Performance Tradeoffs in Amplify-and-Forward Bidirectional Network Beamforming
Bibliographic record
Abstract
We study and compare the performance of two bidirectional network beamforming schemes, namely the multiple access broadcast channel (MABC) strategy and the time division broadcast channel (TDBC) protocol, using joint optimal power control and beamforming design. To do so, we first design two TDBC-based bidirectional network beamformers, through minimization of the total power consumed in the whole network subject to quality of service (QoS) constraints, for the two cases with and without a direct link between the two transceivers. The corresponding power minimization problems are carried out over the transceiver transmit powers as well as relay beamforming weights, thus resulting in a jointly optimal power allocation and beamforming criterion. We devise optimal second-order cone programming based solutions as well as fast gradient-based solutions to these problems. We then use these solutions to compare the performance of the underlying TDBC-based approach to that of the MABC-based technique. This comparison is important because the TDBC approach appears to have certain advantages which can be exploited towards improving the performance of two-way network beamforming. These advantages include the additional degrees of freedom as well as the possibility of benefitting from the availability of a direct link between the two transceivers. Interestingly, in the absence of a direct link between the two transceivers, we show that when the QoS constraints are imposed to meet certain given probabilities of uncoded error [or, equivalently, to meet certain signal-to-noise ratio (SNR) constraints], these two schemes perform closely in terms of the minimum total transmit power. However, when the QoS constraints are used to guarantee certain given rates, the MABC-based scheme outperforms the TDBC counterpart. In the case when a direct link exists between the two transceivers, the TDBC-based approach can outperform the MABC-based method, even for rate satisfying QoS constraints, provided that the direct link is strong enough.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".