# Beamforming and Resource Allocation for Delay Minimization in RIS-Assisted OFDM Systems

arXiv: https://arxiv.org/abs/2506.03586 · HTML: https://mlsanity.com/paper/2506.03586/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 45 references that are in the map.
- First posted on arXiv 4 Jun 2025.

## Builds on (most cited first, 17 of 17)

- [On the Road to 6G: Visions, Requirements, Key Technologies and Testbeds](https://mlsanity.com/paper/2302.14536/) (2023)
- [The Road Towards 6G: A Comprehensive Survey](https://mlsanity.com/paper/2102.01420/) (2021)
- [Wireless Communications Through Reconfigurable Intelligent Surfaces](https://mlsanity.com/paper/1906.09490/) (2019)
- [Reconfigurable Intelligent Surfaces: Principles and Opportunities](https://mlsanity.com/paper/2007.03435/) (2020)
- [Federated Learning and Wireless Communications](https://mlsanity.com/paper/2005.05265/) (2020)
- [Ultra-Reliable and Low-Latency Wireless Communication: Tail, Risk and Scale](https://mlsanity.com/paper/1801.01270/) (2018)
- [Weighted Sum-Rate Maximization for Reconfigurable Intelligent Surface Aided Wireless Networks](https://mlsanity.com/paper/1912.11999/) (2019)
- [Latency Minimization for Intelligent Reflecting Surface Aided Mobile Edge Computing](https://mlsanity.com/paper/1910.07990/) (2019)
- [The Road to 6G: Ten Physical Layer Challenges for Communications Engineers](https://mlsanity.com/paper/2004.07130/) (2020)
- [Low-Latency Millimeter-Wave Communications: Traffic Dispersion or Network Densification?](https://mlsanity.com/paper/1709.08410/) (2017)
- [Joint Power Control and Rate Allocation enabling Ultra-Reliability and Energy Efficiency in SIMO Wireless Networks](https://mlsanity.com/paper/1905.05434/) (2019)
- [Physical Layer Security Enhancement Exploiting Intelligent Reflecting Surface](https://mlsanity.com/paper/1911.02766/) (2019)
- [Queue-Aware STAR-RIS Assisted NOMA Communication Systems](https://mlsanity.com/paper/2202.12333/) (2022)
- [Quantized Phase Alignment by Discrete Phase Shifts for Reconfigurable Intelligent Surface-Assisted Communication Systems](https://mlsanity.com/paper/2303.13046/) (2023)
- [Power-Delay Tradeoff with Predictive Scheduling in Integrated Cellular and Wi-Fi Networks](https://mlsanity.com/paper/1512.06428/) (2015)
- [Secure Wireless Communication in RIS-Aided MISO Systems with Hardware Impairments](https://mlsanity.com/paper/2012.12733/) (2020)
- [Power Allocation for Delay Optimization in Device-to-Device Networks: A Graph Reinforcement Learning Approach](https://mlsanity.com/paper/2505.12902/) (2025)

## Built on by (most cited first, 1 of 1)

- [Optimization Algorithms for Joint OFDM Waveform Design and RIS Configuration in 6G Networks: From Convex Relaxation to Foundation Models](https://mlsanity.com/paper/2606.31334/) (2026)

## Abstract (arXiv)

This paper investigates a joint beamforming and resource allocation problem in downlink reconfigurable intelligent surface (RIS)-assisted orthogonal frequency division multiplexing (OFDM) systems to minimize the average delay, where data packets for each user arrive at the base station (BS) stochastically. The sequential optimization problem is inherently a Markov decision process (MDP), thus falling within the remit of reinforcement learning. To effectively handle the mixed action space and reduce the state space dimensionality, a hybrid deep reinforcement learning (DRL) approach is proposed. Specifically, proximal policy optimization (PPO)-Theta is employed to optimize the RIS phase shift design, while PPO-N is responsible for subcarrier allocation decisions. The active beamforming at the BS is then derived from the jointly optimized RIS phase shifts and subcarrier allocation decisions. To further mitigate the curse of dimensionality associated with subcarrier allocation, a multi-agent strategy is introduced to optimize the subcarrier allocation indicators more efficiently. Moreover, to achieve more adaptive resource allocation and accurately capture the network dynamics, key factors closely related to average delay, such as the number of backlogged packets in buffers and current packet arrivals, are incorporated into the state space. Furthermore, a transfer learning framework is introduced to enhance the training efficiency and accelerate convergence. Simulation results demonstrate that the proposed algorithm significantly reduces the average delay, enhances resource allocation efficiency, and achieves superior system robustness and fairness compared to baseline methods.