Network energy resources are limited in communication systems, which may cause energy shortages in mobile devices at the user end. Active Reconfigurable Intelligent Surfaces (A-RIS) not only have phase modulation properties but also enhance the signal strength; thus, they are expected to solve the energy shortage problem experience at the user end in 6G communications. In this paper, a resource allocation algorithm for maximizing the sum of harvested energy is proposed for an active RIS-assisted Simultaneous Wireless Information and Power Transfer (SWIPT) system to solve the problem of low performance of harvested energy for users due to multiplicative fading. First, in the active RIS-assisted SWIPT system using a power splitting architecture to achieve information and energy co-transmission, the joint resource allocation problem is constructed with the objective function of maximizing the sum of the collected energy of all users, under the constraints of signal-to-noise ratio, active RIS and base station transmit power, and power splitting factors. Second, the considered non-convex problem can be turned into a standard convex problem by using alternating optimization, semi-definite relaxation, successive convex approximation, penalty function, etc., and then an alternating iterative algorithm for harvesting energy is proposed. The proposed algorithm splits the problem into two sub-problems and then performs iterative optimization separately, and then the whole is alternately optimized to obtain the optimal solution. Simulation results show that the proposed algorithm improves the performance by 45.2% and 103.7% compared to the passive RIS algorithm and the traditional without-RIS algorithm, respectively, at the maximum permissible transmitting power of 45 dBm at the base station.

In order to further improve the accuracy of the non‐linear positioning model in the research of ultra wide band (UWB) indoor positioning, a Gaussian unscented Kalman filter (GUKF) algorithm is proposed in this paper. This localisation algorithm first uses a Gaussian function to design a Gaussian smoothing filter template to process the smoothing of experimental data in the GUKF algorithm, and then the filtering algorithm is used to obtain higher positioning accuracy. This paper utilises simulations and actual experiments to verify and analyse the GUKF algorithm, and the actual experiment environment was divided into line‐of‐sight (LOS) and non‐line‐of‐sight (NLOS) experimental environments. The measured experimental results indicate that in the static test of location tags in LOS and NLOS experimental environments, the root mean square error (RMSE) of the GUKF algorithm is reduced by 15.88% and 14.10%, respectively; in the dynamic test, the RMSE of the GUKF algorithm is reduced by 16.67% and 17.89%, respectively, compared with the unscented Kalman filter algorithm. In addition, the positioning performance evaluation method of the mean error and cumulative distribution function curve also verifies that the GUKF algorithm has a higher positioning accuracy than the UKF, Least Squares, and Time of Arrival algorithms.