Joint Path Selection and Rate Allocation Framework for 5G Self Backhauled mmWave Networks in NS2

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 (mm-wave) networks. Enabling multi-hop mm-wave transmissions raises a potential issue of increased latency, and thus, this paper 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, mm-wave bandwidth, and traffic splitting techniques, is proposed to improve the downlink transmission. The studied problem is cast to as a network utility maximization, subject to the upper delay bound constraint, network stability, and network dynamics. By leveraging stochastic optimization, the problem is decoupled into: 1) path selection and 2) rate allocation sub-problems, whereby a framework which selects the best paths is proposed using reinforcement learning techniques. Moreover, the rate allocation is a non-convex program, which is converted into a convex one by using the successive convex approximation method. Via mathematical analysis, the comprehensive performance analysis and convergence proof are provided for the proposed solution.