Optimal Precoder Designs for Sum-Utility Maximization in SWIPT-Enabled Multi-User MIMO Cognitive Radio Networks

Abstract

In this paper, we propose a generalized framework that combines the cognitive radio (CR) techniques for spectrum sharing and the simultaneous wireless information and power transfer (SWIPT) for energy harvesting (EH) in the conventional multi-user MIMO (MuMIMO) channels, which leads to an MuMIMO-CR-SWIPT network. In this system, we have one secondary base-station (S-BS) that supports multiple secondary information decoding (S-ID) and secondary EH (S-EH) users simultaneously under the condition that interference power that affects the primary ID (P-ID) receivers should stay below a certain threshold. The goal of the paper is to develop a generalized precoder design that maximizes the sum-utility cost function under the transmit power constraint at the S-BS, and the EH constraint at each S-EH user, and the interference power constraint at each P-ID user. Therefore, the previous studies for the CR and SWIPT systems are casted as particular solutions of the proposed framework. The problem is inherently non-convex and even the weighted minimum mean squared error (WMMSE) transformation does not resolve the non-convexity of the original problem. To tackle the problem, we find a solution from the dual optimization via sub-gradient ellipsoid method based on the observation that the WMMSE transformation raises zero-duality gap between the primal and the dual problems. We also propose a simplified algorithm for the case of a single S-ID user, which is shown to achieve the global optimum. Finally, we demonstrate the optimality and efficiency of the proposed algorithms through numerical simulation results.

Joint Beamforming and Power Allocation for Multiuser MISO Broadcast Channel SWIPT Employing OFDM

“…The authors in [12] convert the WSR problem to a weighted sum mean square error minimization and formulate it as a quadratic program. Beamforming design through a weighted sum utility maximization problem is considered in [19] for a cognitive MU-MIMO-SWIPT network.…”

“…The proposed algorithm traces are labeled with an "EH" and exhaustive search algorithm points are labeled with an "ExSe". The system parameters include N T = 6, K = 1024, N = 3, and P Total = 30 dBm [19]. Both [9], [11] utilize an iterative method in which multiple SDP problems are solved for one point.…”

“…Beamforming for SWIPT was first examined in [5], which studied optimal transmission strategies and the associated information rate and harvested energy tradeoff. Information and energy beamforming designs were investigated for MISO downlinks in [6], [7], [36], [37], MISO interference channels [33], [34], and for MIMO [11], [19], [29], [30], [32], [35], [39], [52], [53]. For an extensive review of the literature on beamforming in wireless networks with energy harvesting, please refer to [31] as well as [1], [2] and the references therein.…”

Energy Efficiency and User Capacity Optimization of Cognitive MIMO Systems Via the SCMA-Based Nonorthogonal Time Slot Allocation

“…Therefore, we consider introducing MIMO technology into the CR system to improve its performance. In the literature, most of the research on energy efficiency and user capacity of MIMO-assisted CR networks can be divided into two categories: the traditional CR-MIMO network [25][26][27][28][29][30][31][32][33][34][35] and the network combining CR-MIMO with NOMA (CR-MIMO-NOMA) Technology [36][37][38][39][40][41].…”

“…In the traditional CR-MIMO network, spectrum sensing and energy harvesting technology have been the main research directions in recent years [30][31][32][33][34][35], while the EE and user capacity were mainly studied in earlier years [25][26][27][28][29]. The problems of EE optimization of a MIMO-CR system with multiple secondary receivers for the cases of single and multiple data streams was addressed in References [25], respectively.…”