Performance Analysis of Holographic MIMO Based Integrated Sensing and Communications
Bibliographic record
Abstract
A holographic multiple-input multiple-output (MIMO)-based integrated sensing and communications (ISAC) framework is proposed for both downlink and uplink scenarios. The spatial correlation is incorporated into the communication channel modeling, while a spherical wave-based model is used to characterize the sensing link. By considering both instantaneous and statistical channel state information, closed-form expressions are derived for sensing rates (SRs), communication rates (CRs), and outage probabilities under various ISAC designs. This enables an investigation into the theoretical performance limits of the proposed holographic MIMO-based ISAC (HISAC) framework. Further insights are gained by examining the high signal-to-noise ratio (SNR) slopes and diversity orders. Specifically: I) for the downlink case, a sensing-centric (S-C) design and a communications-centric (C-C) design are investigated using different beamforming strategies, and a Pareto optimal design is proposed to characterize the attainable SR-CR region; II) for the uplink case, the S-C design and the C-C design differ in the interference cancellation order between the communication and sensing signals, with the rate region obtained through a time-sharing strategy. Numerical results are provided to demonstrate that HISAC systems outperform both conventional MIMO-based ISAC systems and holographic MIMO-based frequency-division sensing and communications systems, underscoring the superior performance of the HISAC framework.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".