Efficient Resource Allocation in Multiflow Wireless Networks
Middleton, Gareth B.
Doctor of Philosophy
We consider the problem of allocating resources in large wireless net- works in which multiple information flows must be accommodated. In particular, we seek a method for selecting schedules, routes, and power allocations for networks with terminals capable of user-cooperation at the signal level. To that end, we adopt a general information-theoretic communications model, in which the datarate of a wireless link is purely a function of transmission power, pathloss and interference. We begin by studying the case of resource allocation when only point-to-point links are available. The problem is NP-hard in this case, requiring an exponentially-complex exhaustive search to guarantee an optimal solution. This is prohibitively difficult for anything but the smallest of networks, leading us to approximate the problem using a decomposition approach. We construct the solution iteratively, developing polynomial-time algorithms to optimally allocate resources on a per-frame basis. We then update the network graph to reflect the resources consumed by the allocated frame. To manage this decomposition, we present a novel tool, termed the Network-Flow Interaction Chart. By representing the network in both space and time, our techniques trade off interference with throughput for each frame, offering considerable performance gains over schemes of similar complexity. Recognizing that our approach requires a large amount of overhead, we go on to develop a method in which it may be decentralized. We find that while the overhead is considerably lower, the limited solution space results in suboptimal solutions in a throughput sense. We conclude with a generalization of the Network-Flow Interaction Chart to address cooperative resource allocation. We represent cooperative links using "metanodes," which are made available to the allocation algorithms alongside point-to-point links and will be selected only if they offer higher throughput. The data-carrying capability of the cooperative links is modeled using Decode-and-Forward achievable rates, which are functions of transmit power and interference, and so may be incorporated directly into our framework. We demonstrate that allocations incorporating cooperation results in significant performance gains as compared to using point-to-point links alone.