A new method to optimize the satellite broadcasting schedules using the mean field annealing of a Hopfield neural network

A Hybrid Hopfield Network-Simulated Annealing Approach for Frequency Assignment in Satellite Communications Systems

A hybrid Hopfield network-simulated annealing algorithm (HopSA) is presented for the frequency assignment problem (FAP) in satellite communications. The goal of this NP-complete problem is minimizing the cochannel interference between satellite communication systems by rearranging the frequency assignment, for the systems can accommodate the increasing demands. The HopSA algorithm consists of a fast digital Hopfield neural network which manages the problem constraints hybridized with a simulated annealing which improves the quality of the solutions obtained. We analyze the problem and its formulation, describing and discussing the HopSA algorithm and solving a set of benchmark problems. The results obtained are compared with other existing approaches in order to show the performance of the HopSA approach.

A binary Hopfield neural-network approach for satellite broadcast scheduling problems

This paper presents a binary Hopfield neural network approach for finding a broadcasting schedule in a low-altitude satellite system. Our neural network is composed of simple binary neurons on the synchronous parallel computation, which is greatly suitable for implementation on a digital machine. With the help of heuristic methods, the neural network of a maximum of 200000 neurons can always find near-optimum solutions on a conventional workstation in our simulations.

Traffic management of a satellite communication network using stochastic optimization

The performance of nonhierarchical circuit switched networks at moderate load conditions is improved when alternate routes are made available. Alternate routes, however, introduce instability under heavy and overloaded conditions, and under these load conditions network performance is found to deteriorate. To alleviate this problem, a control mechanism is used where, a fraction of the capacity of each link is reserved for direct routed calls. In this work, a traffic management scheme is developed to enhance the performance of a mesh-connected, circuit-switched satellite communication network. The network load is measured and the network is continually adapted by reconfiguring the map to suit the current traffic conditions. The routing is performed dynamically. The reconfiguration of the network is done by properly allocating the capacity of each link and placing an optimal reservation on each link. The optimization is done by using two neural network-based optimization techniques: simulated annealing and mean field annealing. A comparative study is done between these two techniques. The results from the simulation study show that this method of traffic management performs better than the pure dynamic routing with a fixed configuration.