Weighted sum MSE minimization under per-BS power constraint for network MIMO systems

We study joint processing (JP) for network MIMO systems where base stations exchange the user's message and channel state information under per-BS power constraint. In this letter, we propose a weighted sum mean square error (WS-MSE) minimization algorithm for the JP systems by considering the channel gain as the weight factor in the MSE metric. To efficiently solve the formulated WS-MSE problem, an alternating optimization method which iteratively finds a local optimal solution is employed in our algorithm. The simulation results confirm that the proposed algorithm provides the sum rate performance close to the near-optimal gradient ascent approach and outperforms conventional schemes. In addition, we also propose a modified WS-MSE design which is robust to channel mismatch caused by channel estimation and feedback errors.