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Furthermore, articles developing novel communication and networking techniques and systems for distributed/edge machine learning algorithms are of interest. Both theoretical contributions (including new theories, techniques, concepts, algorithms, and analyses) and practical contributions (including system experiments, prototypes, and new applications) are solicited. IEEE TMLCN also particularly encourages the submission of papers that simultaneously advance both the fields of machine learning and wireless networking. The journal also advocates for reproducible and public sharing of codes, datasets, software, and other artefacts related to research contributions. Topics of interest include, but are not limited to, the following: Machine/deep learning for physical layer design, signal detection, channel modeling, estimation, interference mitigation, localization, encoding/decoding, and signal processing. New communication and computing architectures for supporting distributed and large-scale machine learning models. Autonomous resource management, spectrum management, and network optimization techniques using machine learning. Machine learning for integrated radio frequency/non-radio frequency communication systems. Machine learning techniques for information-centric networks, application/user behavior prediction, and user experience modeling and optimization. Machine learning for network slicing, network virtualization, software defined networking, and transport-layer congestion control. AI-native wireless communication systems and architectures (e.g., 5G, 6G, and beyond). Data- and learning-driven cross-layer networking protocols. Distributed and edge learning algorithms, architectures, and implementations over real-world wireless communication systems. Goal-oriented communication techniques for distributed learning and edge AI Device-server or edge-cloud cooperative AI in wireless networks Semantic communication techniques Quantum learning for communication networks, and machine learning for quantum networks Distributed and federated machine learning for efficient network performance. Intent-based networking using machine learning and artificial intelligence. Machine learning and AI techniques for emerging communication systems and applications, such as dronnes, extended reality, metaverse, edge computing, smart cities, sensing/control, connected autonomy, and vehicular networks, among others. Performance analysis and evaluation of machine learning techniques in wired/wireless communication systems. Scalability and complexity of machine learning in networks. Hardware architectures and solutions for implementing machine learning and neural networks in communication systems. Machine learning for enhanced cross-layer wireless/wired network security. Secure machine learning over wireless networks. New software/hardware techniques for realistic dataset generation with applicability to communications and networking systems. Experimental testbeds and systems for real-world implementation of machine learning and AI over wireless networks.</pre><div class="alert alert-warning"> Last updated by <a href="/researcher/1">Dou Sun</a> in 2024-07-24</div> </div> </div>  <div class="portlet" id="yw3"> <div class="portlet-decoration"> <div class="portlet-title"><i class="icon icon-file"></i> Related Journals</div> </div> <div class="portlet-content"> <div class="hidden-phone"> <div id="yw4" class="grid-view"> <table class="table table-hover table-striped table-bordered table-condensed"> <thead> <tr> <th id="yw4_c0">CCF</th><th id="yw4_c1">Full Name</th><th id="yw4_c2">Impact Factor</th><th id="yw4_c3">Publisher</th><th id="yw4_c4">ISSN</th></tr> </thead> <tbody> <tr class="odd"> <td></td><td><a href="/journal/461">Combinatorica</a></td><td>1.000</td><td>Springer</td><td>0209-9683</td></tr> <tr class="even"> <td></td><td><a href="/journal/440">Annals of Operations 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