Single-Carrier Equalization for Asynchronous Two-Way Relay Networks
Bibliographic record
Abstract
We consider an asynchronous bi-directional amplify-and-forward relay network, where two single-antenna transceivers communicate with the help of several single-antenna relay nodes using a single-carrier communication scheme. The propagation delay of each relaying path, (which originates from one transceiver, goes through a certain relay, and ends at the other transceiver) is assumed to be different from those of the other relaying paths. This assumption turns the end-to-end link into a frequency selective channel which can have multiple taps. As such, intersymbol interference (ISI) is inevitable at the two transceivers. Assuming a block transmission/reception scheme, ISI results in interblock interference (IBI) between successive transmitted blocks. To combat IBI, cyclic prefix insertion and deletion as well as block postchannel equalization are used at the two transceivers. Assuming a limited total transmit power budget, we minimize the total mean squared error (MSE) of the estimated received signals at both transceivers by optimally obtaining the transceivers' transmit powers and the relay beamforming weight vector as well as the block post-channel equalizers at the two transceivers. We prove that this optimization problem leads to a relay selection scheme, where only the relays contributing to one tap of the end-to-end channel impulse response are turned on and the remaining relays are switched off. Moreover, we present a semi-closed-form solution for the optimal relay weight vector. Our numerical results show that the proposed algorithm significantly outperforms an equal power allocation scheme, where all nodes receive the same level of transmit power.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".