The IEEE Transactions on Cognitive Communications and Networking (TCCN) is committed to timely publishing of high-quality manuscripts that advance the state-of-the-art of cognitive communications and networking research. The focus of the Transactions will be on “cognitive” behaviors in all aspects of communications and network control, from the PHY functions (including hardware) through the applications (including architecture), and in all kinds of communication networks and systems regardless of type of traffic, transmission media, operating environment, or capabilities of communicating devices. IEEE TCCN will welcome papers dealing with the design, analysis, evaluation, experimentation and testing of cognitive communications and network systems. Inter-disciplinary approaches are encouraged. Papers that focus on experimental infrastructures or tools for cognitive communications and networking will also be considered, provided that they contain significant original contributions in the communications or networking areas.

Since the term “cognitive” may be interpreted in multiple ways, we define here a cognitive entity as one that is capable of selecting and carrying out actions depending on its own goals and its perception of the world and that may also be capable of learning from experience by interacting with the world. Thus, a cognitive entity means an intelligent entity which possesses the following basic components: perception, learning/reasoning and decision making. Papers that will be considered for publication in the IEEE Transactions on Cognitive Communications and Networking must BOTH explicitly include approaches related to the “intelligent entity” AND provide original contributions on communications or networking.

Topics of interest include (but are not limited to):

    Machine learning and artificial intelligence for communications and networking
    Distributed learning, reasoning and optimization for communications and networking
    Architecture, protocols, cross-layer, and cognition cycle design for intelligent communications and networking
    Information/communications theory and network science for intelligent communications and networking
    Ontologies, languages, and knowledge representation for intelligent communications and networking
    Security and privacy issues in intelligent communications and networking
    Cognitive radio and dynamic spectrum access
    Cognitive technologies supporting software-defined radios, systems and networks
    Emerging services and applications enabled by intelligent communications and networks

Special issues will form an integral part of IEEE TCCN. Guest editorial teams are welcome to propose special issues on new emerging areas in cognitive and intelligent communications and networking. Please contact the Editor-in-Chief if you are interested in submitting a proposal.

Special Issue on Machine Learning and Intelligent Signal Processing for Near-Field Technologies
Submission Date: 2025-03-01

The emergence of revolutionary applications, such as extended reality, digital twins, Metaverse, and holographic video, impose stringent requirements in the data rate, latency, reliability, coverage, and energy efficiency of the forthcoming 6G and beyond (B6G) wireless network. To achieve these ambitious objectives, two most important technical trends are (1) the employment of extremely large-scale antenna arrays, such as supermassive multiple-input multiple-output (MIMO), reconfigurable intelligent surfaces (RISs), and continuous-aperture arrays (CAPA); and (2) the use of tremendously high frequencies, i.e., THz. It is worth noting that the large-scale antenna arrays and ultra-high frequencies lead to a qualitative paradigm shift in electromagnetic characteristics, i.e., from the traditional far-field propagation to the near-field propagation. In particular, the far-field propagation is effectively approximated using plane waves, while the near-field propagation has to be modelled using spherical waves. Compared to far-field region, the spherical-wave-based near-field signal propagation brings new degrees of freedom (DoFs) and opportunities to study near-field technologies in B6G. For example, the communication beam pattern in the near field can be designed to be spotlight-like beam focusing instead of the conventional flashlight-like beam steering, thus improving the energy efficiency and reducing the interference. Moreover, the near-field propagation can be exploited to realize precise sensing/localization in the distance domain merely through the narrow bandwidth, which is spectrum-efficient.

Despite the above significant benefits, the development of near-field technologies is challenging and involves a number of unresolved issues. For example, the extremely large-scale MIMO introduces massive number of variables to be optimized and also causes the estimation of channel state information (CSI) and beam training quite challenging. The complicated spherical-wave near-field propagation leads to complicated signal processing when realizing near-field sensing, localization, and positioning. To address these problems, conventional mathematical optimization methods and algorithms might be inefficient due to the high computational complexity and dynamic time-varying environments. Fortunately, advanced machine learning and intelligent signal processing techniques offer potential solutions to tackle these challenges and develop efficient near-field technologies for B6G. This Special Issue invites novel contributions from researchers and practitioners and aims to provide a platform for the state-of-the-art research, innovations, and applications on exploring machine learning and intelligent signal processing enabled near-field technologies. We solicit high-quality original research papers on topics including, but not limited to:

    Advanced near-field CSI estimation and beam training/alignment 
    Near-field intelligent beamforming design 
    Advanced near-field sensing (NISE)/localization/tracking and integrated sensing and communications (ISAC)
    Near-field next generation multiple access (NGMA)
    Near-field techniques with gigantic-MIMO/CAPA/RIS and other new forms of antennas 
    Advanced near-field physical-layer security, wireless power transfer, simultaneous wireless information and power transfer, and etc.
    Pareto-optimal resource management for near-field technologies
    Hardware-efficient transceiver designs for near-field technologies