Abstract
In this paper, we present distributed cooperative and regret-matching-based learning schemes for joint transmit power and beamforming selection for multiple antenna wireless ad hoc networks operating in a multi-user interference environment. Under the total network power minimization criterion, a joint iterative approach is proposed to reduce the mutual interference at each node while ensuring a constant received signal-to-interference and noise ratio at each receiver. In cooperative and regret-matching-based power minimization algorithms, transmit beamformers are selected from a predefined codebook to minimize the total power. By selecting transmit beamformers judiciously and performing power adaptation, the cooperative algorithm is shown to converge to a pure strategy Nash equilibrium with high probability in the interference impaired network. The proposed cooperative and regret-matching-based distributed algorithms are also compared with centralized solutions through simulation results.
Highlights
Multiple-input multiple-output (MIMO) communication techniques have been shown to boost the capacity and spectral efficiency of wireless communication systems [1,2]
7 Conclusion In this paper, we have considered both cooperative and noncooperative joint power control and beamforming in MIMO ad hoc networks using a game theoretic approach
Under constant SINR requirements, the joint transmit beamformer and power selection algorithms were studied in the context of total network power minimization
Summary
Multiple-input multiple-output (MIMO) communication techniques have been shown to boost the capacity and spectral efficiency of wireless communication systems [1,2]. Distributed spatial beamforming algorithms are proposed for multi-user ad hoc MIMO networks in [23,24] under channel reciprocity conditions. In order to lower the communication overhead between transmitter and receiver when channel reciprocity does not hold, a scheme to limit feedback by quantizing the transmit beamformer in single user MIMO systems is proposed in [21]. Latency is reduced in highly mobile and unstable communication networks and user participation is minimized In this scenario, the receiver only feeds back the index of the selected transmit beamformer to the transmitter. The algorithm maximizes the transmission rate in MIMO multi-user ad hoc networks using sequential discrete transmit beamformer selection updates. Designing an optimal distributed algorithm which converges to a set of beamformers to minimize the overall transmit power while meeting target SINRs for all node pairs is not a straightforward task. We will discuss the scenarios where the node pairs are cooperative and noncooperative respectively in order to search for the best results and provide convergence guarantees
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