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Multi-Waveguide Pinching Antennas for ISAC
Weihao Mao, Yang Lu, Yanqing Xu, Bo Ai, Octavia A. Dobre, Dusit Niyato
TL;DR
The paper addresses resource allocation for multi-waveguide pinching-antenna ISAC, where TPAs and RPAs serve a downlink user while detecting a target. It jointly optimizes TPA locations and transmit beamforming through a fine-tuning approximation and SCA-based algorithm. The method achieves near-optimal performance against exhaustive search, while pinching-antenna ISAC shows a distinct communication-sensing trade-off.
Problem
Efficient resource allocation for multi-waveguide pinching-antenna ISAC remains unaddressed because TPA locations have an expanded search space and complicated channel relationships.
Method
The paper jointly optimizes TPA locations and transmit beamforming using a fine-tuning approximation followed by a successive convex approximation algorithm.
Results
The proposed method achieves near-optimal performance compared with computationally intensive exhaustive search, while pinching-antenna ISAC exhibits a distinct communication-sensing trade-off.
Takeaways & Limitations
Multi-waveguide pinching-antenna ISAC supports simultaneous user service and target detection with flexible channel customization at large and small scales.
Abstract
from arXiv · showhide
Recently, a novel flexible-antenna technology, called pinching antennas, has attracted growing academic interest. By inserting discrete dielectric materials, pinching antennas can be activated at arbitrary points along waveguides, allowing for flexible customization of large-scale path loss. This paper investigates a multi-waveguide pinching-antenna integrated sensing and communications (ISAC) system, where transmit pinching antennas (TPAs) and receive pinching antennas (RPAs) coordinate to simultaneously detect one potential target and serve one downlink user. We formulate a communication rate maximization problem subject to radar signal-to-noise ratio (SNR) requirement, transmit power budget, and the allowable movement region of the TPAs, by jointly optimizing TPA locations and transmit beamforming design. To address the non-convexity of the problem, we propose a novel fine-tuning approximation method to reformulate it into a tractable form, followed by a successive convex approximation (SCA)-based algorithm to obtain the solution efficiently. Extensive simulations validate both the system design and the proposed algorithm. Results show that the proposed method achieves near-optimal performance compared with the computational-intensive exhaustive search-based benchmark, and pinching-antenna ISAC systems exhibit a distinct communication-sensing trade-off compared with conventional systems.
I. INTRODUCTION
Pinching-antenna ISAC extends flexible channel customization to large-scale path loss, enabling multi-waveguide TPAs and RPAs to jointly serve a user and detect a target. This paper addresses the resulting TPA-location and beamforming optimization challenge.
- I. INTRODUCTION: Pinching antennas activate at arbitrary points along dielectric waveguides, enabling flexible large-scale path-loss customization and deployment near users for reduced propagation loss or LoS links.Existing flexible-antenna technologies primarily adjust small-scale channel characteristics, whereas pinching antennas support large-scale adjustment.
- I. INTRODUCTION: ISAC jointly integrates communication and sensing at one base station by sharing spectrum and hardware resources.
- I. INTRODUCTION: Efficient resource allocation remains open because multi-waveguide systems expand the TPA-location search space and create complicated location-channel relationships.
- I. INTRODUCTION: Multi-waveguide pinching-antenna ISAC coordinates multiple TPAs to serve one user and illuminate one target while multiple RPAs acquire reflected signals for detection.Each TPA and RPA is independently deployed on a dedicated waveguide.
- I. INTRODUCTION: The paper formulates joint TPA-location and transmit-beamforming optimization under radar SNR, transmit-power, and movement-region constraints, then develops a fine-tuning approximation and SCA-based solution.The paper structure assigns system modeling and problem formulation to Section II and approximation plus SCA solution to Section III.
B. Communication Model
The system transmits a beamformed symbol to one user and uses reflected target signals received by RPAs for target detection. Receive beamforming and Neyman–Pearson testing support sensing, with radar SNR used as the sensing metric.
- B. Communication Model: The TPAs transmit x = ws, where s is a unit-power user symbol and w is the transmit beamforming vector.
- C. Sensing Model: RPAs receive target-reflected signals and apply receive beamforming v to strengthen the sensing signal.The receive beamforming vector satisfies v ∈ C^N.
- C. Sensing Model: A Neyman–Pearson detector tests target absence versus presence using a threshold determined by the false-alarm probability.
- C. Sensing Model: Detection probability is positively proportional to radar SNR, so Γt({w, xT,m}) is adopted as the sensing-performance metric.The RPA locations are placed toward the target to enhance the receive-channel norm, while optimal receive beamforming follows the Cauchy–Schwarz equality condition.
D. Problem Formulation
The paper maximizes the downlink communication rate while enforcing radar SNR, transmit-power, and TPA movement constraints. The resulting problem is non-convex because beamforming and TPA locations are deeply coupled through the channels.
- D. Problem Formulation: The optimization maximizes communication rate over transmit beamforming and TPA locations subject to transmit power, radar SNR, and allowable TPA movement constraints.Pmax denotes the transmit-power budget, while ΓReq is the radar SNR threshold associated with the required detection probability.
- D. Problem Formulation: The problem is non-convex because transmit beamforming and TPA locations are coupled in both the objective and radar-SNR constraint.The channel functions also have complicated dependence on TPA locations.
III. JOINT OPTIMIZATION OF DEPLOYMENT AND BEAMFORMING DESIGN OF PINCHING ANTENNAS
The proposed optimization approach first reformulates the joint deployment and beamforming problem, then constructs an upper-bound approximation and solves it with successive convex approximation.
- III. JOINT OPTIMIZATION OF DEPLOYMENT AND BEAMFORMING DESIGN OF PINCHING ANTENNAS: The method reformulates the original problem using system observations before deriving an upper-bound problem through fine-tuning.
- III. JOINT OPTIMIZATION OF DEPLOYMENT AND BEAMFORMING DESIGN OF PINCHING ANTENNAS: The upper-bound problem is solved by a successive convex approximation algorithm after simplifying notation for the communication and sensing channels.
A. Problem Reformulation
The paper characterizes the optimal beamformer for the reformulated problem and uses this structure to reduce the optimization representation.
- A. Problem Reformulation: The optimal beamformer uses the full transmit power budget, because a feasible higher-rate beamformer can be constructed whenever the budget is not active.The construction preserves feasibility while increasing the communication rate, contradicting optimality of an underpowered solution.
- A. Problem Reformulation: An optimal beamformer lies in the space spanned by the communication and sensing channels, represented through coefficients c_u and c_t.The orthogonal component can be removed without reducing the relevant channel projections, yielding another optimal solution in the channel span.
- A. Problem Reformulation: The beamformer is re-expressed as a function of c_u, c_t, and the TPA locations, allowing Problem P1 to be rewritten using the functions f_u, f_p, and f_t.Proposition 2 provides the coefficient-based representation used in the equivalent reformulation.
B. Problem Approximation
The approximation separates TPA-location effects on path loss and phase, then converts the resulting coupled non-convex problem into successive convex subproblems.
- B. Problem Approximation: The method separates TPA-location effects on path loss and phase shift by fine tuning locations to maximize the communication-sensing channel correlation.This addresses the coupling created because TPA locations affect both large-scale path loss and small-scale phase shift.
- B. Problem Approximation: Fine tuning TPA locations to control channel phase has performance loss on the order of the wavelength and is proven to have negligible system impact.The required location adjustments can remain within a few wavelengths, while the path-loss effect of wavelength-scale adjustments is negligible.
- B. Problem Approximation: Auxiliary variables reformulate the coupled objective and constraints, producing Problem P3 with tractable representations of the relevant functions.The introduced variables include a_u,m, b_u,m, a_t,m, b_t,m and additional coupling variables.
- B. Problem Approximation: First-order Taylor approximations convert the remaining non-convex constraints into convex approximations, and SCA iteratively solves the resulting Problem P5 while updating the variables.The iterative updates are used to improve approximation precision after the reformulations.
IV. SIMULATION RESULTS
The simulations evaluate the proposed pinching-antenna ISAC system and algorithm under fixed default power, radar-SNR, and noise settings.
- IV. SIMULATION RESULTS: The simulation study compares the proposed pinching-antenna ISAC system with the conventional ISAC system and evaluates the proposed algorithm’s effectiveness.Unless otherwise specified, TPAs and RPAs are uniformly distributed within the serving area.
- IV. SIMULATION RESULTS: The default transmit power budget is Pmax = 10 W and the radar SNR requirement is ΓReq = 4.These are the stated default simulation settings.
- IV. SIMULATION RESULTS: The default user and RPA noise powers are σ2_u = −60 dBm and σ2_s = −80 dBm, respectively.The user-noise value is stated in the simulation setup, while the RPA-noise value is given in the following passage.
A. Special Case Analysis
The special-case study examines how user-target geometry affects communication performance and the communication-sensing trade-off while validating the proposed algorithm against exhaustive search.
- A. Special Case Analysis: The proposed algorithm produces communication rates very close to exhaustive search across Case1, Case2, and Case3, validating its effectiveness.The baseline searches each TPA location over [−L/2, L/2] with a 0.5 m step size.
- A. Special Case Analysis: Case2 and Case3 exhibit abrupt communication-rate drops as radar SNR requirements increase, whereas Case1 decreases more smoothly.The abrupt regions correspond to a critical communication-sensing trade-off in the pinching-antenna ISAC system.
- A. Special Case Analysis: As radar SNR requirements increase, the two TPAs approach the target sequentially; in Case2 and Case3, this includes departures from the user.The second TPA departs as the requirement rises from 0 to 0.5, and the first departs from 4.5 to 5, matching the two rate-drop regions.
- A. Special Case Analysis: TPA departures from the user enlarge TPA-user path loss and significantly degrade communication rate, while larger user-target distance complicates the trade-off and produces non-smoothing regions.The reported path loss is inverse-square with transmission distance.
B. Evaluation of Pinching-Antenna ISAC Systems
The proposed pinching-antenna ISAC system outperforms conventional and benchmark designs, while increasing RPAs or TPAs improves performance with diminishing marginal gains.
- The pinching-antenna system achieves the highest communication rate among the evaluated systems, with larger gains as the transmit power budget increases.Its flexible TPA placement reduces propagation loss and helps mitigate communication-sensing interference through joint deployment and beamforming optimization.
- The proposed system obtains the largest communication-sensing region, encompassing the benchmark regions; target-oriented placement becomes similar under high radar SNR requirements.The midpoint design has a significant gap from the proposed design, highlighting the value of optimizing TPA locations.
- Increasing the number of RPAs raises communication rate but approaches an upper bound of about 14.57 bit/s/Hz in this setting.More RPAs improve receiving antenna gain and reduce average RPA-target path loss, but the proposed system remains superior to the benchmarks.
- Increasing the number of TPAs improves performance slightly more than increasing RPAs because TPAs enhance transmit gain, shorten propagation distances, and add spatial degrees of freedom.Both TPA and RPA increases exhibit diminishing marginal gains, suggesting that their numbers should be balanced when partitioning the array.
V. CONCLUSION
The paper develops and evaluates a multi-waveguide pinching-antenna ISAC system for jointly serving one user and detecting one target. Its fine-tuning approximation and SCA-based algorithm achieve near-optimal optimization performance, while the system outperforms conventional systems and three pinching-antenna benchmarks.
- The proposed fine-tuning approximation and SCA-based algorithm solve the non-convex joint optimization problem with negligible performance loss relative to exhaustive search.
- The multi-waveguide system jointly optimizes TPA locations and beamforming to serve one downlink user while detecting one potential target under radar SNR and power constraints.
- Pinching-antenna ISAC systems outperform conventional systems and three pinching-antenna benchmarks, while communication rate has non-smoothing regions as radar SNR requirements increase.Increasing the numbers of TPAs or RPAs produces diminishing marginal performance gains.
APPENDIX
The appendix establishes that fine-tuning changes distances by at most one wavelength and preserves feasibility while achieving an objective value no worse than the original solution.
- Fine-tuning changes the relevant distance by no more than λ, a wavelength-scale perturbation that can be ignored relative to the serving-area length.This supports preservation of the associated distance constraint.
- The appendix uses continuity of θ_m(x_T,m) to establish the existence of a fine-tuned solution satisfying the relevant constraints.
- The fine-tuned TPA configuration satisfies the required constraints and achieves an objective value no worse than the original configuration.