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AFDM-Enabled ISAC in Dynamic Environments: Fundamentals, Technologies and Opportunities
Linchu Chen, Zhendong Li, Zhou Su, Lin Chen, Wen Chen
TL;DR
Dynamic ISAC must handle severe Doppler, rapidly varying channels, and coupled delay-Doppler effects that limit conventional OFDM. This article surveys AFDM fundamentals, advantages, applications, enabling technologies, case studies, and open challenges, concluding that AFDM offers a promising waveform-level foundation for reliable, adaptive, and efficient dynamic ISAC.
Problem
Dynamic environments create severe Doppler, rapid channel variation, and coupled delay-Doppler effects, while OFDM’s orthogonality and stable-channel assumptions become vulnerable.
Method
The article systematically reviews AFDM-enabled ISAC fundamentals, application scenarios, enabling technologies, case studies, and future research directions.
Results
The review identifies AFDM’s delay-Doppler separability, diversity, sensing integration, and configurable waveform parameters as foundations for dynamic ISAC.
Takeaways & Limitations
AFDM provides a promising waveform-level foundation for reliable, adaptive, and efficient ISAC in dynamic environments.
Takeaways & Limitations
OFDM’s orthogonality condition depends on ∆f = 1/Ts and is vulnerable when Doppler and channel variation violate the underlying assumptions.
Abstract
from arXiv · showhide
Dynamic environments pose fundamental challenges to integrated sensing and communication (ISAC), particularly due to severe Doppler effects, rapidly time-varying channels, and the intricate coupling between delay and Doppler shifts. Affine frequency-division multiplexing (AFDM), with its inherent capability of characterizing and separating delay and Doppler effects, has emerged as a promising waveform for dynamic ISAC. This article provides a comprehensive overview on AFDM-enabled ISAC in dynamic environments, covering its fundamental principles, distinctive advantages, representative application scenarios, and key enabling technologies. We first characterize the key features of ISAC in dynamic environments and introduce the fundamentals of AFDM, followed by an analysis of scenarios where AFDM can provide significant performance benefits. Then, several key enabling technologies for AFDM-based ISAC in dynamic environments are elaborated upon, accompanied by case studies on the critical aspects therein. Finally, open challenges and promising future research directions are discussed, aiming to provide a comprehensive reference for researchers and practitioners while inspiring further innovation in this emerging field.
I. INTRODUCTION
Dynamic environments expose OFDM to severe Doppler and rapidly varying channels, motivating AFDM as a chirp-based waveform for delay-Doppler separation and integrated sensing and communication. The article reviews AFDM fundamentals, application scenarios, enabling technologies, and open challenges.
- Doubly dispersive channels in dynamic environments cause Doppler-induced inter-carrier interference that degrades communication and sensing performance.
- AFDM uses chirp basis functions and DAFT-domain processing to represent and separate propagation paths along delay or Doppler dimensions.
- AFDM can suppress inter-carrier interference, achieve full diversity, and enlarge the unambiguous delay-Doppler region while retaining favorable spectral efficiency and peak-to-sidelobe ratio.
- AFDM’s chirp-based structure supports joint communication and sensing through radar-like ambiguity properties and configurable chirp parameters.
- The article examines AFDM-enabled ISAC across satellite, vehicular, UAV-swarm, and high-speed railway scenarios, then surveys enabling technologies and future directions.
II. FUNDAMENTALS OF AFDM-ENABLED ISAC IN DYNAMIC ENVIRONMENTS
Dynamic ISAC environments involve relative motion, rapidly varying propagation, severe Doppler shifts, and fast fading that challenge conventional signal processing. They also require real-time adaptation while communications and sensing increasingly share stringent operational requirements.
- Relative motion among transceivers, targets, and scatterers produces rapidly varying propagation conditions, especially in high-mobility environments.
- Vehicular environments combine severe Doppler shifts and fast time-varying fading, making slowly varying channel assumptions inadequate.
- Rapid channel fluctuations require new signal representations and processing techniques to handle Doppler-induced frequency dispersion.
- Dynamic ISAC also demands real-time adaptation of beam steering, resource allocation, and other system parameters.
B. Limitations of OFDM in Dynamic Environments
OFDM relies on subcarrier orthogonality and a sufficiently stable channel, but Doppler shifts and rapid channel variation undermine both assumptions in dynamic environments. AFDM addresses these limitations by using DAFT-domain processing to distinguish delay-Doppler paths.
- OFDM divides a wideband frequency-selective channel into parallel narrowband subchannels and transmits independent low-rate streams on them.
- OFDM subcarrier orthogonality requires the strict relation ∆f = 1/Ts, where Ts is the OFDM symbol duration.
- Doppler destroys subcarrier orthogonality, causing spectral leakage and inter-carrier interference whose power increases with normalized Doppler frequency fd/∆f.
- Rapid channel variation can make Ts exceed coherence time, preventing effective data recovery through simple equalization.
- AFDM replaces the conventional DFT with a parameterized DAFT whose structured channel representation makes coupled multipath components distinguishable in the delay-Doppler domain.
D. AFDM Advantages
AFDM’s waveform structure is presented as offering full diversity, implementation and design flexibility, and lower BER at a given transmit power than the corresponding OFDM representation in dynamic environments.
- AFDM’s distinctive advantages include full diversity gain, hardware implementation flexibility, and design flexibility.
- The resulting diversity gain can reduce BER at a given transmit power in dynamic environments.
1) Full Diversity Gain:
AFDM converts multipath delay and Doppler shifts into separable DAFT-domain components, providing full diversity while retaining flexible waveform-based integration of communication and sensing.
- Full Diversity Gain: AFDM maps each path to distinct non-overlapping DAFT-domain shifts, producing a diagonal channel matrix that characterizes full delay-Doppler diversity.This diversity can reduce BER at a given transmit power and supports robustness in both communication and sensing.
- Full Diversity Gain: Full delay-Doppler diversity improves reliable transmission under high mobility and increases processing gain for target detection.The paper frames multipath diversity as a robustness resource for both communication and sensing.
- Full Diversity Gain: AFDM can reuse conventional OFDM processing modules and hardware because DAFT combines chirp-related phase operations with FFT processing.This structural similarity supports practical deployment.
- Full Diversity Gain: Tunable chirp parameters shape delay-Doppler ambiguity characteristics while embedding communication information into the waveform.Under appropriate configurations, the ambiguity function retains a sharp mainlobe and favorable sidelobe level.
- Full Diversity Gain: AFDM integrates communication and sensing while preserving key sensing characteristics, with the paper describing zero impairment to sensing performance.Communication-induced waveform changes can be controlled through parameter configuration.
III. REPRESENTATIVE APPLICATIONS OF AFDM-ENABLED ISAC IN DYNAMIC ENVIRONMENTS
Dynamic applications impose stringent communication and sensing requirements because mobility and changing topologies produce rapidly varying channels and complex operating conditions.
- Representative Applications: The representative-scenarios analysis identifies four dynamic environments and examines their system challenges and AFDM’s potential responses.The scenarios are introduced through their distinctive delay-Doppler-domain signal representation.
- UAV Networks: UAV networks combine high mobility, flexible deployment, and changing three-dimensional topologies with airborne communication and mobile sensing roles.These roles require real-time environmental awareness for obstacle avoidance, formation flight, and path planning.
- UAV Networks: UAV-network ISAC requires reliable links over rapidly varying air-to-ground channels, accurate range and velocity estimation, and low energy consumption.
B. Vehicular Networks
Vehicular and satellite environments combine severe mobility-induced channel variation with demanding sensing and communication requirements, while high-speed rail adds complex, rapidly changing multipath conditions.
- Vehicular Networks: Vehicular networks require low-latency, reliable communication and robust real-time sensing for cooperative driving and collision avoidance.High-speed relative motion creates an extremely dynamic propagation environment involving vehicles, pedestrians, and obstacles.
- Vehicular Networks: AFDM maintains delay-Doppler orthogonality under severe doubly selective channels, reducing BER and improving throughput in dynamic multipath scenarios.Its compact ambiguity function also supports range and velocity estimation.
- High-Speed Railway Systems: High-speed railway systems face enormous Doppler shifts, rapid doubly selective fading, and strongly varying multipath across tunnels, bridges, and other environments.Their ISAC requirements include reliable train-control communication and precise sensing for autonomous driving and active safety.
- LEO Satellite Platforms: LEO satellite links experience substantial Doppler spread and severe doubly selective fading, while rapidly changing propagation complicates sensing and conventional channel-state-based transmission.AFDM’s delay-Doppler processing, low pilot overhead, and reduced processing complexity are identified as beneficial for constrained satellite systems.
IV. KEY TECHNOLOGIES
Key technologies for AFDM-enabled ISAC address timely delay-Doppler parameter extraction and adaptive beamforming in rapidly varying environments.
- Parameter Estimation: Accurate delay and Doppler estimation is fundamental because propagation parameters determine communication reliability and sensing accuracy.Rapid channel variation, closely spaced paths, and changing propagation conditions make timely extraction challenging.
- Parameter Estimation: AFDM’s structured delay-Doppler representation supports joint estimation of path delays and Doppler shifts.Learning-based methods can improve adaptability with reduced reliance on prior information.
- Parameter Estimation: Tensor decomposition exploits AFDM’s multidimensional structure for accurate and computationally efficient parameter estimation.Jointly identifying delay, Doppler, and path correspondence can avoid explicit path matching.
- Beamforming Design: Beamforming jointly optimizes antenna amplitudes and phases to focus energy on intended users and sensing targets while suppressing interference.AFDM’s configurable chirp parameters add waveform-design freedom that can be adjusted to channel and target conditions.
C. Index-Modulated AFDM Transmission
Index modulation extends AFDM-enabled ISAC by encoding information through selectable chirp parameters while preserving AFDM’s sensing-oriented signal structure. The case study evaluates tensor-based parameter estimation against MUSIC, OMP, and the CRB using delay and Doppler NMSE.
- Index modulation encodes information by selecting chirp parameter pairs from a predefined lookup table while preserving favorable AFDM sensing characteristics.
- The case study uses a 16×16 MIMO system at 28 GHz with 100 MHz bandwidth, 128 chirp subcarriers, three paths, and AFDM parameters c1 = 7/256 and c2 = 0.
- MUSIC and OMP serve as benchmark estimators, while the CRB provides a lower bound for the variance of unbiased estimators.
- Tensor-based estimation consistently outperforms MUSIC and OMP and approaches the CRB for delay and Doppler NMSE across operating conditions.Delay and Doppler estimates relate closely to target range and velocity.
VI. OPEN CHALLENGES AND FUTURE DIRECTIONS
Future AFDM-enabled ISAC research must address near-field coupling among range, angle, Doppler, and distance, alongside the underexplored challenge of secure transmission in dynamic environments.
- A. Near-Field Coupling in AFDM-Enabled ISAC: Near-field propagation introduces spherical wavefronts and spatial nonstationarity, coupling delay, Doppler, angle, and distance beyond conventional planar-wave models.Future work includes near-field waveform design, joint beam focusing and parameter estimation, and adaptive chirp selection.
- B. Secure Transmission Strategies: Secure transmission remains relatively underexplored, although configurable chirp parameters and index modulation can help conceal waveform characteristics and hinder unauthorized demodulation.Open problems include adaptive chirp design, secure beamforming, and balancing security with communication, sensing, and energy objectives under changing locations.
C. Hardware Impairments and Practical Implementation
AFDM’s practical deployment is constrained by hardware impairments and implementation complexity that can distort its signal structure and degrade communication and sensing. The article identifies these constraints as open validation and design challenges.
- C. Hardware Impairments and Practical Implementation: High PAPR can reduce power-amplifier efficiency and linearity, motivating low-complexity reduction methods that preserve AFDM’s delay-Doppler structure and sensing performance.
- C. Hardware Impairments and Practical Implementation: Phase noise, carrier-frequency offset, sampling errors, and nonlinear amplification may degrade communication and sensing, especially under rapidly varying channels.Their effects require realistic models and experimental measurements.
- C. Hardware Impairments and Practical Implementation: Practical implementation also requires efficient synchronization, low-complexity baseband processing, robust receivers, and over-the-air validation of AFDM’s theoretical advantages.