# Handoff Design in User-Centric Cell-Free Massive MIMO Networks Using DRL

arXiv: https://arxiv.org/abs/2507.20966 · HTML: https://mlsanity.com/paper/2507.20966/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 40 references that are in the map.
- First posted on arXiv 28 Jul 2025.

## Builds on (most cited first, 24 of 24)

- [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)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Soft Actor-Critic Algorithms and Applications](https://mlsanity.com/paper/1812.05905/) (2018)
- [Scalable Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/1908.03119/) (2019)
- [Making Cell-Free Massive MIMO Competitive With MMSE Processing and Centralized Implementation](https://mlsanity.com/paper/1903.10611/) (2019)
- [Machine Learning for Reliable mmWave Systems: Blockage Prediction and Proactive Handoff](https://mlsanity.com/paper/1807.02723/) (2018)
- [Handover Control in Wireless Systems via Asynchronous Multi-User Deep Reinforcement Learning](https://mlsanity.com/paper/1801.02077/) (2018)
- [User-centric Cell-free Massive MIMO Networks: A Survey of Opportunities, Challenges and Solutions](https://mlsanity.com/paper/2104.14589/) (2021)
- [Reinforcement Learning Based Vehicle-cell Association Algorithm for Highly Mobile Millimeter Wave Communication](https://mlsanity.com/paper/2001.07915/) (2020)
- [Downlink Resource Allocation in Multiuser Cell-free MIMO Networks with User-centric Clustering](https://mlsanity.com/paper/2108.06316/) (2021)
- [User-centric Handover in mmWave Cell-Free Massive MIMO with User Mobility](https://mlsanity.com/paper/2105.09041/) (2021)
- [Distributed Resource Allocation Optimization for User-Centric Cell-Free MIMO Networks](https://mlsanity.com/paper/2110.08154/) (2021)
- [Cell-Free Massive MIMO in the O-RAN Architecture: Cluster and Handover Strategies](https://mlsanity.com/paper/2301.07618/) (2023)
- [Handoffs in User-Centric Cell-Free MIMO Networks: A POMDP Framework](https://mlsanity.com/paper/2403.08900/) (2024)
- [Uplink resource allocation optimization for user-centric cell-free MIMO networks](https://mlsanity.com/paper/2406.05576/) (2024)
- [Statistical Analysis of Downlink Zero-Forcing Beamforming](https://mlsanity.com/paper/2008.01875/) (2020)
- [Optimizing RRH Placement Under a Noise-Limited Point-to-Point Wireless Backhaul](https://mlsanity.com/paper/2102.02970/) (2021)
- [Impact of Channel Aging on Zero-Forcing Precoding in Cell-Free Massive MIMO Systems](https://mlsanity.com/paper/2107.01404/) (2021)
- [RWP+: A New Random Waypoint Model for High-Speed Mobility](https://mlsanity.com/paper/2109.05978/) (2021)
- [Analysis and Design of Distributed MIMO Networks with a Wireless Fronthaul](https://mlsanity.com/paper/2111.09826/) (2021)
- [Uplink Performance of High-Mobility Cell-Free Massive MIMO-OFDM Systems](https://mlsanity.com/paper/2201.09622/) (2022)
- [POMDP-based Handoffs for User-Centric Cell-Free MIMO Networks](https://mlsanity.com/paper/2208.02384/) (2022)
- [Soft Handover Procedures in mmWave Cell-Free Massive MIMO Networks](https://mlsanity.com/paper/2209.02548/) (2022)

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

- [Threat-Aware Energy-Efficient Deployment for Dynamic UAV Networks: A Multi-Agent RL Approach](https://mlsanity.com/paper/2609.30690/) (2026)

## Abstract (arXiv)

In the user-centric cell-free massive MIMO (UC-mMIMO) network scheme, user mobility necessitates updating the set of serving access points to maintain the user-centric clustering. Such updates are typically performed through handoff (HO) operations; however, frequent HOs lead to overheads associated with the allocation and release of resources. This paper presents a deep reinforcement learning (DRL)-based solution to predict and manage these connections for mobile users. Our solution employs the Soft Actor-Critic algorithm, with continuous action space representation, to train a deep neural network to serve as the HO policy. We present a novel proposition for a reward function that integrates a HO penalty in order to balance the attainable rate and the associated overhead related to HOs. We develop two variants of our system; the first one uses mobility direction-assisted (DA) observations that are based on the user movement pattern, while the second one uses history-assisted (HA) observations that are based on the history of the large-scale fading (LSF). Simulation results show that our DRL-based continuous action space approach is more scalable than discrete space counterpart, and that our derived HO policy automatically learns to gather HOs in specific time slots to minimize the overhead of initiating HOs. Our solution can also operate in real time with a response time less than 0.4 ms.