QoS‐Aware Optimal Radio Resource Allocation Method for Machine‐Type Communications in 5G LTE and beyond Cellular Networks

Beshley, Halyna; Beshley, Mykola; Medvetskyi, Mykhailo; Pyrih, Yuliia

doi:10.1155/2021/9966366

Cited by 29 publications

(16 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the existing literature, the significance of both QoS and QoE requirements' satisfaction is highlighted. For example, the authors in [56] consider the resource allocation problem in M2M 5G 3GPP cellular systems. An optimal radio resource allocation method in LTE and beyond cellular networks is developed, based on adaptive selection of channel bandwidth, depending on the QoS requirements and priority traffic aggregation.…”

Section: B Representative Recent Non-ml Approachesmentioning

confidence: 99%

“…However, focusing on the outcomes and results of those research efforts, several limitations can be witnessed. In most cases of LTE and early 5G networks [56], [58], [59], [64], the enactment of the RRM policy was based on perfect knowledge of specific parameters, such as the instantaneous CSI and QoS requirements of the active users. Thus, the optimal allocation problem, described in the above paragraphs, is solved through optimization procedures.…”

Section: Limitations Of Non-ml Approachesmentioning

confidence: 99%

See 1 more Smart Citation

ML-Based Radio Resource Management in 5G and Beyond Networks: A Survey

et al. 2022

View full text Add to dashboard Cite

In this survey, a comprehensive study is provided, regarding the use of machine learning (ML) algorithms for effective resource management in fifth-generation and beyond (5G/B5G) wireless cellular networks. The ever-increasing user requirements, their diverse nature in terms of performance metrics and the use of various novel technologies, such as millimeter wave transmission, massive multipleinput-multiple-output configurations and non-orthogonal multiple access, render the multi-constraint nature of the radio resource management (RRM) problem. In this context, ML and mobile edge computing (MEC) constitute a promising framework to provide improved quality of service (QoS) for end users, since they can relax the RMM-associated computational burden. In our work, a state-of-the-art analysis of ML-based RRM algorithms, categorized in terms of learning type and potential applications as well as MEC implementations,is presented, to define the best-performing solutions for various RRM sub-problems. To demonstrate the capabilities and efficiency of ML-based algorithms in RRM, we apply and compare different ML approaches for throughput prediction, as an indicative RRM task. We investigate the problem, either as a classification or as a regression one, using the corresponding metrics in each occasion. Finally, open issues, challenges and limitations concerning AI/ML approaches in RRM for 5G and B5G networks, are discussed in detail.

show abstract

Section: B Representative Recent Non-ml Approachesmentioning

confidence: 99%

Section: Limitations Of Non-ml Approachesmentioning

confidence: 99%

ML-Based Radio Resource Management in 5G and Beyond Networks: A Survey

et al. 2022

View full text Add to dashboard Cite

show abstract

“…They take into account different aspects of 5G services such as traffic qualities, resource demands, as well as QoS requirements. Furthermore, the authors in [32] consider the problem of system saturation in the 3GPP LTE cellular system due to a large number of MTC devices, causing a significant impact on both machine-to-machine (M2M) as well as human to-machine (H2M) traffic. To address this problem, they recommended a sophisticated design made for 5G LTE networks to allow the conjunction of M2M and H2M traffic, sustained by various top priority scheduling algorithms to meet QoS for every traffic.…”

Section: Related Workmentioning

confidence: 99%

Delay-Based Resource Allocation With Fairness Guarantee and Minimal Loss for eMBB in 5G Heterogeneous Networks

2022

View full text Add to dashboard Cite

Heterogeneity is defined as one of the core merits in the 5th Generation (5G) mobile systems. Such a trait is prominent in modern 5G traffic scenarios, i.e., enhanced Mobile Broadband (eMBB), where coarse Quality of Service (QoS) profiles need to be satisfied over the shared transmission channel. For that, Radio Resource Management (RRM) became a challenging problem that should be figured out based on the traffic scenario. Extensive efforts dwelled in the literature attempt to solve the resource allocation problem in the downlink channel by maximizing the gain for a certain aspect in the QoS profile. This, however, poses a challenge for 5G-related traffic scenarios such that multi-dimension QoS shall be attainable. Therefore, in this paper, a Delay-aware Resource Allocation for Guaranteed Fairness and minimal Loss (DRAGFL) is proposed to enhance the QoS for Real-Time (RT) services in eMBB 5G Heterogeneous Networks. The ultimate objective of DRAGFL is to maintain a steadily low latency for delay-sensitive flows without restraining the service fairness and the data loss in the overload states. DRAGFL enhances the efficiency of the MAC layer scheduler throughout three phases. Firstly, generating delay-derived weight matrix of the resources blocks to radio bearers from eMBB different classes. Then, greedy-based flows prioritization for Resource Blocks (RBs) assignment. Finally, the data rate of the RT and Non RT (NRT) flows is further emphasized through a channel-aware principle that is invoked conditioning to the remaining radio resources and available data to be scheduled in the interval. The performance evaluation using system-level simulations reveals prominent gains of DRAGFL in sustaining a steadily low end-to-end delay in the high congestion states. Moreover, it maintains ideal states of service fairness with low data loss for different traffic loads to indicate a promising MAC scheduler for the developing scenarios of 5G.

show abstract

“…Smart grid (SG) is a new type of power system that has emerged in recent years and is widely used by power companies due to its accuracy in power load forecasting [ 1 , 2 , 3 , 4 ]. Energy issues are very important nowadays especially with the spread of appliances and the concepts of the Internet of Things [ 5 , 6 ], smart house [ 7 ], and smart city [ 8 ]. Thus, the problem of energy efficiency is one of the key ones.…”

Section: Introductionmentioning

confidence: 99%

Individualized Short-Term Electric Load Forecasting Using Data-Driven Meta-Heuristic Method Based on LSTM Network

Sun

Qin

Przystupa

et al. 2022

Sensors

View full text Add to dashboard Cite

Short-term load forecasting is viewed as one promising technology for demand prediction under the most critical inputs for the promising arrangement of power plant units. Thus, it is imperative to present new incentive methods to motivate such power system operations for electricity management. This paper proposes an approach for short-term electric load forecasting using long short-term memory networks and an improved sine cosine algorithm called MetaREC. First, using long short-term memory networks for a special kind of recurrent neural network, the dispatching commands have the characteristics of storing and transmitting both long-term and short-term memories. Next, four important parameters are determined using the sine cosine algorithm base on a logistic chaos operator and multilevel modulation factor to overcome the inaccuracy of long short-term memory networks prediction, in terms of the manual selection of parameter values. Moreover, the performance of the MetaREC method outperforms others with regard to convergence accuracy and convergence speed on a variety of test functions. Finally, our analysis is extended to the scenario of the MetaREC_long short-term memory with back propagation neural network, long short-term memory networks with default parameters, long short-term memory networks with the conventional sine-cosine algorithm, and long short-term memory networks with whale optimization for power load forecasting on a real electric load dataset. Simulation results demonstrate that the multiple forecasts with MetaREC_long short-term memory can effectively incentivize the high accuracy and stability for short-term power load forecasting.

show abstract

QoS‐Aware Optimal Radio Resource Allocation Method for Machine‐Type Communications in 5G LTE and beyond Cellular Networks

Cited by 29 publications

References 30 publications

ML-Based Radio Resource Management in 5G and Beyond Networks: A Survey

ML-Based Radio Resource Management in 5G and Beyond Networks: A Survey

Delay-Based Resource Allocation With Fairness Guarantee and Minimal Loss for eMBB in 5G Heterogeneous Networks

Individualized Short-Term Electric Load Forecasting Using Data-Driven Meta-Heuristic Method Based on LSTM Network

Contact Info

Product

Resources

About