  Downlink beamforming techniques with low signaling overhead are proposed for
joint processing coordinated (JP) multi-point transmission. The objective is to
maximize the weighted sum rate within joint transmission clusters. As the
considered weighted sum rate maximization is a non-convex problem, successive
convex approximation techniques, based on weighted mean-squared error
minimization, are applied to devise algorithms with tractable computational
complexity. Decentralized algorithms are proposed to enable JP even with
limited backhaul connectivity. These algorithms rely provide a variety of
alternatives for signaling overhead, computational complexity and convergence
behavior. Time division duplexing is exploited to design transceiver training
techniques for two scenarios: stream specific estimation and direct estimation.
In the stream specific estimation, the base station and user equipment estimate
all of the stream specific precoded pilots individually and construct the
transmit/receive covariance matrices based on these pilot estimates. With the
direct estimation, only the intended transmission is separately estimated and
the covariance matrices constructed directly from the aggregate system-wide
pilots. The impact of feedback/backhaul signaling quantization is considered,
in order to further reduce the signaling overhead. Also, user admission is
being considered for time-correlated channels. The enhanced transceiver
convergence rate enables periodic beamformer reinitialization, which greatly
improves the achieved system performance in dense networks.
