A technical preprint proposes a way for future mobile networks to sense their surroundings through many UEs, treated here as separate sensing nodes. Multiple non-cooperative UEs would form distributed bistatic sensing links to a common base station, or BS, and the network would jointly use uplink signals, downlink signals or both.
The idea is to exploit correlations, or shared patterns, among observations made by those nodes as they interact with common targets and the propagation environment. The paper presents this as a possible new networked-sensing paradigm for perceptive mobile networks. It associates the wider set of observations with potential gains in sensing resolution, robustness, coverage and target identifiability.
Three ways to build the network
The proposal sets out three representative configurations: uplink-based sensing, which uses observations from transmissions sent by UEs to the network; downlink-assisted sensing, which uses transmissions in the other direction; and hybrid sensing, which combines both.
It also separates non-coherent processing from coherent processing. The non-coherent route can use diversity and correlation without stringent synchronization among UEs. The coherent route may add signal-to-noise ratio, bandwidth and distributed-aperture gains, but requires stricter synchronization, calibration and geometry information.
Turning signals into a shared picture
At the processing stage, the proposed chain starts with bistatic-offset cancellation. It then performs correlation-aware joint parameter estimation before fusing multiple views into a unified representation of the environment. The framework links that sequence to localization, tracking, imaging and situational awareness.
How the UEs share access also becomes part of the sensing design. Time-division multiple access, or TDMA, is described as providing temporal diversity. Orthogonal frequency-division multiple access, or OFDMA, provides frequency-domain sampling and bandwidth aggregation, while space-division multiple access, or SDMA, provides spatial and angular diversity. The paper treats those choices as shaping sensing capabilities.
In downlink-assisted sensing, UEs can extract features locally and report compressed measurements or high-level features to the network for fusion instead of forwarding raw received signals. The paper associates this with lower communication overhead. It also lists communication overhead among the tradeoffs whose performance remains unresolved.
An illustrative comparison
The preprint includes an illustrative uplink comparison based on Bayesian compressive sensing and propagation-delay correlation. It describes that correlation-aware approach as achieving substantially higher estimation accuracy than independent per-UE processing. Each UE occupies 128 localized subcarriers in the illustrative setup.
That example is narrower than a system-level validation. The comparison supplies no numerical accuracy values or uncertainty interval in the supplied analysis, and no formal test result is reported. The paper's main contribution is a technical presentation of representative architectures, a multi-view signal-processing framework and open research challenges. Its unit of analysis is a distributed wireless sensing system, not a participant sample.
What remains open
The paper describes multi-UE sensing as early-stage, with fundamental issues still to be solved before practical deployment. It leaves open how performance will scale with the number and density of UEs, how spatial distribution will affect results, and how to balance coverage, resolution, robustness and communication overhead.
The framework's practical tradeoffs include the synchronization, calibration and geometry information needed for coherent processing, as well as the correlation and propagation environment behind the observations. Multiple-access design adds another dimension to the problem. The article also treats performance characterization as open across these design choices.
Even so, the authors interpret multi-UE sensing as a promising path to robust target detection, separation, identification, tracking and network-native environmental perception. The work is a conceptual preprint with performance characterization still unresolved. The conclusion is a forward-looking roadmap, not a demonstrated system-level outcome.
Paper data and sources
Original title: Multi-UE Networked Sensing: A New Paradigm for 6G Perceptive Mobile Networks
Authors: J. Andrew Zhang, Jingying Bao, Kai Wu et al.
Journal/Repository: arXiv
Status: Preprint, not yet peer-reviewed
First online: 2026-08-26
DOI: Not available
Original paper · Full text