JOURNAL of COMMUNICATIONS and NETWORKS (JCN)

In contrast to regular papers, the Editorial Board of JCN periodically announces special issues focused on timely topics within its areas of interest.

Prospective authors interested in submitting to announced Special Issues should upload their manuscripts (in PDF format) via the JCN ScholarOne Manuscripts website (https://mc.manuscriptcentral.com/jcommnet). Submissions should be directed to the Guest Editor of the relevant Special Issue. Please note that Special Issues may include regular papers to help prevent unnecessary publication delays.

Please note that it is the policy of JCN not to solicit special issues proposals.

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Upcoming Special Issues

*JCN is currently not accepting proposals for special issues.

JCN SPECIAL ISSUE ON

“AI-NATIVE RADIO ACCESS NETWORKS (AI-RAN) FOR 5G-ADVANCED AND 6G”

As 5G-Advanced evolves toward 6G, the Radio Access Network (RAN) faces unprecedented complexity and operational challenges caused by the massive scale and stringent latency/throughput requirements of future networks. AI-native RAN (AI-RAN) is emerging as a key paradigm to enable intelligent, autonomous, and efficient RAN design and operation integrated with Artificial Intelligent (AI) / Machine Learning (ML). This Special Issue focuses on three overarching pillars of AI-RAN:

• AI for RAN: applying AI/ML to optimize and automate RAN functions and operations (planning, control, assurance, energy saving, etc.).
• AI on RAN: enabling AI services and workloads over/at the RAN edge with RAN-aware acceleration, orchestration, and real-time constraints.
• AI and RAN: co-designing AI and RAN, where air-interface, architecture, and learning/inference are jointly optimized, including standardization and interoperability aspects.

With the rise of Open RAN (O-RAN), cloud-native virtual RAN (vRAN) architectures, and RAN Intelligent Controller (RIC) frameworks, practical deployment of AI-driven closed-loop control, including Near-Real Time (Near-RT) and NonReal Time (non-RT) applications (correspondingly, xApps and rApps), ( is accelerating. At the same time, integrating AI workloads on/near the RAN and co-designing AI and RAN raise new research challenges such as data reliability, latencybound inference, robustness and security, explainability, and multi-vendor interoperability. This Special Issue solicits original contributions spanning theories, algorithms, architectures, prototyping, testbeds, and field experiences aligned with the three pillars above. The topics of interest include, but are not limited to:

(A) AI for RAN

✓ AI/ML-driven RANoptimization and closed-loop automation (scheduling, power control, mobility, load balancing)
✓ Reinforcement learning / multi-agent learning for real-time RAN control and policy safety
✓ AI-assisted self-organization, self-healing, anomaly detection, and RAN assurance
✓ AI for massive MIMO/beam management and multi-band/multi-RAT optimization
✓ AI-enabled energy saving and sustainable/green RAN (sleep modes, carbon-aware operation)
✓ Data engineering for AI-RAN: telemetry, labeling, synthetic data, digital twins, simulators
✓ Explainable/trustworthy AI for RAN operations (uncertainty, guardrails, reliability)

(B) AI for RAN

✓ Edge AI on/near RAN: low-latency inference, acceleration, and resource-aware deployment
✓ RAN-aware AI workload placement, orchestration, and lifecycle management
✓ Multi-tenant AI services over RAN (QoS/QoE, slicing, intent-based operation for AI workloads)
✓ Federated learning / split learning at the RAN edge with privacy constraints
✓ Efficient AI model compression, distillation, and on-device/on-edge adaptation for RAN environments
✓ RAN telemetry and observability for AI workload performance and Service Level Agreement (SLA) assurance
✓ Security and privacy for AI workloads on RAN (secure aggregation, isolation, attack mitigation)

(C) AI for RAN

✓ Joint design of learning/inference and RAN protocols/architecture (cross-layer co-optimization)
✓ O-RAN RIC (near-RT / non-RT) co-design for xApps/rApps, coordination, and stability of control loops
✓ Foundation models / representation learning for wireless and their integration into RAN control stacks
✓ AI-RAN interoperability, benchmarking, and reproducible evaluation methodologies
✓ Robustness to distribution shift, adversarial conditions, and non-stationary radio environments
✓ Standardization and alignment issues (e.g., O-RAN/3GPP-oriented architectural implications)
✓ System prototypes, testbeds, and field trials demonstrating AI-RAN co-designed gains

Guidelines according to the IEEE/KICS JCN Guidelines: https://www.jcn.or.kr/html/?pmode=InformationforAuthors

Important Dates：

✓ Manuscript Submission Deadline: 4 May 2026
✓ Initial Decision Notification: 30 June 2026
✓ Revised Manuscript Due: 31 July 2026
✓ Final Decision Notification: 31 August 2026
✓ Final Manuscript Due: 14 September 2026
✓ Publication Date: October 2026

Guest Editors:
Prof. Hyun Jong Yang, Seoul National University, Korea / hjyang@snu.ac.kr (Guest EiC)

Prof. Jeongho Kwak, Korea University, Korea / jeonghokwak@korea.ac.kr

Prof. Chunguo Li, Southeast University, China / chunguoli@seu.edu.cn

Prof. Jonggyu Jang, Chungnam National University, Korea / jgjang@cnu.ac.kr

Prof. Tiago Koketsu Rodrigues, Tohoku University, Japan / koketsu.rodrigues.tiago.c4@tohoku.ac.jp

Prof. Jianhua Tang, Shenzhen Smart City Group, China / jtang4@e.ntu.edu.sg

Prof. Jemin Lee, Yonsei University, Korea / jemin.lee@yonsei.ac.kr

Dr. Seungnyun Kim, MIT, MA, USA / snkim94@mit.edu

JCN is a high-quality bimonthly archival journal, published by the Korean Institute of Communications and Information Sciences with the technical co-sponsorship of the IEEE Communications Society, covering the fields of Communication Theory and Systems, Wireless Communications, and Networks and Services. JCN began publication in March 1999.