Large-scale multi-user MIMO iterative detection method for low-precision quantification
An iterative detection, large-scale technology, applied in diversity/multi-antenna systems, digital transmission systems, space transmit diversity and other directions, can solve the problems of unsatisfactory detector performance, low complexity, poor performance, etc., to reduce computational complexity , the effect of improving system performance and improving the degree of information interaction
Active Publication Date: 2021-05-25
SOUTHEAST UNIV
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[0004] For the detection of massive MIMO systems with low-precision ADCs, reference [1] proposed three detection algorithms to avoid matrix inversion by using generalized approximate message passing technology. PDQ) detection, linear (linear) detection, in which DQ detection algorithm involves integral operation and Gaussian distribution table lookup, the complexity is the highest, the performance is optimal, PDQ detection algorithm avoids integral operation, the complexity and performance are in the middle, and the linear detection algorithm is complex Lowest degree but poor performance
[0005] The PDQ detector achieves a good compromise between detection complexity and performance. Its performance is better than that of the classic MMSE detection algorithm and avoids matrix inversion. However, it does not provide a soft quantity extraction method for the detector, and it is equipped with a low-precision ADC. The performance of the detector is not ideal, therefore, in order to further improve the performance, it is necessary to consider the iterative detection method of the low-precision quantization massive MIMO system based on the PDQ detector
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Embodiment 1
[0062] Embodiment 1: A massive multi-user MIMO iterative detection method suitable for low-precision quantization, the method includes the following steps:
[0063] S1, receiving signal quantization processing;
[0064] This step specifically includes:
[0065] S11. For a massive multi-user MIMO system (N≥K) with K single-antenna users and N antennas installed on the base station side, set its uplink channel model to y=Hx+n, where y=[ the y i ]∈C N×1 Indicates the received signal, x=[x j ]∈C K×1 Indicates the transmitted signal with the average transmitted power of the components being 1, x j Modulation constellation point set Ω used for user j j Points on, n∈[n i ]∈C N×1 Represents additive white Gaussian noise (AWGN), and the noise variances corresponding to its components are H=[h ij ]∈C N×K Indicates the channel matrix between the base station and K users, specifically, h ij Indicates the channel gain from the j-th user at the transmitting end to the i-th rece...
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The invention discloses an iterative detection method for a large-scale multi-user multiple-input multiple-output (MIMO) system suitable for low-precision quantization. Aiming at the large-scale multi-user MIMO system in which the receiving antenna is equipped with a low-precision analog-to-digital converter, the present invention uses a joint detector and a decoder to work together to repeatedly perform iterative detection to improve performance, and proposes a low-complexity and excellent-performance iterative detection algorithm. In the inventive method, the detector cooperates with the decoder of the low-density parity-check (LDPC) code to form an iterative detection method that takes both performance and complexity of the receiver into consideration.
Description
technical field [0001] The invention relates to a large-scale multi-user MIMO iterative detection method suitable for low-precision quantization, in particular to a large-scale MIMO iterative detection technology, and belongs to the technical field of wireless communication. Background technique [0002] Massive multiple-input multiple-output (MIMO) technology has become a key technology for the next generation of mobile communications (5G) due to its high spectral efficiency and energy efficiency. For systems using massive MIMO technology, only It is necessary to equip a large number of antennas in the base station to reduce the interference between campuses by using simple signal processing methods. However, the introduction of a large number of antennas makes traditional detection algorithms no longer applicable. For example, for the traditional optimal maximum a posteriori probability (MAP) detection algorithm, its complexity increases exponentially with the increase of ...
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IPC IPC(8): H04B7/0452H04B7/08H04L1/00
CPCH04B7/0452H04B7/08H04L1/0052
Inventor姜明林俊浩
OwnerSOUTHEAST UNIV