Joint iterative beamforming and power adaptation for MIMO ad hoc networks

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.

Power-efficient spatial multiplexing for multiantenna MANETs

“…We prove the existence of a NE and the convergence of the algorithm to the NE. Simulation results show that the algorithm significantly conserves total power consumption in the network, compared with existing methods, e.g., those in [5] [8] [3] [7]. Our algorithm can be implemented distributively without incurring additional signalling overhead.…”

“…The first method was developed similarly to that in [5] [7], where no pricing function was used and the precoding matrices were obtained from problem (4) by using an iterative water-filling algorithm (thus, we name this method as IWF). Note that the performance of IWF should be superior to that of [8] as IWF jointly solves the power allocation and beamforming problems while [8] addressed these problems sequentially. The second method is from [3], in which nodes were assumed to be fullduplex (hence, we refer to this method as FD) and the precoding matrices were found to satisfy the data rate requirement in the both forward and backward directions.…”

“…Using this price function, we propose a price-based iterative water-filling (PIWF) algorithm that minimizes not only the required transmit power but also the network interference. PIWF algorithm conserves more than 53% and 47% total transmission power, compared with the power-efficient precoding method in [3] (for convergent cases) and the greedy method in [5] [8], respectively. Our new definition of network interference allows the price function at a node is calculated locally by taking advantage of the symmetry in the channel gain.…”

“…Moreover, for convergent cases, they are surprisingly less power-efficient than the greedy algorithm. In [8], the power minimization problem was addressed for half-duplex MIMO nodes with beamforming technique. The channel in [8] is not reciprocal, hence transmit beamformers are selected from a finite set of codewords (codebook) to ease feedback requirement.…”

“…In [8], the power minimization problem was addressed for half-duplex MIMO nodes with beamforming technique. The channel in [8] is not reciprocal, hence transmit beamformers are selected from a finite set of codewords (codebook) to ease feedback requirement. The problem of power allocation and beamforming were treated in a sequential order.…”