ViPer NL-COMM: Making Vector Perturbation Precoding Practical

Large MIMO systems rely on efficient downlink precoding to enhance data rates and improve connectivity through spatial multiplexing. However, currently employed linear precoding techniques, such as MMSE, significantly limit the achievable spectral efficiency. To meet practical error-rate targets, existing linear methods require an excessively high number of access point antennas relative to the number of supported users, leading to disproportionate increases in power consumption.Efficient non-linear processing frameworks for uplink MIMO transmissions, such as NL-COMM, have been proposed. However, downlink non-linear precoding methods, such as Vector Perturbation (VP), remain impractical for real-world deployment due to their exponentially increasing computational complexity with the number of supported streams. This work presents ViPer NL-COMM, the first practical algorithmic and implementation framework for VP-based precoding. ViPer NL-COMM extends the core principles of NL-COMM to the precoding problem, enabling scalable parallelization and real-time computational performance while maintaining the substantial spectral-efficiency benefits of VP precoding. ViPer NL-COMM consists of a novel mathematical framework and an FPGA prototype capable of supporting large MIMO configurations (up to 16x16), high-order modulation (256-QAM), and wide bandwidths (100 MHz) within practical power and resource budgets. System-level evaluations demonstrate that ViPer NL-COMM achieves target error rates using only half the number of transmit antennas required by linear precoding, yielding net power savings on the order of hundreds of Watts at the RF front end. Moreover, ViPer NL-COMM enables supporting more information streams than available AP antennas when the streams are of low-rate, paving the way for enhanced massive-connectivity scenarios in next-generation wireless networks.

NL-COMM: Enhanced Video Streaming via Advanced Non-Linear Processing

With video streaming now accounting for the majority of internet traffic, wireless networks face increasing demands, especially in densely populated areas where limited spectral resources are shared among many devices. While multi-user (MU)-MIMO technology aims to improve spectral efficiency by enabling concurrent transmissions over the same frequency and time resources, traditional linear processing methods fall short of fully utilizing available channel capacity. These methods require a substantial number of antennas and RF chains, to support a much smaller number of MIMO streams, leading to increased power consumption and operational costs, even when the supported streams are of low rate. In this demo, we present NL-COMM, an advanced non-linear MIMO processing framework, demonstrated for the first time with commercial off-the-shelf (COTS) user equipment (UEs) in a fully 3GPP-compliant environment. In addition, also for the first time, the audience will compare and assess the quality of live, over-the-air video transmission from four concurrently transmitting UE devices, alternating between current state-of-the-art MIMO detection algorithms and NL-COMM. Key gains of NL-COMM include improved stream quality, halving the number of required base station antennas without compromising stream quality compared to linear approaches, as well as achieving antenna overloading factors of 400\%.

Ultra-Low-Complexity, Non-Linear Processing for MU-MIMO Systems

Non-linear detection schemes can substantially improve the achievable throughput and connectivity capabilities of uplink MU-MIMO systems that employ linear detection. However, the complexity requirements of existing non-linear soft detectors that provide substantial gains compared to linear ones are at least an order of magnitude more complex, making their adoption challenging. In particular, joint soft information computation involves solving multiple vector minimization problems, each with a complexity that scales exponentially with the number of users. This work introduces a novel ultra-low-complexity, non-linear detection scheme that performs joint Detection and Approximate Reliability Estimation (DARE). For the first time, DARE can substantially improve the achievable throughput (e.g., 40%) with less than 2x the complexity of linear MMSE, making non-linear processing extremely practical. To enable this, DARE includes a novel procedure to approximate the reliability of the received bits based on the region of the received observable that can efficiently approach the accurately calculated soft detection performance. In addition, we show that DARE can achieve a better throughput than linear detection when using just half the base station antennas, resulting in substantial power savings (e.g., 500 W). Consequently, DARE is a very strong candidate for future power-efficient MU-MIMO developments, even in the case of software-based implementations, as in the case of emerging Open-RAN systems. Furthermore, DARE can achieve the throughput of the state-of-the-art non-linear detectors with complexity requirements that are orders of magnitude lower.

Towards Radio Designs with Non-Linear Processing for Next Generation Mobile Systems

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, to efficiently facilitate such MIMO transmissions, linear base-station processing is adopted, that translates the MIMO channel into several single-antenna channels. Still, while such approaches are relatively easy to implement, they can leave on the table a significant amount of unexploited MIMO capacity. Recently proposed non-linear base-station processing methods claim this unexplored capacity and promise a substantially increased network throughput. 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 of all the necessary algorithmic modules required by a practical system. This work, outlines our experience by trying to incorporate and evaluate the gains of non-linear base-station processing in a 3GPP standard environment. We discuss the several corresponding challenges and our adopted solutions, together with their corresponding limitations. We report gains that we have managed to verify, and we also discuss remaining challenges, missing algorithmic components and future research directions that would be required towards highly efficient, future mobile systems that can efficiently exploit the gains of non-linear, base-station processing.