Efficient Channel Estimation Techniques for MIMO Systems with 1-Bit ADC

With a low resolution 1-bit ADC on its receiver(RX) side, MIMO with 1-bit ADC took a considerable step in the fulfillment of the hardware complexity constrains of the internet of things(IoT) PHY layer design. However, applying 1-bit ADC at MIMO RX results in severe nonlinear quantization error. By which, almost all received signal amplitude information is completely distorted. Thus, MIMO channel estimation is considered as a major barrier towards practical realization of 1-bit ADC MIMO system. In this paper, two efficient sparsity-based channel estimation techniques are proposed for 1-bit ADC MIMO systems, namely the low complexity sparsity-based channel estimation(LCSCE), and the iterative adaptive sparsity channel estimation(IASCE). In these techniques, the sparsity of the 1-bit ADC MIMO channel is exploited to propose a new adaptive and iterative compressive sensing(CS) recovery algorithm to handle the 1-bit ADC quantization effect. The proposed algorithms are tested with the state-of-the-art 1-bit ADC MIMO constant envelope modulation(MIMO-CEM). The 1-bit ADC MIMO-CEM system is chosen as it fulfills both energy and hardware complexity constraints of the IoT PHY layer. Simulation results reveal the high effectiveness of the proposed algorithms in terms of spectral efficiency(SE) and computational complexity. The proposed LCSCE reduces the computational complexity of the 1-bit ADC MIMO-CEM channel estimation by 86%, while the IASCE reduces it by 96% compared to the recent techniques of MIMO-CEM channel estimation. Moreover, the proposed LCSCE and IASCE improve the spectrum efficiency by 76 % and 73 %, respectively, compared to the recent techniques.

Markovian Cascaded Channel Estimation for RIS Aided Massive MIMO Using 1⁃Bit ADCs and Oversampling

A reconfigurable intelligent surface(RIS)aided massive multiple-input multiple-output(MIMO)system is considered,where the base station employs a large antenna array with low-cost and low-power 1-bit analog-to-digital converters(ADCs).To compensate for the per-formance loss caused by the coarse quantization,oversampling is applied at the receiver.The main challenge for the acquisition of cascaded channel state information in such a system is to handle the distortion caused by the 1-bit quantization and the sample correlation caused by oversampling.In this work,Bussgang decomposition is applied to deal with the coarse quantization,and a Markov chain is developed to char-acterize the banded structure of the oversampling filter.An approximate message-passing based algorithm is proposed for the estimation of the cascaded channels.Simulation results demonstrate that our proposed 1-bit systems with oversampling can approach the 2-bit systems in terms of the mean square error performance while the former consumes much less power at the receiver.

基于深度学习的1-比特超大规模MIMO信道估计

超大规模多输入多输出(Multiple Input Multiple Output,MIMO)技术在未来的移动通信中具有巨大的潜力。然而,采用超大型的天线阵列会出现空间非平稳性信道特征,如果为基站的每根天线都配备一个高精度量化器,系统功耗将大幅度增加,从而阻碍超大规模MIMO系统的广泛应用。因此,假设基站的每根天线都配备有一对1-比特模数转换器(Analog-to-Digital Converters,ADC),利用子阵列与用户之间的映射关系来描述非平稳信道特征,借助深度神经网络(Deep Neural Network,DNN)强大的泛化能力,设计一种新的生成式监督DNN模型,该模型可以使用合理数量的导频进行训练。仿真结果表明,所提出的网络可以使用较少的导频得到较好的估计性能,在性能与计算复杂度之间取得良好的平衡。

基于Sigma-Delta调制技术的高精度数字磁通门磁强计仿真

设计了一种基于1 bit Sigma-Delta环路调制技术的高精度数字磁通门磁强计,建立了数字磁强计信号处理仿真模型,并利用Matlab的Simulink仿真工具开展了数字磁通门磁强计模型的仿真分析,对数字磁强计系统的噪声、线性度、响应速度和频率响应进行了仿真计算。利用本文1 bit Sigma-Delta环路调制技术的数字磁强计在量程超过±10^(5 )nT的情况下,系统在1 Hz处的噪声仅为4.66 pT·Hz^(-1/2),最大线性偏差为0.16 nT,动态响应速度达到2×10^(6) nT·s^(–1),频率响应带宽超过10 Hz。仿真结果表明,基于1 bit Sigma-Delta环路调制技术的数字磁通门磁强计可以有效降低对A/D转换器精度的要求,在保证性能的前提下大幅度降低了电路复杂程度,提高了系统的可靠性,在深空探测、空间磁场测量等领域具有广泛的应用前景。