Predicting the Probability of Spectrum Sensing with LMS Process in Heterogeneous LTE Networks

Adardour, Haroun Errachid; Feham, Mohammed; Hachemi, Mohammed Hicham

doi:10.13164/re.2016.0808

Cited by 10 publications

(8 citation statements)

References 33 publications

(52 reference statements)

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“…Throughout the simulation, we note that the second femto-cell signal sensing probability is null, which results in the decision phase of [10] where the probability of finding eNB signal is imminent. According to Figure 3, sensing probability of triggering handover during the movement of UE#1 by NLMS process is equal to zero.…”

Section: Results and Simulationmentioning

confidence: 99%

“…As a result, the triggering of handover in the present paper will be predicted with using SSP method, which will be quantified via the detection probability. In Hachemi et al, spectrum-sensing probability of the Link Down of the current cell depends on the convergence of the classical NLMS algorithm [10]. For fixed step-size LMS algorithm, the Mean Square Error (MSE) is directly proportional to the adaptation step-size while the convergence rate increases as the step-size decreases [11].…”

Section: Introductionmentioning

confidence: 99%

“…This paper is an improved version of Hachemi et al [10] which is itself inspired by the work developed in [12], [13]. The remainder of this paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Spectrum Sensing with VSS-NLMS Process in Femto/Macro-cell Environments

Irid

Hachemi

Adardour

et al. 2018

IJECE

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<p>Handover is a process that allows a mobile node to change its attachment point. A mobile node connected to a network can, in order to improve the quality of service, have the need to leave it to connect to a cell either of the same network or of a new network. The present paper introduce three techniques using adaptive Variable Step-Size Least Mean Square (VSSLMS) filter combined with spectrum sensing probability method to detect the triggering of handover in heterogeneous LTE networks. These techniques are Normalized LMS (NLMS), Kwong-NLMS and Li-NLMS. The simulation environment is composed of two femtocells belonging to a macrocell. Five User Equipements (UEs) are positioned in one femtocell and are assumed closest to its circumference. Simulation results show that sensing probability with Li-NLMS algorithm has a better performance compared with classical NLMS and Kwong-NLMS.</p>

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Section: Results and Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Spectrum Sensing with VSS-NLMS Process in Femto/Macro-cell Environments

Irid

Hachemi

Adardour

et al. 2018

IJECE

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show abstract

“…To locate the enemy radar, passive system always has distributed stations. Distributed system has a large coverage and inherent superiority at realizing cooperative signal detection (CSD), which can resist the undesirable shadow and multipath fading [18][19][20][21]. Each terminal node of passive system can be regarded as a fusion center (FC).…”

Section: Signal Detectionmentioning

confidence: 99%

Research on Passive Troposcatter Location System

Liu¹,

Chen²,

Liu³

2019

RADIOENGINEERING

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Electromagnetic (EM) wave of enemy radar propagated by troposcatter can be utilized for beyond lineof-sight (b-LoS) location. To provide theoretical basis for passive troposcatter location system, channel characteristics including propagation loss, refraction effect and multipath fading are analyzed. Ray tracing method improved by tropospheric model is employed to describe the refraction effect. Correlation coefficient on the basis of transfer function is deduced to evaluate multipath fading. According to troposcatter characteristics, maximum operating range is estimated, cooperative energy detection is introduced to cope with the received signal without priori knowledge. The principle and accuracy of location algorithm based on azimuth are researched. Spatial smoothing is introduced under the situation that uncorrelated and coherent signals coexist. Through analyzing consequences, future directions are suggested.

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“…Moreover, the variation in transmitting power between the two types of wireless networks (eNB and HeNB) is another issue. When the HeNB location is near the eNB tower, the RSS of the HeNB will always be less than the eNB [9][10][11][12]. Many researches have worked on enhancing the handover procedure in the wireless networks in LTE-A.…”

Section: Introductionmentioning

confidence: 99%

Q-Learning vertical handover scheme in two-tier LTE-A networks

Bathich¹,

Mansor²,

Suliman³

et al. 2020

IJECE

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Global mobile communication necessitates improved capacity and proper quality assurance for services. To achieve these requirements, small cells have been deployed intensively by long term evolution (LTE) networks operators beside conventional base station structure to provide customers with better service and capacity coverage. Accomplishment of seamless handover between Macrocell layer (first tier) and Femtocell layer (second tier) is one of the key challenges to attain the QoS requirements. Handover related information gathering becomes very hard in high dense femtocell networks, effective handover decision techniques are important to minimize unnecessary handovers occurred and avoid Ping-Pong effect. In this work, we proposed and implemented an efficient handover decision procedure based on users’ profiles using Q-learning technique in an LTE-A macrocell-femtocell networks. New multi-criterion handover decision parameters are proposed in typical/dense femtocells in microcells environment to estimate the target cell for handover. The proposed handover algorithms are validated using the LTE-Sim simulator under an urban environment. The simulation results showed noteworthy reduction in the average number of handovers.

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Predicting the Probability of Spectrum Sensing with LMS Process in Heterogeneous LTE Networks

Cited by 10 publications

References 33 publications

Spectrum Sensing with VSS-NLMS Process in Femto/Macro-cell Environments

Spectrum Sensing with VSS-NLMS Process in Femto/Macro-cell Environments

Research on Passive Troposcatter Location System

Q-Learning vertical handover scheme in two-tier LTE-A networks

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