Joint compensation of IQ imbalance, frequency offset and phase noise in OFDM receivers
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Bibliographic record
Abstract
Abstract Zero‐IF receivers are getting a lot of attention because of their potential to enable low‐cost OFDM terminals. However, zero‐IF receivers also introduce IQ imbalance which can have a huge impact on the performance. Rather than increasing component cost to decrease the IQ imbalance, an alternative is to tolerate the IQ imbalance and compensate for it digitally. Current solutions either require additional analog hardware or are based on digital algorithms that converge too slowly for bursty communication. Moreover, the impact of a frequency offset and phase noise on the IQ imbalance estimation/compensation problem is not considered. In this paper, we analyze the joint IQ imbalance/frequency offset/phase noise estimation and propose a low‐cost, highly effective, all‐digital mitigation scheme. For large IQ imbalance large frequency offsets and in the presence of phase noise our solution still results in an average implementation loss below 0.5 dB. It, therefore, enables the design of low‐cost, lowcomplexity OFDM receivers. Copyright © 2004 AEI
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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 it