Multi-scale Spectrum Sensing in 5G Cognitive Networks
share
A multi-scale approach to spectrum sensing is proposed to overcome the huge energy cost of acquiring full network state information over 5G cognitive networks. Secondary users (SUs) estimate the local spectrum occupancies and aggregate them hierarchically to produce multi-scale estimates. Thus, SUs obtain fine-grained estimates of spectrum occupancies of nearby cells, more relevant to resource allocation tasks, and coarse-grained estimates of those of distant cells. The proposed architecture accounts for local estimation errors, delays in the exchange of state information, as well as irregular interference patterns arising in future fifth-generation (5G) dense cellular systems with irregular cell deployments. An algorithm based on agglomerative clustering is proposed to design an energy-efficient aggregation hierarchy matched to the irregular interference structure, resilient to aggregation delays and local estimation errors. Measuring performance in terms of the trade-off between SU network throughput, interference to PUs and energy efficiency, numerical experiments demonstrate a 10 regular tree construction, for a reference value of interference to PUs, and up to 1/4th of the energy cost needed to acquire full network state information.
READ FULL TEXT