arXiv subjects

Konstantinos Nikitopoulos

Publications and source records attributed to Konstantinos Nikitopoulos.

18 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

A Comparative Analysis of Ising Formulations for Neuromorphic Maximum-Likelihood Channel Decoding

Neuromorphic computing has so far been driven predominantly by machine-learning workloads, yet its underlying properties also make it particularly well suited to combinatorial optimization problems expressed in Ising or QUBO form. While neuromorphic Ising solvers have been demonstrated, how a given problem should be formulated to best suit neuromorphic dynamics has received far less attention. Maximum-likelihood (ML) channel decoding can be expressed as an Ising/QUBO problem, and two distinct formulations already exist in the quantum-annealing literature: a squared-penalty formulation that uses few spins but produces dense intra-check couplings, and a chain-product formulation that improves locality at the cost of additional auxiliary spins. Both place the ML codeword at the ground state under sufficient constraint enforcement, but they have not been compared under the constraints that neuromorphic hardware imposes. This work provides the first systematic side-by-side comparison of QUBO/Ising formulations of ML decoding for linear codes. We show that the two formulations impose fundamentally different tradeoffs in neuron count, synaptic density, locality, and convergence behavior. The preferred formulation is inseparable from the choice of solver, and the two must be considered jointly. Finally, we show that ground-state correctness alone is an insufficient design criterion, and that signal processing tasks should ideally be co-formulated with their neuromorphic hardware models if neuromorphic computing is to extend into the receiver pipeline.

cs.ET↗

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.

eess.SP↗

arXiv2025

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.

eess.SP↗

arXiv2025

Power-Efficient RAN Intelligent Controllers Through Optimized KPI Monitoring

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, also for the first time we explore potential power savings through the elimination of redundant KPI transmissions, extending existing techniques for identical subscription removal and KPI selection, achieving significant power consumption gains exceeding 87\% of the overall RIC power consumption.

eess.SY↗

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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arXiv2024

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.

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arXiv2024

Towards Neuromorphic Processing for Next-Generation MU-MIMO Detection

Upcoming physical layer (PHY) processing solutions, leveraging multiple-input multiple-output (MIMO) advances, are expected to support broad transmission bandwidths and the concurrent transmission of multiple information streams. However, the inherent computational complexities of conventional MIMO PHY algorithms pose significant practical challenges, not only in meeting the strict real-time processing latency requirements but also in maintaining practical computational power consumption budgets. Novel computing paradigms, such as neuromorphic computing, promise substantial gains in computational power efficiency. However, it is unknown whether it is feasible or efficient to realize practical PHY algorithms on such platforms. In this work, we evaluate for the first time the potential of neuromorphic computing principles for multi-user (MU)-MIMO detection. In particular, we developed the first spiking-based MU-MIMO simulator that meets practical error-rate targets, suggesting power gains of at least one order of magnitude when realized on actual neuromorphic hardware, compared to conventional processing platforms. Finally, we discuss the challenges and future research directions that could unlock practical neuromorphic-based MU-MIMO systems and revolutionize PHY power efficiency.

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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.

cs.IT↗

arXiv2019

Low-Complexity Hybrid Beamforming for Massive MIMO Systems in Frequency-Selective Channels

Hybrid beamforming for frequency-selective channels is a challenging problem as the phase shifters provide the same phase shift to all of the subcarriers. The existing approaches solely rely on the channel's frequency response and the hybrid beamformers maximize the average spectral efficiency over the whole frequency band. Compared to state-of-the-art, we show that substantial sum-rate gains can be achieved, both for rich and sparse scattering channels, by jointly exploiting the frequency and time domain characteristics of the massive multiple-input multiple-output (MIMO) channels. In our proposed approach, the radio frequency (RF) beamformer coherently combines the received symbols in the time domain and, thus, it concentrates signal's power on a specific time sample. As a result, the RF beamformer flattens the frequency response of the "effective" transmission channel and reduces its root mean square delay spread. Then, a baseband combiner mitigates the residual interference in the frequency domain. We present the closed-form expressions of the proposed beamformer and its performance by leveraging the favorable propagation condition of massive MIMO channels and we prove that our proposed scheme can achieve the performance of fully-digital zero-forcing when number of employed phase shifter networks is twice the resolvable multipath components in the time domain.

cs.IT↗

arXiv2012

Complexity-Efficient Enumeration Techniques for Soft-Input, Soft-Output Sphere Decoding

In this paper two complexity efficient soft sphere-decoder modifications are proposed for computing the max-log LLR values in iterative MIMO systems, which avoid the costly, typically needed, full enumeration and sorting (FES) procedure during the tree traversal without compromising the max-log performance. It is shown that despite the resulting increase in the number of expanded nodes, they can be more computationally efficient than the typical soft sphere decoders by avoiding the unnecessary complexity of FES.

cs.NI↗

arXiv2012

Complexity Adjusted Soft-Output Sphere Decoding by Adaptive LLR Clipping

A-posteriori probability (APP) receivers operating over multiple-input, multiple-output channels provide enhanced bit error rate (BER) performance at the cost of increased complexity. However, employing full APP processing over favorable transmission environments, where less efficient approaches may already provide the required performance at a reduced complexity, results in unnecessary processing. For slowly varying channel statistics substantial complexity savings can be achieved by simple adaptive schemes. Such schemes track the BER performance and adjust the complexity of the soft output sphere decoder by adaptively setting the related log-likelihood ratio (LLR) clipping value.

cs.IT↗

arXiv2012

Approximate MIMO Iterative Processing with Adjustable Complexity Requirements

Targeting always the best achievable bit error rate (BER) performance in iterative receivers operating over multiple-input multiple-output (MIMO) channels may result in significant waste of resources, especially when the achievable BER is orders of magnitude better than the target performance (e.g., under good channel conditions and at high signal-to-noise ratio (SNR)). In contrast to the typical iterative schemes, a practical iterative decoding framework that approximates the soft-information exchange is proposed which allows reduced complexity sphere and channel decoding, adjustable to the transmission conditions and the required bit error rate. With the proposed approximate soft information exchange the performance of the exact soft information can still be reached with significant complexity gains.

cs.IT↗

arXiv2012

MIMO APP Receiver Processing with Performance-Determined Complexity

Typical receiver processing, targeting always the best achievable bit error rate performance, can result in a waste of resources, especially, when the transmission conditions are such that the best performance is orders of magnitude better than the required. In this work, a processing framework is proposed which allows adjusting the processing requirements to the transmission conditions and the required bit error rate. It applies a-posteriori probability receivers operating over multiple-input multiple-output channels. It is demonstrated that significant complexity savings can be achieved both at the soft, sphere-decoder based detector and the channel decoder with only minor modifications.

cs.IT↗