Data networks
Elements of information theory
Elements of information theory
Fair end-to-end window-based congestion control
IEEE/ACM Transactions on Networking (TON)
Impact of fairness on Internet performance
Proceedings of the 2001 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Robust blind source separation by beta divergence
Neural Computation
Scheduling algorithms with error rate consideration in HSDPA networks
Proceedings of the 2006 international conference on Wireless communications and mobile computing
Utility proportional fair bandwidth allocation: an optimization oriented approach
QoS-IP'05 Proceedings of the Third international conference on Quality of Service in Multiservice IP Networks
Opportunistic beamforming using dumb antennas
IEEE Transactions on Information Theory
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The present paper provides a novel characterization of fairness criteria in network resource allocation problems based on information theory. Specifically, the optimization problems that motivate fairness criteria for multi-dimensional resource are characterized using information divergence measures that were originally used in information theory. The characteristics of the fairness criteria clarified herein are summarized as follows: (i) The proportional fairness criterion can be derived through the minimization of the Kullback-Leibler divergence. (ii) The (p, α)-proportional fairness criterion, which is a generalization of the proportional fairness criterion, can be derived through the minimization of the α-divergence and the power-divergence. In addition, the optimization of the fairness criterion is closely related to the Tsallis entropy maximization principle. (iii) The above relationships can be generalized using Csiszár's f-divergence and Bregman's divergence. The information theoretic approach is then applied to a typical example in a practical network resource allocation problem. This example provides a glimpse into the inherent connection between resource allocation problems and information theory.