The recent paradigm shift towards transmitting large numbers of mutually interfering information streams, as in the case of aggressive spatial multiplexing, combined with requirements towards very low processing latency despite the frequency plateauing of traditional processors, initiates a need to revisit the fundamental maximum-likelihood (ML) and, consequently, the sphere-decoding (SD) detection problem. This work presents the design and VLSI architecture of MultiSphere; the first method to massively parallelize the tree-search of large sphere decoders in a nearly-concurrent manner, without compromising their maximum-likelihood performance, and by keeping the overall processing complexity comparable to that of highly-optimized sequential sphere decoders. For a $10 \times 10$ MIMO spatially multiplexed system with 16-QAM modulation and 32 processing elements our MultiSphere architecture can reduce latency by 29× against well-known sequential SDs, approaching the processing latency of linear detection methods, without compromising ML optimality. In MIMO multicarrier systems targeting exact-ML decoding, MultiSphere achieves processing latency and hardware efficiency that are orders of magnitude improved compared to approaches employing one SD per subcarrier. Moreover, for $16 \times 16$ both hard- and soft-output MIMO systems, approximate MultiSphere versions achieve similar error rate performance with state-of-the art approximate SDs with akin parallelization properties, by using only one tenth of the processing elements and to achieve up to approximately 9× increased energy efficiency.

The increasing demand for connectivity and throughput, despite the spectrum limitations, has triggered a paradigm shift towards non-orthogonal signal transmissions. However, the complexity requirements of near-optimal detection methods for such systems becomes impractical, due to the large number of mutually interfering streams and to the rank-deficient or ill-determined nature of the corresponding interference matrix. This work introduces g-MultiSphere; a generic massively parallel and near-optimal sphere-decoding-based approach that, in contrast to prior work, applies to both well- and ill-determined non-orthogonal systems. We show that g-MultiSphere is the first approach that can support large uplink multi-user MIMO systems with numbers of concurrently transmitting users that exceed the number of receive antennas by a factor of two or more, while attaining throughput gains of up to 60% and with reduced complexity requirements in comparison to known approaches. By eliminating the need for sparse signal transmissions for nonorthogonal multiple access (NOMA) schemes, g-MultiSphere can support more users than existing systems with better detection performance and practical complexity requirements. In comparison to state- of-the-art detectors for NOMA schemes and nonorthogonal signal waveforms (e.g., SEFDM) g-MultiSphere can be up to an order of magnitude less complex, and can provide throughput gains of up to 60%.

Multi-user multiple-input, multiple-output (MU-MIMO) designs can substantially increase the achievable throughput and connectivity capabilities of wireless systems. However, existing MU-MIMO deployments typically employ linear processing that, despite its practical benefits, can leave capacity and connectivity gains unexploited. On the other hand, traditional non-linear processing solutions (e.g., sphere decoders) promise improved throughput and connectivity capabilities, but can be impractical in terms of processing complexity and latency, and with questionable practical benefits that have not been validated in actual system realizations. At the same time, emerging new Open Radio Access Network (Open-RAN) designs call for physical layer (PHY) processing solutions that are also practical in terms of realization, even when implemented purely on software. This work demonstrates the gains that our highly efficient, massively parallelizable, non-linear processing (MPNL) framework can provide, both in the uplink and downlink, when running in real-time and over-the-air, using our new 5G-New Radio (5G-NR) and Open-RAN compliant, software-based PHY. We showcase that our MPNL framework can provide substantial throughput and connectivity gains, compared to traditional, linear approaches, including increased throughput, the ability to halve the number of base-station antennas without any performance loss compared to linear approaches, as well as the ability to support a much larger number of users than base-station antennas, without the need for any traditional Non-Orthogonal Multiple Access (NOMA) techniques, and with overloading factors that can be up to 300%.

MIMO mobile systems, with a large number of antennas at the base-station side, enable the concurrent transmission of multiple, spatially separated information streams, and therefore, enable improved network throughput and connectivity both in uplink and downlink transmissions. Traditionally, such MIMO transmissions adopt linear base-station processing, that translates the MIMO channel into several single-antenna channels. While such approaches are relatively easy to implement, they can leave on the table a significant amount of unexploited MIMO capacity and connectivity capabilities. Recently-proposed non-linear base-station processing methods claim this unexplored capacity and promise substantially increased network throughput and connectivity capabilities. Still, to the best of the authors’ knowledge, non-linear base-station processing methods not only have not yet been adopted by actual systems, but have not even been evaluated in a standard-compliant framework, involving all the necessary algorithmic modules required by a practical system. In this work, for the first time, we incorporate and evaluate non-linear base-station processing in a 3GPP standard environment. We outline the required research platform modifications and we verify that significant throughput gains can be achieved, both in indoor and outdoor settings, even when the number of base-station antennas is much larger than the number of transmitted information streams. Then, we identify missing algorithmic components that need to be developed to make non-linear base-station practical, and discuss future research directions towards potentially transformative next-generation mobile systems and base-stations (i.e., 6G) that explore currently unexploited non-linear processing gains.

Non-orthogonal multiple access schemes (NOMA), such as sparse code multiple access (SCMA), are among the most promising technologies to support massive numbers of connected devices. Still, to minimize the transmission delay and to maximize the utilization of the transmission channel, “grant-free” NOMA techniques are required that eliminate any prior information exchange between the users and the base-stations. However, if a large number of users transmit simultaneously in an “unsupervised” manner, (i.e., without any prior signaling for controlling the number of users and the corresponding transmission patterns), it is likely that a large number of users may share the same frequency-resource element, rendering the corresponding user detection impractical. In this context, we present a new multi-user detection approach, which aims to maximize the detection performance, with respect to given processing and latency limitations. We show that our approach enables practical detection for grant-free SCMA schemes that support hundreds of interfering users, with a complexity that is up to two orders of magnitude less than that of conventional detection approaches.