Performance analysis of wireless transmission of compressed images using DCT‐OFDMA system with different compression schemes
Bibliographic record
Abstract
Abstract Transmitting images over an Orthogonal Frequency Division Multiplexing Access (OFDMA) system poses a significant challenge. The process entails sending a substantial amount of data, which consumes a significant amount of bandwidth. Consequently, compressing the transmitted image becomes essential to reduce the required bandwidth. The aim of this paper is to examine and analyse the wireless transmission of a compressed image via Discrete Cosine Transform (DCT‐OFDMA). A comparison is made with Discrete Fourier Transform (DFT‐OFDMA), across various subcarrier mapping schemes (localized and inter‐leaved), and different modulation schemes (16 Quadrature Amplitude Modulation (16QAM) and Quadrature Phase Shift Keying (QPSK)) using vehicular A, Stanford University Interim (SUI3), and uniform channel models. To evaluate the performance of the system, the minimum Signal‐to‐Noise Ratio (SNR) necessary to recover the transmitted compressed image is calculated. This work considers nine standard compression techniques. The results are carried out using the MATLAB simulator. According to the simulation results, the minimum SNR required to recover the transmitted compressed image was found to be 19 dB. This result was achieved when using Discrete Cosine Transform‐Loaclized‐Orthogonal Frequency Division Multiplexing Access (DCT‐LOFDMA) with QPSK modulation and set partitioning in hierarchical trees (SPIHT) compression method over the SUI3 channel model. Moreover, it was observed that the DCT‐LOFDMA and DFT‐IOFDMA systems attained equal SNR while utilizing the SPIHT_3D compression technique and QPSK modulation on the SUI3 channel model. Overall, the results suggest that the performance of DCT‐based localized OFDMA is somewhat superior to DFT‐based localized OFDMA, particularly when utilizing the SUI3 channel model and the QPSK modulation scheme. Therefore, it is feasible to transmit and receive a compressed image effectively over an OFDMA system with DCT.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".