Design of Noncoherent and Coherent Receivers for Chirp Spread Spectrum Systems
Bibliographic record
Abstract
LoRaWAN is a prominent communication standard to enable reliable low-power, long-range communications for the Internet of Things (IoT). The modulation technique used in LoRaWAN, commonly known as LoRa modulation, is based on the principle of chirp spread spectrum (CSS). The main objective of this article is to design a practical, low-complexity coherent receiver for a recently proposed CSS-based modulation scheme, called phase-shift keying CSS (PSK-CSS), that embeds extra information bits in the starting phases of conventional CSS symbols. To this end, a novel method is proposed to perform coarse timing and frequency synchronization that makes use of a preamble consisting of both up and down chirps and the shape of the pulse shaping and/or receive (matched) filters. Furthermore, to enable fine synchronization, timing, and phase loop filters are designed as simple first-order and second-order phase-locked loop (PLL) circuits with dynamic gain control. A natural outcome of our design is a practical noncoherent receiver that can be used for the conventional LoRa/CSS system and has better performance and/or lower computational complexity as compared to other existing designs. Extensive simulation results are presented to demonstrate the excellent performance and merits of the proposed design. In particular, the bit-error-rate (BER) performance of the higher-rate PSK-CSS system obtained with the proposed coherent receiver is only 0.25 dB worse than that obtained with the ideal co-coherent receiver, whereas it enjoys about 0.75-dB power gain over the noncoherent detection performance of the conventional CSS system. Thanks to the extra information bits carried by the phases of CSS symbols, the PSK-CSS system implemented with quadrature phase-shift keying (QPSK) delivers additional 23.44% and 18.75% data rate as compared to the conventional CSS system for the spreading factors (SFs) of 8 and 10, respectively.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".