Classification of LTE Uplink Scheduling Techniques: An M2M Perspective

Machine‐to‐machine (M2M) communication is a challenging topic in the Internet‐of‐Things era. The increasing growth of machines and high rate of packet production have result in massive access to the network. Therefore, one of the media access control (MAC) challenges in 5G cellular networks is the management of massive access to the wireless media regarding quality‐of‐service (QoS) requirements and battery restrictions of the machines. Delay is one of the QoS requirements that should be guaranteed in most applications. To the best of our knowledge, previous studies on scheduled MAC mechanisms have not addressed energy efficiency and delay requirements appropriately. In this paper, the problem of scheduled massive access management has been considered with the aim of simultaneously meeting delay requirements of machines and increasing energy efficiency. The proposed solution exploits gateways to reuse the spectrum dedicated to M2M communications and introduces a clustering algorithm to allocate radio resources to the machines appropriately. Moreover, an optimization problem is defined and solved using genetic algorithms (GA) to adjust the optimal transmission power of machines, aiming at maximizing the energy efficiency. To preserve delay constraints, the proposed method exploits an existing scheduling algorithm. Simulation results demonstrate more than 50% reduction in probability of packet delay violation for various classes of service and in mean packet delay compared to the baseline method. The results have also shown more than 20% improvement in energy efficiency of proposed method compared to the baseline.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy and delay aware massive access management in machine‐to‐machine communications

Tagarian

Ghahfarokhi

2019

“…8 However, because of the diverse characteristics of quality of service (QoS) requirement, power efficiency, transmission periodicity, and payload size, MTC poses huge challenges to LTE/LTE-A technologies. 9,10 Devaluation of user equipment (UE) cost and radio access network (RAN) overload control issue is mainly focused by the LTE-A standards. 11 LTE-A networks are planned fundamentally for H2H communications; therefore, typically, the amount of uplink (UL) traffic is lower than the downlink (DL) traffic.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective cooperative swarm intelligence algorithm for uplink resource allocation in LTE‐A networks

Moses¹,

Balakrishnan²

2019

Nowadays, Long-Term Evolution Advanced (LTE-A) network is the primary innovation in 4G networks. The LTE-A networks convey exceptional data rates and low latency for few sorts of application. Sometimes, in LTE-A, the multiobjective uplink resource allocation is the perplexing optimization problem, and this problem is considered as 0-1 multiobjective knapsack trouble. In order to overcome this trouble, a multiobjective cooperative swarm intelligence algorithm for resource allocation is proposed in this paper. In our proposed work, hybrid firefly algorithm (FFA) and particle swarm optimization (PSO) algorithm are utilized to solve 0-1 multiobjective knapsack problem. Initially, a priority and urgency factor (urgency of packets)-based user ranking and quantifying scheme is designed for scheduling process. After the scheduling process, the resource allocations employ three objective functions, such as maximization of resource utilization, maximization of quality of service (QoS), and interference minimization. The optimization problem is overcome by using the hybrid FFA and PSO algorithm. The experimental outcomes demonstrate that it has the best QoS and less interference in the resource allocation in LTE-A network than state-of-art methods in the proposed strategy.Trans Emerging Tel Tech. 2019;30:e3748.wileyonlinelibrary.com/journal/ett

“…The purpose is to share the physical resources in packet switched networks. In VoLTE, the network capacity signifies its importance in the usage of voice and data . Because of restricted power transmission (in scheduling duration), fading, and intercell interference, uplink transmission deteriorates its data transmission compared to downlink in LTE‐advanced (LTE‐A) networks …”

Section: Introductionmentioning

confidence: 99%

“…In VoLTE, the network capacity signifies its importance in the usage of voice and data. 2,3 Because of restricted power transmission (in scheduling duration), fading, and intercell interference, uplink transmission deteriorates its data transmission compared to downlink in LTE-advanced (LTE-A) networks. 4,5 International Telecommunication Union Radio Communication Sector (ITU-R) has published the guidelines of International Mobile Telecommunications-Advanced (IMT-A) and the performance requirements of fourth-generation networks.…”

Section: Introductionmentioning

confidence: 99%

Analyzing enhanced real‐time uplink scheduling algorithm in 3GPP LTE‐advanced networks using multimedia systems

Deebak

Ever

Al‐Turjman

2018

Third Generation Partnership Project (3GPP) standardizes the Long‐Term Evolution (LTE) to improve the quality of service in modern communication systems using 3GPP LTE‐advanced (LTE‐A) networks. As this technology is converging with modern devices, efficient resource allocation schemes are essential for minimization of the communication delay for the sensitive real‐time devices. To achieve the demands of latest technologies, this paper proposes two novel mechanisms, enhanced real‐time polling system with continuous time–batch Markovian arrival process and proactive resource allocation framework. The objective of the former mechanism is to improvise the service connectivity between the multimedia devices in LTE‐A networks and the latter is to exploit the transmission bits in each user's buffer in addition to the state information of wireless channel. The proposed mechanisms consider the constraints enforced by 3GPP standard in terms of radio resource blocks allocation for LTE‐A networks. In addition, the proposed mechanisms use LTE‐A networks to investigate the Voice over LTE traffic in order to analyze various quality‐of‐service measures, namely, sum throughput, session setup delay, packet delivery ratio, resource utilization ratio, and throughput improvement via real‐time multimedia client‐server systems. The probing results show that the proposed algorithm is superior in comparison to other typical services of IEEE 802.16d/e–based systems as well as LTE‐A system with 7 orthogonal frequency‐division multiplexing symbols over a duration slot 0.5 ms. While the slotted duration is set to 5 ms, the proposed framework effectively utilizes the resource allocation cycle to support the uplink scheduling to outperform at least 38% better throughput performance in comparison with existing mechanisms.