Due to large numbers of antennas and users, matrix inversion is complicated in linear precoding techniques for massive MIMO systems. Several approximated matrix inversion methods, including the Neumann series, have been proposed to reduce the complexity. However, the Neumann series does not converge fast enough. In this paper, to speed up convergence, a new joint Newton iteration and Neumann series method is proposed, with the first iteration result of Newton iteration method being employed to reconstruct the Neumann series. Then, a high probability convergence condition is established, which can offer useful guidelines for practical massive MIMO systems. Finally, simulation examples are given to demonstrate that the new joint Newton iteration and Neumann series method has a faster convergence rate compared to the previous Neumann series, with almost no increase in complexity when the iteration number is greater than or equal to 2.
Along with the development of computer and information technology and the arrival of the digital reading wave, more and more users have switched the way they meet their reading needs to digital reading systems, At the same time, a variety of digital reading systems have also been created. However, most digital reading systems focus on how to present a better reading style, but little research has been done on how to use artificial intelligence and big data technology to provide intelligent information services and user behavior analysis. In such a large environment, a digital reading system that can provide reading behavior collection and intelligent analysis, while providing intelligent reading analysis function, will have broad research prospects. The digital reading system studied in this paper can provide reading behavior analysis and intelligent recommendation service for professional users based on artificial intelligence and big data technology. At the same time, the system uses artificial intelligence technology to realize the functions of bilingual learning reading, new word induction records, etc., which can provide users with knowledge efficiency. In addition, the system utilizes big data technology to provide users with information services such as communication content exchange. The main innovations of this digital reading system are computer automatic clauses based on Chinese and English syntax features, data layering processing mechanism that takes into account the speed and quality of book analysis, and book encryption and decryption schemes across computer systems. The system is based on a C/S and B/S fusion architecture and includes a reading system based on PC and Android.It can present customized ePub electronic resources, and collect users’ reading behavior through mobile screen or mouse and other devices, then use artificial intelligence and big data technology to analyze user data, and finally generate user reading reports. At present, the whole system has been applied in many universities, and the reading level of students and the work efficiency of teachers have been greatly improved, which proves that this digital reading system has high practical value.
The properties of memristor as the fourth basic circuit element are studied. The mathematical models in integral form for memristors with and without border constraint are developed. The simulation is done for the memristor with border constraint. The influences of the source frequency and model parameters on the memristor’s properties are analyzed and some conclusions are drawn. The model parameters considered include the doping ratio and the initial doping width, and their influnce on the current, voltage-current relation and flux-charge relation of the memrister are investigaled.
Several approximation approaches including the Gauss-Seidel (GS) method have been proposed to reduce the complexity of matrix inversion for zero-forcing pre-coding in massive multiple-input-multiple-output systems. However, extra computation is required to obtain the matrix inversion from the iteration result of the GS method. In this paper, we propose a new GS-based matrix inversion approximation (GSBMIA) approach. Unlike the traditional GS method, the GSBMIA approach approximates the matrix inversion, which will simplify further calculations. Furthermore, in order to speed up convergence, we propose a joint algorithm based on the GSBMIA and Newton iteration method where the GSBMIA approach is employed to provide an efficient searching direction for the following Newton iterations. Compared with other approximation methods, the joint algorithm can accommodate more single antenna users for the same base station antenna number. Simulation results demonstrate that the joint algorithm and the GSBMIA approach converge faster than the Neumann series and Newton iteration method.
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