Scheduling algorithms for multihop radio networks
IEEE/ACM Transactions on Networking (TON)
A new model for scheduling packet radio networks
Wireless Networks
Frequency Channel Assignment on Planar Networks
ESA '02 Proceedings of the 10th Annual European Symposium on Algorithms
Strong Edge Coloring for Channel Assignment in Wireless Radio Networks
PERCOMW '06 Proceedings of the 4th annual IEEE international conference on Pervasive Computing and Communications Workshops
Coloring the square of a planar graph
Journal of Graph Theory
Optimal Broadcast Scheduling in Packet Radio Networks via Branch and Price
INFORMS Journal on Computing
A sequential approach for optimal broadcast scheduling in packet radio networks
IEEE Transactions on Communications
A mixed neural-genetic algorithm for the broadcast scheduling problem
IEEE Transactions on Wireless Communications
A novel broadcast scheduling strategy using factor graphs and the sum-product algorithm
IEEE Transactions on Wireless Communications
Optimal broadcast scheduling in packet radio networks using mean field annealing
IEEE Journal on Selected Areas in Communications
IEEE Transactions on Neural Networks
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An important problem that arises in the design of cellular radio networks is the channel assignment problem that addresses the key issue of the scheduling of the transmissions of all stations in the network while avoiding interference. The problem consists of identifying a frame of minimum length that avoids interference while satisfying channel demands of the stations and maximizing channel utilization. The problem is known to be NP-hard and is formulated as a nonlinear integer program in prior research. This paper develops, for the first time, an integer programming formulation for the problem and presents a Probabilistic Greedy Algorithm that avoids the basic shortcoming of the classical greedy approach which is getting trapped in local optima. The algorithm incorporates simultaneously two diversification techniques, namely randomization and perturbation. Computational experiments are conducted on benchmark data sets and randomly generated problem instances with up to 500 stations. The results show the proposed algorithm is very effective in generating good solutions in short computing time. While a general-purpose branch-and-bound algorithm fails to find feasible solutions even for small instances, the proposed algorithm produces solutions that are far better than those obtained using a classical greedy approach in acceptable time.