# Intelligent Trajectory Design for RIS-NOMA aided Multi-robot Communications

arXiv: https://arxiv.org/abs/2205.01647 · HTML: https://mlsanity.com/paper/2205.01647/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 56 references that are in the map.
- First posted on arXiv 3 May 2022.

## Builds on (most cited first, 18 of 18)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [Dueling Network Architectures for Deep Reinforcement Learning](https://mlsanity.com/paper/1511.06581/) (2015)
- [L2 Regularization versus Batch and Weight Normalization](https://mlsanity.com/paper/1706.05350/) (2017)
- [Intelligent Reflecting Surface Enhanced Wireless Network via Joint Active and Passive Beamforming](https://mlsanity.com/paper/1810.03961/) (2018)
- [Towards Smart and Reconfigurable Environment: Intelligent Reflecting Surface Aided Wireless Network](https://mlsanity.com/paper/1905.00152/) (2019)
- [Wireless Communications Through Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/1906.09490/) (2019)
- [Intelligent Reflecting Surface Enhanced Indoor Robot Path Planning: A Radio Map based Approach](https://mlsanity.com/paper/2009.12804/) (2020)
- [Resource Allocation for Multi-Cell IRS-Aided NOMA Networks](https://mlsanity.com/paper/2006.11811/) (2020)
- [Energy-Efficient Design of IRS-NOMA Networks](https://mlsanity.com/paper/2009.05344/) (2020)
- [Machine Learning Empowered Resource Allocation in IRS Aided MISO-NOMA Networks](https://mlsanity.com/paper/2103.11791/) (2021)
- [A Survey of Intelligent Reflecting Surfaces (IRSs): Towards 6G Wireless Communication Networks](https://mlsanity.com/paper/1907.04789/) (2019)
- [A Simple Design of IRS-NOMA Transmission](https://mlsanity.com/paper/1907.09918/) (2019)
- [Sum Rate Maximization for IRS-assisted Uplink NOMA](https://mlsanity.com/paper/2004.10791/) (2020)
- [Robotic Communications for 5G and Beyond: Challenges and Research Opportunities](https://mlsanity.com/paper/2012.05093/) (2020)
- [Multi-Robot Association-Path Planning in Millimeter-Wave Industrial Scenarios](https://mlsanity.com/paper/2003.09662/) (2020)
- [Trajectory and Passive Beamforming Design for IRS-aided Multi-Robot NOMA Indoor Networks](https://mlsanity.com/paper/2011.12703/) (2020)
- [Energy Efficient Reconfigurable Intelligent Surface Enabled Mobile Edge Computing Networks with NOMA](https://mlsanity.com/paper/2105.00093/) (2021)
- [Intelligent Reflecting Surface Aided Wireless Networks: From Single-Reflection to Multi-Reflection Design and Optimization](https://mlsanity.com/paper/2109.13641/) (2021)

## Abstract (arXiv)

A novel reconfigurable intelligent surface-aided multi-robot network is proposed, where multiple mobile robots are served by an access point (AP) through non-orthogonal multiple access (NOMA). The goal is to maximize the sum-rate of whole trajectories for the multi-robot system by jointly optimizing trajectories and NOMA decoding orders of robots, phase-shift coefficients of the RIS, and the power allocation of the AP, subject to predicted initial and final positions of robots and the quality of service (QoS) of each robot. To tackle this problem, an integrated machine learning (ML) scheme is proposed, which combines long short-term memory (LSTM)-autoregressive integrated moving average (ARIMA) model and dueling double deep Q-network (D3QN) algorithm. For initial and final position prediction for robots, the LSTM-ARIMA is able to overcome the problem of gradient vanishment of non-stationary and non-linear sequences of data. For jointly determining the phase shift matrix and robots' trajectories, D3QN is invoked for solving the problem of action value overestimation. Based on the proposed scheme, each robot holds an optimal trajectory based on the maximum sum-rate of a whole trajectory, which reveals that robots pursue long-term benefits for whole trajectory design. Numerical results demonstrated that: 1) LSTM-ARIMA model provides high accuracy predicting model; 2) The proposed D3QN algorithm can achieve fast average convergence; and 3) RIS-NOMA networks have superior network performance compared to RIS-aided orthogonal counterparts.