Bibliographic record
Abstract
Generalized Frequency Division Multiplexing (GFDM) is a multi-domain communication scheme where data symbols are transmitted over a time-frequency block. Tensors, which are multi-way arrays, can be efficiently used to model such systems. This paper presents a system model for a multiple input multiple output (MIMO) GFDM system using the Einstein product of tensors. The input and output are modelled as order 3 tensors where the three modes correspond to space, time and frequency domains. The equivalent channel between the input and output obtained from a cascade of transmit filter, physical channel and receive filter, is defined as an order 6 tensor which takes into account interference across all the domains. An information theoretic analysis of such a tensor channel is presented which is then used to develop a tensor based precoding scheme for MIMO GFDM systems. The effect of various GFDM pulse shape parameters on the capacity of the equivalent channel is explored. A multi-linear minimum mean square error (MMSE) receiver using the tensor framework is also presented.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".