Uplink non-orthogonal multiple access method based on power and code word joint domain

Abstract

The invention discloses an uplink non-orthogonal multiple access method based on a power and code word joint domain. The uplink non-orthogonal multiple access method is characterized by comprising the following steps: 1, allocating different pre-coding matrixes to users; 2, making the users determine transmission power according to amplitude information of own channel responses; and 3, performing user detection and signal recovery by using an iteration algorithm, and performing successive interference cancellation in an iteration process. Specific to the problem of sparse signal reconstruction of large-scale user access, the user overload rate can be increased extremely by the uplink non-orthogonal multiple access method without central scheduling of a base station.

Description

technical field [0001] The invention belongs to the field of communication, and specifically relates to an uplink non-orthogonal multiple access method based on the combined domain of power and code words. Background technique [0002] Compressed sensing technology has been widely used in image and video signal processing, communication signal processing and other fields. Compressed sensing theory shows that when the signal vector is sparse, that is, many elements are zero, and the sampling frequency is lower than the Nyquist sampling frequency, the signal acquisition end or receiver can also reconstruct the original signal. Block sparse signals widely exist in practical applications, such as multi-band signals, sparse channel gain vectors, radar pulse signals, small data packet access, etc. A block-sparse signal means that when a signal sequence is divided into blocks, only some of the signal blocks are non-zero. Existing studies have shown that the introduction of compre...

AI Technical Summary

Problems solved by technology

Taking the block orthogonal matching pursuit (BOMP) iterative recovery algorithm as an example, BOMP needs to select the most likely non-zero signal block position through correlation calculation and comparison in each iteration step, and then perform signal update and The residual signal is updated. When the number of sparse blocks is large, the interference between blocks will make the measurement end unable to correctly identify the sparse blocks, and the corresponding signal recovery cannot be completed.

[0004] Secondly, the power allocation strategy mostly adopts central control scheduling, and the measurement end specifies different power factors for different blocks, which inevitably brings resource consumption and time delay, and is difficult to adapt to communication scenarios with high delay requirements.

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