Beamforming of Secondary System Under QoS Constraint of Primary System
- 주제(키워드) Cognitive radio , MIMO , Beamforming
- 발행기관 고려대학교 대학원
- 지도교수 오성준
- 발행년도 2014
- 학위수여년월 2014. 2
- 학위구분 박사
- 학과 일반대학원 컴퓨터·전파통신공학과
- 세부전공 전파통신공학 전공
- 원문페이지 92 p
- 실제URI http://www.dcollection.net/handler/korea/000000049207
- 본문언어 영어
- 제출원본 000045793741
초록/요약
In the previous decade, cognitive radio (CR) has gained increasing attention and has been developed to mainly address the underutilized spectrum issues within practical communication systems. In CR networks, a hierarchical structure exists between the licensed primary user (PU) or legacy spectrum holders, and and secondary user (SU), who access the licensed spectrum dynamically under the constraint of not inducing quality of service (QoS) degradations intolerable to the PU. Recently, it has gained great attention among the researchers in a CR area using multiple antennas at SU transmitter to reduce or pre-nullify interference to PU receiver. In this dissertation, we discuss some challenges in applying multiple antenna techniques to the CR networks and investigate the beamforming vector designs and power allocations. The first part of this dissertation focuses on a downlink beamforming problem in the cognitive radio (CR) communication system where primary users (PUs) coexist with secondary users (SUs). It is assumed that the SU transmitter has multiple antennas and transmits data to another SU’s single-antenna receiver by employing the beamforming. By properly designing a beamforming vector, a SU can maximize its channel gain while maintaining an interference with PU below a predefined level. Designing a beamforming vector includes steering beamforming vector direction as well as adjusting transmit power. In order to construct the optimal beamforming vector, the SU transmitter requires knowledge of the downlink channel information of both PU and SU receivers sent on the feedback channel. However, it is impractical to assume that the transmitter has the perfect channel state information (CSI) considering the tremendous feedback overhead. One way to tackle this feedback overhead problem is to use a finite number of feedback bits indicating the index of a predefined codeword in the codebook. We analyze interference to the PU receiver and propose an algorithm to design a beamforming vector considering an error attributed to the partial CSI. The simulation shows that the analysis is quite accurate and that the beamforming vector, designed according to the proposed algorithm, keeps interference to PU below a predefined level while taking into account the error owing to the partial CSI. In addition, we propose a feedback bit allocation mechanism, in order to maximize the gain of the SU link. By using the proposed scheme, the CR system become robust against errors attributed to the partial CSI. In the second part of the dissertation, we consider applying the MIMO techniques to a cooperative cognitive radio network (CCRN) in order to achieve increased spectral efficiency. Assuming that the system comprises a single-input single-output (SISO) primary user (PU) pair and a multi-input single-output (MISO) secondary user (SU) pair, we propose jointly optimizing the beamforming vector and power allocation for the SU transmitter in order to maximize the rate for the SU while meeting the rate requirement for the PU. As the problem is nontrivial, we circumvent it using the uplink and downlink duality and equality of the left and right eigenvalues. Further, we introduce an iterative algorithm with a proof of convergence. From the numerical results, the proposed algorithm converges in a small number of iterations and outperforms the zero-forcing (ZF) beamforming lgorithm while providing an upper bound for the CCRN-MIMO performance. Furthermore, the application of the proposed algorithm is not limited to CCRN-MIMO; it can also be extended to the conventional relay MIMO and/or MU-MIMO with a quality-of-service (QoS) requirement.
more목차
Abstract i
Contents iii
List of Figures vi
Chapter 1 Introduction 1
1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.3 Common Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
Chapter 2 Beamforming in a Multi-User Cognitive Radio System with Partial
Channel State Information 7
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
2.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2.1 Application Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
2.2.2 Channel and Signal Model . . . . . . . . . . . . . . . . . . . . . . . 14
2.2.3 Problem Description and Approach . . . . . . . . . . . . . . . . . . 16
iii
2.3 Beamforming Vector with Perfect CSI . . . . . . . . . . . . . . . . . . . . . 17
2.4 Beamforming Vector with Partial CSI . . . . . . . . . . . . . . . . . . . . . 20
2.4.1 Quantization of CDI for a Single PU . . . . . . . . . . . . . . . . . . 21
2.4.2 ZF-beamformer for multiple PUs . . . . . . . . . . . . . . . . . . . 26
2.4.3 Quantization of CMI . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.5 Bit Allocation of CSI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2.6 Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
2.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
Chapter 3 Cognitive Radio Channel with Cooperative Multi-Antenna Secondary
Systems 39
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.2 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
3.3 Design of the Beamforming Vector and Power for Cooperation . . . . . . . . 46
3.3.1 Uplink and Downlink Beamforming Duality . . . . . . . . . . . . . 47
3.3.2 Left Eigenvectors . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.3.3 Iterative Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.4 Performance Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.4.1 Mathematical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.4.2 Numerical Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
3.6 Proof of Lemma 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
3.7 Proof of Lemma 2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.8 Proof of Lemma 3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
iv
3.9 Proof of Lemma 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
Chapter 4 Conclusion 73
Bibliography 76
