Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Robust Beamforming and Time Allocation for Time-Division Cell-Free Near-Field ISAC

Chaedam Son, Si-Hyeon Lee

TL;DR

This work addresses the challenge of jointly enabling sensing and communication in a time-division, near-field cell-free MIMO system. It introduces location-aware channel construction by estimating user positions in a sensing phase and using these estimates to form robust downlink beams in the subsequent communication phase, explicitly modeling how localization errors translate into channel uncertainty. The proposed TD-ISAC-Main algorithm solves a non-convex joint optimization of the sensing covariance, robust beamforming, and time allocation via an alternating-optimization framework with SDP relaxations and the generalized S-procedure; two low-complexity schemes (TD-ISAC-EI and TD-ISAC-MRT) provide practical options with favorable performance-complexity tradeoffs. Numerical results show superior localization accuracy in the near-field multi-static setup and notable throughput gains, illustrating the practical value of location-aware channel estimation in mitigating channel uncertainty for next-generation networks.

Abstract

In this paper, we propose a time-division near-field integrated sensing and communication (ISAC) framework for cell-free multiple-input multiple-output (MIMO), where sensing and downlink communication are separated in time. During the sensing phase, user locations are estimated and used to construct location-aware channels, which are then exploited in the subsequent communication phase. By explicitly modeling the coupling between sensing-induced localization errors and channel-estimation errors, we capture the tradeoff between sensing accuracy and communication throughput. Based on this model, we jointly optimize the time-allocation ratio, sensing covariance matrix, and robust downlink beamforming under imperfect channel state information (CSI). The resulting non-convex problem is addressed via a semidefinite programming (SDP)-based reformulation within an alternating-optimization framework. To further reduce computational complexity, we also propose two low-complexity suboptimal designs: an error-ignorant scheme and a maximum ratio transmission (MRT)-based scheme. Simulation results show that the proposed scheme significantly improves localization accuracy over far-field and monostatic setups, thereby reducing channel estimation errors and ultimately enhancing the achievable rate. Moreover, the error-ignorant scheme performs well under stringent sensing requirements, whereas the MRT-based scheme remains robust over a wide range of sensing requirements by adapting the time-allocation ratio, albeit with some beamforming loss.

Robust Beamforming and Time Allocation for Time-Division Cell-Free Near-Field ISAC

TL;DR

This work addresses the challenge of jointly enabling sensing and communication in a time-division, near-field cell-free MIMO system. It introduces location-aware channel construction by estimating user positions in a sensing phase and using these estimates to form robust downlink beams in the subsequent communication phase, explicitly modeling how localization errors translate into channel uncertainty. The proposed TD-ISAC-Main algorithm solves a non-convex joint optimization of the sensing covariance, robust beamforming, and time allocation via an alternating-optimization framework with SDP relaxations and the generalized S-procedure; two low-complexity schemes (TD-ISAC-EI and TD-ISAC-MRT) provide practical options with favorable performance-complexity tradeoffs. Numerical results show superior localization accuracy in the near-field multi-static setup and notable throughput gains, illustrating the practical value of location-aware channel estimation in mitigating channel uncertainty for next-generation networks.

Abstract

In this paper, we propose a time-division near-field integrated sensing and communication (ISAC) framework for cell-free multiple-input multiple-output (MIMO), where sensing and downlink communication are separated in time. During the sensing phase, user locations are estimated and used to construct location-aware channels, which are then exploited in the subsequent communication phase. By explicitly modeling the coupling between sensing-induced localization errors and channel-estimation errors, we capture the tradeoff between sensing accuracy and communication throughput. Based on this model, we jointly optimize the time-allocation ratio, sensing covariance matrix, and robust downlink beamforming under imperfect channel state information (CSI). The resulting non-convex problem is addressed via a semidefinite programming (SDP)-based reformulation within an alternating-optimization framework. To further reduce computational complexity, we also propose two low-complexity suboptimal designs: an error-ignorant scheme and a maximum ratio transmission (MRT)-based scheme. Simulation results show that the proposed scheme significantly improves localization accuracy over far-field and monostatic setups, thereby reducing channel estimation errors and ultimately enhancing the achievable rate. Moreover, the error-ignorant scheme performs well under stringent sensing requirements, whereas the MRT-based scheme remains robust over a wide range of sensing requirements by adapting the time-allocation ratio, albeit with some beamforming loss.
Paper Structure (19 sections, 1 theorem, 57 equations, 9 figures, 4 algorithms)

This paper contains 19 sections, 1 theorem, 57 equations, 9 figures, 4 algorithms.

Table of Contents

  1. Introduction
  2. System Model and Problem Formulation
  3. Channel Model
  4. Sensing Model
  5. Communication Model
  6. Problem Formulation
  7. Main Algorithm
  8. Sensing Matrix Optimization
  9. Communication Beamforming and Time Allocation Optimization
  10. Beamforming Optimization
  11. Time Allocation Optimization
  12. Overall Algorithm
  13. Low-Complexity Algorithms
  14. Time-Division ISAC Algorithm with Error-Ignorant Beamforming and Time Allocation
  15. Time-Division ISAC Algorithm with Robust Power and Time Allocation under MRT
  16. ...and 4 more sections

Key Result

Theorem 1

(Generalized S-procedure)generalized_S_procedure: Let $f_0(\pmb{x}) = \pmb{x}^H \pmb{A}_0 \pmb{x} + 2 \Re\{\pmb{b}_0^H \pmb{x}\} + c_0$ and $f_i(\pmb{x}) = \pmb{x}^H \pmb{A}_i \pmb{x} + 2 \Re\{\pmb{b}_i^H \pmb{x}\} + c_i$ for $i \in \{1,\dots,L\}$, where $\pmb{A}_i \in \mathbb{C}^{N \times N}$ is a holds if there exist scalars $\lambda_i \geq 0$ such that

Figures (9)

  • Figure 1: Time-division cell-free near-field ISAC system.
  • Figure 2: Antenna configuration at AP $m$.
  • Figure 3: Channel estimation error versus user localization error: (a) different user locations with fixed antenna number ($N_t=20$), (b) different antenna numbers with fixed user location ($c_k^x=12.5$, $c_k^y=21.7$).
  • Figure 4: $\sqrt{\overline{\text{CRLB}}}$ versus $P_{\text{max}}^{\text{s}}$.
  • Figure 5: Normalized MUSIC spectrum comparison between near-field and far-field scenarios with $P_{\text{max}}^{\text{s}}=40$ dBm.
  • ...and 4 more figures

Theorems & Definitions (1)

  • Theorem 1