2026· IEEE Transactions on Communications· Vol 74, pp. 11372-11389· 0 citations· 52 references
Computer Science
Abstract
We consider the downlink of a Rician-faded massive multiple-input-multiple-output multi-cell system, where each base station (BS) serves its user equipments (UEs) with assistance from cell-specific intelligent-reflecting surface (IRS). The BSs employ rate splitting multiple access (RSMA) protocol, and maximal-ratio transmission. We derive a closed-form spectral efficiency (SE) expression for this system, and use it to optimize its global energy efficiency (GEE) metric by jointly optimizing the BS transmit power and IRS phases. We develop novel low-complexity closed-form solution to optimize power by using Lagrangian dual, Quadratic and Dinkelbach transforms. We then optimize IRS phases by developing a novel generative diffusion model (GDM)-based deep reinforcement learning (DRL) framework. We numerically characterize, for the first time, the RSMA SE gains over spatial division multiple access in a multi-cell network. We also compare the impact of inter-cell and residual successive interference cancellation interference on the SE of a multi-cell system. We also show that our GDM-DRL framework provides much higher GEE than multiple state-of-the-art solutions.
A network energy-efficiency (EE) maximization framework for the uplink of acell-free massive MIMO with wireless fronthaul, jointly optimizing the integrated access and fronthaul (IAF) resource split, the adaptive per-AP quantization resolution, and the fronthaul powers, and treating the time-division and frequency-division operating modes in a unified manner.
The proposed framework does not optimize only computational speed, but also clarifies the trade-off among execution time, SINR, spectral efficiency, and fairness under dynamic uplink CF-mMIMO conditions, indicating that this architecture serves as an adaptable platform to evaluate dynamic uplink power distribution across CF-mMIMO networks.
Hussein A. Jasim, M. F. A. Rasid, F. Hashim et al.· Engineer· 0 citations
A cross-layer end-to-end (E2E) resource orchestration framework for green CF-mMIMO ISAC systems with distributed multi-target detection is developed and a fundamental implementation trade-off is revealed: FIS provides lower detector-processing complexity and higher detection performance, whereas PIS substantially reduces fronthaul requirements.
Z. Behdad, Ozlem Tuugfe Demir, Ki Won Sung et al.· arXiv.org· 0 citations
In this paper, we investigate the downlink performance of multi-cell RSMA-enabled ISAC networks in which base stations (BSs), communication users, and sensing targets are spatially distributed according to independent Poisson point processes (PPPs). Each BS simultaneously serves multiple users using RSMA while exploiting the common stream as a dual-functional communication and sensing waveform. The users are equipped with FAS that selects the best antenna port to maximize the received signal quality. Closed-form analytical expressions are derived for the ergodic sum-rates by combining stochastic geometry, order statistics, and Laplace-transform-based interference analysis. Furthermore, a tractable approximation for the average radar SINR is developed by characterizing the statistical properties of the common precoder. Leveraging the derived analytical expressions, a low-complexity analytical resource allocation framework is proposed to jointly optimize the RSMA power allocation, the communication-sensing beam tradeoff, and the number of scheduled users while sat- isfying the sensing quality-of-service constraint. Compared with conventional iterative optimization approaches, the proposed analytical design significantly reduces computational complexity while achieving nearly identical communication performance. Simulation results verify the accuracy of the developed analytical expressions and demonstrate substantial improvements in both RSMA sum-rate and sensing performance over conventional transmission schemes.
Abdelhamid Salem, Hana Shamata, Salma M. Elkawafi et al.· 0 citations
This paper investigates the sum-rate maximization problem for downlink rate-splitting multiple access (RSMA) systems equipped with pattern-reconfigurable fluid antennas (PRFA). Two PRFA models are developed: a deployable discrete-selection PRFA (DS-PRFA) model with finite predefined radiation modes, and an idealized continuous-optimization PRFA (CO-PRFA) model based on spherical harmonic expansion that serves as a performance upper bound. The sum-rate maximization problem is formulated by jointly optimizing digital, analog, and antenna-domain precoders along with RSMA power allocation. To solve this non-convex problem, we propose alternating optimization algorithms based on the weighted minimum mean square error (WMMSE) transformation and block coordinate descent with per-antenna decoupling. For DS-PRFA optimization, closed-form solutions are derived, while for CO-PRFA optimization, a preconditioned Riemannian conjugate gradient method is developed on the spherical manifold. Simulation results under the considered settings show that the proposed tri-hybrid RSMA framework with PRFA improves sum-rate performance compared with conventional hybrid precoding, where the CO-PRFA provides an idealized upper benchmark compared with practical DS-PRFA due to the more flexible reconfigurability.
Yijin Pan, Yifeng Ji, Anzheng Tang et al.· IEEE Open Journal of the Com...· 0 citations
This paper investigates a novel self-sustainable intelligent reflecting surface (IRS)-enhanced multi-input multi-output simultaneous wireless information and power transfer (SWIPT) system under imperfect channel state information. Equipped with an energy harvesting module, the IRS harvests energy from received signals to meet its operational needs. In particular, we focus on maximizing the weighted sum rate (WSR) of all information users (IUs) by jointly optimizing the access point (AP) precoding matrices and IRS reflection coefficient matrix. Meanwhile, the WSR maximization is constrained by the maximum transmit power of the AP and by the harvested power requirements of both IRS and energy users (EUs). To address the highly coupled and non-convex problem, we first adopt the minimum mean square error method to recast the WSR maximization problem into a simpler equivalent problem and split the transformed problem into two sub-problems, which can be tackled alternatively. In addition, we employ the successive convex approximation (SCA) technique to approximate the non-convex sub-problems. Furthermore, we adopt the Lagrangian dual transformation, majorization-minimization method and penalty-based technique to obtain the closed-form solutions. The simulation results demonstrate the effectiveness of the proposed algorithm and show that, with proper IRS deployment, the proposed self-sustainable IRS can achieve 96%–99% of the WSR obtained by the externally powered IRS under the default simulation setup, while eliminating the need for an external power supply.
Wanli Ma, Xiaokai Song, Zhendong Yin et al.· IEEE Transactions on Communi...· 0 citations
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