RIS-Assisted Coordinated Multi-Point ISAC for Low-Altitude Sensing Coverage

The low-altitude economy (LAE) has emerged and developed in various fields, which has gained considerable interest. To ensure the security of LAE, it is essential to establish a proper sensing coverage scheme for monitoring the unauthorized targets. Introducing integrated sensing and communication (ISAC) into cellular networks is a promising solution that enables coordinated multiple base stations (BSs) to significantly enhance sensing performance and extend coverage. Meanwhile, deploying a reconfigurable intelligent surface (RIS) can mitigate signal blockages between BSs and low-altitude targets in urban areas. Therefore, this paper focuses on the low-altitude sensing coverage problem in RIS-assisted coordinated multi-point ISAC networks, where a RIS is employed to enable multiple BSs to sense a prescribed region while serving multiple communication users. A joint beamforming and phase shifts design is proposed to minimize the total transmit power while guaranteeing sensing signal-to-noise ratio and communication spectral efficiency. To tackle this non-convex optimization problem, an efficient algorithm is proposed by using the alternating optimization and semi-definite relaxation techniques. Numerical results demonstrate the superiority of our proposed scheme over the baseline schemes.

💡 Research Summary

This paper addresses the emerging need for continuous monitoring of unauthorized low‑altitude activities in the so‑called low‑altitude economy (LAE). The authors propose a novel integrated sensing and communication (ISAC) architecture that combines coordinated multi‑point (CoMP) transmission from multiple base stations (BSs) with a reconfigurable intelligent surface (RIS) deployed on a building façade. The system consists of J downlink BSs, a single receive‑only BS for radar‑type sensing, K single‑antenna ground users, and an M‑element RIS. The BSs simultaneously transmit communication data to the users and a dedicated sensing waveform toward a prescribed three‑dimensional region O located at height h above ground. Both direct BS‑target links and RIS‑assisted BS‑RIS‑target links are exploited for echo reception at the sensing BS.

The communication channel model incorporates both line‑of‑sight (LOS) and non‑LOS (NLOS) components using a Rician fading model. The effective channel from BS j to user k is the sum of the direct channel h_{j,k} and the reflected channel G_{R,j} Φ h_{R,k}, where Φ is the diagonal phase‑shift matrix of the RIS. Users are assumed to have hardware capable of cancelling the known sensing waveform, thus eliminating cross‑interference. The spectral efficiency (SE) of each user is expressed in the standard Shannon form (5).

For sensing, the receive BS collects echoes that travel either directly from the BSs to the target and back, or via the RIS. The target‑free component of the received signal is assumed to be known and removed, leaving only the reflected signal whose power depends on the transmit beamformers ω_{j,k}, the RIS phase shifts, and the radar cross‑section σ_rcs of the target. The sensing performance is quantified by the signal‑to‑noise ratio (SNR) γ(o) defined in (8) for any point o in the region O.

The core design problem (P1) is to minimize the total transmit power Σ_{j,k}‖ω_{j,k}‖² while guaranteeing (i) a minimum SE R_req for every communication user, (ii) a minimum sensing SNR γ_req for all discretized grid points o_l that approximate O, and (iii) unit‑modulus constraints on each RIS element. This problem is highly non‑convex because the beamforming vectors and RIS phases are coupled in both the SE and SNR constraints.

To obtain a tractable solution, the authors adopt an alternating optimization (AO) framework. In the first sub‑problem (P2), the RIS phase matrix Φ is fixed and the beamforming vectors are optimized. By defining W_k = ω_k ω_kᴴ, the problem is reformulated as a semi‑definite program (SDP) (11) with a rank‑one constraint. The rank constraint is relaxed using semi‑definite relaxation (SDR). The resulting SDP can be solved efficiently with standard convex solvers, and the authors argue (based on prior work) that the optimal solution will be rank‑one, allowing recovery of the beamformers via eigen‑decomposition.

In the second sub‑problem (P3), the beamformers are fixed and the RIS phases are optimized. The authors introduce an augmented vector ˜v =