The SMART Handoff Policy for Millimeter Wave Heterogeneous Cellular Networks

Sun, Yao; Feng, Gang; Qin, Shuang; Liang, Ying-Chang; Yum, T.-S.P.

doi:10.1109/tmc.2017.2762668

Cited by 99 publications

(62 citation statements)

References 24 publications

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“…Recently, RL is widely applied in wireless communication networks, especially in decision-making scenarios [13]- [21]. Specifically, [13] proposed two RL-based user handoff algorithms in a Millimeter wave HetNet. [14] developed an efficient RL-based radio access technology selection algorithm in a HetNet.…”

Section: B Related Workmentioning

confidence: 99%

Deep Reinforcement Learning-Based Modulation and Coding Scheme Selection in Cognitive Heterogeneous Networks

Zhang

Tan

Liang

et al. 2019

IEEE Trans. Wireless Commun.

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126

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We consider a cognitive heterogeneous network (HetNet), in which multiple pairs of secondary users adopt sensing-based approaches to coexist with a pair of primary users on a certain spectrum band. Due to imperfect spectrum sensing, secondary transmitters (STs) may cause interference to the primary receiver (PR) and make it difficult for the PR to select a proper modulation and/or coding scheme (MCS). To deal with this issue, we exploit deep reinforcement learning (DRL) and propose an intelligent MCS selection algorithm for the primary transmission. To reduce the system overhead caused by MCS switchings, we further introduce a switching cost factor in the proposed algorithm. Simulation results show that the primary transmission rate of the proposed algorithm without the switching cost factor is 90% ∼ 100% of the optimal MCS selection scheme, which assumes that the interference from the STs is perfectly known at the PR as prior information, and is 30% ∼ 100% higher than those of the benchmark algorithms. Meanwhile, the proposed algorithm with the switching cost factor can achieve a better balance between the primary transmission rate and system overheads than both the optimal algorithm and benchmark algorithms.

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Section: B Related Workmentioning

confidence: 99%

Deep Reinforcement Learning-Based Modulation and Coding Scheme Selection in Cognitive Heterogeneous Networks

Zhang

Tan

Liang

et al. 2019

IEEE Trans. Wireless Commun.

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126

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“…In [16], the authors developed a general analytical model for a hybrid cellular network constituted of the traditional sub-6 GHz macrocells and mmWave small cells and analyzed how the user should associate with these two types of BSs in the uplink and downlink. To reduce the number of handoffs while maintaining user's Quality of Service (QoS) requirements in mmWave HetNets, a reinforcement learning based handoff policy named SMART was introduced in [17]. The authors in [18] proposed a joint resource allocation and network access problem to investigate the coexistence mechanism for license-assisted access LTE (LAA-LTE) based HetNets.…”

Section: A Heterogeneous Networkmentioning

confidence: 99%

Dynamic Model for Network Selection in Next Generation HetNets With Memory-Affecting Rational Users

Feng

Niyato

et al. 2021

IEEE Trans. on Mobile Comput.

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Recently, due to the staggering growth of wireless data traffic, heterogeneous networks have drawn tremendous attention due to the capabilities of enhancing the capacity/coverage and to save energy consumption for the next generation wireless networks. In this paper, we study a long-run user-centric network selection problem in the 5G heterogeneous network, where the network selection strategies of the users can be investigated dynamically. Unlike the conventional studies on the long-run model, we incorporate the memory effect and consider the fact that the decision-making of the users is affected by their memory, i.e., their past service experience. Namely, the users select the network based on not only their instantaneous achievable service experience but also their past service experience within their memory. Specifically, we model and study the interaction among the users in the framework of fractional evolutionary game based on the classical evolutionary game theory and the concept of the power-law memory. We analytically prove that the equilibrium of the fractional evolutionary game exists, is unique and uniformly stable. We also numerically demonstrate the stability of the fractional evolutionary equilibrium. Extensive numerical results have been conducted to evaluate the performance of the fractional evolutionary game. The numerical results have revealed some insightful findings. For example, the user in the fractional evolutionary game with positive memory effect can achieve a higher cumulative utility compared with the user in the fractional evolutionary game with negative memory effect. Moreover, the fractional evolutionary game with positive memory effect can reduce the loss in the user's cumulative utility caused by the small-scale fading. 2 Network selection, fractional evolutionary game, memory-affecting rationality, and heterogeneous network.

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“…Sun et al 27 A SMART was developed to reduce It spent long handover latency due to the number of handovers while the processing delay in the reinforcement maintaining QoS requirements in learning step in SMART.…”

Section: Approaches Features Limitationsmentioning

confidence: 99%

“…In both types of HO, the vehicles resolved the connection to RSU based on RSSI that announced from RSU. The limitations of previous literature studies [27][28][29][30][31][32] are summarized in Table 2.…”

Section: Prados-garzon Et Al 28 It Proposed X2-based Handovermentioning

confidence: 99%

A comprehensive survey on handover management for vehicular ad hoc network based on 5G mobile networks technology

Ahmed

Alzahrani

2018

Trans Emerging Tel Tech

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Vehicular ad hoc network (VANET) is a style of high‐mobility mobile ad hoc network where the mobile nodes are vehicles traveling across a road or a street. VANET was proposed to provide safety, comfortability, reliability, and security of vehicles driving. One of the serious challenges is handover (HO) problem that occurs when a vehicle moves from one network area covered by a roadside unit (RSU) to another network area covered by a different RSU. The fast mobility of vehicles and the limited coverage area of the RSU (without overlapping signals) should be considered in providing a seamless HO. This paper presents a comprehensive survey on HO management for VANET based on the new features of fifth‐generation (5G) mobile network technologies. The 5G mobile network technology provides enhanced characteristics that will fit the requirements of real‐time video streaming and assist to design an effective solution for the HO problem. In addition, this paper presents the reasons for HO failures and the limitations of available HO failure solutions. Furthermore, it highlights the potential proposed solutions that will tackle the HO failures. This survey will open up the gate of developing an optimal solution based on 5G for HO problem on VANET that might be used in vehicles manufacturing.

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The SMART Handoff Policy for Millimeter Wave Heterogeneous Cellular Networks

Cited by 99 publications

References 24 publications

Deep Reinforcement Learning-Based Modulation and Coding Scheme Selection in Cognitive Heterogeneous Networks

Deep Reinforcement Learning-Based Modulation and Coding Scheme Selection in Cognitive Heterogeneous Networks

Dynamic Model for Network Selection in Next Generation HetNets With Memory-Affecting Rational Users

A comprehensive survey on handover management for vehicular ad hoc network based on 5G mobile networks technology

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