Synchronous compression and recovery algorithm and device for IQ (Intelligence Quotient) data

Abstract

The invention provides a synchronous compression and recovery algorithm for IQ (Intelligence Quotient) data. The synchronous compression and recovery algorithm comprises the following steps that: data to be compressed are grouped, each group comprises n data, and n is a positive integer; a value with a maximum absolute value in the group of data is found out, an absolute value Mt of the value is taken, the data are compressed to j bits, a scale span value Mf of the j bits is divided by the Mt, and a quotient obtained by division is taken as a compression factor k; the data in the group are respectively multiplied by the k, a result obtained by multiplication intercepts data of first j bits, and the obtained data are compressed data; the datum Mt and n compressed data form a data block to be sent to a transmission channel, and the data block is transmitted to a receiving party through the transmission channel; and the receiving party receives the data block, the datum Mt is taken out, the compressed data are respectively multiplied by the Mt, and the recovered data are obtained after multiplication. The invention further provides a synchronous compression and recovery device for the IQ data, so that the data are compressed to be smaller, and the error vector magnitude of a signal in a recovery process becomes smaller.

Description

technical field [0001] The invention relates to an algorithm and device for synchronously compressing and restoring IQ data. Background technique [0002] In a distributed wireless communication system, the near-end device and the remote device perform a large amount of two-way I, Q (real part, imaginary part) data transmission through wired or wireless means, and the data is expressed in binary. [0003] As the standards supported by a single device become larger, the supported frequency bands become wider, and the number of carriers increases, the requirements for transmitting large amounts of data continue to increase. The near-end device and the remote device usually use optical fiber, cable, microwave, etc. for data transmission. For example, two 20M bandwidth LTE signals are transmitted between the far and near-end. If the transmitted I, Q (real part, imaginary part) data The bit width is 14bit, and a 2.5G optical port is required to carry; in the case of meeting the ...

AI Technical Summary

Problems solved by technology

[0004] Currently known IQ data compression schemes include linear compression and nonlinear compression; among them, nonlinear compression, such as JZW compression algorithm, A-law compression algorithm, etc., are more complicated to implement than linear compression, and some of the existing ones are mostly for computing data. The absolute value or the maximum value of the power value, compare the maximum value with the full-scale maximum value of the compression target bit width to obtain the data compression factor, and then truncate the data one by one to obtain the compressed data, and compare the compression factor and the compressed data The data is transmitted to the receiving end together, and the receiving end obtains the shift factor in the data according to the compression factor, and shifts the compressed data according to the shift factor to recover the data. This kind of similar compression scheme directly truncates the data. The signal causes a large distortion, and the implementation shows that the EVM (error vector magnitude) of the signal is seriously deteriorated

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