Francois Chan

By Soheil Salari, Francois Chan and Yiu-Tong Chan

During the last decade, the emerging technique of compressive sampling (CS) has become a popular subject in signal processing and sensor systems. In particular, CS breaks through the limits imposed by the Nyquist sampling theory and is able to substantially reduce the huge amount of data generated by different sources. The technique of CS has been successfully applied in signal acquisition, image compression, and data reduction. Although the theory of CS has been investigated for some radar and localization problems, several important questions have not been answered yet. For example, the performance of CS radar in a cluttered environment has not been comprehensively studied. Applying CS to passive radars and electronic warfare receivers is another concern that needs more attention. Also, it is well known that applying this strategy leads to extra computational costs which might be prohibitive in large-sized localization networks. In this chapter, we first discuss the practical issues in the process of implementing CS radars and localization systems. Then, we present some promising and efficient solutions to overcome the arising problems.

Part of the book: Advanced Electronic Circuits

By Faisal Al-Kamali, Mohamed Alouzi, Claude D’Amours and Francois Chan

Hybrid precoding and combining techniques in millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems with various array architectures have attracted significant interest as a promising technology for the development of 6G wireless communication systems. This approach presents numerous advantages, including reduced complexity, cost, and power consumption, when compared to traditional analog precoding methods. In this chapter, we investigate hybrid precoding and combining techniques for massive MIMO systems operating in the millimeter-wave (mmWave) band, with a focus on different architectures, such as full array (FA), subarray (SA), and hybrid array (HA) architectures. We discuss the system model of each architecture. Additionally, we solve the hybrid precoding and combining optimization problem to maximize the spectral efficiency of each architecture. We then propose iterative hybrid precoding and combining algorithms for all architectures, as well as compare their performance to that of traditional hybrid design methods to demonstrate that the proposed algorithms achieve superior performance with lower complexity and hardware requirements.

Part of the book: MIMO Communications