A Cyclic Correlation-Based Blind SINR Estimation for OFDM Systems

Hong, Shunli; Li, Youming; He, Yu-Cheng; Wang, Gang; Jin, Ming

doi:10.1109/lcomm.2012.100812.122009

Cited by 9 publications

(4 citation statements)

References 15 publications

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“…Cyclostationary detection [17] utilizes the cyclostationary characteristics of a signal, such as symbol rate, carrier frequency, and sampling frequency, to detect the presence of a signal. It can also estimate certain modulation parameters [18]. However, this method has high computational complexity and poor real-time capabilities.…”

Section: Traditional Signal Detectionmentioning

confidence: 99%

Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR

Li,

Shi,

Wang

et al. 2024

Remote Sensing

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In complex electromagnetic environments, satellite telemetry, tracking, and command (TT&C) signals often become submerged in background noise. Traditional TT&C signal detection algorithms suffer a significant performance degradation or can even be difficult to execute when phase information is absent. Currently, deep-learning-based detection algorithms often rely on expert-experience-driven post-processing steps, failing to achieve end-to-end signal detection. To address the aforementioned limitations of existing algorithms, we propose an intelligent satellite TT&C signal detection method based on triplet attention and Transformer (TATR). TATR introduces the residual triplet attention (ResTA) backbone network, which effectively combines spectral feature channels, frequency, and amplitude dimensions almost without introducing additional parameters. In signal detection, TATR employs a multi-head self-attention mechanism to effectively address the long-range dependency issue in spectral information. Moreover, the prediction-box-matching module based on the Hungarian algorithm eliminates the need for non-maximum suppression (NMS) post-processing steps, transforming the signal detection problem into a set prediction problem and enabling parallel output of the detection results. TATR combines the global attention capability of ResTA with the local self-attention capability of Transformer. Experimental results demonstrate that utilizing only the signal spectrum amplitude information, TATR achieves accurate detection of weak TT&C signals with signal-to-noise ratios (SNRs) of −15 dB and above (mAP@0.5 > 90%), with parameter estimation errors below 3%, which outperforms typical target detection methods.

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Section: Traditional Signal Detectionmentioning

confidence: 99%

Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR

Li,

Shi,

Wang

et al. 2024

Remote Sensing

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show abstract

“…In such scenarios, blind SINR estimation methods need to be used since they do not rely on pilot signals. Prior works have investigated maximum likelihood (ML) [26], [27], moment-based [28], and cyclostationary-based [29] SINR estimation methods. However, the accuracy of ML and moment-based methods depend on the availability of accurate fading and interference statistics of the channel, which is often infeasible to acquire in real-time.…”

Section: B Motivationmentioning

confidence: 99%

Semi-Blind Post-Equalizer SINR Estimation and Dual CSI Feedback for Radar-Cellular Coexistence

Rao,

Marojevic,

Reed

2020

Preprint

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Current cellular systems use pilot-aided statistical-channel state information (S-CSI) estimation and limited feedback schemes to aid in link adaptation and scheduling decisions. However, in the presence of pulsed radar signals, pilot-aided S-CSI is inaccurate since interference statistics on pilot and non-pilot resources can be different. Moreover, the channel will be bimodal as a result of the periodic interference.In this paper, we propose a max-min heuristic to estimate the post-equalizer SINR in the case of nonpilot pulsed radar interference, and characterize its distribution as a function of noise variance and interference power. We observe that the proposed heuristic incurs low computational complexity, and is robust beyond a certain SINR threshold for different modulation schemes, especially for QPSK. This enables us to develop a comprehensive semi-blind framework to estimate the wideband SINR metric that is commonly used for S-CSI quantization in 3GPP Long-Term Evolution (LTE) and New Radio (NR) networks. Finally, we propose dual CSI feedback for practical radar-cellular spectrum sharing, to enable accurate CSI acquisition in the bimodal channel. We demonstrate significant improvements in throughput, block error rate and retransmission-induced latency for LTE-Advanced Pro when compared to conventional pilot-aided S-CSI estimation and limited feedback schemes.

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“…Further applications are in the following subjects: capacity evaluation of second-order cyclostationary complex Gaussian noise channels [140], orthogonal overlay channels [371], and power line communication channels [319], timing and other signal parameter estimation [101,134,137,168,224,229,236,246,278,317,334], source separation [16,44,106,169], frequency-domain equalizer (FDE) design [374], blind multiple-input multiple-output (MIMO) system identification [299], signal power (SNR), and signal-to-interference-and-noise ratio (SINR) estimation [8,147,294], Doppler spread estimation [376], microDoppler estimation [214], noise modeling in power lines [130,182,318], and radio frequency interference (RFI) mitigation in radio astronomy [145].…”

Section: Miscellaneousmentioning

confidence: 99%