Hybrid STAR-RIS Architecture for Joint Localization, Communication, and Power Transfer

We propose a hybrid simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) architecture with dynamically switched active and passive elements to support joint localization, communication, and wireless power transfer (WPT). We first pursue a parallel factor analysis with the alternating least squares (PARAFAC-ALS)-based tensor decomposition approach that decouples the base station (BS)-reconfigurable intelligent surface (RIS) and RIS-user channels, thereby enabling low-overhead channel acquisition. Based on this, we formulate a system energy efficiency (EE) maximization problem, subject to the spectral efficiency (SE) requirements of communication users, sensing signal-to-interference-plus-noise ratio constraints, and the nonlinear energy harvesting requirements of energy-harvesting users. The optimization problem is nonconvex since the transmit power allocation, STAR-RIS coefficients, and active/passive mode assignments are tightly coupled in both the objective and constraints. We address this issue by alternating between two subproblems, and solving them via fractional programming, successive convex approximation and a multi-seed greedy strategy employed as an initialization step. Numerical results demonstrate that selectively activating a small, well-chosen subset of STAR-RIS elements achieves 1.5 to 3 times EE improvements compared with fully passive/active architectures, while satisfying communication, sensing, and power-transfer requirements.

Power-Efficient XL-MIMO Design for Mixed Near- and Far-Field SWIPT Systems

This paper examines the power consumption (PC) efficiency of a mixed near- and far-field (MF) simultaneous wireless information and power transfer (SWIPT) system underpinned by a hybrid beamforming (HB)-based modular extra-large multiple-input-multiple output (XL-MIMO) array. Multiple information decoding (ID) and energy harvesting (EH) users are served by multiple constituent subarrays in both the near-field (NF) and far-field (FF) region of the transmit array. A novel decision method is proposed for accurate classification of different field users using Frobenius norm-based frequency correlation of the least square (LS) channel estimates. The NF spatial non-stationarities (SnS) effects entail distinct electromagnetic (EM) visibility regions (VRs), which can be customized to employ strategic activation of the constituent XL-MIMO subarrays. We formulate a two-tier joint optimization problem to minimize the overall PC, considering the power allocation (PA) for both ID and EH users in addition to the subarray activation (SA). This challenging mixed-integer problem is transformed into computationally tractable formulations, accompanied by the development of well-optimized algorithms. Our simulation results demonstrate an overall PC reduction for our proposed PA-SA-HB scheme by up to 93% against the equal PA with full array (FA) and up to 18% with respect to the PA-FA-HB case.

Network-Assisted Full-Duplex Cell-Free Massive MIMO Systems Under Infeasible Circumstances

Cell-free massive multiple-input multiple-output is a potential candidate for future networks with pervasive connectivity by utilizing coherent joint transmission and distributed antenna arrays. This paper studies the exploitation of full-duplex communication for a distributed antenna array. Specifically, we derive a closed-form expression for the uplink and downlink ergodic spectral efficiency (SE) for a network where the APs can flexibly operate in either the full-duplex or half-duplex mode with linear processing and Rayleigh fading channels. A long-term total SE maximization problem is formulated subject to a network operation model and individual SE requirements with limited power budget. Due to the intrinsic nonconvexity and infeasible circumstances where some UEs might not be able to achieve the rate requirements, we adapt differential evolution to design a low computational complexity algorithm that can attain good power allocation and network operation mode in polynomial time. Numerical results demonstrate the effectiveness of our system design and proposed algorithm over state-of-the-art benchmarks with satisfactory service to the majority of UEs, although several ones may be unscheduled under harsh conditions.

Availability of Aerial Heterogeneous Networks for Reliable Emergency Communications

We investigate network availability (NA) in aerial heterogeneous networks (AHetNets) for effective emergency rescue, where diverse delay-constrained communication services must be provided to user equipments (UEs) with varying mobility. The heterogeneity in delay constraints and UE mobility introduces resource allocation conflicts and imbalances, which undermine communication reliability and challenge NA. Although unified resource allocation (URA) can mitigate these issues, it remains unclear whether NA can be sustained under such diverse conditions. To address this, we derive expressions for the lower bound (LB) on NA in AHetNets under URA. Our analysis reveals that extended heterogeneity significantly degrades the LB due to resource limitations-even when the heterogeneity stems from additional services under less stringent delay constraints (LSDC) or from UEs with lower mobility. To overcome this degradation, we formulate and solve a joint optimization problem for the number of UEs sharing time-frequency resources ($K$) and pilot length ($ξ$), aiming to enhance the LB by improving spatial, frequency, and temporal resource efficiency. Simulation results validate our analysis and demonstrate that jointly optimizing $K$ and $ξ$ enables AHetNets to achieve the target NA under greater heterogeneity, outperforming existing resource allocation policies.

Distributed Continuous Aperture Arrays for Multiuser SWIPT

This paper proposes a distributed continuous aperture array (D CAPA) to support simultaneous wireless information and power transfer (SWIPT) to multiple information users (IUs) and energy users (EUs). Each metasurface supports continuous surface currents that radiate electromagnetic (EM) waves for information and energy transmission to the users. These waves propagate through continuous EM channels characterized by the dyadic Green function. We formulate a system power consumption (PC) minimization problem subject to spectral efficiency and energy harvesting quality of service (QoS) requirements, where the QoS requirements are derived under the equal power allocation (EPA) scheme. An efficient two layer optimization algorithm is developed to solve this problem by optimizing the power allocation subject to the QoS violation penalties using augmented Lagrangian transformation. Our numerical results show that well optimized current distributions over each metasurface in the proposed D CAPA achieve up to 65% and 61% reductions in overall system PC compared to the EPA and colocated CAPA (C CAPA) cases, while maintaining the same total aperture size and transmission power.

Cell-Free Massive MIMO-Assisted SWIPT Using Stacked Intelligent Metasurfaces

This study explores a next-generation multiple access (NGMA) framework for cell-free massive MIMO (CF-mMIMO) systems enhanced by stacked intelligent metasurfaces (SIMs), aiming to improve simultaneous wireless information and power transfer (SWIPT) performance. A fundamental challenge lies in optimally selecting the operating modes of access points (APs) to jointly maximize the received energy and satisfy spectral efficiency (SE) quality-of-service constraints. Practical system impairments, including a non-linear harvested energy model, pilot contamination (PC), channel estimation errors, and reliance on long-term statistical channel state information (CSI), are considered. We derive closed-form expressions for both the achievable SE and the average sum harvested energy (sum-HE). A mixed-integer non-convex optimization problem is formulated to jointly optimize the SIM phase shifts, APs mode selection, and power allocation to maximize average sum-HE under SE and average harvested energy constraints. To solve this problem, we propose a centralized training, decentralized execution (CTDE) framework based on deep reinforcement learning (DRL), which efficiently handles high-dimensional decision spaces. A Markovian environment and a normalized joint reward function are introduced to enhance the training stability across on-policy and off-policy DRL algorithms. Additionally, we provide a two-phase convex-based solution as a theoretical robust performance. Numerical results demonstrate that the proposed DRL-based CTDE framework achieves SWIPT performance comparable to convexification-based solution, while significantly outperforming baselines.

Resilient Cell-Free Massive MIMO Networks

This paper proposes a novel optimization framework for enhancing the security resilience of cell-free massive multiple-input multiple-output (CF-mMIMO) networks with multi-antenna access points (APs) and protective partial zero-forcing (PPZF) under active eavesdropping. Based on the main principles of absorption, adaptation, and recovery, we formulate a security-aware resilience metric to quantify the system performance during and after a security outage. A multi-user service priority-aware power allocation problem is formulated to minimize the mean squared error (MSE) between real-time and desired security efficiency, thereby enabling a trade-off between the target user's secrecy performance and multi-user quality of service (QoS). To solve this non-convex problem, a security-aware iterative algorithm based on the successive convex approximation (SCA) is employed. The proposed algorithm determines the optimal power allocation strategy by balancing solution quality against recovery time. At each iteration, it evaluates the overall resilience score and selects the strategy that achieves the highest value. Simulation results confirm that the proposed framework significantly improves the resilience of CF-mMIMO networks, allowing flexible adaptation between rapid recovery and high-quality recovery, depending on system requirements.

Optimized Energy Harvesting in Cell-Free Massive MIMO Using Markov Process Evolution

This paper investigates a discrete energy state transition model for energy harvesting (EH) in cell-free massive multiple-input-multiple-output (CF-mMIMO) networks. A Markov chain-based stochastic process is conceived to characterize the temporal evolution of the user equipment (UE) energy level by leveraging state transition probabilities (STP) based on the energy differential ($\Delta E$) between the EH and consumed energy within each coherence interval. Tractable mathematical relationships are derived for the STP cases using a new stochastic model of non-linear EH, approximated using a Gamma distribution. This derivation leverages closed-form expressions for the mean and variance of the harvested energy. To improve the positive STP of the minimum energy UE among all network UEs, we aim to maximize the $\Delta E$ for this UE using two power allocation (PA) schemes. The first scheme is a heuristic PA using the relative channel characteristics to this UE from all access points (APs). The second scheme is the optimized PA based on the solution of a second-order conic problem to maximize the $\Delta E$ using a responsive primal-dual interior point method (PD-IPM) algorithm with modified backtracking line-search, iterating over multiple PA periods. Our simulation results illustrate that both the proposed PA schemes enhance the dynamic minimum UE energy level by around four-fold over full power control, along with the performance improvement attributed to spatial resource diversification of CF-mMIMO systems.

Cell-Free Massive MIMO-Assisted SWIPT for IoT Networks

This paper studies cell-free massive multiple-input multiple-output (CF-mMIMO) systems that underpin simultaneous wireless information and power transfer (SWIPT) for separate information users (IUs) and energy users (EUs) in Internet of Things (IoT) networks. We propose a joint access point (AP) operation mode selection and power control design, wherein certain APs are designated for energy transmission to EUs, while others are dedicated to information transmission to IUs. The performance of the system, from both a spectral efficiency (SE) and energy efficiency (EE) perspective, is comprehensively analyzed. Specifically, we formulate two mixed-integer nonconvex optimization problems for maximizing the average sum-SE and EE, under realistic power consumption models and constraints on the minimum individual SE requirements for individual IUs, minimum HE for individual EUs, and maximum transmit power at each AP. The challenging optimization problems are solved using successive convex approximation (SCA) techniques. The proposed framework design is further applied to the average sum-HE maximization and energy harvesting fairness problems. Our numerical results demonstrate that the proposed joint AP operation mode selection and power control algorithm can achieve EE performance gains of up to $4$-fold and $5$-fold over random AP operation mode selection, with and without power control respectively.

SWIPT in Cell-Free Massive MIMO Using Stacked Intelligent Metasurfaces

We investigate the integration of stacked intelligent metasurfaces (SIMs) into cell-free massive multiple input multiple output (CF-mMIMO) system to enhance the simultaneous wireless information and power transfer (SWIPT) performance. Closed-form expressions for the spectral efficiency (SE) of the information-decoding receivers (IRs) and the average sum of harvested energy (sum-HE) at the energy-harvesting receivers (ERs) in the novel system model are derived to subsequently formulate a maximum total average sum-HE problem under a minimum SE threshold per each IR. This problem jointly optimizes the SIM phase-shift (PS) configuration and access points' (APs) power allocation, relying on long-term statistical channel state information (CSI). This non-convex problem is then transformed into more tractable forms. Then, efficient algorithms are proposed, including a layer-by-layer heuristic method for SIMs PS configuration that prioritizes sum-HE for the ERs and a successive convex approximation (SCA)-based power allocation scheme to improve the achievable SE for the IRs. Numerical results show that our proposed algorithms achieve an almost 7-fold sum-HE gain as we increase the number of SIM layers, while the proposed power allocation (PPA) scheme often gains up to 40% in terms of the achievable minimum SE, compared to the equal power allocation.