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Adaptive and Distributed Beamforming for Cognitive Radio

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Adaptive and Distributed Beamforming for Cognitive Radio Adaptive and Distributed Beamforming for Cognitive Radio Proefschrift ter verkrijging van de graad van doctor aan de Technische Universiteit Delft, op gezag van de Rector Magnificus prof. ir. K.C.A.M. Luyben, voorzitter van het College voor Promoties, in het openbaar te verdedigen op dinsdag 15 oktober 2013 om 10.00 uur door Xiaohua LIAN Master of Engineering aan de Nanjing University of Aeronautics and Astronautics, P. R. China geboren te Urumqi, P. R. China Dit proefschrift is goedgekeurd door de promotor: Prof. dr. ir. L. P. Ligthart Copromotor Dr. H. Nikookar Samenstelling promotiecommissie: Rector Magnificus, voorzitter Prof. dr. ir. L. P. Ligthart, Technische Universiteit Delft, promotor Dr. H. Nikookar, Technische Universiteit Delft, copromotor Prof. H. Steendam, Universiteit Gent, België Prof. E. Del Re, Università di Firenze, Italië Prof. dr. ir. E. R. Fledderus, Technische Universiteit Eindhoven Prof. dr. ir. W. C. van Etten Universiteit Twente Prof. ir. P. van Genderen Technische Universiteit Delft Prof. dr. ir. G. J. T. Leus, Technische Universiteit Delft, reservelid ISBN 978-94-6186-205-1 Printed by Ipskamp Drukkers Adaptive and Distributed Beamforming for Cognitive Radio Thesis Delft University of Technology Copyright © 2013 by Xiaohua Lian All rights reserved. No parts of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission on writing from the author. Abstract Cognitive Radio (CR) is an energy efficient technique that is capable of optimizing the premium radio resources, such as power and spectrum. In this thesis, we focus on exploiting spatial diversity for CR. We have adopted two spatial signal processing techniques, i.e., Adaptive Beamforming (ABF) and Distributed Beamforming (DB) for CR users and CR networks, respectively. We have investigated and proposed a Bayesian ABF technique to CR Base Station (BS), which is able to direct CR BS main beams to CR users even when the Direction of Arrival (DOA) of each CR user is uncertain or completely unknown. Consequently by using this method, a CR BS at uplink (receiving) can adaptively enhance the signals of CR users by directing its main beams towards their wanted directions. We have also developed a Null Broadening (NB) technique for CR BS to be capable of generating spread nulls in the beampattern around directions of Primary Users (PUs), which guarantees radiated power reduction towards directions of PUs even considering scattering and multipath effects. We have introduced a DB method to distributed CR networks, which are constituted of distributed CR nodes. Two multi beam generating methods have been presented for DB to generate more than one main beam towards Distant CR (DCR) users. Due to the limitation of the DB method, the main beam of the CR network will be extremely narrow considering the possible CR working frequency. Therefore we have proposed a Nodes Selection (NS) method to select proper CR nodes from the CR network to perform DB, and thus the beampattern has a wider main beam while maintaining the lower sidelobe levels towards PUs. I Contents Abstract ........................................................................................................................ I Contents ...................................................................................................................... III Contents of Figures ..................................................................................................... VI Contents of Tables .................................................................................................... VIII Chapter 1 Introduction ................................................................................................. 1 1.1 Research background ............................................................................................... 1 1.2 Research Motivations ............................................................................................... 3 1.3 Scope and Novelties of this thesis ............................................................................ 4 1.4 Outlines of the thesis ................................................................................................ 5 Chapter 2 Adaptive Beamforming (ABF) Techniques for Cognitive Radio (CR) .............. 7 2.1 Introduction of Cognitive Radio (CR) ........................................................................ 7 2.1.1 Concept of CR .................................................................................................... 7 2.1.2 Applications of CR .............................................................................................. 8 2.1.3 OFDM‐ A promising modulation scheme for CR ............................................. 10 2.2 Beamforming techniques ....................................................................................... 11 2.2.1 Basic terminology and concepts of ABF .......................................................... 11 2.2.2 Adaptive beamformers for wireless communications .................................... 15 2.3 Applying Beamforming techniques to CR ............................................................... 16 2.3.1 Beamforming as an interference cancellation scheme for CR ........................ 16 2.3.2 Beamforming and CR functionality ................................................................. 18 2.3.3 Challenges of introducing beamforming to CR ............................................... 19 2.4 Summary ................................................................................................................. 22 Chapter 3 Uplink Adaptive Beamformers for CR ......................................................... 23 3.1 Introduction ............................................................................................................ 23 3.2 Adaptive Bayesian uplink beamformers for CR ...................................................... 26 3.2.1 Adaptive Bayesian beamformer with MMSE criterion ................................... 26 3.2.2 Projection Method (PM) .................................................................................. 28 3.3 Adaptive uplink OFDM beamformers for CR .......................................................... 29 3.3.1 OFDM adaptive beamformer with iterative weights calculating .................... 29 3.3.2 Computation complexity analysis ................................................................... 32 3.3.3 Adaptive OFDM beamformer with weights‐masking ...................................... 33 3.3.4 OFDM adaptive beamformer with weights‐constraint ................................... 34 III 3.3.5 Adaptive OFDM Bayesian beamformer with constrained weights ................. 36 3.4 Simulations and results .......................................................................................... 37 3.5 Summary ................................................................................................................. 43 Chapter 4 Downlink Adaptive Beamformers with Broadened Nulls for the CR system 45 4.1 Introduction ............................................................................................................ 45 4.2 A new Null Broadening (NB) method ‐ VDA ........................................................... 47 4.2.2 VDA method .................................................................................................... 49 4.2.3 VDA with depth control ................................................................................... 52 4.3 Simulation results and analysis .............................................................................. 53 4.4 Summary ................................................................................................................. 59 Chapter 5 Distributed Beamforming (DB) Techniques for CR ...................................... 61 5.1 Introduction ............................................................................................................ 61 5.2 DB of CR networks .................................................................................................. 63 5.2.1 Necessary assumptions ................................................................................... 64 5.2.2 DB for CR networks .......................................................................................... 64 5.2.3 DB with multi main beams .............................................................................. 67 5.3 Nodes Selection (NS) for CR networks with enlarged main beam ......................... 70 5.3.1 A NS method for CR networks ......................................................................... 71 5.3.2 Simulation results of the NS method .............................................................. 74 5.4 Summary ................................................................................................................. 77 Chapter 6 Adaptive and Distributed Beamforming Techniques for Intelligent WiMAX (I‐ WiMAX) ..................................................................................................................... 79 6.1 Introductions and background information ........................................................... 79 6.2 Concept of I‐WiMAX ............................................................................................... 80 6.3 AOFDM for I‐WiMAX .............................................................................................
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