A Power Control Algorithm (PCA) and Software Tool for Femtocells in LTE-A Networks

Khan, Saad Ahmad; Asshad, Muhammad; Küçük, Kerem; Kavak, Adnan

doi:10.16984/saufenbilder.373293

Cited by 18 publications

(5 citation statements)

References 12 publications

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“…Let's also note that, simulation parameters have been set in the following Tables 2 to 4. [30] Outdoor wall penetration loss [23], [24], [43], [45] Low 20 dB --…”

Section: Simulation Resultsmentioning

confidence: 99%

Pedestrian mobility management for heterogeneous networks

Hachemi

Irid²,

Benchehima³

et al. 2022

IJEECS

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Pending the arrival of the next generation of 5G which is not yet deployed in some countries like Algeria, 4G LTE remains one of the main mobile networks to ensure adequate quality services. Mostly, the deployment of femtocells to support the macrocell structure is crucial in the handover decision process. This paper presents a new approach called the epsilon Kalman Filter with normalized least-mean-square (ϵKFNLMS) to realize the handoff triggering in two-tier long-term evolution networks to ensure communication continuity to the pedestrian UE and improve mobility management. ϵKFNLMS uses a two-step process: a tracking process and a prediction process, to produce an optimal future state estimate at ”t+p”, where ”p” is the prediction footstep. The tracking process is performed by the Kalman filter, known for its precision in the state of the signal at time ”t”. It perfectly reduces the estimation error, injected afterward in the variable step-size NLMS algorithm (VSS-NLMS). While the prediction process is performed by the VSS-NLMS algorithm, an adaptive filter known for its prediction of the future state at ”t+p”. Thus, the goal is to achieve a faster convergence with a steady-state. ϵ value provides a precise setting of the handover trigger. Through different numerical simulations in several indoor environments, the results show that the performance and effectiveness of the proposed approach (ϵKFNLMS) provide lower mean square error (MSE), stable physical appearance in the prediction process (convergence with a steady-state), and excellent speed of convergence compared to the classical Normalized LMS (NLMS) and Li-NLMS adaptive filters.

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“…Let's also note that, simulation parameters have been set in the following Tables 2 to 4. [30] Outdoor wall penetration loss [23], [24], [43], [45] Low 20 dB --…”

Section: Simulation Resultsmentioning

confidence: 99%

Pedestrian mobility management for heterogeneous networks

Hachemi

Irid²,

Benchehima³

et al. 2022

IJEECS

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“…Picocells are deployed in congested places or at special occasions, such as stadiums during events, big shopping malls hosting seasonal parties, and other large gatherings, -femtocells cover areas of approx. 10 to 30 m and are used in offices, homes or subways [22]. They are useful in scenarios where a specific user requires a high data rate and the available network offers insufficient throughput.…”

Section: Small Cellsmentioning

confidence: 99%

An Overview of Mobility Management Mechanisms and the Related Challenges in 5G Networks and Beyond

Alkalsh

2023

JTIT

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Ensuring a seamless connection with various types of mobile user equipment (UE) items is one of the more significant challenges facing different generations of wireless systems. However, enabling the high-band spectrum – such as the millimeter wave (mmWave) band – is also one of the important factors of 5G networks, as it enables them to deal with increasing demand and ensures high coverage. Therefore, the deployment of new (small) cells with a short range and operating within the mmWave band is required in order to assist the macro cells which are responsible for operating long-range radio connections. The deployment of small cells results in a new network structure, known as heterogeneous networks (HetNets). As a result, the number of passthrough cells using the handover (HO) process will be dramatically increased. Mobility management (MM) in such a massive network will become crucial, especially when it comes to mobile users traveling at very high speeds. Current MM solutions will be ineffective, as they will not be able to provide the required reliability, ﬂexibility, and scalability.Thus, smart algorithms and techniques are required in future networks. Also, machine learning (ML) techniques are perfectly capable of supporting the latest 5G technologies that are expected to deliver high data rates to upcoming use cases and services, such as massive machine type communications (mMTC), enhanced mobile broadband (eMBB), and ultra-reliable low latency communications (uRLLC). This paper aims to review the MM approaches used in 5G HetNets and describes the deployment of AI mechanisms and techniques in ″connected mode″ MM schemes. Furthermore, this paper addresses the related challenges and suggests potential solutions for 5G networks and beyond.

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“…Release-12 can reach 750 Mbps downlink and 150 Mbps uplink (UL) throughput with a bandwidth of up to 100 MHz, MIMO-OFDM technology, and 256 QAM modulation. LTE-A is a homogenous network that employs advanced base stations (eNodeBs, eNBs) to cover the whole cell and uses the same transmission power level, modulation technique, and antenna configurations to provide service to user units [7]. The downlink quality between eNodeB and user equipment (UE) in LTE-A systems is determined by wireless channel parameters.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis and modeling based on LTE-A field measurements: a city center example

Kurnaz

Kola

Esenalp

2023

Int. j. inf. tecnol.

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The wireless communication channel is the critical parameter that affects the throughput in the LTE-A network. The user equipment measures the quality of the wireless channel as the channel quality indicator (CQI) value and transmits it to eNodeB. The eNodeB uses the CQI value to select the adaptive modulation and coding method to achieve the highest throughput. This study analyzes the LTE-A network based on actual field measurements. Measurements were taken at 80 different locations in Samsun (Turkey) city center using TEMS Investigation software. RSRP, RSRQ, SINR, CQI, and throughput values were recorded for each measurement in a stationary position while downloading 500 MB of data. Then, the averages of these recorded data were calculated, and detailed analyses were performed. At the end of the study, the effects of RSRP, RSRQ, SINR, and CQI on the throughput value in the LTE-A network were examined, and a novel mathematical model was proposed that gives the relationship between them with 88.85% accuracy. It has also been observed that the accuracy of the proposed model can be increased by 4% with GRNN. In the last stage of the study, a new CQI mapping method based on real-field measurements for the LTE-A system was developed.

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A Power Control Algorithm (PCA) and Software Tool for Femtocells in LTE-A Networks

Cited by 18 publications

References 12 publications

Pedestrian mobility management for heterogeneous networks

Pedestrian mobility management for heterogeneous networks

An Overview of Mobility Management Mechanisms and the Related Challenges in 5G Networks and Beyond

Performance analysis and modeling based on LTE-A field measurements: a city center example

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