Weighted sum rate optimization for downlink multiuser MIMO coordinated base station systems: Centralized and distributed algorithms
(2012) IEEE Transactions on Signal Processing — Vol. 60, n° 4, p. 1876-1889 (2012)
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- Abstract
- This paper considers the joint linear transceiver design problem for the downlink multiuser multiple-input-multiple-output (MIMO) systems with coordinated base stations (BSs). We consider maximization of the weighted sum rate with per BS antenna power constraint problem. We propose novel centralized and computationally efficient distributed iterative algorithms that achieve local optimum to the latter problem. These algorithms are described as follows. First, by introducing additional optimization variables, we reformulate the original problem into a new problem. Second, for the given precoder matrices of all users, the optimal receivers are computed using minimum mean-square-error (MMSE) method and the optimal introduced variables are obtained in closed form expressions. Third, by keeping the introduced variables and receivers constant, the precoder matrices of all users are optimized by using second-order-cone programming (SOCP) and matrix fractional minimization approaches for the centralized and distributed algorithms, respectively. Finally, the second and third steps are repeated until these algorithms converge. We have shown that the proposed algorithms are guaranteed to converge. We also show that the proposed algorithms require less computational cost than that of the existing linear algorithm. All simulation results demonstrate that our distributed algorithm achieves the same performance as that of the centralized algorithm. Moreover, the proposed algorithms outperform the existing linear algorithm. In particular, when each of the users has single antenna, we have observed that the proposed algorithms achieve the global optimum. © 2011 IEEE.
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Bogale, T. E., & Vandendorpe, L. (2012). Weighted sum rate optimization for downlink multiuser MIMO coordinated base station systems: Centralized and distributed algorithms. IEEE Transactions on Signal Processing, 60(4), 1876-1889. https://doi.org/10.1109/TSP.2011.2179538 (Original work published 2012)