Achievable Rate Regions for Orthogonally Multiplexed MIMO Broadcast Channels with Multi-Dimensional Modulation
Bibliographic record
Abstract
In this work, we consider a multi-antenna channel with orthogonally multiplexed non-cooperative users, and present its achievable information rate regions with and without channel knowledge at the transmitter. With an informed transmitter, we maximize the rate for each user. With an uninformed transmitter, we consider the optimal power allocation that causes the fastest convergence to zero of the fraction of channels whose mutual information is less than any given rate as the transmitter channel knowledge converges to zero. We assume a deterministic space and time dispersive multipath channel with multiple transmit and receive antennas, generating an orthogonally multiplexed Multiple-Input Multiple-Output (MIMO) broadcast system. Under limited transmit power; we consider different user specific space-time modulation formats that represent assignments of signal dimensions to transmit antennas. For the two-user orthogonally multiplexed MIMO broadcast channels, the achievable rate regions, with and without transmitter channel knowledge, evolve from a triangular region at low SNR to a rectangular region at high SNR. We also investigate the maximum sum rate for these regions and derive the associated power allocations at low and high SNR. Furthermore, we present numerical results for a two-user system that illustrate the effects of channel knowledge at the transmitter, the multi-dimensional space-time modulation format and features of the multipath channel.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".