By Topic

Balancing transport and physical Layers in wireless multihop networks: jointly optimal congestion control and power control

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
$31 $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

1 Author(s)
Mung Chiang ; Electr. Eng. Dept., Princeton Univ., NJ, USA

In a wireless network with multihop transmissions and interference-limited link rates, can we balance power control in the physical layer and congestion control in the transport layer to enhance the overall network performance while maintaining the architectural modularity between the layers? We answer this question by presenting a distributed power control algorithm that couples with existing transmission control protocols (TCPs) to increase end-to-end throughput and energy efficiency of the network. Under the rigorous framework of nonlinearly constrained utility maximization, we prove the convergence of this coupled algorithm to the global optimum of joint power control and congestion control, for both synchronized and asynchronous implementations. The rate of convergence is geometric and a desirable modularity between the transport and physical layers is maintained. In particular, when congestion control uses TCP Vegas, a simple utilization in the physical layer of the queueing delay information suffices to achieve the joint optimum. Analytic results and simulations illustrate other desirable properties of the proposed algorithm, including robustness to channel outage and to path loss estimation errors, and flexibility in trading off performance optimality for implementation simplicity. This work presents a step toward a systematic understanding of "layering" as "optimization decomposition," where the overall communication network is modeled by a generalized network utility maximization problem, each layer corresponds to a decomposed subproblem, and the interfaces among layers are quantified as the optimization variables coordinating the subproblems. In the case of the transport and physical layers, link congestion prices turn out to be the optimal "layering prices.".

Published in:

Selected Areas in Communications, IEEE Journal on  (Volume:23 ,  Issue: 1 )