README.md

RIS-MISO-DRL

DRL-based RIS Configuration in RIS-assisted MU-MISO mmWave Systems for Min-Max MSE Optimization under HWI of Phase errors, Phase-dependent amplitude response model, and Imperfect CSI

Paper

Working paper on IEEE International Conference on Communications
Rejected by IEEE VTC2024-Spring
Working paper on IEEE Wireless Communication Letters
Rejected by IEEE Globecom 2024
Working paper on IEEE Wireless Communications and Networking Conference
Accepted by IEEE WCNC 2025

Note

I'll upload the code once I graduate or the paper gets accepted Never mind.
The code is a mess btw.

Installing

• Install Anaconda
• Import the environment

  conda env create --file sb3.yaml --name sb3
  

Meeting Notes

Spring 2024

• meeting 03/12 Current Progress
  • psi-to-MSE
• meeting 03/05 Current Progress
  • MSE-Matrix vs Signal-Tx
  • Nk-to-MSE
    • PPO-[3, 6, 8, 10]-16-16
  • Nt-to-MSE
    • PPO-2-[8, 16, 32, 64]-16
  • Ns-to-MSE
    • PPO-2-16-[16, 36, 64, 100]
  • beta_min-to-MSE
    • PPO-2-16-36
  • psi-to-MSE
    • PPO-2-16-16
• meeting 02/26 Current Progress
  • Bugs fixing
  • Validate self-identity
  • Ns-to-MSE
• meeting 02/20 Current Progress
  • Ns-to-MSE
• meeting 01/23 Current Progress
  • Nk-to-MSE
• meeting 01/09 Current Progress
  • Baseline method
    • Dominant Eigenvector Matching (DEM) heuristic for RIS Configuration
      • Performance: SDR > DEM > Power method
      • Speed: DEM > Power method > SDR
    • Max Ratio Transmission (MRT) for Precoder Design
  • Appendix
    • Validate MSE values with compute_raw_MSE()
  • Reference
    • N. K. Kundu and M. R. McKay, "RIS-Assisted MISO Communication: Optimal Beamformers and Performance Analysis," 2020 IEEE Globecom Workshops (GC Wkshps, Taipei, Taiwan, 2020, pp. 1-6. (Cited by 13)
    • S. Ragi, E. K. P. Chong and H. D. Mittelmann, "Polynomial-Time Methods to Solve Unimodular Quadratic Programs With Performance Guarantees," in IEEE Transactions on Aerospace and Electronic Systems, vol. 55, no. 5, pp. 2118-2127, Oct. 2019. (Cited by 6)
    • J. Gao, C. Zhong, X. Chen, H. Lin and Z. Zhang, "Unsupervised Learning for Passive Beamforming," in IEEE Communications Letters, vol. 24, no. 5, pp. 1052-1056, May 2020.
• meeting 01/02 Current Progress
  • Inference result: PPO-2-16-[4, 16, 36, 64, 100]
  • Confidence Interval: Random vs. Agent

Fall 2023

• meeting 12/19 Current Progress
  • PPO-2-16-[4, 9, 16, 25, 36, 64]
  • Comparison of different settings
• meeting 12/14 Current Progress
  • M. -A. Badiu and J. P. Coon, "Communication Through a Large Reflecting Surface With Phase Errors," in IEEE Wireless Communications Letters, vol. 9, no. 2, pp. 184-188, Feb. 2020.
  • R. Kozlica, S. Wegenkittl and S. Hiränder, "Deep Q-Learning versus Proximal Policy Optimization: Performance Comparison in a Material Sorting Task," 2023 IEEE 32nd International Symposium on Industrial Electronics (ISIE), Helsinki, Finland, 2023, pp. 1-6.
• meeting 12/13 Current Progress
  • PPO-2-16-[4, 36]
  • PPO-2-16-9
  • PPO-2-16-25
  • PPO-[2, 4, 6, 8, 10]-16-16
  • PPO-10-16-36
• meeting 12/05 Current Progress
  • System validation: Brute force check
    • Try every possible combination of actions
    • Plot the Sum-Rate for every possible actions
  • Update Max Ratio Transmission (MRT)
    • J. Gao, C. Zhong, X. Chen, H. Lin and Z. Zhang, "Unsupervised Learning for Passive Beamforming," in IEEE Communications Letters, vol. 24, no. 5, pp. 1052-1056, May 2020.
    • D. Tse and P. Viswanath, Fundamentals of Wireless Communication, Cambridge, U.K.:Cambridge Univ. Press, 2005.
  • Training results
  • Inference results
  • Plotting functions
  • Future works
    • Adding more neurons in each layer
    • Deepen the network architecture
    • PPO default network architecture is [64, 64] for both actor and critic networks
• meeting 11/28 Current Progress
  • New feature: seed_everything()
  • Bug fixing
  • Training results
    • PPO (1-4-4 to 4-4-4, and 4-16-16)
    • A2C (1-4-4 to 4-4-4)
  • Training of more complex settings with PPO (4-16-16)
  • Training of more episodes with PPO (1000 episodes)
  • Comparison of all continuous agents (TD3, DDPG, A2C, PPO, SAC)
• meeting 11/21 Current Progress
  • Training results
    • Scaling rewards doesn't actually work
  • Channel model
    • General Communication Systems
  • Problem formulations
    • Max-min downlink rate
    • Sum-Rate Maximization
  • Future works
    • Go back to Box discrete
• meeting 11/16 Summary
  • System model
    • Downlink RIS-aided MU-MISO System
  • Channel model
    • mmWave Systems
    • General Communication Systems
  • Steering vectors
    • ULA, UPA, USPA
    • Array response implementations in torch
  • Problem formulations
    • Min-max MSE
    • Max-min downlink rate
    • Sum-Rate Maximization
• meeting 11/14 Channel model - mmWave Systems
  • P. Wang, J. Fang, L. Dai and H. Li, "Joint Transceiver and Large Intelligent Surface Design for Massive MIMO mmWave Systems," in IEEE Transactions on Wireless Communications, vol. 20, no. 2, pp. 1052-1064, Feb. 2021. (Cited by 80)
  • K. Ying, Z. Gao, S. Lyu, Y. Wu, H. Wang and M. -S. Alouini, "GMD-Based Hybrid Beamforming for Large Reconfigurable Intelligent Surface Assisted Millimeter-Wave Massive MIMO," in IEEE Access, vol. 8, pp. 19530-19539, 2020. (Cited by 91)
• meeting 11/07 Steering vectors
  • K. Ying, Z. Gao, S. Lyu, Y. Wu, H. Wang and M. -S. Alouini, "GMD-Based Hybrid Beamforming for Large Reconfigurable Intelligent Surface Assisted Millimeter-Wave Massive MIMO," in IEEE Access, vol. 8, pp. 19530-19539, 2020. (Cited by 91)
  • J. Yuan, Y. -C. Liang, J. Joung, G. Feng and E. G. Larsson, "Intelligent Reflecting Surface-Assisted Cognitive Radio System," in IEEE Transactions on Communications, vol. 69, no. 1, pp. 675-687, Jan. 2021. (Cited by 130)
• meeting 10/31 Random action rewards
  • Random action rewards
  • TODO list
    • Inference
    • more anttenas do help
    • more bits don't actually help
• meeting 10/30 Current Progress
  • Training results
  • Inference results
• meeting 10/24 Current Progress
  • Bugs fixing
  • Training results
    • PPO, A2C DQN
    • Compare differenct models with their best performance
    • Compare different numbers of users
    • Compare the complexity of different settings
• meeting 10/17 Current Progress
  • True Discrete action space version
  • Normalize Box action space
  • Apply GPU acceleration
  • Learn and Save
  • Load and Predict
• meeting 10/03 Custom Gym Environment
  • Environment built
  • Able to train
  • Future works
• meeting 09/26 MU-MISO system model
  • System model
  • Problem formulation
  • MSE derivation
• meeting 09/14 MU-MIMO system model and possible methods
• meeting 09/12 MSE derivation
  • K. -Y. Chen, H. -Y. Chang, R. Y. Chang and W. -H. Chung, "Hybrid Beamforming in mmWave MIMO-OFDM Systems via Deep Unfolding," 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring), Helsinki, Finland, 2022, pp. 1-7.
  • X. Zhao, T. Lin, Y. Zhu and J. Zhang, "Partially-Connected Hybrid Beamforming for Spectral Efficiency Maximization via a Weighted MMSE Equivalence," in IEEE Transactions on Wireless Communications, vol. 20, no. 12, pp. 8218-8232, Dec. 2021.
• meeting 09/05 Paper reading
  • W. -Y. Chen, C. -Y. Wang, R. -H. Hwang, W. -T. Chen and S. -Y. Huang, "Impact of Hardware Impairment on the Joint Reconfigurable Intelligent Surface and Robust Transceiver Design in MU-MIMO System," in IEEE Transactions on Mobile Computing.
• meeting 08/29 Paper reading
  • C. Huang, R. Mo and C. Yuen, "Reconfigurable Intelligent Surface Assisted Multiuser MISO Systems Exploiting Deep Reinforcement Learning," in IEEE Journal on Selected Areas in Communications, vol. 38, no. 8, pp. 1839-1850, Aug. 2020. (Cited by 397)
• meeting 08/22 Paper reading
  • Saglam Baturay, Doga Gurgunoglu, and Suleyman S. Kozat. "Deep Reinforcement Learning Based Joint Downlink Beamforming and RIS Configuration in RIS-aided MU-MISO Systems Under Hardware Impairments and Imperfect CSI." arXiv preprint arXiv:2211.09702 (2022).
    • which was accepted to 2023 IEEE International Conference on Communications the 5th Workshop on Data Driven Intelligence for Networks and Systems (DDINS).