## Jianpo Li and Qiwei Wang## |

Parameter type | Parameter size |
---|---|

Wireless access solution | OFDM |

Base station antenna | 4 |

User number | 2 |

Near end user modulation | QPSK |

Remote user modulation mode | QPSK |

Channel decoding method | Turbo decoding |

Channel model | EVA |

Channel estimation | Ideal |

The SIC uses a joint precoding algorithm and an improved MMSE detection algorithm to detect the signal. It is assumed that a single carrier transmits two user signals, one remote user 1 and one near end user 2 are located in the cell. A power allocation algorithm is used to distribute power for two-user signals at a signal-to-noise ratio (SNR) of 20 dB. The algorithm separately modulates the two-symbol signal by quadrature phase shift keying (QPSK), and multiplies the allocated power by the modulated signal to obtain the modulated signal after carrying the power. After the signal is allocated power, it is needed to superimpose the signal and then transmit it. As shown in Fig. 4, Fig. 4(a) is a constellation diagram of the two-user superimposed signal. The signal is transmitted in the analog channel, and the noise is introduced to interfere with the signal. Fig. 4(b) is a signal constellation diagram transmitted in the channel after adding noise, which contains two useful signals and noise.

The algorithm uses a precoding algorithm to process the superimposed mixed signal, reduce inter¬ference and noise between users, and then transmit the signal to the receiver. According to the SIC interference cancellation principle, at the user 1 receiver, the algorithm directly detects the decoded user 1 signal. At the user 2 receiver, the algorithm needs to first detect the decoding user 1 signal, and then cancel the user 1 signal and then decode the user 2 signal. Here takes user 2 receiving signal as an example to analyze the algorithm performance, as shown in Figs. 5–7.

At the user 2 receiver, the algorithm sorts the total received signals according to the power from large to small. The algorithm first performs the first-level detection and decodes on the signal with the highest power. The detection method uses the MMSE-QR algorithm to decode the detected signal to obtain the signal constellation diagram as shown in Fig. 5. The method subtracts the signal of the first stage detection and decodes from the total signal to obtain the total input signal of the second stage, as shown in Fig. 6, where the signal contains the user 2 signal and the noise. The method uses the same MMSE-QR detection algorithm to detect and decode the signal to obtain a constellation diagram of the user 2 detection signal and the modulated signal, as shown in Fig. 7.

Fig. 8 is a comparison of the performance of ZF, MMSE, ZF-SIC, MMSE-SIC and the proposed algo¬rithm. The simulation results show that when the two signals power distribution ratio (P1, P2) is (0.2, 0.8), the performance of the ZF-SIC algorithm and the MMSE-SIC algorithm combined with the detec¬tion algorithm and the SIC algorithm are better than the traditional ZF algorithm. This result reflects the superiority of joint detection method. The MMSE-SIC detection effect is better than the ZF-SIC detection effect. The proposed algorithm uses the combination of precoding and MMSE-SIC detection based on MMSE-SIC detection. The simulation results show that this algorithm performance is significantly better than MMSE-SIC algorithm and ZF-SIC algorithm. When the BER is 1%, its SINR is lower than the MMSE-SIC and ZF-SIC algorithms by 6 dB and 8 dB, respectively, the BER decreases with the increase of the SNR. When the SNR = 30 dB, the BER of the joint detection algorithm is BER = 0.01%.

Fig. 9 is a performance comparison diagram of the joint detection algorithm and the conventional SIC detection algorithm under different power allocations. The simulation results show that when the power distribution ratio (P1, P2) is (0.2, 0.8) and the SINR is 18 dB, the BER of the traditional SIC receiver is 7.34%, and the error rate of the joint detection algorithm is 0.75%. When the power distribution ratio (P1, P2) is (0.3, 0.7) and the SINR is 18 dB, the BER of the traditional SIC receiver is 9.17%, and the error rate of the joint detection algorithm is 1.32%. Moreover, with the increase of the SNR, the joint detection algorithm performance is better.

This paper studies the signal detection method in the NOMA systems. The algorithm processes the signal at the transmitting end and the receiver to achieve the ability to eliminate interference and correctly detect the signal. At the base station, the algorithm uses RZF precoding technology to process the signals so that the signal has better anti-interference ability before transmitting in the channel. At the receiver, the algorithm uses SIC technology to detect the signals sequentially and the detection technology is realized by MMSE combined with QR decomposition, which has stronger signal detection capability. Simulation results show that the improved algorithm has better performance and lower BER than traditional algorithms. However, this algorithm uses a SIC algorithm, so the signal needs to be detected one by one, which adds the algorithm complexity.

He received his B.S., M.S., and Ph.D. from the Department of Communication Engineering, Jilin University, China, in 2002, 2005, and 2008, respectively. In 2008, he joined the School of Computer Science, Northeast Electric Power University, where he is currently a professor. His research interests are wireless sensor networks, 5G communication, and intelligent signal processing.

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