On the Construction of Radio Environment Maps for Cognitive Radio Networks

TL;DR

This paper analyzes the tradeoff between the number of sensors and the accuracy of Radio Environment Maps in Cognitive Radio Networks, introducing geographic entropy to quantify this relationship and examining sensor deployment impacts.

Contribution

It introduces the concept of geographic entropy and analyzes the convex relationship between sensor count and REM accuracy using information theory.

Findings

REM error decreases convexly with more sensors

Geographic entropy quantifies the measurement-accuracy tradeoff

Sensor deployment significantly influences REM precision

Abstract

The Radio Environment Map (REM) provides an effective approach to Dynamic Spectrum Access (DSA) in Cognitive Radio Networks (CRNs). Previous results on REM construction show that there exists a tradeoff between the number of measurements (sensors) and REM accuracy. In this paper, we analyze this tradeoff and determine that the REM error is a decreasing and convex function of the number of measurements (sensors). The concept of geographic entropy is introduced to quantify this relationship. And the influence of sensor deployment on REM accuracy is examined using information theory techniques. The results obtained in this paper are applicable not only for the REM, but also for wireless sensor network deployment.