5G Scheduling using Reinforcement Learning

Zavyalova, Darya V.; Drozdova, Vera G.

doi:10.1109/fareastcon50210.2020.9271421

Cited by 8 publications

(3 citation statements)

References 5 publications

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“…To ensure the satisfaction of user experience and realize effective network control management, the 3rd Generation Partnership Project standards organization has established a quality of service (QoS) model and a complete QoS guarantee mechanism [2,16] . The QoS model defines the minimum granularity of the service flow management, i.e., the QoS flow.…”

Section: G Quality Of Service Architecturementioning

confidence: 99%

Research on throughput prediction of 5G network based on LSTM

Tao

2022

Intell. and Converged Netw.

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This paper proposes a wireless network traffic prediction model based on long-term and short-term memory cyclic neural networks. Through simulation experiments, the throughput prediction of 5G wireless networks using different scheduling algorithms for many different types of services is studied. The results verify that the long shortterm memory prediction model has acceptable prediction accuracy and algorithm training speed, meets the needs of wireless network traffic prediction, and has a good application prospect.

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Section: G Quality Of Service Architecturementioning

confidence: 99%

Research on throughput prediction of 5G network based on LSTM

Tao

2022

Intell. and Converged Netw.

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“…Nasralla 33 defined hybrid QoS‐aware downlink scheduling algorithms that deliver improved QoS while also balancing the various traffic classes in the bounded channel and bandwidth. Zavyalova and Drozdova 34 stated a novel RL PGagent‐based scheduler for the 5G networks. This method accomplishes better throughput without the knowledge of predefined algorithms.…”

Section: Review Of Related Workmentioning

confidence: 99%

A proportional fair scheduling strategy using multiobjective gradient‐based African buffalo optimization algorithm for effective resource allocation and interference minimization

Kesavan

Periyathambi²,

Chokkalingam

2021

Int J Communication

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Summary The increased usage of Internet of Things (IoT) applications in several areas, like healthcare, agriculture, and business, has aggravated mobile traffic issues to a large extent. The deployment of 5G technology has resulted in increased traffic globally. These coherent devices, on the other hand, use the internet to fine‐tune the quality of service in order to provide scalability, anonymity, and accessibility. Despite its numerous virtues, it is bound to encounter issues with interference management, fairness, throughput, and computational complexities. In this paper, a novel Multi‐Objective Gradient‐based African Buffalo Optimization (MOGABO) algorithm is developed to handle proportional fairness scheduling, improve interference management, increase throughput, and reduce computational complexities in cellular communication systems, particularly device‐to‐device (D2D) communication. The simulation analysis is done against other approaches such as the Hungarian technique, DDDPG technique, heuristic technique, and SC‐FDMA technique demonstrates that this method has improved system fairness over the existing approaches. Our method maximizes throughput, and for 500 UE, the throughput is 10.4 Mbps, with an average queuing delay of 24 ms. As a result, when compared to the existing method, MOGABO method offers a 1.2% increase in throughput. Thus, our method offers better coverage and throughput for D2D cellular communication with the reduction of computational complexities.

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“…The recent era will witness billions of connections of Internet of Things (IoT) devices that will be served simultaneously [5]. Smart mobiles, vehicles, home appliances, and sensors are examples of these IoT devices [6].…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation in Uplink NOMA-IoT Based UAV for URLLC Applications

Karem

Ahmed

Newagy

2022

Sensors

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One of the main targets of future 5G cellular networks is enlarging the Internet of Things (IoT) devices’ connectivity while facing the challenging requirements of the available bandwidth, power and the restricted delay limits. Unmanned aerial vehicles (UAVs) have been recently used as aerial base stations (BSs) to empower the line of sight (LoS), throughput and coverage of wireless networks. Moreover, non-orthogonal multiple access (NOMA) has become a bright multiple access technology. In this paper, NOMA is combined with UAV for establishing a high-capacity IoT uplink multi-application network, where the resource allocation problem is formulated with the objective of maximizing the system throughput while minimizing the delay of IoT applications. Moreover, power allocation was investigated to achieve fairness between users. The results show the superiority of the proposed algorithm, which achieves 31.8% delay improvement, 99.7% reliability increase and 50.8% fairness enhancement when compared to the maximum channel quality indicator (max CQI) algorithm in addition to preserving the system sum rate, spectral efficiency and complexity. Consequently, the proposed algorithm can be efficiently used in ultra-reliable low-latency communication (URLLC).

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5G Scheduling using Reinforcement Learning

Cited by 8 publications

References 5 publications

Research on throughput prediction of 5G network based on LSTM

Research on throughput prediction of 5G network based on LSTM

A proportional fair scheduling strategy using multiobjective gradient‐based African buffalo optimization algorithm for effective resource allocation and interference minimization

Resource Allocation in Uplink NOMA-IoT Based UAV for URLLC Applications

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