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.

Toward Fully Neuromorphic Receivers for Ultra-Power Efficient Communications

Neuromorphic computing, inspired by biological neural systems, has emerged as a promising approach for ultra-energy-efficient data processing by leveraging analog neuron structures and spike-based computation. However, its application in communication systems remains largely unexplored, with existing efforts mainly focused on mapping isolated communication algorithms onto spiking networks, often accompanied by substantial, traditional computational overhead due to transformations required to adapt problems to the spiking paradigm. In this work, we take a fundamentally different route and, for the first time, propose a fully neuromorphic communication receiver by applying neuromorphic principles directly in the analog domain from the very start of the receiver processing chain. Specifically, we examine a simple transmission scenario: a BPSK receiver with repetition coding, and show that we can achieve joint detection and decoding entirely through spiking signals. Our approach demonstrates error-rate performance gains over conventional digital realizations with power consumption on the order of microwatts, comparable with a single very low-resolution Analog-to-Digital Converter (ADC) utilized in digital receivers. To maintain performance under varying noise conditions, we also introduce a novel noise-tracking mechanism that dynamically adjusts neural parameters during transmission. Finally, we discuss the key challenges and directions toward ultra-efficient neuromorphic transceivers.

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\%.

Enabling Ultra-Dense, Open-RAN, Vehicular Networks with Non-Linear MIMO Processing

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 allows for simpler and more densely deployed radio units, paving the way for fully autonomous and sustainable transportation systems.