Performance evaluation, optimal power allocation, and physical layer designs for wireless relaying systems
Bibliographic record
Abstract
Cooperative communication is a promising way to improve wireless network performance by exploiting spatial diversity in fading channels in a distributed manner. Performance of various wireless cooperative configurations are investigated. Theoretical expressions for outage and error probabilities in general fading of amplify-and-forward multi-hop systems are derived using the characteristic function or moment generating function of the inverse of the instantaneous received signal-to-noise ratio. In addition, ergodic capacity of different multi-hop systems is evaluated assuming the channel state information is only available at the receiving terminals. It is shown that decode-and-froward multi-hop systems achieve higher ergodic capacities than amplify-and-forward multi-hop systems. Furthermore, theoretical expressions in the form of single finite integrals for the capacity of different source-adaptive amplify-and-forward multi-hop systems are obtained. New optimal power allocation schemes that maximize the instantaneous received signal-to-noise ratio in an amplify-and-forward multi-hop transmission system are also obtained for short-term and long-term power constraints. The optimal power allocation strategy under short-term power constraint requires a centralized implementation, whereas the optimal power solutions to the long-term power constraints can be implemented in a decentralized manner. Outage probabilities of the proposed power-optimized systems are derived and the performance gains of the optimal power allocation schemes are examined. Previous studies have been primarily focused on cooperative systems in which the functionality of the receivers relies on availability of channel information. Low complexity receivers for coherent amplify-and-forward multi-relay systems requiring no instantaneous fading amplitude information are proposed. Analytical expressions for evaluation of the average output signal-to-noise ratio and symbol error probability are derived and it is demonstrated that these schemes achieve full diversity. Furthermore, upper and lower bounds on the ergodic capacity are obtained. In addition, a maximum energy selection scheme in a noncoherent amplify-and-forward multi-relay system is investigated. An expression for the symbol error probability of this system is derived. It is shown that this scheme achieves full diversity whereas it requires neither instantaneous nor statistical channel gain information at the destination. Finally, performance of different multi-hop diversity transmission systems are studied and expressions for evaluation of their outages and bit error probabilities are derived in Rayleigh fading.
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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 | 0.003 | 0.011 |
| 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".