Unlocking Symbol-Level Precoding Efficiency Through Tensor Equivariant Neural Network

Although symbol-level precoding (SLP) based on constructive interference (CI) exploitation offers performance gains, its high complexity remains a bottleneck. This paper addresses this challenge with an end-to-end deep learning (DL) framework with low inference complexity that leverages the structure of the optimal SLP solution in the closed-form and its inherent tensor equivariance (TE), where TE denotes that a permutation of the input induces the corresponding permutation of the output. Building upon the computationally efficient model-based formulations, as well as their known closed-form solutions, we analyze their relationship with linear precoding (LP) and investigate the corresponding optimality condition. We then construct a mapping from the problem formulation to the solution and prove its TE, based on which the designed networks reveal a specific parameter-sharing pattern that delivers low computational complexity and strong generalization. Leveraging these, we propose the backbone of the framework with an attention-based TE module, achieving linear computational complexity. Furthermore, we demonstrate that such a framework is also applicable to imperfect CSI scenarios, where we design a TE-based network to map the CSI, statistics, and symbols to auxiliary variables. Simulation results show that the proposed framework captures substantial performance gains of optimal SLP, while achieving an approximately 80-times speedup over conventional methods and maintaining strong generalization across user numbers and symbol block lengths.

SCA-LLM: Spectral-Attentive Channel Prediction with Large Language Models in MIMO-OFDM

In recent years, the success of large language models (LLMs) has inspired growing interest in exploring their potential applications in wireless communications, especially for channel prediction tasks. However, directly applying LLMs to channel prediction faces a domain mismatch issue stemming from their text-based pre-training. To mitigate this, the ``adapter + LLM" paradigm has emerged, where an adapter is designed to bridge the domain gap between the channel state information (CSI) data and LLMs. While showing initial success, existing adapters may not fully exploit the potential of this paradigm. To address this limitation, this work provides a key insight that learning representations from the spectral components of CSI features can more effectively help bridge the domain gap. Accordingly, we propose a spectral-attentive framework, named SCA-LLM, for channel prediction in multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Specifically, its novel adapter can capture finer spectral details and better adapt the LLM for channel prediction than previous methods. Extensive simulations show that SCA-LLM achieves state-of-the-art prediction performance and strong generalization, yielding up to $-2.4~\text{dB}$ normalized mean squared error (NMSE) advantage over the previous LLM based method. Ablation studies further confirm the superiority of SCA-LLM in mitigating domain mismatch.

Demonstrator Testbed for Effective Precoding in MEO Multibeam Satellites

The use of communication satellites in medium Earth orbit (MEO) is foreseen to provide quasi-global broadband Internet connectivity in the coming networking ecosystems. Multi-user multiple-input single-output (MU-MISO) digital signal processing techniques, such as precoding, emerge as appealing technological enablers in the forward link of multi-beam satellite systems operating in full frequency reuse (FFR). However, the orbit dynamics of MEO satellites pose additional challenges that must be carefully evaluated and addressed. This work presents the design of an in-lab testbed based on software-defined radio (SDR) platforms and the corresponding adaptations required for efficient precoding in a MEO scenario. The setup incorporates a precise orbit model and the radiation pattern of a custom-designed direct radiating array (DRA). We analyze the main impairments affecting precoding performance, including Doppler shifts and payload phase noise, and propose a synchronization loop to mitigate these effects. Preliminary experimental results validate the feasibility and effectiveness of the proposed solution.

In-Lab Carrier Aggregation Testbed for Satellite Communication Systems

Carrier Aggregation (CA) is a technique used in 5G and previous cellular generations to temporarily increase the data rate of a specific user during peak demand periods or to reduce carrier congestion. CA is achieved by combining two or more carriers and providing a virtual, wider overall bandwidth to high-demand users of the system. CA was introduced in the 4G/LTE wireless era and has been proven effective in 5G as well, where it is said to play a significant role in efficient network capacity management. Given this success, the satellite communication (SatCom) community has put its attention into CA and the potential benefits it can bring in terms of better spectrum utilization and better meeting the user traffic demand. While the theoretical evaluation of CA for SatCom has already been presented in several works, this article presents the design and results obtained with an experimentation testbed based on Software Defined Radio (SDR) and a satellite channel emulator. We first present the detailed implementation design, which includes a Gateway (GW) module responsible for PDU-scheduling across the aggregated carriers, and a User Terminal (UT) module responsible for aggregating the multiple received streams. The second part of the article presents the experimental evaluation, including CA over a single Geostationary (GEO) satellite, CA over a single Medium Earth Orbit (MEO) satellite, and CA combining carriers sent over GEO and MEO satellites. A key contribution of this work is the explicit consideration of multi-orbit scenarios in the testbed design and validation. The testing results show promising benefits of CA over SatCom systems, motivating potential upcoming testing on over-the-air systems.

Enhancing Energy and Spectral Efficiency in IoT-Cellular Networks via Active SIM-Equipped LEO Satellites

This paper investigates a low Earth orbit (LEO) satellite communication system enhanced by an active stacked intelligent metasurface (ASIM), mounted on the backplate of the satellite solar panels to efficiently utilize limited onboard space and reduce the main satellite power amplifier requirements. The system serves multiple ground users via rate-splitting multiple access (RSMA) and IoT devices through a symbiotic radio network. Multi-layer sequential processing in the ASIM improves effective channel gains and suppresses inter-user interference, outperforming active RIS and beyond-diagonal RIS designs. Three optimization approaches are evaluated: block coordinate descent with successive convex approximation (BCD-SCA), model-assisted multi-agent constraint soft actor-critic (MA-CSAC), and multi-constraint proximal policy optimization (MCPPO). Simulation results show that BCD-SCA converges fast and stably in convex scenarios without learning, MCPPO achieves rapid initial convergence with moderate stability, and MA-CSAC attains the highest long-term spectral and energy efficiency in large-scale networks. Energy-spectral efficiency trade-offs are analyzed for different ASIM elements, satellite antennas, and transmit power. Overall, the study demonstrates that integrating multi-layer ASIM with suitable optimization algorithms offers a scalable, energy-efficient, and high-performance solution for next-generation LEO satellite communications.

Robust Design of Beyond-Diagonal Reconfigurable Intelligent Surface Empowered RSMA-SWIPT System Under Channel Estimation Errors

This work explores the integration of rate-splitting multiple access (RSMA), simultaneous wireless information and power transfer (SWIPT), and beyond-diagonal reconfigurable intelligent surface (BD-RIS) to enhance the spectral-efficiency, energy-efficiency, coverage, and connectivity of future sixth-generation (6G) communication networks. Specifically, with a multiuser BD-RIS-empowered RSMA-SWIPT system, we jointly optimize the transmit precoding vectors, the common rate proportion of users, the power-splitting ratios, and scattering matrix of BD-RIS node, under the assumption of imperfect channel state information (CSI). Additionally, to better capture practical hardware behavior, we incorporate a nonlinear energy harvesting model under energy harvesting constraints. We design a robust optimization framework to maximize the system sum-rate, while explicitly accounting for the worst-case impact of CSI uncertainties. Further, we introduce an alternating optimization framework that partitions the problem into several blocks, which are optimized iteratively. More specifically, the transmit precoding vectors are optimized by reformulating the problem as a convex semidefinite programming through successive-convex approximation (SCA), whereas the power-splitting problem is solved using the MOSEK-enabled CVX toolbox. Subsequently, to optimize the scattering matrix of the BD-RIS, we first employ SCA to reformulate the problem into a convex form, and then design a manifold optimization strategy based on the Conjugate-Gradient method. Finally, numerical simulation results reveal that the proposed scheme provides significant performance improvements over existing benchmarks and demonstrates rapid convergence within a reasonable number of iterations.

Optimal Interference Exploitation Waveform Design with Relaxed Block-Level Power Constraints

This paper investigates constructive interference (CI)-based waveform design for phase shift keying and quadrature amplitude modulation symbols under relaxed block-level power constraints in multi-user multiple-input single-output (MU-MIMO) communication systems. Existing linear CI-based precoding methods, including symbol-level precoding (SLP) and block-level precoding (BLP), suffer from performance limitations due to strict symbol-level power budgets or insufficient degrees of freedom over the block. To overcome these challenges, we propose a nonlinear waveform optimization framework that introduces additional optimization variables and maximizes the minimum CI metric across the transmission block. The optimal waveform is derived in closed form using the function and Karush Kuhn Tucker conditions, and the solution is explicitly expressed with respect to the dual variables. Moreover, the original problems are equivalently reformulated as tractable quadratic programming (QP) problems. To efficiently solve the derived QP problems, we develop an improved alternating direction method of multipliers (ADMM) algorithm by integrating a linear-time projection technique, which significantly enhances the computational efficiency. Simulation results demonstrate that the proposed algorithms substantially outperform the conventional CI-SLP and CI-BLP approaches, particularly under high-order modulations and large block lengths.

DT-Aided Resource Management in Spectrum Sharing Integrated Satellite-Terrestrial Networks

The integrated satellite-terrestrial networks (ISTNs) through spectrum sharing have emerged as a promising solution to improve spectral efficiency and meet increasing wireless demand. However, this coexistence introduces significant challenges, including inter-system interference (ISI) and the low Earth orbit satellite (LSat) movements. To capture the actual environment for resource management, we propose a time-varying digital twin (DT)-aided framework for ISTNs incorporating 3D map that enables joint optimization of bandwidth (BW) allocation, traffic steering, and resource allocation, and aims to minimize congestion. The problem is formulated as a mixed-integer nonlinear programming (MINLP), addressed through a two-phase algorithm based on successive convex approximation (SCA) and compressed sensing approaches. Numerical results demonstrate the proposed method's superior performance in queue length minimization compared to benchmarks.

Interference in Spectrum-Sharing Integrated Terrestrial and Satellite Networks: Modeling, Approximation, and Robust Transmit Beamforming

This paper investigates robust transmit (TX) beamforming from the satellite to user terminals (UTs), based on statistical channel state information (CSI). The proposed design specifically targets the mitigation of satellite-to-terrestrial interference in spectrum-sharing integrated terrestrial and satellite networks. By leveraging the distribution information of terrestrial UTs, we first establish an interference model from the satellite to terrestrial systems without shared CSI. Based on this, robust TX beamforming schemes are developed under both the interference threshold and the power budget. Two optimization criteria are considered: satellite weighted sum rate maximization and mean square error minimization. The former achieves a superior achievable rate performance through an iterative optimization framework, whereas the latter enables a low-complexity closed-form solution at the expense of reduced rate, with interference constraints satisfied via a bisection method. To avoid complex integral calculations and the dependence on user distribution information in inter-system interference evaluations, we propose a terrestrial base station position-aided approximation method, and the approximation errors are subsequently analyzed. Numerical simulations validate the effectiveness of our proposed schemes.

Dynamic Beyond 5G and 6G Connectivity: Leveraging NTN and RIS Synergies for Optimized Coverage and Capacity in High-Density Environments

The increasing demand for reliable, high-capacity communication during large-scale outdoor events poses significant challenges for traditional Terrestrial Networks (TNs), which often struggle to provide consistent coverage in high-density environments. This paper presents a novel 6G radio network planning framework that integrates Non-Terrestrial Networks (NTNs) with Reconfigurable Intelligent Surfaces (RISs) to deliver ubiquitous coverage and enhanced network capacity. Our framework overcomes the limitations of conventional deployable base stations by leveraging NTN architectures, including Low Earth Orbit (LEO) satellites and passive RIS platforms seamlessly integrated with Beyond 5G (B5G) TNs. By incorporating advanced B5G technologies such as Massive Multiple Input Multiple Output (mMIMO) and beamforming, and by optimizing spectrum utilization across the C, S, and Ka bands, we implement a rigorous interference management strategy based on a dynamic SINR model. Comprehensive calculations and simulations validate the proposed framework, demonstrating significant improvements in connectivity, reliability, and cost-efficiency in crowded scenarios. This integration strategy represents a promising solution for meeting the evolving demands of future 6G networks.