Abstract
Compressed sensing (CS) has great potential in channel estimation for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems. To solve such a CS-based channel estimation problem, three categories of algorithms, namely convex relaxation algorithms, greedy iteration algorithms and Bayesian inference algorithms, are widely used. In this paper, with a unified massive MIMO framework, comprehensive comparisons among three categories of algorithms are presented in the perspective of the estimated accuracy, which is affected by the received signal-to-noise ratio (SNR), the number of resolvable paths, angular quantization error, the number of pilot symbols and hardware impairments. Specifically, it shows that convex relation algorithms achieve the best estimation accuracy at the high SNR range and it is mainly affected by the received SNR and transmitter’s hardware impairments. At the low SNR range, greedy iteration algorithms outperform others and the estimated accuracy is then limited by the angle quantization error. Furthermore, a tradeoff between the estimated error and complexity is achieved in Bayesian inference algorithms, a;though its estimated error is sensitive to the number of available pilot symbols.
Highlights
Millimeter wave communication has been widely considered as a key technology for B5G due to its potential to provide gigabits-per-second data rates by exploiting the large unused bandwidth [1]
The hardware impairment at transmitter is defined as SNRe =10log10 (1/k e ) dB, where the ratio coefficient k e reflects the degree of hardware impairment
We take the normalized mean squared error (NMSE) as the performance metric to evaluate the estimated quality, which is defined as n o
Summary
Millimeter wave (mmWave) communication has been widely considered as a key technology for B5G due to its potential to provide gigabits-per-second data rates by exploiting the large unused bandwidth [1]. It is challenging for long range wireless communication because of the huge paths loss in mmWave band. MIMO with phase shifter network based hybrid precoding and electromagnetic lens based beamspace has been considered [1,2,3] It is difficult for such an efficient structure to obtain the full channel state information (CSI), since the limited number of RF chains only combines the measurement signal with smaller dimension compared to the entire channel matrix. Some inherent properties including low rank and structural sparsity of mmWave massive MIMO channels have been demonstrated in practical measurement environments [4,5]
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