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White Paper by VTT

March 2012

Cognitive Radio Systems:

Enabler for Intelligent Wireless Telecommunications

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EXECUTIVE SUMMARY The lack of communication resources – especially because of overloaded frequencies - is seen during the last years when wireless communications has been increasingly taken into use by consumers. Wireless operators are now continuously looking for solutions to avoid overloading their frequencies. Much can be done by using existing resources more effectively by taking cognitive radio systems into use. A cognitive radio system (CRS) is aware of its environment and makes decisions considering the performance of the whole radio system and is able to learn of its environment and performance. The spectral efficiency in bit/s/Hz of modern systems in a given frequency band is rather high. The performance of the CRSs is measured in terms of spectrum occupancy which is defined as the percentage of the total bandwidth that is used on the average.

The authorities of public sectors have taken the possibility for more flexible radio technologies seriously for decades. Increasing wireless networks use puts pressure on network operators to develop new business models and new ways to earn in the situation where the lack of available radio resources turns to be the bottleneck for increase in the business. Cognitive and intelligent radio systems may provide new possibilities to funnel traffic to frequencies and technologies that enables the optimal total network usage from operator viewpoint. This trend may provide end-users even free usage to certain nontypical wireless technologies when traffic is offloaded from crowded networks.

We will develop a system concept and innovations for cognitive communication technology based on market needs. This is done by creating new scientific knowledge and combining competencies related

Antennas and RF electronics: Implementation technology focusing on cognitive radio antennas, antenna tuning and matching as well as fast analog signal processing.

Wireless cognitive radio systems: Development of tools for intelligent radio and network resource management, spectral and energy efficient air interfaces as well as cognitive and cooperative techniques to enhance coexistence of various heterogeneous wireless networks.

Network control and management: A concept of a self-controlled network, methods to learn and carry out control operations autonomously, and architecture of learning based network control and management systems as well as a platform for autonomous control systems.

Finally, we integrate a multitechnology test platform that demonstrates cognitive network performance from end-to-end. In addition to spectrum occupancy, energy efficiency is an important aspect of this research.

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1. Driving Forces The lack of communication resources – especially because of overloaded frequencies - is seen during the last years when wireless communication has been increasingly taken into use by consumers. It looks that timing for wireless technologies has been right, and this opportunity has been taken seriously by wireless operators. They are now continuously looking for solutions to avoid overloading their frequencies. During the next years we may witness a trend where mobile operators will invest heavily in offloading wireless traffic into wired systems. On the other hand, much can be done by using existing resources more effectively by taking cognitive radio systems into use.

time20102005 2015 2020

3GPP HSPA

3GPP E-UTRA (LTE)- DL: 300 Mbit/s, OFDMA- UL: 75 Mbit/s, SC-FDMA- 2x2 and 4x4 MIMO- DL: 15 bit/s/Hz- UL: 3.75 bit/s/Hz- Codebook-based beamforming

- DL: 14 Mbit/s, WCDMA- UL: 5.8 Mbit/s, WCDMA

3GPP HSPA+- DL: 42 Mbit/s, WCDMA- UL: 11 Mbit/s, WCDMA- 2x2 and 4x4 MIMO

3GPP LTE-Advanced

IMT-Advanced- Low mobility: 1 Gbit/s- High mobility: 100 Mbit/s- DL: 15 bit/s/Hz- UL: 6.75 bit/s/Hz

- DL: 1 Gbit/s- UL: 500 Mbit/s- DL: 30 bit/s/Hz- UL: 15 bit/s/Hz- Coordinated scheduling/beamforming

Cognitive radio systems

IEEE802.16e-2005- Fixed and mobile applications- MIMO-OFDMA- DL: 63 Mbit/s, 2x2 MIMO, 10 MHz BW- UL: 14 Mbit/s, 2x2 MIMO, 10 MHz BW- Beamforming, space-time coding, spatial multiplexing

IMT-Advanced+- Low mobility: 10 Gbit/s- High mobility: 1 Gbit/s

- Spectrum sharing- Sensing/data base

IEEE802.16m- DL: 15 bit/s/Hz- UL: 6.75 bit/s/Hz

IEEE802.16-2004- Fixed applications- Single carrier, OFDM, OFDMA- 2x2 MIMO with space-time block coding

IMT-2000- Low mobility: 2 Mbit/s- High mobility: 384 kbit/s

Figure 1. Roadmap from standards viewpoint (IMT = International Mobile Telecommunications, 3GPP = Third Generation Partnership Project, HSPA = High-Speed Packet Access, E-UTRA = Evolved UMTS Terrestrial Radio Access, UMTS = Universal Mobile Telecommunications System, LTE = Long-Term Evolution, IEEE = Institute of Electrical and Electronics Engineers, WCDMA = wideband code division multiple access, OFDMA = orthogonal frequency division multiple access, OFDM = orthogonal frequency division multiplexing, SC-FDMA = single-carrier frequency-division multiple access, MIMO = multiple-input multiple-output, DL = downlink, UL = uplink, BW = bandwidth).

2. Cognitive and Intelligent Networks

The Wireless Innovation Forum – www.wirelessinnovation.org – has divided advanced radio systems into three categories: adaptive, cognitive, and intelligent radio systems. An adaptive radio system can monitor its own performance and make some performance enhancements. A cognitive radio system (CRS) is aware of its environment and makes decisions considering the performance of the whole radio system. The intelligent radio system is a cognitive radio system that is able to learn of its environment and performance. In the definition by the International Telecommunications Union – Radiocommunications (ITU-R), learning is included in the concept of cognitive radio systems [1].

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Cognitive radio systems possess the ability to intelligently monitor several parameters in their radio frequency environment and dynamically reconfigure their transmission and reception parameters to enable efficient communications with reduced interference. The most advanced software-defined radios can additionally sense the environment and react appropriately according to the changes in the parameters it has sensed, in real time. A major advantage of CRSs is the optimal usage of spectrum due to their capability to check for available channels and seamlessly transfer the communications to vacant channels. Cognitive radios also make efficient use of licensed bands by using those channels for communications when available and vacating the channel immediately when a licensed user starts using it. The spectral efficiency in bit/s/Hz of modern systems in a given frequency band is rather high. The performance of the CRSs is measured in terms of spectrum occupancy which is defined as the percentage of the total bandwidth that is used on the average.

Besides dynamic spectrum access, CRSs are also capable of adaptive modulation and coding, that is, adaptively modifying transmission characteristics and waveforms according to changing requirements in the environment. Another ability that is possible with such radios is transmitter power control, by which they can control the power transmitted to conserve energy as well as to avoid interference. Cognitive radio systems are capable of coexisting with other CRSs in the network as well as with other legacy radios, and provide easy interoperability between different systems by virtue of their ability to communicate with a variety of waveforms.

When the concept of learning is applied to CRSs, it means turning into the Wireless Innovation Forum defined ”intelligent radio system”. When learning is applied, the CRS uses available heuristic and measured information, such as policies, user preferences, and network configurations and changes in it, to decide on the best course of action. Thus, CRSs – or Intelligent Radio Systems - can learn to understand meaningful characteristics of the region’s telecommunications, and work accordingly. For example, they may learn to know in advance if the signal is bad in a particular area and to avoid using such frequencies or channels.

3. Who Should Be Interested in It?

3.1 Public sector

The authorities of public sectors have taken the possibility for more flexible radio technologies seriously for decades. The reason for this is quite clear: the networks for official communications should be developed and used so that problems in their usage – whatever the reasons for harmful effects were – can be avoided. Communication networks for public authorities can be seen as a target for terrorist attacks and mechanisms to minimize that risk are used largely. With rising terror threats across the globe, governments have been actively seeking to strengthen their public safety infrastructures by adopting next-generation technologies that facilitate seamless communication across public safety units. In addition to homeland security, equipment for military use forms another sector where new innovations are eagerly taken into use.

Public sector forms a kind a use-case for cognitive and intelligent radio systems. Typically the lack of radio resources, such as frequencies, is not the main problem. The main problematic is more on how to ensure reliable and predicted communication channels under dramatic disturbances caused by a civil catastrophe – such as earth quake or flood –

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or by military raid, viruses in IT systems, or any other situation where the communication system is partly or totally down.

3.2 Network Operators

Increasing wireless networks use - enabled with new technologies, services, and devices – puts pressure on network operators to develop new business models and new ways to earn in a situation where the lack of available radio resources turns to be a bottleneck for increase in the business. The current and near future lack of frequencies is not caused by huge number of frequency-dependent services and users, but it is caused by the allocation of frequencies. Frequencies allocated to wireless telecommunication operators is a minority of the whole spectrum. Increasing traffic in the future enforces operators to use their frequencies more effectively than today.

Current trends, when waiting for the real cognitive radio systems, is to agree the frequency use first with authorities and secondly with other operators. National authorities sell frequencies to operators typically by using public auctions. The amount of sold frequencies is typically calculated by using estimates of future needs for wireless communication. Typical is that operators succeed differently in the business, which cause negotiations between them about network resource sharing. Possibilities for this depend heavily on legal issues.

Unlike in public sector, we will see more evolution than revolution in end-user targeted network operator business. Operators are forced to check all possible alternatives to go on with lower prices and growing amount of transferred wireless data. This means for example smaller cell sizes and more emphasis on offloading wireless traffic.

3.3 End-Users

End-users are the end of the business chain and therefore are affected by decisions made by operators. During last decades mobile operators’ business models have changed a lot, and we can expect that trend to continue. Cognitive and intelligent radio systems may provide new possibilities to funnel traffic to such frequencies and technologies that enables the optimal total network usage from operator viewpoint. This trend may provide end-users even free usage to certain nontypical wireless technologies when traffic is offloaded from crowded networks.

4 VTT in Cognitive Networks - Solution to Intelligent Networking The technical quantum jumps of recent years have changed the playground of mobile computing dramatically. We will present potential problems and solutions in three essential fields including antennas and RF, radio systems, and network layer.

4.1 Antennas & RF electronics

Cognitive radio (CR) sets very high requirements for the flexibility and configurability of terminals. In addition, mobile terminals have to fulfill stringent cost, mass/volume and power consumption requirements, whereas in base stations and other fixed assemblies these are important, but the overall technical performance is emphasized. Because the cognitive radios need to adjust their operation to the environment and the available radio spectrum, the radio frequency (RF) parts of the equipment need to have a considerably

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broader bandwidth than in the present mobile radio terminals and base stations. This places new kind of requirements on the RF components and subsystems. The bandwidth either needs to be instantaneous or achieved with quick tuning. The trend has been lately more towards widely tunable narrowband RF front-ends. This improves linearity and channel selection capabilities but on the other hand increases requirements for high performance, high quality factor (Q) tuning elements. Also new kind of functions, such as spectrum sensing devices, are needed.

Many CR applications are based on small mobile terminals where the antennas are small compared to the wavelength. In the near future new tuning and matching methods will be developed further. One development trend is to have a tunable narrow band antenna with several simultaneous independent bands. Passive components together with configuration switches provide tuning. Another approach might be to use active circuits to overcome the theoretical limits of passive implementations, for example nonconventional circuit elements, such as negative inductances, can be realized with active circuits.

An integrated module consisting of antenna, tuning, matching, and amplifier circuits could be feasible and offer several new development alternatives, making use of for example advanced semiconductor and microelectromechanical (MEMS) technologies.

Very fast digital low cost CMOS technology can also be used and has already been used to realize high frequency or broadband analog circuits. CMOS technology has proved to be feasible for at least in less demanding volume applications up to millimetre waves.

Single chip radio front-end has been proposed already for a long time. However, both power amplifiers and filters still require off-chip external components. Power amplifiers are typically realized with GaAs and high quality factor passive technologies. For filters, bulk acoustic wave (BAW) and surface acoustic wave (SAW) technologies can be used.

CR concept requires either a large filter bank or widely tunable filters. Filter banks consist of several filters and switches having a high performance but a rather high cost. Especially on the receiver side, tunable filters combining low noise amplification and filtering with high-Q elements have quite a good performance. These kinds of multi-technological solutions might be cost effective for cognitive radios.

In cognitive radio transmitters, broadband power amplification is also needed. It may not be possible or necessary to make the power amplifier to have an instantaneous broad bandwidth, but active “smart” tuning circuits need to be developed instead.

For spectrum sensing, a new class of circuit and subsystem solutions should be developed. In this area there is more room for new ideas and innovations than in more conventional functions because a need for fast, compact and low cost spectrum sensing solutions has not existed before the era of cognitive radios.

At VTT a large number of antenna solutions applicable to CR have been developed, for example tunable and reconfigurable antenna designs for wireless systems up to 6 GHz and compact 60 GHz and E-band antenna designs. VTT is able to perform advanced antenna designs starting from electromagnetic (EM) simulations up to tested prototypes and accurate modelling of RF components and structures.

In radio frequency electronics, many system, circuit, and component solutions suitable for cognitive radio have been developed at VTT, for example

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very low noise amplifiers and receivers

reconfigurable circuits and sub-systems based on novel technologies such as ferroelectric varactors and MEMS

mm-wave circuits based on several technologies including CMOS, GaAs, InP, SiGe and RF MEMS

customer specific fabrication services in for example ferroelectric varactors, BAW, RF MEMS, and low temperature co-fired ceramics (LTCC) system-on-package technologies.

Key enabling technologies for electrically tuneable and reconfigurable subsystems include for example electric field controlled ferroelectric thin film capacitors and micromechanical switches. VTT offers an affordable solution in fabricating advanced MEMS devices for CR applications. VTT’s other important technologies for CR include integrated passive component technologies and BAW thin film resonators. Active circuit solutions focus on 60 GHz CMOS circuits.

Figure 2. 60 GHz antenna array feed network on ceramic LTCC substrate.

4.2 Wireless communication methods

As presented in Figure 1, 3GPP Long Term Evolution (LTE) is the present developed system for the cellular radio systems. The revolutional LTE is able to benefit from the latest technology development from the evolutional HSPA+ while also being free to adapt new radical technologies without constraints of backward compatibility. LTE-Advanced (LTE-A) systems are coming in 5-10 years. LTE-Advanced (LTE-A) shall fulfil the IMT-Advanced (IMT-A) requirements, and ITU has qualified LTE-A as the true 4G technology on 21 October 2010. Frequencies for IMT-A have been defined in ITU-R report [2]. ITU-R has also defined a cognitive radio system [1], and the cognitive communications is on the agenda of the next WRC-2012 conference. No other decisions on cognition have been made. However, it will be estimated that full cognitive systems are commercially available after 10 years.

Cognition is, however, slowly coming to commercial and military communication systems. For example, the first LTE-A Release 10 includes features such as contiguous and non-contiguous carrier aggregation that support cognitive radios. In five years cognitive principles will be used in some general forms. For example, networks are able to support

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the adaptation to different environments. Base stations are aware of the traffic jams, and they can adapt to the situation. Tools for adaptation and spectrum sensing already exist but reliable demonstrations are needed in order to convince the regulators. The overall technical challenges include amount of control information (distributed vs. centralized control) and minimization of the overall energy consumption in mobile radio networks. A long term vision is that cognitive systems are based on cellular systems and they use databases with which the users reserve spectrum according to their priority levels. Operators cooperate with each other. Opportunistic systems with spectrum sensing are used mainly locally, and self-organized networks are widely used. New intelligent cognitive radio systems and networks under development will use cooperative techniques, and learning from past experience. The complexity and energy consumption versus spectral occupancy need a critical study.

Figure 3 simplifies the ITU-R definition for the cognitive radio systems where the cognitive cycle plays an important role [1]. In general, VTT is working with all the technical aspects of cognitive cycle as well as made contributions in the regulatory domain. A technical challenge is how to collect all the required information and how to manage radio and network resources efficiently? On the other hand, a business challenge is that regulation and standardization are not mature yet.

Internal state

Learning

Obtaining knowledge

Decision and Adjustment

C R SOutside World

Figure 3. ITU-R definition for the cognitive radio systems [1].

Figure 4 summarizes the current VTT’s offering for cognitive radio systems. In general, VTT has developed cognitive techniques that can use cooperation, history database, and intelligent allocation of spectrum sensing and transmission resources. Matlab simulation models exist in order to make performance versus complexity estimations for spectrum sensing using energy detection, and cooperative spectrum sensing with soft or fuzzy decision making. Limits for transmission power for sensing based spectrum access, and adaptive algorithms for fast fading links have been simulated. It has been shown that network capacity improvements can be achieved using smart antennas. Channel selection methods using history and topology information have been studied, and we have also experience on simulation of different system models including different channel models. The results are published in over forty publications including journal and conference papers, book chapters and regulatory contributions. Two patent applications have also been filed for cognitive radio systems. The first application presents a method for adaptive signal detection, and the second one is for device for selecting one or more resources for use from among a set of resources. Finally, an invention report has been filed for model-based learning. The generated results for cognitive radio systems are currently applied to improve energy and spectrum occupancy, e.g., in OFDMA-based femtocell networks that are expected to be widely deployed in an IMT-Advanced network.

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Active awareness

Database

Requirements, policies and administrative information

Spectrum availability information

Database

Selection of spectrum availability

information technique

Selection of transmission

technique

Decision making

Spectrum sensing results combining

Long-term channel selection

Short-term channel selection

Sensingnodes

and learning

Adaptive transmission

Figure 4. Summary of VTT’s offering for cognitive radio systems.

Finally, VTT has around 20 years experience about customized digital signal processing (DSP) algorithms, transmission protocol and HW/SW design for radio modems. VTT has made tailored solutions for GSM, 3G evolution, LTE, WiMAX and microwave links as well as WLAN and sensor networks. VTT has also competence on short range communications, especially in the area of ultra-wideband (UWB) and 60 GHz technologies. Pervasive short-range devices are significant part of future heterogeneous cooperative networks. Physical layer is an important enabler for practical cognitive radios, and a typical bottleneck to increase transmission quality energy efficiently. Implementable MIMO-OFDM algorithms have been developed for beamforming, spatial multiplexing, and space time coding. One critical component in multicarrier communications is a nonlinear power amplifier, and digital compensation and signal design techniques have been evaluated.

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4.3 Networks control and management

Smart phones with video streaming capabilities are taking over personal computers as the main web access device. At the same time, network access technologies are converging, and ad-hoc machine-to-machine (M2M) communication is playing a more significant role in networks. Changes in terminal and network sides are evidently creating new demands for network control and managements, which cannot be fulfilled with existing conventional control and management mechanisms. To meet these new challenges, cognitive features such as sensing and learning are introduced to future networks. The use of expert systems with unsupervised training is compelling because they are mimicking activities of human experts. They have the ability to learn from inferences and use prior experience and knowledge. Moreover, they are developed for dealing with uncertainties and solving problems that have no satisfactory algorithmic solution. That is why they are also ideal for network control and management of future networks where network entities are getting more autonomous and capable of configuring, optimizing and healing themselves without human involvement. However, these future networks do need an efficient collaboration mechanism, and that is the motivation behind the development of Distributed Control Management Framework (DCMF) concept.

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Figure 5. An example of using DCMF to perform hand-off before a user experiences degraded video quality as the train enters a tunnel. In the left picture, the terminal has executed handover to the other base station with the assistance of DCMF. In the right picture, a terminal is trying to keep the existing connection and thus suffers from a degraded video quality.

DCMF tackles future demands in three tiers. The first tier covers both physical and link layers. The aim is to collect as much relevant performance and quality of service (QoS) information as possible from the terminal and network sides to support decision-making. The challenge is that today’s monitoring tools on the network and terminal sides are collecting a vast amount of information, but this data contains a lot of irrelevant information. This data flood prevents us from building real-time and large scale network management solutions that are fast enough to react against encountered problems. Stemming from this, we have used expert systems in order to significantly reduce the

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amount of monitored information as needed for performance and QoS assessments. As part of this development work, a concept of hierarchical monitoring architecture was introduced. It enables highly localised network monitoring and management operations. The aim is to make monitoring even more scalable by extracting most dominant network performance indicators of each subsystem and by forming at a given time the minimum set of indicators or their combinations to provide a sufficient amount of data for reliable performance assessment.

The second tier of DCMF covers the networking layer. The monitored and control information needs to be delegated with minimum delays and overhead to the network entities requesting information. For this, DCMF is exploiting a distributed and event-driven data exchange mechanism called Triggering Engine (TGR). The TGR mechanism supports the DCMF concept well because data and processing complexity is distributed among network entities making them more self-governing and agile to cope with topological and traffic changes. OPEX costs have become a major concern for telecom operators. They are actually hindering the development of new services for end users. To reduce this problem, we have also introduced a concept called distributed Policy Based Network Management (PBNM) to DCMF. In the PBNM approach, policies are used to provide high level abstraction to network management operations. For example in DCMF, policies are used for enforcing the operators’ view of preferred resource and mobility management actions to individual users and their applications.

The third tier of DCMF covers session and application layers. One of the key applications for DCMF is reliable mobile video delivery. For this, dynamic routing over multi-access networks, and adaptive video delivery and Scalable Video Coding (SVC) mechanisms are used. Both mechanisms require detailed and accurate information from the available networks and terminals to operate optimally. The DCMF’s task is to provide this information with the aid of monitoring and decision-making components.

Figure 6. An example of using adaptive Scalable Video Coding (SVC) technology. In the left picture, receiving of the SVC enhancement layer is stopped due to low network QoS level and re-activated again in the right picture when the sufficient network QoS level is retained.

To fulfil the demand for quick response, it is not sufficient to rely only on measured information. We also need a mechanism to provide foresight information to give real-time applications and the network enough time to alleviate raising problems. Here, we are focusing on merging network planning information (interference, coverage and data rate predictions) and context-information (user’s location, direction, and speed) with the

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monitored information in order to provide additional foresight information for decision-making and also to detect possible patterns and trends in the networks’ or users’ behaviour. This information can be used e.g. for adjusting smart antennas in the network.

The drawback of using foresight information is that it can be inaccurate or even totally wrong leading to incorrect decisions. Therefore, it is vital to have a feedback loop from the monitoring elements to both prediction and decision-making elements in order to validate the outcome of these components and supervise them when necessary.

Figure 7. Predicted coverage of three base stations calculated with a propagation model (left) and then optimized (right) with the help of field measurements and context or environment information. In these figures, warm colors (red) indicate high received signal levels whereas cold colors (blue) lower ones. The difference between the pictures shows that the used prediction model overestimates the propagation loss, and fine-tuning with measurements and enviroment information was needed.

[1] “Definitions of Software Defined Radio (SDR) and Cognitive Radio System (CRS),” ITU-R SM.2152, 2009, http://www.itu.int/publ/R-REP-SM.2152-2009/en.

[2] “Requirements, evaluation criteria and submission templates for the development of IMT-Advanced,” ITU-R M.2133, 2008, http://www.itu.int/publ/R-REP-M.2133-2008/en.

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