An hybrid evolutionary multiobjective algorithm for multiuser margin maximization in DSL
Pages 194 - 209
Abstract
This work describes and proposes the application of evolutionary algorithms on the multiuser spectrum and SNR margin optimization problem for multicarrier systems, such as digital subscriber line. The proposed method is designed such that it takes advantage of special characteristics of the well-known power adaptation techniques and uses them to solve the broader and more challenging problem of multiuser margin adaptation. Simulations show that the proposed method provides Pareto-optimal and diverse solutions when compared to a previous method to solve the same problem. Copyright © 2014 John Wiley & Sons, Ltd.
References
[1]
Bikfalvi A, Garcia-Reinoso J, Vidal I, Valera F. A peer-to-peer IPTV service architecture for the ip multimedia subsystem. International Journal of Communication Systems 2010; Volume 23 Issue 6-7: pp.708-801.
[2]
Lloret J, Garcia M, Atenas M, Canovas A. A QoE management system to improve the iptv network. International Journal of Communication Systems 2011; Volume 24 Issue 1: pp.118-138.
[3]
Li J, Ma S. Characterization and modeling of video popularity. International Journal of Communication Systems 2013.
[4]
Kerpez KJ. Automated loop identification on dsl lines. International Journal of Communication Systems 2009; Volume 22 Issue 12: pp.1479-1493.
[5]
Panigrahi S, Xu Y, Le-Ngoc T. Multiuser margin optimization in digital subscriber line DSL channels. IEEE Journal on Selected Areas in Communications 2006; Volume 24 Issue 8: pp.1571-1580.
[6]
Cioffi J, Jagannathan S, Lee W, Zou H, Chowdhery A, Rhee W, Ginis G, Silverman P. Greener Copper with Dynamic Spectrum Management, IEEE Global Communications Conference GLOBECOM, New Orleans, LA, USA, 2008; pp.62-77.
[7]
Krongold BS, Ramchandran K, Jones DL. An efficient algorithm for optimal margin maximization in multicarrier communication systems, IEEE Global Conference in Telecommunication GLOBECOM, Rio de Janeiro, 1999; pp.899-903.
[8]
Starr T, Sorbara M, Cioffi JM, Silverman PJ. DSL Advances, Prentice-Hall: Upper Sadle River, New Jersey, 2003.
[9]
Yu W, Ginis G, Cioffi JM. Distributed multiuser power control for digital subscriber lines. IEEE Journal on Selected Areas in Communications 2002; Volume 20 Issue 5: pp.1105-15.
[10]
DQS: DQM systems functional architecture and requirements. 2012. Technical report TR-198 issue 2, Broadband Forum.
[11]
Cioffi J, Jagannathan S, Lee W, Zou H, Chowdhery A, Rhee W, Ginis G, Silverman P. Greener copper with dynamic spectrum management. In Accessnets, vol.Volume 6, Wang C, Akan O, Bellavista P, Cao J, Dressler F, Ferrari D, Gerla M, Kobayashi H, Palazzo S, Sahni S, Shen X, Stan M, Xiaohua J, Zomaya A, Coulson G eds., <bookSeriesTitle>Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering</bookSeriesTitle>. Springer Berlin Heidelberg, New York, NY, 2009; pp.62-77.
[12]
Jagannathan S, Hwang CS, Cioffi JM. Margin optimization in digital subscriber lines employing level-1 dynamic spectrum management. IEEE International Conference on Communications, ICC'08, Beijing, 2008; pp.435-440.
[13]
Stolle R. Electromagnetic coupling of twisted pair cables. IEEE Journal on Selected Areas in Communications 2002; Volume 20 Issue 5: pp.883-892.
[14]
Starr T, Cioffi JM, Silverman PJ. Understanding Digital Subscriber Line Technology. Prentice-Hall: Upper Sadle River, New Jersey, 1999.
[15]
Cioffi JM, 2010. Stanford class notes - Available from: "http://www.stanford.edu/group/cioffi/" accessed April 15, 2008.
[16]
Konak A, Coit DW, Smith AE. Multi-objective optimization using genetic algorithms: a tutorial. Reliability Engineering & System Safety 2006; Volume 91 Issue 9: pp.992-1007.
[17]
Deb K. Multi-objective genetic algorithms: problem difficulties and construction of test problems. Evolutionary Compution Journal 1999; Volume 7: pp.205-230.
[18]
Mcphee NF. Analysis of genetic diversity through population history. Proceedings of the Genetic and Evolutionary Computation Conference, Morgan Kaufmann, 1999; pp.1112-1120.
[19]
Toffolo A, Benini E. Genetic diversity as an objective in multi-objective evolutionary algorithms. Evolutionary Computation 2003; Volume 11 Issue 2: pp.151-167.
[20]
Hei Y-Q, Li X-H, Li W-T. Investigation on the evolutionary algorithms with their applications in mimo detecting systems. International Journal of Communication Systems 2013; Volume 26 Issue 11: pp.1409-1418.
[21]
Haidine A. Design of reliable fiber-based distribution networks modeled by multi-objective combinatorial optimization. International Journal of Communication Systems 2013; Volume 26 Issue 10: pp.1227-1242.
[22]
urRehman H, Shah SI, Zaka I, Ahmad J. An MBER-BLAST algorithm for OFDM-SDMA communication using particle swarm optimization. International Journal of Communication Systems 2011; Volume 24 Issue 2: pp.185-201.
[23]
Tan X, Zhang H, Liu Z, Hu J. A genetic-based cognitive link decision algorithm for ofdm system. International Journal of Communication Systems 2012.
[24]
Peiravi A, Mashhadi HR, HamedJavadi S. An optimal energy-efficient clustering method in wireless sensor networks using multi-objective genetic algorithm. International Journal of Communication Systems 2013; Volume 26 Issue 1: pp.114-126.
[25]
Zhu F, Wang H. An ant colony optimisation algorithm for aggregated multicast based on minimum grouping model. International Journal of Communication Systems 2013; Volume 26 Issue 3: pp.277-292.
[26]
Zheng-Yi C, Si-Feng Z, Lian-Feng S. Rate adaptive resource allocation in orthogonal frequency division multiple access system using multi-objective immune algorithm. International Journal of Communication Systems 2013.
[27]
Deb K, Pratap A, Agarwal S, Meyarivan T. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 2002; Volume 6: pp.182-197.
[28]
Deb K, Goyal M. A combined genetic adaptive search geneas for engineering design. Computer Science and Informatics 1996; Volume 26: pp.30-45.
[29]
Deb K, Agrawal R. Simulated binary crossover for continuous search space. Complex Systems 1995; Volume 9 Issue 2: pp.115-148.
[30]
Goldberg DE, Deb K. A comparative analysis of selection schemes used in genetic algorithms. Foundations of Genetic Algorithms, Morgan Kaufmann, San Francisco, CA, 1991; pp.69-93.
[31]
Miller BL, Goldberg DE. Genetic algorithms, tournament selection, and the effects of noise. Complex Systems 1995; Volume 9: pp.193-212.
[32]
Wolkerstorfer M, Statovci D, Nordstrom T. Dynamic spectrum management for energy-efficient transmission in DSL. 11th IEEE Singapore International Conference on Communication Systems ICCS'08, 2008; pp.1015-1020.
[33]
Moraes R, Klautau A, Rius J, Dortschy B, Zampolo R. Optimal solution for the fixed margin problem in digital subscriber lines, ISCCSP, St Julians, 2008; pp.1395-1399.
[34]
Monteiro M, Lindqvist N, Klautau A. Spectrum balancing algorithms for power minimization in DSL networks, IEEE International Conference on Communications ICC'09, Dresden, 2009; pp.1-5.
[35]
Tsiaflakis P, Yi Y, Chiang M, Moonen M. Green DSL: Energy-efficient DSM, IEEE International Conference on Communications ICC'09, Dresden, 2009; pp.1-5.
[36]
G.992.5 IStd.Asymmetric Digital Subscriber Line ADSL transceivers - Extended bandwidth ADSL2 ADSL2, 2005.
[37]
Golden P, Jacobsen K, Dedieu H. Implementation and Applications of DSL Technology, Auerbach: Boca Raton, Florida, 2007.
[38]
Papandriopoulos J, Evans JS. Low-complexity distributed algorithms for spectrum balancing in multi-user DSL networks. IEEE International Conference on Communications ICC'06, June 2006; pp.3270-3275.
[39]
Schelstraete S. Upstream power back-off in VDSL. Communications Magazine, IEEE 2001; Volume 46 Issue 8: pp.399-402.
[40]
Jacobsen KS. Methods of upstream power backoff on very high-speed digital subscriber lines. IEEE Communications Magazine 2001; Volume 39 Issue 3: pp.210-216.
[41]
Lindqvist F, Lindqvist N, Dortschy B, Ödling P, Börjesson PO, Ericsson K, Pelaes E. Crosstalk channel estimation via standardized two-port measurements. EURASIP Journal on Advances in Signal Processing 2008; Volume 2008: pp.Article 201, 14 pages.
[42]
Lindqvist N, Lindqvist F, Monteiro M, Dortschy B, Pelaes E, Klautau A. Impact of crosstalk channel estimation on the dsm performance for DSL networks. EURASIP Journal on Advances in Signal Processing 2010; Volume 2010: pp.2-2.
[43]
Wei S, Youming L, Miaoliang Y. Low-complexity grouping spectrum management in multi-user DSL networks. WRI International Conference on Communications and Mobile Computing CMC'09, Yunnan, 2009; pp.381-385.
Index Terms
- An hybrid evolutionary multiobjective algorithm for multiuser margin maximization in DSL
Index terms have been assigned to the content through auto-classification.
Recommendations
Multiobjective Optimization and Hybrid Evolutionary Algorithm to Solve Constrained Optimization Problems
This paper presents a novel evolutionary algorithm (EA) for constrained optimization problems, i.e., the hybrid constrained optimization EA (HCOEA). This algorithm effectively combines multiobjective optimization with global and local search models. In ...
A Hybrid Multiobjective Evolutionary Algorithm for Multiobjective Optimization Problems
Recently, the hybridization between evolutionary algorithms and other metaheuristics has shown very good performances in many kinds of multiobjective optimization problems (MOPs), and thus has attracted considerable attentions from both academic and ...
Decision Aided Hybrid MMSE/SIC Multiuser Detection: Structure and AME Performance Analysis*This work is supported in part by National Science Council of R.O.C. under contract number NSC92-2219-E-011-006.
This paper presents a simple, yet effective hybrid of the minimum mean square error (MMSE) multi-user detection (MUD) and successive interference cancellation (SIC) for direct-sequence code division multiple access (DS-CDMA) systems. The proposed hybrid ...
Comments
Information & Contributors
Information
Published In
Publisher
John Wiley and Sons Ltd.
United Kingdom
Publication History
Published: 10 January 2016
Author Tags
Qualifiers
- Article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 29 Jul 2024
Other Metrics
Citations
View Options
View options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in