There is a surge of interest in efficient resource control for future wireless communication and control systems. Different applications, such as video streaming, energy harvesting, and plant stabilization and optimizations, pose different levels of challenges to the resource control problem. There are many existing literatures on cross-layer resource control to improve the physical layer performance, such as maximization of throughput and SINR, and minimization of bit error rate and mean square error. However, these solution frameworks cannot be extended to solve the above problems, because the optimization objectives therein are the physical layer metrics which may not be directly related to the application level performance metrics in different application scenarios. Furthe...[ Read more ]

There is a surge of interest in efficient resource control for future wireless communication and control systems. Different applications, such as video streaming, energy harvesting, and plant stabilization and optimizations, pose different levels of challenges to the resource control problem. There are many existing literatures on cross-layer resource control to improve the physical layer performance, such as maximization of throughput and SINR, and minimization of bit error rate and mean square error. However, these solution frameworks cannot be extended to solve the above problems, because the optimization objectives therein are the physical layer metrics which may not be directly related to the application level performance metrics in different application scenarios. Furthermore, the resulting control policy is adaptive to the channel state information (CSI) only, which exploits good transmission opportunities from the time-varying physical channels. For real-time multimedia streaming, dynamic control policy adaptive to the instantaneous data queue length (DQSI) is also very important because it gives information about the urgency of the data flows. For energy harvesting, dynamic control policy should be adaptive to the instantaneous energy queue length (EQSI), which gives information about the availability information of the renewable energy. For plant optimization, dynamic control policy should be adaptive to the instantaneous plant state information, which gives information about the urgency of delivering the plant state to the remote controller.

In this thesis, we consider the following four problems: 1) multi-antenna transceiver optimization for multimedia streaming in interference networks, 2) optimal beamforming for video streaming in multi-antenna interference networks via diffusion limit, 3) power control for energy harvesting wireless system with finite energy storage, and 4) power management for networked control systems over correlated wireless fading channels. We formulate the associated stochastic optimization problems as infinite horizon average cost Markov decision process (MDP) problems. It is well-known that conventional solutions, such as value iteration and policy iteration algorithms, have exponential complexity and give no design insights. To obtain low complexity and insightful solutions, we propose a continue-time perturbation (CTP) approach by exploiting the theories of dynamic programming and differential equations. Briefly speaking, CTP approach offers a continuous-time approximation to the original discrete-time system and we solve an approximate equivalent problem in the continuos-time domain to obtain low complexity solutions. However, CTP approach only offers a framework for transforming the original discrete-time problem into a continuous-time problem and it still is a case-by-case problem for solving the associated multi-dimensional partial differential equation (PDE) to truly solve the continuous-time problem. In this thesis, we apply the CTP approach to solve the problems in the above 1), 3), 4) to obtain low complexity and insightful solutions. Note that these three problems pose different challenges to solve the PDE while using the CTP approach. Besides, we apply a diffusion approximation to directly formulate the stochastic optimization in the continuous-time system with dynamics of the form of stochastic differential equation. We apply this approach to solve the above problem 2) to obtain a low complexity and insightful solution. For each of the above four problems, we compare the performance of the proposed solution with the state-of-the-art base policies, and show that significant performance gain can be achieved with low complexity.