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Pinching Antenna-Assisted Full-Duplex Communication Systems

Xuan Li, Xianfu Lei, Mingjiang Wu, Sotiris A. Tegos, Panagiotis D. Diamantoulakis, George K. Karagiannidis

arXiv:2609.29200v1cs.IT

TL;DR

Full-duplex communication is constrained by self-interference and the limited spatial flexibility of fixed antenna arrays. This paper jointly optimizes pinching-antenna positions, beamforming, and power through WMMSE and alternating optimization. Simulations report higher sum-rate than fixed-array benchmarks, including under limited dynamic range.

  • Problem

    Strong self-interference and co-channel interference, together with fixed arrays’ limited spatial flexibility, constrain practical full-duplex communication.

    Source: §I · p. 1

  • Method

    The proposed PASS architecture jointly optimizes antenna positions, base-station beamforming, and uplink power using WMMSE reformulation and alternating optimization.

    Source: §I · p. 1

  • Results

    At M = 2 and PBS = 15 dBm, PASS achieves sum-rate gains of 54.2% over Conv-50 cm and 107.6% over Conv-L.

    Source: §IV · p. 4

  • Takeaways & Limitations

    PASS outperforms conventional fixed arrays under ideal and limited dynamic-range conditions, substantially relaxing stringent requirements for costly and complex analog self-interference cancellation.

    Source: §V · p. 5

Abstract

from arXiv · show

Full-duplex (FD) communication theoretically doubles spectral efficiency. Despite this potential, its practical performance is primarily constrained by severe self-interference (SI) and co-channel interference. Moreover, conventional fixed antenna arrays suffer from limited spatial flexibility, resulting in insufficient spatial isolation for SI suppression. To address this issue, this letter proposes an FD architecture assisted by pinching antenna systems (PASS). By dynamically adjusting transmit and receive antenna positions, the system enables large-scale channel reconfiguration, thereby synergizing with the base station (BS) beamforming to suppress SI and enhance the desired signal reception. To demonstrate the potential of PASS for FD communication, we formulate a weighted sum-rate maximization problem that jointly optimizes antenna positions, BS beamforming, and power allocation. To tackle this non-convex problem, we reformulate it using the weighted minimum mean square error (WMMSE) framework and develop an efficient alternating optimization (AO) algorithm to iteratively update the optimization variables. Simulation results reveal that the proposed PASS-assisted FD architecture significantly outperforms conventional fixed antenna arrays, achieving substantial sum-rate gains while effectively mitigating SI to enable FD operation.

I. INTRODUCTION

Full-duplex communication is limited by strong self-interference and constrained spatial isolation in fixed antenna arrays. The letter proposes using pinching antenna positions with base-station beamforming to reconfigure channels and suppress interference.

I. INTRODUCTION

  • Self-interference is the primary practical bottleneck, with power typically exceeding the desired signal by more than 100 dB.Spatial and analog suppression are needed to keep it within the receiver’s dynamic range and prevent front-end saturation.Source: §I · p. 1
  • Fixed-array spatial constraints weaken intrinsic isolation, increasing reliance on costly, complex, and less scalable analog self-interference cancellation.Hardware impairments can also compromise cancellation precision.Source: §I · p. 1
  • PASS permits large- and small-scale channel reconfiguration by adjusting antenna positions along dielectric waveguides, extending its study from half-duplex links to FD systems.Prior FD work examined antenna activation, while fully exploiting PASS channel reconfiguration together with base-station beamforming remained open.Source: §I · p. 1
  • The proposed architecture jointly optimizes antenna positions, base-station beamforming, and uplink power to maximize weighted sum-rate using WMMSE and alternating optimization.The algorithm uses closed-form beamforming and uplink-power updates and element-wise one-dimensional antenna-position searches.Source: §I · p. 1

II. SYSTEM MODEL AND PROBLEM FORMULATION

The system model places transmit and receive pinching antennas on parallel, interleaved waveguides at an FD base station. It serves one single-antenna downlink user and one single-antenna uplink user.

II. SYSTEM MODEL AND PROBLEM FORMULATION

  • The FD base station has M transmit waveguides and K receive waveguides, each with a dedicated radio-frequency chain and one pinching antenna per waveguide.The one-antenna-per-waveguide assumption is made for analytical tractability and system symmetry; multi-antenna-per-waveguide configurations are left for future work.Source: §II · p. 2
  • All waveguides have length L and are deployed in parallel with uniform spacing across the service-region width W.The model uses a three-dimensional Cartesian coordinate system with the origin at the region center and waveguides parallel to the x-axis.Source: §II · p. 2
  • The system simultaneously serves a single-antenna downlink user and a single-antenna uplink user.The user positions are represented by their x- and y-coordinates in the service region.Source: §II · p. 2

A. System Model

The model represents downlink, uplink, and interference channels for simultaneous FD transmission, including residual self-interference and co-channel interference. Antenna positions and spatial filtering can jointly suppress residual self-interference while preserving desired-signal gains.

A. System Model

  • The downlink and uplink links use spherical-wave channel models, with propagation phases arising from free-space and waveguide paths.The waveguide phase depends on its effective refractive index and the signal wavelength inside the guide.Source: A. System Model · p. 2
  • Simultaneous transmission creates self-interference between transmit and receive pinching antennas, while the user-to-user co-channel channel is modeled as CN(0, ς(dCCI)).The co-channel channel’s large-scale path loss depends on the distance between users; ρ denotes the effective analog self-interference cancellation depth.Source: A. System Model · p. 2
  • The received downlink and uplink signals include the desired transmission, co-channel or self-interference, and additive noise.The uplink receiver uses a combining vector, and its SINR denominator includes residual self-interference power and noise.Source: A. System Model · p. 2
  • Jointly adjusting antenna positions and spatial filtering can suppress residual self-interference while preserving desired-signal gains.Strong self-interference can add receiver noise and distortion under limited dynamic range, making spatial-domain suppression important for FD operation.Source: A. System Model · pp. 2–3

B. Problem Formulation

The problem maximizes weighted sum-rate by jointly selecting pinching-antenna positions, base-station beamforming, and uplink transmit power. Its non-convexity motivates a WMMSE reformulation and alternating optimization.

B. Problem Formulation

  • The weighted sum-rate objective jointly optimizes transmit- and receive-antenna positions, base-station beamforming vectors, and uplink-user transmit power.The formulation constrains base-station and uplink transmit powers and keeps antenna positions within their waveguide ranges.Source: B. Problem Formulation · p. 3
  • The optimization is non-convex because of fractional-logarithmic objective coupling and antenna positions embedded in nonlinear complex-exponential channel terms.The WMMSE method transforms the objective into an equivalent, more tractable weighted MSE.Source: B. Problem Formulation · p. 3

III. PROBLEM REFORMULATION AND ALGORITHM DESIGN

The section reformulates the weighted sum-rate problem into an equivalent weighted-MSE form, then uses alternating optimization to update coupled variables and antenna positions. [Correction: remove unsupported marker?]

III. PROBLEM REFORMULATION AND ALGORITHM DESIGN

  • The weighted-MSE reformulation removes the fractional-logarithmic objective form while retaining equivalent optimal solutions, and alternating optimization addresses the remaining variable coupling.The antenna-position subproblem is also strongly coupled through the self-interference channel.Source: §III · pp. 3–4
  • MSE weights α and β have closed-form updates, while receiver combining vector v and equalization coefficient u are optimized through separate unconstrained convex quadratic problems.The receiver updates follow by setting their first-order derivatives to zero.Source: §III · p. 3
  • The uplink transmit-power objective becomes a convex quadratic in q = √pt, and the feasible solution is obtained by projecting qopt onto [0, √Pt].Source: §III · p. 3
  • The downlink beamforming subproblem is a convex quadratically constrained quadratic program, solved using a Lagrange multiplier found by bisection.An eigenvalue decomposition of A lets each bisection iteration use scalar computations.Source: §III · p. 3
  • Transmit and receive antenna positions are decoupled into two blocks and updated alternately using Gauss-Seidel element-wise one-dimensional searches over the feasible waveguide region.Each search fixes the remaining antennas at their latest updated positions.Source: §III · p. 4
  • The AO objective is monotonically non-increasing and lower-bounded, guaranteeing convergence; total complexity is O(IAO(M^3+MK+MIµ+(M+K)NxMK)).Source: §III · p. 4

IV. SIMULATION RESULTS

Simulations compare PASS with fixed antenna arrays across power budgets, residual self-interference, dynamic-range conditions, and analog cancellation depths. PASS achieves higher reported rates while reducing residual SI and the analog cancellation needed to reach its saturated uplink rate.

IV. SIMULATION RESULTS

  • The evaluation randomly places users in a 40 m × 10 m region and includes a finite transceiver dynamic-range model with Gaussian distortion noise.The carrier frequency is 28 GHz; the transmit and receive waveguides are parallel to the x-axis at a height of 3 m.Source: §IV · p. 4
  • The AO algorithm iteratively updates receiver and equalizer variables, MSE weights, uplink power, beamforming, and antenna positions until convergence or the iteration limit.It outputs the optimized combining vector, uplink power, beamforming vector, and receive and transmit antenna positions.Source: §IV · p. 4
  • Under limited dynamic range, PASS achieves the highest sum-rate and best uplink and downlink performance among the compared schemes.The reported advantage is attributed to spatial SI suppression and enhanced desired-signal power; Conv-50 cm's uplink rate degrades, while Conv-L's fixed placement yields lower desired-channel gain than PASS.Source: §IV · pp. 4–5
  • At M = 2 and PBS = 15 dBm, PASS gains 54.2% over Conv-50 cm and 107.6% over Conv-L in sum-rate.The user-rate comparison is shown in Fig. 2.Source: §IV · p. 4, Figure 2 · p. 5
  • PASS maintains the lowest residual SI power across all configurations.Fig. 3 plots residual SI power before digital combining versus PBS.Source: §IV · p. 4, Figure 3 · p. 5
  • PASS reaches its saturated uplink rate with about 10 dB of analog cancellation, while Conv-50 cm and Conv-L require approximately 40 dB.PASS combines desired-channel gain with SI suppression through spatial channel reconfiguration, relaxing reliance on complex and costly analog cancellation.Source: §IV · p. 5

V. CONCLUSION

The PASS-assisted full-duplex system outperforms conventional fixed antenna arrays under ideal and limited dynamic-range conditions, while relaxing analog self-interference cancellation requirements. The paper jointly optimizes weighted sum-rate and solves the non-convex problem using WMMSE and an alternating optimization algorithm.

V. CONCLUSION

  • PASS outperforms conventional fixed antenna arrays under ideal and limited dynamic-range conditions, substantially relaxing costly and complex analog SI cancellation requirements.Simulations attribute the gains to large-scale spatial-channel reconfiguration.Source: §V · p. 5
  • The non-convex weighted sum-rate problem is reformulated with WMMSE and addressed using an efficient alternating optimization algorithm.Source: §V · p. 5
  • The system jointly optimizes variables to maximize weighted sum-rate.Source: §V · p. 5
  • The reported evaluations plot user rates versus transmit power and uplink rates versus analog SI cancellation depth.Fig. 4 uses K=M=2 and PBS=30 dBm; Fig. 5 uses M=2, PBS=30 dBm, Pt=15 dBm, and κ=γ=−40 dB.Source: Figure 4 · p. 5, Figure 5 · p. 5
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