This paper investigates access point (AP) cooperation cluster formation in user-centric cell-free massive MIMO (CF-mMIMO) communication systems characterized by fronthaul links with capacity restrictions. Specifically, we consider an open radio access network (O-RAN) architecture that, although it favors the deployment of ultra-dense networks, is constrained in the number of APs that can be active simultaneously. In this context, we propose an innovative framework termed pixel-based CF-mMIMO, which enables efficient control of both the AP activation and cooperation cluster formation. Recognizing the parallels with pixel-based reconfigurable antennas, the proposed framework allows dynamic reconfiguration of the network coverage map with reasonably low computational cost. The high scalability and performance of the framework are mainly supported by a learning model based on graph neural networks (GNNs) that effectively exploits the existing graph-like structures in CF-mMIMO systems. Extensive simulation experiments demonstrate that the proposed approach achieves competitive spectral efficiency (SE) in challenging scenarios with dense AP deployments and numerous user equipments (UEs).

User-centric cell-free massive MIMO (CF-mMIMO) communications rely on accurately knowing the channel state information (CSI) to perform coherent signal processing. However, achieving pilot contamination-free channel estimation is challenging due to the limited length of the coherence blocks. In this work, we combine a novel user equipment (UE) clustering method termed C-footprints and a sequential heuristic named cell-free-oriented best first pilot assignment (CF-BFPA) to find pilot allocations that effectively reduce contamination. First, the proposed clustering method groups potentially contaminating UEs based on the degree of orthogonality among the matrices of channel statistics. Subsequently, the CF-BFPA algorithm performs a greedy intra-cluster pilot assignment that ensures low interference between the most contaminating UEs. The performance of the proposed solution (C-footprints+CF-BFPA) is compared with several state-of-the-art algorithms across diverse CF-mMIMO network settings.

Forming effective access point (AP) cooperation clusters is a key challenge in user-centric cell-free massive MIMO (CF-mMIMO). Existing approaches to this task are either computationally prohibitive or overlook the complex interrelationships within communication networks. In this context, we introduce an innovative approach based on graph neural networks (GNNs). By leveraging the inherent graph structure of CF-mMIMO networks, we transform the rate maximization problem into a node classification task, enabling a competitive and robust solution. Simulation results show that the proposed method significantly outperforms conventional baselines in terms of spectral efficiency, computational complexity, and scalability.

Massive MIMO (mMIMO) communication systems are recognized as key enablers of next-generation wireless networks. However, the orthogonal pilot assignments typical of multiple-input multiple-output (MIMO) systems are not well suited to emerging use cases characterized by short channel coherence times and a high number of connected user equipments (UEs). In this work, we propose a novel approach for intra-cell pilot reuse that leverages the spatial features of correlated mMIMO channels to attain low pilot contamination while using a small number of pilot sequences. The first part of the proposed solution is a groundbreaking clustering algorithm termed K-beams, which splits the complex intra-cell pilot allocation into tractable problems without significant loss of optimality. Then, we introduce a heuristic approach called best-first pilot assignment (BFPA), designed to manage intra-cluster pilot assignments by minimizing interference among the most contaminating UEs. We evaluated the performance of our proposed solution (K-beams+BFPA) in terms of sum-normalized mean-squared error (NMSE) and sum-rate under various challenging network setups. The simulation results show that our approach is a robust alternative to more computationally demanding benchmarks.

The deployment of intelligent reflecting surfaces (IRSs) to assist multiple-input multiple-output (MIMO) communications stands as a promising step in overcoming some of the limitations of current vehicular communication systems. In this paper, we focus on the joint optimization of the IRS and the precoders in the uplink of an IRS-assisted MIMO communication established between several connected vehicles and a roadside unit (RSU). We consider a realistic imperfect channel state information (ICSI) model and propose several adjustments to the deep contextual bandit-oriented deep deterministic policy gradient (DCB-DDPG) framework to address this complex optimization problem. Simulation results show that the proposed solution is a robust alternative because it keeps learning from interactions, even when the estimation error statistics are unknown.

We consider the transmission of spatially correlated analog information in a wireless sensor network (WSN) through fading single-input and multiple-output (SIMO) multiple access channels (MACs) with low-latency requirements. A lattice-based analog joint source-channel coding (JSCC) approach is considered where vectors of consecutive source symbols are encoded at each sensor using an$n$-dimensional lattice and then transmitted to a multiantenna central node. We derive a minimum mean square error (MMSE) decoder that accounts for both the multidimensional structure of the encoding lattices and the spatial correlation. In addition, a sphere decoder is considered to simplify the required searches over the multidimensional lattices. Different lattice-based mappings are approached and the impact of their size and density on performance and latency is analyzed. Results show that, while meeting low-latency constraints, lattice-based analog JSCC provides performance gains and higher reliability with respect to the state-of-the-art JSCC schemes.