Integrated Access and Backhauling (IAB) is a viable approach for meeting the
unprecedented need for higher data rates of future generations, acting as a
cost-effective alternative to dense fiber-wired links. The design of such
networks with constraints usually results in an optimization problem of
non-convex and combinatorial nature. Under those situations, it is challenging
to obtain an optimal strategy for the joint Subchannel Allocation and Power
Allocation (SAPA) problem. In this paper, we develop a multi-agent Deep
Reinforcement Learning (DeepRL) based framework for joint optimization of power
and subchannel allocation in an IAB network to maximize the downlink data rate.
SAPA using DDQN (Double Deep Q-Learning Network) can handle computationally
expensive problems with huge action spaces associated with multiple users and
nodes. Unlike the conventional methods such as game theory, fractional
programming, and convex optimization, which in practice demand more and more
accurate network information, the multi-agent DeepRL approach requires less
environment network information. Simulation results show the proposed scheme's
promising performance when compared with baseline (Deep Q-Learning Network and
Random) schemes.

开发了一个基于多智能体深度强化学习框架的集成接入和回传网络的联合优化方法，该方法通过 SAPA (Subchannel Allocation and Power Allocation) 问题的深度 Q 学习网络和随机方案的基线比较，展示了有希望的性能。