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In this paper, we investigate a max–min weighted signal-to-interference-plus-noise-ratio (SINR) problem in a full-duplex multiuser multiple-input-multiple-output system, where a full-duplex-capable base station (BS) equipped with multiple antennas communicates with multiple half-duplex downlink and uplink users under the same system resources. Specifically, we consider a practical scenario where the downlink minimum weighted SINR is maximized under specific SINR constraints for uplink users. Moreover, the optimization is conducted by jointly considering the BS transmit power, the transmit power of uplink users, and BS transmit and receive beamforming. This optimization problem is, therefore, subject to multiple uplink SINR constraints and multiple transmit power constraints. Due to the SINR constraints, negative matrix components arise and hence the optimization problem cannot be directly solved by the standard approach, i.e., Perron–Frobenius theory. We have provided an explicit iterative scheme to solve this joint optimization problem with a strict proof. The proposed algorithm is proved to converge to Karush–Kuhn–Tucker points. Simulation results show that our proposed algorithm has a fast convergence rate and leads to a better performance compared with other optimization techniques that do not jointly consider all parameters. [ABSTRACT FROM PUBLISHER]

Resource allocation is an important strategy to optimize system performance in multiuser cooperative orthogonal frequency-division multiple-access (OFDMA) systems. In this paper, we jointly consider three different transmission modes in cooperative OFDMA systems, i.e., direct transmission (DT) mode, half-duplex (HD) relay cooperative transmission (HDRCT) mode, and full-duplex (FD) relay transmission (FDRT) mode. The joint optimization problem of transmission mode selection, subcarrier assignment, relay selection, subcarrier pairing, and power allocation (PA) is investigated. In this paper, we transform the binary assignment problem into a maximum-weighted bipartite matching problem, which can be solved by the classical Hungarian method. Based on the dual method, we solve the joint PA and binary assignment problem iteratively. Specifically, since the direct link is considered interference in the FD relay transmission mode, the PA problem in FD relay transmission mode is nontrivial. Thus, we provide a novel hierarchical dual method to solve the PA problem in FD relay transmission mode. In addition, in HDRCT mode, the joint transmission of both the source and relay is taken into account, and we provide a simple and insightful PA scheme. Simulation results show that the proposed algorithms can significantly enhance the overall system throughput, compared with previous works. [ABSTRACT FROM AUTHOR]

In this paper, we consider simultaneous wireless information and power transfer (SWIPT) in relay interference channels, where multiple source-destination pairs communicate through their dedicated energy harvesting relays. Each relay needs to split its received signal from sources into two streams: one for information forwarding and the other for energy harvesting. We develop a distributed power splitting framework using game theory to derive a profile of power splitting ratios for all relays that can achieve a good network-wide performance. Specifically, non-cooperative games are respectively formulated for pure amplify-and-forward (AF) and decode-and-forward (DF) networks, in which each link is modeled as a strategic player who aims to maximize its own achievable rate. The existence and uniqueness for the Nash equilibriums (NEs) of the formulated games are analyzed and a distributed algorithm with provable convergence to achieve the NEs is also developed. Subsequently, the developed framework is extended to the more general network setting with mixed AF and DF relays. All the theoretical analyses are validated by extensive numerical results. Simulation results show that the proposed game-theoretical approach can achieve a near-optimal network-wide performance on average, especially for the scenarios with relatively low and moderate interference. [ABSTRACT FROM PUBLISHER]

In this paper, 2-D normalized min-sum (NMS) algorithm and offset min-sum (OMS) algorithm are proposed for efficient decoding of low-density parity-check (LDPC) codes in 2-D intersymbol interference (ISI) ultra-high density magnetic recording channels, such as bit-patterned magnetic recording and 2-D magnetic recording, where a reduced-complexity 2-D detector based on the iterative row-column soft detection feedback with Gaussian approximation detector is employed instead of the full 2-D Bahl–Cocke–Jelinek–Raviv (BCJR) detector. The normalization and offset factors of the 2-D NMS and 2-D-OMS, are optimized based on the extended density evolution for LDPC coded 2-D ISI channel, respectively. Simulation results show the performance loss caused by the reduced-complexity LDPC decoder can be almost fully recovered by the proposed approaches, while retaining the benefit of low complexity in decoder compared with the belief propagation (BP) decoding. Furthermore, both the NMS and OMS exhibit a lower error floor than that of BP decoding in high signal-to-noise ratio region. [ABSTRACT FROM AUTHOR]