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Vol. 10, Issue 3; July-September 2025; 

ISSN: 2837-2964 

Impact Factor: 7.67 

1252 Columbia Rd NW, Washington DC, United States 

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1 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

MEASUREMENT AND ANALYSIS OF SIGNAL QUALITY USING 

ADAPTIVE FILTERING TECHNIQUES IN URBAN LTE NETWORKS 
 
1Onu Kingsley Eyiogwu, 2Obisike Kio Chima, 2Agburuga Goodlife Nkwo, and 2Ugoji 

Prince Chukuladi 
1 Department of Electrical/Electronic Engineering, Rivers State University, Port Harcourt, Nigeria. 
2Department of Electrical/Electronic Technology, Federal College of Education (Technical), Omoku, Rivers 

State, Nigeria 

E-mail: kingsley.onu@ust.edu.ng 

DOI: https://doi.org/10.5281/zenodo.16633176 

 

Abstract: This research investigates the measurement and analysis of signal quality in urban LTE networks using 

adaptive filtering techniques. The study addresses the challenges posed by interference, noise, and multipath 

fading in dense urban environments by employing real-world signal measurements and advanced signal 

processing algorithms. Signal quality metrics including RSRP, SINR, and BER were collected across 20 urban 

measurement points, with SINR values ranging from -2.0 dB to 7.0 dB prior to filtering. Adaptive filters such as 

LMS, RLS, and Kalman were applied to the measured signals, and their eff ectiveness was evaluated. Results 

showed that after filtering, SINR improved by an average of 1.5 to 2.2 dB, while BER decreased significantly, 

with Kalman filter achieving the lowest BER range between 0.0003 and 0.0007. MSE analysis revealed that 

Kalman filtering converged faster than LMS and RLS, reaching an MSE below 0.01 within 10 iterations. 

Additionally, RSRP values improved by 2–3 dB post-filtering, especially at longer distances from the base station, 

indicating enhanced signal retention. The findings confirm that adaptive filtering, particularly with Kalman and 

RLS algorithms, can effectively restore signal quality and reduce error in urban LTE environments, making it a 

strong candidate for deployment in real-time communication systems and future 5G networks. 

Keywords: LTE Network, Signal Quality, Filter, RSRP, BER. 

 

1.0 Introduction 

With the exponential growth in mobile data demand and user density, ensuring robust signal quality in urban 

Long-Term Evolution (LTE) networks has become increasingly critical. In dense metropolitan environments, 

signal degradation due to multipath fading, co-channel interference, shadowing, and environmental noise 

significantly hampers the performance of wireless communication systems. These impairments not only 

deteriorate user experience but also compromise critical Quality of Service (QoS) parameters such as throughput, 

latency, and reliability. 

Adaptive filtering techniques have emerged as powerful tools in digital signal processing to mitigate such 

impairments, offering dynamic noise suppression, channel equalization, and interference cancellation capabilities. 

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 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 3; July-September 2025; 

ISSN: 2837-2964 

Impact Factor: 7.67 

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2 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

Unlike static filtering methods, adaptive filters can continuously update their parameters in real-time, making 

them particularly effective in the time-varying and unpredictable nature of wireless urban channels. 

 

This study presents a comprehensive measurement and analysis of signal quality in urban LTE networks using 

adaptive filtering techniques. Field measurements, including Reference Signal Received Power (RSRP), Signal-

to-Interference-plus-Noise Ratio (SINR), and Bit Error Rate (BER), were collected across different urban 

locations using drive testing equipment. The raw signal data was then processed using various adaptive filtering 

algorithms, including Least Mean Squares (LMS), Recursive Least Squares (RLS), and Kalman filters, to evaluate 

their performance in enhancing signal clarity and stability. 

By integrating real-world measurements with advanced signal processing, this work aims to bridge the gap 

between theoretical filter performance and practical deployment challenges. The findings are expected to inform 

future enhancements in LTE signal optimization and lay the groundwork for adaptive interference mitigation 

strategies in next-generation networks. 

2.0 Literature Review 

The authors in [1] introduced and elaborates on the use of kernel methods in digital signal processing (DSP), 

providing a theoretical and practical framework for nonlinear data analysis. The authors employ reproducing 

kernel Hilbert spaces (RKHS) to extend classical linear DSP techniques into nonlinear domains. The methods 

discussed include kernel adaptive filters, kernel principal component analysis (KPCA), and kernel support vector 

machines (SVMs). Through simulations and real-world examples, the book demonstrates that kernel methods can 

significantly improve classification, regression, and system identification performance over traditional linear 

methods, particularly in non-Gaussian and nonstationary environments. 

The work by [2] presented a comprehensive overview of the role of machine learning (ML) in enabling the 

evolution of 6G wireless networks. The authors survey various ML paradigms, including supervised, 

unsupervised, reinforcement, and federated learning, and discuss their applications to wireless resource 

management, channel estimation, and network slicing. The methodology includes a vision-based analysis and 

conceptual modeling, supported by case studies and literature comparisons. The paper concludes that ML will be 

a foundational component of 6G architecture, enabling self-optimization, reduced latency, and enhanced energy 

efficiency across network layers. Key findings include the necessity for distributed and privacy-preserving 

learning frameworks in future communication systems. 

A detailed study by [3] proposed fractional-order versions of the least mean square (LMS) and normalized LMS 

(NLMS) algorithms for improved performance in line echo cancellation. The authors derive the fractional 

adaptive update rules using fractional calculus and test the algorithms in simulated echo paths with varying delay 

and noise levels. Compared to their conventional counterparts, the fractional LMS and NLMS methods 

demonstrate faster convergence and reduced steady-state error. The results show that for a fixed step size, the 

fractional algorithms maintain lower mean square error (MSE) across iterations, confirming their enhanced 

tracking ability in nonstationary environments. 

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 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 3; July-September 2025; 

ISSN: 2837-2964 

Impact Factor: 7.67 

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3 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

The work by [4] analyzed the performance of adaptive filters using the classic LMS algorithm, particularly 

focusing on convergence behavior, step size impact, and steady-state error. The authors use MATLAB-based 

simulations to evaluate the algorithm under different noise conditions and input signal characteristics. Results 

indicate that small step sizes yield low steady-state error but slow convergence, while larger step sizes speed up 

convergence at the cost of increased error. The analysis confirms the LMS algorithm’s sensitivity to step size 

selection and suggests an optimal range for balancing stability and speed. 

A study by [5] presented a filtered-error recursive least squares (FERLS) algorithm tailored for self-tuning feed 

forward disturbance rejection in vibration control systems. The method is tested on a multi-axis vibration isolator 

to evaluate its real-time adaptation capabilities. Experimental results show that the FERLS algorithm achieves 

better convergence and stability compared to conventional RLS, especially in multi-dimensional control settings. 

The use of filtered errors significantly improves robustness against sensor noise and modeling uncertainties, 

demonstrating the method’s practical applicability to mechatronic systems. 

[6] Proposed a deep neural network (DNN)-based approach for channel estimation in visible light communication 

(VLC) systems. The authors designed a multi-layer DNN to map received signal patterns to channel gain 

estimations, training the network using supervised learning on simulated channel data. The method is evaluated 

under varying noise and mobility conditions. Simulation results show that the DNN-based estimator significantly 

outperforms traditional linear estimators like LS and MMSE in terms of mean square error, especially at low 

signal-to-noise ratios. The study concludes that deep learning enhances VLC robustness in dynamic indoor 

scenarios. 

The work carried out by [7] introduced a convolutional neural network (CNN)-based equalizer enhanced with 

non-linear activation and batch gradient descent optimization for 5G wireless optical communication systems. 

The proposed equalizer is trained to mitigate signal distortions caused by dispersion and nonlinearities in the 

optical channel. Simulation experiments demonstrate improved bit error rate (BER) and convergence speed 

compared to traditional equalizers and earlier CNN models. The method shows particular effectiveness in 

compensating for chromatic dispersion and achieves BER improvements of up to 30% under specific channel 

impairments. 

In [8], the authors explored the applications of deep learning in the physical layer of wireless communication 

systems. The authors summarized use cases including modulation recognition, channel decoding, channel 

estimation, and end-to-end learning-based transceivers. The methodology included extensive literature synthesis 

and theoretical analysis of network architectures like CNNs, RNNs, and auto encoders. The results from surveyed 

experiments suggest that deep learning can outperform traditional techniques in non-linear or high-mobility 

environments, though challenges remain in terms of generalization, training complexity, and interpretability. The 

paper concluded by identifying future research directions for deploying deep learning in real-time and adaptive 

communication systems. 

[9] Offered extensive field measurements of mobile broadband networks in a densely populated city, focusing on 

metrics such as RSRP, SINR, and throughput. The analysis revealed significant signal degradation in urban areas 

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 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 3; July-September 2025; 

ISSN: 2837-2964 

Impact Factor: 7.67 

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4 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

due to obstructions and interference, with RSRP values ranging from –115 dBm to –70 dBm and SINR varying 

widely, reflecting fluctuating signal quality.  

The work done in [10] captured time-varying industrial radio channels for device-to-device (D2D) 

communication on automated guided vehicles (AGVs), demonstrating how channel behavior fluctuates due to 

movement and environmental layout. It employs channel sounding techniques and Doppler spectrum analysis—

highly relevant for observing envelope variations and spectral spreading caused by motion-induced fading. 

[11] Evaluated Wi-Fi reliability under mobility, showing packet loss and throughput instability in enterprise 

environments. Although focused on Wi-Fi, its methodology using empirical KPIs and moving 

transmitters/receivers closely mirrors the approach for the current work. The study supports using envelope 

tracking and time-based performance plots to reveal fading effects on throughput. 

The authors in [12] experimented with private LTE on factory floors showed multipath-induced variability in 

RSRP and throughput. The study is grounded in practical field deployments and supports the use of Rician vs. 

Rayleigh fading models to represent varying LOS conditions.  

A high-frequency channel measurements in indoor industrial settings, comparing path loss, delay spreads, and 

fading depth across 3.7 and 28 GHz was provided in [13]. Frequency used focused between 3.7GHz and 28GHz.  

The authors in [14], using 28 GHz in a smart warehouse, recorded channel impulse responses and multipath 

components under various layouts. It also shows Doppler spread and coherence bandwidth changes due to 

motion.  

[15] Is a dataset-based study that assessed the variability of RSRP, RSRQ, and throughput, confirming that even 

RSRP, often seen as stable, can fluctuate due to multipath and mobility. It provides a valuable benchmark dataset 

and justifies our need to use field-based RSRP plots and fading envelope simulations. 

Also, [16] focused on protocol-level evaluation, this paper quantifies throughput improvements using MultiPath 

TCP, but also notes performance instability due to rapid fading and signal imbalance across paths. This confirms 

that even advanced protocols cannot overcome fading issues without physical layer Mitigation. 

The article in [17] is an open dataset that includes RSRP, SINR, and throughput across a factory environment, 

offering real-world values for comparison. The dataset reflects signal degradation near metallic machinery and 

walls, consistent with the goal of capturing and simulating such environments.  

[18] Used real measurements to analyze key LTE performance indicators. It finds significant SINR degradation 

in urban and high-density areas, correlating directly with increased BER and reduced throughput. The study 

supports using BER vs. SNR curves and envelope statistics to represent signal integrity under multipath fading. 

[19], while focused on machine learning, used RSRP, SINR, and CQI as features to predict downlink throughput. 

It supports collecting these same KPIs during field measurement and links fading-related degradation to QoS 

reduction.  

The work in [20], though more theoretical and based on IIoT applications, this paper’s focus on robustness to 

channel variation via redundancy coding supports the overall relevance of understanding channel fading 

characteristics.  

3.0 Methodology  

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 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 3; July-September 2025; 

ISSN: 2837-2964 

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5 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

To effectively achieve the objective of “Measurement and Analysis of Signal Quality Using Adaptive Filtering 

Techniques in Urban LTE Networks”, the following methodology were adopted. It combines field measurements, 

data preprocessing, adaptive signal processing, and performance evaluation in a step-by-step manner. First, the 

following tools were used: 

 Drive test equipment (e.g., TEMS),  

 Software-defined radio (SDR) modules with MATLAB  

 GPS module for geolocation tagging 

Method of data collection involved: 

1. Study Area Selection and Planning 

The research began with the identification of a representative urban environment (e.g., city centers, residential 

zones, or commercial districts) where LTE services are actively deployed. The area should exhibit typical urban 

characteristics such as: 

 High-rise buildings causing multipath propagation 

 Congested traffic zones 

 Potential interference from multiple eNodeBs 

A route map for drive testing or static signal capture points was generated to cover varying signal conditions. The 

following LTE key performance indicators (KPIs) were measured at regular intervals: 

 RSRP (Reference Signal Received Power) 

 RSRQ (Reference Signal Received Quality) 

 SINR (Signal to Interference plus Noise Ratio) 

 RSSI (Received Signal Strength Indicator) 

Data was recorded for multiple frequency bands (e.g., 800 MHz, 1800 MHz, 2600 MHz) to evaluate frequency-

dependent performance. 

2. Signal Preprocessing and Data Cleaning 

The collected measurement data were filtered to remove outliers and corrupted entries, time-aligned and location-

synchronized using GPS timestamps, transformed into appropriate formats (e.g.,mat) for MATLAB processing, 

signal noise samples (raw I/Q data, where applicable) was also extracted for processing through adaptive filters. 

3. Adaptive Filtering and Signal Processing 

The cleaned signal data was subjected to different adaptive filtering techniques, implemented using MATLAB. 

The following algorithms were tested: 

Least Mean Squares (LMS) Filter, used for simple, lightweight noise reduction and channel equalization. 

Recursive Least Squares (RLS) Filter, known for faster convergence in dynamic signal environments, Kalman 

Filter which is ideal for tracking and estimating state-space variables under uncertainty and noise. Each algorithm 

was applied to enhance the raw signal and suppress noise and interference. 

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 Academic Journal of Science, Engineering and Technology 

Vol. 10, Issue 3; July-September 2025; 

ISSN: 2837-2964 

Impact Factor: 7.67 

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6 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 
Figure 1: Simulation Setup 

 

4. Performance Evaluation and Analysis 

After filtering, both the pre-processed (raw) and post-processed (filtered) signals were evaluated using metrics 

such as: Improvement in SINR, reduction in BER, signal clarity and spectral analysis, Mean Square Error (MSE) 

and Signal-to-Noise Ratio (SNR) gain. Comparative analysis was carried out to determine: the most effective 

filter in different signal conditions (strong, weak, noisy, interference-heavy), the computational complexity and 

real-time applicability of each technique.  

3.1 Received Signal Model 

This basic model represents how the received signal is affected by noise in mobile environment. It is given by: 

𝑌(𝑡) =  𝑆(𝑡) +  𝑛(𝑡)                                      (1)                                      

Where 𝑌(𝑡) is the received signal, 𝑆(𝑡) is the original signal, and 𝑛(𝑡)is the AWGN. 

3.2 Signal-to-Noise Ratio (SNR) 

SNR is a critical performance metrics in this study. It is given by:  

𝑆𝑁𝑅(𝑑𝐵) = 10𝐿𝑔10
𝑃𝑠

𝑃𝑛
                              (2) 

Where 𝑃𝑠the power of the desired signal is, 𝑃𝑛 is the power of the noise. 

3.3 Mean Squared Error (MSE) 

To quantify the accuracy of the denoising process, it is very important that equation (3) should be fully utilized. 

𝑀𝑆𝐸 =  
1

𝑁
∑ (ś(𝑖) − 𝑠(𝑖))2𝑁

𝑖=1                    (3) 

ś(𝑖) is the denoised signal sample, s(𝑖) is the original, clean signal, N is the total number of samples. It is good 

to note that lower MSE values indicate better noise suppression. 

3.4 Bit Error Rate (BER) 

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7 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

After denoising, an essential metric for evaluating communication performance is the bit error rate. It is given 

mathematically as: 

𝐵𝐸𝑅 =  
𝑁𝑒

𝑁𝑡
                                                     (4) 

Where 𝑁𝑒the number of is erroneously received bits and 𝑁𝑡 is the total number of bits. This is especially if 

modulation schemes like BPSK, QPSK, QAM etc. are used. 

3.5 Power Spectral Density (PSD) 

Application of power spectral density is vital to analyze and process noise using frequency-domain approaches. 

For this purpose, equation (5) is very key: 

𝑆𝑦𝑦(𝑓) =  𝑆𝑠𝑠(𝑓) + 𝑆𝑛𝑛(𝑓)                       (5) 

Where 𝑆𝑦𝑦(𝑓): power spectrum of the noisy signal 

𝑆𝑠𝑠(𝑓): power spectrum of the clean signal. 

𝑆𝑛𝑛(𝑓): power spectrum of the noise. 

3.6 Wiener Filter Transfer Function 

The Wiener filter aims to minimize MSE between the desired signal and the actual output: 

𝐻(𝑓) =  
𝑆𝑠𝑠(𝑓)

𝑆𝑠𝑠(𝑓)+𝑆𝑛𝑛(𝑓)
                                   (6) 

Where 𝐻(𝑓) is the frequency response of the optimal filter. 

3.7 Adaptive LMS Filter Weight Update Rule 

For time-domain noise reduction using adaptive filtering application or usage of (7) is necessary:  

𝑤(𝑛 + 1) = 𝑤(𝑛) +  𝜇. 𝑒(𝑛). 𝑥(𝑛)             (7) 

w(n) is the filter weight at iteration n, x(n) is the input signal, 𝜇 is the step-size parameter, and e(n) is the error 

signal given by: 𝑒(𝑛) = 𝑑(𝑛) − 𝑦(𝑛)             

3.8 Loss Function for Machine Learning-Based Denoisers 

𝜑(𝜃 =  
1

𝑁
∑ (ś(𝜃) − 𝑆(𝑖))2𝑁

𝑖=1                        (8) 

Where 𝜃 are the trainable parameters and ś(𝜃) is the network’s output 

4.0 Results and Discussion 

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 Academic Journal of Science, Engineering and Technology 

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8 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 
Figure 2: Graph of SINR Improvement 

The SINR Improvement graph in Figure 2 compares the Signal-to-Interference-plus-Noise Ratio (SINR) before 

and after applying adaptive filtering techniques. In the graph, the SINR values for each of the 20 measurement 

points are plotted on the vertical axis, while the measurement points are plotted on the horizontal axis. Before 

filtering, several of the SINR values fall below 0 dB, indicating severe signal degradation due to interference and 

noise. After applying filtering techniques, especially LMS, RLS, or Kalman, a noticeable improvement in SINR 

is observed. For instance, at point 4, SINR improved from -2.0 dB to approximately -0.5 dB, and at point 8, the 

SINR rose from 7.0 dB to over 8.5 dB. This increase demonstrates the filters’ effectiveness in suppressing 

interference and enhancing signal clarity, thus validating the core objective of the study. 

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9 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 
Figure 3: A Plot of BER vs Filter Type 

Figure 3 illustrates the distribution of Bit Error Rates (BER) across three adaptive filters: LMS, RLS, and Kalman. 

It uses boxplots to show how each filter type performs in minimizing BER. The Kalman filter demonstrates the 

lowest median BER values, with most data points lying in the range of 0.0003 to 0.0007. RLS performs better 

than LMS, with BER values between 0.0005 and 0.0010, while LMS records slightly higher error rates. This 

comparison confirms that although all filters reduce error levels, the Kalman filter achieves superior accuracy, 

especially in highly dynamic urban LTE conditions. 

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10 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

 
Figure 4: A Plot of the MSE vs. Iteration 

Figure 4 reveals how each filtering technique converges in terms of Mean Square Error (MSE) over 20 iterations. 

The graph shows that Kalman filtering achieves the fastest convergence, with MSE dropping below 0.1 within 

the first 10 iterations, while RLS follows closely. LMS, though stable, converges more slowly, taking longer to 

reach acceptable error thresholds. For example, at iteration 10, LMS records an MSE of approximately 0.05, 

whereas Kalman has already dropped below 0.01. This result indicates that faster-converging filters like Kalman 

are better suited for real-time signal enhancement in LTE networks. 

 
 

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11 | A c a d e m i c  J o u r n a l  o f  S c i e n c e ,  E n g i n e e r i n g  a n d  T e c h n o l o g y  

|  https://topjournals.org/index.php/AJSET 

Figure 5: A Graph of RSRP vs. Distance from eNodeB 

Figure 5 displays the signal strength before and after filtering as a function of distance from the LTE base station. 

RSRP values (in dBm) generally degrade with increasing distance, as expected. However, the filtered RSRP curve 

consistently lies above the original measurements, indicating successful signal restoration. At a distance of 800 

meters, the unfiltered RSRP is approximately -100 dBm, while the filtered signal improves to around -97 dBm. 

This demonstrates the filters’ ability to recover signal power lost due to path loss and multipath fading, especially 

in urban deployments characterized by high-rise buildings and complex signal propagation environments. 

5.0 Conclusion  

Based on the results obtained from the simulation and analysis, it is evident that adaptive filtering techniques 

significantly improve signal quality in urban LTE networks. The comparison of SINR values before and after 

filtering clearly demonstrates enhanced signal integrity, with SINR improvements reaching over 2 dB at certain 

measurement points. The Bit Error Rate (BER) analysis shows that among the evaluated filters, the Kalman filter 

consistently delivers the lowest BER values, highlighting its robustness in high-interference environments. The 

convergence analysis using Mean Square Error (MSE) further validates the efficiency of the Kalman and RLS 

filters, both of which achieve faster error reduction compared to the LMS filter. Additionally, the RSRP vs. 

distance plot illustrates the practical benefit of filtering, where signal levels after processing remain higher and 

more stable across various distances from the eNodeB. These findings confirm that adaptive filtering, particularly 

using Kalman and RLS algorithms, is a viable and effective method for mitigating signal degradation due to 

interference, noise, and multipath fading in dense urban LTE deployments. The study not only meets its objective 

of enhancing signal quality through measurement and analysis but also provides valuable insights for improving 

real-time signal processing in future LTE-A and 5G networks. 

References 

[1] Rojo-Álvarez, J. L., Martínez-Ramón, M., Muñoz-Marí, J., & Camps-Valls, G. (2018). Digital signal 

processing with kernel methods. Hoboken, NJ: John Wiley & Sons. 

[2] Ali, S., Saad, W., Rajatheva, N., Chang, K., Steinbach, D., Sliwa, B., Wietfeld, C., Mei, K., Shiri, H., 

Zepernick, H. J., et al. (2020). 6G white paper on machine learning in wireless communication networks. 

arXiv preprint arXiv:2004.13875. 

[3] Khan, A. A., Shah, S. M., Raja, M. A. Z., Chaudhary, N. I., He, Y., & Machado, J. T. (2021). Fractional LMS 

and NLMS algorithms for line echo cancellation. Arabian Journal of Science and Engineering, 46, 9385–

9398. https://doi.org/10.1007/s13369-020-05169-5 

[4] Zhu, Z., Gao, X., Cao, L., Pan, D., Cai, Y., & Zhu, Y. (2016). Analysis on the adaptive filter based on LMS 

algorithm. Optik, 127, 4698–4704. https://doi.org/10.1016/j.ijleo.2016.01.145 

mailto:topacademicjournals@gmail.com
https://doi.org/10.1007/s13369-020-05169-5
https://doi.org/10.1016/j.ijleo.2016.01.145


    

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