Joint Code-Frequency Index Modulation for IoT and Multi-User Communications
Bibliographic record
Abstract
In this paper we propose a family of index modulation systems which can operate with low-power consumption and low operational complexity for multi-user communication. This is particularly suitable for non-time sensitive Internet of Things (IoT) applications such as telemetry, smart metering, and soon. The proposed architecture reduces the peak-to-average-power ratio (PAPR) of orthogonal frequency-division multiplexing (OFDM)-based schemes without relegating the data rate. In the proposed scheme, we implement joint code-frequency-index modulation (CFIM) by considering code and frequency domains for index-modulation (IM). After introducing and analysing the structure of the CFIM, we derive closed-form expressions of the bit error rate (BER) performance over Rayleigh fading channels and we provide extensive simulation results to validate our outcomes. To better exhibit the particularities of the proposed scheme, the PAPR and complexity are thoroughly examined. The obtained results show that the PAPR is reduced compared to conventional OFDM-like IM-based schemes. Therefore, the proposed system is more likely to operate in the linear regime, which can in turn be implemented into low-cost devices with cost effective amplifiers. In addition, the concept is extended to synchronous multi-user communication networks, where full functionality is obtained by using orthogonal spreading codes. With the characteristics demonstrated in this work, the proposed system would constitute an exceptional nominee for IoT applications where low-complexity, low-power consumption and high data rate are paramount.
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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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".