Control Of Wireless Networks Under Uncertain State Information

In shared spectrum, wireless communication systems experience interference that can cause packet transmission failures. The channel conditions that determine these losses are driven by an underlying time-evolving state, which is usually hidden from the wireless network and can only be partially observed through interaction with the channel. This introduces a trade-off between exploration and exploitation: the nodes of the network must schedule their transmissions to both observe the channels and achieve high throughput. The optimal balance between these objectives is determined by the network’s stochastic traffic demand. Solving this joint learning and scheduling problem is complex. In this thesis, we devise queue-length-based scheduling policies that can adapt to the network’s traffic, while simultaneously exploring the channel conditions. We begin by considering controller policies for a transmitter that has multiple available channels. Packets stochastically arrive to the transmitter’s queue, and at each time slot, the transmitter can attempt transmission on one of the channels. For each channel, transmission attempts fail according to a random process with unknown mean. The objective of the transmitter is to learn the channel’s rates while simultaneously minimizing its queue backlog. We proceed to formulate transmission policies that are asymptotically order optimal. Next, we consider transmission scheduling when the network under our control is sharing its channels with an uncooperative network. Transmission collisions cause the uncooperative network to reattempt transmission. Therefore, the experienced interference is correlated over time through the uncooperative network’s queueing dynamics, which are hidden from our network and must be estimated through observation. We derive upper and lower bounds on the maximum attainable rate of successful transmissions in a two user network and use these bounds to characterize the performance of larger networks. These results lead to a queue-length-based method for stabilizing the networks. Finally, we extend our results to networks that have complex constraints on simultaneous transmissions. The network must learn its channel rates while also supporting its stochastic traffic demand. We devise a frame-based max-weight algorithm that learns the channel rates over the duration of a frame to stabilize the network.