A Deep Learning Framework for Optimization of MISO Downlink Beamforming

Beamforming is an effective means to improve the quality of the received signals in multiuser multiple-input-singleoutput (MISO) systems. Traditionally, finding the optimal beamforming solution relies on iterative algorithms, which introduces high computational delay and is thus not suitable for realtime implementation. In this paper, we propose a deep learning framework for the optimization of downlink beamforming. In particular, the solution is obtained based on convolutional neural networks and exploitation of expert knowledge, such as the uplink-downlink duality and the known structure of optimal solutions. Using this framework, we construct three beamforming neural networks (BNNs) for three typical optimization problems, i.e., the signal-to-interference-plus-noise ratio (SINR) balancing problem, the power minimization problem, and the sum rate maximization problem. For the former two problems the BNNs adopt the supervised learning approach, while for the sum rate maximization problem a hybrid method of supervised and unsupervised learning is employed. Simulation results show that the BNNs can achieve near-optimal solutions to the SINR balancing and power minimization problems, and a performance close to that of the weighted minimum mean squared error algorithm for the sum rate maximization problem, while in all cases enjoy significantly reduced computational complexity. In summary, this work paves the way for fast realization of optimal beamforming in multiuser MISO systems

Joint Radar and Communication Design: Applications, State-of-the-Art, and the Road Ahead

Sharing of the frequency bands between radar and communication systems has attracted substantial attention, as it can avoid under-utilization of otherwise permanently allocated spectral resources, thus improving efficiency. Further, there is increasing demand for radar and communication systems that share the hardware platform as well as the frequency band, as this not only decongests the spectrum, but also benefits both sensing and signaling operations via the full cooperation between both functionalities. Nevertheless, the success of spectrum and hardware sharing between radar and communication systems critically depends on high-quality joint radar and communication designs. In the first part of this paper, we overview the research progress in the areas of radar-communication coexistence and dual-functional radar-communication (DFRC) systems, with particular emphasis on application scenarios and technical approaches. In the second part, we propose a novel transceiver architecture and frame structure for a DFRC base station (BS) operating in the millimeter wave (mmWave) band, using the hybrid analog-digital (HAD) beamforming technique. We assume that the BS is serving a multi-antenna user equipment (UE) over a mmWave channel, and at the same time it actively detects targets. The targets also play the role of scatterers for the communication signal. In that framework, we propose a novel scheme for joint target search and communication channel estimation, which relies on omni-directional pilot signals generated by the HAD structure. Given a fully-digital communication precoder and a desired radar transmit beampattern, we propose to design the analog and digital precoders under non-convex constant-modulus (CM) and power constraints, such that the BS can formulate narrow beams towards all the targets, while pre-equalizing the impact of the communication channel. Furthermore, we design a HAD receiver that can simultaneously process signals from the UE and echo waves from the targets. By tracking the angular variation of the targets, we show that it is possible to recover the target echoes and mitigate the resulting interference to the UE signals, even when the radar and communication signals share the same signal-to-noise ratio (SNR). The feasibility and efficiency of the proposed approaches in realizing DFRC are verified via numerical simulations. Finally, the paper concludes with an overview of the open problems in the research field of communication and radar spectrum sharing (CRSS)

Toward Multi-Functional 6G Wireless Networks: Integrating Sensing, Communication, and Security

Integrated sensing and communication (ISAC) has recently emerged as a candidate 6G technology, aiming to unify the two key operations of the future network in a spectrum/energy/cost-efficient way. ISAC systems communicate and sense for targets using a common waveform, a common hardware platform, and ultimately the same network infrastructure. Nevertheless, the inclusion of information signaling in the probing waveform for target sensing raises challenges from the perspective of information security. At the same time, the sensing capability incorporated in ISAC transmission offers unique opportunities to design secure ISAC techniques. This overview article discusses these unique challenges and opportunities for the next generation of ISAC networks. We first briefly discuss the fundamentals of waveform design for sensing and communication. Then we detail the challenges and contradictory objectives involved in securing ISAC transmission, along with state-of-the-art approaches to ensure security. We then identify the new opportunity of using the sensing capability to obtain knowledge target information as an enabling approach against the known weak-nesses of PHY security. Finally, we illustrate some low-cost secure ISAC architectures, followed by a series of open research topics. This family of sensing-aided secure ISAC techniques brings new insight on providing information security, with an eye on robust and hardware-constrained designs tailored for low-cost ISAC devices

Toward Multi-Functional 6G Wireless Networks: Integrating Sensing, Communication, and Security

Integrated sensing and communication (ISAC) has recently emerged as a candidate 6G technology, aiming to unify the two key operations of the future network in a spectrum/energy/cost-efficient way. ISAC systems communicate and sense for targets using a common waveform, a common hardware platform, and ultimately the same network infrastructure. Nevertheless, the inclusion of information signaling in the probing waveform for target sensing raises challenges from the perspective of information security. At the same time, the sensing capability incorporated in ISAC transmission offers unique opportunities to design secure ISAC techniques. This overview article discusses these unique challenges and opportunities for the next generation of ISAC networks. We first briefly discuss the fundamentals of waveform design for sensing and communication. Then we detail the challenges and contradictory objectives involved in securing ISAC transmission, along with state-of-the-art approaches to ensure security. We then identify the new opportunity of using the sensing capability to obtain knowledge target information as an enabling approach against the known weak-nesses of PHY security. Finally, we illustrate some low-cost secure ISAC architectures, followed by a series of open research topics. This family of sensing-aided secure ISAC techniques brings new insight on providing information security, with an eye on robust and hardware-constrained designs tailored for low-cost ISAC devices