Linearized Model for MIMO-MFSK Systems With Energy Detection

Zheng, Lin; Yang, Chao; Deng, Xinyang; Ge, Wei

doi:10.1109/lcomm.2022.3161058

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…However, the matched filter offers poor BER performance. Lin et al [40] proposed to eliminate the need for CSI by using frequency shift keying and energy detection with vertical-Bell laboratories layered space-time (V-BLAST). However, the BER performance of the proposed linear detector system is significantly worse than that of the MLD as well as the coherent detectors.…”

Section: A Related Workmentioning

confidence: 99%

Near-Optimal Efficient MIMO Receiver for Miniature UAV-IoT Communications

Saci,

Al-Dweik,

Jimaa

2024

IEEE Open J. Veh. Technol.

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This article proposes a novel Direct Detection (DD) scheme for multiple-input multipleoutput (MIMO) unmanned aerial vehicle (UAV) communications where tedious, slow, and computationally demanding channel state information (CSI) estimation and detection processes are not required. The proposed detection scheme is achieved by exploiting the frequency and/or temporal correlation among the transmitted signals to directly extract the information symbols from the received signals. The results obtained show that the proposed scheme offers a significant advantage of approximately 15 dB performance gain compared to conventional linear detection schemes, even when such schemes are using perfect CSI. Furthermore, the proposed detection algorithm can be efficiently implemented using the Viterbi Algorithm, or its variants, to achieve further complexity reduction.INDEX TERMS Unmanned aerial vehicle (UAV), fading, diversity, sixth generation (6G), multiple-input multiple-output (MIMO), Internet of things (IoT), wireless sensor networks (WSNs).

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Section: A Related Workmentioning

confidence: 99%

Near-Optimal Efficient MIMO Receiver for Miniature UAV-IoT Communications

Saci,

Al-Dweik,

Jimaa

2024

IEEE Open J. Veh. Technol.

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“…However, the performance of these time-domain signal-processing-based methods sharply declines under restricted conditions. Although energy detection can alleviate this problem under certain conditions [19,20]. As the variety of interference and clutter types grows, devising appropriate threshold levels becomes intricate, especially when the signal energy is weaker than those of interference and noise.…”

Section: Traditional Signal Detectionmentioning

confidence: 99%

“…Furthermore, the quantity of labeled samples is considered a crucial factor in evaluating performance, as it is challenging to obtain under non-collaborative conditions. We investigated the performance of TATR across all SNRs with a proportion of labeled samples in the training dataset, only χ = [5,10,20,30,40,60, 80, 100(%)] samples have labels in the training set. As depicted in Figure 9b, the model's performance improves as the quantity of available labeled samples increases.…”

Section: Validity Analysis and Ablation Experimentsmentioning

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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Linear and Nonlinear Detections for Noncoherent Spatial-multiplexing Based MIMO-MASK

Yang

et al. 2022

2022 IEEE 22nd International Conference on Communication Technology (ICCT)

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Linearized Model for MIMO-MFSK Systems With Energy Detection

Cited by 4 publications

References 14 publications

Near-Optimal Efficient MIMO Receiver for Miniature UAV-IoT Communications

Near-Optimal Efficient MIMO Receiver for Miniature UAV-IoT Communications

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

Linear and Nonlinear Detections for Noncoherent Spatial-multiplexing Based MIMO-MASK

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