Abstract
The intense use of the 2.4 GHz ISM band by several wireless technologies has resulted in increased co-channel interference between networks operating in this frequency band. The aim of this paper is to investigate modeling techniques of co-channel interference affecting bluetooth low energy devices. The models are derived from recorded interference. Two types of models are introduced: a time domain model utilizing IQ data as reference and a spectrum-based model in which the reference signal is captured in frequency domain by a real-time spectrum analyzer. The recorded interference is also used as a reference to analyze the accuracy of proposed models. The proposed IQ-based model shows a degradation in performance for the environments with dominant bluetooth interfering signals. The frequency-based model not only addresses this problem, but also results in a huge data decimation in recording the interference.
Highlights
Interference sources, affecting wireless technologies, can generally be classified into two main categories: intelligent and non-intelligent
We show that signals originated from Wireless local area network (WLAN) sources can be modeled by an additive white Gaussian noise (AWGN) source, which reduces the complexity of the interference model
Apart from that, the Gaussian frequency shift keying (GFSK) interfering signals behave differently compared to noise. This makes it difficult to assume that an AWGN source emulates any type of interference in the Bluetooth low energy (BLE) systems, unlike the claim in [16]
Summary
Interference sources, affecting wireless technologies, can generally be classified into two main categories: intelligent and non-intelligent. Intelligent interferences originate from other wireless systems and the non-intelligent interferences are caused by unintentional electromagnetic emissions [1]. The characterization and modeling of both types have been addressed in many publications. Non-intelligent sources of interference are commonly characterized based on measurement data. Authors in [2] used the results of an extensive measurement campaign to empirically model impulsive interferences, caused by electronic devices. The statistics of peak amplitudes, pulse durations, and interarrival times of interferences are derived by a set of measurements. A similar approach is presented in [3] to model the impulsive interference in digital video broadcasting-terrestrial (DVB-T)
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