pith. sign in

arxiv: 2605.18457 · v1 · pith:QXVEDP5Pnew · submitted 2026-05-18 · 📡 eess.SP

Sense Smarter, Think Better: Edge Perception for Next-Generation Networks

Zhonghao Lyu , Xiaowen Cao , Xianxin Song , Yuchen Li , Jiacheng Wang , Yuanhao Cui , Weijie Yuan , Xianghao Yu
show 5 more authors
Guangxu Zhu Jie Xu Derrick Wing Kwan Ng Dusit Niyato Shuguang Cui
This is my paper

Pith reviewed 2026-05-20 08:37 UTC · model grok-4.3

classification 📡 eess.SP
keywords edge perception6G networkssensing modalitiesedge AIintegrated sensing and communicationmulti-modal fusiontask-driven sensingactive perception
0
0 comments X

The pith

Consolidating research on sensing and edge AI yields design insights for edge perception systems in 6G networks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This survey reviews sensing modalities and edge artificial intelligence techniques as building blocks for edge perception in future wireless networks. It analyzes how edge AI improves sensing through multi-modal fusion and how task-driven sensing supports AI via integrated designs and active perception frameworks. A sympathetic reader would care because these connections point to networks that can interpret and respond to their physical surroundings in a resource-aware, application-specific way rather than treating sensing and computation separately. The work identifies challenges to shape ongoing development toward sixth-generation systems.

Core claim

Edge perception serves as a foundational capability for sixth-generation networks by enabling the network edge to proactively sense, interpret, and interact with the physical environment in a task-oriented and resource-aware manner, built on the synergistic interactions between representative sensing modalities and edge AI techniques including in-band and out-of-band sensing, multi-modal fusion, integrated sensing-communication-computation, and active perception frameworks.

What carries the argument

Synergistic interactions between sensing modalities and edge AI, which allow AI to enhance sensing performance and sensing to facilitate downstream AI tasks through fusion and adaptive strategies.

If this is right

  • Edge AI enhances both in-band and out-of-band sensing capabilities as well as multi-modal data fusion.
  • Task-driven sensing and integrated sensing-communication-computation designs improve support for edge AI applications.
  • Active perception frameworks enable dynamic adaptation of sensing strategies to downstream tasks.
  • Key challenges and open issues identified can direct future research and implementation efforts.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • System designers may prioritize certain modality combinations to reduce overhead in resource-constrained edge deployments.
  • The surveyed interactions could inform sensing strategies in related domains such as autonomous systems or environmental monitoring.
  • Prototype implementations testing the reviewed synergies in controlled wireless testbeds would provide direct validation of the insights.

Load-bearing premise

The selected representative sensing modalities and edge AI techniques are comprehensive and synergistic enough to guide practical 6G system design.

What would settle it

A deployed 6G edge system that achieves comparable or better task performance and resource efficiency without adopting the integrated sensing-AI approaches reviewed in the survey.

Figures

Figures reproduced from arXiv: 2605.18457 by Derrick Wing Kwan Ng, Dusit Niyato, Guangxu Zhu, Jiacheng Wang, Jie Xu, Shuguang Cui, Weijie Yuan, Xianghao Yu, Xianxin Song, Xiaowen Cao, Yuanhao Cui, Yuchen Li, Zhonghao Lyu.

Figure 1
Figure 1. Figure 1: Representative application scenarios of edge perception, where sensing, communication, and edge AI are jointly integrated to support emerging [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Structure of this paper, illustrating the closed-loop co-adaptation between edge sensing and edge AI through edge AI empowered sensing and [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Representative types of edge sensing, including (a) cellular-based in-band sensing leveraging communication waveforms, (b) WLAN-based in-band [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Representative application scenarios of edge AI, illustrating (a) distributed edge learning for smart healthcare via wireless FL, (b) low-latency edge [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Representative AirComp-enabled paradigms in edge perception systems, including (a) distributed sensing with over-the-air aggregation of [PITH_FULL_IMAGE:figures/full_fig_p009_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Basic workflow of semantic/task-oriented communications, where [PITH_FULL_IMAGE:figures/full_fig_p010_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Illustration of multi-modal feature fusion in edge perception, [PITH_FULL_IMAGE:figures/full_fig_p012_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Task-oriented ISCC for federated edge learning, where sensing [PITH_FULL_IMAGE:figures/full_fig_p015_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Task-oriented ISCC for multi-device edge inference, where [PITH_FULL_IMAGE:figures/full_fig_p016_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Active edge perception for downstream AI tasks, where sens [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗
read the original abstract

Edge perception has emerged as a foundational capability for future wireless networks, enabling the network edge to proactively sense, interpret, and interact with the physical environment in a task-oriented and resource-aware manner. This survey provides a comprehensive and structured overview of edge perception. We first review representative sensing modalities and edge artificial intelligence (AI) techniques as the fundamental building blocks. We then examine their synergistic interactions. We systematically analyze how edge AI enhances sensing capabilities, encompassing both in-band and out-of-band modalities, as well as multi-modal sensor data fusion. Moreover, we discuss the role of task-driven sensing in facilitating edge AI, including integrated sensing-communication-computation designs, and active perception frameworks that dynamically adapt sensing strategies for downstream applications. Finally, we identify key challenges and open issues. By consolidating fragmented research across sensing, communication, and edge AI, this survey provides forward-looking insights for the design and implementation of edge perception systems for sixth-generation (6G) networks.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript is a survey on edge perception for 6G networks. It reviews representative sensing modalities and edge AI techniques as building blocks, examines their synergistic interactions (edge AI enhancing in-band/out-of-band sensing and multi-modal fusion; task-driven sensing and active perception frameworks supporting edge AI via integrated sensing-communication-computation designs), and identifies key challenges and open issues to consolidate research and provide forward-looking design insights.

Significance. If the reviewed literature is representative, the survey offers value by structuring fragmented work across sensing, communications, and edge AI, highlighting synergies that could inform practical 6G system architectures. The emphasis on task-oriented and resource-aware perception aligns with emerging 6G priorities and may help researchers navigate the intersection of these areas.

major comments (1)
  1. The abstract and introduction do not specify the selection criteria or search methodology used to identify 'representative' sensing modalities and edge AI techniques. Without this, it is difficult to evaluate whether the consolidation truly supports the claimed forward-looking insights for 6G design, as the central contribution rests on the representativeness of the reviewed works.
minor comments (2)
  1. Clarify the distinction between in-band and out-of-band modalities with concrete examples in the relevant review section to improve readability for readers outside the immediate subfield.
  2. Add a summary table or diagram in the synergy analysis section that maps specific sensing modalities to corresponding edge AI enhancements and task-driven benefits.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive feedback and the recommendation for minor revision. The point about clarifying the literature selection process is valid and will be addressed directly in the revised manuscript to improve transparency and support the survey's claims.

read point-by-point responses

  1. Referee: The abstract and introduction do not specify the selection criteria or search methodology used to identify 'representative' sensing modalities and edge AI techniques. Without this, it is difficult to evaluate whether the consolidation truly supports the claimed forward-looking insights for 6G design, as the central contribution rests on the representativeness of the reviewed works.

    Authors: We agree that explicitly documenting the selection criteria and search methodology would strengthen the manuscript. In the revised version, we will insert a dedicated subsection titled 'Survey Scope and Methodology' immediately following the Introduction. This subsection will describe the databases consulted (IEEE Xplore, ACM Digital Library, arXiv, and Web of Science), the primary search keywords and Boolean combinations employed (e.g., 'edge perception' AND '6G', 'task-oriented sensing' AND 'edge AI'), the publication time window (primarily 2018–2024 with selected foundational works), and the inclusion/exclusion criteria (relevance to integrated sensing-communication-computation, demonstrated task-driven performance gains, and citation impact). We believe this addition will allow readers to better evaluate the representativeness of the reviewed works while preserving the survey's forward-looking focus. revision: yes

Circularity Check

0 steps flagged

No significant circularity: standard literature survey with no derivations or fitted predictions

full rationale

This paper is a survey consolidating external literature on sensing modalities, edge AI techniques, their synergies, and challenges for 6G edge perception. No equations, parameters, or new derivations appear in the provided abstract or structure. The central claim rests on reviewing representative prior work rather than any internal reduction to self-defined inputs, fitted quantities, or self-citation chains. As a review, it has no load-bearing self-referential steps and is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is a survey paper. It introduces no free parameters, axioms, or invented entities of its own; all content draws from cited prior work.

pith-pipeline@v0.9.0 · 5742 in / 899 out tokens · 39945 ms · 2026-05-20T08:37:37.225135+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

What do these tags mean?
matches
The paper's claim is directly supported by a theorem in the formal canon.
supports
The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends
The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses
The paper appears to rely on the theorem as machinery.
contradicts
The paper's claim conflicts with a theorem or certificate in the canon.
unclear
Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

Works this paper leans on

208 extracted references · 208 canonical work pages · 3 internal anchors

  1. [1]

    Framework and overall objectives of the future development of IMT for 2030 and beyond,

    ITU-R, “Framework and overall objectives of the future development of IMT for 2030 and beyond,” 2023. [Online]. Available: https: //www.itu.int/rec/R-RECM.2160-0-202311-I/en

    work page 2030

  2. [2]

    Intelligent multi-modal sensing- communication integration: Synesthesia of machines,

    X. Cheng, H. Zhang, J. Zhang, S. Gao, S. Li, Z. Huang, L. Bai, Z. Yang, X. Zheng, and L. Yang, “Intelligent multi-modal sensing- communication integration: Synesthesia of machines,”IEEE Commun. Surv. Tutor., vol. 26, no. 1, pp. 258–301, 2024

    work page 2024

  3. [3]

    Collaborative sensing in internet of things: A comprehensive survey,

    S. He, K. Shi, C. Liu, B. Guo, J. Chen, and Z. Shi, “Collaborative sensing in internet of things: A comprehensive survey,”IEEE Commun. Surv. Tutor., vol. 24, no. 3, pp. 1435–1474, 2022

    work page 2022

  4. [4]

    Edge intelligence: The confluence of edge computing and artificial intelligence,

    S. Deng, H. Zhao, W. Fang, J. Yin, S. Dustdar, and A. Y . Zomaya, “Edge intelligence: The confluence of edge computing and artificial intelligence,”IEEE Internet Things J., vol. 7, no. 8, pp. 7457–7469, 2020

    work page 2020

  5. [5]

    Pushing AI to wireless network edge: An overview on integrated sensing, communication, and computation towards 6g,

    G. Zhu, Z. Lyu, X. Jiao, P. Liu, M. Chen, J. Xu, S. Cui, and P. Zhang, “Pushing AI to wireless network edge: An overview on integrated sensing, communication, and computation towards 6g,”Science China Information Sciences, vol. 66, no. 3, p. 130301, 2023

    work page 2023

  6. [6]

    Integrated sensing and edge AI: Realizing intelligent perception in 6g,

    Z. Liu, X. Chen, H. Wu, Z. Wang, X. Chen, D. Niyato, and K. Huang, “Integrated sensing and edge AI: Realizing intelligent perception in 6g,”IEEE Commun. Surv. Tutor., pp. 1–1, 2025

    work page 2025

  7. [7]

    Edge perception: Intelligent wireless sensing at network edge,

    Y . Cui, X. Cao, G. Zhu, J. Nie, and J. Xu, “Edge perception: Intelligent wireless sensing at network edge,”IEEE Commun. Mag., vol. 63, no. 3, pp. 166–173, 2025

    work page 2025

  8. [8]

    Embodied AI- empowered low altitude economy: Integrated sensing, communications, computation, and control (ISC3),

    Y . Yang, Y . Chen, J. Wang, G. Sun, and D. Niyato, “Embodied AI- empowered low altitude economy: Integrated sensing, communications, computation, and control (ISC3),” 2024. [Online]. Available: https: //arxiv.org/abs/2412.19996

    work page arXiv 2024

  9. [9]

    Deep AI enabled ubiquitous wireless sensing: A survey,

    C. Li, Z. Cao, and Y . Liu, “Deep AI enabled ubiquitous wireless sensing: A survey,”ACM Comput. Surv., vol. 54, no. 2, Mar. 2021. [Online]. Available: https://doi.org/10.1145/3436729

    work page doi:10.1145/3436729 2021

  10. [10]

    Commodity WiFi sensing in ten years: Status, challenges, and opportunities,

    S. Tan, Y . Ren, J. Yang, and Y . Chen, “Commodity WiFi sensing in ten years: Status, challenges, and opportunities,”IEEE Internet Things J., vol. 9, no. 18, pp. 17 832–17 843, 2022

    work page 2022

  11. [11]

    WiFi-based human sensing with deep learning: Recent advances, challenges, and opportunities,

    I. Ahmad, A. Ullah, and W. Choi, “WiFi-based human sensing with deep learning: Recent advances, challenges, and opportunities,”IEEE open j. Commun. Soc., vol. 5, pp. 3595–3623, 2024

    work page 2024

  12. [12]

    An overview on IEEE 802.11bf: WLAN sensing,

    R. Du, H. Hua, H. Xie, X. Song, Z. Lyu, M. Hu, Narengerile, Y . Xin, S. McCann, M. Montemurro, T. X. Han, and J. Xu, “An overview on IEEE 802.11bf: WLAN sensing,”IEEE Commun. Surv. Tutor., vol. 27, no. 1, pp. 184–217, 2025

    work page 2025

  13. [13]

    WiFi sensing on the edge: Signal processing techniques and challenges for real-world systems,

    S. M. Hernandez and E. Bulut, “WiFi sensing on the edge: Signal processing techniques and challenges for real-world systems,”IEEE Commun. Surv. Tutor., vol. 25, no. 1, pp. 46–76, 2023

    work page 2023

  14. [14]

    Millimeter wave sensing: A review of application pipelines and building blocks,

    B. van Berlo, A. Elkelany, T. Ozcelebi, and N. Meratnia, “Millimeter wave sensing: A review of application pipelines and building blocks,” IEEE Sens. J., vol. 21, no. 9, pp. 10 332–10 368, 2021

    work page 2021

  15. [15]

    A survey of mmwave radar- based sensing in autonomous vehicles, smart homes and industry,

    H. Kong, C. Huang, J. Yu, and X. Shen, “A survey of mmwave radar- based sensing in autonomous vehicles, smart homes and industry,” IEEE Commun. Surv. Tutor., vol. 27, no. 1, pp. 463–508, 2025

    work page 2025

  16. [16]

    Automotive lidar technology: A survey,

    R. Roriz, J. Cabral, and T. Gomes, “Automotive lidar technology: A survey,”IEEE Trans. Intell. Transp. Syst., vol. 23, no. 7, pp. 6282– 6297, 2022

    work page 2022

  17. [17]

    Visual per- ception enabled industry intelligence: State of the art, challenges and prospects,

    J. Yang, C. Wang, B. Jiang, H. Song, and Q. Meng, “Visual per- ception enabled industry intelligence: State of the art, challenges and prospects,”IEEE Trans. Industr. Inform., vol. 17, no. 3, pp. 2204–2219, 2021

    work page 2021

  18. [18]

    Edge video analytics: A survey on applications, systems and enabling techniques,

    R. Xu, S. Razavi, and R. Zheng, “Edge video analytics: A survey on applications, systems and enabling techniques,”IEEE Commun. Surv. Tutor., vol. 25, no. 4, pp. 2951–2982, 2023

    work page 2023

  19. [19]

    Recent progress in micro- and nanotechnology- enabled sensors for biomedical and environmental challenges,

    F. J. Tovar-Lopez, “Recent progress in micro- and nanotechnology- enabled sensors for biomedical and environmental challenges,”Sensors, vol. 23, no. 12, 2023

    work page 2023

  20. [20]

    Context-aware edge-based AI models for wireless sensor networks-an overview,

    A. A. Al-Saedi, V . Boeva, E. Casalicchio, and P. Exner, “Context-aware edge-based AI models for wireless sensor networks-an overview,” Sensors, vol. 22, no. 15, 2022

    work page 2022

  21. [21]

    Radar and camera fusion for object detection and tracking: A comprehensive survey,

    K. Shi, S. He, Z. Shi, A. Chen, Z. Xiong, J. Chen, and J. Luo, “Radar and camera fusion for object detection and tracking: A comprehensive survey,”IEEE Commun. Surv. Tutor., pp. 1–1, 2025

    work page 2025

  22. [22]

    A survey on mobile edge computing: The communication perspective,

    Y . Mao, C. You, J. Zhang, K. Huang, and K. B. Letaief, “A survey on mobile edge computing: The communication perspective,”IEEE Commun. Surv. Tutor., vol. 19, no. 4, pp. 2322–2358, 2017

    work page 2017

  23. [23]

    Machine and deep learning for resource allocation in multi-access edge computing: A survey,

    H. Djigal, J. Xu, L. Liu, and Y . Zhang, “Machine and deep learning for resource allocation in multi-access edge computing: A survey,”IEEE Commun. Surv. Tutor., vol. 24, no. 4, pp. 2449–2494, 2022

    work page 2022

  24. [24]

    Mobile edge intelligence for large language models: A contemporary survey,

    G. Qu, Q. Chen, W. Wei, Z. Lin, X. Chen, and K. Huang, “Mobile edge intelligence for large language models: A contemporary survey,” IEEE Commun. Surv. Tutor., pp. 1–1, 2025

    work page 2025

  25. [25]

    Distributed learning in wireless networks: Recent progress and future challenges,

    M. Chen, D. G ¨und¨uz, K. Huang, W. Saad, M. Bennis, A. V . Feljan, and H. V . Poor, “Distributed learning in wireless networks: Recent progress and future challenges,”IEEE J. Sel. Areas Commun., vol. 39, no. 12, pp. 3579–3605, 2021

    work page 2021

  26. [26]

    Green edge AI: A contemporary survey,

    Y . Mao, X. Yu, K. Huang, Y .-J. Angela Zhang, and J. Zhang, “Green edge AI: A contemporary survey,”Proc. IEEE, vol. 112, no. 7, pp. 880–911, 2024

    work page 2024

  27. [27]

    A survey on trustworthy edge intelligence: From security and reliability to transparency and sustainability,

    X. Wang, B. Wang, Y . Wu, Z. Ning, S. Guo, and F. R. Yu, “A survey on trustworthy edge intelligence: From security and reliability to transparency and sustainability,”IEEE Commun. Surv. Tutor., vol. 27, no. 3, pp. 1729–1757, 2025

    work page 2025

  28. [28]

    A survey on edge intelligence and lightweight machine learning support for future applications and services,

    K. Hoffpauir, J. Simmons, N. Schmidt, R. Pittala, I. Briggs, S. Makani, and Y . Jararweh, “A survey on edge intelligence and lightweight machine learning support for future applications and services,”J. Data Inf. Qual., vol. 15, no. 2, Jun. 2023

    work page 2023

  29. [29]

    Edge intelligence in intelligent transportation systems: A survey,

    T. Gong, L. Zhu, F. R. Yu, and T. Tang, “Edge intelligence in intelligent transportation systems: A survey,”IEEE Trans. Intell. Transp. Syst., vol. 24, no. 9, pp. 8919–8944, 2023

    work page 2023

  30. [30]

    Edge intelligence in smart grids: A survey on architectures, offloading models, cyber security measures, and challenges,

    D. N. Molokomme, A. J. Onumanyi, and A. M. Abu-Mahfouz, “Edge intelligence in smart grids: A survey on architectures, offloading models, cyber security measures, and challenges,”J. Sens. Actuator Netw., vol. 11, no. 3, 2022

    work page 2022

  31. [31]

    Edge-native intelligence for 6G communications driven by federated learning: A survey of trends and challenges,

    M. Al-Quraan, L. Mohjazi, L. Bariah, A. Centeno, A. Zoha, K. Arshad, K. Assaleh, S. Muhaidat, M. Debbah, and M. A. Imran, “Edge-native intelligence for 6G communications driven by federated learning: A survey of trends and challenges,”IEEE Trans. Emerg. Top. Comput. Intell., vol. 7, no. 3, pp. 957–979, 2023

    work page 2023

  32. [32]

    A survey on collaborative DNN inference for edge intelligence,

    W.-Q. Ren, Y .-B. Qu, C. Dong, Y .-Q. Jing, H. Sun, Q.-H. Wu, and S. Guo, “A survey on collaborative DNN inference for edge intelligence,”Machine Intelligence Research, vol. 20, no. 3, pp. 370– 395, 2023

    work page 2023

  33. [33]

    Beyond transmitting bits: Context, semantics, and task-oriented communications,

    D. G ¨und¨uz, Z. Qin, I. E. Aguerri, H. S. Dhillon, Z. Yang, A. Yener, K. K. Wong, and C.-B. Chae, “Beyond transmitting bits: Context, semantics, and task-oriented communications,”IEEE J. Sel. Areas Commun., vol. 41, no. 1, pp. 5–41, 2023

    work page 2023

  34. [34]

    Integrated sensing and communications: Toward dual- functional wireless networks for 6G and beyond,

    F. Liu, Y . Cui, C. Masouros, J. Xu, T. X. Han, Y . C. Eldar, and S. Buzzi, “Integrated sensing and communications: Toward dual- functional wireless networks for 6G and beyond,”IEEE J. Sel. Areas Commun., vol. 40, no. 6, pp. 1728–1767, 2022

    work page 2022

  35. [35]

    Inte- grated sensing and communications: Recent advances and ten open challenges,

    S. Lu, F. Liu, Y . Li, K. Zhang, H. Huang, J. Zou, X. Li, Y . Dong, F. Dong, J. Zhu, Y . Xiong, W. Yuan, Y . Cui, and L. Hanzo, “Inte- grated sensing and communications: Recent advances and ten open challenges,”IEEE Internet Things J., vol. 11, no. 11, pp. 19 094– 19 120, 2024

    work page 2024

  36. [36]

    A survey on integrated sensing, communication, and computation,

    D. Wen, Y . Zhou, X. Li, Y . Shi, K. Huang, and K. B. Letaief, “A survey on integrated sensing, communication, and computation,”IEEE Commun. Surv. Tutor., vol. 27, no. 5, pp. 3058–3098, 2025

    work page 2025

  37. [37]

    S. M. Kay,Fundamentals of Statistical Signal Processing Volume II: Detection Theory. PTR Prentice-Hall, Englewood Cliffs, NJ, 1993

    work page 1993

  38. [38]

    S. M. Kay,Fundamentals of Statistical Signal Processing Volume I: Estimation Theory. PTR Prentice-Hall, Englewood Cliffs, NJ, 1993

    work page 1993

  39. [39]

    Multiple emitter location and signal parameter estima- tion,

    R. Schmidt, “Multiple emitter location and signal parameter estima- tion,”IEEE Trans. Antennas Propag., vol. 34, no. 3, pp. 276–280, Mar. 1986. 21

    work page 1986

  40. [40]

    ESPRIT-estimation of signal parameters via rotational invariance techniques,

    R. Roy and T. Kailath, “ESPRIT-estimation of signal parameters via rotational invariance techniques,”IEEE Trans. Acoust., Speech, Signal Process., vol. 37, no. 7, pp. 984–995, Jul. 1989

    work page 1989

  41. [41]

    Seventy years of radar and communications: The road from separation to integration,

    F. Liu, L. Zheng, Y . Cui, C. Masouros, A. P. Petropulu, H. Griffiths, and Y . C. Eldar, “Seventy years of radar and communications: The road from separation to integration,”IEEE Signal Process. Mag., vol. 40, no. 5, pp. 106–121, Jul. 2023

    work page 2023

  42. [42]

    Integrated sensing, communication, and powering: Toward multi-functional 6G wireless networks,

    Y . Chen, Z. Ren, J. Xu, Y . Zeng, D. W. K. Ng, and S. Cui, “Integrated sensing, communication, and powering: Toward multi-functional 6G wireless networks,”IEEE Commun. Mag., vol. 63, no. 8, pp. 146–153, 2025

    work page 2025

  43. [43]

    Performance analysis and low-complexity design for XL- MIMO with near-field spatial non-stationarities,

    K. Zhi, C. Pan, H. Ren, K. K. Chai, C.-X. Wang, R. Schober, and X. You, “Performance analysis and low-complexity design for XL- MIMO with near-field spatial non-stationarities,”IEEE J. Sel. Areas Commun., vol. 42, no. 6, pp. 1656–1672, 2024

    work page 2024

  44. [44]

    An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs,

    X. Song, Y . Fang, F. Wang, Z. Ren, X. Yu, Y . Zhang, F. Liu, J. Xu, D. W. K. Ng, R. Zhanget al., “An overview on IRS-enabled sensing and communications for 6G: architectures, fundamental limits, and joint beamforming designs,”Sci. China Inf. Sci., vol. 68, no. 5, p. 150301, Apr. 2025

    work page 2025

  45. [45]

    Intelligent reflecting surface enabled sensing: Cram´er-Rao bound optimization,

    X. Song, J. Xu, F. Liu, T. X. Han, and Y . C. Eldar, “Intelligent reflecting surface enabled sensing: Cram´er-Rao bound optimization,”IEEE Trans. on Signal Process., vol. 71, pp. 2011–2026, May 2023

    work page 2011

  46. [46]

    Transformer-based collaborative reinforcement learning for fluid an- tenna system (fas)-enabled 3d uav positioning,

    X. Xu, H. Xu, D. Wei, W. Saad, M. Bennis, and M. Chen, “Transformer-based collaborative reinforcement learning for fluid an- tenna system (fas)-enabled 3d uav positioning,”IEEE J. Sel. Areas Commun., vol. 44, pp. 1128–1143, Oct. 2026

    work page 2026

  47. [47]

    Near- field integrated imaging and communication in distributed MIMO networks,

    K. Zhi, T. Yang, S. Li, Y . Song, A. Rezaei, and G. Caire, “Near- field integrated imaging and communication in distributed MIMO networks,” 2025. [Online]. Available: https://arxiv.org/pdf/2508.17526

    work page arXiv 2025

  48. [48]

    Networked ISAC for low- altitude economy: Coordinated transmit beamforming and UA V trajec- tory design,

    G. Cheng, X. Song, Z. Lyu, and J. Xu, “Networked ISAC for low- altitude economy: Coordinated transmit beamforming and UA V trajec- tory design,”IEEE Trans. Commun., vol. 73, no. 8, pp. 5832–5847, 2025

    work page 2025

  49. [49]

    Networking based ISAC hardware testbed and performance evaluation,

    K. Ji, Q. Zhang, Z. Wei, Z. Feng, and P. Zhang, “Networking based ISAC hardware testbed and performance evaluation,”IEEE Commun. Mag., vol. 61, no. 5, pp. 76–82, May 2023

    work page 2023

  50. [50]

    Robust indoor positioning provided by real-time RSSI values in unmodified WLAN networks,

    S. Mazuelas, A. Bahillo, R. M. Lorenzo, P. Fernandez, F. A. Lago, E. Garcia, J. Blas, and E. J. Abril, “Robust indoor positioning provided by real-time RSSI values in unmodified WLAN networks,”IEEE J. Sel. Topics Signal Process., vol. 3, no. 5, pp. 821–831, Oct. 2009

    work page 2009

  51. [51]

    An enhanced approach to imaging the indoor environment using WiFi RSSI measurements,

    A. Dubey, P. Sood, J. Santos, D. Ma, C.-Y . Chiu, and R. Murch, “An enhanced approach to imaging the indoor environment using WiFi RSSI measurements,”IEEE Trans. Veh. Technol., vol. 70, no. 9, pp. 8415–8430, Sep. 2021

    work page 2021

  52. [52]

    Wi-Fi CSI-based behavior recognition: From signals and actions to activities,

    Z. Wang, B. Guo, Z. Yu, and X. Zhou, “Wi-Fi CSI-based behavior recognition: From signals and actions to activities,”IEEE Commun. Mag., vol. 56, no. 5, pp. 109–115, May 2018

    work page 2018

  53. [53]

    WiFi CSI based passive human activity recognition using attention based BLSTM,

    Z. Chen, L. Zhang, C. Jiang, Z. Cao, and W. Cui, “WiFi CSI based passive human activity recognition using attention based BLSTM,” Trans. Mobile Comput., vol. 18, no. 11, pp. 2714–2724, Nov 2019

    work page 2019

  54. [54]

    M. A. Richardset al.,Fundamentals of Radar Signal Processing. McGraw-Hill New York, 2005, vol. 1

    work page 2005

  55. [55]

    InFi: end-to-end learnable input filter for resource-efficient mobile-centric inference,

    M. Yuan, L. Zhang, F. He, X. Tong, and X.-Y . Li, “InFi: end-to-end learnable input filter for resource-efficient mobile-centric inference,” in Proceedings of MobiCom ’22. New York, NY , USA: Association for Computing Machinery, 2022, p. 228–241

    work page 2022

  56. [56]

    Split computing with scalable feature compression for visual analytics on the edge,

    Z. Yuan, S. Rawlekar, S. Garg, E. Erkip, and Y . Wang, “Split computing with scalable feature compression for visual analytics on the edge,” IEEE Trans. Multimedia, vol. 26, pp. 10 121–10 133, 2024

    work page 2024

  57. [57]

    VideoEdge: Processing camera streams using hierarchical clusters,

    C.-C. Hung, G. Ananthanarayanan, P. Bodik, L. Golubchik, M. Yu, P. Bahl, and M. Philipose, “VideoEdge: Processing camera streams using hierarchical clusters,” in2018 IEEE/ACM Symposium on Edge Computing (SEC), 2018, pp. 115–131

    work page 2018

  58. [58]

    Multi-exit DNN inference acceleration based on multi- dimensional optimization for edge intelligence,

    F. Dong, H. Wang, D. Shen, Z. Huang, Q. He, J. Zhang, L. Wen, and T. Zhang, “Multi-exit DNN inference acceleration based on multi- dimensional optimization for edge intelligence,”IEEE Trans. Mob. Comput., vol. 22, no. 9, pp. 5389–5405, 2023

    work page 2023

  59. [59]

    Towards edge-assisted real-time 3D segmentation of large scale LiDAR point clouds,

    F. McLean, L. Xue, C. X. Lu, and M. Marina, “Towards edge-assisted real-time 3D segmentation of large scale LiDAR point clouds,” in Proceedings of EMDL ’22. New York, NY , USA: Association for Computing Machinery, 2022, p. 1–6

    work page 2022

  60. [60]

    3D LiDAR map compression for efficient localization on resource constrained vehicles,

    H. Yin, Y . Wang, L. Tang, X. Ding, S. Huang, and R. Xiong, “3D LiDAR map compression for efficient localization on resource constrained vehicles,”IEEE Trans. Intell. Transp. Syst., vol. 22, no. 2, pp. 837–852, 2021

    work page 2021

  61. [61]

    Real-time roadside 3D spatial perception with LiDAR AIoT: An edge- cloud–terminal collaborative sensing prototype,

    W. Xiao, P. Li, M. Tang, C. Song, D. Yu, H. Liu, D. Chen, and N. Chen, “Real-time roadside 3D spatial perception with LiDAR AIoT: An edge- cloud–terminal collaborative sensing prototype,”IEEE Internet Things J., vol. 12, no. 9, pp. 12 624–12 639, 2025

    work page 2025

  62. [62]

    EdgeCooper: Network-aware cooperative LiDAR perception for enhanced vehicular awareness,

    G. Luo, C. Shao, N. Cheng, H. Zhou, H. Zhang, Q. Yuan, and J. Li, “EdgeCooper: Network-aware cooperative LiDAR perception for enhanced vehicular awareness,”IEEE J. Sel. Areas Commun., vol. 42, no. 1, pp. 207–222, 2024

    work page 2024

  63. [63]

    AI-driven wearable bioelectronics in digital healthcare,

    G. Huang, X. Chen, and C. Liao, “AI-driven wearable bioelectronics in digital healthcare,”Biosensors, vol. 15, no. 7, 2025

    work page 2025

  64. [64]

    Fundamental CRB-rate tradeoff in multi-antenna ISAC systems with information multicasting and multi-target sensing,

    Z. Ren, Y . Peng, X. Song, Y . Fang, L. Qiu, L. Liu, D. W. K. Ng, and J. Xu, “Fundamental CRB-rate tradeoff in multi-antenna ISAC systems with information multicasting and multi-target sensing,”IEEE Trans. Wireless Commun., vol. 23, no. 4, pp. 3870–3885, Apr. 2024

    work page 2024

  65. [65]

    Joint maneuver and beamforming design for UA V-enabled integrated sensing and communication,

    Z. Lyu, G. Zhu, and J. Xu, “Joint maneuver and beamforming design for UA V-enabled integrated sensing and communication,”IEEE Trans. Wireless Commun., vol. 22, no. 4, pp. 2424–2440, Apr. 2023

    work page 2023

  66. [66]

    On the fundamental tradeoff of integrated sensing and communications under Gaussian channels,

    Y . Xiong, F. Liu, Y . Cui, W. Yuan, T. X. Han, and G. Caire, “On the fundamental tradeoff of integrated sensing and communications under Gaussian channels,”IEEE Trans. Inf. Theory, vol. 69, no. 9, pp. 5723– 5751, Sep. 2023

    work page 2023

  67. [67]

    Random ISAC signals deserve dedicated precoding,

    S. Lu, F. Liu, F. Dong, Y . Xiong, J. Xu, Y .-F. Liu, and S. Jin, “Random ISAC signals deserve dedicated precoding,”IEEE Trans. Signal Process., vol. 72, pp. 3453–3469, Jul. 2024

    work page 2024

  68. [68]

    CP-OFDM achieves the lowest average ranging sidelobe under QAM/PSK constellations,

    F. Liu, Y . Zhang, Y . Xiong, S. Li, W. Yuan, F. Gao, S. Jin, and G. Caire, “CP-OFDM achieves the lowest average ranging sidelobe under QAM/PSK constellations,”IEEE Trans. Inf. Theory, vol. 71, no. 9, pp. 6950–6967, Sep. 2025

    work page 2025

  69. [69]

    Sensing mutual information with random signals in Gaussian channels,

    L. Xie, F. Liu, J. Luo, and S. Song, “Sensing mutual information with random signals in Gaussian channels,”IEEE Trans. Commun., early access, Apr. 2025, doi:10.1109/TCOMM.2025.3564767

    work page doi:10.1109/tcomm.2025.3564767 2025

  70. [70]

    CRB-rate tradeoff for bistatic ISAC with Gaussian information and deterministic sensing signals,

    X. Song, X. Yu, J. Xu, and D. W. K. Ng, “CRB-rate tradeoff for bistatic ISAC with Gaussian information and deterministic sensing signals,” Jul. 2025. [Online]. Available: https://arxiv.org/pdf/2507.21879

    work page arXiv 2025

  71. [71]

    Detection in bistatic ISAC with deterministic sensing and Gaussian information signals,

    ——, “Detection in bistatic ISAC with deterministic sensing and Gaussian information signals,” Nov. 2025. [Online]. Available: https://arxiv.org/pdf/2511.10897

    work page arXiv 2025

  72. [72]

    Wi-Fi sensing based on IEEE 802.11bf,

    C. Chen, H. Song, Q. Li, F. Meneghello, F. Restuccia, and C. Cordeiro, “Wi-Fi sensing based on IEEE 802.11bf,”IEEE Commun. Mag., vol. 61, no. 1, pp. 121–127, Jan. 2023

    work page 2023

  73. [73]

    Enhanced computer vision with microsoft kinect sensor: A review,

    J. Han, L. Shao, D. Xu, and J. Shotton, “Enhanced computer vision with microsoft kinect sensor: A review,”IEEE Trans. Cybern., vol. 43, no. 5, pp. 1318–1334, 2013

    work page 2013

  74. [74]

    Are we ready for real-time LiDAR semantic segmentation in autonomous driving?

    S. A. Haidar, A. Chariot, M. Darouich, C. Joly, and J.- E. Deschaud, “Are we ready for real-time LiDAR semantic segmentation in autonomous driving?” 2024. [Online]. Available: https://arxiv.org/abs/2410.08365

    work page arXiv 2024

  75. [75]

    A multi-modal IoT node for energy-efficient environmental monitoring with edge AI processing,

    P. Wiese, V . Kartsch, M. Guermandi, and L. Benini, “A multi-modal IoT node for energy-efficient environmental monitoring with edge AI processing,” inProc. IEEE COINS, 2025, pp. 1–7

    work page 2025

  76. [76]

    Rethinking resource management in edge learning: A joint pre-training and fine- tuning design paradigm,

    Z. Lyu, Y . Li, G. Zhu, J. Xu, H. Vincent Poor, and S. Cui, “Rethinking resource management in edge learning: A joint pre-training and fine- tuning design paradigm,”IEEE Trans. Wireless Commun., vol. 24, no. 2, pp. 1584–1601, Dec. 2025

    work page 2025

  77. [77]

    Communication-efficient learning of deep networks from decentral- ized data,

    B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. Arcas, “Communication-efficient learning of deep networks from decentral- ized data,” inProc. International Conference on Artificial Intelligence and Statistics, vol. 54, Fort Lauderdale, FL, USA, 20–22 Apr. 2017, pp. 1273–1282

    work page 2017

  78. [78]

    Promptfl: Let federated participants cooperatively learn prompts instead of models – federated learning in age of foundation model,

    T. Guo, S. Guo, J. Wang, X. Tang, and W. Xu, “Promptfl: Let federated participants cooperatively learn prompts instead of models – federated learning in age of foundation model,”IEEE Trans. Mob. Comput., vol. 23, no. 5, pp. 5179–5194, Aug. 2024

    work page 2024

  79. [79]

    Personal- ized edge intelligence via federated self-knowledge distillation,

    H. Jin, D. Bai, D. Yao, Y . Dai, L. Gu, C. Yu, and L. Sun, “Personal- ized edge intelligence via federated self-knowledge distillation,”IEEE Trans. Parallel Distrib. Syst., vol. 34, no. 2, pp. 567–580, Nov. 2023

    work page 2023

  80. [80]

    Optimizing model splitting and device task assignment for deceptive signal-assisted private multi-hop split learning,

    D. Wei, X. Xu, Y . Liu, H. Vincent Poor, and M. Chen, “Optimizing model splitting and device task assignment for deceptive signal-assisted private multi-hop split learning,”IEEE J. Sel. Areas Commun., vol. 44, pp. 1512–1528, Nov. 2026

    work page 2026

Showing first 80 references.