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MWSR over an Uplink Gaussian Channel with Box Constraints: A Polymatroidal Approach

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Published:02 July 2019Publication History

ABSTRACT

The rate capacity region of an uplink Gaussian channel is a generalized symmetric polymatroid. Practical applications impose additional lower and upper bounds on the rate allocations, which are represented by box constraints. A fundamental scheduling problem over an uplink Gaussian channel is to seek a rate allocation maximizing the weighted sum-rate (MWSR) subject to the box constraints. The best-known algorithm for this problem has time complexity O (n5 lnO(1) n). In this paper, we take a polymatroidal approach to developing a quadratic-time greedy algorithm and a linearithmic-time divide-and-conquer algorithm. A key ingredient of these two algorithms is a linear-time algorithm for minimizing the difference between a generalized symmetric rank function and a modular function after a linearithmic-time ordering.

References

  1. J. G. Andrews, Interference cancellation for cellular systems: a contemporary overview, IEEE Trans. Wireless Commun. 12(2): 19--29, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. T. M. Cover and J. A. Thomas, Elements of information theory. John Wiley & Sons, 2012.Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. J. Edmonds, Submodular functions, matroids and certain polyhedra. In Combinatorial structures and their applications, eds. R. Guy, H. Hanani, N. Sauer and J. Schonheim, Pages 69--87, 1970.Google ScholarGoogle Scholar
  4. W. H. Cunningham, On submodular function minimization. Combinatorica 5(3): 185--192, 1985. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. A. Federgruen and H. Groenevelt, The greedy procedure for resource allocation problems: Necessary and sufficient conditions for optimality. Operations Research 34: 909--918, 1986. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. A. Federgruen and H. Groenevelt, Characterization and optimization of achievable performance in general queueing systems, Operations Research 36(5): 733--741, 1988. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. L. Fleischer and S. Iwata, A push-relabel framework for submodular function minimization and applications to parametric optimization. Discrete Applied Mathematics 131(2): 311--322, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. A. Frank and E. Tardos, Generalized polymatroids and submodular flows, Mathematical Programming 42: 489--563, 1988. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. S. Fujishige, Submodular Functions and Optimization, 2nd ed. Annals of Discrete Mathematics vol. 58, Elsevier, Amsterdam, 2005.Google ScholarGoogle Scholar
  10. H. Groenevelt, Two algorithms for maximizing a separable concave function over a polymatroid feasible region. European Journal of Operational Research 54(2): 227--236, 1991.Google ScholarGoogle ScholarCross RefCross Ref
  11. M. Grötschel, L. Lovász, and A. Schrijver. The ellipsoid method and its consequences in combinatorial optimization. Combinatorica 1: 169--197, 1981.Google ScholarGoogle ScholarCross RefCross Ref
  12. S. Iwata, Submodular function minimization. Mathematical Programming 112(1): 45--64, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. S. Iwata and L. Fleischer and S. Fujishige. A combinatorial strongly polynomial algorithm for minimizing submodular functions. Proc. of 32nd Symposium on Theory of Computing (STOC'00), pp. 97--106, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. S. Iwata and J. B. Orlin, A simple combinatorial algorithm for submodular function minimization. In Proceedings of the twentieth Annual ACM-SIAM Symposium on Discrete Algorithms (SODA'09), pages 1230--1237, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. E. L. Lawler and C. U. Martel, Computing maximal "polymatroidal" network flows. Mathematical of Operation Research 7: 334--347, 1982. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Y. T. Lee, A. Sidford, and S. C. Wong, A faster cutting plane method and its implications for combinatorial and convex optimization. In Proceedings of the 56th Annual Symposium on Foundations of Computer Science (FOCS'15), pages 1049--1065, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Y. Liu, Z. Qin, M. Elkashlan, Z. Ding, A. Nallanathan, and L. Hanzo, Nonorthogonal Multiple Access for 5G and Beyond, Proceedings of the IEEE 105(12): 2347--2381, 2017.Google ScholarGoogle ScholarCross RefCross Ref
  18. J.B. Orlin, A faster strongly polynomial time algorithm for submodular function minimization. Mathematical Programming 118(2): 237--251, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. P. Patel and J. Holtzman, Analysis of a simple successive interference cancellation scheme in a DS/CDMA system, IEEE J. Sel. Areas Commun. 12(5): 796--807, 1994. Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. A. Schrijver, A combinatorial algorithm minimizing submodular functions in strongly polynomial time. Journal of Combinatorial Theory, Series B, 80(2): 346--355, 2000. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. A. Schrijver, Combinatorial optimization: polyhedra and efficiency, Springer, 2003.Google ScholarGoogle Scholar
  22. N. V. Shakhlevich, A. Shioura, and V. A. Strusevich, Single machine scheduling with controllable processing times by submodular optimization. Int. J. Found. Comput. Sci. 20: 247--269, 2009.Google ScholarGoogle ScholarCross RefCross Ref
  23. J. G. Shanthikumar and D. D. Yao, Multiclass queueing systems: polymatroidal structure and optimal scheduling control, Operations Research 40(S2): 293--299, 1992.Google ScholarGoogle Scholar
  24. A. Shioura, N. V. Shakhlevich, and V. A. Strusevic, Decomposition algorithms for submodular optimization with applications to parallel machine scheduling with controllable processing times. Math. Program., Ser. A 153: 495--534, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. A. Shioura, N. V. Shakhlevich, and V. A. Strusevich, Application of submodular optimization to single machine scheduling with controllable processing times subject to release dates and deadlines, INFORMS Journal on Computing 28: 148--161, 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. A. Shioura, N. V. Shakhlevich, and V. A. Strusevich, Preemptive models of scheduling with controllable processing times and of scheduling with imprecise computation: A review of solution approaches, European Journal of Operational Research 266(3): 795--818, 2018.Google ScholarGoogle ScholarCross RefCross Ref
  27. D. M. Topkis. Minimizing a submodular function on a lattice. Operations Research 26(2): 305-- 321, 1978. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. D. M. Topkis. Supermodularity and complementarity, Princeton University Press, 2011.Google ScholarGoogle Scholar
  29. D. Tse and S. V. Hanly. Multiaccess fading channels - part I: polymatroid structure, optimal resource allocation and throughput capacities. IEEE Transactions on Information Theory 44(7): 2796--2994, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. D. Tse and P. Viswanath, Fundamentals of wireless communication. Cambridge university press, 2005. Google ScholarGoogle ScholarDigital LibraryDigital Library

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      • Published in

        cover image ACM Conferences
        Mobihoc '19: Proceedings of the Twentieth ACM International Symposium on Mobile Ad Hoc Networking and Computing
        July 2019
        419 pages
        ISBN:9781450367646
        DOI:10.1145/3323679

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        • Published: 2 July 2019

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