Integrated Sensing and Communication (ISAC) Transceiver: Hardware Architectures, Enabling Technologies, and Emerging Trends
Bibliographic record
Abstract
Integrated Sensing and Communication (ISAC)—also known as Joint Radar-Communication (RadCom), Joint Radar-Communication (JRC) and other related variants—has rapidly emerged as a transformative paradigm for future wireless systems. By unifying sensing and communication functions within a shared transceiver framework, ISAC addresses the growing demands for spectral efficiency, multifunctional interplay, situational awareness, and hardware reuse. This convergence is poised to enable emerging and future wireless intelligence-driven applications ranging from intelligent transportation and autonomous factories to beyond-5G and 6G smart self-adaptive networks. This article presents a comprehensive, hardware-centric review of ISAC transceiver technologies. We trace the historical evolution of ISAC systems, survey current transceiver architectures, and analyze key enabling components across diverse frequency bands and application scenarios. Specific focus is given to transceiver design strategies, RF front-end architectures, antenna arrays, and integration techniques. Emerging technologies—such as multi-functional arrays, photonic integration, and reconfigurable intelligent surfaces—are also examined for their role in enhancing ISAC performance and scalability. By synthesizing the current state-of-the-art and highlighting open challenges, this review aims to serve as a valuable reference for researchers and engineers working toward the development of next-generation ISAC hardware platforms.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".