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  1. 1. Cognitive Radio and its RF Challenges Markku Renfors Tampere University of Technology Finland
  2. 2. Contents 1. Dynamic spectrum access and cognitive radio ideas 2. Elements of cognitive radio systems 3. Filter bank approach for cognitive radio physical layer – Spectrum agility 4. Spectrum sensing – Energy detection based spectrum sensing – Wideband sensing using FFT or filter banks 5. RF challenges in cognitive radio
  3. 3. Dynamic spectrum access and cognitive radio techniques • Conventionally, the use of radio frequency bands has been regulated through spectrum allocations with licensing procedure. – However, measurements on the licensed bands show severe temporal and / or spatial underutilization of the assigned spectral resources. – This imposes a great challenge for future wireless communications which attempts to satisfy the ever growing demands for new services and ubiquitous broadband wireless access. • In order to solve the imbalance between spectrum shortage and spectrum underutilization an innovative spectrum access strategy called spectrum pooling has been visioned. • A concept of cognitive radio (CR) has been proposed as a promising technology to fulfill the unique requirements of intelligence and spectrum agility necessary for succesful deployment of such dynamic spectrum access.
  4. 4. Dynamic spectrum access and cognitive radio techniques • Spectrum pooling – Allows opportunistic secondary (unlicenced) access to spectral resources unused by their primary (licensed) owners. Significant improvement in spectrum utilization. – Secondary transmission must avoid any harmful interference to primary systems.  CRs have to regularly perform reliable radio scene analysis to detect the presence of primary user signals with high detection and low false alarm probability. • Cognitive radio – Smart and spectrally flexible radio (secondary user device, SU) that monitors and senses its radio environment for potential spectrum opportunities.
  5. 5. Cognitive radio Definition by FCC: “A radio or system that senses its operational electromagnetic environment and can dynamically and autonomously adjust its radio operating parameters to modify system operation, such as maximize throughput, mitigate interference, facilitate interoperability, access secondary markets.”
  6. 6. Dynamic spectrum access and cognitive radio techniques Concept of opportunistic spectrum sharing: secondary utilization of the identified spectrum holes. • Concepts of a spectrum hole and opportunistic spectrum sharing: Spectral opportunity for secondary access: a spectrum hole.
  7. 7. Contents 1. Dynamic spectrum access and cognitive radio ideas 2. Elements of cognitive radio systems 3. Filter bank approach for cognitive radio physical layer – Spectrum agility 4. Spectrum sensing – Energy detection based spectrum sensing – Wideband sensing using FFT or filter banks 5. RF challenges in cognitive radio
  8. 8. Cognitive radio: Definitions Spectrum gap, spectrum hole, white space  Spatially & temporally unused part of the radio spectrum, which is considered for use by CR Primary user (PU)  Licensed user/privileged user of a frequency band Secondary user (SU)  Opportunistic user of a frequency band Waveform  Signal model & other essential characteristics of the (PU or SU) communication system
  9. 9. Types of CR systems • Underlay systems use wide bandwidth and low-enough power spectral density not to disturb PUs. – Spread spectrum – UWB • Interweave CR is based on identification of spectral holes. • Overlay: Here the SU RX and TX are assumed to know and utilize info about the PU signal – Example: SU TX sends a signal which consists of (1) relayed PU signal and (2) SU signal, possibly with special coding (e.g. dirty paper coding). The relayed PU signal is included to guarantee sufficient SNR for nearby PU receivers. SU receiver detects both PU signal and SU signal, using interference cancellation techniques. • Black space systems: Transmitting at relatively low power level on top of powerful PUs. Detection based on interference cancellation, after detecting PU data. – Like overlay, but no PU relaying needed • The focus in the following discussions is on interweave systems.
  10. 10. Cognitive radio functionalities • Higher layers – Cognitive cycle, adapting to the environment, learning/predicting the PU characteristics and spectrum usage patterns; game-theoretic approaches – Real-time spectrum markets … • Reliable information about primary usage – Cognitive pilot channel – Data base of primary users (PUs) & positioning of seconday user (SU) stations – Spectrum sensing • Dynamic spectrum access (DSA) scheme • Spectrum exploitation – Flexible, spectrum agile waveforms and signal processing for secondary communications – Software defined radio technologies are essential in this context
  11. 11. Knowledge/detection of primary usage - 1 • Cognitive pilot channel – Dedicated control interface ideally to all local users of the radio channel – Could provide explicit information about primary usage – In the first stage, expected to be used as a means for closer coordination of wireles networks using existing technologies (e.g., sharing the same frequency band for GSM/WCMDA/HSPA/LTE/WiMAX/... waveforms in a flexible way) – Means for exchanging information between spectrum sensing stations (cooperative sensing) • Data base of primary users & positioning of seconday user stations – Database maintainance is critical – Central element in the first CR standard IEEE 802.22, along with spectrum sensing
  12. 12. Knowledge/detection of primary usage - 2 • Spectrum sensing – Sensing the radio environment to detect on-going primary transmissions  Idea is to detect spectral holes as opportunities for secondary transmissions – Cooperative sensing by multiple stations helps to mitigate problems, e.g., due to shadow fading.  Reliable detection of primaries in a single mobile station is an extremely challenging task – Minimum infrastructure needed, suitable for ad-hoc networks. – Most challenging scheme to implement from the RF point of view – Discussed later in some more details
  13. 13. Spectrum sensing in general • Occupancy sensing – ‘white’ spaces or spectral holes – ‘grey’ spaces – ’black’ spaces • Methods – (matched filtering) – energy-based detection (radiometer) – feature-based detection • CP autocorrelation for OFDM primaries • Cyclo-stationary detection • Covariance & eigenvalue based methods (within sample sequence and/or between antennas) • Many others • Fixed sample size vs. sequential detection principles • Cooperative sensing
  14. 14. Dynamic spectrum access scheme • MAC protocol for the secondary user system – Usually expected to support, in the same region, multiple independent SU systems – Cognitive pilots support coordinated use of the same resources by multiple operators / SU systems – Opportunistic dynamic spectrum access (DSA) for ad-hoc type operation • Distributed coordination, usually no central control element between different SU systems is assumed. • Basically different SU systems are competing for the spectral resources • To make the idea sensible, some form of distributed co-operation is needed (e.g., good neighbour strategy, fairness) • Game theory & other advanced concepts often considered in the developments.
  15. 15. Spectrum agile waveforms for secondary communications • Very flexible waveforms are needed – Waveforms need to be adapted to the radio environment – Adjustable center frequency, bandwidth, SNR requirements, etc. – Fragmented spectrum use is of interest: The overall transmission band may consist of multiple non-adjacent frequency slots – Well-contained spectrum for high spectral efficiency without leakage to adjacent PU frequency channels. • Multicarrier modulation has many of the desired features
  16. 16. Contents 1. Dynamic spectrum access and cognitive radio ideas 2. Elements of cognitive radio systems 3. Filter bank approach for cognitive radio physical layer – Spectrum agility 4. Spectrum sensing – Energy detection based spectrum sensing – Wideband sensing using FFT or filter banks 5. RF challenges in cognitive radio
  17. 17. Multicarrier modulation: OFDM and FBMC CP-OFDM  Simple and robust.  Spectrum leakage to adjacent subcarriers is a problem, e.g., in non-contiguous OFDM for fragmented spectrum use. o Various attempts for reducing the sidelobes can be found in the literature Filter bank multicarrier (FBMC)  Improved spectral efficiency due to lack of CPs and reduced guardbands  Very good spectral containment → reduced spectral leakage  Possibility to non-synchronized transmission of different groups of subcarriers with relatively small loss in spectrum efficiency
  18. 18. Spectral containment of FBMC vs. OFDM Frequency response of a subchannel filter = Transmited subcarrier spectrum • OFDM: • FBMC:
  19. 19. Filter banks • We consider efficient uniform, modulation-based filter banks, where subchannel frequency responses are obtained as frequency-shifted versions of a prototype. • In our designs, the overall subchannel bandwidth (with transition bands) is 2 x subchannel spacing (i.e., roll-off factor = 1). – Only immediately adjacent subchannels are significantly interacting. – One unused subcarrier is sufficient as a guard-band. 6 7 8 9 10 11 12 13 14 -80 -70 -60 -50 -40 -30 -20 -10 0 10 Subchannel index AmplitudeindB
  20. 20. Filter bank structure • Reduced guardbands between users • No CP's  Improved spectral efficiency • The transmultiplexer system achieves nearly perfect reconstruction.  Distortion is small compared to noise in practical SNR range with ideal channel. 6 7 8 9 10 11 12 13 14 -80 -70 -60 -50 -40 -30 -20 -10 0 10 Subchannel index AmplitudeindB
  21. 21. Transmultiplexer system model OQAMpre-processing + 2M↑ 0 ( )G z 1( )G z 1( )MG z− 2M↑  2M↑  Synthesis filter bank 2M↓  0 ( )F z 1( )F z 1( )MF z− 2M↓ 2M↓  Analysis filter bank OQAMpost-processing  D z− 0 ˆ ( )X z 1 ˆ ( )X z 1 ˆ ( )MX z− 0 ( )X z 1( )X z 1( )MX z− ( )Y z To achieve orthogonality in a spectrally efficient manner, offset-QAM signal model is crucial.  Each QAM symbol is mapped to two consecutive subcarrier samples.  Subcarrier sample sequences are oversampled by a factor of 2.
  22. 22. Receiver side: Efficient polyphase structure for analysis filter bank 2 0 ( )B z 2 1( )B z 2 1( )MB z− FFT 1− z 1− z 2M↓ 2M↓ 2M↓    × × × Subchannel processing Subchannel processing Subchannel processing * 0,nβ * 1,nβ * 1,M nβ − × * 0,nθ × * 1,nθ Re × * 1,M nθ − 0,nd 1,nd 1,M nd −  Re Re   Analysis filter bank OQAM post-processing kR2C kR2C kR2C Proper subchannel processing restores the orthogonality of subcarriers in case of frequency-selective channels. - Synchronization & channel equalization - 2x oversampled subcarrier processing => Fractionally spaced equalization
  23. 23. Prototype filter design based on frequency sampling approach • High frequency selectivity • Exact stopband zeros 6 7 8 9 10 11 12 13 14 -80 -70 -60 -50 -40 -30 -20 -10 0 10 Subchannel index AmplitudeindB
  24. 24. Spectrum efficiency, FBMC vs. OFDM • Link-level simulations with FEC & HARQ • (PHYDYAS (FP7-ICT-2007-211887) , TUT & ALUD):
  25. 25. PHYDYAS: FBMC based cognitive radio physical layer • The idea of FBMC has existed since the 60'ies, but still there are signal processing and communication theoretic issues which are not mature enough for practical use: – Synchronization – Channel estimation and equalization – Adaptation to multi-antenna & MIMO concepts – Multiple access specifics – etc. • EU FP7 project PHYDYAS project is focusing on these open topics. – WiMAX -like system concept as the starting point – Special focus on dynamic spectrum use and cognitive radio
  26. 26. Exploting a spectrum hole © DCE - Tampere University of Technology From deliverable D8.1 of PHYDYAS (FP7-ICT- 2007-211887), contributed by CEA-LETI
  27. 27. Non-contiguous OFDM and FBMC © DCE - Tampere University of Technology From deliverable D8.1 of PHYDYAS (FP7-ICT-2007- 211887), contributed by CEA-LETI
  28. 28. Application of non-contiguous multicarrier: Broadband – narrowband co-existence  Professional Mobile Radio (PMR), Tetra family evolution BB UE#2 BB UE#3 BB UE#4 BB UE#1 Reserved for narrow- band network LTE channel bandwidth Frequency
  29. 29. Fast convolution based flexible channelization filter bank • Supports different waveforms in different channels - Single-carrier - FBMC - Filtered multitone • See [Renfors ECCTD2011]  Independent tunability of the center frequencies of individual subchannels or multiplexes o Individual frequency offset compensation of different users’ signals. o Individual timing offset compensation. o Possibility to process non-synchronized groups of subchannels in a single filter bank.
  30. 30. Multiuser synchronization aspects - 1 OFDM assumes quasi-synchronous operation of different transmissions participating in a multiplex (e.g., uplink): – Delay spread + timing uncertainty within CP – CFOs < 0.01∆f – Tight base-station control required – Tight time-domain multiplexing is possible
  31. 31. Multiuser synchronization aspects - 2 © DCE - Tampere University of Technology FBMC needs 1-subcarrier guardbands  Minor loss in spectrum efficiency  Asynchronous transmission is possible • Coarse frequency synch required • Fine frequency offset and timing offset compensation through subcarrier processing  Suitability to Ad-hoc/DSA/CR  Overheads in tightly time-multiplexed operation • Frame structures should be redesigned k-2 k-1 k+1 k+2 -80 -70 -60 -50 -40 -30 -20 -10 0 10 Subchannel index Amplitude(dB) User YUser X
  32. 32. FBMC as cognitive radio physical layer Advantages: • Spectral efficiency: no CP's, one empty subchannel is sufficient as a guard- band to isolate different secondary users. • The same filter bank can be used for receiver data signal processing and flexible, high-resolution spectrum sensing with high dynamic range.  The possibility of simultaneous sensing and reception (at different parts of the spectrum) facilitates the coexistence of primary and secondary users. • Filter bank of an FBMC receiver as a part of the decimation filtering chain => very flexible channelization • Reduced synchronization requirements; possibility to asynchronous operation with high spectral efficiency Challenges: • High linearity for transmitter power amplifier needed to maintain the clean spectrum provided by the synthesis filter bank. • Filter bank impulse response "tails" (i.e., time-domain overlap of subcarrier symbols) introduce overhead in tightly time-multiplexed operation.
  33. 33. Contents 1. Dynamic spectrum access and cognitive radio ideas 2. Elements of cognitive radio systems 3. Filter bank approach for cognitive radio physical layer – Spectrum agility 4. Spectrum sensing – Energy detection based spectrum sensing – Wideband sensing using FFT or filter banks 5. RF challenges in cognitive radio
  34. 34. Spectrum sensing specifications • Frequency resolution: subchannel spacing – smallest spectral hole – spectral granularity for transmission • Spectral dynamic range: > 50 dB • Noise floor: thermal noise + interference • Out-of-band interference rejection performance of spectrum analyzer: > 80 dB • Sensing latency Max signal level (SU & PU) SU sensitivity level Noise level Spectrum sensing sensitivity RX interference level 50 dB 80 dB  Dynamic range is significantly larger than in data reception!  Important to understand the effect of RF imperfections on different sensing methods Exemplary numeric values!
  35. 35. Energy detection for spectrum sensing 0 1 [ ], :noise only [ ] [ ] [ ], :signal present n l H Y l s l n l H  =  + ( ) 0( | )FAP P T Hγ= >Y 0.8 1 1.2 1.4 1.6 1.8 2 2.2 0 100 200 300 400 500 600 700 Energy detector: P FA = 0.1, N = 100, σ n 2 = 1, SNR = -3.0103 dB γH0 H 1 P MD P FA PD = 1-PMD fY|H 0 (y|H0 ) f Y|H 1 (y|H 1 ) Sensing decision is a binary hypothesis testing problem: Test statistic: ( ) 1( | )MDP P T Hγ= <Y Probability of false alarm: Probability of missed detection:  Interference to primary system.  Lost secondary opportunity.
  36. 36. 36 • The actual PDF’s are chi-square or gamma distributions • Gaussian approximation is usually accurate enough (but care must be exercised). For a complex vector of N samples, the distributions in the absence and presence of the PU signal are: • P: signal variance; σ2: noise variance. Energy detection for spectrum sensing 0.8 1 1.2 1.4 1.6 1.8 2 2.2 0 100 200 300 400 500 600 700 Energy detector: PFA = 0.1, N = 100, σn 2 = 1, SNR = -3.0103 dB γH 0 H 1 PMD PFA P D = 1-P MD fY|H 0 (y|H 0 ) f Y|H 1 (y|H 1 )
  37. 37. Sensing in the presence of noise uncertainty -40 -35 -30 -25 -20 -15 -10 -5 0 0 2 4 6 8 10 12 14 16 SNR in dB log 10 N Sample complexity (N) of the radiometer under noise uncertainty x = 0.001 dB x = 0.1 dB x = 1 dB 1 1 2 2 2[ ( ) (1 )(1 )]FA MDP P SNR N SNR − − Φ − Φ − + = 1 1 2 2 2[ ( ) (1 )] 1 ( ) FA MDP P N SNR ρ ρ − − Φ − Φ − ≈   − −    2 1energy wallSNR ρ ρ − = [1] R. Tandra and A. Sahai, ”SNR Walls for Signal Detection,” IEEE J. Selected Topics in Signal Processing, vol. 2, No. 1, Feb. 2008 The sensing time depends on the primary signal SNR and the noise uncertainty ρ (x in dB units). The noise uncertainty introduces an SNR wall in energy detection [1]:
  38. 38. About noise estimation/calibration • Switching off antenna during calibration. – Various limitations … • If there is a good reason to believe that the channel is initially free of PUs, then we can get a reliable noise reference for spectrum monitoring – For example, using more powerful methods in initial sensing, and energy detection for monitoring. • With frequency hopping PU systems, we should be able to obtain observations in the presence and absence of a transmission burst. • Noise only vs. noise + interference – Interference, e.g., from adjacent channels due to TX PA nonlinearity or RX nonlinearity. – How to distinguish interference due to adjacent channels from a co-channel PU?
  39. 39. Wideband/multichannel spectrum sensing Alternative approaches for analysing the spectrum scene: • Narrowband (per frequency channel) frequency scanning receiver – Simple to implement – Smaller RX dynamic range => less critical to RF imperfections – Relatively slow process when targeting at high sensitivity • Wideband receiver with DSP-based spectrum analysis functionality – Fast: Sensing simultaneously for a high number of possibly available spectral slots. – Frequency resolution determined by subband spacing. – Integration over multiple subbands in case of wideband PUs. – Wideband, high dynamic range ADC needed – Sensitive to RF imperfections – FFT / windowed FFT / filter bank approaches
  40. 40. Commonality of wideband spectrum sensing and Spectrum agile waveforms • Spectrum sensing will be an important element in future networks. • There is also need for devices which can be used for both sensing and data reception.  Commonality of sensing and data reception functions is important.  Similar requirements for spectral agility. • Basically, we need a highly configurable filter bank to do wideband spectrum sensing, simultaneously for a high number of possibly available spectral slots. • Multicarrier techniques can provide the needed commonality and configurability.  OFDM is the common choice - Limited spectral containment introduces various challenges for both sensing and transmission functions  Filter bank based multicarrier (FBMC) techniques have some very interesting characteristics.
  41. 41. FBMC receiver as energy detector -1 -0.5 0 0.5 1 10 -5 10 -4 10 -3 10 -2 10 -1 10 0 Normalized frequency [ω/π] PSD OFDM FBMCPU1 PU3 PU2 SHSH • Here, classical energy detection is considered. • M = 1024 subchannels. • FBMC receiver is not blinded by the presence of high level neighboring signals and is able to identify accurately the spectrum holes.
  42. 42. 42 Multiband spectrum sensing • High flexibility: – Energy measurements for each subcarrier symbol. – Summation over used time-frequency window(s). – The total number of statistically independent (maximally decimated) samples determines sensitivity, together with target PFA & PMD. • Multiple-dwell approaches easy to implement using different window sizes: – Fast reaction to new strong PU signals using short integration. – Fast detection of possible white spaces; longer integration to reach adequate PFA & PMD. Symbol index Subchannelindex Moderate resolution, moderate latency High resolution, high latency Low resolution, low latency 2 , ,∑= nk nkrT Analysis through FFT or filter bank
  43. 43. Sensing time vs. sensing bandwidth • Sensing time as a function of bandwidth for different primary signal SNR's: • With higher SNR's, high bandwidths, and high number of subcarriers, the sensing time is only a few multicarrier symbols, and filter bank impulse response length becomes the limiting factor. 0 1 2 3 4 5 6 7 x 10 5 10 -4 10 -3 10 -2 10 -1 10 0 Sensing bandwidth (in Hz) Sensingtime(inseconds) M = 2048 M = 512 M = 128 M = 32x = 0,1 dB SNR = - 3 dB SNR = - 12 dB SNR = - 6 dB No uncertainty 0.1 0.01 FA MD P P = =
  44. 44. A practical WLAN scenario - 1 Two WLAN signals spectra in 2.4 GHz ISM band with smallest spectral gap of 3 MHz. • We can see the ideal OFDM signal spectrum, which has a deep hole in the considered 3 MHz frequency band. • In the worst case situation allowed by the 802.11g specifications, the power spectral density in the gap can be at about -20 dBr in the considered case. • Figure shows also a third case with modest spectral regrowth at -30 dBr level.
  45. 45. A practical WLAN scenario - 2 Actual false alarm probability of WLAN signals with target PFA=0.1 for 3 MHz sensing bandwidth in AWGN case:
  46. 46. Contents 1. Dynamic spectrum access and cognitive radio ideas 2. Elements of cognitive radio systems 3. Filter bank approach for cognitive radio physical layer – Spectrum agility 4. Spectrum sensing – Energy detection based spectrum sensing – Wideband sensing using FFT or filter banks 5. RF challenges in cognitive radio
  47. 47. RF challenges in CR • Basically the CR idea, in the most aggressive form, leads to extremely high dynamic range requirement for the receiver  In traditional RF systems, the nearby adjacent channels and blockers are assumed to be at a reasonable level from the RF implementation point of view.  In the CR case, there are no specifications for spectral components close to the potential white spaces in the sensing phase or next to the frequency slot to be used for communication once it has been determined to be empty. • Due to the requirement of flexibility and spectral agility, advanced SDR technologies are expected to be used for the RF functions  Wideband sampling with the related ADC-dynamic range and sampling jitter issues.  Also the effect of all other RF imparements becomes more critical. • High spectral purity requirement for the transmitter  SU’s spectral leakage would introduce interference to nearby primary transmissions.  PU’s spectral leakage destroys SU’s transmission
  48. 48. Effects of RF imperfections on spectrum sensing • This is not the whole picture, esp. for FBMC • Spectrum leakage from strong PUs will introduce interference to the spectral gaps • Transmitter related  Imperfect channel filters  Nonlinear distortion in transmitter power amplifier  Acceptable level of interference defined by spectrum masks in specs. © DCE - Tampere University of Technology -1 -0.5 0 0.5 1 10 -5 10 -4 10 -3 10 -2 10 -1 10 0 Normalized frequency [ω/π] PSD OFDM FBMCPU1 PU3 PU2 SHSH • Receiver related  Imperfect channel filters  Nonlinear distortion in active stages due to blockers and strong adjacent channels.  Phase noise, etc.  Specified sensitivity and detection performance sets some limits on these effects  SINR and spectrum sensing performance close to strong primaries is in any case reduced.
  49. 49. About spectrum masks in CR PU side spectrum leakage o Well-defined and fairly tight masks for cellular mobile radio o Relaxed masks for WLAN and other ISM band devices  Limitations to spectrum sensing sensitivity SU side spectrum leakage o Masks could be fairly tight, especially if FBMC waveform is used  Targeting at high spectrum efficiency  Mostly depending on realistic PA spectral regrowth characteristics © DCE - Tampere University of Technology
  50. 50. Spectral regrowth due to PA nonlinearity © DCE - Tampere University of Technology From deliverable D8.1 of PHYDYAS (FP7- ICT-2007-211887), contributed by CEA-LETI Linear PA: Nonlinear PA for different IBO values:
  51. 51. Possible ways to handle RF challenges in CR • Co-operative sensing • Beam-forming • Spectral awareness • DSP-enhanced RF techniques
  52. 52. Reducing the spectral dynamic range observed by CR Radio resource management (RRM) – Cellular mobile radio systems have effective RRM functions, which limit the power level differences in adjacent frequency channels – Due to the distributed nature in opportunistic DSA, we cannot expect such effective control. • It will be a great challenge for CR system to reach the capacity/efficiency of a cellular mobile system! Also the spectral dynamic range is not so well under control. In multiantenna stations, beamforming is an efficient way to control interferences. – Should be implemented on the analog side, in order to relax the ADC dynamic range requirements. In cooperative sensing, dynamic range requirements are reduced Overlay CR Robust waveforms, like MC-CDMA
  53. 53. Coping with RF imperfections • Choice of receiver architecture  Wideband multichannel receiver inevitably needs high dynamic range • DSP-assisted calibration of analog circuits • Interference mitigation => DSP enhanced radio, ‘dirty RF’ • PA nonlinearities (TX) • I/Q imbalance (TX&RX) • oscillator phase noise (TX&RX) • timing jitter in IF/RF sampling (RX) • mixer, LNA and ADC nonlinearities (RX)
  54. 54. Co-operative sensing • Due to large-scale fading (radio shadows), it is very difficult to reach sufficient sensitivity in a single mobile station or access point  The target sensitivity for single-station spectrum sensing is typically deep below the thermal noise level. • In co-operative sensing, the sensing requirement can be relaxed, which directly reduces the dynamic range requirement.  Enhanced decision statistics  ’Sensing diversity’: all stations don’t need to be in good positions to do sensing • Further, in co-operative sensing, different stations are in different radio environments, and some of them might be in particularly good position for securing each specific spectrum hole.  For example, using sensing stations with high antennas.  How to secure that the local sensing network has sufficient coverage & sensitivity?
  55. 55. Beam-forming • Beam-forming is an effective tool for interference management – In a spectrum sensing station, notches can be turned in the direction of the sources of strong interfering spectral components. The same applies during data reception. – Directing the beam towards the actual receiver reduces the general interference level.
  56. 56. Spectral awareness • A specific SU system would usually have multiple white spaces at its disposal. But they might be quite different from the RF requirements point of view.  Choose the one leading to the easiest requirements. • Now we see that the radio scene analysis should include, in addition to identification of spectrum holes, also evaluation of the signal strengths at the nearby frequencies, etc.
  57. 57. Energy efficiency and spectrum awareness • Energy efficiency is important at all levels of radio implementation  Analog, digital, RF, baseband, HW, SW, … • Dependancy also on the radio scene:  Highly loaded network → complicated signal processing to maximize efficient spectrum use  Less congested → relaxed signal processing, e.g., relaxed filter specs → reduced energy consumption • What is needed:  Capabilities to operate with very wide dynamic range (say 80…100 dB)  Ability to relax the signal processing algorithms based on the results of the radio scene analysis
  58. 58. References Survey papers on spectrum sensing: • Y. Zeng, Y.C. Liang, A. T. Hoang, and R. Zhang, “A Review on Spectrum Sensing for Cognitive Radio: Challenges and Solutions,” EURASIP Advances in Signal Proc., vol. 2010, pp. 1-15, January 2010. • T. Yucek and H. Arslan, “A survey of spectrum sensing algorithms for cognitive radio applications,” IEEE Communications Surveys & Tutorials, vol. 11, no. 1, pp. 116–130, March 2009. General references: • J. Mitola, “Cognitive radio for flexible mobile multimedia communications,”in IEEE Int. Workshop Mobile Multimedia Communications, San Diego, USA, Nov. 1999, pp. 3–10. • T. A. Weiss and F. K. Jondral, “Spectrum pooling: An innovative strategy for the enhancement of spectrum efficiency,” IEEE Commun. Mag., vol. 42, pp. S8–S14, Mar. 2004. • C. R. Stevenson, C. Cordeiro, E. Sofer, and G. Chouinard, “Functional requirements for the 802.22 WRAN standard,”, November 2006. • A. Kuzminskiy and Y. Abramovich, “Adaptive antenna array interference mitigation diversity for decentralized DSA in licence-exempt spectrum”, in Proc. of the Int. Conf. on Communications, Dresden, Germany, June 2009. • R. Tandra and A. Sahai, “SNR walls for signal detection,” IEEE J. Select. Topics Signal Processing, vol. 2, pp. 4- 17, February 2008. • B. Farhang-Boroujeny, “Filter bank spectrum sensing for cognitive radios,” IEEE Trans. on Signal Processing, vol. 56, pp. 1801-1811, May 2008. • INFSO-ICT-211887 Project PHYDYAS, Deliverables available at: