Contribution to Distributed Dynamic Spectrum Sharing with...

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Fig. 1: Joint Radio Resource Management (JRRM), Dynamic Spectrum Management (DSM) and Self-x functionalities.

Contribution to Distributed Dynamic Spectrum Sharing with Interference Management

Ghassan R. Alnwaimi

Year 2 PhD Student, C.C.S.R., e-mail: [email protected]

Abstract

Dynamic spectrum assignment (DSA) has been cited as a promising mechanism for managing the radio spectrum. The objectives are to achieve flexible spectrum usage, improve spectrum efficiency, and combat the spectrum scarcity problem. To do so, particularly in competitive and cooperative communication environment, potential inter-operator interference has to be considered. The scope of this work is to address analytically the influence of multi-cell, multi-operator interference on the overall radio resources when multiple competing operators simultaneously co-exist and share a common pool of spectrum. Certainly, distributed DSA algorithm, machine learning and self- organization on the context of two-layer network deployment have been little exploited and thus constitutes a main novelty of this work.

Key words: Dynamic Spectrum Assignment, Joint Radio Re- source Management, Self-x network, Machine Learning.

1. Introduction

In the near future, new network paradigms expected to be deployed as an underlay/overlay to the current existing cellular networks [1], [2]. For instance, some of these scenarios could comprise: Cognitive ad hoc network, Femtocell Access Point (FAP), and multi-RATs/RANs. Although the features of these scenarios play key roles to achieve flexible spectrum usage in the deployment of the next 4G networks, certainly, they introduce new variables that have to be managed accordingly. Clearly, efficient radio resource allocation become more intractable and remains an open problem in which interference is detrimental and this precisely constitutes the main motivation setting out the scope of this research.

2. Envisaged Technical Solution

An envisaged technical solution to the spectrum shortage could be found in the interaction between three strategies; Dynamic Spectrum Assignment (DSA), Joint Radio Resource Management (JRRM), and Self-Organizing Networks (SON), which applied in different time scale, as shown in Fig. 1. Indeed, the optimisation of the utilisation of the radio resources in wireless communication systems is targeted by the provisioning of the dynamic spectrum management, the enhancing of the radio resource management functionalities, self-organising on initiation and decision-making and by exploiting of cognition and re-configurability.

A. Dynamic Spectrum Assignment

The DSA targets to adapt the usage of resources to the current composite network needs and exploit the temporal and spatial variation in the traffic demand, on mid-long-time frame, based on the spectrum usage’s regulatory framework and operators’ policies for spectrum assignment [3]. It is the process that enables the dynamic

management (assignment, de-assignment, sharing, and trading) of spectrum blocks within a single/multi RATs and multi-cells/multi-operators level, while avoiding harmful interference situations.

To this end, two different layers of DSA are identified; intra-operator and inter-operator DSA. Intra-operator DSA enables the dynamic management of radio resources within single or between different RATs, while inter-operator DSA considers managing the spectrum between different operators.

B. Joint Radio Resource Management

The JRRM is responsible for the joint management of the available radio resources in short-term scale, between the different, possibly heterogeneous radio access technologies, in single/multi-operators domain. It is the process that enables the management (assignment, de-assignment) of users to each RAN [3]. Its main task is to select the best radio access for each terminal’s sessions based on the requested QoS, radio conditions, access network conditions, user preferences and network policies. Vertical handover constitutes the key procedure in support of JRRM.

(1)

TABLE I: Simulation parameters.

Fig. 2: QoS, UI, and average power versus traffic density.

The purpose of JRRM is to ensure the efficient use of the available radio resources of the co-existing networks, support of load sharing and policy management across RATs/RANs while sustain the end-to-end QoS, in a self-organised way.

Two layer of JRRM are identified; intra-operator and inter-operator JRRM. The intra-operator JRRM is responsible to assign users to different RATs in the same operator network, while the inter-operator JRRM flexibly assign users, belonging to different operators, to the available RATs, with respect to the policies and regulations.

C. Self-Organizing Networks

A Self-organizing Network (SON) is a communication network composed of several elements that has the ability to perform a set of functions in order to maintain a global goal for the whole network without the need to dedicated central entity or human intervention. The distributed nature and the autonomous behavior of each entity are the main attribute of a self-organized system [4].

SON is addressed in literature with self-x functionalities. It can supports Self-configuration, Self-planning, Self-Optimization, Self-managing and Self-healing. Self-configuration and Self-planning could be performed to ease the planning phase of the network. Any new node will automatically configure itself, e.g. radio parameters, with the system operation in ”plugplay” fashion. Self-Optimization helps to optimise radio resources based on the measurements collected from users terminals or base station node. Self-managing and self-healing are the automation of Operation and Maintenance (OAM) tasks and workflows for the network. Indeed, the main gains of self-x are expected first of all in operational expenditure (OPEX) reductions and network performance improvements.

3. Simulation Results and Conclusion

The performance of the proposed algorithm has been evaluated against FSA; the simulation parameters and assumptions are summarised in Table I. The scenario considered 2 operators with 19 cells each and 3 carriers to share; any cell can be assigned to more than one carrier.

In this work we use the QoS and spectral efficiency gain (Ω) as performance metrics of the DSA algorithm. As shown in Fig. 2, the DSA algorithm improves the QoS level and achieves around 26% spectral efficiency gain compared with the FSA for both operator networks. In addition, the utilisation index ! is proposed to measure

the fairness of access of the both operators to the spectral resources. Utilisation index is defined as how much of the available spectrum has been used by each operator, given:

!m =fmin, jm

fmax"j#Jm

$

where fminm is the required number of carriers vector for the m

operator network, j is the base station number and fmax is the available number of carriers.

Fig. 2 shows ! for both operator networks in DSA scenarios. In addition to the utilisation gain ! , it is shown that the operators have fair access to the spectrum.

Moreover, the proposed DSA algorithm reduced the average required transmission power of the BS by more than 4dB, when the traffic load is 312 MU/Km2, compared with the FSA, as shown in Fig. 2. This is due to the fact that the allocation scheme maintains the minimal level of inter-operator and inter-cell interference between operators.

To conclude, in the initial results, Cell-Cell interaction based approach has been introduced here as a model that might be suitable for the investigation of the influence of communication on the diversity of cell behaviors. A novel, simple medium- term algorithm has been employed in order to enhance the spectrum utilisation given a minimal interference level shared among multi-operators. The simulation results indicate that the proposed algorithm significantly outperforms the FSA in terms of spectral efficiency, fairness, and average required transmission power for BSs. The ultimate objective of this work is to propose dynamic spectrum sharing framework over heterogeneous radio networks to achieve efficient utilization of the spectrum with the necessary support of re-configurability, collaboration capability, self-organize and learning characteristics.

References

[1] J. Mitola, “Cognitive radio architecture evolution,” Proceedings of the IEEE, vol. 97, no. 4, pp. 626–641, Jan 2009.

[2] S. Al-Rubaye, A. Al-Dulaimi, and J. Cosmas, “Cognitive femtocell; future wireless networks for indoor applications,” Vehicular Technology Magazine, IEEE, Jan 2011.

[3] O. Salient, L. Giupponi, J. Nasreddine, R. Agusti, and J. Perez-Romero, “Spectrum and radio resource management,” Vehicular Technology Magazine, IEEE, vol. 3, no. 4, pp. 56–64, 2008.

[4] T. Robertazzi, “Self-organizing communication networks,” Communications Magazine, IEEE, vol. 24, no. 1, Jan 1986.

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Optimisation Algorithms for Collaborative Spectrum Sensing

Kamran Arshad

Research Fellow, C.C.S.R., e-mail: [email protected]

Abstract

Collaborative spectrum sensing (CSS) can offer significant collaboration gain if all the observations or decisions made by collaborating users fused at central node (called fusion centre in this paper) optimally. This paper highlights requirements and algorithms for the selection of best possible fusion rule at the fusion centre in the case of hard decision fusion (HDC) and soft decision fusion (SDC). In contrast to the existing state-of-the-art, we show that just 1-bit information (HDC) and user sensing measurements (SDC) are not enough for the selection of optimal fusion rule at the fusion centre. We proposed a Genetic Algorithm (GA) based weighted CSS scheme and show by simulations that proposed method improves detection performance.

Key words:Cognitive radio, collaborative spectrum sensing, hard decision fusion and soft decision fusion.

1. Introduction

Spectrum sensing is considered as one of the most critical functionalitiesof a Cognitive Radio (CR).Observations of a single CR are not always trustworthy because a CR may have line of sight with the incumbent receiver but may not be able to see the incumbentuser(IU) due to shadowing or fading, known as “hidden node" problem. CSS has been proposed as a solution to the problems that arise due to such uncertainties in the channel [1]. CSS can providesignificant gain and guarantees incumbent protection if data and/or decision fusion algorithms at a fusion centre are optimised.

Various techniques for the optimisation of CSS in terms of fusion rule, number of users, and thresholds have been proposed [1].In the literature, there are studies on the optimisation of the k-out-of-N rule (HDC) to minimise total decision error probability and to maximise the opportunistic users (OUs) throughput; however, those algorithms were designed for a specific scenario of TV bands sharing in an AWGN channel [2]. Similarly, for SDC, most of the prior research work focuses on the case when OUs are far away from the IU and hence the same path loss or Signal-to-Noise Ratio (SNR) was assumed for all collaborating users.CSSschemes with weighted user contributions have been recently proposed in [3]. A linear optimal strategy for CSS was presented and optimal weights for each CR in an AWGN channel were derived. However, the shortcomings of existing literature in weighted CSS are that perfect reporting channels have been assumed, instead of realistic channels.

In this paper, the optimisation of CSS is documented and optimum decision fusion is evaluated for hard and soft decision fusion at the fusion centre. We address the problem of data/decision fusion at fusion centreand answer this simple question: for optimal data/decision fusion what information fusion centre required and how information can be fused optimally? We further elaboratethe data fusion problem in SDC in the presence of realistic reporting channel and propose a weighted GA based CSS framework (assign weight to each user observation).The optimum CSS problem is formulated as a nonlinear optimisation problem in this paper. For a given

probability of false alarm and channel conditions, weights are chosen in such a way that it maximises global probability of detection at the fusion centre. In the presence of channel fading it is hard to derive an analytical expression for the optimum weights hence a GA-based solution is proposed.

2. Technical Approach

We consider a CR network with M users and a fusion centre (may be a CR base station or a node) and formulate the spectrum sensing problem as a binary hypothesis test. For the case of energy detectoras local sensing algorithm[1],detection probability at each node for a given false alarm probability ( ) can be derived as,

(1)

where is the generalised Marcum Q-function, is the number of samples and is the received Signal-to-Noise Ratio

(SNR). We define where is an incomplete gamma function. In the presence of channel fading, average detection probability can be calculated by averaging equation (1) over channel fading statistics.In HDC-CSS, if is the decision made by ith node and K is a parameter (based on fusion rule) then the decision statistic calculated at fusion centre is given by [2]

(2)

Similarly, for weighted SDC in collaborative spectrum sensing, under Gaussian approximation a close form equation

for the detection probability at fusion centre can be derived as (derived in [2])

(3)

where , and represents diagonal matrices formed by placing vectors of (sensing channel noise standard deviation vector), (reporting channel noise standard deviation vector), (SNR vector) and (reporting channel gain vector) on the diagonal respectively. In equation (3), represents global threshold, is the weight vector and is the right tail probability of standard Gaussian distribution.In our proposed strategy for HDC, users need to send their estimated SNR values along with 1-bit decision (in classical HDC users just send 1-bit decision). Based on user’s estimated SNR values and their decisions, fusion centre selects optimum fusion rule. For the case of SDC, the fusion centre receives observations from each user along with their estimated SNR and it also estimates reporting channel gains in order to find optimal weights that should be assigned to each users observation. We propose GA based optimisation of weights in the presence of channel fading as it is difficult to derive optimal weights analytically.

3. Key Results and Discussion

We considered 3 different cases (all users have high SNR – case 1, half of the users have high SNR – case 2, and only 1 user has high SNR – case 3) when users have different mean SNR’s for the case of HDC. Fig. 1 shows that in AWGN channel with i.i.d. observations, fusion centre must use different fusion rules based on a specific scenario. Similar conclusions can be drawn in the presence of channel fading and correlated shadowing (for details see [2]). For SDC, proposed GA-based weighted CSS scheme is simulated and compared with existing weighting schemes proposed in [4], that is, Equal Gain Combining (EGC) and Proportional Combining (PC). Fig. 2 plots the ROC curves for the case when CR users have different SNRs and the reporting channels are not perfect. It can be seen from Fig. 2 that reporting channel gains degrade the performance of spectrum sensing. Without reporting channel gains, PC performs better than EGC, but, in the presence of reporting channel, PC does not perform much better than EGC. This is mainly because of the fact that in the presence of an imperfect reporting channel, optimum weights of cognitive users are not only dependant on SNR values but also depend on reporting channel conditions. We also evaluated performance of proposed approach in fading channel, results are shown in [2]. 4. Summary of the work, potential impact & Conclusion

Data or decision fusion at fusion centre in collaborative spectrum sensing has direct impact on the overall sensing performance. In this paper, suitable approaches have been presented to maximise detection probability and hence providing maximum protection to incumbent users. These

approaches are attractive for system design engineers to enhance probability of incumbent detection.

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Key References

[1] T. Y¨ucek and H. Arslan, “A survey of spectrum sensing algorithms for cognitive radio applications,” IEEE Communications Surveys and Tutorials, vol. 11, no. 1, pp. 116–130, 2009.

[2] Kamran Arshad, Muhammad Ali Imran, and Klaus Moessner,“Collaborative Spectrum Sensing Optimisation Algorithms for Cognitive Radio Networks,” International Journal of Digital Multimedia Broadcasting, vol. 2010, Article ID 424036, 20 pages, 2010. doi:10.1155/2010/424036.

[3] W. Zhang, R. K. Mallik, and K. Ben Letaief, “Cooperative spectrum sensing optimization in cognitive radio networks,” in Proceedings of IEEE International Conference on Communications (ICC ’08), pp. 3411–3415, June 2008.

[4] Z. Quan, S. Cui, and A. Sayed, “Optimal linear cooperation for spectrum sensing in cognitive radio networks,” IEEE Journal on Selected Topics in Signal Processing, vol. 2, no. 1, pp. 28–40,2008.

Improving Fairness by Cooperative Communications and Selection of Critical Users

Juan F. Awad Year 3 PhD Student, C.C.S.R., e-mail: [email protected]

Abstract

Cooperative Transmission can be used in a multicell scenario where base stations are connected to a central processing unit. This cooperation can be used to improve the fairness for users with bad channel conditions–critical users. This paper will look into using cooperative transmission alongside the orthogonal OFDM scheme to improve fairness by careful selection of critical users and a resource allocation and resource division between the two schemes. A solution for power and subcarrier allocations is provided together with a solution for the selection of the critical users. Simulation results are provided to show the fairness achieved by the proposed critical users’ selection method, resource allocation and the resource division method applied under the stated assumptions.

Key words:Critical Users, Cooperative Communications, Radio Resource Allocation, Fairness.

1. Introduction

In recent years, interest in multimedia application, VoIP in and high-speed internet in mobile devices has lead to more demand on data rate thus rapid development in of wireless communications making it the fastest growing segment of the communications industry. With over two billion mobile users around the world, it has become a very lucrative market.

Many users on a base station sector suffer from bad channel condition due to being located at the cell edge suffering from intercell interference and a large path loss or falling in a deep shadowing reason along many other that can lead to a low spectral efficiency, thus the name, critical users. Critical users should be given priority over other users because of their bad channel condition or other criteria that can be chosen.

In this paper, we decided to make the base station able to cooperate to serve the critical users. Base stations cooperation exists in many ways in the literature including, but not limited to, distributed MIMO, beamforming, interference alignment. However, most of the previous work ignored the idea of using base station cooperation to serve users who are critical only; we try to exploit the similar spacing between critical users and all base station in the case that critical users are on the cell edge or to users in deep shadowing which may have better channel to other nearby base stations. In this work, we use an OFDM based maximal ratio transmission, a beamforming technique, to serve the critical users. And the users classified as non critical are served using tradition OFDM system.

In this context, firstly we need to define who of the users should be “critical”. The choice of critical users can be dependent on many factors. These factors should be studied and compared. Some of these criteria could include channel condition (e.g. path loss, shadowing, fading, etc…) or previous achieved rates are even the user’s status as a normal or VIP user. In this paper you chose the previous rate method is it can provide better fairness for its users. In previous literature, critical users are defined as the users at the cell edge only. We looked into different methods, making the choice of critical users fairer.

As for resource allocation, many of authors ignored an optimal power allocation which we include in this paper. And

a method for subcarrier allocation for the maximal ration transmission (MRT) scheme used here is proposed. This complete and join what have been done before in term of resource allocation for a cooperative MRT.


We suggest a three sector scenario, each sector accommodate one base station and each base station is equipped with one antenna. In a downlink scenario, base stations are connected through an unlimited bandwidth backhaul with a central processing unit (CPU). The CPU is responsible for subcarriers and power allocation in addition to selecting who of the users is said to be critical. The channel is assumed to be frequency selective and slow enough for the channel to stay constant over one transmission. All base stations and the CPU are assumed to have a perfect knowledge of the channel state information (CSI). The frequency reuse scheme is shown in Fig. 1 where non-critical users are served by orthogonal frequencies and connected only to their corresponding base stationswhereas the critical users are served by anOFDM-based cooperative MRT technique and connected to all three base stations are the same time, if feasible, on the level of subcarriers. That translates to a certain critical user receiving messages from all base stations on one or more subcarriers while others on different subcarriers. The MRT scheme isapplied by considering each base station as an antenna from a MIMO system controlled by the CPU.

Firstly, we need to define who of the users is critical. After simulating different methods, we found that defining the critical users according to their previous achieved rates can provide best fairness in this specific setup. We introduce a measure that decides the percentage of users which are said to be critical. Furthermore, it decides the amount of resources (power and subcarriers) that are given to each schemefavouring the cooperative scheme. In other words, as

more users are considered to be critical and more resource are given to them under the cooperative scheme.

Figure 1: Frequency Reuse Scheme

Then, subcarriers are assigned to users each under their corresponding scheme. Under the orthogonal scheme subcarrier are allocated according to the method used by authors in [1] which the well known greedy subcarriers allocations. As for the cooperative scheme, the subcarriers assignment for eachuser is done in a greedy approach giving priority to the userswith the worst channel condition. The choice of subcarriersis done from the frequency band assigned to the cooperativescheme. In addition to that, because of maximal ratiotransmission method used here [2], each user has to take thesame subcarrier frequency from all base stations at thesame instance. In other words, the user with the worst channelcondition chooses first the subcarrier that has the best meanSNR from all base stations.

Furthermore, we need to assign the power to the subcarriers. For the orthogonal part of the system, power is distributed using the optimal single user water filling[3]. As for the cooperative part, we found an analytical solutionusing convex optimization analysis and that the power that should be given to each subcarrier is defined by

(1)

Where is the transmit power from the th base station

to the th user on the th and is the unit power SNR. The previous equation resembles single user water filling on every base station and that reduces the computational complexity at the CPU leaving for it the role of subcarrier assignment and the knowledge and passing of CSI to all base stations.

The rate for user in the cooperative scheme is calculated using

(2)

Where the number of subcarriers is, is the number of base stations, is the bandwidth per subcarrier and is the noise spectral density. 3. Key Results and Discussion

We simulate the system described earlier for three base stations and 54 users distributed uniformly and identically in

the three sectors. The number of subcarriers is 256 and the bandwidth is assumed to be 5MHz. The fading channel used is the ITU Pedestrian B and the intersite distance is 3km.

Using different values of between and we studied the spectral efficiency of the system and we also studied the fairness between by plotting the cumulative distribution function (CDF) and Jain’s fairness index . As shown in figure 2, the best fairness is achieved at meaning

of the users with the worst rates are under the cooperative scheme. This compared to a traditional orthogonal reuse scheme of 1/3.Note the significant improvement in fairness measures compared to the orthogonal scheme. However, this improvement comes with a price, here is the spectral efficiency.

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4. Summary of the work, potential impact & Conclusion

In this paper, a method for frequency reuse is proposedusing cooperative communications and maximal ratio transmission. We found a solution for resource allocation alongside a method to choose the most vulnerable users and assign them to the cooperative scheme. What presented here can open the door to use available infrastructure to provide better service to VIP and vulnerable users.

Key References

[1] W. Rhee and J. Cioffi, “Increase in capacity of multiuser OFDMsystem using dynamic subchannel allocation,” in IEEE 51st Vehicular Technology Conference Proceedings, vol. 2, 2000, pp. 1085–1089.

[2] J. Cavers, “Single-user and multiuser adaptive maximal ratio transmission for Rayleigh channels,” IEEE Transactions on Vehicular Technology, vol. 49, no. 6, pp. 2043 –2050, Nov 2000.

[3] J. Jang and K. Bok Lee, “Transmit power adaptation for multiuserOFDM systems,” IEEE Journal on Slected Areas in Communications,vol. 21, no. 2, pp. 171–178, February 2003.

Intra-cell Overlay Opportunistic Spectrum Sharing ByEmploying One-bitFeedback Beamforming

Jiancao Hou a

aYear 2 PhD Student, C.C.S.R., e-mail: [email protected]

Abstract

This paper presents a novel strategy for intra-cell overlay opportunistic spectrum sharing where one-bit feedback beamformingis available. Base station broadcasts messages to two relay stations, and each of them can perfectly decode its own microcell’smessages and messages from the other in order to communicate with proper mobile users. The relay station in secondary microcellattempts to share the overloaded spectrum of primary microcell. By intelligently optimizing the beam vectors, the secondary relaystation can achieve its purpose. Compared to conventional limited feedback beamforming for opportunistic spectrum sharing, myproposed strategy can be achieved with just one-bit feedback.

Key words: opportunistic spectrum sharing, cognitive beamforming, one-bit feedback.

1. Introduction

Rapid growth of wireless services continues to overloadthe spectrum resources.With this growth, most of the spec-trum bands suitable for the wireless communication sys-tem have already been allocated, even though the FederalCommunications Commission (FCC) tries to expand morespectrum bands. On the other hand, based on FCC’s surveyand measurement, some of the allocated spectrum bandsare vastly unused or under-utilized. To deal with this spec-trum inefficient problem, underlay opportunistic spectrumsharing has been proposed that allows secondary users op-portunistically to utilize the spectrum of primary users.To support high efficiency spectrum utilization while keep-ing the introduced interference below a level, secondaryusers should intelligently manage the interference to pri-mary users as well as their own sum-rate.Multiple input and multiple output (MIMO) has a great

potential to increase spectrum efficiency and sum-rate ca-pacity without extending the bandwidth. Recent researchwork towards MIMO broadcast channel has shown thatsum-rate capacity can be achieved by using beamformingwith limited feedback. One solution proposed with usingcodebook based beamforming is performed in [1]. This ef-fective approach computes the beam vectors to maximizethe sum-rate by means of searching optimal beam vectorsbased on codebook and feeding back their labels to thetransmitter, however, the problem of this method is inher-ent quantization error. Another approach named oppor-tunistic beamforming is proposed in [2]. Mobile terminals(MTs) need to send back their best signal-to-interferenceplus noise ratio (SINR) values corresponding to the indexof predetermined beam to the transmitter. This kind of

beamforming introducing diversity gain is suitable for alarge number of MTs.In this paper, I will mainly focus on employing one-

bit feedback beamforming into the intra-cell overlay op-portunistic spectrum sharing in order to achieve my pro-posed strategy. The motivation of this work is that, howto construct the beam vectors by employing one-bit feed-back to let secondary users opportunistically share the over-loaded primary users’ spectrum? Recent research work in[3] presents that transmit beamforming with partial chan-nel knowledge in underlay opportunistic spectrum sharingsystem can intelligently manage the interference to primaryusers and the sum-rate of secondary users. Instead,myworkis focused on the overlay case and based on one bit feed-back beamforming. Assume that primary users’ spectrumis overloaded, the main objective is to let secondary usersmake a deal with primary users for helping them to transmittheir signal messages, meanwhile, maintaining secondaryusers’ own sum-rate by employing one-bit feedback beam-forming.


The intra-cell overlay opportunistic spectrum sharingnetwork is given in Fig. 1. Intra-cell broadcast channelswith one base station (BS), two relay stations (RSs) andmultiple MTs are considered. BS belongs to the cell aim-ing at broadcasting messages to the two RSs. RS-1 is incharge of N MTs around it in primary microcell, and RS-2is in charge ofK MTs around it in secondary microcell. Ex-cept for RS-2 withM antennas, the rest equipments are in-stalled with only one antenna, respectively. The spectrumsof these two microcells are orthogonal. I consider the block

CCSR Research Symposium 2011

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fading channel, which the channel vectors keep constantduring the coherence time interval. Since I consider cellularnetwork, the number of MTs in microcell should be muchlarger than the number of RS’s antennas.BS broadcasts messages to RS-1 and RS-2, both RS-1

and RS-2 can decode their own messages and the messagesfrom the other, and then forward the selected messages tothe appropriate MTs. At this time, overloaded secondarymicrocell attempts to share the spectrum of overloaded pri-marymicrocell. For this reason, RS-2 makes a deal with RS-1 help RS-1 send its messages. Based on this, the objectiveis how to employ one bit feedback beamforming at RS-2 toachieve the intra-cell overlay spectrum sharing strategy. Tocomplete my prposed strategy, let’s get started with con-structing the first beam vector for helping sub-cell 1 senduseful messages.RS-2 firstly constructs a beam vector wM , aiming at

adapting to the interference channel link an and helpingRS-1 send useful messages to chosen MT n in primary mi-crocell. By iteratively sensing the received signal state ofMT n in primary microcell, RS-2 can achieve the purpose.Assume that the SINR of MT n in primary microcell issaved for current time slot, for the next time slot, if theSINR increased, MT n will send 1 to tell RS-1 its SINRincreased, otherwise it will send 0 to tell RS-1 its SINR de-creased. Based on this, RS-2 can sense this information anditeratively adjust the phase of its beam vector wM to findan optimal one to adapt to the interference channel link an.After getting the optimal wM , RS-2 then needs to con-

struct another M − 1 orthogonal beam vectors wk, k =1, . . . ,M −1, for its own microcell’s transmission, and eachof these beam vectors also needs to be orthogonal withthewM for suppressing inter-cell interference. In secondarymicrocell, the number of MTs K need to be implicitly di-vided into M − 1 groups, where each group contains K

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δ = π/10 (100 iterations)without help of RS−2

Fig. 2. Sum-rate performance versus single-to-noise ratio.


In this section, I evaluate the performance of the intra-cell overlay opportunistic spectrum sharing strategy by em-ploying one-bit feedback beamforming, showing in Fig. 2.Compare my strategy with the conventional underlay op-portunistic spectrum sharing case without RS-2’s help, mystrategy increases the sum-rate of primary microcell. Inaddition, setting up suitable phase control δ and iterationtimes can lead to the optimal beam vector wM , give pri-mary microcell the biggest help, and automatically elim-inate the interference from RS-2 to MT n. On the otherhand, based onmy technical analysis, as the number ofMTsK in secondary microcell goes to infinity, the sum-rate ofsecondary microcell can reach its upper bound.

4. Conclusion

In this paper, I proposed a novel strategy for intra-cell overlay opportunistic spectrum sharing by employingone-bit feedback beamforming. The strategy let secondarytransmitter intelligently send useful messages to primaryusers and keep its own sum-rate performance by optimizingthe beam vectors with just one-bit feedback. Meanwhile,by choosing suitable phase control δ and iteration times,we can thoroughly eliminate the inter-cell interference toprimary users. However, based on the system design, thenumber of secondary users had to be sufficient large.

Key References

[1] N. Jindal, “Mimo broadcast channels with finite-rate feedback”,IEEE Transactions on Information Theory, vol. 52, no. 11, pp.5045 – 5060, November 2006.

[2] M. Sharif and B. Hassibi, “On the capacity of mimo broadcastchannel with partial side information”, IEEE Transactions onInformation Theory, vol. 51, no. 2, pp. 506–522, February 2005.

[3] L. Zhang, Y. Liang, Y. Xin, and H. Vincent Poor, “Robustcognitive beamforming with partial channel state information”,IEEE Transactions on Wireless Communications, vol. 8, no. 8,pp. 4143–4153, August 2009.

2

Flexible Frequency Reuse schemes for heterogeneous networks (macrocell and femtocell)

Chrysovalantis Kosta


Abstract

In this article, we introduce a flexible frequency re-use scheme for heterogeneous cellular system to enhance the overall system capacity. In this case our focus is to enhance macrocell cell-edge and femtocell overall throughput. Simulation results confirm the effectiveness of the proposed scheme in both macrocell and femtocell networks compared to a number of schemes in the literature.

Key words: Frequency Partitioning; Inter-cell Interference Coordination; Interference Mitigation schemes

1. Introduction

With advent of femtocell deployment that aims to both extend the radio coverage in a licensed band and provide a large number of bandwidth-hungry multimedia services, the interference coordination techniques have become more challenging and more important than ever [1]. In this article, our focus is to address this spectrum-sharing challenge in the heterogeneous networks through orthogonal patterns for macrocell network with emphasis on tri-sectorized cellular systems.

One of the main drawbacks of existing state-of-art re-use techniques shows a low reuse of the available radio spectrum leading to low spectrum efficiency. For this reason, a flexible scheme is investigated in [2], [3] based on a principle of cyclic difference set from combinatorial theory, which allows the quick construction of orthogonal patterns in radio spectrum. It is found that their employment in interference mitigation scheme has potentially a high value, since with a low-complexity pattern generation it introduces a high degree of orthogonalization among adjacent cells.

Our main contribution is a new reuse frequency scheme which shows high orthogonalization in the case of a trisectorized cellular topology. Additionally, in contract with most of the existing works, we extend our framework to enable a plug-and-play deployment of femtocells in the existing cellular network. Besides this, we investigate the performance of a multi-tier deployment approach where the cell area can be divided up to four distinct sub-cell regions. Thus, this region-based arrangement allows not only to control the degree of coverage but also the interference avoidance successively among these regions.

The rest of the paper is organized as follows. The key idea of the flexible reuse scheme is shown in Section II. Section III, a simulation study is carried out to investigate the effectiveness of the key scheme and Section IV summarizes this article.


The interference avoidance or alternatively, the orthogonalization of wireless resources is achieved by applying radio re-use restrictions on frequency and power domain. However, this approach can be generalized using some fundamental principles of mathematics. This gives us the motivation to investigate a popular technique, which generates orthogonal patterns in a low complexity manner, e.g. the cyclic difference set [4]. In this case, above mention restrictions can be applied through soft frequency reuse (SFR) and fractional frequency reuse (FFR) schemes, respectively. Figure 1 illustrates the key idea of the proposed scheme. Instead of having only a tier on top of the cell-edge region, it is possible to form up to three virtual tiers each reusing a different part of spectrum. For illustration purposes each frequency group is shadowed with unique a hatched color pattern. Note that additional a different power restriction level can be used to enhance the orthogonalization of the radio resource among intercell co-tiers. In this way, the new scheme allows not only to control the degree of coverage but also to flexibly tune the service between the cell-edge and cell-center regions.

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Figure 1. Graphical Illustration of the sub-cell regions of the flexible frequency reuse scheme. Each sub-cell is associated with a frequency groupwhich is orthogonalized with other groups through power amplification (SFR)/reduction or through frequency restriction (FFR).


This study considers active data transmission only in the downlink direction in the context of OFDMA (Orthogonal Frequency-Division Multiple Access), targeting 3GPP LTE (Long Term Evolution). We consider a cellular layout of a 19-cell with three 120-degree sectorized antennas where each cell contains an eNB (LTE eNodeB) in its center. Next, for HeNB (LTE Home eNB) deployment, each house is modeled as a square block with dimension 10x10. Inside of each block, both HeNB and FUE (Femto UE) are dropped according to the uniform distribution. However, for indoor deployment a fixed number of blocks and UEs (User Equipment) are placed in each cell (ref. Table I). Further, to eliminate the boundary effect a wrap-around technique is adopted and therefore the long term geometric Signal-to-Interference plus Noise Ratio (SINR) is calculated including all the interferers. Additionally, we assume that the HeNB deployment is isolated from the eNB by an external wall. The main simulation parameters for this study are listed Table I.

For schemes, with two or more concentric tiers– i.e. SFR 1/3 and SFR x/7 – the HeNB reuses the fraction of bandwidth used in the cell-center region if it located at cell-edge region and vice versa. For evaluation purposes, the rest of the schemes i.e. FR1, the HeNB employs randomly half of the entire spectrum.

Table II shows the average performance of the eNB deployment in terms of cell-center and cell-edge throughput and the relative improvement in parenthesis compared with FR1 scheme defined as the reference scheme. It can be seen that although the FR1 scheme has the highest sector throughput, its cell-edge performance is not satisfactory. The legacy SFR/FFR 1/3 and the proposed SFR/FFR 3/7 schemes have lower cell-center performance compared with FR1; however, the cell-edge performance is much better for these schemes.

Table III shows the average HeNB throughput and eNB throughput with and without femtocells. The relative performance compared with the reference scheme is also shown for the femtocells. Consequently, the flexible reuse scheme with three tiers in the cell-edge area outperforms from other re-use schemes in the case of a co-deployment with femtocells since the improvement can be up to 40%. As it can be seen, all reuse schemes experience a relatively smaller

throughput loss in the case when femtocells are in used; however, the impact is considered detrimental compared with their high performance in throughput.

4. Summary / Future work

A novel frequency-partitioning scheme for heterogeneous network is presented in this paper.. Simulation results show the effectiveness of the proposed scheme in both macrocell and femtocell networks compared with a number of available schemes in the literature.

A further performance improvement is expected through semi-static and dynamic scheme. Radio resource restriction can be applied in such way to benefit the cell and user loading. Since this evaluation scheme assumes only long-term fading component for macro environment, other more realistic fading model can be used to evaluating the performance of the proposed scheme. Other indoor deployment of femtocell e.g. ‘dual stripe’ or ‘5x5 grid’ are also part of the future work.

Key References

[1] A. Quddus, T. Guo, M. Shariat, B. Hunt, A. Imran, Y. Ko, and R. Tafazolli, “Next generation femtocells: An enabler for higher efficiency Multimedia Transmission”, IEEE Comsoc. MMTC Letter, vol. 5, no. 5, pp. 27–31, Sep 2010.

[2] Y.J. Choi, C.S. Kim, S. Bahk, "Flexible Design of Frequency Reuse Factor in OFDMA Cellular Networks”, IEEE ICC '06, vol.4, pp.1784-1788, Jun 2006 .

[3] A. Najjar, N. Hamdi and A. Bouallegue, “Fractional Frequency Reuse Scheme With Two and Three Regions For Multi-cell OFDMA Systems”, 17th TELFOR 2009, Serbia, Belgrade, Nov 2009.

[4] T. Hellesth, D. Jungnickel, A. Pott and P. Kumar, “Difference sets, Sequences and their Correlation Properties”, Kluwer Academic Publisher, 1998.

Table II. Throuput results for macro deployment

Re-use scheme

Average eNB cell-edge T-put in Mbps (% change)

Average eNB cell-center T-put in Mbps (% change)

FR1 19.30 57.08

FR3 23.73 (+23%) 40.20 (-29.6%)

SFR 1/3 37.45 (+94%) 44.40 (-22.2%)

FFR 1/3 44.28 (+129.4%) 38.05 (-33.3%)

SFR 3/7 39.73 (+105.8%) 42.67 (-25.2%)

FFR 3/7 38.97 (+101.9%) 46.46 (-18.6%)

Table III. Throuput results for macro and femto deployment

Re-use scheme

Average HeNB T-put in Mbps

(% change)

Average eNB T-put in Mbps

(% change)

FR1 70.06 46.70

FR3 72.67 (+3.74%) 35.68 (-23.6%)

SFR 1/3 92.74 (+32.38%) 42.49 (-9%)

FFR 1/3 79.18 (+13.03%) 39.76 (-14.9%)

SFR 3/7 99.60 (+42.18%) 41.86 (-10.4%)

FFR 3/7 98.18 (+40.14%) 44.41 (-4.9%)

Table I. List of Main Simulation Parameters

Parameters Values

Total Bandwidth 20 MHz

Site-to-Site Distance 500 m

Antenna model Berger

eNB Power 43 dBm

HeNB Power 10 dBm

Fading model Long-term geometric SINR

Macrocell path loss model L = 128.1 + 37.6 log10D

Femtocell path loss model L = 127 + 30 log10D

External wall loss 10dB

Inter-distance of UEs 1 per 20m2

Inter-distance of FEs 1 per 61m2

Performance Evaluation of Energy Detection in Cognitive Radio

Xing Liu


Abstract

Spectrum sensing plays an important role in cognitive radio systems achieving non-interference operation. Results of spectrum sensing depend on the performance of employed sensing techniques. This paper presents a novel performance evaluation of the most common spectrum sensing techniques – energy detection. New features and characteristics of the energy detector are derived and evaluated by theoretical analysis and simulation in this paper. We show that energy detectors exhibit performance loss due to non-constant-amplitude modulation scheme. In addition, we find that the performance degradation tends to vanish with incremental sensing time. Moreover, the performance of energy detectors under independent and correlated Rayleigh fading channel conditions is investigated.

Key words: Cognitive Radio, spectrum sensing, energy detection, performance evaluation.

1. Introduction

Spectrum is regarded as a precious and limited resource especially with the dramatic development of novel wireless devices and services and increasing demand for broadband communication. Measurements show that most of the licensed frequency bands are underutilized at certain periods of time and in certain geographic areas [1]. Thus, Cognitive Radio (CR) was proposed to opportunistically access the unused spectrum bands for communication. At the same time, the interference caused by such cognitive operation should be kept to a minimum level. Several elements and operations are required to implement a fully functional cognitive radio among which the most important is spectrum sensing.

Up to now, various spectrum sensing algorithms have been proposed in the literature to detect the primary transmissions utilizing null, partial or full knowledge of the primary signal. The strengths and limitations of these techniques have been addressed in many papers. Energy detection is noted for its simplicity and requiring no a priori information of the primary signals. The performance of energy detectors has been extensively studied in the past, and shown to achieve a high probability of detection and low probability of false alarm under good channel conditions with short observation time. However, it suffers from noise uncertainty and estimation error problems [2]. It requires long observation time for the satisfactory detection of a spectrum occupancy state at low SNR levels. It performs much worse when the channel randomness is considered [3]. Moreover, the influences of the hidden terminal and exposed node problems are evaluated in [4].

Although there have been many generic studies of the energy detection, there has been little detailed study of its application in more realistic scenarios including system specific details such as modulation type, channel conditions or sensing time. Herein we provide new analysis of performance for the energy detection in relation to simple system

parameters. This will be used in following work on collaborative spectrum sensing.

The remainder of this paper is organized as follow. Section 2 briefly introduces the system principles and system models for energy detection. Theoretical derivation and simulated performance analysis are given in Section 3. Finally, conclusions are given in Section 4.

2. System Model

We consider a radio spectrum usage model consisting of two elements, primary system and secondary cognitive system. The cognitive radio network in the model consists of one energy detector and the primary system consists of one transmitter/ receiver pair.

When the Neyman-Pearson criterion is applied in spectrum sensing, the optimal test statistic for a signal with an unknown structure in white Gaussian noise is simply the energy summation of the received signal within the observation time. To make the final decision, the measurement is compared to a threshold which is predetermined and based on the noise floor and the target probability of false alarm:

1

02

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2)(

H

HnyM

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n

When the energy of the primary signal is assumed to be constant within a certain time duration, the test statistic of the energy detector follows a central and noncentral chi-square

distribution under hypothesis 0H and 1H respectively. Both

distributions have the same degree of freedom. The noncentrality parameter is essentially the energy of the primary signal within the sensing interval.

3. Results

0.2 0.25 0.3 0.35 0.4 0.45 0.50.75

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Modulation type: QPSK16QAM64QAM

Figure 1 Performance comparison of energy detector for different modulation signals.

The receiver operating characteristic (ROC) curves showing the probability of detection versus the probability of false alarm are utilized to describe the detector performance. Figure 1 depicts the performance of the energy detector for QPSK, 16QAM and 64QAM signals with SNR equal to -2dB. The number of used samples is 10. The energy detector exhibits best performance when the target primary signal is QPSK and degrades for 16 and 64QAM. The reason for this is that the theoretical analysis assumes that the primary signal has deterministic energy over the sensing time period. This assumption holds true when a PSK signal is transmitted, as their constellation points are positioned on a circle such that they are all transmitted with the same energy. However, the amplitude of a primary signal which utilizes more sophisticated modulation approaches can vary. The average received energy fluctuates more intensively and hence results in slight performance deterioration. For instance, the constellation points of a QAM modulated signal is arranged on different circles.

Figure 2 shows that the performance deterioration due to the nondeterministic signal can be alleviated by increasing the number of samples used for detection. The principle behind this phenomenon is that the transmitted data is random with zero mean as with white Gaussian noise. Thus a longer averaging operation will further reduce the effect of non-constant instantaneous power.

Figure 3 shows ROC curves under AWGN, Rayleigh fading and Log-normal shadowing. Two frequency flat Rayleigh fading channels are utilized in the simulation. The sequential channel fading samples of the first Rayleigh fading channel are totally independent while those of the second Rayleigh fading channel are correlated. We observe that independent Rayleigh fading does not significantly degrade the performance of the energy detector. The reason for this is that the energy detector only measures the average energy over the observation time and hence the detection performance does not depend on the relationship between the sequential signal samples although such a relationship is destroyed due to the independence of channel gain samples. On the contrary, the performance of the energy detector under a correlated Rayleigh fading channel deteriorates more markedly. Due to the correlation between sequential channel fading coefficients, the variation of the channel gain is small and the amplitude of the channel gain can even be regarded as

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Figure 2 Performance comparison of energy detector for different modulation type with increased number of samples.

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AWGN + Rayleigh(Independent)

AWGN + Rayleigh(Correlated)

AWGN + Lognormal Shadowing(6dB)

AWGN + Lognormal Shadowing(12dB)

Figure 3 ROC curve under different channel condition

constant over short sensing times. Such a channel may not destroy the correlation between signal samples but can significantly alter the received energy of the primary signal from its real value. Thus, to ensure good performance of the energy detector, independent (at least to some extent) samples must be guaranteed or a more sophisticated algorithm is required to deal with the correlated samples

4. Summary

The advantages of the energy detector are only valid when the wireless channels and equipments are nearly perfect. Besides some well-known disadvantages, the energy detector also suffers from the non-constant amplitude problem and correlated channel problems. Thus, a comprehensive analysis, as given here, is needed to determine practical performance of energy detection.

Key References

[1] FFC, “Spectrum policy task force report,” ET Docket 02-155, Nov, 2002.

[2] R. Tandra and A. Sahai, "Fundamental limits on detection in low SNR under noise uncertainty," in Proc. IEEE IWCMC, Mawii, HI, USA, Jun.2005, vol. 1, pp. 464-469.

[3] F. F. Digham, M. Alouini, and M. K. Simon. “On the energy detection of unknown signals over fading channels,” In Proc. IEEE ICC, Anchorage, Alaska, USA, May 2003, pp. 3575 – 3579.

[4] Q. Ren and Q. Liang, “Performance analysis of energy detection for cognitive radio wireless networks,” in Proc. IEEE WASA, Chicago, USA, Aug. 2007, pp. 139-146.

�

Opportunistic Spectrum Reuse for Femtocell Networks

Research Fellow

Abstract

Femtocell networks are inherently interference

of femtocells make the classical macro cells more susceptible to the resulting interference. In this paper

is proposed for resource allocation for femtocells that opportunistically identifies low

way that would cause minimal impact on the service level of primary macro users. Simulation study confirms

proposed algorithm compared to the benchmarking scenario of macro

tuning of femto throughput at the cost of macro users service.

Key words: Decentralized Resource Scheduling, I

1. Introduction

Recently, the concept of femtocells has attracted much attention in both academia and industry due to the increasing demands for higher data rates. A practically a low-power base station, operating in licensed band that is connected to the core network of an operator via different forms of DSL or residential broadband connectionsHence, femtocells are primarily envisioned to address the classical problem of indoor coverage in cellular networks.

Providing indoor coverage essentially requires a massive deployment of femtocells. As a result, the classical network planning approaches for legacy macro cells would be less efficient at such scales. This unplanned nature in conjunction with the operation within the licensed-band can drastically impact the performance of primary macro users. is important to adopt a proper radio resource allocation scheme to avoid undesirable impact on macro users.

This brief overview shows that radio resource allocation in the context of femtocells is highly coupled with the dynamics of interference. On the other hand, to guarantee scalability, decentralized allocation schemes are more promising to the level of overhead in the network. Different approaches have been adopted in the literature in this directionthis paper, a novel framework is proposed where resource allocation is integrated with opportunistic reuse of spectrum. Here, opportunities are identified in ways to have minimal effect on the primary users.

2. Novel idea of Opportunistic Spectrum Reuse

Considering a cellular environment, there are several levels of diversity that can be exploited based on the dynamics of channel and environment. Multi-user diversity and Route diversity are quite well-known in radio resource scheduling where independence across channel quality experienced by different users is employed to more efficiently utilize scarce resources. However, in a multinode-multiuser environment, another level of opportunity arises based on the isolation factor


Mehrdad Shariat

Research Fellow, C.C.S.R., e-mail: [email protected]�

Femtocell networks are inherently interference-limited similar to other cellular networks. However, massive and unplanned roll

of femtocells make the classical macro cells more susceptible to the resulting interference. In this paper

is proposed for resource allocation for femtocells that opportunistically identifies low-cost resources (in terms of interference) in a

way that would cause minimal impact on the service level of primary macro users. Simulation study confirms

benchmarking scenario of macro-only network operation. The proposed method enables flexible

tuning of femto throughput at the cost of macro users service.

Decentralized Resource Scheduling, Interference Management, Femtocells, OFDMA

Recently, the concept of femtocells has attracted much attention in both academia and industry due to the ever-

. A femtocell is power base station, operating in licensed band

that is connected to the core network of an operator via dential broadband connections.

Hence, femtocells are primarily envisioned to address the assical problem of indoor coverage in cellular networks.

Providing indoor coverage essentially requires a massive deployment of femtocells. As a result, the classical network planning approaches for legacy macro cells would be less

. This unplanned nature in conjunction band can drastically

impact the performance of primary macro users. Therefore, it is important to adopt a proper radio resource allocation scheme

This brief overview shows that radio resource allocation in the context of femtocells is highly coupled with the dynamics of interference. On the other hand, to guarantee scalability, decentralized allocation schemes are more promising to limit the level of overhead in the network. Different approaches have been adopted in the literature in this direction [1]-[2]. In

framework is proposed where resource allocation is integrated with opportunistic reuse of spectrum.

opportunities are identified in ways to have minimal

Reuse

Considering a cellular environment, there are several levels of diversity that can be exploited based on the dynamics of

user diversity and Route known in radio resource scheduling

where independence across channel quality experienced by to more efficiently utilize scarce

multiuser environment, another level of opportunity arises based on the isolation factor

among different pairs of transmitting nodeconcurrent transmission, i.e. to reuse the spectrum. This isolation factor is highly dependent to the chanof a transmitting node to its own users compared to the links to users served by other transmitting nodes. The higher the gap, the better will be the resulting isolation factor.

To better picture the new opportunity leading to higher reuse,here an illustrative example is

Fig. 1 The concept of opportunistic

In this scenario, we try to exploit the isolation factor from a transmitting node to the users served by another node to do concurrent transmission. As shown, the deep fading condition between a macro user (UE1) and Home eNB (HeNB) provides an opportunity to reuse the same resource for concurrent transmission from HeNB to its corresponding user (UE2) as it does not cause any significant interference to the primary transmission. This method effectively provides some lowresources (in terms of interferenscheduled by HeNB to its own users.

3. Proposed Algorithm

Based on the above concept, here we develop a decentralized framework to incorporate opportunistic reuse in radio resource

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limited similar to other cellular networks. However, massive and unplanned roll-outs

of femtocells make the classical macro cells more susceptible to the resulting interference. In this paper, a novel distributed approach

cost resources (in terms of interference) in a

way that would cause minimal impact on the service level of primary macro users. Simulation study confirms the efficiency of

he proposed method enables flexible

among different pairs of transmitting node-user to do concurrent transmission, i.e. to reuse the spectrum. This isolation factor is highly dependent to the channel quality gap of a transmitting node to its own users compared to the links to users served by other transmitting nodes. The higher the gap, the better will be the resulting isolation factor.

To better picture the new opportunity leading to higher reuse, here an illustrative example is provided in Figure 1.

�

opportunistic spectrum reuse

In this scenario, we try to exploit the isolation factor from a transmitting node to the users served by another node to do concurrent transmission. As shown, the deep fading condition between a macro user (UE1) and Home eNB (HeNB) provides

to reuse the same resource for concurrent transmission from HeNB to its corresponding user (UE2) as it does not cause any significant interference to the primary transmission. This method effectively provides some low-cost resources (in terms of interference) in frequency to be scheduled by HeNB to its own users.

Based on the above concept, here we develop a decentralized framework to incorporate opportunistic reuse in radio resource

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allocation for femtocells. This framework involves thefollowing steps:

a) Primary scheduling & listening to channel assignments

At the first stage, the primary macro system follows its scheduling procedure. After the primary scheduling, the channel assignments to macro users should be conveyed to the secondary transmitting node. In this particular case, the eNB acts as the primary serving node.

b) Reuse identification

After receiving the channel assignments, the reuse opportunities can be identified by estimating the channel between secondary serving node (s) and the primary macro user (p), i.e. y

sp

~ . This estimation is done for each macro user on the resources assigned to it in the previous phase.denotes the assigned primary user for resource l.

To identify the opportunities of reuse, Figure 1 is instructive. According to this figure, a lower level of interference on primary user provides a better chance for spectrum reuse. Therefore, the pseudo-code for reuse identification at the HeNB is as follows:

For l = 1 to L

yyifppwherePpthspl

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<=∈∀

Add resource l to the resource pool of HeNB

End

Here, the reusable resources are the one with faded channel

condition on the links between HeNB and primary users. The

fading threshold yth

~ can be tuned adaptively based on the target

level of service for primary users.

c) Secondary scheduling

At the final stage, the identified reusable resources are scheduled according to the adopted policy in secondary node (HeNB) to the corresponding femto users.

4. Simulation Results

To evaluate the efficiency of proposed scheme, a set of simulation studies is carried out on the downlink of an OFDMA-based cellular environment comprising seven wraparound cells. The interference is calculated from the firsttier of neighboring cells as well as serving eNB, HeNB for femto (4) and macro users (8), respectively.benchmarking purpose, a plain macro case is additionally considered where the resource allocation is exclusively for macro users while HeNB is not active. The rest of the simulation parameters are consistent with LTEassumptions [3].

In this scenario, a single femto block (12m x12m) was deployed at the target location (0.8 of cell radius) while macro users are randomly located outdoors in the surroundingthe target femto block.

Figure 2 shows the CDF of macro and femto normalized user throughput. The high throughput gain for the femto users is attributed to the close distance between HeNB and its corresponding femto users and the exterior wall isolation factor

allocation for femtocells. This framework involves the

Primary scheduling & listening to channel assignments

At the first stage, the primary macro system follows its scheduling procedure. After the primary scheduling, the channel assignments to macro users should be conveyed to the

transmitting node. In this particular case, the eNB

After receiving the channel assignments, the reuse opportunities can be identified by estimating the channel

and the primary macro . This estimation is done for each macro user

on the resources assigned to it in the previous phase. Here, pl

*

l.

opportunities of reuse, Figure 1 is instructive. According to this figure, a lower level of interference on primary user provides a better chance for spectrum reuse.

code for reuse identification at the

(1)

Here, the reusable resources are the one with faded channel

condition on the links between HeNB and primary users. The

can be tuned adaptively based on the target

At the final stage, the identified reusable resources are scheduled according to the adopted policy in secondary node

To evaluate the efficiency of proposed scheme, a set of simulation studies is carried out on the downlink of an

based cellular environment comprising seven wraparound cells. The interference is calculated from the first-

of neighboring cells as well as serving eNB, HeNB for , respectively. For the

benchmarking purpose, a plain macro case is additionally the resource allocation is exclusively done

. The rest of the consistent with LTE-Advanced

In this scenario, a single femto block (12m x12m) was deployed at the target location (0.8 of cell radius) while macro

the surrounding area of

the CDF of macro and femto normalized user throughput. The high throughput gain for the femto users is attributed to the close distance between HeNB and its

xterior wall isolation factor

to outdoor macro users. As shown, in spite of from the walls (10 dB), the coordination of interference seems crucial. The proposed opportunistic algorithm can address this issue adequately. As a result, macroperformance to the benchmark plain caseare still able to achieve reasonable throughput (1.8 times improvement in total average throughput over the plain macro) without consuming any additional frequency resourcelevel of opportunities at femtocell can be adaptively tuned by manipulating the fading threshold at the cost of bearing extra interference on the macro users.

Fig. 2 Simulation results, exterior penet

5. Scope of Commercial Utilisation

The proposed solution provides a simple and low cost resource allocation procedure femtocells in existing cellular systems. The algorithm has the potential to exploit some already available signalling procedures as detailed in [4] Advanced standard subject to simple adjustments.

6. Conclusion

In this paper, a decentralized approach is proposed for radio resource allocation in femtocell networks based on opportunistic reuse. As shown, thiscontrol the level of resulting interference on the macro performance. The opportunistic reuse algorithm hugely outperforms the no coordination case in maintaiof macro users where the femto throughput tuned based on the service requirements

Key References

[1] Q. Su, A. Huango, Z. Wu, G. Yu, Z. Zhang, K. Xu, and J.Distributed Dynamic Spectrum Access and Power Allocation algorithm for Femtocell Networks,” IEEE

[2] Z. Bharucha, A. Saul, G. Auer, and H. Haas, “Dynamic Resource Partitioning for Downlink FemtoAvoidance,” EURASIP Journal on wireless Communications and Networking, 2010.

[3] 3GPP, TR 36. 814, “Further advancements for Easpects,” v 9.0.0, Mar. 2010.

[4] M. Shariat, A. Quddus, and R. Tafazolli, “Opportunistic Spectrum Reuse for Femtocell Networks,”

0 0.1 0.2 0.30

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

�

. As shown, in spite of natural isolation coordination of interference seems

he proposed opportunistic algorithm can address this issue adequately. As a result, macro users maintain close

to the benchmark plain case while the femto users are still able to achieve reasonable throughput (1.8 times improvement in total average throughput over the plain macro) without consuming any additional frequency resources. The level of opportunities at femtocell can be adaptively tuned by manipulating the fading threshold at the cost of bearing extra interference on the macro users.

�

results, exterior penetration loss=10 dB

Utilisation

The proposed solution provides a simple and low cost procedure to facilitate integration of

femtocells in existing cellular systems. The algorithm has the to exploit some already available signalling

in future 3GPP releases for LTE-standard subject to simple adjustments.

In this paper, a decentralized approach is proposed for radio resource allocation in femtocell networks based on opportunistic reuse. As shown, this method can efficiently control the level of resulting interference on the macro

he opportunistic reuse algorithm hugely outperforms the no coordination case in maintaining the quality

the femto throughput can be adaptively service requirements of macro users.

, A. Huango, Z. Wu, G. Yu, Z. Zhang, K. Xu, and J. Yang, “ A Distributed Dynamic Spectrum Access and Power Allocation algorithm

IEEE WCSP, 2009, pp. 1-5.

Z. Bharucha, A. Saul, G. Auer, and H. Haas, “Dynamic Resource Partitioning for Downlink Femto-to-Macro-Cell Interference

EURASIP Journal on wireless Communications and

3GPP, TR 36. 814, “Further advancements for E-UTRA physical layer

M. Shariat, A. Quddus, and R. Tafazolli, “Opportunistic Spectrum Reuse for Femtocell Networks,” IEEE VTC, May 2011.

0.4 0.5 0.6 0.7 0.8 0.9 1

CDF

Normalized User T-put

Macro / Opportunistic

Femto / Opportunistic

Macro / No Coordination

Femto / No Coordination

Plain Macro

Spectrum Sensing for OFDM System

by Exploiting Frequency-Domain Pilot Polarity

Zhengwei Lu a

aYear 3 PhD Student, C.C.S.R., e-mail: [email protected]

Abstract

This paper presents a novel spectrum sensing approach for orthogonal frequency division multiplexing (OFDM) system by exploitingfrequency-domain pilot polarity in the scope of signal processing. The proposed technique can achieve a good performance withina short observation time and keep a small computational cost, which is one of the key challenges for spectrum sensing. Thus, it ismore feasible for hardware implementation in real wireless environments. Computer simulations show that the proposed schemeoutperforms the state-of-the-arts pilot based time-domain autocorrelation spectrum sensing approaches around 1 dB and 4 dBrespectively, when the observation period is corresponding to 10 OFDM blocks.

Key words: Spectrum Sensing, OFDM, Pilot Polarity

1. Introduction

Spectrum sensing is a type of advanced signal process-ing technique to monitor user activity. In the opportunis-tic spectrum sharing based networks, such as cognitive ra-dios[1] or self-organizing networks [2], an opportunistic useris allowed to access other user’s spectrum in temporarilybased on provide no harmful interference for other users.To achieve it, the opportunistic user has to sense the spec-trum to detect the presence of other users. Hence, spectrumsensing is one of the key requirements for such systems im-plementation.

Lots of spectrum sensing work has been developed since1940s. A comprehensive survey of spectrum sensing meth-ods can be found in [3]. Most of spectrum sensing tech-niques introduced in the literature suffer either from thenoise uncertainty problem or the latency problem whichcan be due to the observation time delay or the processingtime delay. In this paper, a extreme statistics based spec-trum sensing approach for OFDM system is proposed. Thebasic idea of the proposed approach is based on the ex-treme statistics of the received samples. By exploiting thepolarity of pilot symbols, which are usually either positiveor negative but not always following the same pattern, theenergy spectrum density (ESD) of the received samples af-ter the time-domain autocorrelation will exhibit a fluctua-tion characteristic. A diversity gain can then be obtainedby using frequency-domain differentiation. The proposedscheme is more immune to the noise uncertainty problemand has a small computational cost. Since a spectrum sens-ing approach which can achieve high reliability with lowobservation time and small computational cost is alwaysdesired in fields of industry, our approach is more feasible

for hardware implementation in real wireless environments.Computer simulations show that the proposed scheme out-performs the state-of-the-arts pilot based time-domain au-tocorrelation spectrum sensing approaches around 1 dBand 4 dB respectively when the observation period is cor-responding to 10 OFDM blocks.


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Fig. 1. Block diagram of the proposed detector

To employ the detector, we assume that the opportunis-tic user knows the standard of the primary system by usingsome of location-aided techniques to access the local fin-gerprint database and the noise is the additive white Gaus-sian noise (AWGN). The block diagram of the proposeddetector is shown in Fig. 1. At the opportunistic user re-ceiver’s side, the received signal, after the analog-to-digitalconverter (ADC), is firstly doing the time-domain autocor-relation with a lag to generate an autocorrelation vector,where the vector size is N , which is equal to one OFDMblock size excluding the cyclic-prefix (CP). It is clearly thatthe frequency offset is totally eliminated after the above op-eration. The vector is then performing a fast Fourier trans-form (FFT) operation. Since the autocorrelation of the re-ceived samples on the time domain corresponds to the cir-cular convolution on the frequency domain, the polar dif-ference between pilot symbols will result in the different

CCSR Research Symposium 2011

magnitude for some of sub-carriers and thus result in a di-versity between in such sub-carriers. Next, the energy foreach of those sub-carriers are calculated. Ordering opera-tion is then performed in terms of the energy. After that, afrequency domain differentiation is carried on to obtain thediversity gain. Finally, the maximum diversity gain valueis selected to compare with the threshold to make the de-cision. Since no noise information is required for the teststatistic, the proposed approach is more immune to thenoise uncertainty problem. Although the proposed schemeis based on the frequency domain, in addition, we need toemphasize that only N -points FFT is required for the pro-posed detector, no matter how many blocks received fromthe detector. Hence, the computational cost is saved. Fortiming problem, moreover, if the N -points received sam-ples in the received block belonging to the same transmit-ted block, the whole pilot information will be captured. Todo that, we can switch the initial sampling point for ev-ery CP size samples and repeat the above procedure. Thetotal number of selected initial sampling points are there-fore ⌈ M

Ncp

⌉, where ⌈α⌉ represents the smallest integer which

is larger than or equal to α and Ncp donates the CP size,M = N + Ncp.


In this section, the proposed spectrum sensing schemeis evaluated through the computer simulations. A 3GPPlong term evolution (LTE) system was used as the primarysystem. Each OFDM block contains 1024 sub-carriers withthe CP of length 72. One OFDM block duration is 71.36µs (including the CP), which corresponds to the samplingfrequency at 15.36 MHz. The frequency-domain pilot sym-bols are equally spaced for every 16 sub-carriers. In eachblock, the pilot polarity of the first half block and the secondhalf block are reversed. In addition, the overall pilot polar-ity of two adjacent blocks are also reversed. For example,if we consider a system which contains 4 pilot symbols inone OFDM block, the pilot sequence of the first two blockswould be [1 1 -1 -1] and [-1 -1 1 1]. It should be noted thatthe pilot pattern used in this paper is just an example. Dif-ferent pilot patterns can be used in practice. The channelmodel adopted in the simulations are the WINNER chan-nel model, which is one of the most comprehensive chan-nel model for Beyond-3G (B3G) systems, under the urbanmacro-cell scenario [4]. The mobile station speed setup is13.89 m/s. The maximum Doppler shift is therefore 231.5Hz with the carrier frequency of 5 GHz.

In Fig.2, the performance of the proposed approachis compared with the state-of-the-arts pilot based time-domain autocorrelation spectrum sensing approaches,namely TDSC-NP approach and TDSC-MRC approach,both were proposed in [5]. From the figure, we can see thatthe proposed approach has a fast convergence rate thanother approaches, which means that the proposed schemecan achieve a good performance within a very short obser-

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The Proposed Approach, 10 blocksThe Proposed Approach, 20 blocksThe Proposed Approach, 50 blocksTDSC−MRC Approch, 10 blocksTDSC−MRC Approch, 20 blocksTDSC−MRC Approch, 50 blocksTDSC−NP Approch, 10 blocksTDSC−NP Approch, 20 blocksTDSC−NP Approch, 50 blocks

Fig. 2. Performance comparison between the proposed approach,theTDSC-MRC approach and the TDSC-NP approach with the differentobservation time

vation time, e.g. the observation time corresponding to 10blocks. Although the TDSC-MRC approach can achieve abetter performance comparing to the proposed approachwhen the observation time was corresponding to 50 blocks,the TDSC-MRC approach has to know the noise variancein order to find the weight coefficients to produce the teststatistic. The proposed scheme, however, does not needany noise information for the test statistic. In addition, themaximum ratio combining (MRC) operation can easily in-crease the computational cost. Overall, it can be observedthat the proposed approach outperforms the TDSC-MRCapproach and the TDSC-NP approach around 1 dB and 4dB respectively, when the observation time was set at 10OFDM blocks.

4. Conclusion

A spectrum sensing approach for OFDM Systems by ex-ploiting frequency-domain pilot polarity was proposed. Thenew approach is based on extreme statistics of received sam-ples. Overall, it can be observed that the proposed approachoutperforms the TDSC-MRC approach and the TDSC-NPapproach around 1 dB and 4 dB respectively, when the ob-servation time was set at 10 OFDM blocks.

Key References

[1] J. Mitola and G. Q. Maguire, “Cognitive radio: Making softwareradios more personal”, IEEE Pers. Commun., vol. 6, no. 4, pp.13–18, Aug. 1999.

[2] 3GPP TS 32.500, “Telecommunication management; self-organizing networks (son); concepts and requirements (rel. 8)”,Dec. 2008.

[3] B. Wang and K. J. R. Liu, “Advances in cognitive radio networks:A survey”, IEEE J. Sel. Topics Signal Processing, vol. 5, no. 1,Feb. 2011.

[4] D. S. Baum et al., Final report on link level and system level

channel Models, IST-2003-507581 WINNER D5.4 ver 1.4, Nov.2005.

[5] H. S. Chen, W. Gao, and D. Daut, “Spectrum sensing for ofdmsystems employing pilot tones”, IEEE Trans. Wireless Commun.,vol. 8, no. 12, pp. 5862–5870, December 2009.

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