Wireless Networking

SON Potential for LTE Downlink MAC Scheduler

Xi Li1, Yasir Zaki2, Yangyang Dong1, Nikola Zahariev1, Carmelita Goerg1 1 ComNets, University of Bremen, 28359 Bremen, Germany

1 Email:{xili, ydong, nkz, cg}@comnets.uni-bremen.de 2 Computer Science Department, New York University Abu Dhabi (NYUAD)

2 Abu Dhabi, United Arab Emirates 2 Email: [email protected]

Abstract One of the key radio resource management functions in mobile broadband networks is radio scheduling (also called MAC scheduling), which coordinates the access to shared radio resources. In Long Term Evolution (LTE), advanced scheduling algorithms are needed to provide proper QoS for multi-services and optimize the trade-off between QoS and resource efficiency. Moreover, in order to reduce the overall operation effort and costs, it is also more and more desired by the mobile network operators to develop a self-optimization function which can automatically adapt the optimized settings of the MAC scheduler in accordance with the traffic and network changes during the continuing operations. In this paper we use a novel OSA (Optimized Service Aware) scheduling algorithm for LTE, which provides a good balance between multi-QoS provisioning to support mixes of real-time/non-real-time traffic and system performance maximization in a proportionally fair manner. We present extensive simulation results to investigate the impact of the parameter settings of the OSA scheduler on the service and system performance, and further compare with the well known Proportional Fair (PF) scheduler. In addition, we explore the sensitivity of the optimal setting of the OSA scheduler with respect to different traffic scenarios. Then based on the investigations, we discuss the potential gain of applying SON (Self-Organizing Networks) functions to the OSA scheduler.

KeywordsLTE; MAC Scheduler; QoS; Optimization; SON;

I. INTRODUCTION In LTE Radio Resource Management (RRM) is a

challenging task as many operators nowadays offer unlimited data plans and different services. One of the key RRM functions in LTE is radio scheduling (as called MAC scheduling in this paper), which coordinates the access to shared radio resources. In OFDMA-based LTE systems, this coordination generally considers two distinct dimensions, the time dimension (allocation of time frames) and the frequency dimension (allocation of subcarriers or subcarrier groups). A key challenge in setting parameters for a MAC scheduler is to optimize resource efficiency, while satisfying the users' Quality of Service (QoS) requirements and achieving certain degree of fairness.

The issue of defining an effective LTE scheduling algorithm is the subject of many papers in the literature nowadays. A joint combination of a time and frequency domain scheduler is proven to be the beneficial approach as given in [1], [2], [3], [4], where many different combinations of

standard algorithms for the time and the frequency domain are proposed. In this work, we use OSA (Optimized Service Aware) scheduler in LTEs OFDMA downlink. The OSA scheduler was proposed in [5], which was designed to provide a good balance between multi-QoS provisioning to support mixes of real-time/non-real-time traffic and overall system performance maximization in a proportionally fair manner. In [6] the authors further compare the OSA scheduler against other well-known schedulers such as Blind-Equal Throughput (BET), Maximum Throughput (MaxT), and Weighted Proportional Fair (W-PF) schedulers. However, both [5] and [6] did not explore the parameter optimization problem of the OSA scheduler in different network situations, but only with a default configuration setting. However it becomes more and more important nowadays for the network operators to optimize their system automatically. Therefore, the 3GPP standardization and recent deployment of LTE have highlighted the need of SON (Self-Organizing Networks) focused on self-optimizing and self-organizing capabilities within the network that can bring reductions in operational costs during deployment as well as during continuing operations ([7], [8], [9]). A self-optimized MAC scheduling is aimed to automatically update the optimized settings of the scheduler in accordance with dynamic changes of traffic and network conditions over time. However before implementing self-optimizing functions in the system, it is important to find out whether they can bring significant gain for the user and system performance. A few research papers have started investigating the potential gain of applying self-optimization to the LTE MAC scheduling. For example, [10] found that there is quite limited gain of deploying self-optimization to their MAC scheduling mechanism. However this conclusion is only based on their selected scheduler mechanism. Noting that MAC scheduling schemes are not standardized but rather vendor-specific, the question still remains for other scheduler schemes.

Compared to our previous work ([5], [6]), in this paper we are focused on (i) studying the impact of different parameter setting of the OSA scheduler on the service and system performance and as well comparing against the well known PF scheduler under different parameter settings; (ii) exploring the sensitivity of optimal parameter settings of the OSA scheduler with respect to various traffic scenarios (traffic mix); and (iii) finally investigating the potential gain of applying self-optimization to the OSA scheduler in LTE.

2 This work was done while the second author was working in ComNets, University of Bremen.

The rest paper is organized as follows: Section II gives a detailed introduced of the OSA scheduler. Section III describes the simulation model. The detail results and analysis of OSA scheduler are presented in section IV and a comparison to the PF scheduler is given in section V. Section VI discusses the potential gain of self-optimized algorithm for the OSA scheduler. The end gives the conclusion and future work.

II. LTE MAC SCHEDULER The main target of the Optimized Service Aware (OSA)

scheduler is to satisfy the QoS requirements (e.g., delay budget or loss ratio) of the different LTE bearer types with respect to different services and traffic classes while at the same time providing fairness among all users and maximizing the cell throughput. The OSA general framework is shown in Fig.1. The OSA scheduler is divided into three main stages: QoS Class Identifier (QCI) classification, Time Domain Scheduler (TDS), Frequency Domain Scheduler (FDS).

The TDS deals with issues related to the QoS requirements and user/bearer prioritization, whereas the FDS is responsible for spectrum allocation and multi-user diversity exploitation. The TDS creates a prioritized candidate list of all active users/bearers ready to transmit within the TTI (Transmission Time Interval), and then passes this candidate list to the FDS. In the next step the FDS picks up the users from the list, starting from the highest priority ones, and allocates them with the frequency resources (called physical resource blocks-PRBs) in a way that exploits the different channel conditions of the different users.

The 3GPP defines nine different QCIs, with four of them being defined as Guaranteed Bit Rate (GBR) bearers and five as non-Guaranteed Bit Rate (non-GBR) [11]. In the presented OSA scheduler framework five different MAC-QoS-Classes are defined to differentiate and prioritize between the bearers according to their QoS class. Two classes are defined as GBR and three as non-GBR. In addition, a high priority queue is also defined to handle pending HARQ retransmissions.

Fig. 1: Framework of the OSA Scheduler

A. QCI Classification The first step in the OSA scheduler is the QCI

classification. In each TTI, the scheduler checks the eNodeB buffer and the HARQ buffer of each user. If one of these buffers has data, the user is considered for scheduling within this TTI. The users with pending HARQ retransmissions are given the highest priority. Each traffic type has different QoS requirements, thus each bearer is assigned to a single MAC-

QoS class by mapping its QCI. In this work four different QCI classes are considered; nevertheless this number can easily be extended. Table I shows an example of mapping of the different bearer types.

TABLE I. QCI TO MAC-QOS-CLASS MAPPING EXAMPLE

Bearer Type

Traffic Type

QCI Class MAC QoS Class

WQoS

GBR VoIP QCI-1 MAC-QOS-1 -

Non-GBR

Video Conf.

QCI-7 MAC-QOS-3 5

HTTP QCI-8 MAC-QOS-4 2 FTP QCI-9 MAC-QOS-5 1

B. Time Domain Scheduling (TDS) The time domain scheduler is responsible for prioritizing

the bearers based on their QoS requirements. The TDS separates the bearers prioritization process into two categories: GBR and non-GBR bearers prioritization. In each TTI the TDS sorts all active bearers of the different MAC-QoS-Classes into two separate prioritized candidate lists (one for GBR and one for non-GBR), which are then passed later to the FDS so as to start the spectrum allocation process for that respective TTI. The candidate lists indicate which bearers have to be served with higher priority. Prioritization is based on a Time Domain priority metric that depends on the bearer type.

GBR Bearers

The GBR bearers have a guaranteed bit rate requirement and are normally used for real time applications sensitive to delays. Voice over IP (VoIP) is a typical example of a GBR service, where an application end-to-end delay higher than 150ms is considered as bad call quality. In order to meet these requirements, the GBR bearers are served with strict priority before the Non-GBR bearers. The OSA scheduler prioritizes the GBR bearer k based on its buffering delay at the eNodB and its QoS class according to the following priority metric:

,maxarg)(, pk

TDGBRk ttP = (1)

with tp (p is for packet) being the HOL (head-of-line) packet delay in the bearers buffer (i.e. PDCP buffer).

Non-GBR Bearers

The non-GBR bearers normally carry non-real time services such as buffered video streaming, web browsing (i.e., HTTP), file downloads and uploads (i.e., FTP). In the TD the Non-GBR bearer k is sorted in the Non-GBR candidate list according to the following priority metric:

= jQoS

k

k

k

TDNGBRk Wt

ttP _, )()(maxarg)(

(2)

Here )(tk represents an estimate of the normalized average channel condition of bearer k and )(tk is the normalized average throughput estimate of the bearer k at time instant t. WQoS_j is the QoS weight of the jth MAC QoS class, which is used to enforce priorities and differentiate between

QoS classes. Table I shows the weighting factor in accordance to the bearer type.

The normalized average channel condition is normalized in the range between 0 and 1. It is calculated using the exponential moving average formula as follows:

max

)(/1)1()/11()(SINR

tSINRtt kkk += (3)

Therein denotes the size of the exponential moving average window and can be tuned. SINRk is the instantaneous channel condition of the bearer k and SINRmax is a normalization factor used to normalize the channel condition. The average throughput estimate )(tk is also calculated using the exponential moving average formula given in equation (4).

+=

otherwisetttimeatserved

iskbearerifttt

k

kk

k

)1()/11(

)(/1)1()/11()( max

(4)

k(t) is the instantaneous achievable throughput at time instant t and max as a normalization factor, which is defined to be the maximum throughput that can be achieved if all PRBs are used under perfect channel conditions.

C. Frequency Domain Scheduling (FDS) The frequency domain scheduler is responsible for

distributing the radio interface resources (PRBs) among the different bearers that are sorted by the TD scheduler. The FDS uses the candidate lists provided by the TDS to choose which bearers should be served within the specific TTIs. The FDS starts first with the GBR candidate list, since they have the highest QoS requirements. The FDS uses an algorithm similar to the well known Round Robin for the allocation process: one PRB at a time, with some channel conditions optimization. One PRB is allocated first to the highest priority GBR bearer and then another PRB is allocated to the 2nd highest bearer and so on until all PRBs are distributed. The distribution is done by assigning the bearers their best PRBs out of the spectrum; the best PRB is measured in terms of their SINR value (the higher the better). This is continued in iterations until all GBR bearers have been served within this TTI. Scheduling the non-GBR bearers is very similar to the GBR procedure with only one difference: only a subset of non-GBR bearers are chosen out of the non-GBR candidate list for the PRBs iterative allocation procedure, instead of the complete candidate list, as in the GBR case. This subset is normally chosen to be the maximum number of non-GBR bearers N to be scheduled in one TTI. This procedure was described in greater detail in [5].

D. OSA Optimization Parameters There are mainly three parameters that can be tuned and

optimized in the OSA scheduler. In this work we investigate the impact of the setting of these parameters on the service and system performance in different scenarios, and further derive the optimal parameter setting of the OSA scheduler.

1) Moving average window size: (in number of TTIs) 2) Maximum number of non-GBR bearers per TTI: N 3) QoSWeight of the MAC-QoS class j: WQoS_j

To decide the optimal settings, we aim to make a fair trade-off between providing proper QoS for difference services according to their priority and system performance maximization (i.e. to attain maximum resource utilization in terms of maximum cell throughput). Besides, we want to avoid long window size, which may not be able to properly capture the rapid channel changes. Therefore, in this work we define a utility u to determine the optimum settings.

+= j

jQoSj Wu _ (5)

In the above equation, j stands for the gain on the application quality for the service j, WQoS_j is the QoS weight of the service j. The gain on the application quality per service j is calculated in equation (6), where i is the improvement of the application quality using certain parameter setting and i_worst is the worst application quality among all settings from the measurements. represents the achievable gain on the system throughput, which is calculated in the similar way, as the ratio of the relative increase on the cell throughput under a configured parameter setting to the lowest one of all settings from the measurements.

worstiii _/ = (6) Besides, in order to avoid using too large window size, in

the above utility we introduce , which is the normalized window size in TTIs (in log scale) as a ratio to the maximum allowed window size max (in this work max = 10000 TTIs), as given in equation (7).

)log(/)log( max = (7) In equation (5), the parameters , , and are weighting

factors which can be used to scale performance metrics to comparable ranges and express their relative priority. It is up to the network operators to decide their importance and priority. In this work, they are all equal and set to 1.

III. SIMULATION MODEL The design overview of the LTE simulation model is shown

in Fig. 2. The model has been simplified by focusing on the user-plane end-to-end performance analysis.

Fig. 2: LTE OPNET simulation model

The figure shows that the core modeling represented by the PDN-GW and aGW. The E-UTRAN part is represented by the transport routers, the eNodeBs and the UEs. All user-plan protocols in the simulator have been implemented according to the 3GPP Rel8 specifications. The model also supports several mobility models for the users: e.g. Random Way Point, Radom Walk and Random direction. In addition, a proper channel model has been implemented in the simulation model. More information on this LTE simulation model can be found in [5].

IV. RESULT ANALYSIS In this section we present detailed simulation results to

analyze the impact of different parameters of the OSA scheduler on the service and system performance. The simulation parameters and the traffic models of difference services are summarized in Table II.

A. Impact of Moving average Window Size This section studies the impact of the optimal window size

of the scheduler on the service and cell performances. We consider a scenario with 20 Video users and 10 FTP users (their traffic models are given in Table II). Both video and FTP services belong to the non-GBR class and the video service is given a higher QoS priority than the FTP service (see Table I). The number of non-GBR bearers that can be scheduled within one TTI is set to 5. In this investigation, we change the window size from 2 TTI (2ms) up to 10000 TTI (10s).

TABLE II. MAIN SIMULATION PARAMETERS

Parameter Configurations Cell Layout 1 eNodeB with 5 MHz (~25 PRBs)

Single Cell (350m cell radius), 30 UEs in a cell Channel Model Macroscopic Pathloss model [12], Correlated

Slow Fading [13] and Jakes-like Fast Fading model with user profile ITU-Veh. A.

Mobility Model Random Way Point (RWP) with vehicular speed 120 km/h

OSA Scheduler Moving average window size (in TTIs)

2, 5, 10, 100, 1000 (default), 10000

Number of non-GBR bearers /TTI

1 ~ 16

QoS Weight See Table I Video Traffic Model Frame rate Frame size

24 frame/sec frame size: 2975 bytes

HTTP Traffic Model Number of pages/session 1 (with 1 object of size 1MByte in each page) Reading Time 12 sec FTP Traffic Model Inter-request time File Size

Inter-request time = uniform(1,3) seconds file size = 10MByte

Fig. 3(a) shows the impact of the window size parameter on the video performance in terms of the video packet end-to-end delay. It is seen that when choosing a larger window size (100 TTIs), the average video end to end delay and its standard deviation decreases, which means that we achieve an improved video experience. However, when the window size is further increased from 100 TTIs to 10000 TTIs, the video performance stays constant. On the other hand, it can be seen in Fig. 3(b) that the FTP performance gets worse when a larger window

size is chosen, but similarly its performance is not further degraded when the chosen window size larger than 100 TTIs.

2TTI 5TTI 10TTI 100TTI 1000TTI10000TTI0

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(a) Average video packet delay (ms)


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0.5

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Optimization Utility

Window size (#TTIs)

Util

ity

(d) Optimization utility

Fig.3: Impact of window size on (a) video performance, (b) FTP performance, (c) cell throughput, (d) optimization utility

This can be explained as follows. From equation (3) and (4) it can be seen that with a larger moving average window size, the estimate of the average channel conditions and the average throughput depends more on the history (the estimate from previous TTIs) and less on the instantaneous throughput and channel condition of the current TTI. This also means that when choosing a larger moving average window size, the estimate of the channel conditions and the throughput get closer to the average value since it is considering the changes

over a longer time period. But with smaller window size (lower than 100 TTIs) the estimate of the throughput and the channel conditions will be more dependent on the instantaneous values and thus result in higher variation of the user performance. For the FTP service this means, due to its elastic traffic property, that when there is a higher variation of user throughput - by configuring a smaller window size - we can achieve more multiplexing gain and, in turn, better performance. However, the nature of the video traffic is constant and therefore it suffers from a higher variation of the instantaneous channel conditions due to a smaller window size and results in higher video end-to-end delay. That is why the video performance is decreased whereas the FTP performance gets improved when the window size is reduced from 100 TTIs to 2 TTIs. When the window size is larger than 100 TTIs, the estimate of the channel conditions and throughput become stable and are close to their average values. Therefore, the performances of both services become stable. In addition to service performances, Fig. 3(c) shows that the achieved cell throughput is higher with smaller window size (less than 100 TTIs). This is due to the additional multiplexing gain from the FTP traffic and achieved higher spectral efficiency. Similarly, when the window size is larger than 100 TTIs the cell performance stays constant. Fig. 3(d) illustrates the calculated optimization utility following equation (5), where the weighting factors , , and are all equal and set to 1. It is seen clearly that we get the highest utility at 100 TTIs, where the achieved performance gain of video is 80% and the gain of FTP is 1.5%. Thus, for this case the optimal window size for this scenario is 100 TTIs. As seen from the above results, with 100 TTIs we can achieve minimum delay for the video service though at the cost of longer FTP download time and slightly lower cell throughput. For our objectives this is a proper trade-off between the different criteria, since our first priority is to provide the best QoS for the video service which has much higher QoS priority than the best-effort FTP service in this case.

B. Impact of the number of non-GBRs This section investigates the service performance and

achievable cell throughput with respect to the maximum number of non-GBR bearers per TTI (parameter N). We take the same example scenario as given above, i.e. 20 Video users and 10 FTP users. The moving average window size is set to the default setting of 1000 TTIs. In this investigation, we vary the parameter N (i.e. the number of non-GBR bearers to be scheduled per TTI) between 1 and 16. It can be seen from Fig. 4(a) and (b) that both video and FTP service performance is improved when N is increased from 1 to 7. This is because, when N is set to 1 (each TTI only one non-GBR bearer can be scheduled), the scheduled bearer is not able to use all 25 PRBs and moreover there is no multi-user diversity gain. When N is increased from 1 to 7, each bearer will get less PRBs and thus the PRB efficiency (bit/s per PRB) is decreased according to 3GPP, but in total more PRBs are used (i.e. utilization of PRBs is improved) and additional multi-user diversity gain is achieved. Furthermore, setting a higher N will give higher multi-user diversity gain which is more important in terms of service performance than the impact of the decreased PRB efficiency. Hence, the performance of both services is enhanced. Similarly, the obtained cell throughput also gets

increased due to the higher multi-user diversity gain achieved by increasing N as shown in Fig. 4(c).

1 3 5 7 8 9 10 12 14 15 160

20

40

60

80

100

120

140Video end-to-end delay (ms)

# nGBR bearers served per TTI

Vide

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(ms)

(a) Average video packet delay (ms)

1 3 5 7 8 9 10 12 14 15 160

100

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400FTP download time (s)


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(s)

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1 3 5 7 8 9 10 12 14 15 160

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Thro

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(c) Cell throughput (Mbps)

1 3 5 7 8 9 10 12 14 15 160

0.5

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1.5

2

2.5Optimization Utility


Util

ity

(d) Optimization utility

Fig.4: Impact of N on (a) video performance, (b) FTP performance, (c) cell throughput, (d) optimization utility

Nevertheless, when N is further increased from 8 to 16, it is seen from Fig. 4(a) that the video performance is decreased. The reason is that there are less PRBs per TTI available that can be allocated to each video bearer. On the other hand, the FTP performance shown in Fig. 4(b) is not influenced by further increasing N from 8 to 16, but rather it is approaching constant value close to the best performance. The reason is that whenever the radio resources are fully utilized, the average FTP performance is merely dependent on the total number of

FTP users in the cell other than the number of FTP bearers to be served per TTI due to the elastic property of TCP traffic. With N=8 the maximum multi-user diversity gain has been reached. Therefore, with further increasing N the FTP performance and the cell throughput remain constant at their maximum. Fig. 4(d) gives the calculated optimization utility following equation (5), where the weighting factors , , and are all set to 1. It is shown that in this scenario the optimal value for the parameter N is 7 non-GBR bearers.

In addition to the above presented results, we also studied the sensitivity of the parameter settings of the OSA scheduler with respect to various traffic characteristics, and moreover investigated the impact of the QoS weights on the performance of different services of various QoS classes. However due to space limitations we do not show all results but summarize the main findings below.

1) In data-only scenarios, the optimum window size and the number of non-GBR users per TTI are not sensitive to the file sizes and file size distributions.

2) The setting of QoS weights has significant influence on the service performance of different QoS classes. By setting a relative higher weighting factor for a single QoS class, its performance will be improved at the cost of performance degradation of the lower priority class.

3) By adding the GBR service together with the non-GBR services, the performance of the GBR service is guaranteed since it has the highest priority, and its performance is also independent of the configured parameter settings. But for the non-GBR services, the impact of the parameter settings on their performances as we presented above needs to be considered.

V. COMPARISION OF OSA AND PF SCHEDULER In this section we compare the OSA scheduler against the

well known PF (Proportional Fair) scheduler under different settings of window size (in Fig. 5) and maximum number of non-GBR bearers to be severed per TTI (in Fig. 6), using the same scenario given above ( 20 Video users and 10 FTP users). From Fig. 5 and Fig. 6, we can observe that both schedulers have similar trend of change (increase or decrease) on the service performances and cell throughput with the increase of window size and the maximum number of non-GBR bearers per TTI, which follows the same tendency of the results presented in section IV.

By comparing the performance of OSA and PF scheduler, it is seen that the OSA scheduler is indeed service-aware, which guarantees a much better performance for high priority video service though at the cost of reducing the performance of lower priority FTP service and cell throughput. In comparison to OSA scheduler, the PF scheduler is based upon maintaining a balance between fairness and system throughput, however it does not consider the differences of various services and their QoS requirements. Therefore, it is not able to provide a proper balance between multi-QoS provisioning for the mixed real-time and non-real-time traffic according to their priorities, and hence it is not well suitable for the cases of multi-services with different QoS priority and requirements.

10TTI 100TTI 1000TTI 10000TTI0

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(s)

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5

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Thro

ughp

ut (M

bps)

Cell throughput (Mbps)


Fig.5: Comparing OSA against PF scheduler over different window sizes:

(a) video performance, (b) FTP performance, (c) cell throughput

VI. POTENTIAL GAIN OF SELF-OPTIMIZATION From the results presented in section IV, we have seen that

(=100TTIs, N=7) is the best settings for the window size and the number of non-GBR users per TTI of the OSA scheduler for the example traffic scenario. Without self-optimization of the scheduler, we will use this optimal setting for all scenarios. In the following we compare this case with the case in which the scheduler is self-optimized. Our assumption is that the self-optimized algorithm is able to adapt the optimal setting in every situation.

In Table III we gave the optimal settings for three other traffic scenarios with various mixes of service types. For each traffic scenario, the obtained optimal scheduler parameter settings are different. Comparing to the case (without self-optimization) in which the setting of the scheduler (=100TTIs, N=7) is fixed, the achieved gain on the video performance using their individual optimal settings in the three different traffic scenarios is quite significant. Though there is a slight decrease in the performance of FTP (Best-Effort service) and cell throughput, the resultant overall utility on the performance gain is still considerably high in case if we adapt the optimal settings for every situation. That also implies that there are certainly potentials for applying the self-optimized algorithm for the MAC scheduler.

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Fig.6: Comparing OSA against PF scheduler over different N: (a) video

performance, (b) FTP performance, (c) cell throughput

TABLE III. GAIN OF SELF-OPTIMIZATION OF MAC SCHEDULER

Scenario Optimal settings

Video FTP /HTTP

Cell through.

Utility gain

25 FTP UEs / 5 video UEs

=1000TTIs N=3

22.9% -1.5% -2% 110%

2 FTP UEs / 20 video UEs

=5TTIs N=7

8.6% -1% 0% 42%

10 FTP UEs / 10 HTTP UEs/ 10 video UEs

=1000TTIs N = 3

17.8% HTTP: 1% FTP: -7.6% -2.6% 80.8%

Besides, there is also a potential for self-optimizing the

QoS weights for various QoS classes assuming that each has a different QoS requirement. In different traffic situations, the carried traffic amount of each QoS class is usually different and may change over the time. When we have fixed radio resources in the cell, the scheduler is responsible for distributing the resources among the different QoS classes according to their priorities (QoS weights) and traffic changes so as to accommodate their traffic demand and as well to ensure their individual QoS requirements. For this purpose, we will need a self-optimized algorithm to track the traffic changes of each QoS class and measure their performances, and then according to the measurements to adapt the QoS weighting factors for all QoS classes automatically such that the QoS requirements of each QoS class are satisfied while targeting the best system performance at the same time. For example, if the

traffic demand of one QoS class increases and it is not able to meet its QoS requirements with the current allocated resources, then we need to assign higher QoS weight for this class to allocate it more resources. This will be left for the future work to develop such self-optimization algorithms.

VII. CONCLUSIONS In this paper we explore the optimization potential of the

LTE MAC scheduler by investigating the impact of parameter adjustments on the service and system performances. By comparing to the well known PF scheduler, we can see a clear advantage of OSA scheduler in terms of QoS-aware, which is important to support multi-service LTE networks. Furthermore, we study the sensitivity of the optimal parameter settings for the OSA scheduler with respect to various traffic scenarios (traffic mix). We find potential gain in applying self-optimization to the QoS-aware MAC scheduling mechanism, which justifies the development of self-optimization algorithms for the scheduler. As a future research topic we intend to develop self-optimization solutions for the OSA scheduler and even explore the potential of other types of schedulers.

REFERENCES [1] A. Pokhariyal, T. E. Kolding, and P. E. Mogensen. Performance of

Downlink Frequency Domain Packet Scheduling for the UTRAN Long Term Evolution. In IEEE 17th International Symposium on Personal, Indoor and Mobile Radio Communications, pages 1-5, 2006.

[2] Kian Chung Beh, Simon Armour, and Angela Doufexi. Joint Time-Frequency Domain Proportional Fair Scheduler with HARQ for 3GPP LTE Systems. In Vehicular Technology Conference, 2008. VTC 2008-Fall. IEEE 68th, pages 1-5, 2008.

[3] G. Monghal, K. I. Pedersen, I. Z. Kovacs, and P. E. Mogensen. QoS Oriented Time and Frequency Domain Packet Schedulers for the UTRAN Long Term Evolution. In IEEE Vehicular Technology Conference, VTC, pages 2532-2536, 2008.

[4] A. Pokhariyal, K. I. Pedersen, G. Monghal, I. Z. Kovacs, C. Rosa, T. E. Kolding, and P. E. Mogensen. HARQ Aware Frequency Domain Packet Scheduler with Different Degrees of Fairness for the UTRAN Long Term Evolution. In IEEE 65th Vehicular Technology Conference, VTC, pages 2761-2765, 2007.

[5] Y. Zaki, N. Zahariev, T. Weerawardane, C. Goerg and A. Timm-Giel. Optimized Service Aware LTE MAC Scheduler: Design, Implementation and Performance Evaluation, in OPNET workshop 2011, Washington, D.C., August 29-September 1, 2011, USA.

[6] N. Zahariev, Y. Zaki, T. Weerawardane, Xi Li, C.Goerg, and A. Timm-Giel. Optimized service aware LTE MAC scheduler with comparison against other well known schedulers, in 10th International Conference on Wired/Wireless Internet Communications, WWIC2012, Santorini, Greece, 6-8 June, 2012.

[7] 3GPP TS 36.902, Evolved Universal Terrestrial Radio Access Network (E-UTRAN); Self-Configuring and Self-Optimizing Network (SON) Use Cases and Solutions (V1.0.1). 2008.

[8] Motorola Inc.,LTE Operations and Maintenance Strategy: Using Self-Organizing Networks to Reduce OPEX,, March 2009.

[9] 3G Americas, The Benefits of SON in LTE, December 2009. [10] I. Fernandez Diaz, R. Litjens, J.L. van den Berg, D.C. Dimitrova, K.

Spaey. Sensitivity Analysis of the Optimal Parameter Settings of an LTE Packet Scheduler, in VTC2010-Spring, Taipei, Taiwan, 2010.

[11] 3GPP TS 23.203, Policy and charging control architecture (Release 11), January 2011.

[12] 3GPP TS 25.814 Physical layer aspects for E-UTRA. V7.1.0, 2006. [13] Y. Zaki, T. Weerawardane, A. Timm-Giel and C. Grg. "Multi-QoS-

aware Fair Scheduling for LTE". In IEEE 73rd VTC2011, Budapest, Hungary.

Wireless Networking

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