Robust Energy Efficient Beamforming Design for ISAC Full-Duplex Communication Systems
Bibliographic record
Abstract
This letter examines an integrated sensing and communication (ISAC) scheme for multi-user systems under full-duplex (FD) architecture. We examine an FD-ISAC system where the base station is responsible for target detection and shares the same resources for simultaneous downlink (DL) and uplink (UL) communication. For a green FD-ISAC architecture, our primary focus is addressing the robust energy efficiency maximization (EEM) problem. This involves the integrated beamforming design for the DL, UL, and radar users and UL transmission power. The optimization process follows constraints such as worst-case rate constraints, limitations imposed by bounded channel state information (CSI) error, transmit power constraints, and specific quality of service requirements. To address the EEM problem, we use an iterative algorithm that employs successive convex approximation (SCA) and second-order cone programming (SOCP) to achieve near-optimal resource allocation. Average energy efficiency and average spectral efficiency were compared for the proposed EEM algorithm and the benchmark spectral efficiency maximization (SEM) algorithm. Simulation results show that the FD-ISAC scheme significantly outperforms the conventional half-duplex scheme w.r.t. system performance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".