# Handover Control in Wireless Systems via Asynchronous Multi-User Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1801.02077 · HTML: https://mlsanity.com/paper/1801.02077/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2018.

## Built on by (most cited first, 9 of 9)

- [ColO-RAN: Developing Machine Learning-based xApps for Open RAN Closed-loop Control on Programmable Experimental Platforms](https://mlsanity.com/paper/2112.09559/) (2021)
- [Programmable and Customized Intelligence for Traffic Steering in 5G Networks Using Open RAN Architectures](https://mlsanity.com/paper/2209.14171/) (2022)
- [Asynchronous Hybrid Reinforcement Learning for Latency and Reliability Optimization in the Metaverse over Wireless Communications](https://mlsanity.com/paper/2212.14749/) (2022)
- [Compacting Deep Neural Networks for Internet of Things: Methods and Applications](https://mlsanity.com/paper/2103.11083/) (2021)
- [Can You Fix My Neural Network? Real-Time Adaptive Waveform Synthesis for Resilient Wireless Signal Classification](https://mlsanity.com/paper/2103.03745/) (2021)
- [Neighbor Auto-Grouping Graph Neural Networks for Handover Parameter Configuration in Cellular Network](https://mlsanity.com/paper/2301.03412/) (2022)
- [User-centric Heterogeneous-action Deep Reinforcement Learning for Virtual Reality in the Metaverse over Wireless Networks](https://mlsanity.com/paper/2302.01471/) (2023)
- [Handoff Design in User-Centric Cell-Free Massive MIMO Networks Using DRL](https://mlsanity.com/paper/2507.20966/) (2025)
- [GCN-Based Throughput-Oriented Handover Management in Dense 5G Vehicular Networks](https://mlsanity.com/paper/2505.04894/) (2025)

## Abstract (arXiv)

In this paper, we propose a two-layer framework to learn the optimal handover (HO) controllers in possibly large-scale wireless systems supporting mobile Internet-of-Things (IoT) users or traditional cellular users, where the user mobility patterns could be heterogeneous. In particular, our proposed framework first partitions the user equipments (UEs) with different mobility patterns into clusters, where the mobility patterns are similar in the same cluster. Then, within each cluster, an asynchronous multi-user deep reinforcement learning scheme is developed to control the HO processes across the UEs in each cluster, in the goal of lowering the HO rate while ensuring certain system throughput. In this scheme, we use a deep neural network (DNN) as an HO controller learned by each UE via reinforcement learning in a collaborative fashion. Moreover, we use supervised learning in initializing the DNN controller before the execution of reinforcement learning to exploit what we already know with traditional HO schemes and to mitigate the negative effects of random exploration at the initial stage. Furthermore, we show that the adopted global-parameter-based asynchronous framework enables us to train faster with more UEs, which could nicely address the scalability issue to support large systems. Finally, simulation results demonstrate that the proposed framework can achieve better performance than the state-of-art on-line schemes, in terms of HO rates.