Joint Beamforming and Power Allocation for Multiple Access Channels in Cognitive Radio Networks

• Published in: IEEE journal on selected areas in communications Vol. 26; no. 1; pp. 38 - 51
• Main Authors: Zhang, Lan, Liang, Ying-Chang, Xin, Yan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.01.2008; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Allocations; Array signal processing; Channels; Chromium; Cognitive radio; Constraint optimization; Decision feedback equalizers; Interference constraints; Mathematical models; Maximization; Multiple access; Networks; Operations research; Optimization; Quality of service; Radio spectrum management; Signal to noise ratio; Studies; Throughput
• ISSN: 0733-8716; 1558-0008
• DOI: 10.1109/JSAC.2008.080105

A cognitive radio (CR) network refers to a secondary network operating in a frequency band originally licensed/allocated to a primary network consisting of one or multiple primary users (PUs). A fundamental challenge for realizing such a system is to ensure the quality of service (QoS) of the PUs as well as to maximize the throughput or ensure the QoS, such as signal-to-interference-plus-noise ratios (SINRs), of the secondary users (SUs). In this paper, we study single-input multiple output multiple access channels (SIMO-MAC) for the CR network. Subject to interference constraints for the PUs as well as peak power constraints for the SUs, two optimization problems involving a joint beamforming and power allocation for the CR network are considered: the sum-rate maximization problem and the SINR balancing problem. For the sum-rate maximization problem, zero-forcing based decision feedback equalizers are used to decouple the SIMO-MAC, and a capped multi-level (CML) water-filling algorithm is proposed to maximize the achievable sum-rate of the SUs for the single PU case. When multiple PUs exist, a recursive decoupled power allocation algorithm is proposed to derive the optimal power allocation solution. For the SINR balancing problem, it is shown that, using linear minimum mean-square-error receivers, each of the interference constraints and peak power constraints can be completely decoupled, and thus the multi-constraint optimization problem can be solved through multiple single-constraint sub-problems. Theoretical analysis for the proposed algorithms is presented, together with numerical simulations which compare the performances of different power allocation schemes.