arXiv subjects

Alejandro de la Fuente

Publications and source records attributed to Alejandro de la Fuente.

7 recordsLinked to original sources

arXiv2026

Evolutionary AP Switch ON/OFF Techniques for Energy-efficient Cell-free Massive MIMO Networks

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 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.

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arXiv2026

Deep Learning-Assisted Multicast Subgrouping in Massive MIMO

Efficient content delivery in massive multiple-input multiple-output (mMIMO) multicasting is fundamentally limited by pilot overhead and the need to serve heterogeneous users with a common transmission rate. Conventional approaches either suffer from pilot contamination or are constrained by the worst-user effect, motivating the need for adaptive subgrouping strategies. In this paper, we propose a deep learning-assisted multicast subgrouping framework that infers the number of multicast subgroups directly from users' spatial channel statistics. A snapshot-specific principal component analysis (PCA) is applied to user covariance matrices to obtain a compact representation, which is processed by a sequential long short-term memory (LSTM) encoder capable of handling variable-size user sets. The model predicts the number of subgroups and groups of users based on their statistical similarity. To further improve system performance, we introduce a transfer learning (TL) extension where a pretrained LSTM encoder is reused, and a lightweight dense head is fine-tuned to estimate the sum spectral efficiency (SE) as a function of the subgroup configuration. This enables selecting near-optimal subgrouping solutions without exhaustive search. Simulation results demonstrate that the proposed approach consistently outperforms benchmark methods, including unicast transmission, conventional multicast, random subgrouping, and density-based clustering. The TL-enhanced model achieves up to 85% of the maximum achievable spectral efficiency while maintaining robust performance across diverse spatial user distributions and under imperfect covariance information.

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arXiv2026

Conjugate Beamforming Variants for Multicasting in Cell-Free Massive MIMO Systems

This paper studies scalable conjugate beamforming (CB) variants for physical-layer multicasting in cell-free massive multiple-input multiple-output (CF-mMIMO) systems. Focusing on fully distributed precoding, we analyze classical CB, normalized CB (NCB), and enhanced CB (ECB) within a subgroup-centric multicast framework. Multicast users are partitioned into subgroups based on large-scale fading similarity, which enables composite channel estimation, pilot reuse, and distributed precoding with low complexity. The performance of the different CB variants is evaluated in terms of aggregated spectral efficiency (ASE) under representative user geometries, including uniformly distributed users, spatially clustered deployments, and heterogeneous scenarios combining hotspots with more dispersed users. Monte Carlo simulations reveal a strong spatial geometry-dependent behavior: unicast transmission is preferable in uniform deployments, while subgroup-based multicasting becomes essential in clustered and heterogeneous scenarios. Among the CB-based precoders, NCB offers a robust performance-complexity trade-off across most scenarios, whereas ECB provides additional gains only when sufficient channel hardening is present. These results provide practical insights into the selection of low-complexity distributed precoders and multicast transmission modes in CF-mMIMO systems supporting broadband and multimedia services.

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arXiv2026

RIS-Assisted Cell-Free Massive MIMO: RIS-MS Selection in FR1 and FR3

This paper explores the integration of reconfigurable intelligent surfaces (RISs) into cell-free massive multiple-input-multiple-output (CF-mMIMO) networks operating in FR1 and FR3 frequency bands. We present a comprehensive framework for analyzing RIS-assisted CF-mMIMO systems under realistic propagation conditions, accounting for frequency-dependent characteristics and RIS configurations. A novel RIS-user association algorithm is proposed to optimize phase-shift settings by assigning each RIS to a single user based on line of sight (LoS) connectivity. The system model incorporates spatially correlated Ricean fading channels and employs scalable partial-minimum mean square error (P-MMSE) combining. The numerical results demonstrate that the proposed RIS-user selection strategy significantly improves the spectral efficiency compared to random or exhaustive RIS configurations, particularly when the number of RISs is moderate. We also analyze the trade-off between training overhead and performance gains, showing that excessive pilot requirements can offset benefits when RIS density or element count increases. The results highlight the potential of the FR3 bands for RIS-assisted CF-mMIMO, provided advanced channel estimation techniques are adopted to mitigate overhead. These findings emphasize the importance of intelligent RIS-user pairing and scalable estimation methods for future 6G deployments.

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arXiv2024

User Subgrouping in Scalable Cell-Free Massive MIMO Multicasting Systems

Cell-free massive multiple-input multiple-output (CF-mMIMO) is a breakthrough technology for beyond-5G systems, designed to significantly boost the energy and spectral efficiencies of future mobile networks while ensuring a consistent quality of service for all users. Additionally, multicasting has gained considerable attention recently because physical-layer multicasting offers an efficient method for simultaneously serving multiple users with identical service demands by sharing radio resources. Typically, multicast services are delivered either via unicast transmissions or a single multicast transmission. This work, however, introduces a novel subgroup-centric multicast CF-mMIMO framework that divides users into several multicast subgroups based on the similarities in their spatial channel characteristics. This approach allows for efficient sharing of the pilot sequences used for channel estimation and the precoding filters used for data transmission. The proposed framework employs two scalable precoding strategies: centralized improved partial MMSE (IP-MMSE) and distributed conjugate beam-forming (CB). Numerical results show that for scenarios where users are uniformly distributed across the service area, unicast transmissions using centralized IP-MMSE precoding are optimal. However, in cases where users are spatially clustered, multicast subgrouping significantly improves the sum spectral efficiency (SE) of the multicast service compared to both unicast and single multicast transmission. Notably, in clustered scenarios, distributed CB precoding outperforms IP-MMSE in terms of per-user SE, making it the best solution for delivering multicast content.

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arXiv2024

User Subgrouping and Power Control for Multicast Massive MIMO over Spatially Correlated Channels

Massive multiple-input-multiple-output (MIMO) is unquestionably a key enabler of the fifth-generation (5G) technology for mobile systems, enabling to meet the high requirements of upcoming mobile broadband services. Physical-layer multicasting refers to a technique for simultaneously serving multiple users, demanding for the same service and sharing the same radio resources, with a single transmission. Massive MIMO systems with multicast communications have been so far studied under the ideal assumption of uncorrelated Rayleigh fading channels. In this work, we consider a practical multicast massive MIMO system over spatially correlated Rayleigh fading channels, investigating the impact of the spatial channel correlation on the favorable propagation, hence on the performance. We propose a subgrouping strategy for the multicast users based on their channel correlation matrices' similarities. The proposed subgrouping approach capitalizes on the spatial correlation to enhance the quality of the channel estimation, and thereby the effectiveness of the precoding. Moreover, we devise a max-min fairness (MMF) power allocation strategy that makes the spectral efficiency (SE) among different multicast subgroups uniform. Lastly, we propose a novel power allocation for uplink (UL) pilot transmission to maximize the SE among the users within the same multicast subgroup. Simulation results show a significant SE gain provided by our user subgrouping and power allocation strategies. Importantly, we show how spatial channel correlation can be exploited to enhance multicast massive MIMO communications.

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arXiv2021

User Subgrouping in Multicast Massive MIMO over Spatially Correlated Rayleigh Fading Channels

Massive multiple-input-multiple-output (MaMIMO) multicasting has received significant attention over the last years. MaMIMO is a key enabler of 5G systems to achieve the extremely demanding data rates of upcoming services. Multicast in the physical layer is an efficient way of serving multiple users, simultaneously demanding the same service and sharing radio resources. This work proposes a subgrouping strategy of multicast users based on their spatial channel characteristics to improve the channel estimation and precoding processes. We employ max-min fairness (MMF) power allocation strategy to maximize the minimum spectral efficiency (SE) of the multicast service. Additionally, we explore the combination of spatial multiplexing with orthogonal (time/frequency) multiple access. By varying the number of antennas at the base station (BS) and users' spatial distribution, we also provide the optimal subgroup configuration that maximizes the spectral efficiency per subgroup. Finally, we show that serving the multicast users into two orthogonal time/frequency intervals offers better performance than only relying on spatial multiplexing.

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