Power-Efficient Transceiver Design for Full-Duplex MIMO Multi-Cell Systems With CSI Uncertainty
Bibliographic record
Abstract
With increasing emphasis on incorporating energy awareness in future communication systems, it is desirable to explore power-efficient resource allocation techniques. Therefore, in this paper, we consider the sum-power minimization of base stations (BSs) and users in a full-duplex (FD) multiple-input multiple-output multi-cell system. In particular, we assume that BSs operating in FD transmission mode serve multiple FD mobile users at the same time over the same frequency band. To guarantee a certain quality of service (QoS), we enforce the maintenance of a minimum signal-to-interference-plus-noise ratio for each user. Concerning these design constraints together with realistic FD self-interference models, we investigate the transmit and receive beamforming designs that minimize the joint transmission power of BSs and users. However, the resulting optimization problem is NP-hard. We therefore divide this optimization problem into separate receive and transmit beamforming design steps, which can be solved iteratively. In addition, the non-convex precoder design problem is posed as a difference of convex function programming, which can be efficiently solved via successive convex approximation. In order to account for practical aspects in our design, we also take into account imperfect channel state information by way of stochastic and bounded uncertainties. Numerical results suggest that the FD systems generally outperform the half-duplex ones under a wide range of QoS constraints and transceiver distortions.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".