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 wellsuited to emerging use cases characterized by short channel coherence intervals and high number of connected user equipments (UEs). In this work, we propose a novel approach for intracell 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 Kbeams 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 evaluate 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. Simulation results show that our approach is a robust alternative to more computationally demanding benchmarks.

Efficient resource allocation strategies are pivotal in vehicular communications as connected devices steeply increase in scenarios with much more stringent requirements. In this work, we propose a deep reinforcement learning (DRL)-based sequential scheduling approach for sum-rate maximization in the uplink of intelligent reflecting surface (IRS)-assisted multiuser (MU) multiple-input multiple-output (MIMO) vehicular communications. We formulate the scheduling task as a partially observable Markov decision process (POMDP) and propose a novel stream-level sequential solution based on the proximal policy optimization (PPO) algorithm. We consider a realistic imperfect channel state information (ICSI) model and assess the proposal in several communication setups comprising both spatially uncorrelated and correlated links. Simulation results show that the proposed DRL-based sequential scheduling approach is a robust alternative to more computationally demanding benchmarks.

The combination of multiple-input multiple-output (MIMO) and intelligent reflecting surfaces (IRSs) is foreseen as a key enabler of beyond 5G (B5G) and 6G. In this work, two different approaches are considered for the joint optimization of the IRS phase-shift matrix and MIMO precoders of an IRS-assisted multi-stream (MS) multi-user MIMO (MU-MIMO) system with the aim of maximizing the system sum-rate for every channel realization. The first one is a novel contextual bandit (CB) approach with continuous state and action spaces called deep contextual bandit-oriented deep deterministic policy gradient (DCB-DDPG). The second is an innovative deep reinforcement learning (DRL) formulation where the states, actions and rewards are selected such that the Markov decision process (MDP) property of reinforcement learning (RL) is properly met. Both proposals perform remarkably better than state-of-the-art heuristic methods in high multi-user interference scenarios.

The deep reinforcement learning (DRL)-based deep deterministic policy gradient (DDPG) framework is proposed to solve the joint optimization of the IRS phase-shift matrix and the precoding matrix in an IRS-assisted multi-stream multi-user MIMO communication. The combination of multiple-input multiple-output(MIMO) communications and intelligent reflecting surfaces(IRSs) is foreseen as a key enabler of beyond 5G (B5G) and 6Gsystems. In this work, we develop an innovative deep reinforcement learning (DRL)-based approach to the joint optimization of the MIMO precoders and the IRS phase-shift matrices that is proved to be efficient in high dimensional systems. The proposed approach is termed deep deterministic policy gradient (DDPG)and maximizes the sum rate of an IRS-assisted multi-stream(MS) multi-user MIMO (MU-MIMO) system by learning the best matrix configuration through online trial-and-error interactions. The proposed approach is formulated in terms of continuous state and action spaces, and a sum-rate-based reward function. The computational complexity is reduced by using artificial neural networks (ANNs) for function approximations and it is shown that the proposed solution scales better than other state-of-the-art methods, while reaching a competitive performance.