Self-organizing Feature Maps for Dynamic Control of Radio Resources in CDMA PCS Networks
William S. Hortos · Kluwer Academic Publishers eBooks · 2006
The application of artificial neural networks to the channel assignment problem for cellular code division multiple access (CDMA) cellular networks has previously been investigated. CDMA takes advantage of voice activity and spatial isolation because its capacity is only interference limited, unlike time-division multiple access (TDMA) and frequency-division multiple access (FDMA) where capacities are bandwidth-limited. Any reduction in interference in CDMA translates linearly into increased capacity. To satisfy the demands for new services and improved connectivity for mobile communications, small cell systems are being introduced. For these systems, there is a need for robust and efficient management procedures for the allocation of power and spectrum to maximize radio capacity. Topology-conserving mappings play an important role in biological processing of sensory inputs. The same principles underlie Kohonen’s self-organizing feature maps (SOFMs), which are applied to the adaptive control of radio resources to minimize interference, hence, maximize capacity in direct-sequence (DS) CDMA networks. The approach based on SOFMs is applied to published examples of DS/CDMA networks. Results of the approach for these examples are informally compared to the performance of Hopfield-Tank algorithms and genetic algorithms for the channel assignment problem. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.