Performance Analysis of Full-Duplex Massive MIMO Systems With Low-Resolution ADCs/DACs Over Rician Fading Channels
Bibliographic record
Abstract
This paper analyzes the performance of multi-user full-duplex (FD) massive multiple-input multiple-output (MIMO) systems with low resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) under Rician fading channels. The maximum ratio combining and maximum ratio transmission are used at the base station (BS) for the uplink and downlink, respectively. By leveraging on the additive quantization noise model, tight closed-form approximations of the uplink and downlink achievable rates are obtained for both perfect and imperfect channel state information cases. The results show the impact of the Rician K-factor, ADC/DAC resolution, loop interference, and inter-user interference of the systems. In addition, we adopt the power scaling law to show that to achieve a fixed level of the signal-to-interference-plus-noise ratio, the transmit power of each user and the BS can be scaled down proportionally to the inverse of the BS antenna number. Moreover, we compare the performance of the FD mode and the half-duplex mode, and study the trade-off between the achievable rate and BS energy efficiency. Numerical results show that the use of low-resolution ADCs/DACs can significantly improve the BS energy efficiency with only small reduction in the achievable rate of the FD system.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".