Weighted-Sum-Rate-Maximizing Linear Transceiver Filters for the K-User MIMO Interference Channel

Abstract

This letter is concerned with transmit and receive filter optimization for the K-user MIMO interference channel. Specifically, linear transmit and receive filter sets are designed which maximize the weighted sum rate while allowing each transmitter to utilize only the local channel state information.Our approach is based on extending the existing method of minimizing the weighted mean squared error (MSE) for the MIMO broadcast channel to the K-user interference channel at hand. For the case of the individual transmitter power constraint, however, a straightforward generalization of the existing method does not reveal a viable solution. It is in fact shown that there exists no closed-form solution for the transmit filter but simple one-dimensional parameter search yields the desired solution. Compared to the direct filter optimization using gradient-based search, our solution requires considerably less computational complexity and a smaller amount of feedback resources while achieving essentially the same level of weighted sum rate. A modified filter design is also presented which provides desired robustness in the presence of channel uncertainty.

Decentralized Linear Transceiver Design in Multicell MIMO Broadcast Channels

“…The latter form of E nk in (21) shows that the rate function given by (17) can be equivalently expressed as…”

“…Proof 4: Please refer to Appendix C. In (29), the λ n , ∀n ∈ M, are calculated to satisfy the power constraint at BS n , ∀n ∈ M, by using the KKT condition λ n k∈Kn Tr(T nk T H nk ) − p n = 0 and by utilizing the fact that the transmit power is monotonically decreasing with respect to increasing λ n [21]. The closed-form solution can be obtained by readapting the approach shown in [20] as…”

“…To solve either problem of P WSRP , P WSRM , or P WSRH , an algorithm based on alternating optimization can be used [21]- [25]. The basic idea is to optimize each problem with respect to one variable at a time, while keeping the rest of the variables fixed.…”

“…However, since the original problems P WSRP , P WSRM , and P WSRH are nonconvex, a globally optimal point cannot be found, in general, via alternating optimization. Moreover, different initializations and iteration orders may converge to different local WSRoptima [21]- [25].…”

“…As a result, an iterative algorithm called WSR-WMMSE was proposed, which is based on the alternating optimization technique [26] and solves the quite hard WSR maximization problem indirectly by solving the easier WMMSE minimization problem. This later relation has inspired many extensions, such as to the multicell MIMO-IC [21] and to the multicell MIMO-BC [22]- [25].…”