Large multiple-input multiple-output (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 minimum mean square error (MMSE) precoding, significantly limit the achievable spectral efficiency. To meet practical error-rate targets, existing linear methods require an excessively high number of access point (AP) 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 MIMO streams. This work presents ViPer NL-COMM, the first practical algorithmic and implementation framework for VP-based downlink 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 16×16), 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.

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%.

The Open Radio Access Network (RAN) paradigm envisions a more flexible, interoperable, and intelligent RAN ecosystem via new open interfaces and elements like the RAN Intelligent Controller (RIC). However, the impact of these elements on Open RAN's power consumption remains heavily unexplored. This work for the first time evaluates the impact of Key Performance Indicator (KPI) monitoring on RIC's power consumption using real traffic and power measurements. By analyzing various RIC-RAN communication scenarios, we identify that RIC's power consumption can become a scalability bottleneck, particularly in large-scale deployments, even when RIC is limited to its core operational functionalities and without incorporating application-specific processes. In this context, we also explore potential power savings through the elimination of redundant KPI transmissions for the first time, extending existing techniques for identical subscription removal and KPI selection. We achieve significant power consumption gains exceeding 87% of the overall RIC power consumption.

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.

Future autonomous transportation systems necessitate network infrastructure capable of accommodating massive vehicular connectivity, despite the scarce availability of frequency resources. Current approaches for achieving such required high spectral efficiency, rely on the utilization of Multiple-Input, Multiple-Output (MIMO) technology. However, conventional MIMO processing approaches, based on linear processing principles, leave much of the system’s capacity heavily unexploited. They typically require a large number of power-consuming antennas and RF-chains to support a substantially smaller number of concurrently connected devices, even when the devices are transmitting at low rates. This translates to inflated operational costs that become substantial, particularly in ultra-dense, metropolitan-scale deployments. Therefore, the question is how to efficiently harness this unexploited MIMO capacity and fully leverage the available RF infrastructure to maximize device connectivity. Addressing this challenge, this work proposes an Open Radio Access Network (Open-RAN) deployment, with Massively Parallelizable Non-linear (MPNL) MIMO processing for densely deployed, and power-efficient vehicular networks. For the first time, we quantify the substantial gains of MPNL in achieving massive vehicular connectivity with significantly reduced utilized antennas, compared to conventional linear approaches, and without any throughput loss. We find that an Open-RAN-based realization exploiting the MPNL advancements can yield an increase of over $300 \%$ in terms of concurrently transmitting single-antenna vehicles in urban mobility settings and for various Vehicle-to-Infrastructure (V2I) and Network (V2N) use cases. In this context, we discuss how implementing MPNL within the Open-RAN ecosystem allows for simpler and more densely deployed radio units, paving the way for fully autonomous and sustainable transportation systems.

Open Radio Access Networks (Open-RAN) trigger a shift from conventional monolithic RAN architectures to disaggregated designs with open interfaces, diversifying the 5G supply chain and boosting innovation. It is envisaged that Open-RAN deployments will be heavily software-based, allowing for higher flexibility and faster integration of new features. However, existing software-based solutions, seem to be unable to realize practical and standard-compliant Multiple-Input, Multiple-Output (MIMO) designs with a large number of concurrently transmitted information streams, as the 5G New Radio standard requires in order to substantially improve connectivity and throughput. In this context, we introduce MIMO-SoftiPHY, the first 3GPP and Open-RAN compliant, software-based physical layer (PHY) design and implementation framework that can practically realize MIMO designs with large numbers of information streams in a power-efficient manner. MIMO-SoftiPHY is inherently integrated with OpenAirInterface, enabling practical, software-based MIMO deployments with commercial-off-the-shelf user equipment. Specifically, MIMO-SoftiPHY achieves real-time performance for 12 MU-MIMO streams at a 10 MHz bandwidth and 8 streams at a 20 MHz. In addition, and in contrast to existing designs, MIMO-SoftiPHY can also support non-linear base-station processing in real-time that, as we show, can result in substantial power savings at the radio side, by obviating the need for a “massive” number of base-station antennas.

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%.