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The virtual multiple input multiple output (MIMO) technique can dramatically improve the performance of a multi-cell distributed antenna system (DAS), thanks to its great potentials for inter-cell interference mitigation. One of the most challenging issues for virtual MIMO is the acquisition of channel state information at the transmitter (CSIT), which usually leads to an overwhelming amount of system overhead. In this work, we focus on the case that only the slowly-varying large-scale channel state is required at the transmitter, and explore the performance gain that can be achieved by coordinated transmissions for virtual MIMO with large-scale CSIT. Aiming at maximizing the achievable ergodic sum rate, the input covariances for all the mobile terminals (MTs) are jointly optimized, which turns out to be a complicated non-convex problem with a non-closed-form objective function. Further analysis reveals that the coordinated transmission problem can be recast as a Max-Min problem with a closed-form objective function and linear constraints. Then, by appealing to the successive approximation method and the saddle-point theory of concave-convex functions, we propose an iterative algorithm for coordinated transmissions with large-scale CSIT and establish its convergence. Simulation results corroborate that the proposed scheme converges quickly, and it yields significant performance gains compared to the existing schemes. Moreover, it is observed that the proposed scheme can achieve a nearly globally-optimal point under the diagonal input covariance constraint. Since the acquisition of large-scale CSIT is far less demanding than that of full CSIT, we believe that the proposed coordinated transmissions with large-scale CSIT in DASs shed some light on virtual MIMO in the making.
Feng et al. (Tue,) studied this question.