Cross-Layer Optimization for Spectrum Aggregation-Based Cognitive Radio Ad-Hoc Networks

TY - CHAP

T1 - Cross-Layer Optimization for Spectrum Aggregation-Based Cognitive Radio Ad-Hoc Networks

AU - Chen, Jian

AU - Ping, Shuyu

AU - Jia, Jie

AU - Deng, Yansha

AU - Dohler, Mischa

AU - Aghvami, Hamid

PY - 2017/2/2

Y1 - 2017/2/2

N2 - Spectrum aggregation provides a promising approach to improve the network capacity for Cognitive Radio Ad-Hoc Networks (CRAHNs). Resource allocation for spectrum aggregation-based CRAHNs has become one of the main issues. In this paper, we propose a cross-layer optimization for CRAHNs with the spectrum aggregation. The main objective of our paper is to maximize the network throughput under the network resource constraints. In this regard, we investigate the joint optimization for channel allocation, power control and routing under signal-to-interference-and- noise ratio (SINR) model. This cross-layer optimization problem is decomposed into two sub-problems: a resource allocation at the physical (PHY) layer, and a throughput optimization at the network layer. At the PHY layer, the particle swarm optimization algorithm is proposed to find the suboptimal solution, then at the network layer, linear programming is applied to evaluate the particle's fitness value for throughput maximization. The simulation results demonstrate that the joint optimization of channel allocation and power control is an effective way to improve network throughput, especially when the network has sufficient power supply.

AB - Spectrum aggregation provides a promising approach to improve the network capacity for Cognitive Radio Ad-Hoc Networks (CRAHNs). Resource allocation for spectrum aggregation-based CRAHNs has become one of the main issues. In this paper, we propose a cross-layer optimization for CRAHNs with the spectrum aggregation. The main objective of our paper is to maximize the network throughput under the network resource constraints. In this regard, we investigate the joint optimization for channel allocation, power control and routing under signal-to-interference-and- noise ratio (SINR) model. This cross-layer optimization problem is decomposed into two sub-problems: a resource allocation at the physical (PHY) layer, and a throughput optimization at the network layer. At the PHY layer, the particle swarm optimization algorithm is proposed to find the suboptimal solution, then at the network layer, linear programming is applied to evaluate the particle's fitness value for throughput maximization. The simulation results demonstrate that the joint optimization of channel allocation and power control is an effective way to improve network throughput, especially when the network has sufficient power supply.

KW - CRAHNs

KW - Cross-layer optimization

KW - Particle swarm optimization

KW - Resource allocation

KW - Routing

UR - https://www.scopus.com/pages/publications/85015442726

U2 - 10.1109/GLOCOM.2016.7841708

DO - 10.1109/GLOCOM.2016.7841708

M3 - Conference paper

AN - SCOPUS:85015442726

SN - 9781509013289

BT - 2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 59th IEEE Global Communications Conference, GLOBECOM 2016

Y2 - 4 December 2016 through 8 December 2016

ER -