Browse > Article
http://dx.doi.org/10.5370/KIEE.2016.65.3.463

Efficient Resource Allocation Strategies Based on Nash Bargaining Solution with Linearized Constraints  

Choi, Jisoo (Dept. of Electronics Engineering, Ewha Womans University)
Jung, Seunghyun (Dept. of Electronics Engineering, Ewha Womans University)
Park, Hyunggon (Dept. of Electronics Engineering, Ewha Womans University)
Publication Information
The Transactions of The Korean Institute of Electrical Engineers / v.65, no.3, 2016 , pp. 463-468 More about this Journal
Abstract
The overall performance of multiuser systems significantly depends on how effectively and fairly manage resources shared by them. The efficient resource management strategies are even more important for multimedia users since multimedia data is delay-sensitive and massive. In this paper, we focus on resource allocation based on a game-theoretic approach, referred to as Nash bargaining solution (NBS), to provide a quality of service (QoS) guarantee for each user. While the NBS has been known as a fair and optimal resource management strategy, it is challenging to find the NBS efficiently due to the computationally-intensive task. In order to reduce the computation requirements for NBS, we propose an approach that requires significantly low complexity even when networks consist of a large number of users and a large amount of resources. The proposed approach linearizes utility functions of each user and formulates the problem of finding NBS as a convex optimization, leading to nearly-optimal solution with significantly reduced computation complexity. Simulation results confirm the effectiveness of the proposed approach.
Keywords
Nash bargaining solution; Resource management; Piecewise linear; Convex optimization;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 S. Yoo, Y. Do, and D. Lee, "$H_2$/$H^{\infty}$ Filter Design for Linear Discrete Time Systems via Convex Optimization" The Transaction of the Korean Institute of Electrical Engineers, vol. 45, no. 11, pp. 1603-1607, 1996.
2 S. Boyd and L. Vandenberghe, Convex optimization. Cambridge university press, 2004.
3 M. Johansson, Piecewise linear control systems. Springer, 2003.
4 B. Iem, "Performance Analysis of A Variable Bit Rate Speech Coder", The Transaction of the Korean Institute of Electrical Engineers, vol. 62, no. 12, pp. 1750-1754, 2013.   DOI
5 E. S. Levitin and B. T. Polyak, "Constrained minimization methods", USSR Computational mathematics and mathematical physics, vol. 6, no. 5, pp. 1-50, 1966.   DOI
6 G. B. Dantzig and R. Cottle, The Basic George B. Dantzig. Stanford University Press, 2003.
7 J. Callahan, K. Homan, D. Cox, D. O'Shea, H. Pollatsek, and L. Senechal, Calculus in context: the five college calculus project. WH Freeman, 1995.
8 K. Stuhlmuller, N. Farber, M. Link, and B. Girod, "Analysis of video transmission over lossy channels", IEEE Journal on Selected Areas in Communications, vol. 18, no. 6, pp. 1012-1032, 2000.   DOI
9 A. B. Tal and A. Nemirovski, Lectures on modern convex optimization: analysis, algorithms and engineering applications, Siam, 2001.
10 J. Nash, "The bargaining problem", Econometrica, vol. 18, pp. 155-162, 1950.   DOI
11 M. J. Osborne and A. Rubinstein, A course in game theory, MIT press, 1994.
12 K. Binmore, Fun and Games: A Text on Game Theory, Lexington, MA:D.C. Health, 1992.
13 K. Lee, "A Study on Transaction Pricing of Generation Bidding in Electricity Market by Using Game Theory", The Transaction of the Korean Institute of Electrical Engineers, vol. 52A, no. 6, pp. 333-339, 2003.
14 J. E. Suris, L. DaSilva, Z. Han, A. B. MacKenzie, R. S. Komali et al., "Asymptotic optimality for distributed spectrum sharing using bargaining solutions", IEEE Transactions on Wireless Communications, vol. 8, no. 10, pp. 5225-5237, 2009.   DOI
15 J. Shin and K. Lee, "Analysis on Incomplete Information in an Electricity Market using Game Theory", The Transaction of the Korean Institute of Electrical Engineers, vol. 55A, no. 5, pp. 214-219, 2006.
16 H. Park and Mihaela van der Schaar, "Bargaining Strategies for Networked Multimedia Resource Management", IEEE Transactions on Signal Processing, vol. 55, no. 7, pp. 3496-3511, 2007.   DOI
17 Z. Han, Z. J. Ji, and K. Liu, "Fair multiuser channel allocation for ofdma networks using nash bargaining solutions and coalitions", IEEE Transactions on Communications, vol. 53, no. 8, pp. 1366-1376, 2005.   DOI
18 A. Z. Shifat, M. Z. Chowdhury, and Y. M. Jang, A game theoretical approach for QoS provisioning in heterogeneous networks. ICT Express, 2015.   DOI
19 N. Enneya, R. Elmeziane, and M. Elkoutbi, "A game theory approach for enhancing QoS-Aware routing in mobile ad hoc networks", in 2009. NDT'09. First International Conference on Networked Digital Technologies, pp. 327-333, Czech, Ostrava, July 2009.
20 E. Kim, H. Park, and P. Frossard, "Low complexity iterative multimedia resource allocation based on game theoretic approach", in 2012 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1099-1102, Seoul, Korea, May 2012.
21 E. Choi and H. Park, "Transformation based low complexity algorithm for nash bargaining solutions in dynamic networks", in 2013 International Conference on Information Networking (ICOIN). pp. 365-370, Bangkok, Thailand, Jan. 2013.