Description

Abstract:

             In cognitive radio networks (CRNs), secondary users must be able to accurately and reliably track the location of small-scale mobile primary users/devices such as wireless microphones. In order to efficiently utilize spatial spectrum opportunities, while protecting primary communications.However accurate tracking of the location of mobile primary users is difficult due mainly to the CR-unique constraint, such as localization must rely solely on reported sensing results, which can easily be compromised by malicious sensors (or attackers). To cope with this challenge, we propose a new framework, called Sequential MOnte carLo combIned with shadow-faDing estimation (SOLID), for a ccurate,tracking of small scale mobile primary users. Specifically, SOLID augments conventional Sequential Monte Carlo (SMC)-based target tracking with shadow-fading estimation. . By examining the shadow-fading gain between the primary transmitter and CRs/sensors, SOLID 1) significantly improves the accuracy of primary tracking regardless of the presence/absence of attack, and 2) successfully masks the abnormal sensing reports due to sensor faults or attacks, preserving localization accuracy and improving spatial spectrum efficiency.