Please use this identifier to cite or link to this item: https://doi.org/10.1109/JSAC.2008.080105
Title: Joint beamforming and power allocation for multiple access channels in cognitive radio networks
Authors: Zhang, L.
Liang, Y.-C.
Xin, Y. 
Keywords: Beamforming
Cognitive radio network
Multiple access channel
Power allocation
Issue Date: Jan-2008
Source: Zhang, L.,Liang, Y.-C.,Xin, Y. (2008-01). Joint beamforming and power allocation for multiple access channels in cognitive radio networks. IEEE Journal on Selected Areas in Communications 26 (1) : 38-51. ScholarBank@NUS Repository. https://doi.org/10.1109/JSAC.2008.080105
Abstract: 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 subproblems. Theoretical analysis for the proposed algorithms is presented, together with numerical simulations which compare the performances of different power allocation schemes. © 2008 IEEE.
Source Title: IEEE Journal on Selected Areas in Communications
URI: http://scholarbank.nus.edu.sg/handle/10635/56433
ISSN: 07338716
DOI: 10.1109/JSAC.2008.080105
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