Differential back‐pressure routing for single‐queue time‐varying wireless networks. Issue 18 (1st November 2019)

• Record Type: Journal Article
• Authors: Wang, Nan; Zhao, Hai; Hai, Long
• Is Part Of: IET communications. Volume 13:Issue 18(2019)
• Page Start: 3117
• Page End: 3123
• Publication Date: 2019-11-01
• Subjects: optimisation -- telecommunication traffic -- radio networks -- delays -- routing protocols -- queueing theory -- resource allocation -- time‐varying channels -- wireless channels -- telecommunication network routing; single‐queue time‐varying wireless networks -- intelligent control -- data transmission -- wireless multihop networks -- network throughput -- optimises resource allocation -- multihop wireless networks -- time‐varying channels -- slow routing convergence -- complex control -- data queues -- end‐to‐end delay -- network resources -- network loads -- differential back‐pressure -- single‐queue management -- traditional back‐pressure routing -- data pressure -- data backlog calculation -- queue length differences -- single queue -- queue cap -- routing metric -- routing algorithm -- random networks; Telecommunication systems -- Periodicals; Speech processing systems -- Periodicals; 621.38205
• DOI: 10.1049/iet-com.2019.0371
• Languages: English
• ISSNs: 1751-8628

Abstract

Abstract : Dynamic resource control and routing are important for realising the intelligent control of data transmission in wireless multi‐hop networks. It is well known that back‐pressure routing based on a max‐weight policy maximises network throughput and optimises resource allocation in multi‐hop wireless networks with time‐varying channels. Due to the slow routing convergence and complex control of data queues, however, back‐pressure routing also results in large end‐to‐end delays and a waste of network resources, particularly when the network loads are light or moderate. In this study, a differential back‐pressure routing scheme with single‐queue management is proposed to improve the packet delay performance and simplify the management of data queues. Unlike in traditional back‐pressure routing, the authors use the differences in the rates of change in data queue length to calculate data pressure. Compared with the method of data backlog calculation based on queue length differences, this method achieves faster routing convergence. They also consider the ceiling problem for a single queue and enhance the effect of the queue cap on the routing metric by means of dynamic weighting. Simulation results show that the proposed routing algorithm achieves a 20% decrease in end‐to‐end delay in grid and random networks.