Distributed Fronthaul-Constrained Joint Transmission Design and Selection Using Augmented Consensus-based Dual Decomposition

User-centric coordinated multipoint (CoMP) joint transmission (JT) is a novel technique to manage interference and enhance system performance with single frequency reuse, where user equipment (UE) communicates with their closest transmission points (TPs). Unfortunately, in coherent JT, requirement of strict network synchronization accuracy makes it difficult and expensive to be practically deployed. The noncoherent JT has therefore received growing attention since it requires less strict network synchronization accuracy compared to its coherent counterpart as it does not require the signal to be phase-aligned at the receiver. Moreover, cell-free massive MIMO, which is regarded as combining CoMP with massive MIMO systems, has recently been touted as a solution for avoiding intercell interference and provide uniform coverage over a large area. However, the operational costs of CoMP, such as the associated control signaling and communication overhead and the increase of network complexity, could prevent the practical implementation of CoMP. A distributed joint transmission CoMP (JT-CoMP) scheme is proposed herein that allows distributed design and selection of cooperating transmission nodes. To maximize user capacity, the proposed distributed consensus optimization problem assumes spectrum underlay transmission is used so that the solution can achieve non-orthogonal multiple access (NOMA) for cell-free user centric JT-CoMP systems. The proposed algorithm is different from others in the literature because it solves a design problem that involves a coupling constraint that no existing algorithm can solve. Analytical results based on spectral graph theory are given to prove its convergence and characterize its rate of convergence. The more practical scenario is further considered, where limited fronthaul capacity is also included in the problem. A successive convex approximation (SCA) method is used to solve the resulting nonconvex problem, which is shown to maximize spectral efficiency. Simulation results are provided to show that the performance of both proposed distributed algorithms (that address problem without and with fronthaul constraint) is comparable to its centralized counterpart.