By Topic

Multiuser Effective Capacity analysis for Queue Length Based Rate Maximum wireless scheduling

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Zhengyong Feng ; Sch. of Comm. & Inform. Eng., Univ. of Elec. Sci. & Tech. of China, Chengdu, China ; Guangjun Wen ; Chang Wen Chen

Recently the Effective Capacity of multi-user wireless scheduling has been analyzed based on large deviation principle. The users' queue length distribution bound of wireless scheduling algorithm such as round robin and rate maximum was discussed based on Effective Capacity analysis. But for Queue Length Based (QLB) Rate Maximum scheduling algorithm, the analysis result is only limited to system bound performance and not for each user's bound performance. In this paper we consider each user's amount of input traffic and channel statistical characteristics and introduce a new Effective Capacity analysis model for QLB scheduling algorithm. The queue length distribution bound of the QLB scheduling algorithm for each user is then predicted by the proposed analysis model. Based on the predicted queue length distribution bound, we can set different queue length threshold, that is, delay constraint (queue length can be translated to delay) for each user to obtain different queue length violation (or delay constraint violation or queue overflow) probability. Then the effect of each user's amount of input traffic to their queue length (or delay constraint) violation probability in multiuser environment is analyzed. The proposed analysis model and the estimation results have been verified by numerical simulations.

Published in:

Communications in China (ICCC), 2012 1st IEEE International Conference on

Date of Conference:

15-17 Aug. 2012