Learning contention patterns and adapting to load/topology changes in a MAC scheduling algorithm

Aggregate traffic loads and topology in multi-hop wireless networks may vary slowly, permitting MAC protocols to 'learn' how to spatially coordinate and adapt contention patterns. Such an approach could reduce contention, leading to better throughput and energy consumption. To that end we propose a new family of distributed MAC scheduling algorithms combining synchronous two-level priority RTS/CTS handshaking with randomized time slot selection. We prove that for any fixed admissible load such algorithms converge to a feasible schedule (i.e., throughput-optimal). Furthermore, by adaptively biasing time-slot selection probabilities based on past history, one can develop variations that are also provably throughput-optimal and exhibit better convergence rates. Additionally under moderate loads local changes in load would lead to only local changes in contention patterns leading once again to fast convergence. This makes the case for adopting such protocols in wireless multi- hop networks, where aggregate loads and network topology are slowly varying.