2026· IEEE Transactions on Wireless Communications· Vol 25, pp. 21783-21798· 0 citations· 43 references
Computer Science
Abstract
To address the challenges of inter-cell interference/reflection and static clutter, a clutter-aware waveform design for a multi-cell multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system is proposed. Various levels of coordination among base stations (BSs) are investigated to enhance target detectability in cluttered environments while maintaining the quality of service (QoS) for communication users. Specifically, two coordination schemes are investigated: 1) coordinated beamforming (CBF), where only channel state information (CSI) is shared, and 2) coordinated multipoint (CoMP), where both CSI and user data are exchanged among BSs. The waveform design problem is formulated as a non-convex optimization that maximizes the radar output signal-to-clutter-plus-interference-plus-noise ratio (SCINR), subject to robust symbol-level QoS and constant-modulus power constraints to ensure uncertainty in shared and estimated CSI. To tackle this problem, the single-ratio SCINR objective is decoupled via Dinkelbach’s transform (or a quadratic transform for multiple-ratio objectives) and reformulated on a Riemannian manifold to accommodate the constant-modulus constraint guarantees a practical peak-to-average-power ratio (PAPR). The resulting problem is further converted into an unconstrained form using the augmented Lagrangian method (ALM) and solved through a Riemannian conjugate gradient (RCG) algorithm. Simulation results demonstrate that the proposed designs achieve superior radar and communication performance compared to baseline schemes that underestimate the effects of multi-cell deployment.
We investigate waveform optimization for integrated sensing and multi-user communications. The key idea is to minimize the integrated sidelobe level of the multiple transmit waveforms while ensuring that the signal-to-interference-plus-noise ratio of each communication user remains above a prescribed threshold. Moreover, by exploiting the available spatial degrees of freedom, we enforce identical effective channel gains for all desired noise-free symbols. This design decouples the signal strength from spatial beamforming and simplifies symbol detection at the users. The main challenge lies in handling a non-convex quartic objective with multiple coupled constraints, where the weight vector appears inside correlation terms, making the problem difficult to solve directly. To address this issue, we resort to the majorization-minimization (MM) technique to convert the objective into a tractable form. We then derive a series of surrogate functions and supporting results for the MM steps, enabling the solution to be obtained iteratively. Simulation results demonstrate the effectiveness of the proposed waveform design.
Chunxuan Shi, Yongzhe Li, Ran Tao· IEEE Signal Processing Lette...· 0 citations
Integrated sensing and communication (ISAC) requires transmit waveforms that simultaneously preserve communication quality, provide reliable sensing, and remain compatible with practical radio-frequency front ends. This paper considers a discrete-time ISAC waveform design problem that minimizes transmit power from the perspective of a novel metric termed the ambiguity-domain sensing signal-to-interference-plus-noise ratio (AF-SINR). The proposed AF-SINR quantifies the ratio between the desired ambiguity-function mainlobe power and the weighted aggregate sidelobe leakage plus noise within a local delay-Doppler region of interest, thereby providing a localized and noise-aware sensing-QoS measure. To enable ISAC operation, the waveform is further required to satisfy per-user effective communication-SINR constraints, while a peak-to-average power ratio (PAPR) constraint is imposed to facilitate practical implementation. The resulting energy-minimization problem is nonconvex due to the fractional QoS expressions, quartic ambiguity terms, and waveform-dependent PAPR constraint. To address this challenge, we propose a fractional-programming successive-convex-approximation (FP-SCA) algorithm. Simulation results verify that the proposed method satisfies all requirements while preserving localized ambiguity suppression over the local delay-Doppler region.
A modulation- and receive-filter-aware framework for the sensing-interference management in multi-cell OFDM-ISAC systems is developed and closed-form signal-to-interference-plus-noise ratio (SINR) expressions for each range--Doppler bin under matched filtering (MF) and reciprocal filtering (RF).
Kaitao Meng, Kawon Han, C. Masouros et al.· 0 citations
In integrated sensing and communication (ISAC) systems, stringent sensing performance constraints can severely limit the power available for communication. Hybrid reconfigurable intelligent surfaces (HRISs) with capabilities of both passive reflection and active signal amplification can significantly improve communication performance in the power-limited regime. This motivates us to analyze and optimize the performance of an HRIS-aided multiple-input-multiple-output (mMIMO) ISAC system. We first estimate the effective uplink/downlink channels using the minimum mean square error method. We then derive closed-form expressions for the communication sum-rate and sensing Cram\'er-Rao lower bound (CRLB). It is shown that under the equal power allocation strategy, the CRLB remains independent of the HRIS coefficients. Then, we formulate a joint optimization problem of power allocation and HRIS beamforming to maximize the communication sum-rate while ensuring specified sensing CRLB constraints. To solve the formulated non-convex problem, we propose an alternating optimization algorithm based on fractional programming and successive convex approximation. Extensive simulations validate our analysis and proposed algorithm, showing significant improvements in both communication and sensing performances enabled by the HRIS. For example, an HRIS with only $4$ active elements offers $97.30\%$ improvement in the communication sum-rate, while ensuring a sensing CRLB constraint of $-30$ dB.
Smriti Uniyal, Tian-Yu Fang, M. di Renzo et al.· 0 citations
Integrated sensing and communications (ISAC) is a key technology for next-generation wireless networks, enabling communication and radar sensing over shared spectral and hardware resources. In practical multi-user multiple-input multiple-output (MU-MIMO) ISAC transmitters, however, mutual coupling (MC) between antenna elements distorts the array steering vector and each communication user (CU) channel, so that the sensing beampattern deviates from the desired one and the communication link to each user degrades. To address this limitation, we propose a robust MC-compensated beamforming design that guarantees both the sensing and communication performance of MU-MIMO ISAC transmitters against the residual MC error. We introduce a residual error on the MC matrix, so that a norm-bounded residual error induces both the sensing beampattern uncertainty and the communication channel uncertainty. The transmit covariance is then optimized against the worst-case of each uncertainty, minimizing the worst-case beampattern matching mean-squared error (MSE) for sensing while guaranteeing the signal-to-interference-plus-noise ratio (SINR) for each CU. Each worst-case constraint is converted into a linear matrix inequality, and the problem becomes a convex semidefinite program (SDP). Numerical results show that the proposed robust design attains both a lower sensing beampattern matching MSE and a higher communication SINR than those of the conventional designs, with an advantage that widens as the residual error grows.
Coordinated multi-point (CoMP) integrated sensing and communications (ISAC) architecture enhances cell-edge coverage, yet its sensing performance is severely limited when target statistics (e.g., radar cross-section and noise variance) are unknown a priori. Treating the unknown parameters as deterministic, we propose a generalized likelihood ratio test (GLRT) based invariant detector that maintains constant false alarm rate (CFAR). Leveraging the monotonic relationship between the detection probability and the non-centrality parameter to model the intractable detection probability constraint, we formulate a joint transmit beamforming problem that maximizes the communication sum-rate under radar detection and minimum user signal-to-interference-plus-noise ratio (SINR) constraints, robustly accommodating both perfect and imperfect CSI. To solve this highly non-convex optimization problem, a two-stage algorithm is developed: Stage I extracts a feasible initial point, while Stage II employs fractional programming for iterative sum-rate maximization. Numerical results show that the proposed joint design significantly enlarges the feasible region and achieves substantial gains over non-CoMP baselines.
