A Preliminary Investigation of Multi-user Interference Cancellation Techniques at Roadside Unit in Vehicular Networks
Bibliographic record
Abstract
In this paper, we investigate multi-user interference cancellation (MUI) schemes for deployment at roadside unit in vehicular ad hoc networks. Generally, MUI schemes can be divided into linear and nonlinear groups. In linear MUI schemes, successive and parallel interference cancellation schemes are widely used. In successive interference cancellation (SIC) schemes, the receiver will detect the user's data on a per user base, and immediately cancel the interference of the detected user for the next detection. On the contrary, parallel interference cancellation (PIC) schemes detect a group of users' data simultaneously, then cancel the interference of all users in the next round of operation. There exists error propagation problem in both successive and parallel interference cancellation schemes. To address the problem, ordered successive interference cancellation scheme has been proposed to improve the bit error rate (BER) performance by the receiver detecting the user with the highest instantaneous signal-to-interference-plus-noise ratio (SINR) and canceling the interference of that user for another continuously. The process repeats until all users' data are detected. Besides, iterative processing techniques are also introduced to further improve the system BER performance. In this paper, we study and compare the interference cancellation schemes for uplink transmission in vehicular ad hoc networks. Simulation results show that the BER performance is much better in ordered successive interference cancellation schemes than both the SIC and PIC schemes, especially in high signal-to-noise ratio regions where the multi-user interference becomes dominant. It is also shown through the study that the ordering procedure can efficiently avoid the error propagation problem in vehicular networks.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".