A Framework for Rate Efficient Control of Distributed Discrete Systems
For designers of distributed control systems, this work provides theoretical bounds and a practical optimization method to minimize communication overhead while maintaining control quality.
The paper derives the minimum information exchange required for distributed controllers in Markov decision processes to achieve the same performance as an omniscient controller, and provides an alternating optimization framework to balance control performance and communication cost. Examples from wireless resource allocation demonstrate tradeoffs.
A key issue in the control of distributed discrete systems modeled as Markov decisions processes, is that often the state of the system is not directly observable at any single location in the system. The participants in the control scheme must share information with one another regarding the state of the system in order to collectively make informed control decisions, but this information sharing can be costly. Harnessing recent results from information theory regarding distributed function computation, in this paper we derive, for several information sharing model structures, the minimum amount of control information that must be exchanged to enable local participants to derive the same control decisions as an imaginary omniscient controller having full knowledge of the global state. Incorporating consideration for this amount of information that must be exchanged into the reward enables one to trade the competing objectives of minimizing this control information exchange and maximizing the performance of the controller. An alternating optimization framework is then provided to help find the efficient controllers and messaging schemes. A series of running examples from wireless resource allocation illustrate the ideas and design tradeoffs.