An Optimal Game Approach for Heterogeneous Vehicular Network Selection with Varying Network Performance

Zhao, Xiangmo; Li, Xiaochi; Xu, Zhigang; Chen, Ting

doi:10.1109/mits.2019.2919563

Cited by 20 publications

(16 citation statements)

References 19 publications

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“…Hence, new definitions are needed based on QoS indicators of service reliability and energy efficiency/consumption (mMTC) and reliability/latency (URLLC) [260]. A similar observation can be done for novel vehicular communications, for which initial GT-based RAT selection approaches can be found in [261] [262]. Even when they have similar goals, users may still show different capabilities in terms of context observation and computation, eventually adopting different learning schemes.…”

Section: Open Challengesmentioning

confidence: 99%

“…Data-driven modeling, analysis, and testing: The overview of RAT selection literature shows an ongoing transition from pure theoretical modeling (e.g., under perfect and complete information assumptions) to more practical implementation of learning schemes (e.g., assuming imperfect and incomplete information). More recent investigations have also proposed empirical analyses due to higher availability of data collected in experimental open platforms, testbeds, and large-scale measurement campaigns [93] [181] [261]. On this aspect, differently from RAT selection, MP scheduling solutions are most often analyzed via experiments in real scenarios, also thanks to the possibility of using existing software implementations for several MP protocols.…”

Section: Open Challengesmentioning

confidence: 99%

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User-Centric Radio Access Technology Selection: A Survey of Game Theory Models and Multi-Agent Learning Algorithms

et al. 2021

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User-centric radio access technology (RAT) selection is a key communication paradigm, given the increased number of available RATs and increased cognitive capabilities at the user end. When considered against traditional network-centric approaches, user-centric RAT selection results in reduced network-side management load, and leads to lower operational costs for RATs, as well as improved quality of service (QoS) and quality of experience (QoE) for users. The complex between-users interactions involved in RAT selection require, however, specific analyses, toward developing reliable and efficient schemes. Two theoretical frameworks are most often applied to user-centric RAT selection analysis, i.e., game theory (GT) and multi-agent learning (MAL). As a consequence, several GT models and MAL algorithms have been recently proposed to solve the problem at hand. A comprehensive discussion of such models and algorithms is, however, currently missing. Moreover, novel issues introduced by next-generation communication systems also need to be addressed. This paper proposes to fill the above gaps by providing a unified reference for both ongoing research and future research directions in the field. In particular, the review addresses the most common GT and MAL models and algorithms, and scenario settings adopted in user-centric RAT selection in terms of utility function and network topology. Regarding GT, the review focuses on non-cooperative models, because of their widespread use in RAT selection; as for MAL, a large number of algorithms are described, ranging from game-theoretic to reinforcement learning (RL) schemes, and also including most recent approaches, such as deep RL (DRL) and multi-armed bandit (MAB). Models and algorithms are analyzed by comparatively reviewing relevant literature. Finally, open challenges are discussed, in light of ongoing research and standardization activities.

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Section: Open Challengesmentioning

confidence: 99%

Section: Open Challengesmentioning

confidence: 99%

User-Centric Radio Access Technology Selection: A Survey of Game Theory Models and Multi-Agent Learning Algorithms

et al. 2021

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“…However, the handover increases with increase in vehicle density. Authors in (Zhao et al, 2019) have presented an optimal game approach for network selection using probabilistic strategy. The threshold value was validated and then handoff probability was computed to perform handover.…”

Section: Prior Work On Handovermentioning

confidence: 99%

Artificial Intelligence-Based Network Selection and Optimized Routing in Internet of Vehicles

Hussain

Yusof

Hussain

2021

Transport and Telecommunication Journal

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Internet of Vehicles (IoV) is a network of vehicles communicating with each other by exchanging road traffic information via radio access technologies. Two potential technologies of V2X that have gained attention over the past years are DSRC and cellular networks such as 4G LTE and 5G. DSRC is suitable for low latency communications, however provides a shorter coverage range whereas, 4G LTE offers a wide coverage range but has high transmission time intervals. In contrast, 5G offers higher data rates, low latencies but prone to blockages. Single technology might not fully accommodate the requirements of vehicular communications. Hence, it is required to interwork with more than one radio access network to satisfy the requirements of safety vehicular applications. One issue identified when working with multiple radio access networks is the selection of the most appropriate network for vertical handover. Usually, in the previous works, the network is selected directly or will be connected to the available network due to which the handover had to take place frequently resulting in unnecessary handovers. Hence, in the existing state-of-the-art, the need for handover is not validated. In this paper, we have proposed a dynamic Q-learning algorithm to validate the need for handover, and then, appropriate selection of network would take place by using a fuzzy convolutional neural network. Besides, a modified jellyfish optimization algorithm is proposed to select the shortest paths by forming V2V pairs that take into account channel metrics, vehicle metrics, and vehicle performance metrics. The proposed algorithms are then evaluated using OMNET++ and compared with the existing state-of-the-art concerning mean handover, HO failure, throughput, delay, and packet loss as the performance metrics.

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“…This work results to increase the number of handover when the vehicle density increases. An optimal game approach was presented in [17], for network selection using probabilistic strategy. The main limitation of using game theory is that it operates with assumptions and also the initial threshold was fixed static.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence based handover decision and network selection in heterogeneous internet of vehicles

Hussain

Yusof

Asuncion

et al. 2021

IJEECS

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Internet of vehicles (IoV) is an emerging area that gives support for vehicles via internet assisted communication. IoV with 5G provides ubiquitous connectivity due to the participation of more than one radio access network. The mobility of vehicles demands to make handover in such heterogeneous network. The vehicles at short range uses dedicated short range communication (DSRC), while it has to use better technology for long range and any type of traffic. Usually, the previous work will directly select the network for handover or it connects with available radio access. Due to this, the occurrence of handover takes place frequently. In this paper, the integration of DSRC, LTE and mmWave 5G on IoV is incorporated with novel handover decision making, network selection and routing. The handover decision is to ensure whether there is a need for vertical handover by using Dynamic Q-learning algorithm that uses entropy function for threshold prediction as per the current characteristics of the environment. Then the network selection is based on fuzzy-convolution neural network (F-CNN) that creates fuzzy rules from signal strength, distance, vehicle density, data type and line of sight. V2V chain routing is proposed to select V2V pairs using jellyfish optimization algorithm (JOA) that takes in account of channel, vehicle and transmission metrics. This system is developed in OMNeT++ simulator and the performances are evaluated in terms of success probability, handover failure, unnecessary handover, mean throughput, delay and packet loss.

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An Optimal Game Approach for Heterogeneous Vehicular Network Selection with Varying Network Performance

Cited by 20 publications

References 19 publications

User-Centric Radio Access Technology Selection: A Survey of Game Theory Models and Multi-Agent Learning Algorithms

User-Centric Radio Access Technology Selection: A Survey of Game Theory Models and Multi-Agent Learning Algorithms

Artificial Intelligence-Based Network Selection and Optimized Routing in Internet of Vehicles

Artificial intelligence based handover decision and network selection in heterogeneous internet of vehicles

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