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Integrating Sensing and Communications for Ubiquitous IoT: Applications, Trends and Challenges
Yuanhao Cui, Fan Liu, Xiaojun Jing, Junsheng Mu
TL;DR
IoT requires stronger sensing capabilities while communication and sensing systems increasingly share technical and resource characteristics. This overview defines ISAC, analyzes its driving forces and use cases, classifies integration solutions, and reviews advances and challenges. It reports practical sensing capabilities in compact radar hardware and Wi-Fi services, alongside regulatory and shared-spectrum pressures.
Problem
Growing IoT sensing demands and congested resources motivate integrating sensing and communication functions instead of treating them as separate capabilities.
Method
The paper defines ISAC, analyzes technical, commercial, and regulatory forces, presents use cases, classifies solutions by integration layer, and surveys related advances and challenges.
Results
The overview identifies signaling-layer convergence and reports compact radar and Wi-Fi sensing results demonstrating practical ISAC-related capabilities.
Takeaways & Limitations
ISAC can provide integration and coordination gains while enabling sensing in existing communication infrastructures and supporting new IoT use cases.
Abstract
from arXiv · showhide
Recent advances in wireless communication and solid-state circuits together with the enormous demands of sensing ability have given rise to a new enabling technology, integrated sensing and communications (ISAC). The ISAC captures two main advantages over dedicated sensing and communication functionalities: 1) Integration gain to efficiently utilize congested resources, and even, 2) Coordination gain to balance dual-functional performance or/and perform mutual assistance. Meanwhile, triggered by ISAC, we are also witnessing a paradigm shift in the ubiquitous IoT architecture, in which the sensing and communication layers are tending to converge into a new layer, namely, the signaling layer. In this paper, we first attempt to introduce a definition of ISAC, analyze the various influencing forces, and present several novel use cases. Then, we complement the understanding of the signaling layer by presenting several key benefits in the IoT era. We classify existing dominant ISAC solutions based on the layers in which integration is applied. Finally, several challenges and opportunities are discussed. We hope that this overview article will serve as a primary starting point for new researchers and offer a bird's-eye view of the existing ISAC-related advances from academia and industry, ranging from solid-state circuitry, signal processing, and wireless communication to mobile computing.
I. INTRODUCTION
ISAC emerges as sensing demands grow and communication and sensing systems become increasingly similar. The paper presents ISAC as a driver of signaling-layer convergence in ubiquitous IoT and surveys its forces, use cases, advances, and challenges.
- IoT devices increasingly require sensing alongside communication, but conventional pipelines implement the two functions in separate black-box modules.
- Wireless sensing from received RF patterns is motivated by emerging services including extended reality, digital twins, autonomous systems, and flying vehicles.
- Communication and sensing can share hardware, spectrum, signal processing, and control because their architectures, channels, and processing pipelines increasingly resemble one another.
- ISAC is associated with a signaling-layer paradigm that can reduce hardware cost, power consumption, latency, and product size while improving spectral efficiency.
- The paper defines ISAC, analyzes influencing forces, presents use cases, classifies integration solutions, and reviews advances and open challenges across academia, industry, and standards.
- Research interest in ISAC is accelerating, as illustrated by the growth of peer-reviewed publications in Fig. 1.
A. What is ISAC?
ISAC integrates sensing and communication systems to use congested resources efficiently and pursue mutual benefits. Its definition encompasses multiple integration levels, whose specific location determines the efficiencies and architectural effects obtained.
- ISAC is a design paradigm integrating radio sensing and communication systems to use congested resources efficiently and pursue mutual benefits.
- Integration means combining two or more systems wholly or partly, including hardware, signaling, or spectrum integration.
- The integration level determines which resources can be saved, including hardware, spectral, temporal, signaling, and energy resources.
- Levels of Integration: A unified communication-and-sensing waveform is the tightest configuration because one radio emission conveys information and extracts information from echoes.
- Levels of Integration: Looser integration configurations can improve size, hardware, spectral, and energy efficiency while reducing latency and signaling cost.
- Some Terminology: Related terminology distinguishes ISAC from DFRC, RF convergence, RadCom, and JCAS by emphasis on functionality, radio scope, equipment, infrastructure, or network architecture.
B. Why Do We Need ISAC in the IoT?
Technical progress, commercial demand, and regulatory pressure make integrated sensing and communications increasingly relevant to IoT. Reported hardware and Wi-Fi results illustrate that sensing capabilities can fit practical devices and services, while shared-spectrum constraints remain significant.
- Dedicated RF circuits contributed to limited ISAC development after early coded-pulse concepts because devices were specialized for sensing or communications.
- Hardware: A 192-virtual-receiver MIMO radar SoC achieved ±1° angular resolution and 0.099 km/h Doppler resolution in a 71 mm^2 overall silicon area.
- Signal Processing: MmWave and massive MIMO create similarities between communication and radio-sensing hardware, channels, and information-processing pipelines.
- Mobile Computing: Wi-Fi sensing faces ambiguity in range and velocity estimates because communication black boxes hide timing and frequency offsets from sensing modules.
- Mobile Computing: Breaking cross-system isolation allows necessary offset information to be exchanged between sensing and communication functions in the baseband processor.
- Commercial and Regulatory Forces: ISAC applications extend beyond academic studies, particularly through Wi-Fi sensing, while civilian radio sensors face substantial regulatory burdens and shared-spectrum conflicts.
- Commercial Progress: Wi-Fi sensing has reported 97%–100% conference-room presence accuracy, 73%–100% activity-recognition accuracy, and imaging error below 4.5 cm/±1°.
- Use Cases: The paper illustrates seven scenarios and 34 ISAC-enabled IoT use cases, with parameter indicators, descriptions, and challenges.
III. LEVERAGING ISAC IN THE IOT: A PARADIGM SHIFT
ISAC motivates a shift from separate sensing and communication layers toward a signaling layer that jointly handles radio emission, post-processing, information extraction, and data transmission. This convergence supports information sharing and co-design while enabling sensing-assisted IoT applications.
- The conventional IoT architecture separates sensing, communication, and processing layers, with radio sensing and wireless communication traditionally assigned to different layers.This communication-aftersensing arrangement limits information sharing and co-designed signaling strategies.
- Joint signaling strategies can overcome interlayer constraints and enable optimal co-design and operation of sensing and communication.They represent the most tightly integrated setting and support partial convergence toward signaling-level integration through waveform unification.
- The signaling layer handles radio emission and related post-processing strategies for sensing and communication, including information extraction and data transmission.Joint signal processing is conducted in this layer to provide access to information needed by both functions.
- Abandoning interlayer isolation allows signaling-level information exchange, more design degrees of freedom, flexible resource allocation, and mutual assistance between sensing and communication.The architecture is intended to preserve the original information-extraction and data-transmission tasks while enabling coordinated operation.
- A Wi-Fi antenna array can form a directional sensing beam that reduces floor and wall reflections affecting human activity recognition.The resulting sensing signal can convey purer activity information to the receiver than the original black-box-like arrangement.
IV. FEATURES OF ISAC IN THE IOT
The paper presents ISAC's IoT-era advantages and uses Fig. 3 to relate them to shared architecture and signaling strategies. The figure distinguishes communication-specific, sensing-specific, and shared components.
- ISAC's potential IoT advantages include improved integration across sensing and communication functions, although the paper notes that systematic quantification remains limited.The section introduces these advantages as potential benefits rather than reporting a quantified evaluation.
- Fig. 3 distinguishes communication-specific architecture in yellow, sensing-specific architecture in blue, and architecture shared between sensing and communication in green.Its left side presents four signaling strategies and labels X_s and X_c as the resources allocated to sensing and communication.
A. Integration Gain
Integration gain is the core advantage of ISAC over separate sensing and communication, arising from coupled components or resources that improve efficiency and enable shared information and signaling.
- Integration gain couples sensing and communication components or resources to improve size, hardware, spectral, and energy efficiency while lowering latency and signaling costs.The benefits depend on the level at which integration is applied.
- Separating radar and communication antenna-array groups reduces spatial degrees of freedom and angular resolution while adding interference-management constraints.These constraints impose extra expenses compared with a shared antenna array.
- Separated antenna groups still improve energy and hardware efficiency compared with entirely separate sensing and communication layers.This illustrates that a less tightly coupled setting can retain integration benefits despite reduced spatial degrees of freedom.
- Spectrum sharing and joint signaling can support simultaneous sensing and communication through non-overlapped resources or a fully unified waveform.Joint signaling also addresses interference management in spectrum integration.
- Shared situational awareness, including channel-state information, directions, and channel maps, can support cross-function signaling design.Depending on the integration level, this information may be shared through memory or a common processor.
- Synchronizing transmitted communication symbols with sensing provides a reference signal for partially matched filtering and pulse compression.The shared signal can improve sensing detection probability through prior knowledge in post-processing.
V. DOMINANT ISAC SOLUTIONS
The paper connects the IoT signaling-layer paradigm with dominant ISAC implementations spanning software-defined radio, spatial parameter processing, and SoC/SiP integration. These approaches trade reconfigurability, coordination, and hardware integration in different ways.
- Software-defined radio enables one device to run different transmitter and receiver strategies by moving signal processing from specialized RF circuits to general-purpose processors.For large IoT devices, SDR can provide hardware reuse across sensing and communication.
- A frequency-independent signal-path representation can be obtained by estimating spatial channel parameters such as angle of arrival, path attenuation, phase shift, and distance.
- SDR reconfiguration is typically time-consuming and function-independent, so co-design limitations result in rare coordination gain.This constrains the coordination benefits available from hardware reuse alone.
- ISAC SoC/SiP designs integrate multichannel RF transceivers, high-performance ADCs, and antenna arrays to pursue both integration and coordination gain.Such integration can implement radar and communication functionality in a chipset for tiny IoT devices.
B. Signaling Layer Integration
Signaling-layer integration harmonizes sensing and communications through shared waveform, spectrum, time, or spatial resources. Strategies range from loosely coupled division schemes to spatial designs requiring interference-channel knowledge.
- Signaling-Layer Rationale: Shared spectrum makes communication and radio-sensing systems similar in hardware, propagation, waveform, and beamforming, supporting harmonized emissions.Resource allocation can be orthogonal or non-overlapped.
- Time Division: Time-division ISAC schedules sensing and communication waveforms in separate time slots, enabling inexpensive sensing additions to existing protocols.Examples include 802.11p and 802.11ad, with potential for cellular IoT devices.
- Spectral Division: Spectral-division ISAC assigns sensing and communication waveforms to different subcarriers or frequency bands, often with minor OFDM-system modifications.A binary subcarrier-selection indicator can map the two waveforms to separate subcarriers.
- Spatial Division: Spatial-division ISAC forms multiple beams to serve communication receivers while performing sensing tasks such as target detection.Sensing waveforms may be selected within another system’s null space.
- Spatial Division: Because propagation determines the channel null space, interference CSI estimation is essential for spatial waveform and filter design.The designer cannot directly control the propagation-defined null space.
2) Fully Unified Waveform:
Fully unified waveforms share wireless resources and offer the greatest potential integration and coordination gains. They can be designed around sensing, communication, or a flexible joint optimization.
- Fully Unified Waveform: A fully unified ISAC waveform shares wireless resources and offers the potential for the highest integration and coordination gains.It is the most tightly integrated signaling-layer setting.
- Sensing-Centric Design: Sensing-centric design embeds communication data into existing radar waveforms or sensing infrastructures across signal domains.One classical approach modulates ASK, PSK, or FSK symbols onto chirp carriers.
- Communication-Centric Design: Communication-centric design reuses known communication waveforms for monostatic radar sensing, including OFDM for extracting range and Doppler.Range and Doppler parameters can be obtained through IFFT and FFT processing.
- Joint Design: Joint design creates an ISAC waveform from the ground up to provide a flexible sensing–communication performance trade-off.Sensing-centric and communication-centric designs represent its two extremes.
- Joint Design: Joint waveform design can optimize sensing or communication metrics subject to constraints preserving the other function’s performance.An example optimizes a MIMO-radar beampattern under per-user communication SINR constraints.
C. Application Layer Integration
Application-layer integration lets sensing and communication functions use one another’s outputs, but typical pipelines remain serial and loosely coupled. Open challenges concern theory, parameter adjustment, receivers, and networked cooperation.
- Application Layer Integration: Application-layer integration uses one function’s output to provide information for the other, typically through a serial sensing-after-communication or reverse pipeline.This arrangement is characterized as loosely coupled.
- Human Activity Recognition: Wi-Fi human-activity recognition processes CSI through augmentation, target-signal extraction, and model-based or learning-based detection.Processing includes noise reduction, outlier removal, thresholding, filtering, or signal compression.
- Coordinated S&C Control: Coordinated S&C control can improve vehicular-radar tracking using shared communication-system situational information while improving communication efficiency and reliability.
- Fundamental Limitations and Trade-offs: Theoretical ISAC analysis still needs unified performance bounds and achievable design objectives linking sensing estimation with communication capacity.A proposed direction connects information theory and detection theory through MMSE and possibly CRLB.
- Practical ISAC Parameter Adjustment: Practical signaling parameters must balance sensing focus, communication multipath needs, target shape, communication quality, and temporal resolution.Pulse repetition frequency determines frame rate, which matters for applications such as gesture recognition.
- ISAC Receiver Solutions: ISAC receivers face difficulty separating sensing echoes from communication signals in rich-scattering environments, motivating time-separated echo and uplink-data protocols.
- Networked ISAC: Networked ISAC must address information exchange, cooperative sensing, and assignment and management of sensing and communication beams across devices.Multiple devices could operate as a multistatic radar for joint sensing.
VII. CONCLUSION
The paper defines ISAC, examines its drivers and use cases, and organizes solutions by integration layer. It concludes that ISAC can converge sensing and communication into a signaling layer while leaving important challenges and research directions.
- Conclusion: The article analyzes forces driving ISAC, presents new use cases, and argues that the sensing and communication layers may partially converge into a signaling layer.
- Conclusion: The signaling-layer paradigm may reduce hardware cost, power consumption, signaling latency, and product size while improving spectral efficiency.
- Conclusion: The paper identifies integration gains from resource efficiency and coordination gains from mutual assistance, and classifies solutions across hardware, signaling, and application layers.
- Conclusion: The article discusses major ISAC challenges, opportunities, and future research directions, concluding that ISAC will play an essential role in the IoT era.