Improved Resource Allocation in 5G MTC Networks

Rehman, Waheed Ur; Salam, Tabinda; Almogren, Ahmad; Haseeb, Khalid; Din, Ikram Ud; Bouk, Safdar Hussain

doi:10.1109/access.2020.2974632

Cited by 22 publications

(14 citation statements)

References 33 publications

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“…The researchers of [ 145 ] proposed a scheme for categorizing resource allocation into two main parts named as resource allocation and medium access. The medium access influences the transmission nature of the wireless signal and MTC devices’ wait time to allocate priorities utilizing capillary band in an integral way.…”

Section: Discussionmentioning

confidence: 99%

Resource Allocation Schemes for 5G Network: A Systematic Review

Kamal

Raza

Alam

et al. 2021

Sensors

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Fifth-generation (5G) communication technology is intended to offer higher data rates, outstanding user exposure, lower power consumption, and extremely short latency. Such cellular networks will implement a diverse multi-layer model comprising device-to-device networks, macro-cells, and different categories of small cells to assist customers with desired quality-of-service (QoS). This multi-layer model affects several studies that confront utilizing interference management and resource allocation in 5G networks. With the growing need for cellular service and the limited resources to provide it, capably handling network traffic and operation has become a problem of resource distribution. One of the utmost serious problems is to alleviate the jamming in the network in support of having a better QoS. However, although a limited number of review papers have been written on resource distribution, no review papers have been written specifically on 5G resource allocation. Hence, this article analyzes the issue of resource allocation by classifying the various resource allocation schemes in 5G that have been reported in the literature and assessing their ability to enhance service quality. This survey bases its discussion on the metrics that are used to evaluate network performance. After consideration of the current evidence on resource allocation methods in 5G, the review hopes to empower scholars by suggesting future research areas on which to focus.

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Section: Discussionmentioning

confidence: 99%

Resource Allocation Schemes for 5G Network: A Systematic Review

Kamal

Raza

Alam

et al. 2021

Sensors

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“…ETSI defines QoS class identifier for M2M communications in LTE [110], [144], [145], as shown in Table 4. As the M2M communication system has a massive number of devices with varying QoS requirements, therefore designing scheduling schemes for M2M communication over the cellular network is a challenging task [71], [146]. A massive number of MTC devices are infrequently sending varying sizes of data packets; the LTE bandwidth offers a limited number of physical resources and that are optimized for H2H communication [72].…”

Section: Quality Of Service Based Techniquesmentioning

confidence: 99%

A Survey on LTE/LTE-A Radio Resource Allocation Techniques for Machine-to-Machine Communication for B5G Networks

et al. 2021

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The Machine-to-Machine (M2M) communication refers to an autonomous communication among devices that aims for a massive number of connected devices. M2M communication can support ubiquitous communication and full mechanical automation, and it will change everything from industry to ourselves. Recent developments in communication technology make Long Term Evolution (LTE)/Long Term Evolution-Advance (LTE-A) a promising technology for supporting M2M communication. LTE can support the diverse characteristic of M2M communication due to its IP connectivity, coverage area, and scalability. Therefore, the LTE schedulers should satisfy the need for M2M communication.Motivated from these facts, in this paper, we present a survey on classification of LTE / LTE-A scheduling methodologies from the perspective of M2M communication. We classify the schedulers based on their objectives, such as energy efficiency, spectrum efficiency, group-based, and Quality-of-Service (QoS) support for Machine Type Communication Devices (MTCDs). We also highlight the scope of future research direction for the scheduling work.

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“…An efficient rewarding function influences the process for finding optimal policy [28]. The proposed reward function is closely related to the energy-minimizing problem (7). It can be achieved by efficient computation offloading.…”

Section: ) Reward Functionmentioning

confidence: 99%

“…supervision [3]- [6]. The traffic characteristics of MTCDs vary from best-effort services such as utility metering, and environmental monitoring systems to ultra-reliable ones such as mission-critical industry, public safety, and health-care [7]. Most of these services are compute-intensive and require computational support to be provided in reality [8].…”

Section: Introductionmentioning

confidence: 99%

Advanced Energy-Efficient Computation Offloading Using Deep Reinforcement Learning in MTC Edge Computing

et al. 2020

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Mobile edge computing (MEC) supports the internet of things (IoT) by leveraging computation offloading. It minimizes the delay and consequently reduces the energy consumption of the IoT devices. However, the consideration of static communication mode in most of the recent work, despite varying network dynamics and resource diversity, is the main limitation. An energy-efficient computation offloading method using deep reinforcement learning (DRL) is proposed. Both delay-tolerant and non-delay tolerant scenarios are considered using capillary machine type communication (MTC). Depending upon the type of service, an intelligent MTC edge server using DRL decides either process the incoming request at the MTC edge server or sends it to the cloud server. To control communication, we draft a markov decision problem (MDP). This minimizes the long-term power consumption of the system. The formulation of the optimization problem is considered under the constraint of computing power resources and delays. Simulation results delineate the significant performance gain of 12% in computation offloading through the proposed DRL approach. The effectiveness and superiority of the proposed model are compared with other baselines and are demonstrated numerically. INDEX TERMS Machine type communication, mobile edge computing, computation offloading, deep reinforcement learning, energy efficiency.

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Improved Resource Allocation in 5G MTC Networks

Cited by 22 publications

References 33 publications

Resource Allocation Schemes for 5G Network: A Systematic Review

Resource Allocation Schemes for 5G Network: A Systematic Review

A Survey on LTE/LTE-A Radio Resource Allocation Techniques for Machine-to-Machine Communication for B5G Networks

Advanced Energy-Efficient Computation Offloading Using Deep Reinforcement Learning in MTC Edge Computing

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