This paper investigates the joint design of beamforming and radar receive filters in a multiuser bistatic integrated sensing and communications (ISAC) system, aiming to maximize the minimum radar signal-to-interference-plus-noise ratio (SINR) under communications SINR and transmit power constraints. We consider two scenarios: transmitted signals are either known or unknown at the radar receiver. We develop tractable solutions to the resulting non-convex optimization problems in both cases. For the known-signal case, we derive closed-form radar receive filters and iteratively design beamforming using fractional programming (FP) and successive convex approximation (SCA). For the unknown case, we adopt an alternating optimization (AO) approach to jointly design the beamforming and receive filters. Numerical results demonstrate that, while both approaches achieve comparable performance under per-slot optimization, knowledge of the transmitted symbols provides significant gains in multi-slot processing via coherent integration. Moreover, the proposed ISAC designs perform close to the radar-only benchmark under moderate communication requirements.
Mohammad Hatami, N. Nguyen, Markku J. Juntti· 0 citations
Simulation results demonstrate that the proposed sub-6 GHz-assisted GNN-based beamformer achieves competitive and often superior sum-rate performance compared to classical baselines that rely on full mmWave CSI.
Sina Tavakolian, Abolfazl Zakeri, Ahmed Alkhateeb et al.· 0 citations
This paper investigates joint beamforming design and subcarrier allocation in a multicarrier integrated sensing and communications (ISAC) system that operates as a monostatic multiple-input multiple-output (MIMO) radar while simultaneously providing downlink communications services to multiple users. The main objective is to jointly optimize the beamforming and subcarrier allocation to maximize the minimum radar signal-to-interference-plus-noise ratio (SINR), subject to communications SINR and total power constraints. To address the resulting mixed-integer nonconvex optimization problem, we first derive a closed-form solution for the radar receive filter using the well-known minimum variance distortionless response (MVDR) beamforming scheme, and then employ an alternating optimization (AO) framework to decompose the original problem into two subproblems: beamforming design and subcarrier allocation. For the beamforming design, we propose an efficient approach that combines fractional programming (FP) and successive convex approximation (SCA) techniques. Furthermore, by leveraging the block-diagonal form of the matrices in the radar SINR formulation, we derive a simplified expression for the radar SINR, which significantly reduces the computational complexity and memory usage of the proposed method. Numerical results validate the convergence and effectiveness of the proposed algorithm and illustrate the trade-off between sensing and communications performances. The results show that the proposed method performs close to the radar-only benchmark under moderate communications SINR requirements and achieves substantial performance gains compared with a beampattern-mismatch-driven baseline scheme.
Mohammad Hatami, N. Nguyen, Markku J. Juntti· IEEE Transactions on Communi...· 1 citation
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
A deep-unfolded APG framework that maps iterative APG updates onto a finite number of neural network layers, where parameters such as step sizes and penalty coefficients are learned from data to reduce the computational complexity and runtime of APG.
Phuong Nam Tran, N. Nguyen, H. Ngo et al.· 0 citations
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