Impacts of Imperfect CSI, Residual Hardware Impairments, and Imperfect SIC on Alamouti-Coded Short-Packet NOMA Systems With Hybrid Multicast–Unicast Transmission
Bibliographic record
Abstract
This paper analyzes the performance of Alamouti coded short-packet non-orthogonal multiple access (NOMA) systems with hybrid multicast-unicast transmission over Nakagami-mfading, where only the statistical channel state information is available at the transmitter, and the multicast and unicast signals are intended for all users and a particular user, respectively. Due to practical limitations, channel estimation errors (CEEs), residual hardware impairments (RHIs), and imperfect successive interference cancellation (SIC) are considered. We first derive approximate closed-form expressions for the average block error rate (BLER) and the corresponding asymptotic expressions at all users. Using such expressions, we analyze the diversity performance including conventional diversity order and finite signal-to-noise ratio (SNR) diversity order. After this, we quantify the relationship among the blocklength of information transmission, power allocation, and pilot sequence length under users’ reliability constraints. Finally, numerical and simulation results show that CEEs, RHIs, and imperfect SIC greatly affect the transmission blocklength. Moreover, RHIs lead to the error floor at high SNRs and finite-SNR diversity order is an effective performance metric at low or medium SNRs. They also show that there exist optimal values for the power allocation coefficients, blocklength of information transmission, and pilot sequence length that minimize the transmission blocklength in the considered hybrid multicast-unicast system. They further show that the NOMA scheme is superior to the orthogonal multiple access counterpart by achieving low-latency transmission.
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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 | 0.002 | 0.007 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".