Frequency Index Modulation for Low Complexity Low Energy Communication Networks
Bibliographic record
Abstract
In this paper, we propose a low-complexity multi-user communication system based on frequency index modulation that suits Internet of Things applications. This system aims to reduce the transmitted energy and the peak-to-average-power ratio (PAPR) of orthogonal frequency-division multiplexing (OFDM) systems and to perform without sacrificing data rate in comparison with conventional OFDM. In this design, OFDM-like signals are used to make the implementation of the system easy by the virtue of fast Fourier transform (FFT). In the proposed scheme, an OFDM bandwidth of$N_{\text {FIM}}$total sub carriers is divided into$N_{B}$equal number of sub-bands, with$N$subcarriers in every sub-band. At the transmitter side of each sensor, the outgoing bit stream is divided into two blocks: mapped and modulated. The bits within the mapped block are used to activate the corresponding subcarrier in its predefined sub-band in order to carry the data content of the modulated block, while the other$N-1$subcarriers are nulled out. At the receiver, the FFT is performed first, and then the square-law envelope detector is applied to estimate the active frequency index to recover the mapped bits, followed by a conventional demodulation process to demodulate the transmitted bits. Once the system is presented and analyzed, energy efficiency, PAPR and complexity are studied to show the features of the proposed scheme. Moreover, we derive closed-form expressions of the bit error rate performance over Rayleigh fading channels and we validate the outcome by simulation results. With the characteristics exhibited in this paper, the proposed system would constitute an excellent candidate for wireless sensor applications where it represents a simpler substitution for frequency-hopping-based architectures, in which the hops carry extra bits.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".