Improving call admission control in 5G for smart cities applications

Slalmi, Ahmed; Saadane, Rachid; Chaibi, Hasna; Aroussi, Hatim Kharraz

doi:10.1145/3368756.3369031

Cited by 5 publications

(6 citation statements)

References 10 publications

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“…Three articles [212][213][214] look at the latest cooperative MIMO antenna technologies, which are the bridging technologies to 5G, by first looking at the benefits of cooperative MIMO in smart city applications like smart transportation, home automation, and security, and then evaluating the impact of cooperative MIMO technologies on achieving higher spectral efficiency.…”

Section: Smart Citiesmentioning

confidence: 99%

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

Mahdi

Ahmad

Qassim³

et al. 2021

Applied Sciences

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Due to the rapid development of the fifth-generation (5G) applications, and increased demand for even faster communication networks, we expected to witness the birth of a new 6G technology within the next ten years. Many references suggested that the 6G wireless network standard may arrive around 2030. Therefore, this paper presents a critical analysis of 5G wireless networks’, significant technological limitations and reviews the anticipated challenges of the 6G communication networks. In this work, we have considered the applications of three of the highly demanding domains, namely: energy, Internet-of-Things (IoT) and machine learning. To this end, we present our vision on how the 6G communication networks should look like to support the applications of these domains. This work presents a thorough review of 370 papers on the application of energy, IoT and machine learning in 5G and 6G from three major libraries: Web of Science, ACM Digital Library, and IEEE Explore. The main contribution of this work is to provide a more comprehensive perspective, challenges, requirements, and context for potential work in the 6G communication standard.

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Section: Smart Citiesmentioning

confidence: 99%

From 5G to 6G Technology: Meets Energy, Internet-of-Things and Machine Learning: A Survey

Mahdi

Ahmad

Qassim³

et al. 2021

Applied Sciences

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“…Hence for CAC, the GSM network allocates both frequency and time slots to each new user. However, high‐speed circuit switched data (HSCSD), general packet radio service (GPRS), and enhanced data rates for global evolution (EDGE) allocate more than one‐time slot per user by using, for instance, statistic multiplexing 4 …”

Section: Call Admission Control (Cac) In Mobile Networkmentioning

confidence: 99%

“…RRC states are a solution for system access, energy‐saving, and mobility optimization. 5G technology must support eMBB, uRLLC, and mMTC services with the same cost and energy dissipation per day and zone 4 …”

Section: Call Admission Control (Cac) In Mobile Networkmentioning

confidence: 99%

“…The discontinuous transmission mode, DTX, is used to reduce the base station power consumption by switching into sleep mode during small periods in each frame 4‐11 . We denote by:

γ_{1} = P_{0} + P_{T X} + P_{f L} + P_{P A} with 0 \leq P_{T X} < P_{m a x} for active mode,

γ_{2} = P_{S M} for sleep mode,

γ_{2} = μ P_{S M} for deep‐sleep mode,

and

λ = N_{T X} N_{S e c t} N_{c} (1 - G_{D T X}),

where…”

Section: Power Consumed In the Macrocell In A Udnmentioning

confidence: 99%

“…The minimum transmission power ( P TX ) min is given by: 4

{(P_{T X})}_{m i n} = (2^{\frac{{(C_{k})}_{m i n}}{B_{k}}} - 1) (I + B_{k} N_{0}) P L_{k j},

where PL kj is path loss model. If a new user is accepted, the consumption power will be given by:

{(P_{T}^{B S})}_{n e w} = P_{T}^{B S} + {(P_{T X})}_{m i n} .

…”

Section: Call Admission Control With Lower Energy Consumption In 5gmentioning

confidence: 99%

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5G NB‐IoT: Efficient network call admission control in cellular networks

Slalmi

Chaibi²,

Saadane³

et al. 2020

Concurrency and Computation

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Summary The International Telecommunications Union defines in its IMT‐2020 recommendations three types of use of 5G services: mMTC (massive Machine‐type Communications), eMBB (enhanced Mobile Broadband), and uRLLC (ultra‐Reliable Low Latency Communications). The mMTC service allows a considerable number of machines and devices to communicate while guaranteeing a good quality of service. The eMBB service allows very high data throughput, even at the cell border. The uRLLC service is used for ultra‐reliable communication for critical needs requiring very low latency. These services are provided separately in a given cell. However, the number of connected objects is starting to increase rapidly as well as the bit rates and energy consumption. The 5G network must make it possible to provide access to a vast number of users of its different service categories. Call admission control (CAC) techniques focus more on availability in terms of bit rate and coverage. In this article, we suggest an algorithm for modeling CAC in an area served by the three categories of services in a 5G access network, mainly based on minimum energy consumption. This technique will allow connected objects that consume low energy to connect to the network with an adequate quality of service and enable the development of the Internet of Things.

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