Multiple Antenna Broadcast Channels with Random Channel Side Information
Bibliographic record
Abstract
The performance of multiple input single output (MISO) broadcast channels is strongly dependent on the availability of channel side\ninformation (CSI) at the transmitter. In many practical systems, CSI may be available to the transmitter only in a corrupted and\nincomplete form. It is natural to assume that the flaws in the CSI are random and can be represented by a probability distribution\nover the channel. This work is concerned with two key issues concerning MISO broadcast systems with random CSI: performance analysis and system design. First, the impact of noisy channel information on system performance is investigated. A simple model is formulated where the channel is Rayleigh fading, the CSI is corrupted by additive white Gaussian noise and a zero forcing precoder is formed by the noisy CSI. Detailed analysis of the\nergodic rate and outage probability of the system is given. Particular attention is given to system behavior at asymptotically\nhigh SNR. Next, a method to construct precoders in a manner that accounts for the uncertainty in the channel information is\ndeveloped. A framework is introduced that allows one to quantify the tradeoff between the risk (due to the CSI randomness) that is\nassociated with a precoder and the resulting transmission rate. Using ideas from modern portfolio theory, the risk-rate problem is\nmodified to a tractable mean-variance optimization problem. Thus, we give a method that allows one to efficiently find a good\nprecoder in the risk-rate sense. The technique is quite general and applies to a wide range of CSI probability distributions.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".