Relay-assisted downlink transmissions to support increased data rates for single antenna users
Bibliographic record
Abstract
In this paper, we exploit cooperation between mobile stations and transmissions scheduling in a wireless network to enhance the downlink data rates to multiple single antenna users. The objective is to increase the number of multiplexed signals within a channel use close to what is offered in interference alignment approaches without incurring the corresponding system complexity. Specifically, we divide the communication process into two stages. In the first stage, the transmitter with multiple antennas using time division multiplexing sends to different users in their allocated time slots a number of messages equal to the number of transmit antennas in a MISO frame. In the next stage, every user and its assigned relays form an independent network, and relays using an amplify-and-forward scheme aid the recovery of messages by the user solving linear system of equations. All of these subnetworks utilize the available channel concurrently producing an acceptable level of multiple access interference (MAI) where the MAI is controlled using a clustering technique tailored to location of users and potential relays. Simulation results are provided demonstrating the capacity improvements in the proposed scheme and showing good performance in a low SNR region of operation.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".