Chathura Jayawardena

Future wireless networks are expected to deliver enhanced spectral efficiency while being energy efficient. MIMO and other non-orthogonal transmission schemes, such as non-orthogonal multiple access (NOMA), offer substantial theoretical spectral efficiency gains. However, these gains have yet to translate into practical deployments, largely due to limitations in current signal processing methods. Linear transceiver processing, though widely adopted, fails to fully exploit non-orthogonal transmissions, forcing massive MIMO systems to use a disproportionately large number of RF chains for relatively few streams, increasing power consumption. Non-linear processing can unlock the full potential of non-orthogonal schemes but is hindered by high computational complexity and integration challenges. Moreover, existing message-passing receivers for NOMA depend on specially designed sparse signals, limiting resource allocation flexibility and efficiency. This work presents NL-COMM, an efficient non-linear processing framework that translates the theoretical gains of non-orthogonal transmissions into practical benefits for both the uplink and downlink. NL-COMM delivers over 200% spectral efficiency gains, enables 50% reductions in antennas and RF chains (and thus base station power consumption), and increases concurrently supported users by 450%. In distributed MIMO deployments, the antenna reduction halves fronthaul bandwidth requirements, mitigating a key system bottleneck. Furthermore, NL-COMM offers the flexibility to unlock new NOMA schemes. Finally, we present both hardware and software architectures for NL-COMM that support massively parallel execution, demonstrating how advanced non-linear processing can be realized in practice to meet the demands of next-generation networks.

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.

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%.