Efficient resource allocation plays a critical role in wireless communication systems. Many resource allocation problems can be formulated into constrained optimization problems, and when the problem is convex, there are many efficient iterative algorithms to find the optimal solution that yields the desired control policy. In the literature, most of the convergence results of these algorithms were established under the assumption that all the problem specific parameters are time invariant. However, such assumption is unrealistic in wireless systems, because the communication systems usually operate in a time-varying environment, where the system parameters may be changing in a very short timescale. Moreover, message passing is usually involved at each iteration among wireless n...[ Read more ]

Efficient resource allocation plays a critical role in wireless communication systems. Many resource allocation problems can be formulated into constrained optimization problems, and when the problem is convex, there are many efficient iterative algorithms to find the optimal solution that yields the desired control policy. In the literature, most of the convergence results of these algorithms were established under the assumption that all the problem specific parameters are time invariant. However, such assumption is unrealistic in wireless systems, because the communication systems usually operate in a time-varying environment, where the system parameters may be changing in a very short timescale. Moreover, message passing is usually involved at each iteration among wireless nodes. As a result, the iterative algorithms may not be able to converge fast enough to catch up with the effects of parameter variations. Since the algorithm convergence error may significantly affect the system performance, it is highly important to understand the convergence behavior of iterative algorithms under time-varying parameters.

In this thesis, a general convergence analysis framework for gradient-based iterative algorithms under time-varying parameters is developed. The dynamics of the convergence errors are modeled by a family of nonlinear virtual dynamical systems (VDS), and the VDS can be viewed as an asymptotically stable dynamical system being disturbed by exogenous force due to by parameter variations. It is shown that studying the algorithm convergence is equivalent to studying the stability of the VDS. Following this result, various techniques from the control theory are applied to study the VDS, and the algorithm convergence errors under time-varying parameters are characterized. With the insight that stabilizing the VDS improves the convergence performance, a family of adaptive compensation algorithms are derived. The compensation algorithm demonstrates a significant performance advantage from both analytical and numerical results.