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

Irid, Sidi Mohammed Hadj; Hachemi, Mohammed Hicham; Adardour, Haroun Errachid; Hadjila, Mourad

doi:10.11591/ijece.v8i6.pp5185-5194

Cited by 4 publications

(5 citation statements)

References 31 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%

“…Indeed, there are two binary hypotheses: H 0,i the null hypothesis which indicates the absence of PU compared to UE i and H 1,i which designates the hypothesis of the presence of PU signal as a function of UE i . To decide between them, we translate the received signal energy E into sensing probability metrics by applying the probability theory (the chi-squared law [23], [24], [38]. Giving thus, two metrics, the false-alarm probability P f a and the probability of detection P d .…”

Section: Non-cooperative Spectrum Sensingmentioning

confidence: 99%

“…VSS-NLMS algorithms and their variants are based on the principle of updating a variable step size used subsequently in the update of a weight multiplied by an error and this comes down to Table 1 [24], where ∥.∥ 2 is the Euclidean norm square.…”

Section: Vss-nlms Usedmentioning

confidence: 99%

“…Unfortunately, the traditional technique in the 4G LTE network initiates handoff by adjusting values of handover margin (HOM) and time to trigger (TTT) which can cause significant data loss during the session due to limited 3GPP group settings like, false handoff triggering or unnecessary handoff [9], [11] due to limited 3GPP group settings [23]- [26]. Knowing that, our previous works [23] and [24], have proven by using variable step-size normalized least mean square (VSS-NLMS) adaptive filter family, more especially, a VSS-NLMS combined with spectrum sensing (SS) probability method, can predict the detection of handoff triggering spectrum in 4G LTE heterogeneous networks (HetNets) with a rather long convergence time, mainly due to the estimation error in VSS-NLMS process.…”

Section: Introductionmentioning

confidence: 99%

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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%

Section: Non-cooperative Spectrum Sensingmentioning

confidence: 99%

Section: Vss-nlms Usedmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Pedestrian mobility management for heterogeneous networks

Hachemi

Irid²,

Benchehima³

et al. 2022

IJEECS

Self Cite

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show abstract

“…of algorithm must consider the computational cost, performance, and robustness. In this applied study we select the LMS algorithm [22][23][24][25] rather than the other two well-known algorithms namely, Recursive Least Squres RLS and Recursive Least Squres Lattice RLSL algorithms that could be employed to solve problems related to the field of equalization. The main objective of using this strategy is to eliminate noise from the corrupted input signal and adapt the ATF weights in the way that the mean square of the estimated error is to be minimized.…”

mentioning

confidence: 99%

LMS Algorithm for Adaptive Transversal Equalization of a Linear Dispersive Communication Channel

Arslan

2020

Review of Computer Engineering Research

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The presence of any type of distortion in communication system, regardless of the causes, is undesirable and undeniably has a negative impacts on the system in general and therefore it is necessary to eliminate its effects. This study employs one of the wellknown algorithms for adaptive equalization of linear dispersive communication channel which is Least Mean Square (LMS) algorithm. The LMS technique is basically utilized to eliminate the noise in communication channel. The novelty of this paper includes the profoundly analyzing of the influence of rate of convergence, miss-adjustment, computational requirement, and sensitivity to Eigen-value spread in sufficient details in a simple and plain way. Moreover, the system performance improvement employing the feedback equalizer technique is intensively presented which shows that our methodology is very effective to eliminate the noise in the system. The simulation work has been performed with MATLAB software. Contribution/Originality: This study uses new estimation methodology which is regarded as profoundly analyzing of the influence of rate of convergence, miss-adjustment, computational requirement, and sensitivity to Eigen-value spread in sufficient details in a simple and plain way. 1. INTRODUCTION Adaptive filters are used extensively in statistical signal processing and offer a great improvement in performance compared with the conventional fixed filters [1-7].The subject of adaptive filters in general and linear adaptive filters in particular has drawn the attention of many researchers and therefore a various methodologies have been developed and implemented to solve any given problem in the area of statistical signal processing. The linear adaptive filter includes a filter whose function is to produce a desired output, and an adaptive algorithm to set the filter parameters. The selected algorithm is significantly affected by the filter structure which is mainly classified into finite impulse filter (FIR) [8-14] and infinite impulse response (IIR) [15-19]. Generally, adaptive algorithm attempts to minimize the error function in the input, reference, and output signals to near zero value. The most commonly minimization methods used for adaptive filters are Quasi-Newton techniques and the steepest-descent gradient technique [20, 21]. The latter is easy to perform but the quasi-Newton strategy basically has better convergence rate. Therefore the best choice is the Quasi-Newton techniques which have better computational performance and good convergence. But disadvantage of this method is very sensitive to the instability matters. In all these strategies, it is necessary to select the convergence factor carefully based on the specific adaptation issue. The error signal normally is created in different ways but the most popular techniques are Mean Square Error (MSE) methodology, and Least Squares (LS) technique. MSE is requiring an

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Spectrum Resource for Cognitive Radio Networks

Maharaj

Awoyemi

2020

Developments in Cognitive Radio Networks

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

Cited by 4 publications

References 31 publications

Pedestrian mobility management for heterogeneous networks

Pedestrian mobility management for heterogeneous networks

LMS Algorithm for Adaptive Transversal Equalization of a Linear Dispersive Communication Channel

Spectrum Resource for Cognitive Radio Networks

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