Converting a 1×K Static Rayleigh Channel to K Parallel AWGN Using Media-based Modulation
Bibliographic record
Abstract
The idea of media-based modulation (MBM) [1] [2] is to embed information in the variations of the transmission media (channel states). Using a single traditional antenna surrounded by a closure with w radio frequency (RF) walls, MBM creates a set of 2wstates for the end-to-end channel, and the data is mapped into the index of these channel states. Each channel state results in an independent complex channel gain to each receive antenna, which specifies an MBM constellation point coordinate. In a rich scattering environment, MBM constellation points are independent of each other, with coordinates that follow an independent identically distributed (i.i.d.) complex Gaussian density. In a 1 ×K MBM, this property mimics the random code-book generation for signaling over K parallel additive white Gaussian noise (AWGN) channels. Accordingly, it is shown in [2] that the capacity of a 1 ×K MBM system with one unit of transmit energy and AWGN variance σ2over each receive antenna is equal to K times the capacity of an AWGN with a signal to noise ratio of snr = 1/σ2. The current article provides an alternative proof based on a novel formulation that reveals several interesting features of MBM. It is shown that the capacity in a 1×K MBM, as a random variable defined over the sample space of MBM constellation of cardinality M, follows a normal distribution with a mean of K log(1 + snr) and variance of (K/M)(snr/(1 + snr))2→ 0 as the M →∞. This entails, in contrast to legacy MIMO where the singularity of the channel matrix governs the outage probability, in MBM the outage is determined by the realized energy of the MBM constellation, and for any multiplexing gain r < K, the outage probability decreases exponentially fast as the number of points increases.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".