Dynamic resource allocation in multiuser multicarrier fading environments

Interference among users results in the degradation of signal quality and leads to poor performance in multi-user interference systems such as DSL system, ad-hoc wireless network. Study of the interaction of interference among users in the network is crucial for achieving better system performance. Efficient utilization of available resources in an interference system has two major complementary approaches: interference cancellation and resource allocation. The principal objective of this thesis is to develop efficient resource allocation algorithms that can optimize joint perfounance of multiple users in multi-user multi-carrier interference channels. Such resource allocation problems in multi-user interference channels can be formulized as nonconvex optimization problems. The search for the optimal solution is very challenging for these problems: conventional local optimization techniques are only capable of finding local optimum; In addition, resource allocation problems encountered in practice generally are large-scale ones, as the number of users or the number of sub-carriers in the system is typically large. For instance, digital subscriber lines (DSL) system, which is employed as a motivating practical model in this dissertation, has thousands of sub-carriers and large number of lines residing in a binder that cause interference to each other. Designing distributed and centralized resource allocation strategies to achieve good optimality and complexity tradeoff for multi-user interference systems by exploiting the underlining problem structures is the main focus of this thesis. This thesis develops two dynamic resource allocation strategies, responding to different requirements that might arise in various practical scenarios of DSL environment: one is a low-complexity, quasi-distributed algorithm that achieves near-optimal performance with very little centralized coordination; the other is a centralized algorithm based on global difference of convex (d.c.) optimization that guarantees optimal performance with substantial complexity reduction. Our global d.c. approach reveals the hidden convexity of nonconvex optimization problems in interference systems, which were once thought of completely being devoid of any convexity structure. The d.c. structure provides a general framework for designing efficient global optimization algorithm for resource allocation in multi-user interference channel. In particular, a modified prismatic branch and bound (PBnB) is proposed to find global optimum efficiently and its global convergence is also established by analysis. Furthermore, motivated by the observation that the excessive transmission power penalty incurred by zero forcing (ZF) precoding scheme with user selection algorithm for MIMO Broadcast (BC) channels contributing to sum-rate capacity loss, we also explore joint power allocation and interference pre-cancellation (precoding) for wireless MIMO BC channels. A channel inversion regularization (CIR) strategy is proposed to replace ZF as the precoding scheme to alleviate the excessive transmission power penalty and we formulate the maximization of the sum-rate capacity with CIR precoding into d.c. structure and thereafter its global optimum can be achieved with PBnB algorithm. Moreover, we propose a local optimization method based on gradient projection (GP) to achieve near-optimal solution efficiently and we also conduct asymptotic analysis to show that the proposed CIR precoding scheme can achieve asymptotically optimum sum rate equal to that of dirty paper coding (DPC) strategy.

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