Data-Reuse Adaptive Algorithms for Graph Signal Estimation Over Sensor Network

The least mean square (LMS) algorithm of graph signal processing (GSP) can be used for sensor signal processing due to its simplicity and low computational complexity, and the recursive least squares (RLS) algorithm of GSP can be applied to sensor signal processing since it has fast convergence rate. However, the GSP LMS algorithm has poor convergence performance, and the tracking performance of the GSP RLS algorithm turns weak after signal mutation. To solve the mentioned problems, we focus on the data-reuse strategy, aiming to improve the convergence/tracking performance of the related algorithms by reusing the same set of data several times, and thus, the GSP data-reuse LMS (GSP-DR-LMS) algorithm and the GSP data-reuse RLSs (GSP-DR-RLSs) algorithm are proposed. Moreover, to make the GSP-DR-LMS algorithm achieve better coordination between steady-state error and convergence rate, we propose the variable step size (VSS) strategy applicable to the GSP-DR-LMS algorithm, and thus, the GSP VSS-DR LMS algorithm is proposed. In addition, the performance analysis of the related algorithms is performed. Ultimately, we verify the superiority of the proposed algorithms in terms of convergence/tracking performance by performing computer simulations.