Distributed Structured Compressive Sensing-Based Time-Frequency Joint Channel Estimation for Massive MIMO-OFDM Systems
| Autor: | Wenjie Zhang, Shanlin Wei, Rong Jin, Hui Li, Penglu Liu, Wei Cheng, Weisi Kong |
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| Rok vydání: | 2019 |
| Předmět: |
Article Subject
Computer Networks and Communications Orthogonal frequency-division multiplexing Computer science 020206 networking & telecommunications 020302 automobile design & engineering TK5101-6720 02 engineering and technology Spectral efficiency MIMO-OFDM Computer Science Applications symbols.namesake 0203 mechanical engineering Channel state information Gaussian noise Frequency domain Telecommunication 0202 electrical engineering electronic engineering information engineering symbols Overhead (computing) Algorithm Computer Science::Information Theory Communication channel |
| Zdroj: | Mobile Information Systems, Vol 2019 (2019) |
| ISSN: | 1875-905X 1574-017X |
| DOI: | 10.1155/2019/2634361 |
| Popis: | In massive multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) systems, accurate channel state information (CSI) is essential to realize system performance gains such as high spectrum and energy efficiency. However, high-dimensional CSI acquisition requires prohibitively high pilot overhead, which leads to a significant reduction in spectrum efficiency and energy efficiency. In this paper, we propose a more efficient time-frequency joint channel estimation scheme for massive MIMO-OFDM systems to resolve those problems. First, partial channel common support (PCCS) is obtained by using time-domain training. Second, utilizing the spatiotemporal common sparse property of the MIMO channels and the obtained PCCS information, we propose the priori-information aided distributed structured sparsity adaptive matching pursuit (PA-DS-SAMP) algorithm to achieve accurate channel estimation in frequency domain. Third, through performance analysis of the proposed algorithm, two signal power reference thresholds are given, which can ensure that the signal can be recovered accurately under power-limited noise and accurately recovered according to probability under Gaussian noise. Finally, pilot design, computational complexity, spectrum efficiency, and energy efficiency are discussed as well. Simulation results show that the proposed method achieves higher channel estimation accuracy while requiring lower pilot sequence overhead compared with other methods. |
| Databáze: | OpenAIRE |
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