Block compressive sensing non-orthogonal multiple-address system multiuser detection method

Abstract

The invention belongs to the wireless communication signal detection technical field and relates to a block compressive sensing non-orthogonal multiple-address system multiuser detection method. The invention provides a sparseness adaptive detection method which can recombine structured sparseness into block sparseness. According to the block compressive sensing non-orthogonal multiple-address system multiuser detection method of the invention, a block sparseness-adaptive sub-space tracking algorithm is adopted for recombined block sparseness signals; according to the algorithm, a noise power setting threshold is utilized to realize the adaptive evaluation of the sparseness of users, and SER (Symbol Error Rate) performance is improved greatly compared with that of the prior art under a condition that the a priori information of sparseness degree is not required; and a simulation result indicates that the performance of the algorithm can reach an optimal theoretical value after a signal-to-noise ratio is increased to a certain extent.

Description

Technical field [0001] The invention belongs to the technical field of wireless communication signal detection, and relates to a block compressed sensing non-orthogonal multiple access system multi-user detection method. Background technique [0002] Large-scale machine communication is one of the core application scenarios of 5G in the future. It has the characteristics of small packet transmission, massive connections, low latency access, and low power consumption. The existing multiple access technology has been difficult to meet the requirements of large-scale machine communication, mass connection and high scalability, non-orthogonal multiple access technology is considered to be a more suitable solution; at the same time, the uplink of large-scale machine communication is common There is a characteristic of sporadic communication, that is, the number of users accessing the base station at a certain moment is much smaller than the total number of users. Due to the character...

AI Technical Summary

Problems solved by technology

The present invention adopts a block sparsity self-adaptive subspace tracking algorithm for the recombined block sparse signal. The algorithm realizes the self-adaptive estimation of the user sparsity by using the noise power to set the threshold. In the case of prior information, the performance of SER has been greatly improved compared with the background technology; at the same time, the simulation results show that the performance of the algorithm can fully reach the best theoretical value after the signal-to-noise ratio increases to a certain extent

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