An Evolutionary Game for User Access Mode Selection in Fog Radio Access Networks

Yan, Shi; Peng, Mugen; Abana, Munzali Ahmed; Wang, Wenbo

doi:10.1109/access.2017.2654266

Cited by 57 publications

(18 citation statements)

References 30 publications

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“…The authors have presented analytical results on successful delivery probability based on different transmission schemes in a cluster-centric small cell network, and contributed to analyze the tradeoff between transmission diversity and content diversity in [11]. In addition, the authors have investigated the coverage probability and ergodic rate in a PPP-based F-RANs with D2D users in [12] [13].…”

Section: A Related Workmentioning

confidence: 99%

“…Recently, there have been some comprehensive evaluations with regard to latency performance. In [13], tractable expressions of the effective capacity, which reflects both latency and capacity performance, are derived in PPP-based C-RANs. Normalized delivery time is presented from the aspect of fundamental information-theoretic, which reflects the interplay between cloud processing and edge caching in F-RANs [14].…”

Section: A Related Workmentioning

confidence: 99%

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Tradeoff Between Ergodic Rate and Delivery Latency in Fog Radio Access Networks

Yin

Peng

Yan

et al. 2020

IEEE Trans. Wireless Commun.

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Wireless content caching has recently been considered as an efficient way in fog radio access networks (F-RANs) to alleviate the heavy burden on capacity-limited fronthaul links and reduce delivery latency. In this paper, an advanced minimal delay association policy is proposed to minimize latency while guaranteeing spectral efficiency in F-RANs. By utilizing stochastic geometry and queueing theory, closed-form expressions of successful delivery probability, average ergodic rate, and average delivery latency are derived, where both the traditional association policy based on accessing the base station with maximal received power and the proposed minimal delay association policy are concerned. Impacts of key operating parameters on the aforementioned performance metrics are exploited. It is shown that the proposed association policy has a better delivery latency than the traditional association policy. Increasing the cache size of fog-computing based access points (F-APs) can more significantly reduce average delivery latency, compared with increasing the density of F-APs. Meanwhile, the latter comes at the expense of decreasing average ergodic rate. This implies the deployment of large cache size at F-APs rather than high density of F-APs can promote performance effectively in F-RANs.

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Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Tradeoff Between Ergodic Rate and Delivery Latency in Fog Radio Access Networks

Yin

Peng

Yan

et al. 2020

IEEE Trans. Wireless Commun.

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“…In this FVNET, the F-APs can cache multiple content files from the centralized cache library in the cloud sever, which stores N contents N ≡ {1, 2, ..., N } that may be requested by users [22]. Without loss of generality, each content v has a fixed size C f .…”

Section: B Cache Modelmentioning

confidence: 99%

Joint Access Mode Selection and Spectrum Allocation for Fog Computing Based Vehicular Networks

et al. 2019

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The explosive growth of data with the requirements of high reliability and low latency has posed huge challenges to the vehicular networks. One potential solution is to deploy the fog computing servers geographically closer to the vehicles to serve the vehicle-based applications in real time. However, due to the constraint of caching storage space as well as lack of tractable access mode selection and spectrum allocation algorithm, it is very challenging to balance the network transmission performance and fronthaul savings. In this paper, we investigate the joint optimization problem of access mode selection and spectrum allocation in fog computing-based vehicular networks. To solve the problem in an efficient way, the original high complexity optimization problem is divided into two subproblems. Wherein, the vehicle access mode selection problem is solved by Q-learning-based algorithm by considering spectrum allocation profiles and the fronthaul link cost. The randomly vehicular network topologies are modeled as CoX processes and the closed-form payoff expressions are derived by the stochastic geometry tools. On the other hand, the optimal spectrum allocation indicator value can be finally obtained via convex optimization. The analytical results for the proposed algorithm as well as the traditional baseline approaches are evaluated with different weight factors, which verify our theoretical analysis and confirm the proposed approach can achieve significant performance gains.

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“…We use replicator dynamics to model the evolution of MTD cluster size preferences, based on high data correlation and reduced transmission power [33], [34], [38]. The evolution of cluster type j, is given by:ẋ…”

Section: A Dynamics Of Cluster Formationmentioning

confidence: 99%

Evolutionary Games for Correlation-Aware Clustering in Massive Machine-to-Machine Networks

Sawyer

Soorki

Saad

et al. 2019

IEEE Trans. Commun.

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In this paper, the problem of self-organizing, correlation-aware clustering is studied for a dense network of machine-type devices (MTDs) deployed over a cellular network. In dense machine-tomachine networks, MTDs are typically located within close proximity and will gather correlated data, and, thus, clustering MTDs based on data correlation will lead to a decrease in the number of redundant bits transmitted to the base station. To analyze this clustering problem, a novel utility function that captures the average MTD transmission power per cluster is derived, as a function of the MTD location, cluster size, and inter-cluster interference. Then, the clustering problem is formulated as an evolutionary game, which models the interactions among the massive number of MTDs, in order to decrease MTD transmission power. To solve this game, a distributed algorithm is proposed to allow the infinite number of MTDs to autonomously form clusters. It is shown that the proposed distributed algorithm converges to an evolutionary stable strategy (ESS), that is robust to a small portion of MTDs deviating from the stable cluster formation at convergence. The maximum fraction of MTDs that can deviate from the ESS, while still maintaining a stable cluster formation is derived. Simulation results show that the proposed approach can effectively cluster MTDs with highly correlated data, which, in turn, enables those MTDs to eliminate a large number of redundant bits. The results show that, on average, using the proposed approach yields reductions of up to 23.4% and 9.6% in terms of the transmit power per cluster, compared to forming clusters with the maximum possible size and uniformly selecting a cluster size, respectively. A preliminary version of this work appeared in IEEE GLOBECOM 2017 [1]. 2 I. INTRODUCTION Machine-to-machine (M2M) communications is an important component of the emerging Internet of Things (IoT) system, as it enables advanced networked applications such as smart home technologies, smart grid, healthcare, drone systems, manufacturing systems, and surveillance [2]-[7]. Within an M2M network, a massive number of machine-type devices (MTDs) will be densely deployed over wireless cellular networks [8]. An MTD can be a sensor, actuator, or smart meter whose typical role is to sense or measure an environment, and transmit the collected data to cellular base stations (BSs). MTDs enable real-time monitoring and control of any physical environment, without direct human involvement, thus making processes more efficient and improving human welfare [4], [9]. Since the number of MTDs is expected to be massive and much larger than the number of cellular-type devices (CTDs), it is expected that a massive-scale MTD deployment will lead to quality-of-service (QoS) degradation, increased traffic and signaling, increased latency, and reduced reliability [4]. Therefore, deploying MTDs over cellular networks faces many challenges ranging from network modeling to resource management, massive-scale, access, and MTD clustering as mention...

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An Evolutionary Game for User Access Mode Selection in Fog Radio Access Networks

Cited by 57 publications

References 30 publications

Tradeoff Between Ergodic Rate and Delivery Latency in Fog Radio Access Networks

Tradeoff Between Ergodic Rate and Delivery Latency in Fog Radio Access Networks

Joint Access Mode Selection and Spectrum Allocation for Fog Computing Based Vehicular Networks

Evolutionary Games for Correlation-Aware Clustering in Massive Machine-to-Machine Networks

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