High rate signal processing schemes for correlated channels in 5G networks.

Fifth generation (5G) cellular networks must support dense user environments where close proximity users and their associated highly correlated channel vectors are prevalent. Therefore, this thesis considers a multi-user multiple-input multiple-output (MU-MIMO) communication system, where both highly correlated users and semiorthogonal users are present. Specifically, this thesis provides a performance analysis of several established and novel linear signal processing schemes for users with highly correlated channels. The main focus of this thesis is divided into two scenarios. First, we have proposed two novel schemes namely decorrelating zero forcing (DZF) and hybrid zero forcing (HZF), which are robust in user environments where highly correlated users are present. DZF simply decorrelates the channels of two highly correlated users exploiting the advantage of mutual orthogonality of eigenvectors of highly correlated users, i.e., DZF employs the first and second eigenvectors while designing the precoders for two highly correlated users. DZF is as simple as conventional zero forcing (CZF), but achieves higher rates in highly correlated channel environments. Analysis and numerical results are presented for MU-MIMO systems demonstrating the impact of the DZF scheme, which improves user rates and provides fairness while scheduling the highly correlated users. Then, a more robust HZF scheme is designed by integrating the CZF and DZF schemes. In CZF, semiorthogonal users are scheduled to achieve higher sum-rate, meaning that fairness among correlated users is compromised. HZF allows us to harness the advantages of both CZF and DZF, hence providing robustness against the joint scheduling of semi-orthogonal and highly correlated users with very little additional complexity. Second, we studied the existing downlink non-orthogonal multiple access zero-