Large-scale multi-user MIMO iterative detection method applicable to low precision quantification
It is an iterative detection and large-scale technology, which is applied in diversity/multi-antenna systems, space transmit diversity, radio transmission systems, etc., and can solve problems such as the absence of detector soft quantity extraction methods, unsatisfactory detector performance, and low complexity. question
Active Publication Date: 2018-09-07
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 a large-scale multi-user MIMO system iterative detection method applicable to low precision quantification. The invention aims at the large-scale multi-user MIMO system of which a receiving antenna is provided with a low precision analog-digital converter, performs iterative detection repeatedly to enhance performance through cooperative work of a detector and a decoder, and provides an iterative detection algorithm with relatively low complexity and excellent performance. According to the method provided by the invention, the detector cooperatively works with the decoder with low density parity check, and thus the iterative detection method with both good receiver performance and complexity can be formed.
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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Login to View More IPC IPC(8): H04B7/0452H04B7/08H04L1/00
CPCH04B7/0452H04B7/08H04L1/0052
Inventor姜明林俊浩
OwnerSOUTHEAST UNIV