Jul 2026· IEEE Open Journal of the Communications Society· Vol 7, pp. 8905-8925· 0 citations· 66 references
Engineering
TL;DR
The proposed methods consistently outperform state-of-the-art greedy benchmarks, delivering noticeable improvements in energy efficiency for both conjugate beamforming and minimum mean square error (MMSE) processing, while simultaneously enhancing the energy–spectral efficiency tradeoff, which is typically difficult to improve without incurring penalties elsewhere.
Abstract
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.
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.
K. A. Bonsu, Ebenezer Baidoo Baidoo Bediako, K. Darkwah et al.· Applied Mathematics and Stat...· 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.
Sixth-generation (6G) mobile communication poses unprecedented challenges for resource scheduling under personalized demands. Cell-free massive multiple-input multiple-output (CF-mMIMO), with its user-centric characteristics, has emerged as a key technology for satisfying personalized demands. However, faced with heterogeneous quality-of-service (QoS) requirements, existing reinforcement learning schemes are constrained by partial observability, making it difficult to balance overall system performance and personalized demands. Consequently, we propose a graph-embedded multi-agent deep deterministic policy gradient (G-MADDPG) scheme. Guided by personalized demands, proposed G-MADDPG formulates a maximization problem for system weighted sum spectral efficiency and introduces differentiated QoS penalties. In addition, graph neural networks (GNNs) are embedded into the policy learning and value estimation processes of reinforcement learning, endowing agents with enhanced structural reception and cooperative capabilities. Simulation results demonstrate that proposed G-MADDPG scheme outperforms existing benchmark schemes in both convergence speed and performance evaluation.
Yu-Heng An· 2026 8th International Confe...· 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
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, Özlem Tuğfe Demir, Ki Won Sung et al.· 0 citations
This research is among the first to employ MARL to this extent, and it offers an end-to-end solution that combines cellular, Wi-Fi, and device-to-device (D2D) communications and considers practical network environments like user mobility and channel conditions.
Nabeel Abdolrazagh Yaseen Alrashedi, Rasool Sadeghi, Wael Hussein Zayer Al-Lamy et al.· Journal of universal compute...· 0 citations
