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

Reconfigurable Intelligent Surfaces for Energy Efficiency in Wireless Communication

The adoption of a Reconfigurable Intelligent Surface (RIS) for downlink multi-user communication from a multi-antenna base station is investigated in this paper. We develop energy-efficient designs for both the transmit power allocation and the phase shifts of the surface reflecting elements, subject to individual link budget guarantees for the mobile users. This leads to non-convex design optimization problems for which to tackle we propose two computationally affordable approaches, capitalizing on alternating maximization, gradient descent search, and sequential fractional programming. Specifically, one algorithm employs gradient descent for obtaining the RIS phase coefficients, and fractional programming for optimal transmit power allocation. Instead, the second algorithm employs sequential fractional programming for the optimization of the RIS phase shifts. In addition, a realistic power consumption model for RIS-based systems is presented, and the performance of the proposed methods is analyzed in a realistic outdoor environment. In particular, our results show that the proposed RIS-based resource allocation methods are able to provide up to $300\%$ higher energy efficiency, in comparison with the use of regular multi-antenna amplify-and-forward relaying.Comment: Accepted by IEEE TWC; additional materials on the topic are included in the 2018 conference publications at ICASSP (https://ieeexplore.ieee.org/abstract/document/8461496) and GLOBECOM 2018 (arXiv:1809.05397

Applying Deep Learning for Phase-Array Antenna Design

Master of Engineering (Electrical Engineering), 2021Hybrid beamforming (HBF) can provide rapid data transmission rates while reducing the complexity and cost of massive multiple-input multiple-output (MIMO) systems. However, channel state information (CSI) is imperfect in realistic downlink channels, introducing challenges to hybrid beamforming (HBF) design. For HBF designs, we had a hard time finding the proper labels. If we use the optimized output based on the traditional algorithm as the label, the neural network can only be trained to approximate the traditional algorithm, but not better than the traditional algorithm. This thesis proposes a hybrid beamforming neural network based on unsupervised deep learning (USDNN) to prevent the labeling overhead of supervised learning and improve the achievable sum rate based on imperfect CSI. Compared with the traditional HBF method, the unsupervised learning-based method can avoid the labeling overhead as well as obtain better performance than the traditional algorithm. The network consists of 5 dense layers, 4 batch normalization (BN) layers and 5 activation functions. After training, the optimized beamformer can be obtained, and the optimized beamforming vector can be directly output. The simulation results show that our proposed method is 74% better than manifold optimization (MO) and 120% better than orthogonal match pursuit (OMP) systems. Furthermore, our proposed USDNN can achieve near-optimal performance