Multi-Scale Spectrum Sensing in Dense Multi-Cell Cognitive Networks

TL;DR

This paper introduces a multi-scale spectrum sensing approach for dense multi-cell cognitive networks, enabling efficient spectrum occupancy estimation and decentralized traffic adaptation with minimal information exchange.

Contribution

It proposes a hierarchical aggregation method and interference-matched tree design to improve spectrum sensing efficiency and network control in dense cognitive networks.

Findings

Achieves up to 15% throughput degradation with only one-third of the communication cost.

Outperforms random tree and consensus-based algorithms in spectrum estimation accuracy.

Provides relevant multi-scale spectrum information for better network management.

Abstract

Multi-scale spectrum sensing is proposed to overcome the cost of full network state information on the spectrum occupancy of primary users (PUs) in dense multi-cell cognitive networks. Secondary users (SUs) estimate the local spectrum occupancies and aggregate them hierarchically to estimate spectrum occupancy at multiple spatial scales. Thus, SUs obtain fine-grained estimates of spectrum occupancies of nearby cells, more relevant to scheduling tasks, and coarse-grained estimates of those of distant cells. An agglomerative clustering algorithm is proposed to design a cost-effective aggregation tree, matched to the structure of interference, robust to local estimation errors and delays. Given these multi-scale estimates, the SU traffic is adapted in a decentralized fashion in each cell, to optimize the trade-off among SU cell throughput, interference caused to PUs, and mutual SU interference. Numerical evaluations demonstrate a small degradation in SU cell throughput (up to 15% for a 0dB interference-to-noise ratio experienced at PUs) compared to a scheme with full network state information, using only one-third of the cost incurred in the exchange of spectrum estimates. The proposed interference-matched design is shown to significantly outperform a random tree design, by providing more relevant information for network control, and a state-of-the-art consensus-based algorithm, which does not leverage the spatio-temporal structure of interference across the network.