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September 2024
Sensing in Bistatic ISAC Systems With Clock Asynchronism: A signal processing perspective
Integrated Sensing And Communication (ISAC) has been identified as a pillar usage scenario for the impending 6G era. Bi-static sensing, a major type of sensing in ISAC, is promising to expedite ISAC in the near future, as it requires minimal changes to the existing network infrastructure. However, a critical challenge for bi-static sensing is clock asynchronism due to the use of different clocks at far-separated transmitters and receivers.
In-Band Full-Duplex Multiple-Input Multiple-Output Systems for Simultaneous Communications and Sensing: Challenges, methods, and future perspectives
In-band full-duplex (FD) multiple-input, multiple-output (MIMO) systems offer a significant opportunity for integrated sensing and communications (ISAC) due to their capability to realize simultaneous signal transmissions and receptions. This feature has been recently exploited to devise spectrum-efficient simultaneous information transmission and monostatic sensing operations, a line of research typically referred to as MIMO FD-ISAC.
Index Modulation for Integrated Sensing and Communications: A signal processing perspective
A joint design of both sensing and communication can lead to substantial enhancement for both subsystems in terms of size and cost as well as spectrum and hardware efficiency. In the last decade, integrated sensing and communications (ISAC) has emerged as a means to efficiently utilize the spectrum on a single and shared hardware platform.
July 2024
Interdisciplinarity: The Clear Path Forward
We opened the year with the theme of “embracing interdisciplinarity,” emphasizing the fact that signal processing naturally builds bridges across different domains and disciplines. The front cover image of an organic bridge across mature trees giving birth to a sapling helped convey our message. After two special issues (two parts of one special issue), we come back to you with an issue comprised of feature articles and columns, which all reinforce the message in our first issue of 2024.
Deep Internal Learning: Deep learning from a single input
Deep learning, in general, focuses on training a neural network from large labeled datasets. Yet, in many cases, there is value in training a network just from the input at hand. This is particularly relevant in many signal and image processing problems where training data are scarce and diversity is large on the one hand, and on the other, there is a lot of structure in the data that can be exploited.
