We consider the problem of the joint design of transmit and receive beamforming (TRBF) vectors for integrated sensing and communication systems in the signal-dependent interference scenario. The dual-functional base station can communicate with downlink users and detect targets simultaneously. By adopting the signal-to-interference-plus-noise ratio (SINR) as performance metrics for both radar and communication, we propose the joint design of the TRBF strategy to manifest the balance of radar and communication in both perfect and imperfect channel state information scenarios. Meanwhile, the upper bound performance of the SINR for target detection is analyzed. Numerical results show the SINR trade-offs between target detection and user communication.

In this letter, we consider a multi-input multi-output (MIMO) dual-functional radar and communication (DFRC) system, where a co-designed waveform is optimized for dual purposes of detecting multiple targets and serving several single-antenna users. In particular, two novel waveform design optimization problems based on constructive interference (CI) are proposed, where a constraint of waveform similarity error (WSE) and a joint radar-communication objective function are imposed under a Pareto optimization framework, by taking into account different transmit power constraints. To tackle the proposed optimization problems, we propose specifically tailored convex optimization techniques by using manifold algorithms. Numerical results show that the CI-based methods outperform the existing least-squares (LS) based methods, in terms of the symbol error rate (SER), without the need of knowing the exact constellation power.

Sensing-assisted communication is a feasible technology for the rapid development of 5G and 6G communication systems, which can achieve greater channel capacity with limited energy consumption. In this letter, we propose a deep learning-based beam selection and power allocation algorithm (DL-BSPA), which includes a beam selection network and a power allocation network. The beam selection network uses real millimeter wave radar data to learn how to accurately and efficiently select beams. An unsupervised power allocation network learns and optimizes the power allocation based on the results of the beam selection network. In addition, the DL-BSPA algorithm adopts the joint deployment optimization method to maximize the channel capacity between mobile users and base stations (BS). Simulation results show that the proposed scheme is robust and can achieve competitive performance.

Radar target detection on the sea surface is challenging due to the influence of sea clutter. Traditional radar target detection methods cannot model the sea clutter distributions precisely, resulting in poor target detection performance. In this letter, we propose a novel PointNet-based method to parallelly detect multiple targets. We extract the global features to solve a classification problem, i.e., detecting whether there exist the targets in a radar echo frame, and extract the local features to solve a segmentation problem, i.e., detecting whether it has a target in each range cell. In addition, to implement constant false alarm rate (CFAR) detection, we apply a statistical method by precisely adjusting the detection threshold to keep a desired probability of false alarm (PFA). Simulation results show that the proposed method can realize the target classification with 95.985% total accuracy rate when PFA is 0.1, and achieve a larger detection probability under a desired PFA based on the IPIX radar dataset compared with the baselines.

The diversity and complexity of the environment, such as wind speed, wave height, sea clutter, etc., are the main reasons for the current limitation in the spaceborne Global Navigation Satellite System-Reflectometry(GNSS-R) sea surface height(SSH) accuracy. This letter proposes a modified ResNet architecture for sea surface altimetry, named fractional Fourier transform-ResNet (F-ResNet), using filtered Delay-Doppler Maps(DDMs) and other information from Cyclone Global Navigation Satellite System(CYGNSS) Level 1 data. The DDMs are filtered by fractional Fourier transform(FrFT) to keep the doppler and delay information in the five maps according to the different fractions. F-ResNet is based on convolutional layers for the filtered DDMs, along with fully connected layers for processing ancillary technical and higher-level input parameters. The validity of our proposed method is demonstrated using the Jason-3 data set for the whole year 2020. The RMSE of the SSH using F-ResNet is improved by 15.2% compared to other machine learning methods, which can achieve 1.17 meters. And the influence of different signal-to-noise ratios on prediction accuracy is analyzed.

Influenced by the randomnesses in the realistic vehicle-to-infrastructure (V2I) scenarios, traditional deterministic optimization algorithms cannot be adopted directly to promote sensing-communication performance. In this letter, we consider a vehicle served by several base stations (BSs), model the integrated performance based on information theory, and formulate an optimization problem to enlarge the total information amount. A reinforcement-learning-based algorithm of jointly optimizing the bandwidth allocation and BS selection is proposed, where a double deep Q-network agent is adopted. Simulation results verify the system performance, which shows performing better than baselines.