Low-computation-complexity self-adaptive in-band processing restraining amplitude limiting algorithm

Abstract

The invention discloses a low computation complexity self-adaptive in-band processing restraining amplitude limiting method. Through studying the relation between time-domain signal change amount and frequency-domain signal change amount in an amplitude limiting method, and probability distribution of the time-domain signal amplitude, the invention provides an method for determining selected parameters during in-band processing according to the condition that the amplitude in a time-domain signal of an OFDM (orthogonal frequency division multiplexing) symbol exceeds the energy of differential value signals of some threshold under the condition of satisfying the threshold requirements of EVM (error vector magnitude) of a system, so that the in-band self-adaptive restraining processing is realized. Theoretical analysis and the simulation show that a PAPR (powered air purifying respirator) can be better reduced by the method provided by the invention compared with the traditional method, and the calculation amount of the method is greatly reduced.

Description

technical field [0001] The present invention relates to the technical field of improving OFDM signals in an Orthogonal Frequency Division Multiplexing (OFDM) mobile communication system, in particular to an adaptive method for reducing PAPR of an OFDM system with low computational complexity. In-band constraint clipping method. Background technique [0002] However, the shortcoming of peak-to-average power ratio of OFDM system seriously affects the performance of the system. In order to reduce the peak-to-average power ratio, many solutions have been proposed, which are roughly divided into three categories: encoding methods, scrambling methods, and predistortion methods. Each method has its own characteristics, but also has its own defects. The encoding method is a linear process, but it is troublesome to encode and decode the signal, and the information rate is low. The scrambling method focuses on reducing the probability of signal peak occurrence. Although it can effe...

AI Technical Summary

Problems solved by technology

The algorithm proposed in the document "Li Qiang, "Research on the key technologies of PAR reduction for WIMAX (chineses)" Master Dissertation, University of Electronic Science and Technology of China, July 2008" adjusts the EVM of some OFDM symbols after clipping, As a result, the average EVM cannot meet the system requirements, and the fixed number of iterations also leads to a very slow convergence speed.

The literature "Wanzhi Ma, Shihai Shao, and Youxi Tang, "Adaptive Step Size for OFDM CFR with Cognitive Clipping," IEEE Conferences, pp.1-4, 2010" proposed an adaptive constraint clipping algorithm, but in-band When processing, the best EVM value cannot be directly selected at one time, and it is necessary to continuously change the EVM according to each effect in the subsequent iterative clipping to make it tend to the best value

Therefore, more iterations are required to achieve the desired effect, which increases the amount of calculation of the system.

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