Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/14399
Title: Resource Optimization for multi-antenna Cognitive radio networks
Authors: ZHANG LAN
Keywords: Resource,Optimization,Multi-antenna,Cognitive,Networks,Radio
Issue Date: 8-Jan-2010
Source: ZHANG LAN (2010-01-08). Resource Optimization for multi-antenna Cognitive radio networks. ScholarBank@NUS Repository.
Abstract: One of the fundamental challenges faced by the wireless communication industry is how to meet rapidly growing demands for wireless services and applications with limited radio spectrum. Cognitive radio (CR) is a promising solution to tackle this challenge by supporting the secondary (unlicensed) users to opportunistically or con-concurrently access the spectrum allocated to primary (licensed) users. However, such spectrum access by secondary users (SUs) needs to avoid casuing detrimental interference to the primary users. (PUs). There are two popular CR modules: the opportunistic spectrum access model and the spectrum sharing model. In the opportunistic spectrum access model, the SUs are allowed to coexist with the PUs, subject to an interference power constraint that specifies the maximum tolerable interference power from the SUs to the PUs. This thesis studise a number of topics on multo-antenna CR networks under the spectrum sharing model. First, we study the resource optimization problems for the three different multi-antenna CR channels, including the CR single-input multiple output multiple access channel (SIMO-MAC), the CR multiple-input multiple-output broad-cast channel (MIMO-BC), and the CR multiple-input single-output (MISO) channel. Then, we apply the solution of the resource allocation problem for CR MIMO channels to solve the capacity computation problem for secrecy MIMO channels.Specifically, for the CR SIMO-MAC, we first consider the joint beamforming and power allocation for the sum rate maximization problem subject to transmit and interference power constraints. A capped multi-level water filling algorithm is proposed to obtain the optimal power allocation. Secondly, we consider the signal-to-interference-plus-noise ratio (SINR) balancing problem, in which the minimal ratio of the achievable SINRs relative to the target SINRs of the users is maximized. It is proved that the linear power constraints can be completely decoupled, and accordingly a high-efficiency algorithm is proposed to solve the corresponding problem. For the CR MINMO-BC, we focus on determining the optimal transmit covariance matrix to achieve the entire capacity region. Conventionally, the MIMO-BC is subject to single rsum power constraint, and the corresponding capacity computation problem can be transformed into that of a dual MIMO-MAC by using the conventional BC-MAC duality. This daulity, however, cannot be applied to the CR case due to the existence of the extra interference power constraints. To handle this difficulty, a generalized BC-MAC daulity is proposed for the MIMO-BC with multiple linear constraints. By exploring the new duality, a subgradient based algorithm is developed. For the CR MISO channel, we consider a robust design problem, in which the channel state information (CSI) of the link from the SU transmitter to the PU is assumed to be partially known by the SU. Our design objective is to determine the transmit covarience matrix that maximizes the rate of the SU while the interference power constraint is satisifed for all possible channel realizations. This problem is formulated as a semi-infinite programming (SIP) problem. Two solutions, including a closed-form solution and a second order cone programming (SOCP) based solution, are proposed. Finally, we apply the resource allocation solution for CR MIMO channels to solve the capacity computation problem for secrecy MIMO channels. By exploiting the relationship between these two channels, the capacity computation problem for secrecy MIMO channels is transfored to a sequence of optimization problems for CR MIMO channels, through which several efficient algorithms are proposed.
URI: http://scholarbank.nus.edu.sg/handle/10635/14399
Appears in Collections:Ph.D Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
thesis_lanzhang.pdf844.42 kBAdobe PDF

OPEN

NoneView/Download

Page view(s)

365
checked on Dec 11, 2017

Download(s)

372
checked on Dec 11, 2017

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.