Performance Analysis of Massive MIMO Multi-Way Relay Networks with\n Low-Resolution ADCs
Bibliographic record
Abstract
High power consumption and hardware cost are two barriers for practical\nmassive multiple-input multiple-output (mMIMO) systems. A promising solution is\nto employ low-resolution analog-to-digital converters (ADCs). In this paper, we\nconsider a general mMIMO multi-way relaying system with a multi-level mixed-ADC\narchitecture, in which each antenna is connected to an ADC pair of an arbitrary\nresolution. By leveraging on Bussgang's decomposition theorem and Lloyd-Max\nalgorithm for quantization, tight closed-form approximations are derived for\nthe average achievable rates of zero-forcing (ZF) relaying considering both\nperfect and imperfect channel state information (CSI). To conquer the\nchallenges caused by multi-way relaying, the complicated ZF beam-forming\nmatrix, and the general mixed-ADC structure, we develop a novel method for the\nachievable rate analysis using the singular-value decomposition (SVD) for\nGaussian matrices, distributions of the singular values of Gaussian matrices,\nand properties of Haar matrices. The results explicitly show the achievable\nrate behavior in terms of the user and relay transmit powers and the numbers of\nrelay antennas and users. Most importantly, it quantifies the performance\ndegradation caused by low-resolution ADCs and channel estimation error. We\ndemonstrate that the average achievable rate has an almost linear relation with\nthe square of the average of quantization coefficients pertaining to the ADC\nresolution profile.\n
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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 | 0.001 |
| 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".