Concurrent Dual-Band Six-Port Receiver for Multi-Standard and Software Defined Radio Applications
Bibliographic record
Abstract
This paper proposes a novel concurrent dual-band receiver architecture that uses only one six-port correlator circuit to downconvert two signals in two different bands concurrently. The receiver is reconfigurable over a broadband to simultaneously receive two different signals with different modulation techniques and bandwidths. There are no limitations on the carrier frequencies of the two signals except that they have to be within the bandwidth of the six port receiver. The mathematical model for the receiver is derived and subsequently implemented to evaluate its performance. This approach shows that, by analytically choosing the frequencies of the two local oscillator signals sent into the six-port correlator, the in-phase (I) and the quadrature (Q) components of each of the two input RF signals can be obtained from the filtered high-pass and low-pass components of the diode detectors outputs. A black box model, which uses a modified memory polynomial, is used to calibrate the receiver. The calibration constants are estimated by sending a training signal of similar characteristics as the signal to be received. Two signals pairs with different modulation types are received to verify the model, and to evaluate the performance and test the robustness of the receiver. A 64-QAM signal at 2.5 GHz and a 16-QAM signal at 3.0 GHz, both with a data rate of 2 Mbps are received. The measured EVMs were 1.9% for the 64-QAM and 1.8% for the 16-QAM. Real communication signals, WCDMA and LTE were also received concurrently with measured EVMs of 1.9% and 2.0%, respectively. A bit error rate (BER) profile of the receiver for a 16QAM and 64QAM, both at 2Mbps is also plotted to evaluate the receiver.
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 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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".