  Network management is a fundamental ingredient for efficient operation of
wireless networks. With increasing bandwidth, number of antennas and number of
users, the amount of information required for network management increases
significantly. Therefore, distributed network management is a key to efficient
operation of future networks. This paper focuses on the problem of distributed
joint beamforming control and power allocation in ad-hoc mmWave networks. Over
the shared spectrum, a number of multi-input-multi-output links attempt to
minimize their supply power by simultaneously finding the locally optimal power
allocation and beamformers in a self-organized manner. Our design considers a
family of non-convex quality-of-service constraint and utility functions
characterized by monotonicity in the strategies of the various users. We
propose a two-stage, decentralized optimization scheme, where the adaptation of
power levels and beamformer coefficients are iteratively performed by each
link. We first prove that given a set of receive beamformers, the power
allocation stage converges to an optimal generalized Nash equilibrium of the
generalized power allocation game. Then we prove that iterative
minimum-mean-square-error adaptation of the receive beamformer results in an
overall converging scheme. Several transmit beamforming schemes requiring
different levels of information exchange are also compared in the proposed
allocation framework. Our simulation results show that allowing each link to
optimize its transmit filters using the direct channel results in a near
optimum performance with very low computational complexity, even though the
problem is highly non-convex.
