Study of Clustering Techniques and Scheduling Algorithms with Fairness for Cell-Free MIMO Networks

In this work, we propose a clustering technique based on information rates for cell-free massive multiple-input multiple-output (MIMO) networks. Unlike existing clustering approaches that rely on the large scale fading coefficients of the channels and user-centric techniques, we develop an approach that is based on the information rates of cell-free massive MIMO networks. We also devise a resource allocation technique to incorporate the proposed clustering and schedule users with fairness. An analysis of the proposed clustering approach based on information rates is carried out along with an assessment of its benefits for scheduling. Numerical results show that the proposed techniques outperform existing approaches.

Sequential Multiuser Scheduling and Power Allocation for Cell-Free Multiple-Antenna Networks

Resource allocation is a fundamental task in cell-free (CF) massive multi-input multi-output (MIMO) systems, which can effectively improve the network performance. In this paper, we study the downlink of CF MIMO networks with network clustering and linear precoding, and develop a sequential multiuser scheduling and power allocation scheme. In particular, we present a multiuser scheduling algorithm based on greedy techniques and a gradient ascent {(GA)} power allocation algorithm for sum-rate maximization when imperfect channel state information (CSI) is considered. Numerical results show the superiority of the proposed sequential scheduling and power allocation scheme and algorithms to existing approaches while reducing the computational complexity and the signaling load.

Study of MMSE-Based Resource Allocation for Clustered Cell-Free Massive MIMO Networks

In this paper, a downlink cell-free massive multiple-input multiple-output (CF massive MIMO) system and a network clustering is considered. Closed form sum-rate expressions are derived for CF and the clustered CF (CLCF) networks where linear precoders included zero forcing (ZF) and minimum mean square error (MMSE) are implemented. An MMSE-based resource allocation technique with multiuser scheduling based on an enhanced greedy technique and power allocation based on the gradient descent (GD) method is proposed in the CLCF network to improve the system performance. Numerical results show that the proposed technique is superior to the existing approaches and the computational cost and the signaling load are essentially reduced in the CLCF network.