Allocation of Control Resources for Machine-to-Machine and Human-to-Human Communications Over LTE/LTE-A Networks

Andrade, Tiago Faustino; Astudillo, Carlos A.; Fonseca, Nelson L. S. da

doi:10.1109/jiot.2016.2557240

Cited by 47 publications

(11 citation statements)

References 20 publications

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“…For human resource allocation, one of the core business processes is to identify and recruit people with corresponding skills, abilities, knowledge, and experience to fill the posts in enterprise organization [17][18][19]. Although there are many human resource scheduling strategies and mechanisms in the field of human resource scheduling, there are still some drawbacks in the existing research work.…”

Section: Related Workmentioning

confidence: 99%

Semi-supervised human resource scheduling based on deep presentation in the cloud

Lin

Wang

2020

J Wireless Com Network

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Traditional competitive human resource allocation is no longer suitable for contemporary requirements. To improve the effectiveness of human resource management and the degree of matching between jobs and staffs, we propose a novel auto-encoder neural network-based method in the cloud environment, which is a semi-automatic manner in the business process of human resource allocation. The proposed method is based on deep learning architecture by using appropriate cloud resources and takes into account the similarities and deep presentation between staff modules. The construction between human resource network is combined with a priori information about the human resource and set up by the evaluation index system of human resource planning. Our proposed method enables the modeling of semi-automatic human resource allocation process and can be used to facilitate optimized human resource allocation. Experimental results show that our method can bring significant improvements to personnel position matching and effectively enhance the efficiency of human resource allocation based on the cloud environment.

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Section: Related Workmentioning

confidence: 99%

Semi-supervised human resource scheduling based on deep presentation in the cloud

Lin

Wang

2020

J Wireless Com Network

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“…In [15], several possible scheduling algorithms for LTE-PDCCH have been discussed. Further, considering the joint effect of random access procedure and PDCCH, a novel scheduling algorithm has been proposed in [15]. Based on the simulation results, it has been shown that the proposed scheduling algorithm improves QoS provisioning for realtime traffic.…”

Section: B On Scheduling Of the Devices In Control Channelmentioning

confidence: 99%

Scheduling and Decoding of Downlink Control Channel in 3GPP Narrowband-IoT

et al. 2020

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Narrowband Internet of Things (NB-IoT) is a low power wide area network technology introduced by the 3 rd Generation Partnership Project (3GPP). It is a derivative of the existing 3GPP Long Term Evolution (LTE) that will enable cellular service to a massive number of IoT devices. In comparison with LTE and 5G New Radio, the NB-IoT devices will be of low cost, low throughput, and delay-tolerant. The reduction in available bandwidth and introduction of repetitions for achieving wider coverage requires modified Narrowband Physical Downlink Control Channel (NPDCCH) search space design and decoding as compared to the LTE. Hence, in this paper, we first explain the NPDCCH physical layer procedures, along with the search space decoding. Unlike LTE, there is no channel feedback mechanism in NB-IoT. Therefore, we propose a novel resource mapping scheme for NPDCCH based on the uplink reference signals. We perform system-level simulations and analyze the impact of the proposed mapping for varying operating frequencies and channel conditions. Further, the NB-IoT devices have limitations on the battery power, and hence, the existing control channel schedulers cannot be reused for the NB-IoT scenario. Thus, we propose a novel scheduler for NPDCCH. We have also modified the current state-of-the-art algorithms to meet the NPDCCH constraints and compared them against the proposed scheduler. We derive bounds for such scheduling algorithms and show that the proposed scheduler additionally conserves up to 25% of the IoT device battery power. Through Monte Carlo simulations, we show that the proposed scheduler better achieves the various trade-offs between power consumption, search space utilization, and fairness as compared to the existing schedulers.

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“…Third, multi-hop based scheduling uses several hops to send data over short distances instead of sending them over long distances using a single hop, which saves energy. Finally, scalable network based scheduling uses the native IP LTE-A connectivity feature to support a considerable number of M2M communications [15]. Scheduling is the process implemented by eNodeB to allocate radio resource PRB to UE or MTCD in the downlink or uplink direction.…”

Section: Resource Allocation For M2m Communicationsmentioning

confidence: 99%

New Method Based on Priority of Heterogeneous Traffic for Scheduling Techniques in M2M Communications over LTE Networks

Ouaissa¹,

Rhattoy²

2018

IJIES

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Machine to Machine (M2M) communication is a technology for machines communicating with each other without human intervention. Long Term Evolution (LTE) and Long-Term Evolution-Advanced (LTE-A) cellular networks technologies provide the resources required for M2M communication. However, LTE is required to meet M2M communication requirements, such as power management and support for a large number of devices while maintaining the Quality of Service (QoS) requirements for these devices. Resource allocation or scheduling is one of the key challenges facing M2M communications over Long-Term Evolution networks. M2M traffic has unique characteristics; it usually consists of a large number of small data packets, with specific delays, generated by a potentially large number of devices competing on scarce radio resources. In this article, we will present the scheduling techniques on the Packet Downlink Control Channel (PDCCH) in a M2M scenario based on the priority and differentiation between heterogeneous traffic (real time and non-real time) to achieve high spectral efficiency in cellular systems and provide quality of service guarantees for system users. The results obtained show that the FLS downlink scheduler is the optimal solution based on our proposed method which offers more priority to the real time service (video and voip) than the non real time service (best effort).

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Allocation of Control Resources for Machine-to-Machine and Human-to-Human Communications Over LTE/LTE-A Networks

Cited by 47 publications

References 20 publications

Semi-supervised human resource scheduling based on deep presentation in the cloud

Semi-supervised human resource scheduling based on deep presentation in the cloud

Scheduling and Decoding of Downlink Control Channel in 3GPP Narrowband-IoT

New Method Based on Priority of Heterogeneous Traffic for Scheduling Techniques in M2M Communications over LTE Networks

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