Resource Allocation for Low-Latency Vehicular Communications: An Effective Capacity Perspective

Guo, Chongtao; Liang, Le; Li, Geoffrey Ye

doi:10.1109/jsac.2019.2898743

Cited by 120 publications

(49 citation statements)

References 24 publications

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“…7) decrease. Given the weight values as in Table II, the packet drops and the AoI jointly dominate the utility function (11). It can be hence observed from Fig.…”

Section: ) Efficiency Of Offline Trainingmentioning

confidence: 83%

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

Chen

et al. 2020

IEEE Trans. Wireless Commun.

151

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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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“…7) decrease. Given the weight values as in Table II, the packet drops and the AoI jointly dominate the utility function (11). It can be hence observed from Fig.…”

Section: ) Efficiency Of Offline Trainingmentioning

confidence: 83%

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

Chen

et al. 2020

IEEE Trans. Wireless Commun.

151

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“…UL multi-cell reception design has been extensively addressed in [23] by comparing the achievable capacity of various receivercombining techniques. Several studies explore URLLC for vehicular communications [24]- [29].…”

Section: A Brief Overview Of the State Of The Art On Urllcmentioning

confidence: 99%

On the Multiplexing of Data and Metadata for Ultra-Reliable Low-Latency Communications in 5G

Karimi

Pedersen

Mahmood

et al. 2020

IEEE Trans. Veh. Technol.

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This paper addresses the problem of downlink radio resource management for ultra-reliable low-latency communications (URLLC) in fifth generation (5G) systems. To support low-latency communications, we study performance of two multiplexing schemes namely in-resource control signalling and joint encoding of data and metadata. In the former, the metadata and data are separately encoded and the metadata is sent at the beginning of transmission time prior to the data. Thus, it benefits from a low-complexity receiver structure to decode the data block. The latter takes advantages of transmitting a larger blocklength to enhance the reliability and improve spectrum efficiency by jointly encoding data and metadata as a single codeword. Dealing with small URLLC payloads, the overhead and error of sending metadata are not negligible and have a significant impact on the system performance in terms of resource usage the reliability of transmission. For each scheme, we derive expressions for the outage probability and resource usage by taking into account impacts of the finite blocklength payloads, overhead and error of sending metadata, and probability of error in uplink feedback channel. We propose a novel framework for joint data and metadata link adaptation to minimize the average number of allocated resources, while ensuring the stringent URLLC quality of service requirements. An optimization problem is formulated for each scheme that is mixed-integer nonconvex problem, difficult to solve in polynomial time. Solutions based on successive convex optimization are proposed. Numerical evaluations show that the proposed algorithms perform close to the optimal solution and demonstrate remarkable gains of up to 27% improvement in resource usage. Finally, we present sensitivity analysis of the results for various network parameters.

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“…The EC model is used to estimate the optimal distance between a vehicle and road-side unit and to investigate the transmission of QoSaware data with loose delay requirements. Provisioning of low latency in vehicular communications with EC perspective has been studied in [281]. The concept of latency violation probability (LVP) with optimal resource allocation such as power and spectrum has also been investigated.…”

Section: G Vehicular Adhoc Networkmentioning

confidence: 99%

Effective Capacity in Wireless Networks: A Comprehensive Survey

Amjad

Musavian

Rehmani

2019

IEEE Commun. Surv. Tutorials

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Low latency applications, such as multimedia communications, autonomous vehicles, and Tactile Internet are the emerging applications for next-generation wireless networks, such as 5th generation (5G) mobile networks. Existing physicallayer channel models, however, do not explicitly consider qualityof-service (QoS) aware related parameters under specific delay constraints. To investigate the performance of low-latency applications in future networks, a new mathematical framework is needed. Effective capacity (EC), which is a link-layer channel model with QoS-awareness, can be used to investigate the performance of wireless networks under certain statistical delay constraints. In this paper, we provide a comprehensive survey on existing works, that use the EC model in various wireless networks. We summarize the work related to EC for different networks such as cognitive radio networks (CRNs), cellular networks, relay networks, adhoc networks, and mesh networks. We explore five case studies encompassing EC operation with different design and architectural requirements. We survey various delay-sensitive applications such as voice and video with their EC analysis under certain delay constraints. We finally present the future research directions with open issues covering EC maximization.

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Resource Allocation for Low-Latency Vehicular Communications: An Effective Capacity Perspective

Cited by 120 publications

References 24 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

On the Multiplexing of Data and Metadata for Ultra-Reliable Low-Latency Communications in 5G

Effective Capacity in Wireless Networks: A Comprehensive Survey

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