Optimum Power Allocation for Fading Relay Channels
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Optimum power allocation is a key technique to realize the full potentials of relay-assisted transmission promised by the recent information-theoretic results. In this paper, we present a comprehensive framework for power allocation problem in a single-relay scenario taking into account the effect of relay location. In particular, we aim to answer the two fundamental questions: Q1) How should the overall transmit power be shared between broadcasting and relaying phases?; Q2) In the relaying phase, how much power should be allocated to relay-to-destination and source-to-destination links? The power allocation problem is formulated to minimize a union bound on the bit error rate (BER) performance assuming amplify-and-forward (AaF) relaying. We consider three TDMA-based cooperation protocols which correspond to distributed implementations of MIMO (multi-input-multi-output), SIMO (single-input-multi-output), and MISO (multi-input-single-output) schemes. Optimized protocols demonstrate significant performance gains over their original versions which assume equal sharing of overall transmit power between the source and relay terminals as well as between broadcasting and relaying phases. It is observed that optimized virtual (distributed) antenna configurations are able to demonstrate a BER performance as close as 0.4 dB within their counterpart co-located antenna configurations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it