Cooperative beamforming for the MISO interference channel

Abstract

Abstract-A distributed beamforming algorithm is proposed for the two-user multiple-input single-output (MISO) interference channel (IFC). The algorithm is iterative and uses as bargaining value the interference that each transmitter generates towards the receiver of the other user. It enables cooperation among the transmitters in order to increase both users' rates by lowering the overall interference. In every iteration, as long as both rates keep on increasing, the transmitters mutually decrease the generated interference. They choose their beamforming vectors distributively, solving the constrained optimization problem of maximizing the useful signal power for a given level of generated interference. The algorithm is equally applicable when the transmitters have either instantaneous or statistical channel state information (CSI). The difference is that the core optimization problem is solved in closed-form for instantaneous CSI, whereas for statistical CSI an efficient solution is found numerically via semidefinite programming. The outcome of the proposed algorithm is approximately Pareto-optimal. Extensive numerical illustrations are provided, comparing the proposed solution to the Nash equilibrium, zero-forcing, Nash bargaining, and maximum sum-rate operating points.

Beamforming Design Based on Virtual SINR Maximization for Interference Networks

“…Compared to the centralized scheme presented in the previous section, A l is replaced bŷ A l which depends only on v l and {h k,l , ∀k}. Now, we compare the proposed distributed scheme with the conventional distributed algorithms in [8] and [9]. In these schemes, transmitter l computes its beamformer as a function of {h k,l , P I k , ∀k} where P I k = k =l |h H k,l v l | 2 is the interference power at receiver k. Since P I k is a function of all beamformers, the receivers should report their interference power to the transmitters until convergence.…”

Multicell Interference Mitigation with Joint Beamforming and Common Message Decoding

“…As shown in [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], such a beamforming level coordination can already provide considerable gain as compared to an uncoordinated system. The present paper goes one step further in pointing out that even larger performance gain is possible if the beamformers are explicitly designed to account for the possibility of interference subtraction.…”

Distributed weighted sum-rate maximization in multicell MU-MIMO OFDMA downlink

“…In [9], the authors make high signal-to-interference-plus-noise ratio (SINR) approximation to decouple the WSRM problem which involves the beamforming vectors of all BSs into a distributed WSRM problem as a function of local channel state information (CSI), and then solve each decoupled problem by employing a zero-gradient based algorithm. Furthermore, the distributed solutions proposed in [10] and [11] for WSRM are not fully distributed in a sense that at each iteration the BSs have to notify their interference power that depend on other usersf beamformers, and a single user is served per BS in these schemes. However, all these iterative WSRM optimization designs are for a single career system with the users equipped with a single antenna.…”

“…In the iterative procedure, each BS optimizes its own beamformers considering the beamformers used by other BSs as fixed, while keeping the optimization of the May 1, 2018 DRAFT weighted sum-rate of the whole system as a global perspective. Unlike [10], [11], our solution does not require the BSs to report the interference powers at each iteration, and therefore, substantially reduces the system overhead.…”