Joint compensation of IQ imbalance, frequency offset and phase noise in OFDM receivers
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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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".