The object of study in the recent theory of Mean Field Games has been primarily large populations of agents interacting through a population dependent coupling term, entering through individual cost or dynamics. However, there are situations where agents are essentially independent, except for measurement interference. This is the case for example in cellular communications networked control across noisy channels. In previous work, we formulated the case of interference coupled linear partially observed stochastic agents as a game. Conditions were developed under which naively ignoring the interference term leads to asymptotically in population size, optimal control laws which are Riccati gain based. We tackle here the case of exact decentralized filtering under a class of time invariant certainty equivalent feedback controllers, and numerically investigate both stabilization ability and performance of such controllers as the state estimate feedback gain varies. While the optimum filters have memory requirements which become infinite over time, the stabilization ability of their finite memory approximation is also tested.
Published January 2017 , 27 pages