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

Special Issue on Artificial General Intelligence for Low-Altitude Economy Networking
Submission Date: 2025-06-01

The advancement of drone technology coupled with the increasing congestion of terrestrial road resources is catalyzing the exploration of low-altitude economy. It denotes the utilization of the airspace up to 3000 meters above ground level, where flying equipment such as unmanned aerial vehicles (UAVs) are employed to foster various applications. These initiatives are designed to exploit the low-altitude range to revolutionize industries such as urban transportation, logistics, agriculture, and tourism, thereby attracting significant attention from various industries across several countries. In comparison to terrestrial networks, the airspace offers greater freedom of movement, which provides more space for multi-UAV path optimization, communication strategy selection, etc. Moreover, unlike the traditional UAV networks, applications within the low-altitude economy framework typically involve massive flying devices acting as air taxis, aerial base stations, and airborne charging stations, each tasked with different missions and having distinct communication needs. This requires advanced capabilities from both drones and low-altitude communication networks.

Artificial general intelligence (AGI), bolstered by diverse AI technologies, such as deep learning models, generative AI models, deep transfer learning techniques, and large language models, possesses capabilities such as autonomous perception, learning, decision-making, execution, and social collaboration. These capabilities enable AGI to independently handle a variety of tasks effectively at or above human level. Within the low-altitude economy, AGI empowers massive UAVs to navigate under varying weather conditions, evade random obstacles like birds, and optimize flight paths, thereby enhancing energy efficiency and promoting sustainability. In low-altitude network communications, AGI enhances the network's cognitive, learning, and decision-making capabilities, playing a crucial role in optimizing network resources, facilitating self-organization and repair, and assessing security vulnerabilities. Despite its promise, using AGI in this context poses significant challenges. These include the training of various robust AGI models that prioritize the security of UAV communications and can autonomously detect and rectify network faults. Moreover, given the highly dynamic nature of low-altitude airspace, developing AGI models capable of robustly handling complex tasks, including the integration of sensor data, real-time processing, and prompt decision-making, also presents difficulties. Therefore, this special issue aims to delve deeply into these challenges and opportunities, inviting contributions on theoretical advancements, technological solutions, and case studies that support various applications within low-altitude airspace via different AI models, to facilitate the development of the low-altitude economy. Potential topics of interest include but are not limited to the following:

    AGI-based integrated communication, sensing, and computing for low-altitude transport networks
    AGI-based resource management and optimization for low-altitude communication networks
    AGI-based cognitive communication capabilities enhancement 
    Structure and protocol designs for low-altitude communication system via AGI
    Integrated satellite, low altitude, and terrestrial communication networks based on AGI
    AGI-enabled semantic communication for low-altitude communication 
    AGI-based UAV detection, positioning, and navigation for low altitude logistics
    AGI-enabled swarm technology for low-altitude economy applications
    AGI-based environment perception and reconstruction technology via low-altitude UAV
    AGI model design, deployment, and evaluation for low-altitude applications
    Testbed and real-world evaluation of AGI-enabled UAV systems
    Security and privacy of AGI model in low-altitude communication networks

Submission Guidelines

Prospective authors are invited to submit their manuscripts electronically, adhering to the IEEE Transactions on Cognitive Communications and Networking guidelines. Note that the page limit is the same as that of regular papers. Please submit your papers through the online system and be sure to select the special issue or special section name. Manuscripts should not be published or currently submitted for publication elsewhere. Please submit only full papers intended for review, not abstracts, to the ScholarOne portal. If requested, abstracts should be sent by e-mail directly to the Guest Editors.

Important Dates

Manuscript Submission: 1 June 2025
First Review Round: 1 August 2025
Revision Papers Due: 15 September 2025
Acceptance Notification: 1 November 2025
Final Manuscript Due: 1 December 2025
Publication: 2026

Guest Editors

Jiacheng Wang
Nanyang Technological University, Singapore

Liehuang Zhu
Beijing Institute of Technology, China

Lan (Emily) Zhang
Clemson University, USA

Mubashir Husain Rehmani
Munster Technological University, Ireland

Dusit (Tao) Niyato
Nanyang Technological University, Singapore

Dong In Kim
Sungkyunkwan University, Korea