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Integrated sensing, communications, and identification for low-altitude economy

Shanxing Zeng, Jun Wang, Ying-Chang Liang

arXiv:2610.23602v1cs.ITmath.IT

TL;DR

Low-altitude networks need to support communication, sensing, and identification, while existing ISAC addresses only the joint design of communication and sensing. This review organizes ISCAI around BackCom-based aircraft identification and its integration with sensing and communications. It concludes that identity mechanisms must fit the ISAC waveform and echo-processing chain for joint acquisition of target states and identities.

  • Problem

    Low-altitude networks need communication, sensing, and identification, but the paper identifies a gap in incorporating identification into joint communication-and-sensing design.

    Source: §1 · p. 3

  • Method

    The review synthesizes low-altitude ISAC foundations and designs, then develops a framework for BackCom-based identification and its integration into ISAI and ISCAI.

    Source: §1 · p. 3, §6 · p. 33

  • Results

    The review identifies BackCom-based electronic license plates as a key route toward ISCAI, with identification compatible with the ISAC waveform and echo-processing chain for joint acquisition and association of target states and identities.

    Source: §6 · pp. 32–33

  • Takeaways & Limitations

    The survey frames ISCAI as an integrated design in which aircraft identity is acquired alongside target state and directly associated with it.

    Source: §6 · p. 32

    BackCom-based identification requires aircraft to carry backscatter devices and generally has a shorter effective identification range than active identity reporting because of round-trip propagation loss.

    Source: §4.2.4 · p. 23

Abstract

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With the rapid development of low-altitude economy (LAE), low-altitude information networks (LAINs) must provide reliable communication, environmental sensing, and target identification to support efficient and safe airspace operation. Although integrated sensing and communications (ISAC) provides a foundation for the joint design of communication and sensing, identification must also be incorporated to meet airspace safety and regulatory requirements. This paper reviews integrated sensing, communications, and identification (ISCAI) for LAE. We first review low-altitude communication, sensing, and identification approaches and explain how representative applications motivate their integration. We then summarize low-altitude ISAC fundamentals, including system architectures, channel modeling, unified signal models, and performance metrics, followed by communication-centric and sensing-centric designs, resource allocation, and aircraft trajectory optimization. Building on these, we introduce ISCAI, focusing on a backscatter communication (BackCom)-based identification mechanism in which backscatter devices serve as electronic license plates for aircraft. We present system and signal models for integrated sensing and identification (ISAI) and ISCAI, together with multifunctional receiver designs and mutual integration gains. Finally, we discuss open problems and future directions, including an information-theoretic framework, artificial intelligence-empowered system design, and reconfigurable intelligent surface-enhanced ISCAI. This review provides a structured perspective on the evolution of LAINs toward integrated communication, sensing, and identification.

1 Introduction

LAE growth makes reliable, safe airspace operation depend on low-altitude networks that support communication, sensing, and identification. Because these functions are coupled in practice but identification remains outside joint ISAC design, the paper reviews ISCAI and its technical foundations.

Motivation

  • LAE spans activities such as UAV logistics, urban air mobility, emergency response, and aerial inspection, making efficient and safe operation an increasingly important concern.Industry forecasts cited in the passage project a $163 billion global UAV market by 2030 and a $1 trillion UAM market by 2040.Source: §1 · p. 1
  • LAINs must support communication, sensing, and identification because safety-critical low-altitude operations couple reliable links, environmental awareness, and continuous identity traceability.Communication alone cannot provide obstacle-avoidance sensing, while sensing without reliable communication cannot deliver timely updates for coordination.Source: §1 · p. 2

Research gap

  • Existing low-altitude ISAC research jointly designs communication and sensing, but identification remains largely standalone and is not jointly designed with both functions.The review notes that only a few ISAC studies mention identification as a requirement, while none designs identification jointly with communication and sensing.Source: §1 · pp. 2–3

Scope and organization

  • The paper reviews ISCAI for LAE, covering low-altitude functions and applications, ISAC foundations and designs, BackCom-based identification, ISAI and ISCAI models and receivers, integration gains, and future directions.It identifies BackCom devices as electronic license plates and discusses information-theoretic, AI-enabled, and RIS-enhanced research directions.Source: §1 · p. 3

2 Overview of LAINs

The overview surveys low-altitude communication, sensing, and identification approaches alongside application requirements, showing why these functions motivate integrated network design. It covers terrestrial, satellite, and aerial-ad-hoc communication, multiple sensing and identification modalities, and four representative LAE scenarios.

2 Overview of LAINs

  • LAINs are reviewed through standalone communication, sensing, and identification approaches and representative applications, with the section concluding that their coupled operational needs motivate more integrated design.The next step identified by the review is low-altitude ISAC, which jointly designs communication and sensing.Source: §2 · p. 4, §2.5 · p. 10

2.1 Low-altitude communication

  • Low-altitude communication must support control, coordination, and mission data across dynamic three-dimensional airspace, using terrestrial cellular networks supplemented by satellite and aerial ad hoc layers.Terrestrial networks offer primary infrastructure, satellites extend geographic coverage, and air-to-air links support local cooperation and topology extension.Source: §2.1 · pp. 4–5, §2.1.2 · p. 6, §2.1.3 · pp. 6–7

2.1.1 Terrestrial cellular network-based low-altitude communication

  • Ground cellular systems face limited vertical coverage, severe interference under high line-of-sight probability, and rapidly changing channels from aircraft mobility.Responses described include adjustable-elevation antennas and coordinated resource management, channel models, and RIS-assisted virtual line-of-sight links.Source: §2.1.1 · pp. 5–6

2.1.2 Satellite network-assisted low-altitude communication

  • Satellite links can extend or back up low-altitude connectivity beyond ground coverage, but LEO mobility brings changing geometry, Doppler shifts, beam misalignment, and cross-network interference.The cited research also addresses routing, security, random access, and trajectory design in satellite-assisted systems.Source: §2.1.2 · p. 6

2.1.3 Aerial ad hoc network-based low-altitude communication

  • Air-to-air links support local cooperation and multi-hop access, while highly mobile peers, frequent route reconstruction, access collisions, and relay-energy demands constrain network operation.Research spans channel characterization, predictive routing, distributed resource management, and formation-aware topology control.Source: §2.1.3 · pp. 6–7

2.2 Low-altitude sensing

  • Low-altitude sensing spans target detection, localization, velocity estimation, and tracking, with radar approaches ranging from single-site FMCW to multistatic, networked, and passive configurations.Optical, infrared, LiDAR, and acoustic sensors can complement radar, though their performance is more sensitive to environmental conditions or range limits.Source: §2.2 · pp. 7–8

2.3 Low-altitude identification

  • Identification distinguishes target classification from acquisition of an aircraft’s individual identity, using RF, vision, acoustic, radar, multimodal, broadcast, network-based, and fingerprinting approaches.Broadcast and network reporting suit cooperative or connected aircraft, whereas RFF complements them for non-cooperative targets but depends on active transmissions and robust fingerprints.Source: §2.3 · p. 8, §2.3.1 · p. 8, §2.3.2 · pp. 8–9

2.4 Typical applications for LAINs

  • Smart logistics, UAM, emergency rescue, and low-altitude agriculture couple distinct communication, sensing, and identification demands, supporting the case for integrated LAIN design.Examples include intruder identification for delivery airspace, precise awareness and identity in shared UAM corridors, rescue operations under degraded infrastructure, and agricultural mission traceability.Source: §2.4 · p. 9, §2.4.1 · p. 10, §2.4.2 · p. 10

3 ISAC for LAINs

Low-altitude ISAC combines reliable data transmission with target localization, velocity estimation, and trajectory tracking through reflected signals. The review covers its technical foundations and design and optimization approaches.

Low-altitude ISAC

  • Low-altitude ISAC supports data transmission alongside target localization, velocity estimation, and trajectory tracking using reflected signals.The review covers system architectures, channel modeling, unified signal models, and performance metrics.Source: §3 · p. 10
  • The section reviews communication-centric and sensing-centric waveform and receiver designs for low-altitude ISAC.Source: §3 · p. 10
  • The review also examines resource allocation and aircraft trajectory optimization as parts of low-altitude ISAC design.Source: §3 · p. 10

3.1 Fundamentals of low-altitude ISAC

Low-altitude ISAC fundamentals cover deployment architectures, channel representations, unified signal models, and metrics for communication, sensing, and their joint tradeoff.

3.1.1 System architectures

  • Monostatic, bistatic, and distributed deployments trade implementation simplicity against self-interference, synchronization, backhaul, and the geometric diversity needed for 3D localization.Monostatic nodes are co-located; bistatic nodes separate transmitter and receiver; distributed systems fuse measurements from multiple cooperating nodes.Source: §3.1.1 · p. 11

3.1.2 Channel modeling

  • Monostatic channel modeling uses planar-array steering vectors and represents communication paths, target echoes, clutter, and target radar cross section.RCS approaches include deterministic physics-based models and statistical Swerling models; more detailed frameworks combine large-scale, angle-dependent, and random components.Source: §3.1.2 · pp. 11–12

3.1.3 Unified ISAC signal models

  • Unified ISAC signal models describe a beamformed transmitted waveform and the resulting communication and sensing receiver signals, including noise, echoes, and clutter.The transmitted-signal representation does not assume a particular modulation, allowing OFDM, OTFS, and FMCW waveforms.Source: §3.1.3 · pp. 12–13

3.1.4 Performance metrics

  • ISAC evaluation covers communication efficiency and reliability, sensing detection and estimation, and their joint performance tradeoff under shared power and bandwidth.Communication measures include achievable rate, BER, outage probability, and delay; sensing measures include detection curves, MSE or CRLB, and range and velocity resolution.Source: §3.1.4 · p. 13

3.2 Communication-centric ISAC

Communication-centric ISAC adapts communication waveforms for sensing while prioritizing communication performance. The review covers OFDM receiver methods and alternatives designed for high mobility.

3.2 Communication-centric ISAC

  • Communication-centric ISAC adapts communication waveforms for sensing while prioritizing communication quality of service, making it suitable when communication is the primary requirement.OFDM is the section’s main focus because it is widely adopted in 4G and 5G and is the most studied and validated communication-centric ISAC waveform in current research.Source: §3.2 · p. 13

3.2.1 Transmitted OFDM signal

  • The OFDM transmitted signal is represented using subcarrier symbols and a time-domain waveform, within a unified framework that does not restrict the modulation waveform.The framework can incorporate OFDM, OTFS, and FMCW.Source: §3.2.1 · pp. 13–14

3.2.2 Parameter estimation at the sensing receiver

  • OFDM sensing receivers suppress clutter and estimate target parameters; conventional estimators suit low-mobility cases, while ICI-aware methods address high mobility at greater receiver complexity.At high mobility or millimeter-wave frequencies, ICI can spread signal power across subcarriers; the review contrasts efficient separable estimators with more complex ICI-aware approaches.Source: §3.2.2 · pp. 14–15

3.2.3 Data demodulation at the communication receiver

  • OFDM communication receivers use conventional channel estimation and detection when ICI is negligible, and sparse channel recovery or iterative receivers when ICI is significant.Receiver selection depends on aircraft mobility, channel sparsity, pilot resources, and real-time processing constraints.Source: §3.2.3 · p. 15

3.2.4 New communication-centric ISAC waveforms for high-mobility scenarios

  • OTFS and AFDM are considered for high-mobility scenarios where OFDM subcarrier orthogonality is difficult to maintain, with different processing and estimation tradeoffs.OTFS uses a delay–Doppler representation that facilitates target delay and Doppler estimation but increases receiver-processing overhead; AFDM uses chirp basis functions and may require less processing overhead than 2D OTFS.Source: §3.2.4 · p. 16

3.3 Sensing-centric ISAC

Sensing-centric ISAC embeds communication data into radar waveforms while prioritizing sensing, with FMCW as the focus. The review covers embedding schemes and receiver designs for sensing and communication.

3.3 Sensing-centric ISAC

  • Sensing-centric ISAC prioritizes sensing performance and is suited to applications requiring high sensing precision; the review focuses on FMCW-enabled systems.FMCW is highlighted for Doppler-shift resistance, extended sensing range, and simple self-interference suppression.Source: §3.3 · p. 16

3.3.1 Basic FMCW waveform

  • FMCW transmits repeated chirps and has an approximately 0 dB PAPR, allowing higher average transmit power than OFDM under a common peak-power constraint.The increased energy on target is described as beneficial for detecting low-RCS objects such as small low-altitude UAVs.Source: §3.3.1 · p. 16

3.3.2 Schemes embedding communication data into FMCW

  • Four FMCW data-embedding approaches differ in how communication symbols perturb sensing: inter-chirp phase, chirp-parameter, intra-chirp multiplicative, and index modulation.Inter-chirp phase modulation preserves chirp structure but couples data phase with Doppler rotation; intra-chirp multiplicative modulation can raise communication rate while degrading sensing.Source: §3.3.2 · pp. 16–17

3.3.3 Parameter estimation at the sensing receiver

  • FMCW sensing receivers form a spatial, slow-time, and fast-time tensor for parameter estimation, with estimator choice balancing resolution, complexity, and embedding-induced distortion.FFT methods are relatively low-complexity but grid-limited; MUSIC can offer finer resolution with greater processing, and some embedding schemes require compensation before estimation.Source: §3.3.3 · pp. 17–18

3.3.4 Data demodulation at the communication receiver

  • Communication demodulation strategies for inter-chirp phase modulation trade pilot overhead, detection reliability, and computational complexity because data phase and Doppler are coupled.Differential detection avoids explicit Doppler estimation but incurs an inherent BER penalty; other strategies use pilots or jointly estimate parameters and data.Source: §3.3.4 · pp. 18–19

3.4 Resource allocation and aircraft trajectory optimization

Low-altitude ISAC treats aircraft trajectories as controllable spatial resources that can be jointly optimized with communication and sensing variables. The reviewed approaches span aircraft as communication users and targets, and UAVs as aerial mobile base stations, with cooperation and joint design introducing added complexity.

3.4 Resource allocation and aircraft trajectory optimization

  • Trajectory optimization can improve the communication–sensing tradeoff by changing aircraft positions to affect channel quality and sensing geometry.The review groups trajectory and resource-allocation work into two categories based on aircraft roles.Source: §3.4 · p. 19
  • When aircraft act as communication users and sensing targets, studies jointly design trajectories with beamforming or other network variables, sometimes using cooperative base stations.These designs address objectives such as communication rate or imaging quality under sensing, mission, power, and collision constraints; cooperation can improve spatial diversity, interference management, and sensing coverage but increases interference and optimization complexity.Source: §3.4 · p. 19
  • When UAVs act as aerial mobile base stations, designs combine trajectory planning with user association, sensing schedules or selection, and beamforming or resource allocation.Reviewed approaches include periodic or on-demand sensing and weighted communication–sensing objectives; multi-UAV settings further couple trajectory, association, and radio-resource decisions.Source: §3.4 · p. 19
  • Most reviewed formulations assume finite mission periods, deterministic LoS channels, and known or estimated user and target states.Scaling to more users, targets, and cooperative base stations increases model-based computational complexity, while learning-based methods require extensive interaction data and training overhead.Source: §3.4 · pp. 19–20
  • Scalable online joint trajectory and resource optimization remains open under imperfect channel information, uncertain target motion, limited onboard energy, residual interference, and clutter.Beamforming, power allocation, and scheduling can update much faster than physical aircraft trajectories.Source: §3.4 · p. 20

3.5 Summary

Low-altitude ISAC research has developed a technical foundation across architectures, models, design, and optimization. However, sensing physical states does not itself provide aircraft identity, motivating the transition to ISCAI.

3.5 Summary

  • Low-altitude ISAC studies cover system architectures, channel and signal models, waveform and receiver design, resource allocation, and aircraft trajectory optimization.The reviewed work establishes a substantial technical foundation for integrating communication and sensing.Source: §3.5 · p. 20
  • ISAC estimates target positions, velocities, and trajectories but lacks identity awareness.Physical-state estimates cannot directly determine who a target is.Source: §3.5 · p. 20
  • The section therefore turns to ISCAI, which incorporates identification into ISAC.Source: §3.5 · p. 20

4 ISCAI for LAINs

ISCAI for low-altitude information networks integrates communication, sensing, and identification, with this section focusing on individual identity acquisition. It frames identification as an extension of ISAC and develops BackCom-based identification as a route to integrated sensing and identification systems.

4 ISCAI for LAINs

  • The section introduces a unified ISCAI framework and focuses primarily on individual identity acquisition rather than target classification.It examines system and signal models, multifunctional receivers, and mutual integration gains for ISAI and ISCAI.Source: §4 · p. 20

4.1 From ISAC to ISCAI

  • ISCAI jointly considers communication, sensing, and identification in system modeling, signal processing, receiver design, and optimization, sharing waveform, spectrum, and hardware resources.Identification-related information or variables are explicitly considered alongside communication symbols and sensing parameters.Source: §4.1 · p. 20
  • ISAC estimates physical target states but cannot acquire identity, which is increasingly relevant to airspace safety and regulatory compliance.Identity information can support differentiated measures such as prioritized tracking, early warnings, or countermeasures.Source: §4.1 · p. 20
  • ISCAI combines sensing and identification so acquired identities can support authentication, regulatory authorization, trajectory association, and differentiated airspace management.The review characterizes ISAC as making targets visible and ISCAI as also aiming to distinguish them accurately and control them effectively.Source: §4.1 · p. 20
  • Parallel identity reporting is not ISCAI when identifiers remain ordinary payloads uncoupled from ISAC system modeling or signal processing.Existing active reporting and RFF-based identification differ from typical ISAC signal origins, propagation links, and echo processing, preventing natural sharing of waveforms, spectrum, and hardware.Source: §4.1 · p. 20
  • The review treats BackCom-based identification as a technical bridge to ISAI and ISCAI while leaving other identification mechanisms within the broader ISCAI definition.Source: §4.1 · p. 21

4.2 BackCom-based identification paradigm

The review presents BackCom-based aircraft identification using backscatter devices as lightweight electronic license plates. It describes the device and signal process, identification metrics, and how BackCom can integrate with ISAC while retaining important deployment and link limitations.

4.2 BackCom-based identification paradigm

  • Backscatter devices serve as electronic license plates by storing aircraft identity information for BackCom-based identification.The paradigm is inspired by traffic-control systems that capture vehicle license plates through visual sensors.Source: §4.2 · p. 21, §4.2.2 · p. 22

4.2.1 The basic principles of BackCom

  • A BackCom device transmits information by modulating and reflecting an incident RF signal rather than generating its own carrier.A typical system has an RF source, a backscatter device, and a reader; the device includes modulation, control, memory, encoding, and energy-management components.Source: §4.2.1 · p. 21
  • The device switches antenna-load impedance states to encode symbols in its reflection, and the reader detects the resulting sequence.BackCom avoids power-intensive carrier-generation components and can harvest energy from ambient electromagnetic waves.Source: §4.2.1 · pp. 21–22

4.2.2 The process of BackCom-based identification

  • An identification station wakes aircraft-mounted devices, sends excitation carriers, and the receiver demodulates their reflected identity sequences for database comparison.The device may also transmit sensor data; unlike a typical independent RFID system, this approach can reuse existing communication or sensing RF signals.Source: §4.2.2 · p. 22

4.2.3 Identification performance metrics

  • Identification performance is characterized by decision reliability and timeliness, including missed-identification, identity-false-alarm, false-identification probabilities, and latency.For BackCom, latency includes wake-up and synchronization, identity-sequence transmission, and joint parameter-estimation and symbol-detection time.Source: §4.2.3 · pp. 22–23

4.2.4 The main advantages and limitations of BackCom-based identification

  • BackCom can reuse existing RF signals and integrate with ISAC, but requires pre-equipped aircraft and has a shorter effective range than active identity reporting.The two-way propagation path can weaken the signal and increase missed identification; wake-up and synchronization add latency, and passive reflection supports a limited data rate. Acquired identity still requires subsequent authentication and regulatory authorization.Source: §4.2.2 · p. 22, §4.2.4 · p. 23

4.3 Integrated sensing and identification

ISAI combines low-altitude sensing with BackCom-based aircraft identification, sharing spectrum and hardware while associating identities with physical parameters. The integration supports reuse of sensing signals for identification and offers gains in sensing accuracy and resource adaptation.

4.3.1 System model

  • An ISAI station wakes aircraft backscatter devices, illuminates aircraft-BD pairs, and jointly estimates aircraft parameters and extracts identities; without a valid BD sequence, physical tracking can continue but identity acquisition has failed for that observation.The architecture may be monostatic, bistatic, or distributed.Source: §4.3.1 · pp. 23–24

4.3.2 Signal model

  • The FMCW beat-signal model combines BD antenna backscatter with aircraft-surface reflection, and a fixed BD reflection state reduces the composite term to the conventional FMCW reflection coefficient.Residual self-interference and time-varying clutter can degrade identification, and increasing transmit power alone cannot eliminate that degradation.Source: §4.3.2 · p. 25

4.3.3 Joint sensing and identification receiver design

  • Joint receiver recovery is nonlinear because BD symbols couple with aircraft delay and Doppler, while BD synchronization and clock errors degrade parameter estimation and symbol detection.The bilinear coupling makes cascaded FFT processing no longer directly applicable when symbols and aircraft parameters are both unknown.Source: §4.3.3 · pp. 25–26
  • Moving-target receiver approaches include element-wise complex division, zero-padded BD patterns with low-rank matrix decomposition, and differential-symbol tensor decomposition [166], [157], and [167].The approaches use sensing-parameter estimates to assist demodulation, mitigate inter-symbol interference, or recover aircraft parameters and BD symbols.Source: §4.3.3 · p. 26

4.3.4 Mutual integration gains

  • Identification reuses the sensing signal as the BackCom carrier, avoiding dedicated identification waveforms and separate frequency allocations.This is attractive for spectrum-constrained and cost-sensitive deployments.Source: §4.3.4 · p. 26
  • BD antennas can enlarge aircrafts’ equivalent radar cross section, lowering parameter-estimation CRLBs and improving sensing accuracy, especially for small or low-observable aircraft.The passage attributes the CRLB improvement to results depicted in [167].Source: §4.3.4 · p. 26
  • Identification can guide adaptive sensing allocation: high-threat targets may receive more frequent revisits, higher-resolution sensors, or additional tracking channels, while low-priority targets may use fewer resources.Source: §4.3.4 · pp. 26–27
  • In distributed architectures, decoded identities provide cues for associating observations across stations, but missed detections or decoding errors can still cause association failures.Position and trajectory information should also be considered.Source: §4.3.4 · p. 27

4.4 Integrated sensing, communications, and identification

ISCAI extends ISAC with backscatter-based aircraft identification, sharing spectrum and hardware to support communication, sensing, and identity acquisition. Its receiver designs must address coupled signal recovery and practical interference, while integration also creates gains across the three functions.

4.4 Integrated sensing, communications, and identification

  • ISCAI combines communication, target sensing, and individual identity acquisition using shared spectrum and hardware.Backscatter devices provide the identification mechanism within low-altitude ISAC.Source: §4.4 · p. 27

4.4.1 System model

  • The station uses communication signals as triple-function waveforms, serving users while sensing aircraft and decoding identity-bearing backscatter echoes.It estimates aircraft positions and velocities, then detects BD symbol sequences and matches the resulting identities.Source: §4.4.1 · pp. 27–28

4.4.2 Signal model

  • BDs encode identity by modulating their reflection coefficients in synchronization with incident OFDM symbols, with one BD symbol per OFDM-symbol period.Aircraft echoes contain both structural reflections and antenna-mode backscatter carrying identity information.Source: §4.4.2 · p. 28

4.4.3 Multifunctional receiver design •

  • Joint receiver recovery is difficult because BD symbols, aircraft delay and Doppler, and transmitted data are coupled, while BD timing errors can invalidate the ideal signal model.For nearby aircraft-BDs, identity-modulated reflections can also interfere with user demodulation; prior approaches use subcarrier or waveform-domain orthogonality [170], [171].Source: §4.4.3 · p. 29
  • Reported joint recovery methods use pilot-aided estimation with 3D-Newtonized OMP [168] or alternating ML estimation and detection [176], but handle ICI differently.In [168], treating ICI as noise limits achievable delay and Doppler accuracy; [176] exploits ICI for initialization. Earlier approaches address static scenarios and omit structural reflection components [174], [172], [169], [175].Source: §4.4.3 · p. 29

4.4.4 Mutual integration gains

  • Integration enables BD reuse of communication and sensing signals, larger equivalent aircraft RCS, sensing-guided resource allocation, and identity-assisted data association.The Miller–Chang bounds for delay and Doppler estimation decrease after adding BDs [176]. Sensed locations and identities can guide communication scheduling, while uplink sensor data can cross-validate identity and support cooperative environmental sensing.Source: §4.4.4 · p. 30

4.5 Summary

The section summarizes the progression from ISAC to ISCAI and the BackCom-based identification approach, then reviews ISAI and ISCAI models, receiver designs, and integration gains.

4.5 Summary

  • The section synthesizes ISCAI’s development for LAINs, from BackCom principles and performance measures through system models, receiver design, and mutual integration gains.It also compares ISAC, ISAI, and ISCAI in Table 4.Source: §4.5 · p. 30

5 Open problems and future directions

The paper identifies tradeoffs among communication, sensing, and identification, alongside complex system design, as open challenges for ISCAI. It outlines information-theoretic, AI-based, and RIS-enhanced research directions.

5.1 Information-theoretic framework of ISCAI

  • A unified information-theoretic framework must characterize performance interdependencies and joint Pareto boundaries across communication, sensing, and identification.Their metrics differ, and the sensing parameters are continuous while aircraft identities and BD symbols are discrete, complicating framework construction.Source: §5.1 · p. 31

5.2 AI-empowered ISCAI system design

  • AI methods are proposed for joint waveform and receiver design where analytical approaches face intractable solutions, high computational complexity, or poor environmental adaptability.Deep reinforcement learning could optimize waveforms under BD activation constraints; model-driven deep unfolding could learn joint recovery of communication symbols, sensing parameters, and identities.Source: §5.2 · pp. 31–32

5.3 RIS-enhanced ISCAI

  • RIS can enhance communication links and target echoes, suppress clutter, and create virtual LoS links for occluded users or targets.Identification requires BD incident power to exceed its harvesting threshold, and high mobility can make cascaded channel estimates outdated before RIS configuration; the paper calls for mobility-aware prediction, low-overhead training, and robust optimization.Source: §5.3 · p. 32

6 Conclusions

LAINs are evolving beyond connectivity toward integrated communication, sensing, and identification. The review identifies waveform-compatible BackCom-based electronic license plates as a route to jointly acquire aircraft states and identities.

6 Conclusions

  • ISCAI requires identification compatible with the ISAC waveform and echo-processing chain to jointly acquire target states and identities and directly associate them.The review cautions that simply adding an existing identity-reporting system alongside ISAC is insufficient.Source: §6 · p. 32
  • BackCom-based electronic license plates are identified as a key route for organizing ISAI and ISCAI research.In this paradigm, backscatter devices serve as aircraft license plates.Source: §6 · pp. 32–33
  • The review covers low-altitude communication, sensing, and identification approaches, ISAC models and metrics, system designs, resource allocation, and aircraft trajectory optimization.It also presents ISAI and ISCAI system and signal models, multifunctional receiver design, and mutual integration gains.Source: §6 · p. 33
  • Future directions include an information-theoretic framework, AI-empowered ISCAI design, and RIS-enhanced ISCAI.The review aims to guide research and practical deployments toward more capable, intelligent, and safe LAINs.Source: §6 · p. 33

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