Next generation wireless systems shall satisfy the increasing demand of higher and higher data rates at very competitive prices as well as be able to efficiently accommodate for and adapt to a huge dynamic range of services, applications, and types of devices expected in the near future. Appealing architectural solutions have been leveraged on ultra-densification of antennas. Ultra-dense (UD) wireless systems envision ultra-dense distributed antenna systems (UD-DAS) based on remote distributed antennas empowered by the e-cloud for a centralized processing. However, neither DAS nor massive MIMO technology will meet the increasing data rate demands of the next generation wireless communications due to the inter-cell interference and large quality of service (QoS) variations. To address these limitations and provide uniformly service to all the users, beyond-5G networks need to enter the cell-free (CF) paradigm, where the absence of cell boundaries mitigates the inter-cell interference and handover issues but also causes new challenges.
In this thesis, we consider a CF massive MIMO system in Uplink comprising a massive number of geographically distributed access points (APs) jointly and coherently serving a smaller number of users distributed over a wide area on the same time-frequency resources. One of the major issues in large-scale networks such as CF massive MIMO systems is complexity at the receivers. In centralized massive MIMO systems, the phenomenon of favorable propagation has been observed when the number of receive antennas tends to infinity while the number of transmit antennas remains finite, the users’ channels become almost orthogonal and low complexity detection via matched filtering is almost optimal. In this regard, the first part of this thesis is devoted to analyzing the favorable propagation property of CF massive MIMO systems through the characteristics of the channel eigenvalue moments in asymptotic conditions when the network dimensions go to infinity with given intensities of the transmit and receive antenna point processes (PP). The performance of CF massive MIMO systems is critically affected by the so-called pilot contamination. This impairment originates from the reuse of training sequences or pilots utilized in the channel estimation, which prevents the possibility of obtaining an adequate estimate of the channel state information (CSI). Therefore, in the second part of this thesis, we study semi-blind joint channel estimation and data detection methods for exploiting the sparsity of the channel support in CF massive MIMO systems to combat pilot contamination. An extensive attention has been dedicated to the design of detectors relying on message passing (MP) algorithms in recent years. MIMO detections based on expectation propagation (EP) which is also a kind of MP algorithm, obtains near-optimal performance with acceptable complexity, under specific conditions. Finally, in the last part of this thesis, we propose an MP algorithm based on the EP principle to iteratively conduct the Bayesian semi-blind method for channel estimation and data detection in CF massive systems.