Characterizing Mutual Information of Multistream MIMO-SVD Systems in Heterogeneous Random Networks
Bibliographic record
Abstract
The main focus of this paper is on multistream multiantenna (multiple-input multiple-output) communications in large-scale heterogeneous random networks. Previous work in this area mainly studies the outage probability from the perspective of a given stream, which is inaccurate as it generally overlooks possible interference-driven cross stream signal-to-interference correlation in each communication link. To tackle this issue, in this paper, we focus on a link-level performance evaluation by characterizing the complementary cumulative distribution function (CCDF) of random mutual information (RMI) under heterogeneous interference. We focus on the scenarios that no coordination among transmitters is persuaded, and the receivers do not attempt to cancel out part of interuser interference. Assume that channel state information is known at both the transmitter and receiver of each communication pair, Rayleigh fading, that the singular value decomposition (SVD) is applied to construct pre-coding/post-coding filters for single-user detection, and that transmitted signals of antennas are i.i.d. Gaussian. We first provide accurate expression for the achievable data rate (ADR) of each link, and show that, regardless of the interference power, when transceivers are equipped with equal number of antennas it grows sublinearly with the number of antennas. We then approximate RMI via Gaussian distribution that its mean is the ADR and its variance is derived based on network's parameters. It is seen that the Gaussian approximation is credible even when two streams of data are transmitted. This paper provides various insights regarding the impact of system parameters, including density of transmitters, path-loss exponent, and the number of antennas, on the ADR and CCDF of RMI. We finally spot tremendous benefits of the SVD scheme over other prominent multiplexing systems, including maximum ratio combining, zero-forcing, and diagonal BLAST superstructure with minimum mean square error receivers.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".