Increasing throughput in multi-way three-user MIMO networks using successive relaying and IRI cancellation
Bibliographic record
Abstract
This paper considers multi-way communications among three users equipped with multiple-input multiple-output (MIMO) transceivers that exchange massages between each other via two amplify-and-forward (AF) co-located relays. In this network with half-duplex MIMO relays, the wireless medium is simultaneously utilized for time-slotted transmissions by one of the users and one of the relays in order to increase the network capacity. The relays successively relay the signals creating inter-relay interference (IRI), which limits the bit error rate (BER) performance. To mitigate the IRI, this paper develops a cancellation technique at the receiving side of the users through specialized signal processing and scheduling of transmissions with opportunistic listening. The full IRI cancellation is possible assuming that every transceiver knows its receiving channel state information (CSI) and the inter-relay CSI. When all nodes - users and relays - are equipped with M antennas, the proposed scheme allows to exchange M messages per time slot which doubles the capacity of the network over the conventional AF system operating with a single relay in one-way setup. Simulation results document the effectiveness of the developed scheme in terms of channel capacity and the BER performance.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".