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Weighted Sum Rate Optimization for Movable Antenna Enabled Near-Field ISAC

Nemanja Stefan Perović, Keshav Singh, Chih-Peng Li, Mark F. Flanagan

TL;DR

This work addresses near-field ISAC with movable antennas (MAs) by formulating a non-convex weighted sum-rate (WSR) maximization under a sensing SINR constraint $\gamma_s \ge \gamma_0$. It develops an alternating-optimization (AO) algorithm that separately optimizes the sensing combiner $\mathbf{u}$ (closed-form), the communication precoders $\{\mathbf{W}_k\}$ (via a concave surrogate in a SCA framework), the sensing beamformer $\mathbf{v}$ (via SDR with a Taylor surrogate), and the MA positions $\mathbf{q}_k$ (via a gradient projection method). Simulations show substantial WSR gains of MA-enabled near-field ISAC over fixed-antennas baselines, with larger gains when more weight is assigned to nearer users; the sensing performance is more sensitive to the sensing SINR threshold than the communication performance. Overall, the proposed AO framework demonstrates the practical value of MAs in expanding degrees of freedom and improving performance in near-field ISAC.

Abstract

Integrated sensing and communication (ISAC) has been recognized as one of the key technologies capable of simultaneously improving communication and sensing services in future wireless networks. Moreover, the introduction of recently developed movable antennas (MAs) has the potential to further increase the performance gains of ISAC systems. Achieving these gains can pose a significant challenge for MA-enabled ISAC systems operating in the near-field due to the corresponding spherical wave propagation. Motivated by this, in this paper we maximize the weighted sum rate (WSR) for communication users while maintaining a minimal sensing requirement in an MA-enabled near-field ISAC system. To achieve this goal, we propose an algorithm that optimizes the sensing receive combiner, the communication precoding matrices, the sensing transmit beamformer and the positions of the users' MAs in an alternating manner. Simulation results show that using MAs in near-field ISAC systems provides a substantial performance advantage compared to near-field ISAC systems with only fixed antennas. Additionally, we demonstrate that the highest WSR is obtained when larger weights are allocated to the users placed closer to the BS, and that the sensing performance is significantly more affected by the minimum sensing signal-to-interference-plus-noise ratio (SINR) threshold compared to the communication performance.

Weighted Sum Rate Optimization for Movable Antenna Enabled Near-Field ISAC

TL;DR

This work addresses near-field ISAC with movable antennas (MAs) by formulating a non-convex weighted sum-rate (WSR) maximization under a sensing SINR constraint . It develops an alternating-optimization (AO) algorithm that separately optimizes the sensing combiner (closed-form), the communication precoders (via a concave surrogate in a SCA framework), the sensing beamformer (via SDR with a Taylor surrogate), and the MA positions (via a gradient projection method). Simulations show substantial WSR gains of MA-enabled near-field ISAC over fixed-antennas baselines, with larger gains when more weight is assigned to nearer users; the sensing performance is more sensitive to the sensing SINR threshold than the communication performance. Overall, the proposed AO framework demonstrates the practical value of MAs in expanding degrees of freedom and improving performance in near-field ISAC.

Abstract

Integrated sensing and communication (ISAC) has been recognized as one of the key technologies capable of simultaneously improving communication and sensing services in future wireless networks. Moreover, the introduction of recently developed movable antennas (MAs) has the potential to further increase the performance gains of ISAC systems. Achieving these gains can pose a significant challenge for MA-enabled ISAC systems operating in the near-field due to the corresponding spherical wave propagation. Motivated by this, in this paper we maximize the weighted sum rate (WSR) for communication users while maintaining a minimal sensing requirement in an MA-enabled near-field ISAC system. To achieve this goal, we propose an algorithm that optimizes the sensing receive combiner, the communication precoding matrices, the sensing transmit beamformer and the positions of the users' MAs in an alternating manner. Simulation results show that using MAs in near-field ISAC systems provides a substantial performance advantage compared to near-field ISAC systems with only fixed antennas. Additionally, we demonstrate that the highest WSR is obtained when larger weights are allocated to the users placed closer to the BS, and that the sensing performance is significantly more affected by the minimum sensing signal-to-interference-plus-noise ratio (SINR) threshold compared to the communication performance.
Paper Structure (13 sections, 1 theorem, 26 equations, 5 figures, 1 algorithm)

This paper contains 13 sections, 1 theorem, 26 equations, 5 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Communication Model
  4. Sensing Model
  5. Problem Formulation
  6. Proposed Algorithm
  7. Optimization of Sensing Receive Combiner
  8. Optimization of Communication Precoding Matrices
  9. Optimization of Sensing Transmit Beamformer
  10. Optimization of MA Positions of User $k$
  11. Overall Algorithm and Convergence Analysis
  12. Simulation Results
  13. Conclusion

Key Result

Lemma 1

The gradients of $R_{k}$wrt the x and y coordinates of the b-th MA of user $k$ are given by (eq:dRk_x) and (eq:dRk_y) respectively, where $\mathbf{C}_{1,k}=\mathbf{T}_{1}\mathbf{H}_{k}^{H}\mathbf{A}_{1}^{-1},\mathbf{C}_{2,k}=\mathbf{T}_{2}\mathbf{H}_{k}^{H}\mathbf{A}_{2,k}^{-1}$,$\mathbf{T}_{1}=\sum

Figures (5)

  • Figure 1: Convergence of the proposed algorithm.
  • Figure 2: WSR versus the number of user antennas.
  • Figure 3: WSR versus the users rate weights.
  • Figure 4: WSR versus the maximum transmit power.
  • Figure 5: WSR and sensing signal power versus the sensing SINR threshold.

Theorems & Definitions (1)

  • Lemma 1