An Energy Efficient Coherent IR-UWB Receiver With Non-Coherent-Assisted Synchronization
Bibliographic record
Abstract
This paper presents a non-coherent assisted synchronization mechanism for low data rate coherent impulse radio ultra-wide band (IRUWB) receivers. A two-step coarse and fine acquisition mechanism is utilized to simplify the coherent synchronization and minimize the total required packet length. This hybrid scheme reduces the total power consumption of the receiver. The reception of a UWB packet begins with an energy efficient non-coherent synchronization portion in on-off keying (OOK) modulation non-coherent acquisition. This reduces the search space for the coherent reception by first finding the best integration window that has the most energy of the received signal. Afterwards, the receiver searches coherently in binary phase-shift keying (BPSK) modulation within only the pre-selected integration window instead of the whole symbol time. Self-mixing and template-based correlation are utilized for the non-coherent and coherent reception, respectively. Most of the front-end blocks are shared between the coherent and non-coherent modes, including the LNA, mixer, and the baseband circuitry that follows to minimize the total power consumption of the receiver. A differential architecture including a low noise amplifier (LNA), a mixer, a fast start-up template generator, an integrator, and a differential comparator are utilized for the receiver front-end. A prototype of the proposed receiver operating from 3.5 to 5 GHz over four bands, each spaced by 500 MHz, is implemented in 65-nm CMOS technology. The proposed receiver architecture achieves a −68 dBm, −70.5 dBm, and −70.8 dBm sensitivity at a 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> BER at 50 MHz pulse repetition frequency (PRF) in the non-coherent, coherent, and proposed hybrid synchronization modes, respectively. The receiver consumes 6.8 mW and 8.8 mW in the non-coherent and coherent mode, respectively, when continuously ON. Using its novel synchronization mechanism, the receiver can reduce the ON time of the receiver by 68% and 34% in order to coherently synchronize and receive a 100-bit and 1024-bit payload, respectively. As a result, the energy per useful bit (EPUB) of the receiver is reduced by a factor of 2.9 from 1.24 nJ/b to 422 pJ/b for 100-bit payloads, and by a factor of 1.5 from 309 pJ/b to 204 pJ/b for 1024-bit payloads, increasing energy efficiency while maintaining the sensitivity benefits of coherent detection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.001 |
| 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".