Bursty Impulsive Noise Mitigation in NOMA: A MAP Receiver-Based Approach
Bibliographic record
Abstract
Non-orthogonal multiple access (NOMA) was recently proposed as a promising multiple access technique that can enable improved spectral efficiency, massive connectivity, and low latency. In this letter, we analyze the performance of power domain NOMA in the presence of impulsive noise, which is encountered in various practical applications, such as smart-grid communications. Such noise is known to degrade overall system performance. Moreover, since the noise in question is bursty, it is an arduous task to detect the signals with conventional receivers. Therefore, this letter sheds light on the performance degradation and mitigation of bursty impulsive noise, modeled by a two-state Markov-Gaussian process, in the context of uplink NOMA systems. More specifically, we propose a maximum a posteriori (MAP) receiver combined with successive interference cancellation to mitigate the effect of impulsive noise on the detection of the different users' signals. Simulation results prove the effectiveness of the proposed MAP-based bursty impulsive noise mitigation scheme in terms of bit error rate.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".