Optimal Resource Allocation for MIMO Ad Hoc Cognitive Radio Networks

Abstract

Abstract-Maximization of the weighted sum-rate of secondary users (SUs) possibly equipped with multiantenna transmitters and receivers is considered in the context of cognitive radio (CR) networks with coexisting primary users (PUs). The total interference power received at the primary receiver is constrained to maintain reliable communication for the PU. An interference channel configuration is considered for ad hoc networking, where the receivers treat the interference from undesired transmitters as noise. Without the CR constraint, a convergent distributed algorithm is developed to obtain (at least) a locally optimal solution. With the CR constraint, a semidistributed algorithm is introduced. An alternative centralized algorithm based on geometric programming and network duality is also developed. Numerical results show the efficacy of the proposed algorithms. The novel approach is flexible to accommodate modifications aiming at interference alignment. However, the stand-alone weighted sum-rate optimal schemes proposed here have merits over interference-alignment alternatives especially for practical SNR values.Index Terms-Ad hoc network, cognitive radio, interference network, MIMO, optimization.

Sum-rate maximization in the multicell MIMO broadcast channel with interference coordination

“…Note that f q (Q q , Q −q ) is a convex function with respect to Q q ∈ S q {{Q qi }|Q qi ≽ 0, ∑ K i=1 Tr{Q qi } ≤ P q } [7], [9]. Thus, the first-order condition of the convex function f q [28] dictates that…”

“…Different numerical methods for designing the precoders that maximize this WSR have been investigated in the literature [7]- [9]. Specifically, the gradient projection method was applied in [7] to search for a locally optimal transmit strategy.…”

“…Specifically, the gradient projection method was applied in [7] to search for a locally optimal transmit strategy. Successive convex approximation was applied in [8], [9] to decompose the original nonconvex problem into a sequence of simpler convex problems, which can be solved separately at the transmitters. Note that these works only considered the network with one MS per cell.…”

“…The main focus of this work is to jointly optimize the precoding covariance matrices at the BSs in order to maximize the network-wide WSR under the IC mode. While most of the works considered linear precoding at each BS for the multicell MIMO-BC system [3]- [9], our focus in this paper is on nonlinear precoding design. Specifically, in the BC with multiple MSs per cell, each BS utilizes dirty paper coding (DPC) to encode the data for the MSs within its cell.…”

“…Thus, one can obtain the globally optimal solution to problem (19) by adopting the approach in solving problem (9). The difference is in the change of variables whereQ i = (A+λI)…”