Throughput enhanced scheduling (TES) scheme for ultra‐dense networks

Hacı, Hüseyin; Abdelbari, Amr

doi:10.1002/dac.4229

Cited by 6 publications

(12 citation statements)

References 38 publications

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“…Future 5G cellular networks are expected to be dense because of the deployment of numerous small cells [71]. In this section, we have studied the impact of small cell density on the performance of the HUDN.…”

Section: B Results From Network Density Perspectivementioning

confidence: 99%

An Adaptive Cell Selection Scheme for 5G Heterogeneous Ultra-Dense Networks

Alablani

Arafah

2021

IEEE Access

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Fifth-generation (5G) cellular networks are a promising technology to meet the rapid growth in wireless traffic. Small cells are critical in fulfilling the requirements of 5G networks. A heterogeneous ultra-dense network (HUDN) is an enabling technology consisting of several types of small cells to enhance the performance of 5G networks effectively. A critical issue of HUDN is the cell selection method because the traditional technique for cell selection is inapplicable in such a network. This study proposes a novel adaptive cell selection (ADA-CS) scheme. It adapts to various characteristics of HUDNs and vehicle movements. It performs six phases to select the best base station with which to be associated. Simulation results show that, with low-and medium-speed vehicles, the ADA-CS scheme outperforms the traditional protocol in terms of the average number of handovers by 42.39%. In addition, it is superior to some relevant recent schemes by up to 36.53%. The adaptation feature of the proposed protocol provides additional improvements regarding the average number of handovers with high-speed vehicles. Therefore, it achieves superiority in terms of the average number of handover failures and unnecessary handovers. In addition, the ADA-CS scheme enhances the average achievable downlink data rates and spectral efficiency per vehicle by 3.98% and 2.79% compared with the traditional and the relevant recent schemes.

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Section: B Results From Network Density Perspectivementioning

confidence: 99%

An Adaptive Cell Selection Scheme for 5G Heterogeneous Ultra-Dense Networks

Alablani

Arafah

2021

IEEE Access

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“…In [17], the throughput enhanced scheduler (TES) is described. The TES algorithm defines two scheduling techniques.…”

Section: Network Slicing In Vehicular Networkmentioning

confidence: 99%

“…The strong aspects of the URLLC-AVN [11], FLSA-CC [15] and TES [17] schemes, as well as of aerial networks, are combined to accomplish enhanced performance regarding the allocation of the RBs to vehicular users. In particular, the design of the URLLC-AVN was extended, resulting in a three-layer architecture according to the operating principles of the FLSA-CC algorithm, in a way similar to [36].…”

Section: Contributionmentioning

confidence: 99%

“…In each TTI, the middle layer allocates to each GBR service the number of RBs estimated from the upper layer. The allocation is performed by applying an improved version of the scheduling metric that the throughput enhanced scheduler (TES) [17] algorithm defines for real-time services, where the maximum acceptable packet loss ratio is considered along with the other parameters that consider the aforementioned metric. Specifically, this metric is calculated using Formula (6), where τ i stands for the maximum acceptable delay, DHOL i represents the head of line delay, b is a constant, SI NR i represents the signal-to-interference plus noise ratio, r i is the available throughput and R i (t) is the past average throughput for the ith flow.…”

Section: The Middle Layer Of the Network Slicing Schemementioning

confidence: 99%

“…Specifically, this metric is calculated using Formula (6), where τ i stands for the maximum acceptable delay, DHOL i represents the head of line delay, b is a constant, SI NR i represents the signal-to-interference plus noise ratio, r i is the available throughput and R i (t) is the past average throughput for the ith flow. The α i value is calculated using Formula (7) where δ i represents the maximum acceptable packet loss ratio: The lower layer allocates available RBs to best effort flows using the scheduling metric that the TES [17] algorithm defines for non-real-time services. In particular, the metric is estimated using Formula (8):…”

Section: The Middle Layer Of the Network Slicing Schemementioning

confidence: 99%

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A Network Slicing Framework for UAV-Aided Vehicular Networks

Skondras

Michailidis

Michalas

et al. 2021

Drones

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In a fifth generation (5G) vehicular network architecture, several point of access (PoA) types, including both road side units (RSUs) and aerial relay nodes (ARNs), can be leveraged to undertake the service of an increasing number of vehicular users. In such an architecture, the application of efficient resource allocation schemes is indispensable. In this direction, this paper describes a network slicing scheme for 5G vehicular networks that aims to optimize the performance of modern network services. The proposed architecture consists of ground RSUs and unmanned aerial vehicles (UAVs) acting as ARNs enabling the communication between ground vehicular nodes and providing additional communication resources. Both RSUs and ARNs implement the LTE vehicle-to-everything (LTE-V2X) technology, while the position of each ARN is optimized by applying a fuzzy multi-attribute decision-making (fuzzy MADM) technique. With regard to the proposed network architecture, each RSU maintains a local virtual resource pool (LVRP) which contains local RBs (LRBs) and shared RBs (SRBs), while an SDN controller maintains a virtual resource pool (VRP), where the SRBs of the RSUs are stored. In addition, each ARN maintains its own resource blocks (RBs). For users connected to the RSUs, if the remaining RBs of the current RSU can satisfy the predefined threshold value, the LRBs of the RSU are allocated to user services. On the contrary, if the remaining RBs of the current RSU cannot satisfy the threshold, extra RBs from the VRP are allocated to user services. Similarly, for users connected to ARNs, the satisfaction grade of each user service is monitored considering both the QoS and the signal-to-noise plus interference (SINR) factors. If the satisfaction grade is higher than the predefined threshold value, the service requirements can be satisfied by the remaining RBs of the ARN. On the contrary, if the estimated satisfaction grade is lower than the predefined threshold value, the ARN borrows extra RBs from the LVRP of the corresponding RSU to achieve the required satisfaction grade. Performance evaluation shows that the suggested method optimizes the resource allocation and improves the performance of the offered services in terms of throughput, packet transfer delay, jitter and packet loss ratio, since the use of ARNs that obtain optimal positions improves the channel conditions observed from each vehicular user.

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BeSleep: Blockchain‐enabled distributed sleeping strategies of small base stations in ultra dense networks

Venkateswararao,

Swain

2023

Int J Communication

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SummarySmall cell networks can fulfill the increasing demandfor the high data rate of wireless applications. Energy efficiency is an important design parameter of the ultra dense small cell network (UDSCN). The sleeping strategy of small base stations (s‐BSs) is used to enhance the network's energy efficiency. An efficient sleeping strategy of s‐BSs is required while preserving users' quality of service (QoS). The idle s‐BSs can be switched to sleep mode. This paper proposes a blockchain‐enabled solution for the sleeping strategy of s‐BSs. Here, a blockchain‐enabled small cell network is created between the s‐BSs. The network is decentralized, which eliminates the workload of the macro base station (MBS). The proposed network architecture is enabled as a decentralized network through blockchain. The blockchain provides distributed control over the s‐BS operations through a smart contract. Here, smart contracts act as distributed self organizing network features to handle self‐transactions among small cells for switching off s‐BSs in the network. All the software logic required to perform s‐BS operations is written in a smart contract using Ethereum. The proposed solution improves energy efficiency and enables the ultra dense small cell network to be decentralized.

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Throughput enhanced scheduling (TES) scheme for ultra‐dense networks

Cited by 6 publications

References 38 publications

An Adaptive Cell Selection Scheme for 5G Heterogeneous Ultra-Dense Networks

An Adaptive Cell Selection Scheme for 5G Heterogeneous Ultra-Dense Networks

A Network Slicing Framework for UAV-Aided Vehicular Networks

BeSleep: Blockchain‐enabled distributed sleeping strategies of small base stations in ultra dense networks

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