In the realm of wireless communications, geographically distributing the serving antennas is pivotal to harness the full potential of multiple-input multiple-output (MIMO) technology. By strategically positioning the network's transmitters, herein called access points (APs), in close proximity to users, we strengthen the desired signal while capitalizing on macro diversity and cooperation. Numerous architectures have been proposed for distributing the antennas and setting cooperation. Herein, we focus on the emerging network scheme called the user-centric cell-free massive MIMO (UC-mMIMO) network.

In the first part of this thesis, we consider the problem of placing the APs through a wireless fronthaul. We analyze two possible MIMO fronthaul solutions:​ multicast and zero-forcing beamforming. Using these solutions, we develop a statistical model that maximizes the achievable rate on the access channel while respecting a fronthaul outage constraint. In this part, we focus on a cell-centric clustering scheme.

In the second part, we handle the problem of user-scheduling and beamforming in the UC-mMIMO scheme. We propose two different paradigms for resource allocation. The first is centralized resource allocation scheme where user-scheduling and beamforming are performed at a central unit (CU) located at the network core. The second is distributed or decentralized resource allocation with two variants of decentralization;​ which we denote as the AP-distributed system and the CU-distributed system.

In the third part, we study the management of handoffs (HOs) within the UC-mMIMO network scheme. Due to the user-centric clustering and the dense deployment of the APs, a moving user may frequently encounter HOs, where HOs entail the addition/removal of APs to/from the serving set of the user. This potentially could affect the system performance. We propose two distinct methodologies for the management of HOs within the UC-mMIMO context. The first approach involves modeling HOs as a partially observable Markov decision process (POMDP), where we adopt a divide-and-conquer approach to obtain a HO policy using the value iteration algorithm. While the second approach harnesses the power of reinforcement learning, where we use deep reinforcement learning (DRL) to construct a deep neural network (DNN)-based HO policy that exhibits real-time control and low computational complexity.