Wireless Networks

, Volume 24, Issue 5, pp 1405–1418 | Cite as

Resource allocation for real-time traffic in unreliable wireless cellular networks

  • Jun Xu
  • Chengcheng Guo
  • Hao Zhang
  • Jianfeng Yang


Providing reliable transmission for real-time traffic in wireless cellular networks is a great challenge due to the unreliable wireless links. This paper concentrates on the resource allocation problem aiming to improve the real-time throughput. First, the resource allocation problem is formulated as a Markov Decision Process and thus the optimal resource allocation policy could be obtained by adopting the value iteration algorithm. Considering the high time complexity of the optimal algorithm, we further propose an approximate algorithm which decomposes the resource allocation problem into two subproblems, namely link scheduling problem and packet scheduling problem. By this method, the unreliable wireless links are only constrained in the link scheduling problem, and we can focus on the real-time requirement of traffic in packet scheduling problem. For the link scheduling problem, we propose the maxRel algorithm to maximize the long-term network reliability, and we theoretically prove that the maxRel algorithm is optimal in scenarios with dynamic link reliabilities. The Least Laxity First algorithm is adopted for the packet scheduling problem. Extensive simulation results show that the proposed approximate resource allocation algorithm makes remarkable improvement in terms of time complexity, packet loss rate and delay.


Resource allocation Wireless cellular network Network reliability Real-time MDP 



This work is supported by Grant No. 413000016 from Wuhan University.


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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Jun Xu
    • 1
  • Chengcheng Guo
    • 1
  • Hao Zhang
    • 2
  • Jianfeng Yang
    • 1
  1. 1.Electronic Information SchoolWuhan UniversityWuhanChina
  2. 2.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote SensingWuhan UniversityWuhanChina

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