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Multi-UE Networked Sensing: A New Paradigm for 6G Perceptive Mobile Networks
J. Andrew Zhang, Jingying Bao, Kai Wu, Henk Wymeersch, Christos Masouros, Y. Jay Guo
TL;DR
Distributed wireless nodes provide fragmented views, motivating networked sensing for richer environmental perception in ISAC. This article introduces multi-UE sensing, presents uplink, downlink, and hybrid architectures with synchronization, correlation-aware estimation, and fusion, and identifies open challenges including correlation modelling, compression, and co-optimization.
Problem
Individual sensing devices provide fragmented and limited observations, while exploiting correlated observations from distributed UEs remains insufficiently addressed for networked ISAC sensing.
Method
The article develops a multi-UE sensing framework spanning uplink, downlink, and hybrid architectures, with synchronization, correlation-aware parameter estimation, and multi-view fusion.
Results
Multi-UE sensing offers a scalable paradigm that exploits multi-view diversity and correlation-aware processing for high-resolution, robust, and wide-coverage sensing.
Takeaways & Limitations
Multi-UE sensing provides a pathway toward network-native environmental perception using distributed UEs and fusion at the network edge or a single BS.
Takeaways & Limitations
Processing must remove synchronization-induced offsets while preserving target-dependent correlation, and theoretical performance scaling and tradeoffs remain to be established.
Abstract
from arXiv · showhide
Networked sensing, which jointly exploits observations from multiple distributed nodes, is essential for unlocking the full sensing potential of integrated sensing and communications (ISAC). This article introduces multi-UE sensing, a new networked sensing paradigm for future perceptive mobile networks that exploits the correlated sensing observations naturally arising from distributed user equipment devices (UEs) interacting with common targets. Representative uplink, downlink, and hybrid sensing architectures are presented, together with a multi-view signal processing framework encompassing synchronization, correlation-aware parameter estimation, and sensing fusion. Key open challenges, including correlation modelling, target association, sensing information compression, and communication-sensing co-optimization, are also discussed.
1. INTRODUCTION
Multi-UE sensing extends networked sensing from cooperating BS receivers to distributed, non-cooperative UEs whose correlated multi-view observations can be fused at a single BS. The paradigm targets scalable, cost-effective environmental sensing while supporting both non-coherent and potentially coherent processing.
- Motivation: 6G perceptive mobile networks reuse communication signals, spectrum, and infrastructure to provide large-scale environmental sensing alongside wireless connectivity.Applications include intelligent transportation, autonomous systems, disaster monitoring, infrastructure protection, and low-altitude sensing.
- Motivation: Networked sensing jointly exploits distributed observations to obtain multi-view, high-resolution perception, addressing fragmented views caused by restricted perspectives, blockage, and fading.Distributed observations can improve sensing resolution, robustness, coverage, and target identifiability.
- From Multi-CoopRx to Multi-UE: Existing multi-CoopRx architectures rely on coordinated BS receivers and require dense BS deployment, tight synchronization, interference management, and high-capacity backhaul.These coordination requirements motivate alternative networked sensing architectures.
- From Multi-CoopRx to Multi-UE: Multi-UE single-BS multistatic sensing uses non-cooperative UEs to form distributed bistatic links whose correlated parameters support estimation, target association, and multi-view fusion.Uplink, downlink, or hybrid signals can be jointly exploited at one BS.
- Processing Modes: Non-coherent processing avoids stringent inter-UE synchronization, while coherent processing can add SNR improvement, bandwidth aggregation, and distributed aperture formation when synchronization and calibration are accurate.The two processing modes offer different sensing-diversity and implementation requirements.
- Scope: The framework is primarily presented for a single-BS architecture but can extend to multiple cooperating BSs and other multi-access systems such as WiFi, low-altitude networks, and LoRa.This defines the stated scope and portability of the proposed framework.
2. MULTI-UE SENSING ARCHITECTURES
Multi-UE sensing supports uplink-only, downlink-assisted, and hybrid architectures that obtain diverse observations from distributed UEs. Their sensing benefits and processing requirements depend on observation direction, fusion location, synchronization, and multiple-access design.
- Uplink-Only Multi-UE Sensing: Uplink-only sensing forms distinct UE-BS bistatic links whose distributed transmitter locations provide multiple target views at a common BS.The architecture aligns with cellular protocols and supports scalable sensing with minimal additional signaling overhead.
- Uplink-Only Multi-UE Sensing: Distributed UE perspectives can improve target observability, separability, blockage robustness, coverage, and scalability as the number of participating UEs increases.These advantages are especially relevant when multiple targets, scatterers, and interference sources coexist outdoors.
- Uplink-Only Multi-UE Sensing: Uplink sensing requires multiple UEs to share communication resources, so orthogonal allocation or spatial multiplexing introduces resource partitioning and possible inter-user interference.Each UE may occupy only a subset of available sensing resources.
- Downlink-Assisted Multi-UE Sensing: Downlink-assisted sensing lets UEs locally extract features and report compressed measurements instead of raw signals, reducing communication overhead while enriching spatial sampling.Broadcast transmissions can potentially provide many more sensing observations than uplink-based sensing.
- Hybrid Multi-UE Sensing: Hybrid sensing combines uplink observations from transmitting UEs with downlink observations at receiving UEs to exploit complementary sensing information.The combined observations increase available sensing paths and strengthen geometric constraints for localization, tracking, and environmental reconstruction.
- Hybrid Multi-UE Sensing: Hybrid sensing uses more sensing nodes, paths, and spatial perspectives than either uplink-only or downlink-only sensing, improving coverage, observability, robustness, and target separability.Additional diversity may occur across space, time, and frequency.
- Multiple-Access-Aware Sensing: Multiple-access choices such as TDMA, OFDMA, and SDMA shape sensing diversity and coherent-processing gains, including SNR, robustness, and detection reliability.OFDMA allocation can trade stitched wideband delay resolution against frequency-domain sampling diversity and reduced range ambiguity.
3. MULTI-UE SENSING SIGNAL PROCESSING FRAMEWORK
The framework transforms heterogeneous multi-UE observations into environmental perception through synchronization, correlation-aware parameter estimation, and multi-view fusion. It exploits shared target geometry and observation correlation while balancing fusion level, overhead, and synchronization constraints.
- Core processing pipeline: Payload-based sensing requires user-separable signal processing because received time-domain signals superimpose transmissions propagated through different channels.Residual interference, decoding errors, and reconstruction uncertainty can weaken correlation-aware estimation and fusion.
- Core processing pipeline: The processing pipeline applies bistatic offset cancellation, jointly estimates target parameters, and fuses uplink or downlink observations into a unified environmental representation.The resulting representation supports localization, tracking, imaging, and situational awareness.
- Synchronization and bistatic offset cancellation: Synchronization suppression can preserve relative delay, Doppler, and angular information, but often converts absolute sensing parameters into relative measurements.Recovering absolute parameters requires references, calibration, or bidirectional signaling, while offset processing can also alter useful correlation structure.
- Sensing parameter estimation with correlation exploitation: Correlation-aware estimation exploits parameter links induced by common target geometry and motion rather than treating UE observations as independent.Representative approaches include structured sparse recovery, statistical estimation, subspace methods, and learning-assisted techniques.
- Sensing parameter estimation with correlation exploitation: Bayesian compressive sensing jointly exploits propagation-delay correlation among nearby UEs and achieves substantially higher estimation accuracy than independent per-UE processing.The uplink example uses an OFDMA system in which each UE occupies 128 localized subcarriers.
- Multi-view fusion: Multi-view fusion combines observations across UEs and can operate from raw signals through measurements, features, or semantic representations.Higher-level fusion reduces transmitted information but provides different communication-overhead and prior-information tradeoffs.
4. CHALLENGES AND OPEN RESEARCH PROBLEMS
Multi-UE sensing remains early-stage and faces open problems in correlation modelling, performance characterization, association, compression, participation, and joint communication-sensing optimization. These challenges arise from dynamic UE populations, heterogeneous observations, overhead, and incomplete system knowledge.
- Overview: Multi-UE sensing is at an early development stage, with challenges amplified by the large number and dynamic nature of participating UEs.The article identifies these problems as major open research challenges.
- Correlation modelling and performance characterization: Correlation models must capture how UE distribution, target motion, sensing geometry, propagation, and multiple-access schemes shape shared observations.These models support correlation-aware estimation, fusion, and resource optimization.
- Correlation modelling and performance characterization: A rigorous theory is needed to characterize how accuracy and resolution scale with UE number and density while accounting for coverage, robustness, and communication overhead.Performance bounds such as the Cramér-Rao lower bound and information-theoretic metrics are identified as useful tools.
- Sensing parameter association and tracking: Associating jointly estimated parameters with physical targets is difficult in dense environments containing clutter, missed detections, heterogeneous observations, and incomplete overlaps.Scalable methods must combine geometric, statistical, temporal, and semantic information as UE conditions change.
- Sensing information compression: Downlink-assisted and hybrid systems need sensing information compression because many participating UEs can create substantial reporting overhead.The objective is to preserve task-relevant information for association, tracking, and fusion rather than reconstructing original measurements.
- UE grouping and participation selection: UE grouping and participation selection should account for differing locations, geometries, propagation conditions, and target visibility instead of activating every available UE.Selection aims to limit communication overhead and computational complexity while maximizing sensing utility.
- Communication-sensing co-optimization: Communication and sensing objectives may be jointly optimized through UE selection, resource allocation, and correlation exploitation.Such optimization depends on accurate correlation modelling and performance characterization.
- Practical system constraints: Practical systems may be constrained by unavailable or private UE locations, orientations, and synchronization states.When accurate UE state information is unavailable, systems may need relative measurements or higher-level feature and semantic fusion.
5. CONCLUSION
Multi-UE sensing is presented as a scalable paradigm for perceptive mobile networks, supported by architectures, signal processing frameworks, and enabling technologies. Its multi-view, correlation-aware processing offers a pathway toward robust network-native environmental perception.
- The article presents multi-UE sensing as a scalable networked sensing paradigm for future perceptive mobile networks.
- It develops representative architectures, signal processing frameworks, and key enabling technologies for multi-UE sensing.
- Multi-view diversity and correlation-aware processing across distributed UEs are positioned to support robust target detection, separation, identification, and tracking.