# Exploiting Deep Reinforcement Learning for Edge Caching in Cell-Free Massive MIMO Systems

arXiv: https://arxiv.org/abs/2208.12453 · HTML: https://mlsanity.com/paper/2208.12453/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 26 Aug 2022.

## Builds on (most cited first, 10 of 10)

- [Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor](https://mlsanity.com/paper/1801.01290/) (2018)
- [Addressing Function Approximation Error in Actor-Critic Methods](https://mlsanity.com/paper/1802.09477/) (2018)
- [Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation](https://mlsanity.com/paper/1903.10611/) (2019)
- [Prospective Multiple Antenna Technologies for Beyond 5G](https://mlsanity.com/paper/1910.00092/) (2019)
- [Structured Massive Access for Scalable Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/2006.10275/) (2020)
- [Uplink Performance of Cell-Free Massive MIMO with Multi-Antenna Users Over Jointly-Correlated Rayleigh Fading Channels](https://mlsanity.com/paper/2110.04962/) (2021)
- [Uplink Performance of Cell-Free Massive MIMO Over Spatially Correlated Rician Fading Channels](https://mlsanity.com/paper/2110.05796/) (2021)
- [Improving Sum-Rate of Cell-Free Massive MIMO with Expanded Compute-and-Forward](https://mlsanity.com/paper/2111.10717/) (2021)
- [Cell-Free Massive MIMO-OFDM for High-Speed Train Communications](https://mlsanity.com/paper/2203.00900/) (2022)
- [Local Partial Zero-Forcing Combining for Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/2203.10224/) (2022)

## Abstract (arXiv)

Cell-free massive multiple-input-multiple-output is promising to meet the stringent quality-of-experience (QoE) requirements of railway wireless communications by coordinating many successional access points (APs) to serve the onboard users coherently. A key challenge is how to deliver the desired contents timely due to the radical changing propagation environment caused by the growing train speed. In this paper, we propose to proactively cache the likely-requesting contents at the upcoming APs which perform the coherent transmission to reduce end-to-end delay. A long-term QoE-maximization problem is formulated and two cache placement algorithms are proposed. One is based on heuristic convex optimization (HCO) and the other exploits deep reinforcement learning (DRL) with soft actor-critic (SAC). Compared to the conventional benchmark, numerical results show the advantage of our proposed algorithms on QoE and hit probability. With the advanced DRL model, SAC outperforms HCO on QoE by predicting the user requests accurately.