Optimal broadcast scheduling in packet radio networks using mean field annealing

We present an efficient broadcast scheduling algorithm based on mean field annealing (MFA) neural networks. Packet radio (PR) is a technology that applies the packet switching technique to the broadcast radio environment. In a PR network, a single high-speed wideband channel is shared by all PR stations. When a time-division multi-access protocol is used, the access to the channel by the stations´ transmissions must be properly scheduled in both the time and space domains in order to avoid collisions or interferences. It is proven that such a scheduling problem is NP-complete. Therefore, an efficient polynomial algorithm rarely exists, and a mean field annealing-based algorithm is proposed to schedule the stations´ transmissions in a frame consisting of certain number of time slots. Numerical examples and comparisons with some existing scheduling algorithms have shown that the proposed scheme can find near-optimal solutions with reasonable computational complexity. Both time delay and channel utilization are calculated based on the found schedules