New Weight Function for Adapting Handover Margin Level over Contiguous Carrier Aggregation Deployment Scenarios in LTE-Advanced System

Shayea, Ibraheem; Ismail, Mahamod; Nordin, Rosdiadee; Ergen, Mustafa; Ahmad, Norulhusna; Abdullah, Nor Fadzilah; Alhammadi, Abdulraqeb; Mohamad, Hafizal

doi:10.1007/s11277-019-06463-2

Cited by 40 publications

(31 citation statements)

References 26 publications

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“…The channel model consists of several components that include the antenna array response matrices for the transmitter, for the receiver and the propagation channel response matrix for cluster mas follows [2]:…”

Section: Channel Modelsmentioning

confidence: 99%

“…Path loss models for the different WINNER situations have been created dependent on aftereffects of estimations did inside WINNER, just as results from the open writing. These path loss models are ordinarily of the accompanying structure [2] (4)…”

Section: Figure2: Illustration Of Path Lossmentioning

confidence: 99%

“…The models can be applied in the recurrence run from 2 -6 GHz and for various reception apparatus statures. The handling of estimating the qualities from exact perception of the factors A, B, C and K of Equation are portrayed in [2]. The free-space way misfortune, 〖 PL 〗 _free can be composed as pursues (5) The path loss models utilized in various situations of WINNER channel model depend on estimated information got basically at 2 and 6 GHz.…”

Section: Figure2: Illustration Of Path Lossmentioning

confidence: 99%

“…These include the Pico cells, Femtocels, as well as the new relay nodes. The European WINNER model [1] has built up another radio idea for Beyond-3G (B3G) remote correspondence framework which utilizes a channel data transfer capacity of up to 100 MHz for one radio connection, and radio frequencies in all probability somewhere in the range of 2 and 6 GHz [2]. Thus, new channel models were created during the WINNER venture, in light of channel estimations [3], which covers progressively nitty gritty system situations.…”

mentioning

confidence: 99%

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Performance Optimal for Deploying Relays in Urban Micro Cells on LTE-Advanced Technology

Saif*,

Ismail,

Nordin

et al. 2020

IJITEE

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Cooperative Relay is a promising enhancement to modern radio technologies which has been considered in LTE-Advanced and IEEE 802.16m technologies. Cooperative Relay is one of the technologies that used to improve coverage in downlink mobile network. The work is done by designing numerous environments for LTE-Advanced networks involving relays, whereby using vertical distributions of relays for both AAF & DAF techniques as well as comparison between the performances optimal of both techniques on urban micro cell for LTE-Advanced Technologies, Insertion the channel model from WINNER project. The performances optimal of non-Cooperative Relay and Cooperative Relay are evaluated in terms of Symbol Error Rate (SER) and Signal-to Noise-Ratio (SNR). The simulation results show that relay technologies can effectively improve system performance. On the other hand, 10-Relay in vertical distributions clearly shows outperforms on other environments and, when the relay put near, middle and far from Evolved Node B (eNB), the result shows at SER of 10-3 the system that employs AAF technique outperforms on the system that employs DAF technique in LTE-A system by about 6.5 dB, 2.5 dB and 2 dB sequent.

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Section: Channel Modelsmentioning

confidence: 99%

Section: Figure2: Illustration Of Path Lossmentioning

confidence: 99%

Section: Figure2: Illustration Of Path Lossmentioning

confidence: 99%

mentioning

confidence: 99%

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Performance Optimal for Deploying Relays in Urban Micro Cells on LTE-Advanced Technology

Saif*,

Ismail,

Nordin

et al. 2020

IJITEE

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“…The HO decision in the proposed algorithm depends on the signal-to-interference-plus-noise-ratio (SINR) rather than on the received signal strength indicator, which is then used to calculate the actual level of the HOM. Shayea et al [12][13][14] have proposed an HO optimization technique based on a weighted function for carrier aggregation. The proposed algorithm automatically adjusts the values of the HOM in accordance with three functions: SINR, traffic load, and velocity.…”

Section: Introductionmentioning

confidence: 99%

Velocity-Aware Handover Self-Optimization Management for Next Generation Networks

et al. 2020

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The fifth generation (5G) network is an upcoming standard for wireless communications that coexists with the current 4G network to increase the throughput. The deployment of ultra-dense small cells (UDSC) over a macro-cell layer yields multi-tier networks, which are known as heterogeneous networks (HetNets). HetNets play a key role in the cellular network to provide services to numerous users. However, the number of handovers (HOs) and radio link failure (RLF) greatly increase due to the increase in the UDSC in the network. Therefore, mobility management becomes a very important function in a self-organizing network to improve the system performance. In this paper, we propose a velocity-based self-optimization algorithm to adjust the HO control parameters in 4G/5G networks. The proposed algorithm utilizes the user's received power and speed to adjust the HO margin and the time to trigger during the user's mobility in the network. Simulation results demonstrate that the proposed algorithm achieves a remarkable reduction in the rate of ping-pong HOs and RLF compared with other existing algorithms, thereby outperforming such algorithms by an average of more than 70% for all HO performance metrics.

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Deep Artificial Immune System With Malicious Node Detection and Secure Routing Protocol in MANET

Jamaesha,

Gowtham,

Ramkumar

2024

Trans Emerging Tel Tech

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In the context of Mobile Ad Hoc Networks (MANETs), the dynamic and decentralized topology poses significant challenges like unreliable connectivity, limited bandwidth, node mobility, and vulnerability to security threats from malicious nodes. Ensuring secure and energy‐efficient data transmission in such environments is crucial for mission‐critical applications. This research addresses these pressing challenges by introducing a robust routing protocol capable of detecting and mitigating malicious nodes, thereby enhancing MANET's Quality of Service (QoS). The proposed approach, the Dendritic Cell with Adaptive Trust Q‐learning Protocol (dDC‐ATQP), integrates several innovative techniques to tackle these issues. Firstly, the trust evaluation mechanism assesses the behavior of nodes to identify potential malicious actors, mitigating the effect of malicious nodes on system execution. Secondly, the adaptive routing strategy optimizes data transmission paths based on real‐time network conditions, reducing latency and packet loss. To evaluate the effectiveness of this approach, extensive simulations are conducted using a range of performance metrics. The results demonstrate significant improvements over existing methods, including a throughput increase of (79.2% in 50 s), lower end‐to‐end‐delay (0.075 s for 20 nodes), energy consumption of (38.55/J), higher packet delivery ratio (98% for 20 nodes), reduced packet loss ratio (5% for 100 nodes), enhanced security (80% for 70 nodes).

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New Weight Function for Adapting Handover Margin Level over Contiguous Carrier Aggregation Deployment Scenarios in LTE-Advanced System

Cited by 40 publications

References 26 publications

Performance Optimal for Deploying Relays in Urban Micro Cells on LTE-Advanced Technology

Performance Optimal for Deploying Relays in Urban Micro Cells on LTE-Advanced Technology

Velocity-Aware Handover Self-Optimization Management for Next Generation Networks

Deep Artificial Immune System With Malicious Node Detection and Secure Routing Protocol in MANET

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