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 Smart Environment Engineering for Integrated Sensing and Communication
Día de Entrega: 2024-12-01

Sixth-generation (6G) networks are expected to revolutionise various emerging applications, such as intelligently connected vehicles, smart cities and homes, smart manufacturing, and environmental monitoring. These applications require both extreme wireless connectivity and highly accurate and reliable sensing capabilities. Among many 6G visions, there is a prevailing consensus that sensing will play an even more significant role than before. The integration of sensing and communication (ISAC) is a crucial enabler for 6G networks. It facilitates high-throughput, ultra-reliable, and low-latency wireless communications, as well as ultra-precise, high-resolution, and resilient wireless sensing. However, for conventional networks with stationary antennas and fixed base station topology, as well as random or uncontrolled channel fading, the performance of sensing and communication may be severely restricted by transmission blockages, excessive connectivity demands, and conflicting design objectives of ISAC networks.

By harnessing cutting-edge technologies such as intelligent surfaces, advanced antennas (e.g., fluid antenna systems (FAS) and movable antenna (MA)), and holographic MIMO, in conjunction with innovative cell-free and mobile air-ground network, a more comprehensive and holistic view of smart environment engineering can be achieved for propagation channel control, transmission topology management, and antenna geometry modification. These advanced antenna techniques and network architectures offer promising avenues for designing cooperative ISAC networks that facilitate signal power enhancement and efficient interference mitigation. Although these smart technologies hold great potential for enhancing sensing and communication performance, their benefits are offset by the inherent drawbacks of increased latency, overhead, and power consumption due to control and interaction operations. Fortunately, leveraging sensing capabilities can aid in understanding the locations of served/detected users/targets, increasing awareness of potential link blockages, and minimizing signaling overhead required for environmental control. Consequently, ISAC technologies play a crucial role in improving propagation environments, hence resulting in a win-win integration with mutual benefits.

This Special Issue seeks to bring together contributions from researchers and practitioners in wireless communications and signal processing, with an emphasis on new approaches and techniques for smart environment aided ISAC designs. We solicit high-quality original research papers on topics including, but not limited to:

    Network architectures/standardizations designs for smart environment and ISAC
    Smart environment engineering technique for ISAC
    ISAC for smart environment engineering design
    Cooperative smart environment techniques for sensing/communication
    UAV/RIS/IRS/FAS/MA… aided sensing and communication
    Intelligent signal processing and transmission for ISAC
    Resource optimization for ISAC and smart environment
    Performance analysis for smart environment-aided ISAC
    Network security and privacy issues for ISAC and smart environment
    Machine Learning/Artificial intelligence/edge computing for ISAC
    Waveform/beamforming design for ISAC
    ISAC with mmWaves/THz smart environment technology
    Smart environment opportunities with near-field ISAC
    Distributed/decentralized ISAC frameworks
    MIMO/Massive MIMO/Holographic MIMO surface for ISAC 
    Smart environment for air-space-ground ISAC
    Environment perception and reconstruction for ISAC
    Software and hardware verification platform for ISAC

Submission Guidelines

Prospective authors should submit their manuscripts following the IEEE TCCN guidelines. Authors should submit a PDF version of their complete manuscript to the IEEE Author Portal according to the following schedule:

Important Dates

Manuscript Submission: 1 December 2024
First Review Round: 30 January 2025
Revision Papers Due: 15 March 2025
Acceptance Notification: 30 April 2025
Final Manuscript Due: 30 May 2025
Publication: 2025

Guest Editors

Robert Schober
Friedrich-Alexander-University Erlangen-Nürnberg (FAU), Germany 

Kaitao Meng
University College London, UK

Athina Petropulu
Rutgers University, USA

Fan Liu
Southern University of Science and Technology, China

George C. Alexandropoulos
National and Kapodistrian University of Athens, Greece

Sundeep Prabhakar Chepuri
Indian Institute of Science, India

Ruiqi Liu
ZTE Corporation, China

Special Issue on Machine Learning and Intelligent Signal Processing for Near-Field Technologies
Día de Entrega: 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