Radar and Communication Coexistence Enabled by Interference Exploitation

In this paper, we propose a novel approach for the spectrum sharing between Multi-Input-Multi-Output (MIMO) radar and downlink multi-user Multi-Input- Single-Output (MU-MISO) communication system. To obtain a power-efficient beamforming at the base station (BS), we utilize the constructive multi- user interference (MUI) as a source of green signal power. The proposed beamforming design mainly focuses on two optimization problems, i.e., transmit power minimization for BS and interference minimization for radar, subject to given performance requirements of the two systems. We further consider the impact of the proposed methods on radar, where the detection probability for MIMO radar in the presence of the interference from BS is analytically derived, and important trade-offs are revealed. Numerical results show that the proposed approach outperforms the conventional beamforming designs by achieving a significant performance gain under the discussed coexistence scenario

Towards Dual-functional Radar-Communication Systems: Optimal Waveform Design

We focus on a dual-functional multi-input-multi-output (MIMO) radar-communication (RadCom) system, where a single transmitter communicates with downlink cellular users and detects radar targets simultaneously. Several design criteria are considered for minimizing the downlink multi-user interference. First, we consider both the omnidirectional and directional beampattern design problems, where the closed-form globally optimal solutions are obtained. Based on these waveforms, we further consider a weighted optimization to enable a flexible trade-off between radar and communications performance and introduce a low-complexity algorithm. The computational costs of the above three designs are shown to be similar to the conventional zero-forcing (ZF) precoding. Moreover, to address the more practical constant modulus waveform design problem, we propose a branch-and-bound algorithm that obtains a globally optimal solution and derive its worst-case complexity as a function of the maximum iteration number. Finally, we assess the effectiveness of the proposed waveform design approaches by numerical results.Comment: 13 pages, 10 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Joint Beamforming for RIS-Assisted Integrated Sensing and Communication Systems

Integrated sensing and communications (ISAC) is an emerging critical technique for the next generation of communication systems. However, due to multiple performance metrics used for communication and sensing, the limited degrees-of-freedom (DoF) in optimizing ISAC systems poses a challenge. Reconfigurable intelligent surfaces (RIS) can introduce new DoF for beamforming in ISAC systems, thereby enhancing the performance of communication and sensing simultaneously. In this paper, we propose two optimization techniques for beamforming in RIS-assisted ISAC systems. The first technique is an alternating optimization (AO) algorithm based on the semidefinite relaxation (SDR) method and a one-dimension iterative (ODI) algorithm, which can maximize the radar mutual information (MI) while imposing constraints on the communication rates. The second technique is an AO algorithm based on the Riemannian gradient (RG) method, which can maximize the weighted ISAC performance metrics. Simulation results verify the effectiveness of the proposed schemes. The AO-SDR-ODI method is shown to achieve better communication and sensing performance, than the AO-RG method, at a higher complexity. It is also shown that the mean-squared-error (MSE) of the estimates of the sensing parameters decreases as the radar MI increases.Comment: 30 pages, 8 figures. This paper has been submitted to IEEE Transactions on Communication