We address comprehensive optimization of uplink spectral efficiency (SE) and resource allocation in multiple reconfigurable intelligent surfaces (RIS)-aided cell-free massive MIMO (CF-mMIMO) systems. While integrating CF-mMIMO with RISs enhances SE, existing solutions assume ideal conditions or separately optimize access point (AP) clustering, large-scale fading decoding (LSFD), RIS phase-shifts, and power allocation. To bridge these gaps, we propose a unified statistical channel state information (CSI)-based optimization (SCOP) framework that jointly optimizes AP clustering, LSFD, RIS phase-shift control, and uplink power allocation to maximize the minimum uplink SE. A closed-form SE expression is derived for maximum ratio (MR) combining, accounting for both direct and cascaded channels with spatially correlated Ricean fading. Leveraging statistical CSI significantly reduces real-time acquisition overhead while enabling robust and efficient uplink transmission design. SCOP is solved via a multi-step strategy: (i) for fixed power, an iterative algorithm computes joint optimization parameters (JOP) vector representing a combination of AP clustering, LSFD, and RIS phase-shift parameters; (ii) a closed-form solution updates power allocation; and (iii) an alternating optimization jointly refines both. We also introduce a novel method to extract the optimal system parameters from the JOP. Simulation results show that, in a representative 60 APs, 30 users, and 4 RISs scenario, the proposed SCOP framework lifts the median uplink SE from 2.4 bit/s/Hz to 3.9 bit/s/Hz (+65%) and more than doubles the bottom 5% rate, with similar 60–120% gains in other setups.

This paper explores the challenges of user equipment (UE) association and downlink power control in scalable cell-free massive multiple-input multiple-output (MIMO) systems. Initially, we develop a scalable UE association algorithm, which ensures that each UE is connected to only a small set of access points (APs). This approach effectively reduces both fronthaul requirements and the computational load on the APs. The algorithm employs a competition-based mechanism to ensure that APs efficiently distribute workloads while satisfying the quality of service (QoS) requirements of UEs, preventing them from losing network connectivity. Second, we introduce a distributed downlink power control method based on deep neural networks (DNNs) to improve the long-term downlink spectral efficiency (SE) of the entire network. This method relies solely on locally collected large-scale fading information as DNN input, making it adaptable to dynamic scenarios involving varying numbers of associated UEs. To further enhance the training efficiency of the DNN, we design a distributed training framework that fully leverages the computational resources of distributed edge processors (EPs). Simulation results show that the proposed UE association algorithm and downlink power control method exhibit significant advantages over the benchmarks.

Unmanned Aerial Vehicles (UAVs) integrated with Internet of Things (IoT) systems represent a powerful solution for reestablishing connectivity in emergency scenarios where terrestrial infrastructure is damaged or overloaded. However, ensuring service continuity under UAV mobility and resource constraints poses significant orchestration challenges. This paper presents a mobility-aware orchestration framework tailored for UAV-enabled IoT networks in such critical contexts. Leveraging Kubernetes for service orchestration and Prometheus for telemetry monitoring, we design and implement a modular architecture that dynamically reallocates containerized services across mobile UAV nodes in real time. Our framework is validated through physical testbed experiments on Raspberry Pi-equipped UAVs, where container migration is triggered by geographic constraints and monitored across varying pod workloads and link conditions. Results demonstrate robust adaptability, sub-second recovery under favorable link quality, and a scalable orchestration strategy for mission-critical operations. This work advances the practical deployment of orchestrated UAV swarms, bridging the gap between theoretical frameworks and real-world mobility-aware computing.

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) is one of the most promising technologies from 5G-based Internet of Things (IoT) to future wireless communication-based IoT, which usually relies on beamforming codebooks for data transmission. However, traditional codebooks often consist of numerous narrow beams, which causes substantial training overhead. Although centralized machine learning-based methods can address this issue to some extent, they overlook minority IoT devices scattered across various areas, which is vital for the coverage equity of the environmental adaptive codebook and the optimal average achievable rate. To circumvent the problem, we propose a distributed learning (DL) framework for codebook design in mmWave massive MIMO systems with uneven user distribution. Specifically, the user channel set is first divided into subsets by pre-classification based on the power responses of the featured combining vectors from different subregions. Then, a novel DL architecture processes these subsets, each assigned to different baseband processing boards (BPBs) in building baseband units, alleviating the centralized machine learning burden on the active antenna unit (AAU) or its directly connected BPB. Meanwhile, the current algorithms lack the hardware perspective or only implement the inference stage of the model. Thus, we deploy an FPGA-adapted DL-based codebook training prototype that runs on FPGA, which fully explores the “Backward-While-Forward" strategy for data reuse in the forward and backward passes. Simulation validates the effectiveness of distributed learning. Notably, the FPGA implementation on the embedded-level board outperforms consumer-grade CPU and GPU in terms of both latency and energy efficiency.

This paper considers an antenna structure where a (non-large) array of radiating elements is placed at short distance in front of a reconfigurable intelligent surface (RIS), herein nicknamed reconfigurable intelligent base station (RIBS). We firstly derive a closed-form expression for the channel between the array of radiating elements and the RIS that captures the near-field effects, and give some considerations on the channel hardening and favorable propagation in this scenario. Focusing on both active and passive RIS, we describe channel estimation and downlink signal processing techniques suitable for the RIBS structure. Additionally, we formulate and solve an optimization problem aimed at maximizing the fairness among the users with respect to the downlink power coefficients and RIS configuration both in the cases of active and passive RIBS. Numerical results show that the proposed structure is effective and capable of outperforming conventional non-RIS aided MIMO systems, especially in the case of active RIBS. The proposed antenna structure is thus shown to be able to approach massive MIMO performance levels in a cost-effective way with reduced hardware resources.

This article studies the cell-free massive multi-input multi-output (MIMO) two-way relay systems with low-resolution analog-to-digital converters (ADCs). Primarily, we analyze the normalized mean square error (NMSE) and derive a closed-form expression for NMSE’s expectation${\mathrm {Exp}}_{\mathrm {nmse}}$. Asymptotic analysis showcases that, with an infinite number of access point (AP) antennas M or ideal ADCs,${\mathrm {Exp}}_{\mathrm {nmse}}$converges to a finite value. Particularly, as pilot power decreases with M in a power law, the channel estimation performance can be maintained at a desired level. Secondly, we derive two closed-form expressions of spectral efficiencies (SE) for multiple-access channel (MAC) phase and broadcasting (BC) phases, respectively. The corresponding analysis implies that, as AP number$L\rightarrow \infty $, the SE tends to infinity with MAC phase and approaches to constant with BC phase. Interestingly, all SE expressions can reduce to the conventional cases with high-resolution ADCs. Finally, based on geometric programming, an effective power successive approximation (PSA) scheme is provided to maximize the sum SE. Results prove that the proposed PSA scheme can significantly improve the SE compared to baseline benchmarks, particularly for median AP antenna regime. All the derived closed-form expressions are validated through Monte Carlo simulations.

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) technology represents a promising technology in wireless communication. This technology relies on beamforming codebooks for initial access and transmission. However, conventional codebooks comprise a multitude of single-lobe narrow beams, resulting in redundant beams that may never be utilized in beam training. While centralized machine learning methods can partially address the concern of redundancy, they tend to overlook the presence of minority users scattered across diverse regions. The equitable coverage of environmental adaptive codebooks depends on addressing this issue. Hence, we devise a distributed learning (DL) framework for codebook design, which is tailored for scenarios with uneven user distribution and fully exploits the decentralized and online learning features of DL. Our approach begins by segmenting the user channels into various subsets through a pre-classification process. Then, we introduce a novel DL architecture designed to process the subsets that are assigned to individual user equipments (UEs). Each UE then generates a phase shift matrix that contributes to the concatenation-based global aggregation in the base station. The simulation results confirm the effectiveness of DL in improving the performance of mmWave massive MIMO systems in scenarios with unevenly distributed users.

Ultradense cell-free massive multiple-input multiple-output (CF-MMIMO) has emerged as a promising technology expected to meet the future ubiquitous connectivity requirements and ever-growing data traffic demands in sixth generation (6G). This article provides a contemporary overview of ultradense CF-MMIMO networks and addresses important unresolved questions on their future deployment. We first present a comprehensive survey of state-of-the-art research on CF-MMIMO and ultradense networks. Then, we discuss the key challenges of CF-MMIMO under ultradense scenarios such as low-complexity architecture and processing, low-complexity/scalable resource allocation, fronthaul limitation, massive access, synchronization, and channel acquisition. Finally, we answer key open questions, considering different design comparisons and discussing suitable methods dealing with the key challenges of ultradense CF-MMIMO. The discussion aims to provide a valuable roadmap for interesting future research directions in this area, facilitating the development of CF-MMIMO for 6G.

The coupling of cell-free massive MIMO (CF-mMIMO) with Mobile Edge Computing (MEC) is investigated in this paper. A MEC-enabled CF-mMIMO architecture implementing a distributed user-centric approach both from the radio and the computational resource allocation perspective is proposed. A multi-objective optimization problem (MOOP) for the joint allocation of radio and remote computational resources is formulated, aimed at striking an optimal balance between total uplink power minimization and sum spectral efficiency maximization, under resource budget and latency constraints. In order to solve such a challenging non-convex problem, we convert the MOOP to an equivalent single-objective optimization problem (SOOP) through the weighted sum method and propose an iterative algorithm based on alternating optimization and sequential convex programming, along with an alternative heuristic resource allocation for distributed networks. Finally, we provide a detailed performance comparison between the proposed MEC-enabled CF-mMIMO architecture with its co-located counterpart, and its small-cell implementation. Numerical results reveal the effectiveness of the proposed resource allocation scheme, under different access point selection strategies, and the natural suitability of CF-mMIMO in supporting computation-offloading applications with benefits over users’ transmit power and energy consumption, the effective latency experienced, and the computation offloading efficiency.

The non-orthogonal coexistence between the enhanced mobile broadband (eMBB) and the ultra-reliable low-latency communication (URLLC) in the downlink of a multi-cell massive MIMO system is investigated in this work. We provide a unified information-theoretic framework blending an infinite-blocklength analysis of the eMBB spectral efficiency (SE) in the ergodic regime with a finite-blocklength analysis of the URLLC error probability. Puncturing (PUNC) and superposition coding (SPC) are considered as alternative coexistence strategies to deal with the inter-service interference. eMBB and URLLC performances are then evaluated over different precoding techniques and power control schemes, by accounting for imperfect channel state information knowledge at the base stations, pilot-based estimation overhead, spatially correlated channels, and the structure of the radio frame. Simulation results reveal that SPC is, in many operating regimes, superior to PUNC in providing higher SE for the eMBB yet achieving the target reliability for the URLLC with high probability. However, PUNC turns to be necessary to preserve the URLLC performance in scenarios where the multi-user interference cannot be satisfactorily alleviated.

This paper considers a mobile edge computing-enabled cell-free massive MIMO wireless network. An optimization problem for the joint allocation of uplink powers and remote computational resources is formulated, aimed at minimizing the total uplink power consumption under latency constraints, while simultaneously also maximizing the minimum SE throughout the network. Since the considered problem is non-convex, an iterative algorithm based on sequential convex programming is devised. A detailed performance comparison between the proposed distributed architecture and its co-located counterpart, based on a multi-cell massive MIMO deployment, is provided. Numerical results reveal the natural suitability of cell-free massive MIMO in supporting computation-offloading applications, with benefits over users’ transmit power and energy consumption, the offloading latency experienced, and the total amount of allocated remote computational resources.

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.

In cell-free massive multiple-input multiple-output (MIMO) the fluctuations of the channel gain from the access points to a user are large due to the distributed topology of the system. Because of these fluctuations, data decoding schemes that treat the channel as deterministic perform inefficiently. A way to reduce the channel fluctuations is to design a precoding scheme that equalizes the effective channel gain seen by the users. Conjugate beamforming (CB) poorly contributes to harden the effective channel at the users. In this work, we propose a variant of CB dubbed enhanced normalized CB (ECB), in that the precoding vector consists of the conjugate of the channel estimate normalized by its squared norm. For this scheme, we derive an exact closed-form expression for an achievable downlink spectral efficiency (SE), accounting for channel estimation errors, pilot reuse and user's lack of channel state information (CSI), assuming independent Rayleigh fading channels. We also devise an optimal max-min fairness power allocation based only on large-scale fading quantities. ECB greatly boosts the channel hardening enabling the users to reliably decode data relying only on statistical CSI. As the provided effective channel is nearly deterministic, acquiring CSI at the users does not yield a significant gain.

Cell-free Massive MIMO (multiple-input multiple-output) is a promising distributed network architecture for 5G-and-beyond systems. It guarantees ubiquitous coverage at high spectral efficiency (SE) by leveraging signal co-processing at multiple access points (APs), aggressive spatial user multiplexing and extraordinary macro-diversity gain. In this study, we propose two distributed precoding schemes, referred to as local partial zero-forcing (PZF) and local protective partial zero-forcing (PPZF), that further improve the spectral efficiency by providing an adaptable trade-off between interference cancelation and boosting of the desired signal, with no additional front-hauling overhead, and implementable by APs with very few antennas. We derive closed-form expressions for the achievable SE under the assumption of independent Rayleigh fading channel, channel estimation error and pilot contamination. PZF and PPZF can substantially outperform maximum ratio transmission and zero-forcing, and their performance is comparable to that achieved by regularized zero-forcing (RZF), which is a benchmark in the downlink. Importantly, these closed-form expressions can be employed to devise optimal (long-term) power control strategies that are also suitable for RZF, whose closed-form expression for the SE is not available.

Ubiquitous cell-free massive MIMO (multiple-input multiple-output) combines massive MIMO technology and user-centric transmission in a distributed architecture. All the access points (APs) in the network cooperate to jointly and coherently serve a smaller number of users in the same time-frequency resource. However, this coordination needs significant amounts of control signalling which introduces additional overhead, while data co-processing increases the back/front-haul requirements. Hence, the notion that the “whole world” could constitute one network, and that all APs would act as a single base station, is not scalable. In this study, we address some system scalability aspects of cell-free massive MIMO that have been neglected in literature until now. In particular, we propose and evaluate a solution related to data processing, network topology and power control. Results indicate that our proposed framework achieves full scalability at the cost of a modest performance loss compared to the canonical form of cell-free massive MIMO.

Cell-free Massive MIMO (multiple-input multiple-output) refers to a distributed Massive MIMO system where all the access points (APs) cooperate to coherently serve all the user equipments (UEs), suppress inter-cell interference and mitigate the multiuser interference. Recent works demonstrated that, unlike co-located Massive MIMO, the channel hardening is, in general, less pronounced in cell-free Massive MIMO, thus there is much to benefit from estimating the downlink channel. In this study, we investigate the gain introduced by the downlink beamforming training, extending the previously proposed analysis to non-orthogonal uplink and downlink pilots. Assuming single-antenna APs, conjugate beamforming and independent Rayleigh fading channel, we derive a closed-form expression for the per-user achievable downlink rate that addresses channel estimation errors and pilot contamination both at the AP and UE side. The performance evaluation includes max-min fairness power control, greedy pilot assignment methods, and a comparison between achievable rates obtained from different capacity-bounding techniques. Numerical results show that downlink beamforming training, although increases pilot overhead and introduces additional pilot contamination, improves significantly the achievable downlink rate. Even for large number of APs, it is not fully efficient for the UE relying on the statistical channel state information for data decoding.

In this paper, we analyze the benefits of including downlink pilots in a cell- free massive MIMO system. We derive an approximate per-user achievable downlink rate for conjugate beamforming processing, which takes into account both uplink and downlink channel estimation errors, and power control. A performance comparison is carried out, in terms of per-user net throughput, considering cell-free massive MIMO operation with and without downlink training, for different network densities. We take also into account the performance improvement provided by max-min fairness power control in the downlink. Numerical results show that, exploiting downlink pilots, the performance can be considerably improved in low density networks over the conventional scheme where the users rely on statistical channel knowledge only. In high density networks, performance improvements are moderate.