Massive MIMO Channel-aware Decision Fusion Aided by Reconfigurable Intelligent Surfaces

This paper investigates channel-aware decision fusion empowered by massive MIMO systems and reconfigurable intelligent surfaces (RIS). By integrating both, we aim to improve goal-oriented (fusion) performance despite the unique propagation challenges introduced. Specifically, we investigate traditional favorable propagation properties in the context of RIS-aided Massive MIMO decision fusion. The above analysis is then leveraged (i) to design three sub-optimal simple fusion rules suited for the large-array regime and (ii) to devise an optimization criterion for RIS reflection coefficients based on long-term channel statistics. Simulation results confirm the appeal of the presented design.

Design of RIS-aided mMTC+ Networks for Rate Maximization under the Finite Blocklength Regime with Imperfect Channel Knowledge

Within the context of massive machine-type communications+, reconfigurable intelligent surfaces (RISs) represent a promising technology to boost system performance in scenarios with poor channel conditions. Considering single-antenna sensors transmitting short data packets to a multiple-antenna collector node, we introduce and design an RIS to maximize the weighted sum rate (WSR) of the system working in the finite blocklength regime. Due to the large number of reflecting elements and their passive nature, channel estimation errors may occur. In this letter, we then propose a robust RIS optimization to combat such a detrimental issue. Based on concave bounds and approximations, the nonconvex WSR problem for the RIS response is addressed via successive convex optimization (SCO). Numerical experiments validate the performance and complexity of the SCO solutions.

Holographic & Channel-Aware Distributed Detection of a Non-cooperative Target

This work investigates Distributed Detection (DD) in Wireless Sensor Networks (WSNs), where spatially distributed sensors transmit binary decisions over a shared flat-fading channel. To enhance fusion efficiency, a reconfigurable metasurface is positioned in the near-field of a few receive antennas, enabling a holographic architecture that harnesses large-aperture gains with minimal RF hardware. A generalized likelihood ratio test is derived for fixed metasurface settings, and two low-complexity joint design strategies are proposed to optimize both fusion and metasurface configuration. These suboptimal schemes achieve a balance between performance, complexity, and system knowledge. The goal is to ensure reliable detection of a localized phenomenon at the fusion center, under energy-efficient constraints aligned with IoT requirements. Simulation results validate the effectiveness of the proposed holographic fusion, even under simplified designs.

Energy Efficiency Maximization of MIMO Systems through Reconfigurable Holographic Beamforming

This study considers a point-to-point wireless link, in which both the transmitter and receiver are equipped with multiple antennas. In addition, two reconfigurable metasurfaces are deployed, one in the immediate vicinity of the transmit antenna array, and one in the immediate vicinity of the receive antenna array. The resulting architecture implements a holographic beamforming structure at both the transmitter and receiver. In this scenario, the system energy efficiency is optimized with respect to the transmit covariance matrix, and the reflection matrices of the two metasurfaces. A low-complexity algorithm is developed, which is guaranteed to converge to a first-order optimal point of the energy efficiency maximization problem. Moreover, closed-form expressions are derived for the metasurface matrices in the special case of single-antenna or single-stream transmission. The two metasurfaces are considered to be nearly-passive and subject to global reflection constraints. A numerical performance analysis is conducted to assess the performance of the proposed optimization methods, showing, in particular, that the use of holographic beamforming by metasurfaces can provide significant energy efficiency gains compared to fully digital beamforming architectures, even when the latter achieve substantial multiplexing gains.

Channel-Aware Holographic Decision Fusion

This work investigates Distributed Detection (DD) in Wireless Sensor Networks (WSNs) utilizing channel-aware binary-decision fusion over a shared flat-fading channel. A reconfigurable metasurface, positioned in the near-field of a limited number of receive antennas, is integrated to enable a holographic Decision Fusion (DF) system. This approach minimizes the need for multiple RF chains while leveraging the benefits of a large array. The optimal fusion rule for a fixed metasurface configuration is derived, alongside two suboptimal joint fusion rule and metasurface design strategies. These suboptimal approaches strike a balance between reduced complexity and lower system knowledge requirements, making them practical alternatives. The design objective focuses on effectively conveying the information regarding the phenomenon of interest to the FC while promoting energy-efficient data analytics aligned with the Internet of Things (IoT) paradigm. Simulation results underscore the viability of holographic DF, demonstrating its advantages even with suboptimal designs and highlighting the significant energy-efficiency gains achieved by the proposed system.

Rate Optimization for RIS-Aided mMTC Networks in the Finite Blocklength Regime

Reconfigurable intelligent surfaces (RISs) have become a promising candidate for the development of future mobile systems. In the context of massive machine-type communications (mMTC), a RIS can be used to support the transmission from a group of sensors to a collector node. Due to the short data packets, we focus on the design of the RIS for maximizing the weighted sum and minimum rates in the finite blocklength regime. Under the assumption of non-orthogonal multiple access, successive interference cancelation is considered as a decoding scheme to mitigate interference. Accordingly, we formulate the optimizations as non-convex problems and propose two sub-optimal solutions based on gradient ascent (GA) and sequential optimization (SO) with semi-definite relaxation (SDR). In the GA, we distinguish between Euclidean and Riemannian gradients. For the SO, we derive a concave lower bound for the throughput and maximize it sequentially applying SDR. Numerical results show that the SO can outperform the GA and that strategies relying on the optimization of the classical Shannon capacity might be inadequate for mMTC networks.

Rate Region of RIS-Aided URLLC Broadcast Channels: Diagonal versus Beyond Diagonal Globally Passive RIS

We analyze the finite-block-length rate region of wireless systems aided by reconfigurable intelligent surfaces (RISs), employing treating interference as noise. We consider three nearly passive RIS architectures, including locally passive (LP) diagonal (D), globally passive (GP) D, and GP beyond diagonal (BD) RISs. In a GP RIS, the power constraint is applied globally to the whole surface, while some elements may amplify the incident signal locally. The considered RIS architectures provide substantial performance gains compared with systems operating without RIS. GP BD-RIS outperforms, at the price of increasing the complexity, LP and GP D-RIS as it enlarges the feasible set of allowed solutions. However, the gain provided by BD-RIS decreases with the number of RIS elements. Additionally, deploying RISs provides higher gains as the reliability/latency requirement becomes more stringent.

Secrecy Energy Efficiency Maximization in RIS-Aided Wireless Networks

This work proposes a provably convergent and low complexity optimization algorithm for the maximization of the secrecy energy efficiency in the uplink of a wireless network aided by a Reconfigurable Intelligent Surface (RIS), in the presence of an eavesdropper. The mobil users' transmit powers and the RIS reflection coefficients are optimized. Numerical results show the performance of the proposed methods and compare the use of active and nearly-passive RISs from an energy-efficient perspective.

Energy Efficiency in RIS-Aided Wireless Networks: Active or Passive RIS?

This work addresses the comparison between active and passive RISs in wireless networks, with reference to the system energy efficiency (EE). To provably convergent and computationally-friendly EE maximization algorithms are developed, which optimize the reflection coefficients of the RIS, the transmit powers, and the linear receive filters. Numerical results show the performance of the proposed methods and discuss the operating points in which active or passive RISs should be preferred from an energy-efficient perspective.

Energy Efficiency Maximization in RIS-Aided Networks with Global Reflection Constraints

This work addresses the issue of energy efficiency maximization in a multi-user network aided by reconfigurable intelligent surface (RIS) with global reflection capabilities. Two optimization methods are proposed to optimize the mobile users' powers, the RIS coefficients and the linear receive filters. Both methods are provably convergent and require only the solution of convex optimization problems. The numerical results show that the proposed methods largely outperform heuristic resource allocation schemes.