A Resource Allocation Algorithm for Collaborative Networks Using Inferred Information

Abstract

Even with the advent of 5G wireless communications and the millimeter wave spectrum, there will always be crowded frequency bands where multiple uncoordinated networks will have to contend (or collaborate) to squeeze as much throughput as possible while avoiding interference. This work proposes a branch and bound algorithm for maximizing the overall sum rate over multiple interfering networks with a pre-fixed set of offered flows, as well as a heuristic algorithm that individual networks can follow to collaboratively share the available bandwidth with that same objective. The latter algorithm finds a greedy solution by independently optimizing the links and routes for each network, and then refines that solution by discarding inefficient and potentially harmful links. It does not require any direct communication between the networks, relying instead on location estimates which could be inferred from interference powers. Simulation results show that, when the networks have different traffic loads, the proposed algorithm outperforms the original greedy solution as well as those based on partitioning the resources among the networks for their exclusive use.

Keywords

• Collaborative networks
• branch and bound
• resource allocation
• power and rate control
• link scheduling
• non-convex optimization