On the Duality between MIMO Systems with Distributed Antennas and MIMO Systems with Colocated Antennas
Bibliographic record
Abstract
Multiple-input multiple-output (MIMO) systems are known to offer huge advantages over single-antenna systems, both with regard to capacity and error performance. Usually, quite restrictive assumptions are made in the literature on MIMO systems concerning the spacing of the individual antenna elements. On the one hand, it is typically assumed that the antenna elements at transmitter and receiver are colocated, that is, they belong to some sort of antenna array. On the other hand, it is often assumed that the antenna spacings are sufficiently large, so as to justify the assumption of uncorrelated fading on the individual transmission links. From numerous publications it is known that spatially correlated links caused by insufficient antenna spacings lead to a loss in capacity and error performance. We show that this is also the case when the individual transmit or receive antennas are spatially distributed on a large scale, which is caused by unequal average signal-to-noise ratios (SNRs) on the individual transmission links. Possible applications include simulcast networks as well as future mobile radio systems with joint transmission or reception strategies. Specifically, it is shown that there is a strong duality between MIMO systems with colocated antennas (and spatially correlated links) and MIMO system with distributed antennas (and unequal average link SNRs). As a result, MIMO systems with distributed and colocated antennas can be treated in a single, unifying framework. An important implication of this finding is that optimal transmit power allocation strategies developed for MIMO systems with colocated antennas may be reused for MIMO systems with distributed antennas, and vice versa.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| 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".