Jul 2026· Applied Mathematics and Statistics· 0 citations· 24 references
TL;DR
Validation of the APG algorithm's resilience revealed that it outperformed benchmark algorithms in terms of energy efficiency and execution time, demonstrating its usefulness for challenging optimization tasks, particularly those involving bursty communication.
Abstract
The study investigates a power allocation model for cell-free massive Multiple Input Multiple Output (CF-mMIMO) systems using the Accelerated Proximal Gradient (APG) algorithm.CF- mMIMO systems are a crucial component of sixth-generation networks (6G) that aim to improve system energy efficiency. Unlike conventional optimization approaches that rely on computationally intensive convex relaxation or second-order solvers, the proposed method directly addresses the inherently non-convex fractional energy-efficiency maximization problem under Quality of Service (QoS) and access point (AP) power constraints. A differentiable quadratic penalty mechanism is introduced to seamlessly incorporate the QoS constraints into the objective function, thereby transforming the constrained non-convex problem into a tractable penalized optimization problem suitable for first-order iterative optimization. Validation of the APG algorithm's resilience revealed that it outperformed benchmark algorithms in terms of energy efficiency and execution time, demonstrating its usefulness for challenging optimization tasks, particularly those involving bursty communication. Furthermore, the APG technique guarantees that no penalty functions (PFs) are broken, ensuring that the overall loss converges to zero, irrespective of the beginning conditions.
Cell-free massive multiple input multiple output (CF-mMIMO) is an emerging technology for next-generation wireless systems, where dynamically adapting the set of active access points (APs) is crucial to balance quality of service (QoS) requirements and network energy consumption under highly time-varying and spatially non-uniform traffic loads. Existing AP ON/OFF mechanisms—typically based on worst-case dimensioning or greedy heuristics—explore the combinatorial activation space inadequately, leading to suboptimal energy-efficiency outcomes. This paper introduces two evolutionary AP-selection strategies tailored to cell-free massive multiple input multiple output (CF-mMIMO) networks. The first, a constrained genetic algorithm (CGA), identifies the near-optimal subset of active APs for any fixed activation cardinality, while an outer search determines the globally optimal operating point. The second, a Pareto-driven genetic algorithm (PDGA), jointly optimizes spectral and energy efficiency by evolving a Pareto front over all feasible activation patterns. A detailed computational-complexity analysis is provided for both techniques. Simulations conducted under realistic spatially inhomogeneous traffic and considering both conjugate beamforming (CB) and minimum mean square error (MMSE) processing confirm consistent performance gains. The proposed methods consistently outperform state-of-the-art greedy benchmarks, delivering noticeable improvements in energy efficiency for both CB and MMSE schemes, while simultaneously enhancing the energy–spectral efficiency tradeoff, which is typically difficult to improve without incurring penalties elsewhere. These results highlight the strong potential of evolutionary optimization as a powerful and reliable approach for energy-efficient CF-mMIMO deployments.
Jan Garc'ia-Morales, Alejandro de la Fuente, D. Gualda et al.· IEEE Open Journal of the Com...· 0 citations
—Reconfigurable intelligent surface (RIS)-assisted wireless networks offer significant performance gains; however, energy-efficiency maximization in such systems remains fundamentally challenging due to the coupled active–passive beamforming design and the fractional nature of the objective function. Existing studies typically rely on semidefinite relaxation or optimize surrogate spectral-efficiency metrics, without establishing theoretical equivalence to the original fractional formulation or providing formal convergence guarantees. This paper addresses this gap by formulating joint transmit and RIS phase optimization as a non-convex fractional program with unit-modulus constraints, which is NP-hard. We develop a unified optimization framework that integrates Dinkelbach fractional programming, a rigorously derived weighted minimum mean square error (WMMSE) reformulation, second-order cone programming (SOCP), and Riemannian manifold optimization within a provably convergent alternating structure. Unlike prior approaches, the proposed method preserves stationary-point equivalence to the original fractional objective and is proven to converge to a Karush–Kuhn–Tucker (KKT) point under standard constraint qualification conditions. A detailed per-stage computational complexity analysis is provided, together with empirical runtime evaluation. Simulation results, averaged over 1000 independent Monte Carlo realizations with 95% confidence intervals, demonstrate up to 40.9% improvement in energy efficiency compared with state-of-the-art benchmark schemes under identical system assumptions with 95% confidence interval (CI) [38 . 7% , 43 . 1%] . Sensitivity analysis further confirms robustness with respect to RIS size, user density, transmit power, and phase quantization resolution. These results establish both the theoretical soundness and practical scalability of the proposed framework for energy-efficient RIS-assisted communications.
I. Hburi, A. Abdulameer, N. Abbas et al.· Journal of Communications So...· 0 citations
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
Cell-free massive MIMO with wireless fronthaul is a promising architecture for energy-efficient 6G networks, but the access and fronthaul links must then share the same scarce spectrum, and, under the fully centralized (option-8) functional split, the fronthaul rate is dictated by the finite quantization resolution used at the access points (APs). This paper develops a network energy-efficiency (EE) maximization framework for the uplink of such a system, 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 (TD) and frequency-division (FD) operating modes in a unified manner. Each AP may be switched off (put to sleep) when it is not worth activating, so the resolution allocation is inherently coupled with AP selection. The resulting mixed-integer, nonconvex fractional program is solved by an alternating-optimization algorithm with per-block optimality guarantees---a closed-form optimal time split, bandwidth bisection, and optimal per-AP bit selection---that applies verbatim to both modes. While the design relies on the tractable additive quantization noise model, the reported performance is obtained end-to-end with the actual Lloyd--Max quantizers and a Bussgang decomposition-based achievable-rate bound.
Results indicate that the proposed AO-SCA framework provides an effective and practical solution for fairness-aware power allocation in downlink MN-NOMA systems, and provides a balanced fairness-efficiency tradeoff.
Sudhir Kumar Dhotre, S. Nalbalwar, A. Nandgaonkar· International Research Journ...· 0 citations
Integrated sensing, communication, and computation (ISCC) enables next-generation wireless networks to perform environmental perception while processing massive data under stringent quality-of-service (QoS) requirements. Energy consumption is a crucial indicator for the ISCC system design. However, accounting for energy heterogeneity in ISCC system design is an open problem. Specifically, battery-constrained user equipments (UEs) and energy-abundant access points (APs) require fundamentally different energy allocation strategies based on device computational capabilities, battery states, and QoS constraints. In this paper, we introduce a nonconvex energy cost minimization problem by considering a user-specific energy cost ratio coefficient that explicitly balances UE-AP energy consumption according to heterogeneous device energy states. To efficiently address this problem, a double-loop framework combining successive convex approximation and alternating direction method of multipliers is also developed. Numerical results demonstrate that the proposed scheme significantly outperforms the fixed offloading baselines (full offloading, full local and half offloading) in terms of the total energy cost. In particular, the proposed scheme achieves up to $25-47.6\%$ energy cost reduction at moderate latency constraints over fixed offloading baselines, thereby supporting time-sensitive applications. Moreover, this work provides an effective solution for energy-efficient and QoS-aware 6G ISCC systems serving diverse devices with conflicting energy priorities.
Kai Dong, Lei Wang, S. Vorobyov et al.· IEEE Transactions on Wireles...· 0 citations