Reframing JCAS Through Sequential Beamforming: Joint Positioning and Passive Target Sensing
Joint Communication and Sensing (JCAS) is often framed as a spectrum-sharing problem: one infrastructure, one waveform, multiple functions. In reality, the deeper challenge lies in estimation design [1]. A practical JCAS system must localize mobile users, sense passive objects, and allocate limited pilot, power, and beamforming resources across tasks that favor different spatial strategies [2]. This challenge becomes especially acute in mobile scenarios, where the network must simultaneously track a moving user and detect passive targets, and where both sensing quality and positioning accuracy depend on how effectively resources are coordinated between Monostatic Sensing (MS) and Bistatic Positioning (BP). In such settings, the problem is no longer purely one of finding a good operating point; it is one of designing the right sequence of decisions over time.
Why This Problem Matters
In many future wireless applications, the network will need to know more than whether a link is connected. It will need to estimate where a user is, how fast that user is moving, and what objects are present in the surrounding environment. This is especially relevant in dense urban settings, industrial spaces, and autonomous mobility scenarios, where GNSS may be unreliable and situational awareness must come directly from the wireless infrastructure.
Traditional approaches often optimize sensing and positioning as separate functions, even though they share the same transmitter, the same scene, and the same radio resources. That separation can lead to inefficient beam allocation, weaker estimation quality, and missed opportunities to reuse information collected in one phase to improve the next [2].
The Core Idea
A more effective strategy is to treat sensing and positioning as a coordinated sequence rather than as isolated operations [3]. Instead of viewing sensing and positioning as competing modes, future architectures may benefit more from treating them as tightly linked steps in a shared inference pipeline. Monostatic sensing can be performed first so that the base station can build a prior estimate of the environment and the mobile user before the system moves to the bistatic positioning phase.
That prior is not just a rough guess. It is encoded through covariance information and forwarded to support the positioning stage, helping the User Equipment (UE) refine its own state estimate while also improving awareness of Passive Targets (PTs) [3]. In other words, the first stage does not end when its measurements are collected; it actively prepares the second stage to work better.
Fig. 1. Illustration of MS and BP in an urban canyon scenario. The BS simultaneously performs i) MS (orange dotted lines) to detect UE and PTs (e.g., stationary cars) and ii) BP (blue dashed lines) to enable localization and velocity estimation for the mobile UE.
Why Sequential Beamforming Helps
MS and BP do not ask for the same spatial illumination pattern [3]. Sensing tends to favor beam patterns that illuminate reflectors and environmental targets, while positioning needs beamforming that strengthens the useful geometry and observability of the mobile user state at the receiver.
Fig. 2. Beamforming design for MS (left) and BP (right). The UE and PTs are placed symmetrically at equal distances to isolate distance-dependent path-loss disparities, focusing on beamforming for MS and BP.
If these phases are optimized independently, each stage may look locally efficient while the overall sequence remains suboptimal. The study therefore evaluates a sequential design in which the two stages are jointly coupled in time and compares a shared beamformer with a separate greedy two-stage strategy.
What the Results Show
To make the results easy to read, the paper tracks two key metrics: the Position Error Bound (PEB) and the Velocity Error Bound (VEB), which indicate how accurately the system can theoretically estimate position and velocity. Lower PEB and VEB mean better localization and tracking performance, so they give a compact way to compare different sensing and positioning strategies.
The main takeaway is clear: the shared sequential beamformer performs better than stage-specific greedy designs, both in localizing the user and passive targets and in estimating user velocity. It also runs faster, because it avoids re-optimizing separate beamformers for each stage and instead solves one globally coupled design problem.
Across different monostatic sensing allocation ratios (i.e., how much symbols are allocated between MS and BP in a fixed time horizon), the shared sequential design consistently achieves lower UE PEB, UE VEB, and passive-target PEB than the separate sequential approach, while also outperforming the full-monostatic and full-bistatic baselines. In practical terms, this means the shared design is not only more accurate but also more efficient, and under the reported simulation settings, it reaches centimeter-level positioning accuracy while maintaining robust velocity estimation.
Fig. 3. Performance of full MS, full BP, shared sequential, and separate sequential beamforming designs vs. the MS symbol ratio
Practical Takeaway
For practical JCAS deployment, the main question is no longer how to divide resources between communication and sensing. An equally important question is how to order sensing and positioning tasks, how to share their information, and how to optimize beamforming so that the full sequence produces the strongest final estimate. That is why sequential beamforming stands out as more than an algorithmic refinement. It offers a system-level design principle for making integrated sensing and positioning more robust, more efficient, and more deployable in real mobile networks.
References
- Bauhofer, S. Mandelli, M. Henninger, T. Wild and S. t. Brink, “Multi-Target Localization in Multi-Static Integrated Sensing and Communication Deployments,” 2023 2nd International Conference on 6G Networking (6GNet), Paris, France, 2023, pp. 1-4, doi: 10.1109/6GNet58894.2023.10317749.
- Zhang et al., “Joint Bistatic Positioning and Monostatic Sensing: Optimized Beamforming and Performance Tradeoff,” in IEEE Transactions on Cognitive Communications and Networking, vol. 11, no. 5, pp. 2834-2847, Oct. 2025, doi: 10.1109/TCCN.2025.3578477.
- Hamrouni, S. Pollin, and H. Sallouha, “Joint Mobile User Positioning and Passive Target Sensing using Optimized Sequential Beamforming,” to appear in IEEE VTC 2026, [arXiv:2605.15808](https://arxiv.org/abs/2605.15808)
An article by Aymen Hamrouni