Impact of impulsive interference on wireless communication links

The realm of wireless communications is progressively complex and densely populated. The expanding potential for signal interference among various sources is becoming increasingly apparent. Impulsive interference is a type of electromagnetic disturbance that can have a substantial risk of affecting the signal quality and functionality of wireless communication links. Impulsive interference in communication systems is a multifaceted problem that arises from various sources, such as electromagnetic interference (EMI) and radio frequency interference (RFI) caused by electronic devices, power lines, atmospheric conditions, lightning strikes, and human-generated disturbances. Various factors such as power line noise, switching transients, faults in electronic components, and intermodulation distortion can all play a role in causing abrupt disruptions in communication signals. The presence of this interference, which occurs due to short bursts of energy, follows a random pattern and can greatly compromise the integrity of the signal and it is a representation example presented in Fig. 1. Unforeseen surges from various electronic devices such as microwave ovens and power tools, as well as external factors like lightning strikes, have the potential to cause disruptions, elevate bit error rates, and even result in communication link failures.

A impulsive noise is illustrated in below figure.

Active noise cancellation algorithms for impulsive noise - ScienceDirect

When it comes to wireless communication systems, it is essential to carefully evaluate the impact of impulsive noise. This is because impulsive noise tends to deviate from Gaussian characteristics and display unpredictable behaviour, making it inappropriate to rely on Gaussian approximation [2]. The presence of impulsive noise, which does not follow a Gaussian distribution, can have a negative impact on the performance of wireless communication systems [4]. In order to tackle these challenges, various adaptive demodulation algorithms have been put forward. The log-likelihood ratios are computed by these algorithms using a strategic approach that takes into account interference classification and the estimation of noise models. These noise models include Middleton’s Class A noise model and the symmetric α-Stable (SαS) model. These adaptive techniques have shown significant enhancements in terms of bit error rate (BER) and energy per bit to noise power ratio (Eb/N0) in comparison to conventional non-adaptive methods. Through the utilisation of adaptive strategies, wireless communication systems have the ability to effectively minimise the negative effects of impulsive interference, resulting in an overall improvement in system performance.

Impulsive Interference Mitigation Techniques

There are several methods available to safeguard communication systems against impulsive interference, ensuring their protection from potential interference. Different techniques can be classified as either passive or active mitigation techniques.

Passive Mitigation: Passive mitigation measures encompass the utilisation of shielding, filtering, and grounding techniques. Shielding is a crucial technique employed in the field of electronics to effectively enclose and protect sensitive components or cables. By utilising conductive materials, shielding acts as a barrier against impulsive noise coupling, ensuring optimal performance and reliability. Filtering is a crucial technique employed to effectively mitigate the impact of interference by selectively blocking specific frequency bands using specialised filters. Grounding serves the purpose of establishing a shared reference point for electrical currents and effectively managing EMI.

Active Mitigation: Active mitigation measures utilise electronic circuitry to identify and counteract impulsive interference. One can employ various techniques such as error correction coding, adaptive modulation, and adaptive equalisation. By incorporating redundant information into transmitted data, error correction coding enables the identification and rectification of errors resulting from impulsive interference. Adaptive modulation is a technique that optimises signal integrity by adjusting the modulation scheme based on the interference conditions. Adaptive equalisation is a technique that helps to counteract signal distortions caused by interference. It achieves this by making necessary adjustments to the filtering and gain characteristics of the receiver.

In order to successfully address the negative impact of impulsive interference on communication links, it is crucial to adopt a holistic approach. In order to safeguard against electromagnetic interference, it is crucial to implement techniques that were defined previously. To adapt to changing interference conditions, it is recommended to employ adaptive signal processing algorithms. When it comes to designing communication systems, it is crucial to pay close attention to frequency planning, power line conditioning, and environmental factors. These factors play a significant role in ensuring the resilience of the system. By adhering to regulatory compliance, implementing isolation of components, following proper grounding practices, and incorporating surge protection measures, the system’s resilience against impulsive interference is significantly improved. Through the implementation of these measures, communication systems can effectively reduce disruptions, enhance signal reception, and guarantee the consistent and resilient performance of communication links, even when confronted with impulsive interference.


[1] Li, P., & Yu, X. (2013). Active noise cancellation algorithms for impulsive noise. Mechanical Systems and Signal Processing, 36(2), 630-635. ISSN 0888-3270.

[2] U. Ashraf and G. R. Begh, “Effect of Impulsive Noise on IRS-Aided Communication Systems,” in IEEE Transactions on Vehicular Technology, vol. 72, no. 1, pp. 648-653, Jan. 2023, doi: 10.1109/TVT.2022.3203192.[3] L. Clavier, G. W. Peters, F. Septier, and I. Nevat, “Impulsive Noise Modeling and Robust Receiver Design,” EURASIP Journal on Wireless Communications and Networking, vol. 13, 2021.