Analysis of Massive Ultra-Reliable and Low-Latency Communications Over the <i>κ</i>-<i>μ</i> Shadowed Fading Channel
Bibliographic record
Abstract
We investigate the performance of massive ultra-reliable and low-latency communications (mURLLC) under massive active users, and non-uniform small-scale and shadow fading in the uplink (UL) of a next-generation multiple access (NGMA) system that integrates massive multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) techniques. We first derive new closed-form expressions to accurately approximate the probability density function (PDF) and cumulative distribution function (CDF) of the channel gains in MIMO systems under the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\kappa $ </tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> shadowed fading. Then, we derive the post-processing signal-to-noise ratio (SNR) and its closed-form PDFs and CDFs in the NGMA system, under both perfect and imperfect channel state information of the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\kappa $ </tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> shadowed fading channel. Given the post-processing SNRs and their PDFs, the general expressions are established for the error probability (EP) to analyze the mURLLC of NGMA by applying finite blocklength information theory. Corroborated by extensive simulations, our analysis reveals that with the increasing reliability requirements of the users, the relative gaps in EPs enlarge between users experiencing different fading channels, and the feasible system configurations (i.e., the transmit powers of the users, and the numbers of antennas, active users, and subcarriers) also increasingly differ between the users. The impact of different fading on mURLLC implementations cannot be overlooked, and the research of mURLLC under the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\kappa $ </tex-math></inline-formula> - <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu $ </tex-math></inline-formula> shadowed fading model is indispensable. The NGMA system considered in this paper is capable of achieving mURLLC under non-uniform small-scale and shadow fading.
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How this classification was reachedexpand
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 | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".