This paper proposes novel linear precoding algorithms for Multiple-Input Multiple-Output Bit-Interleaved Coded Modulation (MIMO-BICM) systems that maximize the achievable rate subject to power constraints. To overcome the nonlinear and nonconvex nature of the optimization problem, we rewrite the achievable rate in terms of the log-likelihood ratio (LLR) and introduce manifold-based gradient ascent (MGA) precoding and low-complexity non-iterative algorithms. Simulation results show significant gains in achievable rate and block error rate compared to existing techniques. Additionally, we extend our investigation to linear precoding with the constraint that the precoding matrix is selected from the codebook type-I adopted in Fifth-Generation New Radio (5G NR) networks. We propose heuristic algorithms that exploit the Kronecker and Discrete Fourier Transform (DFT) structure of the codebook and consider the singular vector decomposition (SVD) precoder as the optimal reference precoder. The traditional exhaustive search methods require a high complexity, especially for large codebook sizes. However, our proposed algorithms apply a combination of direct estimation and a low-dimensional search for deriving the indices, resulting in a reduced number of codebook precoder candidates. Simulation results show that our proposed low-complexity algorithms perform comparably to exhaustive search baselines.

In this paper, the receive spatial modulation (RSM) scheme is studied in the downlink of the multi-cell multi-user systems, assuming that the base station (BS) is equipped with a large number of antennas and serves a large number of multi-antenna users, while the user-antenna ratio is bounded. The system model is practical and applicable in a comprehensive manner, accounting for channel estimation, spatially correlated channels, pilot contamination, and path loss. Combining the concepts of RSM and massive multiple-input multiple-output (MIMO), the spectral efficiency of linear preprocessing zero-forcing (ZF) and regularized ZF (RZF) methods is studied by approximating the probability of antenna detection and deriving a lower bound for the spatial-domain mutual information. With the aid of the large-system analysis, the deterministic equivalent expressions are provided for the achievable rates that are asymptotically exact. The Monte Carlo simulations validate this claim and show that for limited dimensions, the proposed deterministic equivalents yield accurate approximations, even for strongly correlated channels. The results further show that in comparison with the conventional massive MIMO systems with single-antenna users, equipping each user with multiple antennas and applying the RSM scheme enhances the spectral efficiency when each cell is not heavily loaded.

Massive multiple‐input multiple‐output (MIMO) as a promising technology to achieve high spectral efficiency encounters the problem of high cost and complexity of hardware implementation for digital signal processing. One proposed solution is hybrid analogue–digital processing to reduce the number of radio‐frequency (RF) chains required at massive antenna arrays. In this study, hybrid analogue–digital preprocessing aided spatial modulation is introduced to the downlink of multi‐user massive MIMO systems. Linear zero‐forcing (ZF) and regularised ZF preprocessors which are near‐optimal in massive MIMO systems and a low‐complexity two‐stage preprocessing scheme based on block‐diagonalisation are proposed. These full‐digital schemes are implemented in hybrid RF‐baseband domain through a low‐dimensional processor based on the baseband effective channel and phase controllers at the RF domain. The spatially correlated and sparsely scattered millimetre wave channels are adopted to consider the proposed preprocessing schemes. Under an imperfect channel state information at the transmitter, the robust design of full digital and hybrid analogue‐digital preprocessors is presented using stochastic robust approximation. Furthermore, the hybrid RF‐baseband preprocessors are realised using heavily quantised angles and the preprocessing schemes are investigated from the data rate point of view. The simulation results show desirable performance and close to that of full digital preprocessing is achieved by proper configuration of system.

In this study a new approach to filter‐and‐forward (FF) relay design in an overlay cognitive radio (CR) network with frequency selective channels is proposed. In this system, to implement two‐way relaying scheme, a secondary multi‐antenna node acts as a relay and helps two primary users (PUs) communicate with each other and in return the secondary node superposes its signal to filtered signals of PUs. The authors intend to design secondary transceiver under two criteria, one from the viewpoint of improving the performance of secondary network and the other to guarantee quality of service (QoS) of primary network. The first criterion is to maximise the signal‐to‐interference‐plus‐noise ratio (SINR) of secondary receiver subject to constraints on QoS of PUs and the relay transmitted power. For the other one, the goal is to achieve max‐min fairness among PUs through maximising the worse signal‐to‐interference‐plus‐noise ratio (SINR) of PUs subject to constraints on QoS of secondary receiver and transmit power of relay. The authors employ an alternating optimisation algorithm to find the beamforming matrices and the allocated power to secondary receiver's message in these non‐convex problems. Simulation results show that the proposed FF approach has better performance compared with simple AF relaying strategy.