Minimum Mean Squared Error Holographic Beamforming for Sum-Rate Maximization

This paper studies the problem of hybrid holographic beamforming for sum-rate maximization in a communication system assisted by a reconfigurable holographic surface. Existing methodologies predominantly rely on gradient-based or approximation techniques necessitating iterative optimization for each update of the holographic response, which imposes substantial computational overhead. To address these limitations, we establish a mathematical relationship between the mean squared error (MSE) criterion and the holographic response of the RHS to enable alternating optimization based on the minimum MSE (MMSE). Our analysis demonstrates that this relationship exhibits a quadratic dependency on each element of the holographic beamformer. Exploiting this property, we derive closed-form optimal expressions for updating the holographic beamforming weights. Our complexity analysis indicates that the proposed approach exhibits only linear complexity in terms of the RHS size, thus, ensuring scalability for large-scale deployments. The presented simulation results validate the effectiveness of our MMSE-based holographic approach, providing useful insights.

Multi-Agent Actor-Critic with Harmonic Annealing Pruning for Dynamic Spectrum Access Systems

Multi-Agent Deep Reinforcement Learning (MADRL) has emerged as a powerful tool for optimizing decentralized decision-making systems in complex settings, such as Dynamic Spectrum Access (DSA). However, deploying deep learning models on resource-constrained edge devices remains challenging due to their high computational cost. To address this challenge, in this paper, we present a novel sparse recurrent MARL framework integrating gradual neural network pruning into the independent actor global critic paradigm. Additionally, we introduce a harmonic annealing sparsity scheduler, which achieves comparable, and in certain cases superior, performance to standard linear and polynomial pruning schedulers at large sparsities. Our experimental investigation demonstrates that the proposed DSA framework can discover superior policies, under diverse training conditions, outperforming conventional DSA, MADRL baselines, and state-of-the-art pruning techniques.

Graph-CNNs for RF Imaging: Learning the Electric Field Integral Equations

Radio-Frequency (RF) imaging concerns the digital recreation of the surfaces of scene objects based on the scattered field at distributed receivers. To solve this difficult inverse scattering problems, data-driven methods are often employed that extract patterns from similar training examples, while offering minimal latency. In this paper, we first provide an approximate yet fast electromagnetic model, which is based on the electric field integral equations, for data generation, and subsequently propose a Deep Neural Network (DNN) architecture to learn the corresponding inverse model. A graph-attention backbone allows for the system geometry to be passed to the DNN, where residual convolutional layers extract features about the objects, while a UNet head performs the final image reconstruction. Our quantitative and qualitative evaluations on two synthetic data sets of different characteristics showcase the performance gains of thee proposed advanced architecture and its relative resilience to signal noise levels and various reception configurations.

How Do Microstrip Losses Impact Near-Field Beam Depth in Dynamic Metasurface Antennas?

The convergence of eXtremely Large (XL) antenna arrays and high-frequency bands in future wireless networks will inevitably give rise to near-field communications, localization, and sensing. Dynamic Metasurface Antennas (DMAs) have emerged as a key enabler of the XL Multiple-Input Multiple-Output (MIMO) paradigm, leveraging reconfigurable metamaterials to support large antenna arrays. However, DMAs are inherently lossy due to propagation losses in the microstrip lines and radiative losses from the metamaterial elements, which reduce their gain and alter their beamforming characteristics compared to a lossless aperture. In this paper, we address the gap in understanding how DMA losses affect near-field beamforming performance, by deriving novel analytical expressions for the beamforming gain of DMAs under misalignments between the focusing position and the intended user's position in 3D space. Additionally, we derive beam depth limits for varying attenuation conditions, from lossless to extreme attenuation, offering insights into the impact of losses on DMA near-field performance.

Joint Beamforming and 3D Location Optimization for Multi-User Holographic UAV Communications

This paper pioneers the field of multi-user holographic unmanned aerial vehicle (UAV) communications, laying a solid foundation for future innovations in next-generation aerial wireless networks. The study focuses on the challenging problem of jointly optimizing hybrid holographic beamforming and 3D UAV positioning in scenarios where the UAV is equipped with a reconfigurable holographic surface (RHS) instead of conventional phased array antennas. Using the unique capabilities of RHSs, the system dynamically adjusts both the position of the UAV and its hybrid beamforming properties to maximize the sum rate of the network. To address this complex optimization problem, we propose an iterative algorithm combining zero-forcing digital beamforming and a gradient ascent approach for the holographic patterns and the 3D position optimization, while ensuring practical feasibility constraints. The algorithm is designed to effectively balance the trade-offs between power, beamforming, and UAV trajectory constraints, enabling adaptive and efficient communications, while assuring a monotonic increase in the sum-rate performance. Our numerical investigations demonstrate that the significant performance improvements with the proposed approach over the benchmark methods, showcasing enhanced sum rate and system adaptability under varying conditions.

Joint Holographic Beamforming and User Scheduling with Individual QoS Constraints

Reconfigurable holographic surfaces (RHS) have emerged as a transformative material technology, enabling dynamic control of electromagnetic waves to generate versatile holographic beam patterns. This paper addresses the problem of joint hybrid holographic beamforming and user scheduling under per-user minimum quality-of-service (QoS) constraints, a critical challenge in resource-constrained networks. However, such a problem results in mixed-integer non-convex optimization, making it difficult to identify feasible solutions efficiently. To overcome this challenge, we propose a novel iterative optimization framework that jointly solves the problem to maximize the RHS-assisted network sum-rate, efficiently managing holographic beamforming patterns, dynamically scheduling users, and ensuring the minimum QoS requirements for each scheduled user. The proposed framework relies on zero-forcing digital beamforming, gradient-ascent-based holographic beamformer optimization, and a greedy user selection principle. Our extensive simulation results validate the effectiveness of the proposed scheme, demonstrating their superior performance compared to the benchmark algorithms in terms of sum-rate performance, while meeting the minimum per-user QoS constraints

On the Rate-Exponent Region of Integrated Sensing and Communications With Variable-Length Coding

This paper considers the achievable rate-exponent region of integrated sensing and communication systems in the presence of variable-length coding with feedback. This scheme is fundamentally different from earlier studies, as the coding methods that utilize feedback impose different constraints on the codewords. The focus herein is specifically on the Gaussian channel, where three achievable regions are analytically derived and numerically evaluated. In contrast to a setting without feedback, we show that a trade-off exists between the operations of sensing and communications.

A Doubly-Dispersive MIMO Channel Model Parametrized with Stacked Intelligent Metasurfaces

Introduced with the advent of statistical wireless channel models for high mobility communications and having a profound role in communication-centric (CC) integrated sensing and communications (ISAC), the doubly-dispersive (DD) channel structure has long been heralded as a useful tool enabling the capture of the most important fading effects undergone by an arbitrary time-domain transmit signal propagating through some medium. However, the incorporation of this model into multiple-input multiple-output (MIMO) system setups, relying on the recent paradigm-shifting transceiver architecture based on stacked intelligent metasurfaces (SIM), in an environment with reconfigurable intelligent surfaces (RISs) remains an open problem due to the many intricate details that have to be accounted for. In this paper, we fill this gap by introducing a novel DD MIMO channel model that incorporates an arbitrary number of RISs in the ambient, as well as SIMs equipping both the transmitter and receiver. We then discuss how the proposed metasurfaces-parametrized DD (MPDD) channel model can be seamlessly applied to waveforms that are known to perform well in DD environments, namely, orthogonal frequency division multiplexing (OFDM), orthogonal time frequency space (OTFS), and affine frequency division multiplexing (AFDM), with each having their own inherent advantages and disadvantages. An illustrative application of the programmable functionality of the proposed model is finally presented to showcase its potential for boosting the performance of the aforementioned waveforms. Our numerical results indicate that the design of waveforms suitable to mitigating the effects of DD channels is significantly impacted by the emerging SIM technology.

Joint Communications and Sensing for 6G Satellite Networks: Use Cases and Challenges

Satellite Networks (SN) have traditionally been instrumental in providing two key services: communications and sensing. Communications satellites enable global connectivity, while sensing satellites facilitate applications such as Earth observation, navigation, and disaster management. However, the emergence of novel use cases and the exponential growth in service demands make the independent evolution of communication and sensing payloads increasingly impractical. Addressing this challenge requires innovative approaches to optimize satellite resources. Joint Communications and Sensing (JCAS) technology represents a transformative paradigm for SN. By integrating communication and sensing functionalities into unified hardware platforms, JCAS enhances spectral efficiency, reduces operational costs, and minimizes hardware redundancies. This paper explores the potential of JCAS in advancing the next-generation space era, highlighting its role in emerging applications. Furthermore, it identifies critical challenges, such as waveform design, Doppler effect mitigation, and multi-target detection, that remain open for future research. Through these discussions, we aim to stimulate further research into the transformative potential of JCAS in addressing the demands of 6G and beyond SN.

Holographic Metasurfaces Enabling Wave Computing for 6G: Status Overview, Challenges, and Future Research Trends

Recent advancements in wave computing using metasurfaces are poised to transform wireless communications by enabling high-speed, energy-efficient, and highly parallelized signal processing. These capabilities are essential to meet the ultra-high data rates of up to 1 terabit per second and minimal latency as low as 1 millisecond required by next-generation wireless networks. Diverging from traditional digital processing, wave computing adopts continuous analog signals to foster innovative functions such as over-the-air computation, integrated sensing and communications, computational electromagnetic imaging, and physical-layer security. This article explores the potential of reconfigurable multi-functional metasurfaces in wave computing, emphasizing their pivotal role in facilitating seamless communications and addressing the escalating computational demands for sixth generation (6G) networks. As artificial intelligence has become one of the most prominent and rapidly advancing fields of research over the last decade, we also introduce a wave-domain-based machine learning approach aimed at achieving power-efficient, fast training and computation. Future research directions are discussed, underscoring how metasurface-based systems can merge computation with communication to innovate components of 6G networks, thus creating smarter, faster, and more adaptable wireless infrastructures.