Adaptive Baseband Processing Techniques for Cognitive Radio Systems

Cognitive Radio is a new paradigm in the wireless communications. It puts the intelligence and awareness dimension to the radio communication system. A Cognitive Radio system will be aware of the changing condition in its environment. The awareness in this thesis is related to the spectrum and the channel stipulation. By intelligent learning and understanding from the environment, a Cognitive Radio device will adapt its transmission parameters to the changing environment. The objective of a Cognitive Radio system is to have a reliable communication and efficient spectrum utilization. Although most of the spectral ranges are already licensed, studies and measurements have shown that most of the time the spectrum is not fully occupied, and even some bands are rarely occupied. The congestions happen because of the existing poor spectrum access technique. This condition raises the opportunity to rent or to access the spectrum while the licensed user is in idle condition or by having the Cognitive Radio side by side with the licensed users band occupying the spectrum holes. In this way, the spectrum is utilized in a more efficient way. The challenging problem is to settle a “win-win” co-existence between the Cognitive Radio, which is the rental system, with the legacy licensed system. Spectrum pooling has been proposed in the literature as a technique of sharing the spectrum with the licensed system by using the Orthogonal Frequency Division Multiplexing (OFDM) as modulation technique. Some of the OFDM carriers located in the licensed user’s band will be deactivated in order not to interfere the legacy system access. Due to this flexibility, the OFDM is considered as a proper modulation technique to be applied to a Cognitive Radio system. Deactivation of more carriers adjacent to the licensed user’s band, as well as windowing and several signal processing techniques could reduce the interference contribution of the OFDM based Cognitive Radio system to the licensed users signal. These techniques are studied and presented in this dissertation. The spectrum pooling technique could only work upon the reception of the accurate spectrum occupancy information. The spectrum occupancy information is derived from a spectrum sensing module. Spectrum sensing necessitates such a sophisticated module and requires a proper attention. This thesis does not focus on the spectrum sensing module, but rather it is assumed that the spectrum occupancy information is available and accurate. The work on spectrum sensing is conducted by out partner at the Twente University . Following the objective of Cognitive Radio to have reliable communications, the observation or emphasis of the technique is not only on the licensed users side but also on the rental user. Besides assuring the reliable communications of the licensed system, the target Quality of Service (QoS) of the Cognitive Radio system should be attained. The practical parameters to be observed are the Bit Error Rate (BER) and the bit rate of the system. The application of OFDM with spectrum shaping for the purpose of reducing the interference contribution to the licensed system can be achieved with the cost of self QoS degradation. Results from literature have shown that application of adaptive bit loading could enhance the BER of an OFDM system. By adaptive bit loading, the bits are allocated to each of the OFDM carrier intelligently according to the channel condition by setting the target BER and bit rate of the overall system. In this work, we propose to combine the adaptive bit loading with the spectrum shaping to preserve the Cognitive Radio system’s QoS. The impact of this combination on the OFDM Peak to Average Power Ratio (PAPR) growth is evaluated through simulations. There is a growing interest in replacing the Fourier transform in OFDM with wavelet basis functions. The technique is termed as the Wavelet Packet Multicarrier Modulation (WPMCM). Investigations reported in literature have presented the evaluation of WPMCM and compared the results with OFDM. Following the successful application of a frequency selective wavelet in the Ultra Wide Band system, in this dissertation we evaluate the suitability of the frequency selective wavelet in WPMCM combined with the spectrum pooling concept to be applied to the Cognitive Radio system. As the efficient spectrum utilization is one of the major objectives of Cognitive Radio, it is reasonable to include Multiple Input Multiple Output (MIMO) to the Cogntive Radio system. This subject is studied in this thesis and the performance of MIMO in the OFDM and WPMCM based Cogntive Radio system is evaluated. Channel estimation is the crucial module in every OFDM system. We propose an effective channel estimation scheme based on optimum pilot patterns of conventional OFDM using virtual pilots and apply it to Cognitive Radio systems. The virtual pilots are derived from the combination of the linear interpolation/extrapolation between two real pilots with the so called decision directed method. Without loss of generality we use the Wiener filter as the channel estimation technique due to its efficient and straightforward method in utilizing the channel correlation property according to the distance between pilots and data. First we adopt the Single Input Single Output (SISO) OFDM based Cognitive Radio system, and then we expand the scheme to the MIMO application. Beside OFDM and WPMCM based Cognitive Radio system, recently Transform Domain Communication System (TDCS) and Wavelet Domain Communication System (WDCS) have been introduced as promising modulation techniques for Cognitive Radio application. TDCS and WDCS have bit rate limitations. As an effort to enhance the bit rate of TDCS and WDCS, we propose to add an extra embedded symbol to TDCS and WDCS. We analyze the impact of the embedded symbol on the conventional TDCS and WDCS data detection. The impact of the embedded symbol on the data detection in a multi-user environment, which is inherently supported by the conventional TDCS and WDCS, is observed. In addition, we also evaluate the performance of the TDCS with an embedded symbol in the MIMO system. As a sort of verification platform for Cognitive Radio we proposed a practical Demonstrator that involved a spectrum scanning module and baseband processing transceiver module. The spectrum sensing is employed by the Universal Software Defined Radio Peripheral (USRP) while the baseband processing transceiver module is applied to an FPGA Development board. The current made Cognitive Radio verification platform is simple and still has a limited feature. While further developments are required in this field, details of this effort are also provided in this thesis.

[1]  Erik H. Kjeldsen A Novel Interpolated Tree Orthogonal Multiplexing (ITOM) Scheme with Compact Time-Frequency Localization: an Introduction and Comparison to Wavelet Filter Banks and Polyphase Filter Banks , 2006 .

[2]  Lajos Hanzo,et al.  Blind-detection assisted sub-band adaptive turbo-coded OFDM schemes , 1999, 1999 IEEE 49th Vehicular Technology Conference (Cat. No.99CH36363).

[3]  R. Rajbanshi,et al.  A Novel Sidelobe Suppression Technique for OFDM-Based Cognitive Radio Transmission , 2008, 2008 3rd IEEE Symposium on New Frontiers in Dynamic Spectrum Access Networks.

[4]  F.K. Jondral,et al.  Mutual interference in OFDM-based spectrum pooling systems , 2004, 2004 IEEE 59th Vehicular Technology Conference. VTC 2004-Spring (IEEE Cat. No.04CH37514).

[5]  Per Ola Börjesson,et al.  Timing and frequancy synchronization in OFDM systems using the cyclic prefix , 1995 .

[6]  H. Nikookar,et al.  Efficient Pilot Pattern for OFDM-based Cognitive Radio Channel Estimation - Part 1 , 2007, 2007 14th IEEE Symposium on Communications and Vehicular Technology in the Benelux.

[7]  M. Reuter,et al.  Cyclic code shift keying: a low probability of intercept communication technique , 2003 .

[8]  Jelena Kovacevic,et al.  Wavelets and Subband Coding , 2013, Prentice Hall Signal Processing Series.

[9]  Stephan Sand,et al.  Adaptive Pilot Symbol Aided Channel Estimation for OFDM Systems , 2004 .

[10]  Stefan Kaiser Multi-Carrier CDMA Mobile Radio Systems-Analysis and Optimization of Detection , 1998 .

[11]  Georgios B. Giannakis,et al.  Cyclic prefixing or zero padding for wireless multicarrier transmissions? , 2002, IEEE Trans. Commun..

[12]  Marion F. Lee Wavelet Domain Communication System (WDCS): Packet-Based Wavelet Spectral Estimation and M-ARY Signaling , 2002 .

[13]  N. Chotikakamthorn,et al.  On identifiability of OFDM blind channel estimation , 1999, Gateway to 21st Century Communications Village. VTC 1999-Fall. IEEE VTS 50th Vehicular Technology Conference (Cat. No.99CH36324).

[14]  Michael A. Temple,et al.  Wavelet domain communication system: bit error sensitivity characterization for geographically separated transceivers (U) , 2002, MILCOM 2002. Proceedings.

[15]  Ivan Cosovic,et al.  Special Issue on MC-SS Suppression of sidelobes in OFDM systems by multiple-choice sequences , 2006, Eur. Trans. Telecommun..

[16]  O. Rioul,et al.  A Remez exchange algorithm for orthonormal wavelets , 1994 .

[17]  José Manuel Páez-Borrallo,et al.  Peak power reduction for OFDM systems with orthogonal pilot sequences , 2006, IEEE Transactions on Wireless Communications.

[18]  Masoud Salehi,et al.  Communication Systems Engineering , 1994 .

[19]  I. Daubechies Ten Lectures on Wavelets , 1992 .

[20]  Jie Zhu,et al.  Channel estimation with power-controlled pilot symbols and decision-directed reference symbols [OFDM systems] , 2003, 2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484).

[21]  Michael A. Temple,et al.  Cognitive radio - an adaptive waveform with spectral sharing capability , 2005, IEEE Wireless Communications and Networking Conference, 2005.

[22]  Petri Mähönen,et al.  Wavelet packet modulation for wireless communications , 2005, Wirel. Commun. Mob. Comput..

[23]  H. Nikookar,et al.  Efficient Pilot Pattern for OFDM-based Cognitive Radio Channel Estimation - Part 2 , 2007, 2007 14th IEEE Symposium on Communications and Vehicular Technology in the Benelux.

[24]  H. Nikookar,et al.  Wavelet-based multicarrier transmission over multipath wireless channels , 2000 .

[25]  Homayoun Nikookar,et al.  On the Use of Virtual Pilots with Decision Directed Method in OFDM Based Cognitive Radio Channel Estimation Using 2x1-D Wiener Filter , 2008, 2008 IEEE International Conference on Communications.

[26]  Homayoun Nikookar,et al.  Maximally Frequency Selective Wavelet Packets Based Multi-Carrier Modulation Scheme for Cognitive Radio Systems , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[27]  Hüseyin Arslan,et al.  Sidelobe suppression in OFDM-based spectrum sharing systems using adaptive symbol transition , 2008, IEEE Communications Letters.

[28]  Homayoun Nikookar,et al.  Cognitive Radio Dynamic Access Techniques , 2008, Wirel. Pers. Commun..

[29]  Edgar Bolinth,et al.  A blockwise loading algorithm for the adaptive modulation technique in OFDM systems , 2001, IEEE 54th Vehicular Technology Conference. VTC Fall 2001. Proceedings (Cat. No.01CH37211).

[30]  Friedrich Jondral,et al.  Spectrum pooling: an innovative strategy for the enhancement of spectrum efficiency , 2004, IEEE Communications Magazine.

[31]  Robert F. H. Fischer,et al.  A new loading algorithm for discrete multitone transmission , 1996, Proceedings of GLOBECOM'96. 1996 IEEE Global Telecommunications Conference.

[32]  R. Brodersen,et al.  Corvus: a Cognitive Radio Approach for Usage of Virtual Unlicensed Spectrum List of Figures Figure 1 , 2004 .

[33]  Yong-Hwan Lee,et al.  Optimum pilot pattern for channel estimation in OFDM systems , 2005, IEEE Transactions on Wireless Communications.

[34]  M. Heskamp,et al.  A node architecture for disaster relief networking , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[35]  Santiago Zazo,et al.  Pilot patterns for channel estimation in OFDM , 2000 .

[36]  M. Sandell,et al.  Low-complex frame synchronization in OFDM systems , 1995, Proceedings of ICUPC '95 - 4th IEEE International Conference on Universal Personal Communications.

[37]  Stéphane Mallat,et al.  A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[38]  Homayoun Nikookar,et al.  Cognitive Radio with Single Carrier TDCS and Multicarrier OFDM Approach with V-BLAST Receiver in Rayleigh Fading Channel , 2008, Mob. Networks Appl..

[39]  Michael Schnell,et al.  A technique for sidelobe suppression in OFDM systems , 2005, GLOBECOM '05. IEEE Global Telecommunications Conference, 2005..

[40]  Simeon Furrer,et al.  Adaptive bit loading for wireless OFDM systems , 2001, 12th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications. PIMRC 2001. Proceedings (Cat. No.01TH8598).

[41]  Peter Adam Hoeher,et al.  A statistical discrete-time model for the WSSUS multipath channel , 1992 .

[42]  J.M. Paez-Borrallo,et al.  Efficient pilot patterns for channel estimation in OFDM systems over HF channels , 1999, Gateway to 21st Century Communications Village. VTC 1999-Fall. IEEE VTS 50th Vehicular Technology Conference (Cat. No.99CH36324).

[43]  R. Hassun,et al.  Effective evaluation of link quality using error vector magnitude techniques , 1997, Proceedings of 1997 Wireless Communications Conference.

[44]  L. Hanzo,et al.  Adaptive multicarrier modulation: a convenient framework for time-frequency processing in wireless communications , 2000, Proceedings of the IEEE.

[45]  Ivan Cosovic,et al.  Sidelobe Suppression in OFDM Spectrum Sharing Systems Via Additive Signal Method , 2007, 2007 IEEE 65th Vehicular Technology Conference - VTC2007-Spring.

[46]  Michael Schnell,et al.  Reduction of out-of-band radiation in OFDM systems by insertion of cancellation carriers , 2006, IEEE Communications Letters.

[47]  V. Almenar,et al.  Performance enhancements in OFDM-WLAN systems using MIMO access techniques , 2004, 1st International Symposium onWireless Communication Systems, 2004..

[48]  Michael Schnell,et al.  Subcarrier weighting: a method for sidelobe suppression in OFDM systems , 2006, IEEE Communications Letters.

[49]  Alan V. Oppenheim,et al.  Discrete-time Signal Processing. Vol.2 , 2001 .

[50]  Maja Sliskovic,et al.  Carrier and sampling frequency offset estimation and correction in multicarrier systems , 2001, GLOBECOM'01. IEEE Global Telecommunications Conference (Cat. No.01CH37270).

[51]  A. Jensen,et al.  Ripples in Mathematics - The Discrete Wavelet Transform , 2001 .

[52]  Per Ola Börjesson,et al.  ML estimation of time and frequency offset in OFDM systems , 1997, IEEE Trans. Signal Process..

[53]  Reinaldo A. Valenzuela,et al.  V-BLAST: an architecture for realizing very high data rates over the rich-scattering wireless channel , 1998, 1998 URSI International Symposium on Signals, Systems, and Electronics. Conference Proceedings (Cat. No.98EX167).

[54]  Patrick Robertson,et al.  Two-dimensional pilot-symbol-aided channel estimation by Wiener filtering , 1997, 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[55]  Ramjee Prasad,et al.  Weighted OFDM for Wireless Multipath Channels , 2000 .

[56]  Hongbin Li,et al.  Blind channel estimation, equalisation and CRB for OFDM with unmodelled interference , 2007, IET Commun..

[57]  Joseph Mitola,et al.  Cognitive Radio An Integrated Agent Architecture for Software Defined Radio , 2000 .

[58]  L.P. Ligthart,et al.  Performance Evaluation of OFDM Based Cognitive Radio System with Wiener Filter Channel Estimation Using Frequency Hopping GSM Channel Model at 900 MHz , 2007, 2007 European Conference on Wireless Technologies.

[59]  Lajos Hanzo,et al.  Adaptive Wireless Transceivers , 2002 .

[60]  Sharath B. Reddy,et al.  An Efficient Blind Modulation Detection Algorithm for Adaptive OFDM Systems , 2003 .

[61]  Patrick Robertson,et al.  Pilot-symbol-aided channel estimation in time and frequency , 1997 .

[62]  L.P. Ligthart,et al.  Combined Spectrum Pooling and Adaptive Bit Loading for Cognitive Radio OFDM Based System , 2006, 2006 Symposium on Communications and Vehicular Technology.

[63]  Andrea M. Tonello,et al.  Improved Nyquist pulses , 2004, IEEE Communications Letters.

[64]  P. Vaidyanathan Multirate Systems And Filter Banks , 1992 .

[65]  Homayoun Nikookar,et al.  Wavelet Packet Multi-Carrier Modulation MIMO Based Cognitive Radio Systems with VBLAST Receiver Architecture , 2008, 2008 IEEE Wireless Communications and Networking Conference.

[66]  Norman C. Beaulieu,et al.  Reduced ICI in OFDM systems using the "better than" raised-cosine pulse , 2004, IEEE Communications Letters.

[67]  Francois P. S. Chin,et al.  Two-Dimensional Iterative Sampling Frequency Offset Estimation for MB-OFDM System , 2006, 2006 IEEE 63rd Vehicular Technology Conference.

[68]  C. Burrus,et al.  Introduction to Wavelets and Wavelet Transforms: A Primer , 1997 .

[69]  B. Nauwelaers,et al.  Wavelet packet based multicarrier modulation , 2000, IEEE Benelux Chapter on Vehicular Technology and Communications. Symposium on Communications and Vehicular Technology. SCVT-2000. Proceedings (Cat. No.00EX465).

[70]  M. Fischler,et al.  Intelligence: The Eye, the Brain, and the Computer , 1987 .

[71]  Guerino Giancola,et al.  Understanding Ultra Wide Band Radio Fundamentals , 2004 .

[72]  Alexander M. Wyglinski,et al.  Peak-to-Average Power Ratio Analysis for NC-OFDM Transmissions , 2007, 2007 IEEE 66th Vehicular Technology Conference.

[73]  Ping Zhang,et al.  Subband bit and power loading for adaptive OFDM , 2003, 2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484).

[74]  C. Muschallik,et al.  Improving an OFDM reception using an adaptive Nyquist windowing , 1996, 1996. Digest of Technical Papers., International Conference on Consumer Electronics.

[75]  Homayoun Nikookar,et al.  MIMO TDCS with Extra Embedded Symbol for Higher Data Rates in Overlay Spectrum Sharing System , 2009, 2009 IEEE Wireless Communications and Networking Conference.

[76]  Homayoun Nikookar,et al.  Cognitive Radio Dynamic Access Techniques for Mutual Interference Reduction and Efficient Spectrum Utilization , 2009 .

[77]  M. Schnell,et al.  Reduction of out-of-band radiation in OFDM based overlay systems , 2005, First IEEE International Symposium on New Frontiers in Dynamic Spectrum Access Networks, 2005. DySPAN 2005..

[78]  L.P. Ligthart,et al.  On the utilization of embedded symbol for CCSK BER improvement in TDCS dynamic spectrum access , 2008, 2008 European Conference on Wireless Technology.

[79]  Friedrich Jondral,et al.  OFDM-Based Overlay Systems: A Promising Approach for Enhancing Spectral Efficiency [Topics in Radio Communications] , 2007, IEEE Communications Magazine.

[80]  Alexander M. Wyglinski,et al.  Sidelobe Suppression for OFDM-Based Cognitive Radios Using Constellation Expansion , 2008, 2008 IEEE Wireless Communications and Networking Conference.

[81]  Marc C. Necker,et al.  Totally blind channel estimation for OFDM over fast varying mobile channels , 2002, 2002 IEEE International Conference on Communications. Conference Proceedings. ICC 2002 (Cat. No.02CH37333).

[82]  Michael A. Temple,et al.  TDCS, OFDM, and MC-CDMA: a brief tutorial , 2005, IEEE Communications Magazine.

[83]  John M. Cioffi,et al.  A practical discrete multitone transceiver loading algorithm for data transmission over spectrally shaped channels , 1995, IEEE Trans. Commun..

[84]  Homayoun Nikookar,et al.  Waveshaping of multicarrier signal for data transmission over wireless channels , 1997, Proceedings of ICUPC 97 - 6th International Conference on Universal Personal Communications.

[85]  Dan E. Dudgeon,et al.  Multidimensional Digital Signal Processing , 1983 .

[86]  A.H. Aghvami,et al.  On the guard band-based coarse frequency offset estimation technique for burst OFDM systems , 2000, VTC2000-Spring. 2000 IEEE 51st Vehicular Technology Conference Proceedings (Cat. No.00CH37026).

[87]  Ramjee Prasad,et al.  OFDM for Wireless Multimedia Communications , 1999 .

[88]  Simon Haykin,et al.  Cognitive radio: brain-empowered wireless communications , 2005, IEEE Journal on Selected Areas in Communications.

[89]  M. Schnell,et al.  Sidelobe suppression in OFDM systems by insertion of cancellation carriers , 2005, VTC-2005-Fall. 2005 IEEE 62nd Vehicular Technology Conference, 2005..

[90]  Haiyan Che,et al.  Adaptive OFDM and CDMA Algorithms for SISO and MIMO Channels , 2005 .

[91]  Klaus Witrisal,et al.  OFDM Air-interface design for multimedia communications , 2002 .

[92]  H. Yamaguchi,et al.  Active interference cancellation technique for MB-OFDM cognitive radio , 2004, 34th European Microwave Conference, 2004..

[93]  Hua Li,et al.  Low-Complexity Blind Symbol Timing Offset Estimation in OFDM Systems , 2005, EURASIP J. Adv. Signal Process..