Trajectory and Passive Beamforming Design for IRS-aided Multi-Robot NOMA Indoor Networks
This work addresses the problem of maximizing data rates for mobile robots in indoor environments, which is an incremental improvement for wireless communication systems.
This paper proposes an intelligent reflecting surface (IRS)-aided multi-robot network to maximize the sum-rate of robots served by an access point using non-orthogonal multiple access (NOMA). The Dueling Double Deep Q-Network (D3QN) algorithm is used to jointly optimize robot trajectories, NOMA decoding orders, IRS reflecting coefficients, and AP power allocation, outperforming conventional algorithms and OMA networks.
A novel intelligent reflecting surface (IRS)-aided multi-robot network is proposed, where multiple mobile wheeled robots are served by an access point (AP) through non-orthogonal multiple access (NOMA). The goal is to maximize the sum-rate of all robots by jointly optimizing trajectories and NOMA decoding orders of robots, reflecting coefficients of the IRS, and the power allocation of the AP, subject to the quality of service (QoS) of each robot. To tackle this problem, a dueling double deep Q-network (D^{3}QN) based algorithm is invoked for jointly determining the phase shift matrix and robots' trajectories. Specifically, the trajectories for robots contain a set of local optimal positions, which reveals that robots make the optimal decision at each step. Numerical results demonstrated that the proposed D^{3}QN algorithm outperforms the conventional algorithm, while the performance of IRS-NOMA network is better than the orthogonal multiple access (OMA) network.