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In this paper, a novel approach, namely realcomplex hybrid modulation (RCHM), is proposed to scale up multiuser multiple-input multiple-output (MU-MIMO) detection with particular concern on the use of equal or approximately equal service antennas and user terminals. By RCHM, we mean that user terminals transmit their data sequences with a mix of real and complex modulation symbols interleaved in the spatial and temporal domain. It is shown, through the system outage probability, RCHM can combine the merits of real and complex modulations to achieve the best spatial diversity-multiplexing trade-off that minimizes the required transmit-power given a sum-rate. The signal pattern of RCHM is optimized with respect to the real-to-complex symbol ratio as well as power allocation. It is also shown that RCHM equips the successive interference canceling MU-MIMO receiver with near-optimal performances and fast convergence in Rayleigh fading channels. This result is validated through our mathematical analysis of the average biterror-rate as well as extensive computer simulations considering the case with single or multiple base-stations.

Multi-user (MU), multiple-input, multiple-output (MIMO) detection has been extensively investigated, and many techniques have been proposed. However, further performance improvements may be constrained by limitations in classical computation. The motivation for this work is to test whether a machine that exploits quantum principles can offer improved performance over conventional detection approaches. This paper presents an evaluation of MIMO detection based on quantum annealing (QA) when run on an actual QA quantum processing unit (QPU) and describes the challenges and potential improvements. The evaluations show promising results in some cases, such as near-optimality in a QPSK-modulated 8×8 MIMO case, but poor results in other cases, such as for larger systems or when using 16-QAM. We show that some challenges of QA detection include dealing with integrated control errors (ICE), the limited dynamic range of QA QPUs, an exponential increase in the number of qubits to the problem size, and a high computation overhead. Solving these challenges could make QA-based detection superior to conventional approaches and bring a new generation of MU-MIMO detection methods.

This work introduces Gyre Precoding (GP), a novel linear multi-user multiple-input multiple-output (MU-MIMO) precoding approach. GP performs rotations of the symbols of each spatial layer to optimize the precoding performance. To find the rotation angles, we propose a near-optimal, gradient descent-based low-complexity algorithm. GP is constellationagnostic and does not require significant changes to conventional receiver procedures or wireless standards. Computer evaluation results show that GP can achieve 8 dB SNR gains over linear precoding techniques and 2 dB over suboptimal symbol-level precoding (SLP) methods for a 16 × 16 MU-MIMO system. Furthermore, in a 64×12 massive-MIMO scenario in a 5G New Radio (5GNR) setup, GP achieves a 13% higher throughput gain over zero-forcing precoding.

Next-generation 6G networks are expected to feature an extremely high density of network and user devices. MU-MIMO non-linear processing can provide substantially improved performance over linear processing in dense conditions, but suffers from a high complexity and processing latency. The use of the massively parallel non-linear (MPNL) processing framework can overcome such limitations. This work discusses three potential 6G transmission scenarios and evaluates their detection and precoding performance using link-level simulations and a system-level, over-the-air, 3GPP standards-based testbed. The results validate that MPNL processing has the potential to transform the way 6G MU-MIMO systems are designed.