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Journal articles on the topic 'Interference Mitigation'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 May 2024

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1

Ekers, R. D., and J. F. Bell. "Radio Frequency Interference." Symposium - International Astronomical Union 199 (2002): 498–505. http://dx.doi.org/10.1017/s0074180900169669.

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We describe the nature of the interference challenges facing radio astronomy in the next decade. These challenges will not be solved by regulation only, negotiation and mitigation will become vital. There is no silver bullet for mitigating against interference. A successful mitigation approach is most likely to be a hierarchical or progressive approach throughout the telescope and signal conditioning and processing systems. We summarise some of the approaches, including adaptive systems.

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2

Xu, Zhengguang, and Shanyong Wei. "FMCW Radar System Interference Mitigation Based on Time-Domain Signal Reconstruction." Sensors 23, no. 16 (August 11, 2023): 7113. http://dx.doi.org/10.3390/s23167113.

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In this study, an interference detection and mitigation method is proposed for frequency-modulated continuous-wave radar systems based on time-domain signal reconstruction. The interference detection method uses the difference in one-dimensional fast Fourier transform (1D-FFT) results between targets and interferences. In the 1D-FFT results, the target appears as a peak at the same frequency point for all chirps within one frame, whereas the interference appears as the absence of target peaks within the first or last few chirps within one frame or as a shift in the target peak position in different chirps. Then, the interference mitigation method reconstructs the interference signal in the time domain by the estimated parameter from the 1D-FFT results, so the interference signal can be removed from the time domain without affecting the target signal. The simulation results show that the proposed interference mitigation algorithm can reduce the amplitude of interference by about 25 dB. The experimental results show that the amplitude of interference is reduced by 20–25 dB, proving the effectiveness of the simulation results.

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3

Ayoughi, S. Arvin, and Wei Yu. "Interference Mitigation via Relaying." IEEE Transactions on Information Theory 65, no. 2 (February 2019): 1137–52. http://dx.doi.org/10.1109/tit.2018.2878452.

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4

Garzia, Fabio, Johannes Rossouw van der Merwe, Alexander Rügamer, Santiago Urquijo, and Wolfgang Felber. "HDDM Hardware Evaluation for Robust Interference Mitigation." Sensors 20, no. 22 (November 13, 2020): 6492. http://dx.doi.org/10.3390/s20226492.

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Interference can significantly degrade the performance of global navigation satellite system (GNSS) receivers. Therefore, mitigation methods are required to ensure reliable operations. However, as there are different types of interference, robust, multi-purpose mitigation algorithms are needed. This paper describes the most popular state-of-the-art interference mitigation techniques. The high-rate DFT-based data manipulator (HDDM) is proposed as a possible solution to overcome their limitations. This paper presents a hardware implementation of the HDDM algorithm. The hardware HDDM module is integrated in three different receivers equipped with analog radio-frequency (RF) front-ends supporting signals with different dynamic range. The resource utilization and power consumption is evaluated for the three cases. The algorithm is compared to a low-end mass-market receiver and a high-end professional receiver with basic and sophisticated interference mitigation capabilities, respectively. Different type of interference are used to compare the mitigation capabilities of the receivers under test. Results of the HDDM hardware implementation achieve the similar or improved performance to the state of the art. With more complex interferences, like frequency hopping or pulsed, the HDDM shows even better performance.

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Deng, Wen, Xiang Wang, Zhitao Huang, and Qiang Xu. "Interference Mitigation using Data-driven Sparse Component Analysis." Journal of Physics: Conference Series 2722, no. 1 (March 1, 2024): 012010. http://dx.doi.org/10.1088/1742-6596/2722/1/012010.

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Abstract To address the challenge of mitigating asynchronous and non-stationary interference under single-channel conditions, we proposes a sparse component analysis interference mitigation method based on the recurrent neural network. This method aims to recover the desired signal from the received time-frequency over-lapped co-channel signal. Unlike previous interference mitigation methods, our proposed method achieves ”end-to-end” signal recovery in the time domain without any priori requirements on the received signals, making it more universal than existing methods. Numerical results validate the effectiveness of our proposed method and demonstrate its significantly superior mitigation performance compared to existing schemes under different environmental noises, intensities of interfering signals, and generalization test conditions.

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6

Baig, Mirza Uzair, Anders Host-Madsen, and Aria Nosratinia. "Discrete Modulation for Interference Mitigation." IEEE Transactions on Information Theory 66, no. 5 (May 2020): 3026–39. http://dx.doi.org/10.1109/tit.2019.2953916.

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Chen, Gang, Zhengyu Zhao, Guoqiang Zhu, Yujie Huang, and Ting Li. "HF Radio-Frequency Interference Mitigation." IEEE Geoscience and Remote Sensing Letters 7, no. 3 (July 2010): 479–82. http://dx.doi.org/10.1109/lgrs.2009.2039340.

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8

Minasian, R. A., K. E. Alameh, and E. H. W. Chan. "Photonics-based interference mitigation filters." IEEE Transactions on Microwave Theory and Techniques 49, no. 10 (2001): 1894–99. http://dx.doi.org/10.1109/22.954804.

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9

Ramezanpour, Parham, and Mohammad Reza Mosavi. "DNN-based interference mitigation beamformer." IET Radar, Sonar & Navigation 14, no. 11 (November 1, 2020): 1788–94. http://dx.doi.org/10.1049/iet-rsn.2020.0234.

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10

Lee, Jungwon, Dimitris Toumpakaris, and Wei Yu. "Interference Mitigation via Joint Detection." IEEE Journal on Selected Areas in Communications 29, no. 6 (June 2011): 1172–84. http://dx.doi.org/10.1109/jsac.2011.110606.

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11

van der Merwe, Johannes Rossouw, Fabio Garzia, Alexander Rügamer, Santiago Urquijo, David Contreras Franco, and Wolfgang Felber. "Wide-Band Interference Mitigation in GNSS Receivers Using Sub-Band Automatic Gain Control." Sensors 22, no. 2 (January 16, 2022): 679. http://dx.doi.org/10.3390/s22020679.

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The performance of global navigation satellite system (GNSS) receivers is significantly affected by interference signals. For this reason, several research groups have proposed methods to mitigate the effect of different kinds of jammers. One effective method for wide-band interference mitigation (IM) is the high-rate DFT-based data manipulator (HDDM) pulse blanker (PB). It provides good performance to pulsed and frequency sparse interference. However, it and many other methods have poor performance against wide-band noise signals, which are not frequency-sparse. This article proposes to include automatic gain control (AGC) in the HDDM structure to attenuate the signal instead of removing it: the HDDM-AGC. It overcomes the wide-band noise limitation for IM at the cost of limiting mitigation capability to other signals. Previous studies with this approach were limited to only measuring the carrier-to-noise density ratio (C/N0) performance of tracking, but this article extends the analysis to include the impact of the HDDM-AGC algorithm on the position, velocity, and time (PVT) solution. It allows an end-to-end evaluation and impact assessment of mitigation to a GNSS receiver. This study compares two commercial receivers: one high-end and one low-cost, with and without HDDM IM against laboratory-generated interference signals. The results show that the HDDM-AGC provides a PVT availability and precision comparable to high-end commercial receivers with integrated mitigation for most interference types. For pulse interferences, its performance is superior. Further, it is shown that degradation is minimized against wide-band noise interferences. Regarding low-cost receivers, the PVT availability can be increased up to 40% by applying an external HDDM-AGC.

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12

Xu, Zhihuo, Shuaikang Xue, and Yuexia Wang. "Incoherent Interference Detection and Mitigation for Millimeter-Wave FMCW Radars." Remote Sensing 14, no. 19 (September 27, 2022): 4817. http://dx.doi.org/10.3390/rs14194817.

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Current automotive radar technology is almost exclusively implemented using frequency modulated continuous wave (FMCW) radar in the millimeter wave bands. Unfortunately, incoherent interference is becoming a serious problem due to the increasing number of automotive radars in dense traffic situations. To address this issue, this article presents a sparsity-based technique for mitigating the incoherent interference between FMCW radars. First, a low-pass filter-based technique is developed to detect the envelope of the interference. Next, the labeled regions where interference is present are considered as missing data. In this way, the problem of mitigating interference is further formulated as the restoration of the echo using L1 norm-regularized least squares. Finally, the alternating direction method of the multipliers-based technique is applied to restore the radar echoes. Extensive experimental results demonstrate the effective performance of the proposed approach. Compared to state-of-the-art interference mitigation methods, the proposed method remarkably improves the quality of radar targets.

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13

., S. Gospel Ruben. "SCHEDULING FOR INTERFERENCE MITIGATION USING ENHANCED INTERCELL INTERFERENCE COORDINATION." International Journal of Research in Engineering and Technology 03, no. 02 (February 25, 2014): 624–27. http://dx.doi.org/10.15623/ijret.2014.0302111.

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14

Aughenbaugh, Jason M., Brian R. La Cour, and James M. Gelb. "Interference Mitigation for Multistatic Active Sonar." IEEE Journal of Oceanic Engineering 40, no. 3 (July 2015): 570–82. http://dx.doi.org/10.1109/joe.2014.2332951.

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15

Towliat, Mohammad, and Seyyed Mohammad Javad Asgari Tabatabaee. "GFDM Interference Mitigation Without Noise Enhancement." IEEE Communications Letters 22, no. 5 (May 2018): 1042–45. http://dx.doi.org/10.1109/lcomm.2018.2813393.

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16

Uysal, Faruk. "Synchronous and Asynchronous Radar Interference Mitigation." IEEE Access 7 (2019): 5846–52. http://dx.doi.org/10.1109/access.2018.2884637.

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17

Bergel, I., E. Fishler, and H. Messer. "Narrowband Interference Mitigation in Impulse Radio." IEEE Transactions on Communications 53, no. 8 (August 2005): 1278–82. http://dx.doi.org/10.1109/tcomm.2005.852819.

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18

Rhodes, C. W., and G. J. Sgrignoli. "Interference mitigation for improved DTV reception." IEEE Transactions on Consumer Electronics 51, no. 2 (May 2005): 463–70. http://dx.doi.org/10.1109/tce.2005.1467988.

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19

Wang, I.-Hsiang, and David N. C. Tse. "Interference Mitigation Through Limited Transmitter Cooperation." IEEE Transactions on Information Theory 57, no. 5 (May 2011): 2941–65. http://dx.doi.org/10.1109/tit.2011.2120170.

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20

Wang, I.-Hsiang, and David N. C. Tse. "Interference Mitigation Through Limited Receiver Cooperation." IEEE Transactions on Information Theory 57, no. 5 (May 2011): 2913–40. http://dx.doi.org/10.1109/tit.2011.2120290.

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21

Urick, Vincent J., Modesto E. Godinez, and Dennis C. Mikeska. "Photonic Assisted Radio-Frequency Interference Mitigation." Journal of Lightwave Technology 38, no. 6 (March 15, 2020): 1268–74. http://dx.doi.org/10.1109/jlt.2019.2955921.

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22

Zirwas, Wolfgang, Wolfgang Mennerich, and Aneeq Khan. "Main enablers for advanced interference mitigation." Transactions on Emerging Telecommunications Technologies 24, no. 1 (August 28, 2012): 18–31. http://dx.doi.org/10.1002/ett.2567.

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23

Same, Mohammad Hossein, Gabriel Gandubert, Preslav Ivanov, René Landry, and Gabriel Gleeton. "Effects of Interference and Mitigation Using Notch Filter for the DVB-S2 Standard." Telecom 1, no. 3 (December 4, 2020): 242–65. http://dx.doi.org/10.3390/telecom1030017.

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The abundance of radio signals and their increasing number creates interferences on adjacent signals and sometimes, with co-channel communication. Jammers, which are operated by hackers or by military forces, are another source of smart and powerful interferences. This paper will discuss the effect of the continuous wave interference (CWI) on a radio communication receiver, specifically with the Digital Video Broadcasting for Satellite Second Generation (DVB-S2) communication standard. It investigates the general effect of the interference on a Quadrature Phase Shift Keying (QPSK) signal over each part of the DVB-S2 receiver. It also focuses on the impact of the center frequency and power of the interference on the critical blocks of a DVB-S2 receiver. This study also tries to determine the deviation from the normal operation in the format of mathematical expressions and simulation results. Based on the obtained results, there is a vulnerability in the chain of the receiver’s blocks that allows a smart jammer to affect the device with low power interference. The notch filter is utilized as a solution to mitigate the interference. In addition, the effects of this technique on the system’s performance are studied. The simulation results show that there is a great improvement after CWI removal according to the Jamming to Signal Ratio (JSR), the Signal-to-Noise Ratio (SNR), and the Bit Error Rate (BER). In some cases, the JSR was reduced by 15 dB, the SNR was improved by 10 dB and BER also improved by 7 dB. However, the notch filter deletes some information from the original signal. This study introduces new ways to clarify the tradeoff between the amount of interference power reduction and removed bandwidth from the signal with notch filtering.

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24

Huang, Zhitao, and Xin Cai. "Interference Mitigation via Sparse Coding in ${K}$ -User Interference Channels." IEEE Wireless Communications Letters 8, no. 6 (December 2019): 1596–99. http://dx.doi.org/10.1109/lwc.2019.2930693.

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25

Thakur, Ajit Kumar, Adarsh Kumar Arya, and Pushpa Sharma. "Prediction and mitigation of AC interference on the pipeline system." Corrosion Reviews 40, no. 2 (February 1, 2022): 149–57. http://dx.doi.org/10.1515/corrrev-2021-0061.

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Abstract The purpose of this paper is to predict and mitigate AC interference on buried pipeline systems due to transmission lines. Modeling and field verification of AC interference is done. The article also presents the issue of optimizing the mitigation measures. The paper uses the field data on soil resistivity, transmission line, and pipeline details to develop a model using current distribution electromagnetic interference grounding and soil structure analysis (CDEGS) software to predict the AC interference on the pipeline system. The model is validated with field measurements, and post-mitigation measures are considered. Mitigation measures are optimized to develop an economical mitigation plan. The case demonstrates the use of modeling techniques to predict and mitigate AC interference on pipelines. The field validation of modeling results helps improve the modeling results and plan optimized mitigation measures. The study requires providing comprehensive field data relevant to the pipeline system under consideration. The accuracy of the field data may have a bearing on the outcome of the study. The study enables designing and optimizing mitigation measures using modeling. Comparisons with field measurements help achieve desired pipeline system integrity against AC corrosion.

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26

Fan, Weiwei, Feng Zhou, Mingliang Tao, Xueru Bai, Pengshuai Rong, Shuang Yang, and Tian Tian. "Interference Mitigation for Synthetic Aperture Radar Based on Deep Residual Network." Remote Sensing 11, no. 14 (July 11, 2019): 1654. http://dx.doi.org/10.3390/rs11141654.

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Radio Frequency Interference (RFI) is a key issue for Synthetic Aperture Radar (SAR) because it can seriously degrade the imaging quality, leading to the misinterpretation of the target scattering characteristics and hindering the subsequent image analysis. To address this issue, we present a narrow-band interference (NBI) and wide-band interference (WBI) mitigation algorithm based on deep residual network (ResNet). First, the short-time Fourier transform (STFT) is used to characterize the interference-corrupted echo in the time–frequency domain. Then, the interference detection model is built by a classical deep convolutional neural network (DCNN) framework to identify whether there is an interference component in the echo. Furthermore, the time–frequency feature of the target signal is extracted and reconstructed by utilizing the ResNet. Finally, the inverse time–frequency Fourier transform (ISTFT) is utilized to transform the time–frequency spectrum of the recovered signal into the time domain. The effectiveness of the interference mitigation algorithm is verified on the simulated and measured SAR data with strip mode and terrain observation by progressive scans (TOPS) mode. Moreover, in comparison with the notch filtering and the eigensubspace filtering, the proposed interference mitigation algorithm can improve the interference mitigation performance, while reducing the computation complexity.

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27

Same, Mohammad Hossein, Gabriel Gleeton, Gabriel Gandubert, Preslav Ivanov, and Rene Jr Landry. "Multiple Narrowband Interferences Characterization, Detection and Mitigation Using Simplified Welch Algorithm and Notch Filtering." Applied Sciences 11, no. 3 (February 2, 2021): 1331. http://dx.doi.org/10.3390/app11031331.

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By increasing the demand for radio frequency (RF) and access of hackers and spoofers to low price hardware and software defined radios (SDR), radio frequency interference (RFI) became a more frequent and serious problem. In order to increase the security of satellite communication (Satcom) and guarantee the quality of service (QoS) of end users, it is crucial to detect the RFI in the desired bandwidth and protect the receiver with a proper mitigation mechanism. Digital narrowband signals are so sensitive into the interference and because of their special power spectrum shape, it is hard to detect and eliminate the RFI from their bandwidth. Thus, a proper detector requires a high precision and smooth estimation of input signal power spectral density (PSD). By utilizing the presented power spectrum by the simplified Welch method, this article proposes a solid and effective algorithm that can find all necessary interference parameters in the frequency domain while targeting practical implantation for the embedded system with minimum complexity. The proposed detector can detect several multi narrowband interferences and estimate their center frequency, bandwidth, power, start, and end of each interference individually. To remove multiple interferences, a chain of several infinite impulse response (IIR) notch filters with multiplexers is proposed. To minimize damage to the original signal, the bandwidth of each notch is adjusted in a way that maximizes the received signal to noise ratio (SNR) by the receiver. Multiple carrier wave interferences (MCWI) is utilized as a jamming attack to the Digital Video Broadcasting-Satellite-Second Generation (DVB-S2) receiver and performance of a new detector and mitigation system is investigated and validated in both simulation and practical tests. Based on the obtained results, the proposed detector can detect a weak power interference down to −25 dB and track a hopping frequency interference with center frequency variation speed up to 3 kHz. Bit error ratio (BER) performance shows 3 dB improvement by utilizing new adaptive mitigation scenario compared to non-adaptive one. Finally, the protected DVB-S2 can receive the data with SNR close to the normal situation while it is under the attack of the MCWI jammer.

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28

Yousif, Tasneem, and Paul Blunt. "Interference Mitigation for GNSS Receivers Using FFT Excision Filtering Implemented on an FPGA." Eng 3, no. 4 (October 31, 2022): 439–66. http://dx.doi.org/10.3390/eng3040032.

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GNSS receivers process signals with very low received power levels (<−160 dBW) and, therefore GNSS signals are susceptible to interference. Interference mitigation algorithms have become common in GNSS receiver designs in both professional and mass-market applications to combat both unintentional and intentional (jamming) interference. Interference excision filters using fast Fourier transforms (FFTs) have been proposed in the past as a powerful method of interference mitigation. However, the hardware implementations of this algorithm mostly limited their use to military GNSS receivers where greater power and resources were available. Novel implementation of existing FPGA technology should make interference mitigation feasible with limited hardware resources. This paper details the practicalities of implementing excision filters on currently available FPGAs trading off the achievable performance against the required hardware resources. The hardware implementation of the FFT excision mitigation algorithm is validated with the GNSS software receiver. The results indicate that the desired performance of the developed algorithm has achieved the expectations and can provide significant improvement on mitigation techniques in current GNSS receiver hardware. Two hardware implementation designs (fixed-point and float-point data type format) are developed and compared to achieve the optimal design that can provide the best performance with the possible minimum hardware resources.

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29

Hechenberger, Stefan, Stefan Tertinek, and Holger Arthaber. "Low-Complexity Wideband Interference Mitigation for UWB ToA Estimation." Sensors 23, no. 13 (June 21, 2023): 5806. http://dx.doi.org/10.3390/s23135806.

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Reliable time of arrival (ToA) estimation in dense multipath (dm) environments is a difficult task, especially when strong interference is present. The increasing number of multiple services in a shared spectrum comes with the demand for interference mitigation techniques. Multiple receiver elements, even in low-energy devices, allow for interference mitigation by processing coherent signals, but computational complexity has to be kept at a minimum. We propose a low-complexity, linearly constrained minimum variance (LCMV) interference mitigation approach in combination with a detection-based ToA estimator. The performance of the method within a realistic multipath and interference environment is evaluated based on measurements and simulations. A statistical analysis of the ToA estimation error is provided in terms of the mean absolute error (mae), and the results are compared to those of a band-stop filter-based interference blocking approach. While the focus is on receivers with only two elements, an extension to multiple elements is discussed as well. Results show that the influence of strong interference can be drastically reduced, even when the interference bandwidth exceeds 60% of the signal bandwidth. Moreover, the algorithm is robust to uncertainties in the angle of arrival (aoa) of the desired signal. Based on these results, the proposed mitigation method is well suited when the interference bandwidth is large and when computational power is a critical resource.

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30

Chen, Bingxu, Zongsen Lv, Pingping Lu, Gaofeng Shu, Yabo Huang, and Ning Li. "Extension and Evaluation of SSC for Removing Wideband RFI in SLC SAR Images." Remote Sensing 14, no. 17 (August 31, 2022): 4294. http://dx.doi.org/10.3390/rs14174294.

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Synthetic aperture radar (SAR), as a wideband radar system, is easily contaminated by radio frequency interference (RFI), which affects the imaging quality of SAR. The subband spectral cancellation (SSC) method and its modifications utilize the SAR single-look complex (SLC) image to realize RFI extraction and mitigation by subtracting between sub-images, which are robust and efficient for engineering applications. In the past, the traditional SSC was often applied to narrowband interference (NBI) mitigation. However, when it was used for wideband interference (WBI) mitigation, it would cause the mitigated image to lose much of its useful information. In contrast, this paper proposes an improved SSC method based on successive cancellation and data accumulation (SSC-SCDA) for WBI mitigation. First, the fast Fourier transform (FFT) is used to characterize the SAR SLC data in the frequency domain, and the average range spectrum algorithm is used to detect whether there are interference components in the SAR SLC data. Then, according to the carrier frequency and bandwidth of the RFI in the frequency domain, the subbands are divided, and a cancellation strategy is formulated. Finally, based on the successive cancellation and data accumulation technology, WBIs can be removed by using only a small percentage of the clean subbands. Based on the simulated experiments, the interference mitigation performance of the proposed method is analyzed when the interference-to-signal bandwidth ratio (ISBR) varies from 20% to 80% under different signal-to-interference-to-noise ratios (SINR). The experimental results based on WBI-contaminated European Space Agency (ESA) Sentinel-1A SAR SLC data demonstrate the effectiveness of the proposed method in WBI mitigation.

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31

Ghavami, Siavash, and Bahman Abolhassani. "A new practical interference mitigation scheme for interference cognitive radio networks." International Journal of Communication Systems 26, no. 12 (March 6, 2012): 1617–35. http://dx.doi.org/10.1002/dac.2339.

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Ab. Ghani, Hadhrami, Azlan Abd Aziz, Azizul Azizan, and Salwani Mohd Daud. "Adaptive Interference Mitigation with User Grouping for Fast Transmission in Cellular Networks." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 2 (May 1, 2018): 702. http://dx.doi.org/10.11591/ijeecs.v10.i2.pp702-712.

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Designing uplink systems which group users with adaptive interference mitigation techniques is the objective of this research. Reduction in error rates and improvement in the energy efficiency is expected with this approach in addition to spectral efficiency. This paper reports a study on interference mitigation and transmission designs for groups of users in the uplinks. New formulations for the interference mitigation are produced based on the minimum mean square error and successive interference cancellation approach. By reducing the interference, the energy efficiency can be maintained and improved although the number of users per group increases. The measured error rates of this approach with user grouping achieve gains between 1 to 3 dB against that of the existing approach. With reduced complexity, the proposed scheme should be viable for practical deployment.

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Ab. Ghani, Hadhrami, Azlan Abd Aziz, Azizul Azizan, and Salwani Mohd Daud. "Adaptive Interference Mitigation with User Grouping for Fast Transmission in Cellular Networks." Indonesian Journal of Electrical Engineering and Computer Science 10, no. 2 (May 1, 2018): 704. http://dx.doi.org/10.11591/ijeecs.v10.i2.pp704-712.

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Designing uplink systems which group users with adaptive interference mitigation techniques is the objective of this research. Reduction in error rates and improvement in the energy efficiency is expected with this approach in addition to spectral efficiency. This paper reports a study on interference mitigation and transmission designs for groups of users in the uplinks. New formulations for the interference mitigation are produced based on the minimum mean square error and successive interference cancellation approach. By reducing the interference, the energy efficiency can be maintained and improved although the number of users per group increases. The measured error rates of this approach with user grouping achieve gains between 1 to 3 dB against that of the existing approach. With reduced complexity, the proposed scheme should be viable for practical deployment.

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34

Steeb, Jan-Willem W., David B. Davidson, and Stefan J. Wijnholds. "Mitigation of non-narrowband radio-frequency interference." URSI Radio Science Bulletin 2018, no. 365 (June 2018): 10–19. http://dx.doi.org/10.23919/ursirsb.2018.8572495.

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35

LIEN, Shao-Yu, Shin-Ming CHENG, and Kwang-Cheng CHEN. "Interference Mitigation in CR-Enabled Heterogeneous Networks." IEICE Transactions on Communications E96.B, no. 6 (2013): 1230–42. http://dx.doi.org/10.1587/transcom.e96.b.1230.

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36

VU, Trung Kien, Sungoh KWON, and Sangchul OH. "Cooperative Interference Mitigation Algorithm in Heterogeneous Networks." IEICE Transactions on Communications E98.B, no. 11 (2015): 2238–47. http://dx.doi.org/10.1587/transcom.e98.b.2238.

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37

Borio, Daniele, Haoqing Li, and Pau Closas. "Huber’s Non-Linearity for GNSS Interference Mitigation †." Sensors 18, no. 7 (July 10, 2018): 2217. http://dx.doi.org/10.3390/s18072217.

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38

Hansen, Chad K., Karl F. Warnick, Brian D. Jeffs, J. Richard Fisher, and Richard Bradley. "Interference mitigation using a focal plane array." Radio Science 40, no. 5 (June 25, 2005): n/a. http://dx.doi.org/10.1029/2004rs003138.

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39

Siafarikas, Dimitrios, Elias A. Alwan, and John L. Volakis. "Interference Mitigation for 5G Millimeter-Wave Communications." IEEE Access 7 (2019): 7448–55. http://dx.doi.org/10.1109/access.2018.2889620.

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Li, Mao, Zishu He, and Wencai Li. "Transient Interference Mitigation via Supervised Matrix Completion." IEEE Geoscience and Remote Sensing Letters 13, no. 7 (July 2016): 907–11. http://dx.doi.org/10.1109/lgrs.2016.2553082.

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Bizaki, Hossein Khaleghi, and Mohsen Farhang. "Precoded GFDM system for self-interference mitigation." International Journal of Systems, Control and Communications 10, no. 4 (2019): 329. http://dx.doi.org/10.1504/ijscc.2019.10022002.

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Farhang, Mohsen, and Hossein Khaleghi Bizaki. "Precoded GFDM system for self-interference mitigation." International Journal of Systems, Control and Communications 10, no. 4 (2019): 329. http://dx.doi.org/10.1504/ijscc.2019.102743.

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Oteri, O., and A. Paulraj. "Multicell Optimization for Diversity and Interference Mitigation." IEEE Transactions on Signal Processing 56, no. 5 (May 2008): 2050–61. http://dx.doi.org/10.1109/tsp.2007.909338.

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Sun, Yanzan, Roger Piqueras Jover, and Xiaodong Wang. "Uplink Interference Mitigation for OFDMA Femtocell Networks." IEEE Transactions on Wireless Communications 11, no. 2 (February 2012): 614–25. http://dx.doi.org/10.1109/twc.2011.120511.101794.

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Elsherif, Ahmed R., Zhi Ding, and Xin Liu. "Dynamic MIMO Precoding for Femtocell Interference Mitigation." IEEE Transactions on Communications 62, no. 2 (February 2014): 648–66. http://dx.doi.org/10.1109/tcomm.2013.122913.130062.

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Che, Enlong, Hoang Duong Tuan, Ho Huu Minh Tam Tam, and Ha H. Nguyen. "Successive Interference Mitigation in Multiuser MIMO Channels." IEEE Transactions on Communications 63, no. 6 (June 2015): 2185–99. http://dx.doi.org/10.1109/tcomm.2015.2429127.

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Rinija, G. N., Meenu B. Menon, Nisheena V. Iqbal, and A. Gopakumar. "Interference Mitigation Techniques Using UWB Pulse Shaping." Procedia Technology 24 (2016): 804–11. http://dx.doi.org/10.1016/j.protcy.2016.05.100.

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Jia, Qiongqiong, Renbiao Wu, Wenyi Wang, Dan Lu, Lu Wang, and Jie Li. "Multipath interference mitigation in GNSS via WRELAX." GPS Solutions 21, no. 2 (May 3, 2016): 487–98. http://dx.doi.org/10.1007/s10291-016-0538-9.

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Babich, F., M. Comisso, M. D'Orlando, and L. Manià. "Interference Mitigation on WLANs Using Smart Antennas." Wireless Personal Communications 36, no. 4 (March 2006): 387–401. http://dx.doi.org/10.1007/s11277-005-9007-4.

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Kaushal, Shifa, and Amanjot Singh. "Mitigation of interference from ECG: A Review." International Journal of Computer Applications 141, no. 9 (May 17, 2016): 26–30. http://dx.doi.org/10.5120/ijca2016909802.

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