Noise Reduction on Received Signals in Wireless Ultraviolet Communications Using Wavelet Transform

Peng Song, Yumei Tan, Xiaojun Geng, Taifei Zhao
2020 IEEE Access  
In wireless ultraviolet optical communications, noise is one of the most essential factors affecting the communication system performance. This paper presents a scheme of reducing noise in the received ultraviolet signal using wavelet transform algorithms. An effective signal-to-noise ratio (SNR) calculation method for the received signals is also proposed, and adopted by our wavelet denoising scheme so that an optimal wavelet basis function can be selected. The proposed denoising method is
more » ... ising method is applied to both the ultraviolet signals generated from the multi-scattering transmission simulation model and the signals received from physical experiments, under different conditions of transceiver elevation angles and communication distances. The results show consistently that the wavelet transform algorithm can significantly improve the SNRs at the receiving end. When the wavelet basis is coif2, the best denoising effect is achieved where the improved SNR reaches 11.9925 dB on average for various physical conditions. INDEX TERMS Ultraviolet optical communication, scattering transmission, wavelet transform, signal-tonoise ratio, signal denoising. I. INTRODUCTION Ultraviolet Communication (UVC) is a new type of wireless optical communication method [1], [2]. Ultraviolet (UV) rays with wavelength between 200∼280nm in the solar blind zone are used as carriers of information transmission. Signals are transmitted through the scattering of UV by atmospheric particles, aerosols, and dust in the atmosphere. For this reason, UVC can realize non-line-of-sight (NLOS) optical communications [3] and avoid the shortcomings of traditional line-ofsight (LOS) communications. Also, UVC has the advantages of high confidentiality and strong anti-interference ability, and can work in all-weather conditions. These features enable the wireless UVC to work well in complex terrain environments [4], and to have broad application prospects in the field of covert communications [5]-[7]. At present, most of the research work related to UVC systems focuses on establishing UVC channel models [8], [9], studying channel characteristics [10], examining noise The associate editor coordinating the review of this manuscript and approving it for publication was Xianfu Lei . components, and exploring signal-to-noise (SNR) estimation methods. In UVC systems, noise can cause serious interference to signal; therefore, extensive research has been conducted on noise-related issues to improve communication performance. Authors in [11] studied the SNR and the bit error rate (BER) in LOS and NLOS wireless UV links, and presented a SNR-estimation method for UVC systems. Background noise in UVC channels is studied in [12] using the Monte Carlo (MC) simulation method. Reference [13] developed a multiple-input multiple-output (MIMO) receiver system to suppress shot noise. Manchester coding was adopted to suppress the optical background noise in a LED wireless communication system [14] . A SNR estimation method suitable for NLOS UV communications was proposed in [15] to estimate the channel capacity. Reference [16] studied the influence of mutual crosstalk between impulse response sequences on the communication rate. In order to analyze the channel capacity, the SNR of a NLOS non-coplanar UV system was studied using the quantum limit method [17] . In the same year researchers [18] proposed the use of anti-multipath UV multiple access technology to solve the problem of signal 131626 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see VOLUME 8, 2020 basis, and coif wavelet basis. As the communication distance increases, all four curves show a downward trend, which confirms that UV is more suitable for short distance communication. In addition, agreeing with the earlier case where the transceiver elevation angle varies, using the coif wavelet basis results in the highest SNR values consistently for all communication distances. Then, we use different orders of the wavelet bases in the coif family-the coif1, coif2, coif3, coif4, and coif5 wavelet bases to reprocess the noisy signal. As before, the communication distance is varied from 10m to 100m with step of 10m. The result, as shown in Fig. 11(b) , suggests that the coif2 is superior to the others; therefore, we select the coif2 wavelet for the subsequent analysis. Next, using the coif2 wavelet, we apply the same wavelet denoising algorithm to the UV signals received with different communication distances. SNRs are evaluated for both the original signals as well as the denoised signals. The results are depicted in Fig. 12 . FIGURE 12. Comparison of SNRs before and after denoising of the UV signals received at different communication distances.
doi:10.1109/access.2020.3009944 fatcat:yzxmtz6slzeupeswviu3bxqeze