5G Network Slicing for Vehicle-to-Everything Services

Campolo, Claudia; Molinaro, Antonella; Iera, Antonio; Menichella, Francesco

doi:10.1109/mwc.2017.1600408

Cited by 272 publications

(183 citation statements)

References 3 publications

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“…for an urban area using 5.9 GHz carrier frequency [13], [33], where we assume that H is a finite space of the channel states 2 , | · | is the absolute value norm of a one-dimensional vector, · 1 1 The Rayleigh distribution of the fast fading component is averaged out in this work by applying the previous result in [15]. 2 The assumption is reasonable due to the discrete-time Manhattan mobility model adopted in this paper. and · 2 are the 1-norm and the 2-norm of a vector, η is the path loss exponent, while both ϕ and ρ are the path loss coefficients satisfying ρ < ϕ · (ℓ 0 /2) η .…”

Section: A Network and Channel Modelsmentioning

confidence: 99%

Age of Information Aware Radio Resource Management in Vehicular Networks: A Proactive Deep Reinforcement Learning Perspective

Chen

et al. 2020

IEEE Trans. Wireless Commun.

152

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In this paper, we investigate the problem of age of information (AoI)-aware radio resource management for expected long-term performance optimization in a Manhattan grid vehicle-to-vehicle network.With the observation of global network state at each scheduling slot, the roadside unit (RSU) allocates the frequency bands and schedules packet transmissions for all vehicle user equipment-pairs (VUEpairs). We model the stochastic decision-making procedure as a discrete-time single-agent Markov decision process (MDP). The technical challenges in solving the optimal control policy originate from high spatial mobility and temporally varying traffic information arrivals of the VUE-pairs. To make the problem solving tractable, we first decompose the original MDP into a series of per-VUE-pair MDPs.Then we propose a proactive algorithm based on long short-term memory and deep reinforcement learning techniques to address the partial observability and the curse of high dimensionality in local network state space faced by each VUE-pair. With the proposed algorithm, the RSU makes the optimal frequency band allocation and packet scheduling decision at each scheduling slot in a decentralized way in accordance with the partial observations of the global network state at the VUE-pairs. Numerical experiments validate the theoretical analysis and demonstrate the significant performance improvements from the proposed algorithm.X. Chen and T. Chen are with the ). M. Bennis is with the Centre for Wireless Communications, University of Oulu, Finland (email: mehdi.bennis@oulu.fi). Index TermsVehicular communications, multi-user resource scheduling, Markov decision process, long shortterm memory, deep reinforcement learning, Q-function decomposition.

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Section: A Network and Channel Modelsmentioning

confidence: 99%

Age of Information Aware Radio Resource Management in Vehicular Networks: A Proactive Deep Reinforcement Learning Perspective

Chen

et al. 2020

IEEE Trans. Wireless Commun.

152

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“…These resources can be defined as remote cloud. When the capability of edge cloud cannot meet the QoS requirements of V2X services, offloading the service requests of 7 VUEs in its serivce area to the remote cloud becomes a good alternative. However, to access the remote cloud, it needs not only wireless links but also wired links, which will increase latency of V2X services.…”

Section: A Cloud-based Framework For Vehicular Networkmentioning

confidence: 99%

Intelligent Network Slicing for V2X Services Toward 5G

2019

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Benefiting from the widely deployed LTE infrastructures, the fifth generation (5G) wireless networks has been becoming a critical enabler for the emerging vehicle-to-everything (V2X) communications.However, existing LTE networks cannot efficiently support stringent but dynamic requirements of V2X services. One effective solution to overcome this challenge is network slicing, whereby different services could be supported by logically separated networks. To mitigate the increasing complexity of network slicing in 5G, we propose to leverage the recent advancement of Machine Learning (ML) technologies for automated network operation. Specifically, we propose intelligent network slicing architecture for V2X services, where network functions and multi-dimensional network resources are virtualized and assigned to different network slices. In achieving optimized slicing intelligently, several critical techniques, including mobile data collection and ML algorithm design, are discussed to tackle the related challenges.Then, we develop a simulation platform to illustrate the effectiveness of our proposed intelligent network slicing. With the integration of 5G network slicing and ML-enabled technologies, the QoS of V2X services is expected to be dramatically enhanced. Index TermsV2X services, network slicing, artificial intelligence, deep reinforcement learning Jie Mei is with the Intelligent Computing and Communication (IC 2 ) Lab, Wireless Signal Processing and Network

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“…The whole system operates over the discrete scheduling slots, each of which is of equal duration δ and is indexed by a positive integer t ∈ N + . Let x t k = x (1),t k , x (2),t k and y t k = y (1),t k , y…”

Section: System Modelmentioning

confidence: 99%

Learning to Entangle Radio Resources in Vehicular Communications: An Oblivious Game-Theoretic Perspective

Chen

Bennis

et al. 2019

IEEE Trans. Veh. Technol.

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This paper studies the problem of non-cooperative radio resource scheduling in a vehicle-to-vehicle communication network. The technical challenges lie in high vehicle mobility and data traffic variations.Over the discrete scheduling slots, each vehicle user equipment (VUE)-pair competes with other VUEpairs in the coverage of a road side unit (RSU) for the limited frequency to transmit queued packets. The frequency allocation at the beginning of each slot by the RSU is regulated following a sealed secondprice auction. Each VUE-pair aims to optimize the expected long-term performance. Such interactions among VUE-pairs are modelled as a stochastic game with a semi-continuous global network state space.By defining a partitioned control policy, we transform the stochastic game into an equivalent game with a global queue state space of finite size. We adopt an oblivious equilibrium (OE) to approximate the Markov perfect equilibrium (MPE), which characterizes the optimal solution to the equivalent game.The OE solution is theoretically proven to be with an asymptotic Markov equilibrium property. Due to the lack of a priori knowledge of network dynamics, we derive an online algorithm to learn the OE policies. Numerical simulations validate the theoretical analysis and show the effectiveness of the proposed online learning algorithm. Index TermsVehicle-to-vehicle communications, multi-user resource scheduling, stochastic games, Markov decision process, Markov perfect equilibrium, oblivious equilibrium, learning.X. Chen is with the ). 2 I. INTRODUCTION The next generation vehicle-to-everything (V2X) technologies have been receiving increasing attentions for enabling emerging vehicular services, such as traffic safety, congestion reporting and in-vehicle infotainment [1]-[3]. In particular, vehicle-to-vehicle (V2V) communication, operating in an ad hoc manner, provides more flexibility to render more attractive vehicle-related applications [4]. This type of vehicular applications have an ontological feature of requiring coordinations among the vehicles in close proximity [5]. However, the topology of a V2V communication network changes dynamically across the time horizon because of the high vehicle mobility. Without the support of an infrastructure, this in turn makes the design of radio resource management (RRM) techniques extremely challenging [6]. In the literature, there are a number of works focusing on RRM in V2V communications. In [7], Bai et al. proposed a low-complexity outage-optimal distributed channel allocation scheme for V2V communications based on maximum matching. In [8], Sun et al. investigated RRM for device-to-device based V2V communications, for which a separate resource block and power allocation algorithm was proposed. Yao et al. proposed in [9] a loss differentiation rate adaptation scheme to meet the stringent delay and reliability requirements for V2V safety communications. In [10], Egea-Lopez et al. proposed a fair adaptive beaconing rate for the intervehicular communications algorithm to solve the...

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5G Network Slicing for Vehicle-to-Everything Services

Cited by 272 publications

References 3 publications

Age of Information Aware Radio Resource Management in Vehicular Networks: A Proactive Deep Reinforcement Learning Perspective

Age of Information Aware Radio Resource Management in Vehicular Networks: A Proactive Deep Reinforcement Learning Perspective

Intelligent Network Slicing for V2X Services Toward 5G

Learning to Entangle Radio Resources in Vehicular Communications: An Oblivious Game-Theoretic Perspective

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