A NOMA Scheme for a Two-User MISO Downlink Channel With Unknown CSIT
Bibliographic record
Abstract
The notion of non-orthogonal multiple access (NOMA) for 5G essentially relies on the availability of the channel state information at the transmitter (CSIT). Such knowledge is used to judiciously allocate power among users to make their signals separable at their respective receivers while employing successive interference cancellation (SIC). Feeding back the CSI from the users to the BS (transmitter) is obviously bandwidth consuming. Reducing such an overhead is of great importance and has been of interest in recent years. Furthermore, existing NOMA techniques become inapplicable when the CSI is unavailable at the BS. In this case, the BS has only the option of allocating power among users blindly, including equal power splitting, which has been shown to yield poor performance in terms of outage probability and error probability. This motivates us to develop a NOMA scheme that does not require CSI knowledge at the BS. We make use of a nonlinear interference alignment technique that we have proposed recently, namely, interference dissolution, to develop the proposed NOMA scheme, which allows the BS to communicate with two users simultaneously while keeping signals perfectly separable at their respective receivers. We develop the proposed scheme for multiple-input single-output and single-input single-output downlink channels. We analyze the proposed technique analytically in terms of the achievable degrees-of-freedom and achievable rate per user. We show that the proposed NOMA scheme outperforms existing NOMA techniques in terms of the outage probability and error probability.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 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".