Secured Distributed Cognitive MAC and Complexity Reduction in Channel Estimation for the Cross Layer based Cognitive Radio Networks

TL;DR

This paper proposes a secure, efficient cross-layer cognitive radio network with optimized channel allocation and reduced complexity in channel estimation, improving link reliability and network performance.

Contribution

It introduces a novel secure MAC protocol with dynamic channel allocation and a complexity-reduced channel estimation method for cognitive radio networks.

Findings

CR-MMSE outperforms MMSE and LS in SER performance.

CR-ML surpasses MMSE and ML in accuracy.

Enhanced security and efficiency in channel utilization.

Abstract

Secured opportunistic Medium Access Control (MAC) and complexity reduction in channel estimation are proposed in the Cross layer design Cognitive Radio Networks deploying the secured dynamic channel allocation from the endorsed channel reservation. Channel Endorsement and Transmission policy is deployed to optimize the free channel selection as well as channel utilization to cognitive radio users. This strategy provide the secured and reliable link to secondary users as well as the collision free link to primary users between the physical and MAC layers which yields the better network performance. On the other hand, Complexity Reduction in Minimum Mean Square Errror (CR-MMSE) and Maximum Likelihood (CR-ML) algorithm on Decision Directed Channel Estimation (DDCE) is deployed significantly to achieve computational complexity as Least Square (LS) method. Rigorously, CR-MMSE in sample spaced channel impulse response (SS-CIR) is implemented by allowing the computationally inspired matrix inversion. Regarding CR-ML, Pilot Symbol Assisted Modulation (PSAM) with DDCE is implemented such the pilot symbol sequence provides the significant performance gain in frequency correlation using the finite delay spread. It is found that CRMMSE demonstrates outstanding Symbol Error Rate (SER) performance over MMSE and LS, and CR-ML over MMSE and ML.