An Energy Efficient Uplink Scheduling and Resource Allocation for M2M Communications in SC-FDMA Based LTE-A Networks

Li, Qiyue; Ge, Yuling; Yang, Yangzhao; Zhu, Yadong; Sun, Wei; Li, Jie

doi:10.1007/978-3-030-17513-9_9

Cited by 9 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…MTCDs are clustered by a transmission protocol and then by QoS features and needs. Thus, resource allocation is based on sum throughput maximization [17].…”

Section: M2m Resource Allocation and Clustering Schemes A Recent Stud...mentioning

confidence: 99%

Algorithm for Energy Resource Allocation and Sensor-Based Clustering in M2M Communication Systems

Ajay

Nagaraj

Jain

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Recent years have seen a surge in curiosity in machine-to-machine (M2M) collaborations between academics and industry. Machine-to-machine communication devices (MTCDs) are able to communicate automatically and with minimum human intervention in an M2M communications infrastructure. While MTCDs are anticipated to deliver a range of services, resource allocation and clustering approaches in M2M transceivers face issues and limits due to the diverse quality of service (QoS) needs in various network conditions. A major issue in M2M communication systems is how to distribute and cluster resources. This article presents a clustering technique and collaborative resource allocation for MTCD resource management. The clustering and integrated resource allocation challenge is characteristic as a maximization of energy efficiency problem. As a consequence of the original optimization model’s inability to tackle nonlinear fractional utilizations, we separate the issue into two subproblems: power redistribution and cluster. We begin by obtaining the optimal power distribution plan through an iterative energy efficiency maximization algorithm and then offer a modified K -means technique for clustering. The effectiveness of the proposed approach is shown by the numerical solution.

show abstract

“…MTCDs are clustered by a transmission protocol and then by QoS features and needs. Thus, resource allocation is based on sum throughput maximization [17].…”

Section: M2m Resource Allocation and Clustering Schemes A Recent Stud...mentioning

confidence: 99%

Algorithm for Energy Resource Allocation and Sensor-Based Clustering in M2M Communication Systems

Ajay

Nagaraj

Jain

2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…4: The approximate latency of an M2M user is found using Equation (22) probability that exactly ñ other users are in scheduling stage 2. Using Bayes' rule, the unconditional probability of scheduling an M2M user is therefore given as:…”

Section: Latency Analytic Equationmentioning

confidence: 99%

“…Results indicate that low data packets dropping ratios and optimal energy efficiency for a large number of M2M nodes were achieved. The inclusion of MCS in the scheduling and resource allocation in [22] is unique as it caters for modulation and coding aspects that impact this performance measures in wireless communication. The paper, however, did not consider circuit energy consumption.…”

Section: Related Workmentioning

confidence: 99%

“…However, some emerging research challenges of cooperative M2M communication indicated in [21] are an increase in end‐to‐end latency, complex scheduling, increased overhead, increased intra and inter network interferences, and the difficulty of clustering process due to the problem of uneven distribution of M2M devices. Maximum energy efficient uplink M2M data packets transmission is investigated in [22] by formulating it into a joint problem of modulation and coding scheme (MCS) selection, resource allocation, and power control. The allocation and scheduling scheme were formulated into a mixed‐integer linear fractional programming problem which was reconstructed into a linear form and an optimisation scheme based on the Charnes–Cooper transformation and the Glover linearisation scheme was proposed.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Energy efficiency, latency and reliability trade‐offs in M2M uplink scheduling

et al. 2021

View full text Add to dashboard Cite

Issues of energy efficiency, latency and reliability have engaged researchers' attention in wireless communications for a long time due to their importance in the dependable delivery of data over wireless networks. The emergence of M2M communication and IoT has intensified this attention because energy efficiency and latency are critical factors affecting their performance. In this paper, a scheduler is designed by utilising the probability density function of the signal-to-noise ratio of Rayleigh fading channels to define a threshold used for resource allocation. This threshold, combined with the mean SNR of an M2M device, determines whether or not an M2M device is eligible for scheduling, given its instantaneous channel conditions. The trade-off between energy efficiency, latency and packet drop of this proposed scheduling strategy is investigated. The performance of the proposed scheduler is compared to round robin, maximum throughput, and proportional fair schedulers. Compared to these standard scheduling strategies, the scheduler provides the advantage of trading off latency for energy efficiency by tuning the threshold parameter. INTRODUCTIONMachine-to-Machine (M2M) communication has recently developed into an important technology generating significant revenues to mobile network operators. In this technology, nodes are equipped with sensor modules to monitor and collect various forms of data such as temperature, humidity, and pressure for onward transmission through communication networks.There are many applications of M2M communication, some of which are: smart metering, intelligent transportation, health care, smart city, public safety and consumer devices [1][2][3][4]. M2M communication, however, comes with peculiar challenges which are dependent on its typical characteristics, including a massive number of devices, small size data transmission, infrequent traffic patterns, and battery limitation in contrast to human-to-human (H2H) communications. These problems are well-known and are variously discussed [5][6][7][8][9][10][11]. Energy efficiency and low latency are very critical considerations in current and future wireless networks. Since the types of wireless services become increasingly diverse as technologies evolve, networks must also be built to meet the corresponding applications and different delay requirements, knowing when and how to tradeThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

show abstract

Throughput Optimal Uplink Scheduling in Heterogeneous PLC and LTE Communication for Delay Aware Smart Grid Applications

Cao

Sun

et al. 2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

Self Cite

View full text Add to dashboard Cite

Smart grid is an energy network that integrates advanced power equipment, communication technology and control technology. It can transmit two-way power and data among all components of the grid at the same time. The existing smart grid communication technologies include power line carrier (PLC) communication, industrial Ethernet, passive optical networks and wireless communication, each of which have different advantages. Due to the complex application scenarios, massive sampling points and high transmission reliability requirements, a single communication method cannot fully meet the communication requirements of smart grid, and heterogeneous communication modes are required. In addition, with the development of cellular technology, long term evolution (LTE)-based standards have been identified as a promising technology that can meet the strict requirements of various operations in smart grid. In this paper, we analyze the advantages and disadvantages of PLC and LTE communication, and design a network framework for PLC and LTE communication uplink heterogeneous communication in smart grid. Then, we propose an uplink scheduling transmission method for sampling data with optimized throughput according to the requirements of system delay and reliability. Then, we use the formula derivation to prove the stability and solvability of the scheduling system in theory. Finally, the simulation results show that under the condition of satisfying the delay requirement, our proposed framework can optimally allocate the wireless communication resource and maximize the throughput of the uplink transmission system.

show abstract

An Energy Efficient Uplink Scheduling and Resource Allocation for M2M Communications in SC-FDMA Based LTE-A Networks

Cited by 9 publications

References 16 publications

Algorithm for Energy Resource Allocation and Sensor-Based Clustering in M2M Communication Systems

Algorithm for Energy Resource Allocation and Sensor-Based Clustering in M2M Communication Systems

Energy efficiency, latency and reliability trade‐offs in M2M uplink scheduling

Throughput Optimal Uplink Scheduling in Heterogeneous PLC and LTE Communication for Delay Aware Smart Grid Applications

Contact Info

Product

Resources

About