Joint low-complexity equalization and CFO estimation and compensation for UWA-OFDM communication systems

2020 ◽  
Vol 33 (3) ◽  
pp. e3972 ◽  
Author(s):  
Khaled Ramadan ◽  
Khalil F. Ramadan ◽  
Ahmed S. Fiky ◽  
Hasna Alam ◽  
Moawad I. Dessouky ◽  
...  
Keyword(s):  
Communication Systems ◽  
Low Complexity

scholarly journals Low-complexity beam-domain channel estimation and power allocation in hybrid architecture massive MIMO systems

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Xiao Chen ◽  
Zaichen Zhang ◽  
Liang Wu ◽  
Jian Dang
Keyword(s):  
Channel Estimation ◽  
Power Allocation ◽  
Spectral Efficiency ◽  
Communication Systems ◽  
Mimo Systems ◽  
Beam Steering ◽  
Estimation Method ◽  
Low Complexity ◽  
Estimation Accuracy ◽  
Hybrid Architecture

Abstract In this journal, we investigate the beam-domain channel estimation and power allocation in hybrid architecture massive multiple-input and multiple-output (MIMO) communication systems. First, we propose a low-complexity channel estimation method, which utilizes the beam steering vectors achieved from the direction-of-arrival (DOA) estimation and beam gains estimated by low-overhead pilots. Based on the estimated beam information, a purely analog precoding strategy is also designed. Then, the optimal power allocation among multiple beams is derived to maximize spectral efficiency. Finally, simulation results show that the proposed schemes can achieve high channel estimation accuracy and spectral efficiency.

scholarly journals Joint low-complexity equalisation and CFO compensation for DCT–OFDM communication systems based on SIC

2020 ◽  
Vol 14 (20) ◽  
pp. 3549-3559
Author(s):  
Khaled Ramadan ◽  
Moawad I. Dessouky ◽  
Fathi E. Abd El-Samie
Keyword(s):  
Communication Systems ◽  
Low Complexity ◽  
Cfo Compensation

scholarly journals A low complexity digital frequency calibration with high jitter immunity for ultra-low-power oscillators

2019 ◽  
Vol 17 ◽  
pp. 145-150
Author(s):  
Markus Scholl ◽  
Ralf Wunderlich ◽  
Stefan Heinen
Keyword(s):  
Low Power ◽  
Communication Systems ◽  
Calibration Method ◽  
Low Complexity ◽  
Frequency Stability ◽  
Wireless Communication Systems ◽  
Ultra Low Power ◽  
Wireless Transceiver ◽  
Digital Frequency ◽  
Frequency Calibration

Abstract. This paper presents a highly efficient digital frequency calibration method for ultra-low-power oscillators in wireless communication systems. This calibration method locks the ultra-low-power oscillator's output frequency to the reference clock of the wireless transceiver during its send- and receive-state to achieve frequency stability over process variation and temperature drifts. The introduced calibration scheme offers high jitter immunity and short locking periods overcoming frequency calibration errors for typical ultra-low-power oscillator's by utilizing non-linear segmented feedback levels. In measurements the proposed calibration method improves the frequency stability of an ultra-low-power 32 kHz oscillator from 53 to 10 ppm ∘C−1 over a wide temperature range for temperature drifts of less than 1 ∘C s−1 with an estimated power consumption of 185 nW while coping with relocking periods of 7 ms.

scholarly journals Channel Covariance Matrix Estimation via Dimension Reduction for Hybrid MIMO MmWave Communication Systems

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3368
Author(s):  
Rui Hu ◽  
Jun Tong ◽  
Jiangtao Xi ◽  
Qinghua Guo ◽  
Yanguang Yu
Keyword(s):  
Covariance Matrix ◽  
Communication Systems ◽  
Low Complexity ◽  
Low Rank ◽  
Covariance Matrix Estimation ◽  
Hardware Complexity ◽  
Matrix Estimation ◽  
Planar Arrays ◽  
Rank Matrix ◽  
Low Rank Matrix

Hybrid massive MIMO structures with lower hardware complexity and power consumption have been considered as potential candidates for millimeter wave (mmWave) communications. Channel covariance information can be used for designing transmitter precoders, receiver combiners, channel estimators, etc. However, hybrid structures allow only a lower-dimensional signal to be observed, which adds difficulties for channel covariance matrix estimation. In this paper, we formulate the channel covariance estimation as a structured low-rank matrix sensing problem via Kronecker product expansion and use a low-complexity algorithm to solve this problem. Numerical results with uniform linear arrays (ULA) and uniform squared planar arrays (USPA) are provided to demonstrate the effectiveness of our proposed method.

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