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Joint Path Selection and Rate Allocation Framework for 5G Self-Backhauled mmWave Networks

JOINT PATH SELECTION AND RATE ALLOCATION FRAMEWORK FOR 5G SELF-BACKHAULED MMWAVE NETWORKS

  • Project Code :
  • TMRECO19_07
  • .
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JOINT PATH SELECTION AND RATE ALLOCATION FRAMEWORK FOR 5G SELF-BACKHAULED MMWAVE NETWORKS

Owing to severe path loss and unreliable transmission over a long distance at higher frequency bands, this paper investigates the problem of path selection and rate allocation for multi-hop self-backhaul millimeter wave (mmWave) networks. Enabling multi-hop mmWave transmissions raises a potential issue of increased latency, and thus, this work aims at addressing the fundamental questions: “how to select the best multi-hop paths and how to allocate rates over these paths subject to latency constraints?”. In this regard, a new system design, which exploits multiple antenna diversity, mmWave bandwidth, and traffic splitting techniques, is proposed to improve the downlink transmission. The studied problem is cast as a network utility maximization, subject to an upper delay bound constraint, network stability and network dynamics. By leveraging stochastic optimization, the problem is decoupled into: ¹iº path selection and ¹iiº rate allocation sub-problems, whereby a framework which selects the best paths is proposed using reinforcement learning techniques. Moreover, the rate allocation is a nonconvex program, which is converted into a convex one by using the successive convex approximation method. Via mathematical analysis, a comprehensive performance analysis and convergence proof are provided for the proposed solution. Numerical results show that the proposed approach ensures reliable communication with a guaranteed probability of up to 99:9999%, and reduces latency by 50:64% and 92:9% as compared to baseline models. Furthermore, the results showcase the key trade-off between latency and network arrival rate.

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