A Hopfield neural-network-based dynamic channel allocation with handoff channel reservation control

As channel allocation schemes become more complex and computationally demanding in cellular radio networks, alternative computational models that provide the means for faster processing time are becoming the topic of research interest. These computational models include knowledge-based algorithms, neural networks, and stochastic search techniques. This paper is concerned with the application of a Hopfield (1982) neural network (HNN) to dynamic channel allocation (DCA) and extends previous work that reports the performance of HNN in terms of new call blocking probability. We further model and examine the effect on performance of traffic mobility and the consequent intercell call handoff, which, under increasing load, can force call terminations with an adverse impact on the quality of service (QoS). To maintain the overall QoS, it is important that forced call terminations be kept to a minimum. For an HNN-based DCA, we have therefore modified the underlying model by formulating a new energy function to account for the overall channel allocation optimization, not only for new calls but also for handoff channel allocation resulting from traffic mobility. That is, both new call blocking and handoff call blocking probabilities are applied as a joint performance estimator. We refer to the enhanced model as HNN-DCA++. We have also considered a variation of the original technique based on a simple handoff priority scheme, here referred to as HNN-DCA+. The two neural DCA schemes together with the original model are evaluated under traffic mobility and their performance compared in terms of new-call blocking and handoff-call dropping probabilities. Results show that the HNN-DCA++ model performs favorably due to its embedded control for assisting handoff channel allocation