As wireless technology begins to utilize physically larger arrays and/or higher frequencies, the transmitter and receiver will reside in each other’s radiative near field. This fact gives rise to unusual propagation phenomena, such as spherical wavefronts and beam focusing, creating the impression that new spatial dimensions—called degrees of freedom (DOF)—can be exploited in the near field.
After nearly a century of specialized applications in optics, remote sensing, and acoustics, the near-field (NF) electromagnetic (EM) propagation zone is experiencing a resurgence in research interest. This renewed attention is fueled by the emergence of promising applications in various fields, such as wireless communications, holography, medical imaging, and quantum-inspired systems.
Multichannel signal processing technologies are moving toward the deployment of small and densely packed sensors yielding extremely large aperture arrays (ELAAs) in order to provide higher angular resolution and beamforming gain. In particular, technologies are moving beyond the fifth-generation (5G) networks, wherein the adoption of ELAAs or surfaces and the exploitation of higher-frequency bands, e.g., terahertz
Multichannel acoustic signal processing is a well-established and powerful tool to exploit the spatial diversity between a target signal and nontarget or noise sources for signal enhancement. However, the textbook solutions for optimal data-dependent spatial filtering rest on the knowledge of second-order statistical moments of the signals, which have traditionally been difficult to acquire.
“All models are wrong, but some are useful” - understanding “models” as analytical mathematical models, this aphorism, originating from George Box in 1976, motivates the synthesis of model-based and data-driven audio signal processing as the leitmotif of this special issue.
Multichannel acoustic signal processing is a well-established and powerful tool to exploit the spatial diversity between a target signal and nontarget or noise sources for signal enhancement. However, the textbook solutions for optimal data-dependent spatial filtering rest on the knowledge of second-order statistical moments of the signals, which have traditionally been difficult to acquire.
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.
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.
This paper addresses the topic of integrated sensing and communications (ISAC) in 5G and emerging 6G wireless networks. ISAC systems operate within shared, congested or even contested spectrum, aiming to deliver high performance in both wireless communications and radio frequency (RF) sensing. The expected benefits include more efficient utilization of spectrum, power, hardware (HW) and antenna resources.
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.
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