# Combined Channel Estimation with Interference Suppression in CPSS
## 1 Introduction  
The cyber-physical system (CPS) was coined to describe the mapping, interaction and collaboration between computational and physical resources [1]. With the tight junction between human behaviour and CPS, social characteristics integrated into CPS, which is called a cyber-physical-social system (CPSS) [2]. CPSS indicates the integration of cyber interactions, physical perceptions and social connections. Due to the subjectivity of human behaviour, the wireless channel has been a complex electromagnetic environment of mixed radio signals between human-to-human and human-to-object [3]. Under the wireless connectivity of all things, the signal types are diverse, and not only the ambient noise is increased, but also the mutual interference will appear. Especially in the connection in multimedia interaction, the signal has a broad bandwidth which is easily interrupted. It leads to short communication distance, low transmission rate and even information coverage hole in electromagnetic interference [4]. There is an interesting example: when you enter a WiFi coverage room with a Bluetooth headset, the transmission rate of WiFi will decrease because of the interference by Bluetooth [5]. Channel estimation is used for the recognition of fading characteristics in communication channel. For broadband communication, channel estimation is often realized by inserting training sequences of signals in time domain and frequency domain. The transform domain signal processing theory has achieved a good compromise between performance and complexity for channel estimation, which canthen be applied in broadband communication [6]. Studies by [PERSON] proposed to cancel the noise in the channel impulse response (CIR). of time domain, which was transformed by least square (LS) channel estimation in frequency domain [7]. Studies by [PERSON] took two times of samples than that in the largest multipath delay, while the other samples in CIR were set to zero and treated as noise [8]. Studies by [PERSON] proposed taking the samples in the largest multipath delay in CIR. The other samples in CIR are set to zero which are treated as noise [9]. Studies by [PERSON] presented the leakage of CIR in the practical Orthogonal Frequency Division Multiplexing (OFDM) systems, the noise existed during signal multipath delay, the frequency domain window was used to reduce the leakage of CIR, and the noise of CIR was detected by threshold [10]. With the development of machine learning in recent years, [PERSON] and [PERSON] have put forward discriminant analysis, clustering analysis in CIR. Through the machine learning of the distance between the samples in CIR, the clustering of noise and the distinction of signal taps in time domain were realized [11; 12]. There are two typical strategies for anti-interference. One is to pre-process before transmitting, such as transmission power allocation, spectrum sharing or reusing [13; 14; 15]. Pre-processing is used to avoid the intended interference signal. The other strategy for anti-interference is to suppress or cancel interference by recognition of the receiving signal feature. Interference suppression or cancellation aims at uncertain interference signal. The technologies of time domain prediction, code-aided and transform domain for interference suppression are summarized in [16]. The algorithm based on time domain prediction takes advantage of the pseudo-randomness of spread spectrum signal, which has a slow convergence rate. In addition, the code-aided algorithm is based on a pseudo-random sequence with high computational complexity. The transform domain interference suppression is simple in implementation and available in application, which can fulfill the demand of CPSS for energy efficient [17]. Due to the interference in suppression and channel estimation aimed at different objects, the technologies evolved through relatively independent roadmap.