Power-optimal MIMO two-way relay networks
Bibliographic record
Abstract
In this dissertation, we study MIMO two-way relay networks consisting of singleantenna users which wish to exchange information with the help of multiple multiantenna relays. The problem we aim to solve is to minimize the total transmit power consumed in the entire network while certain quality-of-service constraints are satisfied at the transceivers. To do so, we optimize jointly the relays??? beamforming matrices and the transceivers??? transmit powers. We assume that networks use the multiple access broadcast (MABC) relaying scheme where each round of information exchange between the transceivers takes place in two time-slots. In Chapter 3, the network is assumed to be synchronous while in Chapter 4, we study asynchronous networks. In asynchronous networks, the data transmitted from transceivers will arrive with different delays at relays and the data forwarded from relays will arrive with different delays at each transceiver. In Chapter 5, we use a massive number of relay antennas in a two-way relay network with multiple peer-to-peer communications established with the help of multi-antenna relays. We observe that under the assumption that the relay beamforming matrices are symmetric, the total power minimization problems in synchronous and asynchronous networks are amenable to semi-closed-form solutions. Considering asynchronous networks, we prove rigorously that at the optimum, only those relays corresponding to the power-optimal synchronous sub-network of relays must contribute to the data exchange between transceivers. Equipping relays in the multipair two-way relay networks with massive number of antennas, we study performance of linear relaying techniques such as the maximum ratio transmitting/combining (MRT/MRC) and the zero-forcing (ZF) schemes. Exploiting the approximate orthogonality among relaytransceiver channel vectors when number of relay antennas are very large, we show that the total power minimization problem for networks with a massive number of relay antennas will be amenable to a semi-closed form solution.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".