A DQN-Based Handover Management for SDN-Enabled Ultra-Dense Networks

Wu, Mengting; Huang, Wei; Sun, Kai; Zhang, Haijun

doi:10.1109/vtc2020-fall49728.2020.9348779

Cited by 14 publications

(12 citation statements)

References 13 publications

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“…The DNN not only greatly improves the computational efficiency but also improves the summation rate of the system. In [35], a DQN method is used to control the Handover (HO) procedure of the User Equipments (UEs) by well capturing the characteristics of wireless signals interference and network load. Experimental results show that the proposed scheme can reduce HO rate and guarantee the system throughput, which is better than the traditional HO scheme.…”

Section: Related Workmentioning

confidence: 99%

Optimization of LoRa SF Allocation Based on Deep Reinforcement Learning

Zhong

Ning

Wang

et al. 2022

Wireless Communications and Mobile Computing

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LoRa is an IoT communication technology that realizes ultra-long-distance transmission through spread spectrum modulation. However, its ultra-long-distance transmission also sacrifices the corresponding rate, and data conflicts are prone to occur when the number of nodes is large. In this article, we investigate various types of data collisions in LoRa wireless work, most of which are affected by Spreading Factor (SF) assignment. At present, the distribution of the SF for LoRa in the industry is mostly based on Min-airtime and Min-distance. In the case of a large number of nodes, the data collision between nodes will increase sharply. This paper proposes a SF redistribution scheme under limited network resources, in order to improve the terminal capacity of the LoRa gateway. First, the problem of minimizing the data collision rate without expanding gateway or network resources is presented. Specifically, the reallocation of SF with increasing number of terminals is studied. Finally, considering the randomness of the data sent by the terminal, SF redistribution schemes based on deep reinforcement learning (DRL) are developed. The simulation results show that the collision rate of the proposed SF redistribution scheme is nearly 30% lower than Min-airtime and Min-distance, and its total energy consumption is close to Min-distance. Therefore, the proposed SF redistribution scheme can effectively improve the gateway capacity of LoRa wireless network.

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Section: Related Workmentioning

confidence: 99%

Optimization of LoRa SF Allocation Based on Deep Reinforcement Learning

Zhong

Ning

Wang

et al. 2022

Wireless Communications and Mobile Computing

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“…en, the authors proposed an NB-IoT downlink scheduling algorithm. Wu et al [21] proposed a deep Q-learning network (DQN) method used to control the hand-over (HO) procedure of the user equipment (UE) by well capturing the characteristics of wireless signals/interferences and network load. In [22], a multiagent deep Q-network-(DQN-)based dynamic joint spectrum access and mode selection (SAMS) scheme is proposed for the SUs in the partially observable environment.…”

Section: Relate Workmentioning

confidence: 99%

Deep Grid Scheduler for 5G NB-IoT Uplink Transmission

Zhong

Sun

Mei

et al. 2021

Security and Communication Networks

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Since the birth of narrowband Internet of Things (NB-IoT), the Internet of Things (IoT) industry has made a considerable progress in the application for smart cities, smart manufacturing, and healthcare. Therefore, the number of UEs is increasing exponentially, which brings considerable pressure to the efficient resource allocation for the bandwidth and power constrained NB-IoT networks. In view of the conventional algorithms that cannot dynamically adjust resource allocation, resulting in a low resource utilization and prone to resource fragmentation, this paper proposes a double deep Q-network (DDQN)-based NB-IoT dynamic resource allocation algorithm. It first builds an NB-IoT environment model based on the real environment. Then, the DDQN algorithm interacts with the NB-IoT environment model to learn and optimize resource allocation strategies until it converges to the optimum. Finally, the simulation results show that the DDQN-based NB-IoT dynamic resource allocation algorithm is better than the traditional algorithm in the resource utilization, average transmission rate, and UE average queuing time.

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“…Therefore, the modeling and analyzing of HO are complicated, and it is difficult to configure proper HO parameters for different network scenarios. Our research on mobility management issues in UDN has been partially published in [25], and this study has been extended in the following ways [25]: (1) the HO parameters are now considered; (2) the HOF and ping-pong HO are considered in proposed mobility management method; (3) a minimum threshold throughput is given to perform the judgment of HOF. In this paper, a new strategy based on DRL and the technique for order of preference by similarity to ideal solution (TOPSIS) is developed to adaptively adjust the HO parameters setting for SDN-enabled UDN, in which the TOPSIS algorithm determines the target BS, and DRL method obtains the optimal HO decision point.…”

Section: Introductionmentioning

confidence: 99%

Self-Adapting Handover Parameters Optimization for SDN-Enabled UDN

Huang

Yang

et al. 2022

IEEE Trans. Wireless Commun.

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Increasing the deployment density of small base stations (SBS) is a key method designed to satisfy high data traffic in 5th generation mobile network (5G). However, a large number of SBSs in such ultradense network (UDN) may cause ping-pong handovers (HOs), accompanied by increased delay and HO failure. In addition, because of the separation of control and data signaling in 5G, the HO procedure must be performed in both layers. In this paper, we introduce an SDN-based intelligent dynamic HO parameter optimization strategy to minimize both HO failures and ping-pong HOs together. The goal of the proposed strategy is to reduce the HO failure rate and redundant HO (i.e. ping-pong HO) while enabling user equipment (UE) to make full use of the benefits of dense deployment of BSs. Simulation results present that the method proposed in this paper effectively suppresses the ping-pong effect and keeps it at a low level in all of the investigated scenes. In addition, compared with the other algorithms, the HO failure rate is significantly reduced and the throughput of UE is greatly increased, especially in the case of high BS density. Therefore, the benefits of intensive BS deployment are retained.

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A DQN-Based Handover Management for SDN-Enabled Ultra-Dense Networks

Cited by 14 publications

References 13 publications

Optimization of LoRa SF Allocation Based on Deep Reinforcement Learning

Optimization of LoRa SF Allocation Based on Deep Reinforcement Learning

Deep Grid Scheduler for 5G NB-IoT Uplink Transmission

Self-Adapting Handover Parameters Optimization for SDN-Enabled UDN

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