Efficient Channel Estimation in Millimeter Wave Hybrid MIMO Systems with Low Resolution ADCs
Efficient Channel Estimation in Millimeter Wave Hybrid MIMO Systems with Low Resolution ADCs
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DOI：
10.23919/eusipco.2018.8553303
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发表时间：
2018-09
期刊：
影响因子：
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通讯作者：
Aryan Kaushik;Evangelos Vlachos;J. Thompson;A. Perelli
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作者：
Aryan Kaushik;Evangelos Vlachos;J. Thompson;A. Perelli
提出了一种用于毫米波系统的高效信道估计算法，该算法采用模拟-数字混合多输入多输出(MIMO)结构，在接收端采用少比特量化。利用毫米波MIMO信道的稀疏性来描述问题，同时在接收机结构中使用有限分辨率的模数转换器(ADC)。估计问题可以通过基于Stein的无偏风险估计(SURE)的参数去噪器和广义近似消息传递(GAMP)框架使用压缩感知来解决。期望最大化(EM)密度估计用于避免由于EM-SURE-GAMP算法估计信道而需要指定信道统计的需要。当然，根据噪声观测，最小化以在每次迭代时自适应地优化参数类内的去噪器。与期望最大化广义AMP(EM-GAMP)解相比，均方误差(MSE)在低信噪比(SNR)和高信噪比(SNR)、ADC比特数和训练长度方面都表现出更好的性能。低分辨率模数转换器的使用降低了功耗，并导致了高效的毫米波MIMO系统。
This paper proposes an efficient channel estimation algorithm for millimeter wave (mmWave) systems with a hybrid analog-digital multiple-input multiple-output (MIMO) architecture and few-bits quantization at the receiver. The sparsity of the mmWave MIMO channel is exploited for the problem formulation while limited resolution analog-to-digital converters (ADCs) are used in the receiver architecture. The estimation problem can be tackled using compressed sensing through the Stein's unbiased risk estimate (SURE) based parametric denoiser with the generalized approximate message passing (GAMP) framework. Expectation-maximization (EM) density estimation is used to avoid the need of specifying channel statistics resulting the EM-SURE-GAMP algorithm to estimate the channel. SURE, depending on the noisy observation, is minimized to adaptively optimize the denoiser within the parametric class at each iteration. The proposed solution is compared with the expectation-maximization generalized AMP (EM-GAMP) solution and the mean square error (MSE) performs better with respect to low and high signal-to-noise ratio (SNR) regimes, the number of ADC bits, and the training length. The use of the low resolution ADCs reduces power consumption and leads to an efficient mmWave MIMO system.