###### Abstract
Traditional methods of threshold determinations include unbiased risk estimation, heuristic thresholds, fixed thresholds, and Minimax thresholds. To achieve a better denoising effect, there are different threshold determination methods for various situations. To improve the accuracy of motion parameter estimation, ref. [12] introduces inter-scale correlation to determine the threshold. Such an approach not only improves the adaptability of the estimation function but also makes the threshold estimation smoother. For partial high-voltage electrical signals, ref. [13] reduces the computational amount by constructing dual-tree complex wavelet pairs for signal decomposition. It calculates the wavelet coefficients by the block threshold, considering the correlation of neighboring wavelet coefficients. Ref. [14] uses the statistical properties of wavelet coefficients to construct adaptive wavelet threshold denoising. Ref. [15] combines the simulated annealing algorithm with the artificial bee colony algorithm and uses it to select the optimal threshold. Reconfiguration algorithms are also crucial in MWC systems. In recent years, for the optimization of reconstruction algorithms, researchers have conducted a lot of research. Refs. [16; 17; 18] optimize the algorithm by reducing the amount of computation. Generalized Orthogonal Matching Pursuit (GOMP) is a generalization of the OMP [16]. Although GOMP is a simple improvement, its indices are significantly improved compared with OMP. Ref. [17] proposed nearest orthogonal matching pursuit (N-OMP) algorithms, which directly determine the occupancy of two neighboring sub-bands by calculating the correlation coefficients between the residual vectors and the two neighboring sub-bands. Ref. [18] reduces the number of iterations and the computational complexity by exploiting the correlation between the observation matrix and the sample. Refs. [19; 20; 21; 22] enhance algorithms' accuracy by filtering multiple times or by proposing multiple filtering conditions. Ref. [19] uses a backtracking elimination strategy to filter the initially selected support set indices. Ref. [20] performs the screening of sampling channels before reconstruction. Ref. [21]sets additional support set screening conditions to achieve accurate reconstruction. Double Threshold Matching Pursuit (DTMP) is an energy-based dual threshold algorithm [22]. DTMP compares the power of its reconstructed signal with the corresponding noise power for the initially selected support set. If it is below a certain threshold, it is determined that it contains only noise and it is removed from the support set. DTMP has improved the performance of the support set in terms of the false alarm rate. In summary, we propose the following optimization scheme for the communication and sensing system of wireless devices:  
1. To minimize the impact of noise in the sampled signal on subsequent sensing and communication, we propose a dynamic flexible processing method based on spatial information entropy. The method first dynamically denoises the signal according to the noise characteristics, and second, for the subsequent reconstruction, needs to construct the spatial information entropy and use it to further process the denoised signal. 2. To further improve the accuracy of communication and sensing, we optimized the OMP algorithm based on the concept of the genetic algorithm. According to the characteristics of the traditional OMP algorithm, the spatial information gain and spatial information characteristics are utilized to constitute an information feature factor, which helps the algorithm to further filter the support set. The incorporation of randomness reduces the false alarm rate of the support set and the Bit Error Rate (BER) of the signal, and overcomes the defect of the OMP algorithm falling into the local optimal solution. The particulars of the remaining sections of this paper are as follows.