Design And Implementation Of Wireless Transceiver Under Low-Resolution Quantization

With the commercial deployment of 5G and the development of next-generation mobile communication research,the importance of network energy efficiency in massive MIMO systems is gradually highlighted.As an important means to reduce system hardware overhead and power consumption,low-resolution quantized signal recovery technology,In actual deployment,it still faces challenges such as high algorithm complexity,poor robustness,and difficulty in digital synchronization.This paper focuses on the deep integration of low-resolution quantization technology with massive MIMO and OFDM technology,and studies key issues such as massive machine type communication network access,channel estimation,and OFDM link adaptive transmission scheme.First,the basic theory of low-resolution quantized signal recovery algorithm and network access method is summarized.Combined with the low-resolution quantized transmission model,the expectation consistant approximation method,generalized Turbo algorithm and generalized expectation consistant algorithm are briefly described.Based on the problem of large-scale network access,this paper introduces the traditional random access method and analyzes its limitations,and then introduces the grant-free random access method.According to its research focus,it summarizes the existing grant-free random access active user detection algorithms for follow-up.This chapter lays the foundation for the design of low-resolution quantization receivers and the research on grant-free random access methods.Then,a low-cost joint channel estimation and activity detection algorithm for massive connectivity network is designed.Facing the problems of high hardware cost and power consumption,and excessive network access load caused by the huge base station antenna scale and massive user terminals in the mMTC scenario,it is proposed to use a low-resolution DAC on the user side and a mixed-ADC architecture on the base station side.The Bayesian optimal MMSE channel estimation algorithm and user activity detection algorithm are derived,and their state evolution equations are derived for performance analysis.In addition,a complexity optimization scheme of the algorithm is proposed to further reduce the computational complexity.Simulation results show that the proposed mixed-ADC channel estimator and active user detector can achieve performance close to high-resolution systems with low overhead,and the proposed complexity optimization scheme can effectively accelerate the algorithm convergence.Finally,a low-resolution quantized OFDM prototype verification system is developed,and a low-resolution quantized OFDM link adaptive transmission scheme is designed.In order to verify the feasibility of the low-resolution quantization receiver,a low-resolution quantization OFDM prototype verification system is developed for online air interface testing.According to the low-resolution quantization receiver’s poor robustness when the channel conditions change,a low-resolution quantization OFDM link adaptive transmission scheme is proposed.The communication quality is evaluated at the receiving end and the system parameter adjustment strategy is designed.The simulation results show that the signal reconstruction performance of the low-resolution quantized OFDM system varies greatly under different parameter configuration modes.When the channel conditions change,the proposed link adaptive transmission scheme can maintain the reliable transmission of the system within a certain range.The air interface test results verify the feasibility of the proposed link adaptive transmission scheme.