Joint Opportunistic Routing with Autonomic Forwarding Angle Adjustment and Channel Assignment for Throughput Maximization in Cognitive Radio Ad Hoc Networks

摘要

In this paper, we investigate the cross-layer optimization method to maximize the end-to-end throughput by proposing the joint opportunistic routing and channel assignment scheme in cognitive radio ad hoc networks (CRANETs). We firstly devise the selection metric of the possible forwarding candidate cognitive users (CUs) by exploiting the joint impact of the queue length and the contact degree of CUs on the reliable data packet transfer. The selection algorithm is further presented to identify the set of the forwarding candidate CUs. Then the scheme of the opportunistic routing with autonomic forwarding angle adjustment (FAOR) is proposed wherein the forwarding angle for a CU can be dynamically calculated and updated. Moreover, we put forward the joint FAOR and channel assignment (FACA) scheme by characterizing the SNR of each idle channel and also taking into account the interrupt time of available channels to describe the usage pattern of available channels used by primary users (PUs). Besides, we quantify the forwarding probability of data packet for CUs by employing the metric of expected transmission count to compute the packet loss rate. Next, we formulate the end-to-end throughput from the source CU to the destination CU as a nonlinear programming problem. Extensive simulation results show that the proposed FACA scheme significantly outperforms the existing schemes.