## 1 Introduction  
With the development of mobile internet, the wireless data transmission rate demands increase rapidly [1]. The main bottleneck to achieve such a requirement is the shortage of available licensed spectrum. To solve this problem, one effective solution is to reuse the same resources in heterogeneous networks, which is named non-orthogonal resource sharing. For example, the ultra density network is an effective way to improve the spectral efficiency. Since the small cell base stations generally reuse the same channels with the main base station, strong co-channel interference overlapping on the time-frequency plane may be brought to the ultra density network [2; 3]. In addition, in Device-to-Device communications (D2D) underlaying cellular network, the signals of cellular users and D2D users with the non-orthogonal resource sharing mode are also superposed in time-frequency domain [4]. In both cases, one important issue is to suppress the non-orthogonal or co-channel interferences while maintaining the desired signal's frequency characteristic. In current communication systems, the signals are analyzed by Fourier transform, which shows what frequency components one signal contains, but does not tell when the frequency components exist. In contrast to Fourier transform, time frequency analysis is a powerful tool which shows the energy concentration characteristics of non-stationary signals on the time-frequency plane. The time-frequency distributions have been utilized in radar signal analysis, the linear frequency modulated signal detection, and the PSK/FSK signal distribution by extracting the signal features such as instant frequency, instant energy and instant phase [5; 6; 7; 8; 9]. [PERSON] et al. [5] applied the proposed time-frequency distribution based on compressed sensing to radar backscattered signals for signature analysis, where the compressed sensing joint time frequency distribution works well for signals since the cross-term energy and signal energy are well separated in the ambiguity plane. Other authors [6; 7; 8] used the time frequency distribution to solve the interference problems in GNSS systems, and real GNSS data collected in the presence of jamming were used to demonstrate the effectiveness of time frequency-based antijam approaches. [PERSON] et al. [9] used Wigner-Ville distribution slice and fractional Fourier transform to detect the moving target in SAR system, and [PERSON] et al. [10] addressed the problem of direction of arrival estimation and blind source separation for nonstationary signals in the underdetermined case, and used high-resolution quadratic time-frequency distributions to deal with the problem. [PERSON] et al. [11] used Choi-Williams time-frequency distribution to extract the feature of radar waveform in automatic radar waveform recognition systems. However, limited research effort has been devoted to studying the bandwidth variations of the communication signals. In this paper, the Gaussian pulse shaping signal is analyzed by Choi-Williams time frequency analysis which has good cross-term suppression characteristics [12]. It shows that the signal has a time-varying spectrum, i.e., the signal's bandwidth is not constant, but varies with time in the passband. This brings an opportunity to suppress the co-channel interferences with time varying filter whose pass region is designed according to the desired signal's energy distribution. For example, in a time frequency area \(\Delta\) within the passband, the desired signal's energy approximates to zero, but the energies of interferences are not negligible. In this case, if the received signal passes through a classical bandpass filter, the filter will collect all the energies in area \(\Delta\), which are mainly interferences. However, if the received signal passes through the time-varying filter, the filter will filter out area \(\Delta\) and the strong interferences can be suppressed. Therefore, the time-varying filter can provide better SINR performance.