arXiv subjects

Chathura Jayawardena

Publications and source records attributed to Chathura Jayawardena.

8 recordsLinked to original sources

arXiv2026

Aggressive Non-Orthogonal Transmission with DFT-s-OFDM for Direct Device-to-Satellite Communications

Direct Device-to-Satellite (D2S) communications promise global connectivity to unmodified user equipment (UE), extending coverage beyond terrestrial networks. Realizing this promise is fundamentally challenging: severe path loss and limited UE transmit power push uplink SNRs far below terrestrial norms, while suitable spectrum remains scarce. Together, these constraints impose a spectral-efficiency (SE) bottleneck, and under such conditions the efficiency of the UE power amplifier becomes critical, jointly governing transmit power and battery life. To improve UE-side power efficiency, 3GPP has adopted Discrete Fourier Transform-spread OFDM (DFT-s-OFDM) as an optional uplink waveform, exploiting its substantially lower Peak-to-Average Power Ratio (PAPR) relative to OFDM. To break the SE bottleneck, we show that aggressive non-orthogonal transmission, in which the number of concurrent users exceeds the number of receive antennas by more than 2x, can unlock substantial capacity gains that remain entirely unexploited. Realising these gains, however, requires receiver architectures that, to the best of our knowledge, have not yet been developed. DFT-s-OFDM intensifies the difficulty: the DFT spreading couples signal components across subcarriers, inflating the effective dimensionality of the detection problem. We address both challenges with a novel receiver design that jointly exploits the SE gains of aggressive non-orthogonal transmission and the power-efficiency benefits of DFT-s-OFDM. Simulations under realistic channel-estimation errors and high-mobility Doppler show that the proposed scheme achieves 2x the SE of baseline, surpasses recent nonlinear MIMO receivers by 40% at 15% of their complexity, and reduces PAPR by up to 6 dB relative to DFT-s-OFDM MIMO and 11 dB relative to OFDM.

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arXiv2026

NL-COMM-Sat: Breaking the Direct Device-to-Satellite Communication Barrier via "Aggressive" Non-Orthogonal Transmissions and Non-Linear Processing

Direct Device-to-Satellite (D2S) communications, which enable direct satellite connectivity with unmodified user equipment (UE), not only expand global coverage but also reshape the evolution of future access networks. However, D2S links face fundamental challenges due to inherently low signal-to-noise ratios (SNRs) and limited spatial multiplexing gains arising from near line-of-sight propagation, both of which severely constrain achievable spectral efficiency. Despite the lack of spatial multiplexing, this work shows that aggressive non-orthogonal transmissions, where multiple users (e.g., four) transmit concurrently over the same frequency resources, even to a single receive antenna, can unlock substantial capacity gains that remain entirely unexploited by existing systems. Realizing these gains in practice, however, requires receiver architectures that, to the best of our knowledge, have not yet been developed. To this end, we introduce NL-COMM-Sat, an efficient and flexible framework that overcomes this limitation by enabling aggressive non-orthogonal signal transmissions. In contrast to conventional non-orthogonal multiple access (NOMA) schemes, NL-COMM-Sat supports more than two UEs per receive antenna on the same frequency resource. The framework revisits optimal receiver design principles and proposes computationally efficient processing schemes that translate previously unexplored theoretical gains into tangible throughput improvements, even under realistic channel estimation errors and high-mobility Doppler conditions. Our evaluation shows that NL-COMM-Sat achieves up to a 2x increase in spectral efficiency compared to orthogonal multiple access and NOMA baselines across all considered SNR and Doppler regimes, even with a single-antenna receiver and user speeds of up to 500 km/h.

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arXiv2026

Low-Complexity, Space Splitting-based User Selection in MU-MIMO for Massive Connectivity and AI-Native Traffic

The rise of Artificial Intelligence (AI)-driven services, machine-type communications, and massive Internet of Things (IoT) deployments is reshaping wireless traffic toward dense, uplink-oriented, bursty, and latency-critical patterns. In these regimes, Multi-User Multiple-Input Multiple-Output (MU-MIMO) is essential to support massive concurrent connectivity through spatial multiplexing. However, the need for frequent, low-latency scheduling decisions exposes fundamental scalability barriers in existing user selection approaches. The inherently combinatorial nature of MU-MIMO user selection leads computational complexity to grow rapidly with both the number of candidate users and spatial layers, rendering existing near-optimal heuristic methods impractical in dense and highly dynamic scenarios. This paper introduces the Space Splitting-based User Selection (SS-US) algorithm, a complexity barrier-breaking, massively parallelizable method that departs from subset-based selection by constructing orthonormal spatial bases and independently matching users to spatial directions. Simulation results across diverse MIMO configurations, channel conditions, and user densities show that SS-US reduces computational complexity by over three orders of magnitude while achieving spectral efficiency comparable to state-of-the-art practical baselines.

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arXiv2026

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.

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arXiv2025

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\%.

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arXiv2024

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.

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arXiv2024

NL-COMM: Demonstrating Gains of Non-Linear Processing in Open-RAN Ecosystem

Multi-user multiple-input, multiple-output (MU-MIMO) designs can substantially increase wireless systems' achievable throughput and connectivity capabilities. However, existing MU-MIMO deployments typically utilize linear processing techniques that, despite their practical benefits, such as low computational complexity and easy integrability, can leave much of the available throughput and connectivity gains unexploited. They typically require many power-intensive antennas and RF chains to support a smaller number of MIMO streams, even when the transmitted information streams are of low rate. Alternatively, non-linear (NL) processing methods can maximize the capabilities of the MIMO channel. Despite their potential, traditional NL methods are challenged by high computational complexity and processing latency, making them impractical for real-time applications, especially in software-based systems envisioned for emerging Open Radio Access Networks (Open-RAN). Additionally, essential functionalities such as rate adaptation (RA) are currently unavailable for NL systems, limiting their practicality in real-world deployments. In this demo, we present the latest capabilities of our advanced NL processing framework (NL-COMM) in real-time and over-the-air, comparing them side-by-side with conventional linear processing. For the first time, NL-COMM not only meets the practical 5G-NR real-time latency requirements in pure software but also does so within a standard-compliant ecosystem. To achieve this, we significantly extended the NL-COMM algorithmic framework to support the first practical RA for NL processing. The demonstrated gains include enhanced connectivity by supporting four MIMO streams with a single base-station antenna, substantially increased throughput, and the ability to halve the number of base-station antennas without any performance loss to linear approaches.

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arXiv2021

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.

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