Multi-way Communications: An Information Theoretic Perspective
Bibliographic record
Abstract
Multi-way communication is a means to significantly improve the spectral efficiency of wireless networks. For instance, in a bi-directional (or two-way) communication channel, two users can simultaneously use the transmission medium to exchange information, thus achieving up to twice the rate that would be achieved had each user transmitted separately. Multi-way communications provides an overview on the developments in this research area since it has been initiated by Shannon. The basic two-way communication channel is considered first, followed by the two-way relay channel obtained by the deployment of an additional cooperative relay node to improve the overall communication performance. This basic setup is then extended to multi-user systems. For all these setups, fundamental limits on the achievable rates are reviewed, thereby making use of a linear high-SNR deterministic channel model to provide valuable insights which are helpful when discussing the coding schemes for Gaussian channel models in detail. Several tools and communication strategies are used in the process, including (but not limited to) computation, signal-space alignment, and nested-lattice codes. Finally, extensions of multi-way communication channels to multiple antenna settings are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".