Optimal Resource Allocation in Cellular Networks With H2H/M2M Coexistence

Alhussien, Nedaa; Gulliver, T. Aaron

doi:10.1109/tvt.2020.3016239

Cited by 20 publications

(6 citation statements)

References 20 publications

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“…In addition, in order to meet the massive access of MTC devices, researchers have also proposed other methods. For example, reference [25] has proposed a two-stage resource optimization algorithm for H2H/M2M coexistence. The results show that the algorithm can balance the demands of M2M and H2H traffic, and meet their diverse QoS requirements.…”

Section: Relate Workmentioning

confidence: 99%

A Novel Group Paging Control Method for Massive MTC Accesses in LTE Networks

Wang

Wei

et al. 2022

Wireless Pers Commun

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The fifth-generation (5G) systems have to deal with massive deployment of machine-type-communication (MTC) devices. System overload may occur during a random access procedure under a limited number of preamble resources and physical uplink shared channel resources especially when there exist massive MTC devices in a cell. In order to resolve the system overload (caused by the massive MTC deployment), the 3GPP proposed the adopted a group paging (GP)-based uplink access technique. But its performances dramatically decrease as the number of MTC devices increases. In this paper we propose a novel method, named ACB-based group paging overload control method (AGO). To reduce the number of simultaneous access MTC devices, AGO first scatters the MTC devices over a GP interval, and then automatically adjusts ACB parameters according to the load conditions. By doing so, AGO achieves high-channel access probability for MTC devices. Simulation results show that this method is superior to the GP and Pre-backoff mechanisms in terms of success and collision probability, average access latency and resource utilization rate.

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Section: Relate Workmentioning

confidence: 99%

A Novel Group Paging Control Method for Massive MTC Accesses in LTE Networks

Wang

Wei

et al. 2022

Wireless Pers Commun

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“…The small scale Rayleigh fading follows an exponential distribution with unit mean, and the large scale shadowing follows a log-normal distribution with zero mean and standard deviation σ s = 8 dB [44]. The AWGN power is σ 2 0 = −100 dBm [5]. The circuit power consumption is p cir = 5 dBm and the power amplifier efficiency ζ is 30%.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…On the other hand, cellular networks provide extended wireless connectivity, more flexible radio resource management, and higher capacity to support M2M deployment. However, the characteristics and qualityof-service (QoS) requirements of M2M communications are distinct from those of traditional cellular communications that these networks were originally designed for [5]. While the majority of cellular traffic is on the downlink, M2M traffic is mainly on the uplink with infrequent transmissions under delay limits [6].…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Interference-Aware Cognitive Machine-to-Machine Communications Underlaying Cellular Networks

Alhussien

Gulliver

2022

IEEE Access

Self Cite

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Machine-to-machine (M2M) communications can effectively utilize cognitive radio (CR) to coexist with cellular users in what is known as cognitive M2M (CM2M) communications. In this system, underlay CR is used to manage spectrum sharing among machine type communication devices (MTCDs) and cellular user equipment (CUE) where the CUE are considered to be primary users (PUs) and the MTCDs are secondary users (SUs). Moreover, due to the limited battery capacity of MTCDs and diverse quality-of-service (QoS) requirements of both MTCDs and CUE, energy efficiency (EE) is critical to prolonging network lifetime. This paper investigates the power allocation problem for energy-efficient CM2M communications underlaying cellular networks. Underlay CR is employed to manage the coexistence of MTCDs and CUE and exploit spatial spectrum opportunities to improve spectrum utilization. Two power allocation problems are proposed where the first targets MTCD power consumption minimization while the second considers MTCD EE maximization subject to MTCD transmit power constraints, MTCD minimum data rate requirements, and CUE interference limits. The proposed power consumption minimization problem is transformed into a geometric programming (GP) problem and solved iteratively. The proposed EE maximization problem is a nonconvex fractional programming problem. Hence, a parametric transformation is used to convert it into an equivalent convex form and this is solved using an iterative approach. Simulation results are presented which show that the proposed algorithms provide MTCD power allocation with lower power consumption and higher EE than the equal power allocation (EPA) scheme while satisfying the constraints.INDEX TERMS Cellular user equipment (CUE), energy efficiency (EE), machine-to-machine (M2M) communications, power allocation, underlay cognitive radio (CR).

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“…To our best knowledge, there are no other works that can predict analytically the exact distribution of the traffic pattern at the base station. Regarding the traffic distribution at single mMTC users, the general assumption is that inter-generation times for sparse traffic are either periodic (i.e., deterministic) [13], uniform [14], [15], or exponential [6] for sparse traffic, and for mMTC users with more intense traffic, the number of packets underlie a beta distribution [1], whereas the inter-generation time is governed by a Pareto distribution [14]. In all these models, the sizes of the packets are fixed, and very small.…”

Section: Related Workmentioning

confidence: 99%

Modeling and Analysis of mMTC Traffic in 5G Base Stations

Mehmeti¹,

Porta²

2021

Preprint

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Massive Machine-Type Communications (mMTC) are one of the three types of services that should be supported by 5G networks. These are distinguished by the need to serve a large number of devices which are characterized by nonintensive traffic and low energy consumption. While the sporadic nature of the mMTC traffic does not pose an exertion to efficient network operation, multiplexing the traffic from a large number of these devices within the cell certainly does. Therefore, planning carefully the network resources for this traffic is of paramount importance. To do this, the statistics of the traffic pattern that arrives at the base station should be known. To this end, in this paper, we derive the distribution of the inter-arrival times of the traffic at the base station from a general number of mMTC users within the cell, assuming a generic distribution of the traffic pattern by individual users. We validate our results on traces. Results show that adding more mMTC users in the cell increases the variability of the traffic pattern at the base station almost linearly, which is not the case with increasing the traffic generation rates.

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Optimal Resource Allocation in Cellular Networks With H2H/M2M Coexistence

Cited by 20 publications

References 20 publications

A Novel Group Paging Control Method for Massive MTC Accesses in LTE Networks

A Novel Group Paging Control Method for Massive MTC Accesses in LTE Networks

Energy-Efficient Interference-Aware Cognitive Machine-to-Machine Communications Underlaying Cellular Networks

Modeling and Analysis of mMTC Traffic in 5G Base Stations

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