Joint Design of Access and Backhaul in Densely Deployed MmWave Small Cells
Ziqi Guo, Yong Niu, Shiwen Mao, Ruisi He, Ning Wang, Zhangdui Zhong,, Bo Ai

TL;DR
This paper proposes a multi-agent deep reinforcement learning approach to jointly optimize user association and backhaul resource allocation in mmWave HetNets, significantly improving network throughput amid dynamic link conditions.
Contribution
It introduces a novel MADRL-based scheme for joint access and backhaul design in mmWave HetNets, addressing non-convex optimization and link variability.
Findings
Enhanced total link throughput in simulations
Adaptive user association and resource allocation
Effective handling of dynamic mmWave link conditions
Abstract
With the rapid growth of mobile data traffic, the shortage of radio spectrum resource has become increasingly prominent. Millimeter wave (mmWave) small cells can be densely deployed in macro cells to improve network capacity and spectrum utilization. Such a network architecture is referred to as mmWave heterogeneous cellular networks (HetNets). Compared with the traditional wired backhaul, The integrated access and backhaul (IAB) architecture with wireless backhaul is more flexible and cost-effective for mmWave HetNets. However, the imbalance of throughput between the access and backhaul links will constrain the total system throughput. Consequently, it is necessary to jointly design of radio access and backhaul link. In this paper, we study the joint optimization of user association and backhaul resource allocation in mmWave HetNets, where different mmWave bands are adopted by the…
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Taxonomy
TopicsMillimeter-Wave Propagation and Modeling · Advanced MIMO Systems Optimization · Microwave Engineering and Waveguides
