Low complexity multi-layer optimization for multi-hop wireless networks

We design a low-complexity solution to multi-layer optimization in multi-hop wireless networks with throughput objectives. Considering channel sensing and power control at the physical layer, we formulate resource allocation as a non-convex throughput optimization problem that allows distributed implementation. We develop a genetic algorithm to solve this physical layer problem with local information only and then formulate a localized back-pressure algorithm to make routing, scheduling, and frequency band assignments at the link and network layers along with physical-layer considerations. We extend our multi-layer solution to cognitive radio networks with different user classes and evaluate our analytical solution via simulations. We also present hardware-in-the-loop emulation test results obtained with real radio transmissions over emulated channels and verify the performance of our distributed multilayer optimization solution for multi-hop wireless networks.