IEEE Signal Processing Magazine

Over-the-air computation (AirComp) leverages the signal superposition characteristic of wireless multiple-access channels (MACs) to perform mathematical computations. Initially introduced to enhance communication reliability in interference channels and wireless sensor networks (WSNs), AirComp has more recently found applications in task-oriented communications like wireless distributed learning and in wireless control systems.

The tutorial “Introducing Information Metrics for Statistical Signal Processing,” [A1] provided the inspiration for our cover in this issue. In their “Lecture Notes” column, Steve Kay and Kaushallya Adhikari invite us to “a leisurely stroll through the garden of the beautiful information-theoretic flowers that have blossomed over the years.” This ends up being not only a pleasant stroll, but also an enriching one with useful insights into these measures and their interrelationships recounted with precision.

In his January 2012 column for IEEE Signal Processing Magazine, K. J. Ray Liu, then president of the IEEE Signal Processing Society (SPS), highlighted the importance of establishing a formal community when individuals come together to create a professional society [1]. He emphasized that the Society’s primary mission, its raison d’être, is to serve its community, and the key to achieving this mission is to serve its members effectively.

Hypercomplex signal processing (HSP) provides state-of-the-art tools to handle multidimensional signals by harnessing the intrinsic correlation of the signal dimensions through Clifford algebra. Recently, the hypercomplex representation of the phase retrieval (PR) problem, wherein a complex-valued signal is estimated through its intensity-only projections, has attracted significant interest.

Hypercomplex signal and image processing extends upon conventional methods by using hypercomplex numbers in a unified framework for algebra and geometry. The special issue is divided into two parts and is focused on current advances and applications in computational signal and image processing in the hypercomplex domain.