Multi-user (MU)-multiple-input, multiple-output (MIMO) technology has been central to the evolution of wireless networks, since it can provide substantial network gains by enabling the concurrent transmission of a large number of information streams, over the same frequency. However, reliably detecting these mutually interfering streams comes at a very high computational cost that increases exponentially with the number of concurrently transmitted streams. This makes the corresponding MU-MIMO systems highly inefficient in terms of power consumption and processing latency. In this context, and in order to unlock the full MU-MIMO potential, alternative computing architectures are required, able to efficiently detect a large number of information streams, in a power-efficient manner. In this context, NeuroMIMO, is the first attempt to apply the principles of neuromorphic computing to achieve highly efficient MIMO detection. NeuroMIMO suggests and evaluates two different ways to translate the MIMO detection problem into a neuromorphic one. The first (i.e., Massive-NeuroMIMO) is appropriate for massive MIMO systems, where the number of receive, base-station/access-point antennas is much higher than the number of information streams. The second (i.e., Highly-Efficient-NeuroMIMO) is appropriate for the case where the number of transmitted streams approaches the number of base station antennas, and can reach the performance of the optimal Maximum-Likelihood detector. We discuss the trade-offs between the two NeuroMIMO approaches, and we show that both can provide substantial power gains compared to their traditional counterparts, while accounting for the preprocessing overhead required to translate the MIMO detection problem into a neuromorphic one. In addition, despite the current limitations in the "speed" of existing neuromorphic chips, we discuss that real-time processing detection can be achieved, even for a 5G NR system with 100 MHz operating bandwidth.

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.

It is well documented that the achievable throughput of MIMO systems that employ linear beamforming can significantly degrade when the number of concurrently transmitted information streams approaches the number of base-station antennas. To increase the number of the supported streams, and therefore, to increase the achievable net throughput, non-linear beamforming techniques have been proposed. These beamforming approaches are typically evaluated via simulations or via simplified over-the-air experiments that are sufficient for validating their basic principles, but they neither provide insights about potential practical challenges when trying to adopt such approaches in a standards-compliant framework, nor they provide any indication about the achievable performance when they are part of a standards-compliant protocol stack. In this work, for first time, we evaluate non-linear beamforming in a 3GPP standards-compliant framework, using our recently-proposed SWORD research platform. SWORD is a flexible, open for research, software-driven platform that enables the rapid evaluation of advanced algorithms without extensive hardware optimizations that can prevent promising algorithms from being evaluated in a standards-compliant stack. We show that in an indoor environment, vector perturbation-based non-linear beamforming can provide up to 46% throughput gains compared to linear approaches for 4×4 MIMO systems, while it can still provide gains of nearly 10% even if the number of base-station antennas is doubled.

In this paper, a novel approach, namely, real-complex 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 (UTs). By RCHM, we mean that UTs transmit their data sequences with a mix of real and complex modulation symbols interleaved in the spatial and temporal domain. It is shown that, through the system outage probability, RCHM can combine the merits of real and complex modulations to achieve the best spatial diversity-multiplexing tradeoff 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 bit-error-rate as well as extensive computer simulations considering the case with single or multiple base stations.