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Joint EM-BB Beamforming Design for ISAC Systems With Reconfigurable Antenna Arrays

Jvxiang Hu, Mengzhen Liu, Rang Liu, Qian Liu, Ming Li

arXiv:2610.08461v1eess.SP

TL;DR

ISAC must balance target illumination with communication quality, while fixed antenna responses limit available spatial and polarization degrees of freedom. This paper jointly designs pixel-antenna switch states and BB beamforming using polarization-aware communication and sensing models. Simulations report higher sensing SNR and target-angle robustness than the fixed-patch benchmark.

  • Problem

    ISAC must illuminate sensing targets while maintaining user SINR, but fixed antenna responses limit spatial and polarization degrees of freedom.

    Source: §I · p. 1

  • Method

    The paper models switch-configurable pixel antennas and alternately optimizes EM switch states, a BB precoder, and a sensing combiner.

    Source: §I · p. 1, §III · p. 4

  • Results

    The proposed design improves sensing SNR and target-angle robustness over the fixed-patch benchmark.

    Source: §V · p. 6

  • Takeaways & Limitations

    The simulations show that increasing the number of controllable ports yields larger gains over the fixed-patch benchmark.

    Source: §V · p. 6

Abstract

from arXiv · show

This paper investigates the joint electromagnetic (EM)-domain and baseband (BB)-domain beamforming for integrated sensing and communication (ISAC) systems enabled by pattern- and polarization-reconfigurable pixel-antenna arrays. A multiport-network-based antenna model is developed to map each binary switch configuration to its radiation response using the impedance and open-circuit radiation data extracted from Computer Simulation Technology (CST) Studio Suite simulations. Based on this model, polarization-aware virtual channels are constructed for downlink communication and statistical target sensing, leading to a mixed-integer nonconvex problem that maximizes the average sensing signal-to-noise ratio (SNR) subject to user signal-to-interference-plus-noise ratio (SINR) and transmit-power constraints. To handle the coupled binary and continuous variables, we propose an alternating optimization algorithm, in which the EM-domain switch-state matrices are updated using successive exhaustive Boolean optimization (SEBO), the BB-domain precoder is optimized via a minorization-maximization (MM) and second-order cone programming (SOCP) procedure, and the sensing combiner is obtained from a Rayleigh-quotient update. Simulation results show that the proposed design achieves higher sensing SNR and stronger target-angle robustness than the fixed-patch benchmark.

I. INTRODUCTION

The paper motivates joint EM- and BB-domain design to address ISAC requirements that fixed antenna responses and BB beamforming alone do not fully capture. It models switch-configurable pixel antennas through a multiport network, mapping switch states to polarization-resolved radiation responses.

I. INTRODUCTION

  • Joint EM-BB design addresses ISAC needs to illuminate targets while meeting user SINR requirements, which fixed antenna responses and BB-only beamforming do not fully capture.Source: §I · p. 1
  • The proposed framework maps pixel-antenna switch configurations to polarization-resolved radiation responses and constructs virtual channels for communication and statistical sensing.Source: §I · p. 1

A. Single Pixel Antenna Modeling

B. Pixel Antenna Array Modeling

The array model combines independently controlled pixel-antenna responses with array phase and polarization-aware propagation models. It yields virtual channels for communication and sensing.

B. Pixel Antenna Array Modeling

  • The base station uses separate transmit and receive ULAs of N pixel antennas, serving K single-antenna users and one target.Source: B. Pixel Antenna Array Modeling · p. 2
  • Each antenna’s switch configuration controls its radiation characteristics, and transmit and receive switch states form array-level configuration matrices.Source: B. Pixel Antenna Array Modeling · p. 3
  • The effective array responses combine polarization-resolved antenna radiation patterns with the shared spatial phase response across sampled angles.Source: B. Pixel Antenna Array Modeling · p. 3
  • A reduced Jones-rotation model captures polarization rotation and mismatch, while the angular channel and polarization response form polarization-aware virtual channels.Source: B. Pixel Antenna Array Modeling · p. 3

C. Communication and Sensing Signal Models

The signal models account for communication transmission and radar probing, while representing sensing through statistical polarization-aware channels. User reception is evaluated by SINR and radar reception by post-combining sensing SNR.

C. Communication and Sensing Signal Models

D. Problem Formulation

The formulation jointly selects transmit and receive antenna switch configurations, a BB beamformer, and a sensing combiner. Its objective is average sensing SNR, subject to user SINR and transmit-power constraints.

D. Problem Formulation

III. JOINT EM-BB BEAMFORMING DESIGN

The design alternates among EM-domain switch-state updates, digital precoder optimization, and sensing-combiner optimization.

III. JOINT EM-BB BEAMFORMING DESIGN

  • The mixed-integer nonconvex design alternately optimizes binary antenna switch states, the digital precoder, and the sensing combiner.SEBO updates the switch states, an MM-SOCP-based algorithm updates the precoder, and a Rayleigh-quotient maximization gives the combiner in closed form.Source: §III · p. 4

A. EM-domain Beamforming Designs

With baseband beamforming fixed, the EM-domain design searches antenna switch states to maximize sensing SNR while accounting for communication QoS. It uses block-wise exhaustive search and a penalty for SINR violations.

A. EM-domain Beamforming Designs

  • The EM-domain switch-state problem maximizes sensing SNR with the baseband beamforming matrices fixed.Candidate configurations are evaluated subject to communication QoS requirements.Source: A. EM-domain Beamforming Designs · p. 4
  • A penalty merit function ranks switch configurations by sensing SNR while penalizing the degree of communication-constraint violation.Unlike the hard rule, it does not directly discard currently infeasible candidates, which may become feasible after precoder re-optimization.Source: A. EM-domain Beamforming Designs · p. 4
  • SEBO partitions each antenna's switch states into disjoint blocks and cyclically optimizes each block by exhaustive search with the remaining states fixed.The transmit-side states are updated antenna by antenna, followed by receive-side updates in the same manner.Source: A. EM-domain Beamforming Designs · p. 4
  • One SEBO sweep requires at most 2NG2^Qb candidate evaluations, compared with 2^2QN for global exhaustive search.Thus, enumeration grows exponentially with block size Qb rather than the total number of switch variables.Source: A. EM-domain Beamforming Designs · p. 4

B. BB-domain Beamforming Designs

After EM-domain switch configurations determine the array radiation characteristics, the BB-domain design formulates its optimization using the resulting target channel and sensing-SNR objective.

B. BB-domain Beamforming Designs

  • The BB-domain beamforming problem is formulated after the EM-domain antenna switch configurations are designed.The objective sensing SNR is further rewritten using the effective target channel.Source: B. BB-domain Beamforming Designs · pp. 4–5
  • The target's effective channel is defined using the radiation characteristics determined by the EM-domain switch configurations.The channel incorporates the antenna radiation responses.Source: B. BB-domain Beamforming Designs · p. 5
  • The digital precoder is initialized by RZF or, if necessary, a feasibility SOCP before its objective is rewritten in an equivalent form.The passage does not specify the subsequent optimization steps.Source: 1) Digital Precoder Design · p. 5

1) Digital Precoder Design:

The digital-precoder update uses an MM surrogate and an SOCP transformation to form a convex subproblem, which can be solved with CVX.

1) Digital Precoder Design:

  • The MM method relaxes the nonconcave maximization into a linear surrogate because the objective is convex in F.This provides the surrogate used in the digital-precoder update.Source: 1) Digital Precoder Design · p. 5
  • The fractional communication SINR constraint is transformed into a tractable second-order cone constraint with the desired signal constrained to be real-valued.The resulting convex subproblem optimizes the digital precoder.Source: 1) Digital Precoder Design · p. 5
  • The resulting convex subproblem can be tackled by CVX.Source: 1) Digital Precoder Design · p. 5

2) Digital Combiner Design:

The digital combiner is updated by maximizing a Rayleigh quotient, yielding the normalized dominant-eigenvalue eigenvector. The alternating design also updates transmit and receive switch states, the BB beamformer, and the combiner until sensing-SNR convergence.

2) Digital Combiner Design:

  • The algorithm initializes BT, BR, and F, then returns BT, BR, F, and w as its output, retaining the three initialized variables together with the combiner.It computes Ψ by (31) and updates the combiner w by (39).Source: 2) Digital Combiner Design · p. 5
  • The alternating algorithm sequentially updates transmit and receive switch configurations with SEBO, then updates the BB beamformer and digital combiner until sensing SNR converges.It computes Ψ from the switch states before updating the BB beamformer.Source: 2) Digital Combiner Design · p. 5
  • The combiner maximizes a Rayleigh quotient using the eigenvector associated with the largest eigenvalue of Cw, then normalizes it to satisfy the unit-norm constraint.This gives the update in (39).Source: 2) Digital Combiner Design · p. 5

IV. SIMULATION RESULTS

Simulations compare switch-configurable pixel antennas with a fixed-radiation patch benchmark across transmit-power budgets and target angles. The proposed schemes improve sensing SNR over the patch benchmark, with the 80-port design offering greater gains and angle robustness than the 12-port alternative.

IV. SIMULATION RESULTS

  • The evaluation uses CST-derived antenna data at 2.45 GHz, with separate four-element transmit and receive arrays serving two users.The patch benchmark has the same aperture and is optimized only in the BB domain.Source: §IV · p. 5
  • Optimizing 80 switch-configurable ports yields greater sensing gains than optimizing 12 preselected ports, but requires higher hardware-control and optimization complexity.The 12-port option still provides sensing gains over the patch benchmark at lower complexity.Source: §IV · pp. 5–6
  • As transmit-power budget increases, sensing SNR rises for all schemes, and proposed pixel-antenna schemes outperform the patch benchmark in both ISAC and radar-only cases.Radar-only curves are higher without user-SINR constraints; the 80-port scheme consistently outperforms the 12-port scheme.Source: Figure 3 · p. 6, §IV · p. 6
  • Across a wide target-angle range, proposed pixel-antenna schemes maintain much higher sensing SNR than the fixed-radiation patch benchmark, which degrades off boresight.The 80-port scheme further improves SNR stability over the 12-port scheme.Source: Figure 4 · p. 6, §IV · p. 6

V. CONCLUSION

The conclusion presents a joint EM-BB beamforming design for ISAC pixel-antenna arrays, solved by a SEBO-based alternating algorithm. CST-based simulations show improved sensing SNR and target-angle robustness over the fixed-patch benchmark, with larger gains from more controllable ports.

V. CONCLUSION

  • The design integrates a multiport-network antenna model with polarization-aware communication and statistical sensing channels to maximize average sensing SNR under user-SINR and transmit-power constraints.The resulting problem is mixed-integer and nonconvex.Source: §V · p. 6
  • A SEBO-based alternating algorithm is developed to solve the joint EM-BB beamforming problem.Source: §V · p. 6
  • CST-based simulations show improved sensing SNR and target-angle robustness over the fixed-patch benchmark, with larger gains from increasing the number of controllable ports.Source: §V · p. 6
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