Abstract
User-centric base station (BS) cooperative transmission strives to satisfy the quality of service of each user no matter where the user is located. The resulting user-dependent cooperative clusters are inevitably overlapped. To minimize the mean square error of channel estimation assisting user-centric downlink cooperative transmission, the training signals sent from the BSs in each cluster or from the users selecting the same BS in their clusters should be mutually orthogonal. In this paper, we study the orthogonal training resource-allocation problem for user-centric cooperative network aiming at minimizing the overall training overhead. We find the optimal solution through a graph-theoretic approach. To provide a feasible solution for large-scale networks, a low-complexity algorithm is then proposed. Simulation results show that the algorithm performs closely to the optimal solution, and both provide remarkably higher net throughput than the system with fixed clustering.
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