Power optimization for multiple QoS, delay, and BER classes relying on finite-delay information theory

Research Paper
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Abstract

Future communication systems will include diffierent types of messages requiring diffierent transmission rates, packet lengths, and service qualities. We address the power-optimization issues of communication systems conveying multiple message types based on finite-delay information theory. Given both the normalized transmission rate and the packet length of a system, the actual residual decoding error rate is a function of the transmission power. We propose a generalized power allocation framework for multiple message types. Two diffierent optimization cost functions are adopted: the number of service-quality violations encountered and the sum log ratio of the residual decoding error rate. We provide the optimal analytical solution for the former cost function and a heuristic solution based on a genetic algorithm for the latter one. Finally, the performance of the proposed solutions are evaluated numerically.

Keywords

5G communication short packet ultra-dense delay-limited finite-length information theory power optimization 

References

  1. [1]
    5G System [Z]. Ericsson White Paper, 2015.Google Scholar
  2. [2]
    5G Vision [Z]. DMC Research and Development Center. Samsung Electronics Co., Ltd., 2015.Google Scholar
  3. [3]
    5G radio access [Z]. Ericsson White Paper, 2015.Google Scholar
  4. [4]
    Rethink mobile communications for 2020+ [Z]. Future Mobile Communication Forum White Paper, 2014.Google Scholar
  5. [5]
    P. Smulders. The road to 100 Gb/s wireless and beyond: basic issues and key directions [J]. IEEE Communications Magazine, 2013, 51(12): 86–91.CrossRefGoogle Scholar
  6. [6]
    N. Bhushan, J. Y. Li, D. Malladi, et al. Network densification: the dominant theme for wireless evolution into 5G [J]. IEEE Communications Magazine, 2014, 52(2): 82–89.CrossRefGoogle Scholar
  7. [7]
    IWPC. Evolutionary and disruptive visions towards ultra high capacity networks, for crowd and broadband/dense applications access, backhaul and user equipment [Z]. White Paper, 2014.Google Scholar
  8. [8]
    P. Lin, W. Chou, T. Lin. Achieving airtime fairness of delay sensitive applications in multirate IEEE 802.11 wireless LANs [J]. IEEE Communications Magazine, 2013, 49(9): 169–175.CrossRefGoogle Scholar
  9. [9]
    J. Akhtman, R. Maunder, L. Hanzo. Constrained capacity of delay-limited wireless transceivers [C]//IEEE 70th Vehicular Technology Conference Fall, Anchorage, USA, 2009: 1–4.Google Scholar
  10. [10]
    J. Akhtman, R. Maunder, N. Bonello. Closed-form approximation of maximum free distance for binary block codes [C]//IEEE 70th Vehicular Technology Conference Fall, Anchorage, USA, 2009: 1–3.Google Scholar
  11. [11]
    Y. Polyanskiy, H. V. Poor, S. Verdu. Channel coding rate in the finite blocklength regime [J]. IEEE Transactions on Information Theory, 2012, 56(5): 2307–2359.MathSciNetCrossRefGoogle Scholar
  12. [12]
    B. Soret, K. I. Pedersen, N. T. K. Jorgensen, et al. Interference coordination for dense wireless networks [J]. IEEE Communications Magazine, 2015, 53(1): 102–109.CrossRefGoogle Scholar
  13. [13]
    S. F. Yunas, M. Valkama, J. Niemela. Spectral and energy efficiency of ultra-dense networks under different deployment strategies [J]. IEEE Communications Magazine, 2015, 53(1): 90–101.CrossRefGoogle Scholar
  14. [14]
    A. Asadi, V. Sciancalepore, V. Mancuso. On the efficient utilization of radio resources in extremely dense wireless networks [J]. IEEE Communications Magazine, 2015, 53(1): 126–132.CrossRefGoogle Scholar
  15. [15]
    Y. M. Shi, J. Zhang, B. O'Donoghue, et al. Largescale convex optimization for dense wireless cooperative networks [J]. IEEE Transactions on Signal Processing, 2015, 63(18): 4729–4743.MathSciNetCrossRefGoogle Scholar
  16. [16]
    J. Akhtman, L. Hanzo. Power versus bandwidthefficiency in wireless communications: the economic perspective [C]//IEEE 70th Vehicular Technology Conference Fall, Anchorage, USA, 2009: 1–5.Google Scholar

Copyright information

© Posts & Telecom Press and Springer Singapore 2017

Authors and Affiliations

  • Chen Gong
    • 1
  • Qian Gao
    • 1
  • Lajos Hanzo
    • 3
  • Zhengyuan Xu
    • 1
    • 2
  1. 1.Key Laboratory of Wireless-Optical Communications, Chinese Academy of Sciences, School of Information Science and TechnologyUniversity of Science and Technology of ChinaHefeiChina
  2. 2.Shenzhen Graduate SchoolTsinghua UniversityShenzhenChina
  3. 3.School of Electronics and Computer ScienceUniversity of SouthamptonSouthamptonUK

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