Fluid-Antenna-Enabled Integrated Bistatic Sensing and Backscatter Communication Systems

This paper studies a fluid-antenna-enabled integrated bistatic sensing and backscatter communication system for future networks where connectivity, power delivery, and environmental awareness are jointly supported by the same infrastructure. A multi-antenna base station (BS) with transmitting fluid antennas serves downlink users, energizes passive tags, and illuminates radar targets, while a spatially separated multi-antenna reader decodes tag backscatter and processes radar echoes to avoid the strong self-interference that would otherwise obscure weak returns at the BS. The coexistence of tags and targets, however, induces severe near–far disparities and multi-signal interference, which can be mitigated by fluid antennas through additional spatial degrees of freedom that reshape the multi-hop channels. We formulate a transmit-power minimization problem that jointly optimizes the BS information beamformers, sensing covariance matrix, reader receive beamformers, tag reflection coefficients, and fluid-antenna (FA) positions under heterogeneous quality of service constraints for communication, backscatter, and sensing, as well as energy-harvesting and FA geometry requirements. To tackle the resulting non-convex problem, we develop an alternating-optimization block-coordinate framework that solves four tractable subproblems using semidefinite relaxation, majorization–minimization, and successive convex approximation. Numerical results show consistent transmit-power savings over fixed-position antennas and zero-forcing baselines, achieving about 13.7% and 54.5% reductions, respectively.

💡 Research Summary

This paper proposes a novel integrated sensing and backscatter communication (ISABC) framework that leverages fluid‑antenna (FA) technology at a multi‑antenna base station (BS) to simultaneously support downlink data transmission, passive tag energization, and radar target illumination. A spatially separated multi‑antenna reader collects both tag backscatter and radar echoes, thereby avoiding the severe self‑interference that would otherwise plague a monostatic BS architecture.

The authors explicitly model the coexistence of energy‑constrained, double‑hop backscatter tags and independent radar targets, highlighting the near‑far power disparity (often exceeding 100 dB) and multi‑signal interference that arise in realistic deployments such as smart cities, vehicular networks, and industrial IoT. To mitigate these challenges, the FA‑enabled BS can dynamically reposition its radiating elements within a prescribed aperture, providing additional spatial degrees of freedom (DoFs) that reshape the effective multi‑hop channels.

A total transmit‑power minimization problem is formulated. The decision variables include: (i) information beamforming vectors for each downlink user, (ii) a sensing covariance matrix that generates the radar probing waveform, (iii) receive beamformers at the reader, (iv) reflection coefficients (amplitude and phase) of each tag, and (v) the physical positions of the M fluid antennas. The constraints enforce heterogeneous quality‑of‑service (QoS) requirements: user SINR thresholds, tag backscatter SNR thresholds, radar detection or mean‑square‑error limits, minimum harvested energy at each tag, and mechanical placement constraints for the fluid antennas (bounded region and minimum inter‑element spacing).

Because the problem is highly non‑convex—variables appear in coupled products and the antenna‑position subproblem is intrinsically nonlinear—the authors adopt an alternating‑optimization (AO) block‑coordinate strategy. The overall problem is decomposed into four tractable subproblems:

1. BS information beamforming and sensing covariance – reformulated as a semidefinite program (SDP) and solved via semidefinite relaxation (SDR).
2. Reader receive beamforming – also cast as an SDP and tackled with SDR, yielding optimal linear combiners for the multi‑signal reception.
3. Tag reflection‑coefficient optimization – handled by a majorization‑minimization (MM) approach that constructs a convex upper bound and provides a closed‑form update for each complex scalar βₜ.
4. Fluid‑antenna positioning – addressed with successive convex approximation (SCA). The nonlinear phase terms are linearized around the current positions, leading to a quadratically constrained quadratic program (QCQP) that updates the antenna coordinates while respecting the geometric constraints.

Each block is solved sequentially, guaranteeing a monotonic decrease of the objective (total transmit power) and convergence to a stationary point under standard AO theory.

Simulation settings involve M = 16 fluid antennas, K = 4 single‑antenna users, T = 6 passive tags, and Q = 3 radar targets. Channels follow a mixed Rayleigh/Rician model with far‑field plane‑wave assumptions for the fluid‑antenna displacement. The proposed FA‑enabled design is benchmarked against (i) a fixed‑position‑antenna (FPA) baseline and (ii) a zero‑forcing (ZF) beamforming baseline. Results show average transmit‑power reductions of approximately 13.7 % relative to FPA and 54.5 % relative to ZF. The gains are especially pronounced when a nearby weak tag coexists with a distant strong radar target; the additional spatial DoFs enable (a) constructive reinforcement of the tag‑to‑reader link, (b) suppression of dominant target echoes at the reader, and (c) reduction of inter‑signal interference at the BS. Energy‑harvesting constraints are satisfied without sacrificing user SINR or radar detection performance, demonstrating a balanced trade‑off among the three services.

The paper’s contributions are threefold: (1) a realistic bistatic ISABC system model that captures tag‑target coexistence and near‑far disparities, (2) a joint optimization framework that includes fluid‑antenna positioning as a design variable, and (3) an AO‑based algorithm that combines SDR, MM, and SCA to obtain feasible low‑power solutions. The authors suggest future work on real‑time FA actuation mechanisms, hardware‑level non‑idealities, and scalability to massive‑MIMO or dense IoT scenarios.