Precoding for Multi-Cell ISAC: from Coordinated Beamforming to Coordinated Multipoint and Bi-Static Sensing

TL;DR

This work develops a CRB-based, robust precoding framework for multi-cell ISAC systems, contrasting Coordinated Beamforming (CBF) and Coordinated Multipoint (CoMP) under CSI uncertainty. It derives CRB expressions for mono-static (CBF) and bistatic (CoMP) sensing, and formulates joint CRB minimization with minimum SINR under power constraints using Block-Level (BLP) and Symbol-Level (SLP) precoding. The non-convex problems are addressed via Semidefinite Relaxation (SDR) and Alternating Optimization (AO), with extensive simulations showing inter-cell links can both help (in CoMP bistatic sensing) and hurt (through leakage) performance, and that CoMP-SLP yields the best overall sensing and communications performance. These results highlight the importance of inter-cell coordination and constructive interference exploitation for practical ISAC deployments in dense networks.

Abstract

This paper proposes a framework for designing robust precoders for a multi-input single-output (MISO) system that performs integrated sensing and communication (ISAC) across multiple cells and users. We use Cramer-Rao-Bound (CRB) to measure the sensing performance and derive its expressions for two multi-cell scenarios, namely coordinated beamforming (CBF) and coordinated multi-point (CoMP). In the CBF scheme, a BS shares channel state information (CSI) and estimates target parameters using monostatic sensing. In contrast, a BS in the CoMP scheme shares the CSI and data, allowing bistatic sensing through inter-cell reflection. We consider both block-level (BL) and symbol-level (SL) precoding schemes for both the multi-cell scenarios that are robust to channel state estimation errors. The formulated optimization problems to minimize the CRB in estimating the parameters of a target and maximize the minimum communication signal-to-interference-plus-noise-ratio (SINR) while satisfying a given total transmit power budget are non-convex. We tackle the non-convexity using a combination of semidefinite relaxation (SDR) and alternating optimization (AO) techniques. Simulations suggest that neglecting the inter-cell reflection and communication links degrades the performance of an ISAC system. The CoMP scenario employing SL precoding performs the best, whereas the BL precoding applied in the CBF scenario produces relatively high estimation error for a given minimum SINR value.

Figures (9)

• Figure 1: System setup.
• Figure 2: SLP: the $\langle d_{mks}$ rotated noiseless received signal $\hat{y}_{mks}=\mathbf{\tilde{h}}^{T}_{m,mks} \mathbf{{x}}_{ms}d_{mks}$ should fall in the CI region of the transmitted QPSK symbol $d_{mks}$.
• Figure 3: RCRB Vs minimum SINR performance when $N_{\mathrm{tx}}=10$, $N_{\mathrm{rx}}=10$, $K=3$, $\delta =0$, $M_{\mathrm{psk}}=4$. Single-cell solutions in the proximity of additional BSs are sub-optimal, motivating exploration of multi-cell ISAC setups.
• Figure 4: Final beampattern for BS1, when $\gamma=40$ dB, $N_{\mathrm{tx}}=16$, $N_{\mathrm{rx}}=6$, $K=3$, $\delta =0$, $M_{\mathrm{psk}}=4$, $M_{\mathrm{psk}}=4$.
• Figure 5: RCRB Vs. comm SINR threshold when $N_{\mathrm{tx}}=6$, $N_{\mathrm{rx}}=6$, $K=3$, $\delta=0$, $M_{\mathrm{psk}}=4$ .

Theorems & Definitions (6)

• Proposition 1
• proof
• Proposition 2
• proof
• Proposition 3
• proof