Application of complex quantized feedback in direct conversion receivers for wireless applications
Bibliographic record
Abstract
The recent resurgence in radio frequency (RF) transceiver design for wireless applications is accompanied by aggressive design goals such as low cost, low power dissipation; small form factor, and high-speed data transfer. To address these objectives, extensive research has been focused on the development of monolithic transceiver architectures, especially using low-cost CMOS technology. It is in this context that there is renewed interest in the direct-conversion receiver (DCR) architecture, the subject of this research. Currently, the use of DCRs involves a number of challenges. Issues of DC offset, flicker noise, LO leakage and radiation, I/Q mismatch, and intermodulation distortion should be carefully considered and addressed in a DCR design. Also, because of cost incentives and performance, one of the main trends in the evolution of wireless receivers is to implement more and more functionality by way of digital signal processing (DSP). The main objective of this thesis is the development and implementation of solutions to overcome these impairments in the DCR architecture and facilitate DCR design using DSP. To achieve this goal, we begin by using AC coupling to remove DC offset and to reduce flicker noise. Then, quantized feedback (QFB) is employed in both I and Q channels to reduce the baseline wander effect caused by AC-coupling. Such modifications are unable to combat carrier phase error and I/Q mismatch problems. However, they can be effectively reduced using a cross-coupled or complex QFB (CQFB), the key contribution in the thesis. The performance of this CQFB-enhanced DCR architecture is theoretically analyzed and experimentally validated. Next, the design issues for adaptive implementation of CQFB using DSP techniques are addressed. Further, its use is illustrated in the context of a direct-conversion orthogonal frequency-division multiplexing (OFDM) receiver. We show that digital CQFB is very effective in compensation of DC offset, I/Q mismatch, and carrier phase error in a DCR for OFDM signaling. To assist in validation of the developed approach, a prototype RF front-end of the receiver is designed and fabricated in TSMC 0.18μm CMOS process for 2.4GHz wireless applications. The developed prototype provides for user control of I/Q mismatch and includes all the main blocks of the RF front-end of the receiver, namely, a low-noise amplifier (LNA), two mixers and a voltage-controlled oscillator (VCO). This prototype can be used as test vehicle for evaluation of various I/Q mismatch compensation methods implemented in the back-end.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".