Data-driven design for multihop and multi-band cellular networks

Date

2023-01-03

Authors

Gupta, Manan

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Abstract

Millimeter wave (mmWave) integrated access and backhaul (IAB) and multiband heterogeneous networks allow operators to avail more spectral resources and keep up with the intense consumer demand for faster data connectivity. However, these promising network architectures pose new challenges to network resource management, such as multihop routing, link scheduling, and traffic steering, and motivate the re-thinking of traditional solutions. IAB facilitates cost-effective deployment of mmWave cellular networks via multihop self-backhauling, albeit at the cost of poor rate scaling and packet latency. In the first part of this dissertation, we develop data-driven link scheduling policies for IAB networks to minimize the multihop delay while accounting for practical network constraints like feedback delays, choice of half-duplex (HD) or full-duplex (FD) transceivers, and scheduling restrictions. We formulate the link scheduling problem as a Markov decision process (MDP) with a continuous action space and solve it using the deep deterministic policy gradient (DDPG) algorithm. Detailed system-level simulations show that the reinforcement learning RL-based scheduler can reduce the mean delay by 230% and 260% compared to a backpressure scheduler and max-min scheduler respectively. In the second part, we investigate the network-level benefits of upgrading an IAB network with FD transceivers as a potential means to overcome latency and throughput challenges faced by IAB networks. We formulate a network utility maximization problem with practical and tractable throughput and latency constraints to evaluate both FD-IAB and HD-IAB networks and analytically characterize the latency gain from an FD upgrade. Even when the residual self-interference is significantly above the noise floor, this transceiver-level upgrade can improve throughput by 8x and reduce latency by 4x for a fourth-hop user. In the third part, we develop a novel learning-based model predictive control (MPC) approach for base station (BS) selection and band assignment while accounting for user mobility. We first train a deep recurrent neural network to reliably forecast the mobile users' future rates. The MPC controller then uses this forecast to optimize the association decisions to maximize the service rate-based network utility. To efficiently solve the MPC, we also develop an optimization algorithm based on the Frank-Wolfe method. The MPC approach improves the 5th percentile service rate by 2.7x compared to the traditional signal strength-based association. Its performance approaches that of a genie-aided scheme in terms of the number of handovers.

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