Joint Beamforming and Antenna Selection for Sum Rate Maximization in Cognitive Radio Networks

Abstract

This letter studies joint transmit beamforming and antenna selection at a secondary base station (BS) with multiple primary users (PUs) in an underlay cognitive radio multiple-input single-output broadcast channel. The objective is to maximize the sum rate subject to the secondary BS transmit power, minimum required rates for secondary users, and PUs' interference power constraints. The utility function of interest is nonconcave and the involved constraints are nonconvex, so this problem is hard to solve. Nevertheless, we propose a new iterative algorithm that finds local optima at the least. We use an inner approximation method to construct and solve a simple convex quadratic program of moderate dimension at each iteration of the proposed algorithm. Simulation results indicate that the proposed algorithm converges quickly and outperforms existing approaches.Index Terms-Antenna selection, cognitive radio, nonconvex programming, sum rate, transmit beamforming.

Impact of Multiple Primaries and Partial CSI on Transmit Antenna Selection for Interference-Outage Constrained Underlay CR

“…The multiple PRxs model considered is more general than the single PRx model considered in [6], [11]- [13], and [25]. Additionally, the partial CSI model and statistically nonidentical STx-PRx channel gain model are more general than the full CSI model assumed in [3]- [5]. Our paper differs in many ways compared to [10].…”

“…For Multiple PRxs: For an STx that can adapt its transmit power and is required to satisfy the peak-interference constraint at each of the PRxs, the antenna with the highest STx-SRx channel power gain is selected in [3] and [4]. A low complexity iterative algorithm for jointly determining the antenna subset and beamforming vector is proposed in [5].…”

“…It is practically appealing because it enables the use of high-efficiency power amplifiers and also simplifies the design of the other components of the RF chain. We focus on the interference-outage constraint, which is a generalization of the widely studied peak-interference constraint [3]- [5]. Given its stochastic nature, STx need not perfectly know the instantaneous channel power gains from it to all the PRxs, which is unlike the peak-interference constraint.…”

“…This models the scenario in which the PRxs are at different distances from the STx, and, thus, have different path-losses. Our model differs from [3]- [5], which assume the peak-interference constraint and statistically identical STx-PRx channel gains, all of which are known to the STx.…”

Cooperative Prediction-and-Sensing-Based Spectrum Sharing in Cognitive Radio Networks

“…Depending on the power usage, the spectral efficiency problem has been studied with a sum power constraint [14], [17], [19] and per-antenna power constraints [15], [16]. However, the minimum rate requirements for all SUs were not addressed in [13]- [16], [18], [19], [28], although such rate constraints are crucial to resolving the so-called user fairness.…”