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Handover Control in Wireless Systems via Asynchronous Multi-User Deep Reinforcement Learning
Zhi Wang, Lihua Li, Yue Xu, Hui Tian, Shuguang Cui
TL;DR
Frequent handovers in dense wireless networks can disrupt data flows and consume resources, while heterogeneous mobility and random-start learning complicate controller design. The paper clusters UEs by mobility and uses asynchronous multi-user deep reinforcement learning, with supervised initialization. Its simulations report lower handover rates and higher throughput than UCB after equal learning time, with further improvement from clustering.
Problem
Heterogeneous mobility patterns call for different handover controllers, while prior model-based approaches rely on network-dynamics knowledge that may be difficult to assume in practice.
Source: §I · p. 2Method
A centralized controller clusters UEs by mobility, then asynchronous multi-user reinforcement learning with DNN controllers learns handover policies within each cluster.
Source: §I · p. 2Results
After equal learning time, the proposed methods achieved better throughput and lower handover rates than UCB, and clustering with a global parameter server further improved performance.
Source: B. Simulation Results · p. 11Takeaways & Limitations
The asynchronous framework is reported to accelerate individual handover-controller learning as more UEs join the system.
Source: B. Asynchronous Joint Learning Scheme · p. 6
Abstract
from arXiv · showhide
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.
I. INTRODUCTION
In ultra-dense networks, frequent handovers can disrupt data flows and consume network resources, while heterogeneous mobility and cold starts complicate learning effective controllers. The paper proposes clustering UEs and learning handover controllers within each cluster.
I. INTRODUCTION
- Frequent handovers can disrupt data flows, consume energy and other resources, and diminish capacity gains from network densification.The paper defines handover rate as the expected number of handovers per unit time.Source: §I · p. 1
- Different mobility patterns across scenarios require different optimal controllers, while new scenarios and arriving UEs may face poor performance from random starting points.The paper identifies these as two issues with applying prior learning-based methods to large-scale mobility optimization.Source: §I · p. 2
- A centralized controller clusters UEs by mobility pattern, and reinforcement learning then learns a handover controller for each UE in each cluster.The paper models handover processes within a cluster as similar MDPs.Source: §I · p. 2
- The proposed model-free A3C framework learns handover policies asynchronously within clusters and is described as accelerating learning as UE numbers increase.The paper contrasts this with prior methods whose learning time increases at least linearly with the number of UEs,.Source: §I · p. 2
- DNNs approximate the Q-function, and newly arriving UEs can obtain pretrained cluster-network weights for online learning or static offline control.The paper cautions that performance may degrade if the DNN cannot generalize across the whole state space.Source: §I · p. 2
- Supervised learning initializes the DNN from outputs of the traditional 3GPP handover scheme before reinforcement learning begins.This initialization is intended to improve performance before RL training, for both online and offline methods.Source: §I · p. 2
II. PRELIMINARY
The preliminaries introduce reinforcement learning and its MDP formulation. In an MDP, an agent acts from observed states and receives rewards and new states, with value functions representing expected discounted rewards.
II. PRELIMINARY
- The paper introduces MDPs, value functions, policy-gradient methods, and LSTM networks as preliminaries for its learning scheme.The LSTM is later used as the DQN function approximator in the proposed RL framework.Source: §II · p. 3
A. Markov Decision Process
- At each time step, an RL agent observes a state, chooses an action under its policy, and receives a reward and the next state.The MDP is represented by state and action spaces, transition probabilities, a policy, a reward function, and a discount factor.Source: A. Markov Decision Process · p. 3
- Action-value and state-value functions describe expected discounted rewards under a policy, and the optimal policy is characterized through Bellman fixed points.The passage identifies Qπ and Vπ as fixed points of the policy Bellman operator, and Q* and V* as fixed points of the optimal Bellman operator.Source: A. Markov Decision Process · p. 3
B. Value-Function Prediction with Deep Neural Network
The section describes DNN-based value-function prediction and policy-gradient updates. Value prediction estimates action values from sampled experience, while policy gradients update a parameterized policy directly.
B. Value-Function Prediction with Deep Neural Network
- DNN approximators estimate value functions over large state spaces when transition probabilities are difficult to obtain in practical systems.The prediction problem is posed as an optimization over the state distribution, which generally depends on unknown transition probabilities and policy.Source: B. Value-Function Prediction with Deep… · pp. 3–4
- Stochastic-gradient updates use sampled state-action pairs and an estimated target because the true action value is unknown.The passage gives Monte Carlo and TD(0) bootstrapping as target choices.Source: B. Value-Function Prediction with Deep… · p. 4
C. Policy Gradient Methods
- Policy-gradient methods parameterize and update the policy directly, using stochastic gradients and value-function estimates for the gradient terms.The policy is parameterized by θ, and Qπθ can be estimated with the previously described value-function prediction methods.Source: C. Policy Gradient Methods · p. 4
D. LSTM
The system groups UEs by mobility pattern and learns handover controllers within each cluster. Its clustering uses mobility features, while the DNN architecture and cluster setup define the learning context.
D. LSTM
- The DNN approximator uses an LSTM unit with a fully connected input layer and separate output layers for value function and policy.The LSTM gates update cell and hidden states to retain information across time steps.Source: D. LSTM · p. 4
III. SYSTEM MODEL
- The system models UEs across potentially nonadjacent areas as episodic MDPs, assuming UEs in the same area share similar mobility patterns.The centralized controller assigns UEs to mobility-based clusters, and each UE selects a serving SBS at each time step.Source: §III · p. 4
A. Clustering by Centralized Controller
- The controller clusters mobility feature vectors containing UE coordinates and speeds over a fixed observation period, using K-means to minimize intra-cluster variance.The distance between a UE feature vector and a cluster centroid uses a weight factor.Source: A. Clustering by Centralized Controller · p. 5
- Because the cluster count is unknown, clustering validation compares candidate counts up to the number of areas and selects the best result by internal validation indices.New UEs fetch parameters from their cluster's server; all UEs are periodically re-clustered on the mobility-pattern timescale, usually hours.Source: A. Clustering by Centralized Controller · p. 5
- Newly arriving UEs fetch parameters from their cluster's server, and all UEs are periodically re-clustered on the mobility-pattern timescale, usually hours.The paper skips the details of handling these aspects to focus on other core issues.Source: A. Clustering by Centralized Controller · p. 5
B. Asynchronous Joint Learning Scheme
Within each cluster, UEs collaborate through a parameter server to learn handover controllers asynchronously. The framework's convergence property is established, and the paper reports faster individual-controller learning as more UEs join.
B. Asynchronous Joint Learning Scheme
- Each UE fetches the latest controller parameters, interacts with the environment to compute a gradient, then sends the gradient to the cluster's parameter server for a global update.The server updates global controller weights asynchronously and lock-free.Source: B. Asynchronous Joint Learning Scheme · p. 5
- The framework uses an advantage actor-critic algorithm to compute controller-network gradients, assuming DQN is used.The UE pushes each new gradient back to the cluster's parameter server.Source: B. Asynchronous Joint Learning Scheme · p. 5
- The asynchronous framework's convergence property has been established, and the paper reports that individual handover-controller learning accelerates as more UEs join.The figure caption depicts UEs in area l belonging to cluster h.Source: B. Asynchronous Joint Learning Scheme · p. 6, Figure 4 · p. 6
C. Action
A UE chooses a serving SBS by index, while its state combines radio measurements with its previous serving choice. The reward balances throughput against handovers and shapes intermediate feedback.
C. Action
- The action is a scalar identifying the serving SBS selected by a UE at each time step.The choice is made from the UE's active SBS set.Source: C. Action · p. 6
D. State Vector
- The state combines RSRQs from candidate SBSs with a one-hot representation of the UE's previous serving-SBS choice.The previous action encodes exploration information alongside the RSRQ situation information.Source: D. State Vector · p. 6
E. Reward Signal
- The reward balances averaged throughput against handover rate, with reward shaping providing informative feedback at each time step.The throughput and handover-rate measures divide, respectively, summed rate and handover count by episode time steps; the reward subtracts βE when a handover occurs.Source: E. Reward Signal · p. 6
IV. PROBLEM FORMULATION AND LEARNING ALGORITHM
The section formulates per-UE policy learning after clustering and describes trajectory-based updates, policy evaluation, and the implementation loop. It combines policy improvement with value estimation, whose iterative evaluation is described as convergent under the stated contraction condition.
A. Problem Formulation and Learning Algorithm
- Each UE’s objective is to learn policy parameters, with sampled trajectory gradients used to update its policy incrementally.The derivation omits area and cluster subscripts for simplicity.Source: A. Problem Formulation and Learning… · pp. 6–7
- The n-step estimator combines Monte Carlo and temporal-difference prediction to balance variance against bias; terminal trajectories reduce to Monte Carlo sampling.Monte Carlo estimates are unbiased but high-variance, whereas TD can lower variance while introducing bias when estimation is inaccurate.Source: A. Problem Formulation and Learning… · p. 6
- A value-function baseline is subtracted from the estimated action value to form an advantage and further decrease gradient variance.The passage also describes a separate value-network gradient calculation.Source: A. Problem Formulation and Learning… · p. 7
- Policy improvement and value estimation are iterated; if both stabilize, the estimated value function and policy are at least locally optimal.The policy gradient is described as converging to a local optimum, while value evaluation converges through repeated applications of a γ-contraction Bellman operator.Source: A. Problem Formulation and Learning… · p. 7
B. Learning Algorithm Implementation
- The UE repeats interaction under its policy, storing each transition and continuing until the n-step trajectory is collected.The passage identifies the resulting process as the learning algorithm implementation shown in Fig. 5.Source: B. Learning Algorithm Implementation · p. 7
- A UE collects n-step state-action-reward trajectories, estimates values and advantages by forward propagation, then back-propagates accumulated gradients.The implementation overview is identified as Fig. 5.Source: B. Learning Algorithm Implementation · p. 7, Figure 5 · p. 7
C. DQN in HO Controller
The HO controller uses a three-layer actor-critic network with shared encoding and LSTM components. Its recurrent design captures movement history, while truncated back-propagation and asynchronous RMSProp support learning and parameter updates.
C. DQN in HO Controller
- The controller uses a three-layer network with encoding, LSTM, and value and policy output layers; actors and critics share the encoding and LSTM weights.The repeating module contains the encoding layer, LSTM unit, and value and policy output layers.Source: C. DQN in HO Controller · pp. 7–8, Figure 7 · p. 8
- The policy head outputs a softmax distribution over candidate SBSs, while the critic outputs a scalar state-value estimate.The action is selected according to the policy distribution.Source: C. DQN in HO Controller · p. 8
- LSTM units capture movement patterns from location history and accumulate exploration information for the exploration–exploitation tradeoff.The repeating module’s LSTM can learn long-range temporal information using forget, input, cell, and output gates.Source: C. DQN in HO Controller · p. 8
- Truncated back-propagation through time runs every n steps to alleviate error accumulation, carrying the previous block’s cell and hidden states forward.If the previous block ends in a terminal state, both initial states for the next block are zero vectors.Source: C. DQN in HO Controller · p. 8
- Asynchronous RMSProp uses a decayed gradient accumulator to update shared cluster-level DQN weights from UE gradients.The passage states that the same global-update procedure applies to the value-network weights.Source: C. DQN in HO Controller · p. 8
D. Initialization with Supervised Learning
The method initializes parts of the HO controller through supervised learning on actions generated by a traditional HO scheme. This uses five HHM/TTT pairs and five training episodes per cluster; the value output remains randomly initialized.
D. Initialization with Supervised Learning
- The asynchronous learning algorithm fetches cluster parameters, accumulates UE gradients, and applies updates at the parameter server.Its loop uses local and global time counters and repeats until learning is done.Source: D. Initialization with Supervised… · p. 9
- Supervised initialization uses actions generated by a traditional HO scheme from RSRPs in each UE’s active set.The resulting state includes active-set RSRPs and the serving SBS index.Source: D. Initialization with Supervised… · p. 9
- Each cluster’s training data comprise five episodes sampled using five different HHM and TTT pairs.The training examples are state-action sequences from those episodes.Source: D. Initialization with Supervised… · p. 9
- Supervised learning initializes the encoding, LSTM, and policy-softmax layers, but the value-output layer is initialized randomly because system dynamics are unknown.The passages present this initialization as a way to boost RL-framework performance.Source: D. Initialization with Supervised… · pp. 8–9
V. SIMULATION
The simulation section presents its setup, then its results, and finally discusses an upper bound on the number of UEs per cluster.
A. Simulation Setups
The simulations assess clustering, the HO-rate/throughput tradeoff, learning behavior, and performance against UCB. The proposed methods achieve lower HO rates and higher throughput than UCB after equal learning time, with clustering further improving performance.
A. Simulation Setups
- The simulation uses three nonadjacent 16×16 m² areas with six SBSs each, random-walk UE mobility, and speeds of 1–3 m/s.Interference is not considered.Source: A. Simulation Setups · p. 9
B. Simulation Results
- Two clusters are preferred under the tested setup, based on the Calinski-Harabaz index (CHI) averaged over 100 runs.CHI assesses clustering goodness across datasets with 4, 40, and 400 UEs per area; the table reports the validation results.Source: B. Simulation Results · p. 9, Table I · p. 9
- Larger reward weight β encourages lower HO rates at the cost of throughput.Fig. 8 compares the HO-rate/throughput tradeoff for β = 5, 10, 15, 20, and 25.Source: B. Simulation Results · p. 10, Figure 8 · p. 10
- With β = 25 and a 119-second learning period, SL initialization mitigates early degradation, while clustering yields larger state values than no clustering.The comparison concerns UEs in A1 and contrasts shared parameter servers for A1 and A2 against one for A1 alone.Source: B. Simulation Results · p. 10, Figure 9 · p. 10
- After equal learning time, SL-initialized A3C online and offline methods achieve higher throughput and lower HO rates than UCB; clustering further improves performance.The comparison averages results over 500 random runs; UCB is averaged over its learning and testing periods.Source: B. Simulation Results · pp. 10–11, Figure 10 · p. 10
C. Discussion
The discussion identifies delayed gradients as a constraint on asynchronous learning as the number of UEs grows. The conclusion presents a clustered asynchronous framework with pretrained DQN approximators and supervised initialization to lower HO rates while ensuring throughput.
C. Discussion
- Delayed-gradient delay is roughly proportional to UE count, and the discussion states that UE numbers should be bounded to ensure asynchronous learning accelerates.The conclusion describes clustered asynchronous learning aimed at lowering HO rates while ensuring system throughput.Source: C. Discussion · p. 11, §VI · p. 11
VI. CONCLUSIONS
- The framework clusters UEs by mobility pattern and shares cluster-level DQN approximators; supervised learning initializes them from traditional HO data before reinforcement learning.Newly arriving UEs can fetch pretrained networks from their cluster's parameter server.Source: §VI · p. 11