On the ergodic capacities of decode‐and‐forward MIMO relay network with simultaneous wireless information and power transfer
Bibliographic record
Abstract
Abstract In this article, simultaneous wireless information and power transfer (SWIPT) has been studied for point‐to‐point decode‐and‐forward (DF) based multiple‐input‐multiple‐output (MIMO) relay network with an energy‐constrained relay. This relay is capable of harvesting energy from the wireless signals received from the source terminal. In particular, three different SWIPT schemes, that is, time switching, power splitting, and antenna switching are considered for EH at relay. New closed‐form approximations for the sum ergodic capacity of a DF MIMO relay network have been derived with spatial multiplexing at source, zero‐forcing receivers at relay and destination. Based on the sum capacity approximations, optimal splitting coefficients for aforementioned SWIPT schemes that maximize the sum capacity are presented. Moreover, the impact of system parameters on these optimal coefficients has been studied. In addition to this, first optimization problems for the number of antennas at relay have been formulated and then these problems are transformed into concave form by relaxing constraints to be defined over positive real numbers instead of taking only discrete values. New closed‐form solutions for the optimum number of antennas at relay have been derived. Furthermore, new closed‐form approximations for the ergodic capacities in the presence of an external co‐channel interference are also presented. Numerical results show that approximations for the sum capacity match well with the exact ones obtained through Monte‐Carlo simulations, particularly when the channel hardening effect kicks in, that is, for large degrees of freedom. Finally, the solutions for the optimization problems have also been validated by the numerical examples.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".