Novel Wavelet Convolutional Neural Networks for Signal Detection in OFDM-IM Systems
Why this work is in the frame
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Bibliographic record
Abstract
Orthogonal Frequency Division Multiplexing with Index Modulation (OFDM-IM) is regarded as a promising candidate for next generation communications due to its remarkable efficiency and flexibility. In the field of wireless communications, deep learning, particularly Convolutional Neural Networks (CNNs), has been extensively utilized for tasks such as channel estimation and signal detection. However, CNNs' limited receptive field growth poses a challenge in capturing long range dependencies. To achieve efficient deep learning based OFDM-IM detection, this paper proposes two novel OFDM-IM signal detection networks that integrate wavelet transforms with CNNs (WTConv). The first proposed network, referred to as Dual Stage Wavelet Convolutions (DS-WTConv), adopts a dual stage architecture. It comprises an Index Feature Extraction Sub-Network (IdxNet) and a Signal Feature Reconstruction Sub-Network (DetNet). The second network, named Single Network Wavelet Convolutions (SN-WTConv), features a more compact single stage design that combines wavelet convolution and CNN layers. Extensive simulation results demonstrate that both the DS-WTConv and SN-WTConv networks exhibit superior bit error rate (BER) performance and lower computational complexity compared to existing conventional and deep learning-based approaches. Compared to the existing deep learning based detection schemes, the proposed WTConv-based networks reduce the BER by up to 35.3%, and the running time by up to 30.1%. Compared to the optimal Maximum likelihood (ML) method, the proposed DS-WTConv and SN-WTConv achieve approximately 19.2 times and 11.3 times faster runtime, respectively.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it