Orthogonal signal space alignment for MIMO Y channels with redundant antennas
Bibliographic record
Abstract
In this paper, we design a signal alignment scheme that exploits orthogonality of spatial signalling dimensions in the multiple-input multiple-output (MIMO) Y channel, where three users send messages to each other through a relay. The antenna configuration in the original MIMO Y channel system imposes limitations on controlling the minimum distance between signaling points representing network coded messages and affects symbol error rate performance. To mitigate this problem, this paper adds one redundant antenna at every user and designs the corresponding precoding vectors to obtain the flexibility in the selection of orthogonal signaling dimensions and to simplify the decoding at the relay. Specifically, as demonstrated through simulations, the design of precoding vectors in the proposed scheme for users improves the bit error rate (BER) performance but the BER is not balanced among the three users. We even the performances via power allocation applied to distribute energy among the users based on their channel conditions. In addition, iterative optimization of the signaling dimensions and time scheduling of transmissions according to channel states are employed to further improve the BER performance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".