The IEEE Open Journal of the Communications Society (OJ-COMS) is an open access, all-electronic journal that publishes original high-quality manuscripts on advances in the state of the art of telecommunications systems and networks. Submissions reporting new theoretical findings (including novel methods, concepts, and studies) and practical contributions (including experiments and development of prototypes) are welcome. Additionally, review and survey articles are considered; however, tutorials are not included.

The IEEE Open Journal of the Communications Society covers science, technology, applications and standards for information organization, collection and transfer using electronic, optical and wireless channels and networks, including but not limited to: Systems and network architecture, control and management; Protocols, software and middleware; Quality of service, reliability and security; Modulation, detection, coding, and signaling; Switching and routing; Mobile and portable communications; Terminals and other end-user devices; Networks for content distribution and distributed computing; and Communications-based distributed resources control.

Hallmarks of the IEEE Open Journal of the Communications Society (OJ-COMS) are a rapid peer review process and open access of all published papers.  The broad scope of the journal comprises, but is not limited to:

Big Data and Machine Learning for Communications
Cloud Computing, Edge Computing, and Internet of Things
Communications and Information Security
Communications Theory and Systems
Green, Cognitive, and Intelligent Communications and Networks
Multimedia Communications
Network and Service Management

Network Science and Economics
Optical Communications and Optical Networks
Resource Management and Multiple Access
Signal Processing for Communications
Underwater Communications and Networks
Wired Communications and Networks
Wireless Communications and Networks

Special Issue on Trustworthy AI-Enabled Edge Computing in Next-Generation Wireless Networks
截稿日期: 2024-07-31

The evolution of next-generation wireless networks presents new opportunities and challenges for secure and privacy-aware AI-enabled edge computing. These networks introduce ultra-low latency, high data rates, and massive connectivity, enabling various innovative edge computing applications. However, ensuring robust security and protecting user privacy in this dynamic and heterogeneous environment requires novel approaches.

The ultra-low latency capabilities of next-generation networks empower time-sensitive applications like autonomous vehicles, remote surgeries, and immersive augmented reality experiences. High data rates enable seamless transmission of large volumes of data, facilitating real-time analytics, video streaming, and resource-intensive applications. Furthermore, the massive connectivity of these networks allows for seamless integration of a vast array of devices, including Internet of Things (IoT) devices, into the network fabric. However, the dynamic and heterogeneous nature of next-generation wireless networks also introduces new security and privacy challenges. With an increased number of connected devices and diverse applications, the attack surface expands, making security breaches and unauthorized access more likely. Additionally, the immense volume of data generated and processed in edge computing environments raises concerns about user privacy and data protection.

This research aims to investigate secure and privacy-aware AI-enabled edge computing in next-generation wireless networks, exploring techniques to mitigate threats, preserve privacy, and enhance the trustworthiness of edge-based AI applications. Topics include, but are not limited to the following:

- Explainable and trustworthy AI in edge computing
- Efficient training and fast inference in the edge-cloud continuum
- Privacy-preserving data aggregation in AI-enabled edge computing
- Secure authentication and access control in edge computing
- Threat detection and mitigation in AI-enabled edge computing
- Secure and privacy-preserving machine learning algorithms for edge computing
- Lightweight cryptography for secure communication in edge computing
- Adversarial defense in privacy protection for AI-driven edge computing
- Federated learning with privacy and security in edge computing
- Secure and private data sharing in AI-driven edge computing
- Fairness and bias mitigation in federated learning
- AI-driven secure and private communication in edge computing
- AI-driven intrusion detection and prevention in edge computing 

Special Issue on Emerging Modulation Techniques Towards 6G Networks
截稿日期: 2024-08-31

With the commercialization of 5G, early explorations of the game-changing 6G concept have been initiated by a collection of countries, which is expected to facilitate a plethora of future data applications like extended reality (XR), digital twins, autonomous driving, smart home, etc. These cutting-edge services induce unprecedented demands on data rate, energy consumption, mobility, and positioning accuracy. For instance, 6G is envisioned to attain 50 times’ peak rate and 20 times’ sensing accuracy enhancements over existing 5G. However, it seems to be rather challenging to achieve these goals with traditional microwave frequencies and mature modulation formats like OFDM/SC-FDE.

Against this background, there have been preliminary efforts on the emerging modulation techniques from the physical-layer perspective, which aim to fulfill the requirements of different performance indicators for 6G networks. For instance, to achieve Tbps data rate level, exploitation of terahertz (THz) frequencies and above can be mandatory under the spectrum scarcity of the microwave counterpart. Despite the ultra-broad bandwidth and high carrier frequency over 100 GHz, the resultant severe path loss, frequency-selective fading, and Doppler shifts (even more pronounced under high mobility) make existing modulation formats no longer suitable. This motivates new modulation designs to enhance the resilience to harsh channel conditions, e.g., orthogonal time frequency space (OTFS), orthogonal delay-Doppler division multiplexing (ODDM), orthogonal chirp division multiplexing (OCDM), and affine frequency division multiplexing (AFDM). Besides, new modulation schemes have been highlighted to fully utilize the communication bandwidth with reduced energy consumption levels, such as extremely-large-scale reconfigurable intelligent surface (XL-RIS) and index modulation. Moreover, novel dual-functional waveform design for integrated sensing and communications (ISAC) can be crucial to support accurate sensing and high-rate transmission in a full-duplex manner, which is a key enabler for next-generation applications like metaverse and robotics. To this end, our Special Issue (SI) will focus on the modulation-related topics for 6G networks that have just begun to attract extensive attention from both academia and industry. Both researchers and engineers are invited to submit their recent research results and innovations.

We seek original work not currently under review by any other journal/magazine/conference. Topics of interest include, but are not limited to:

- Emerging modulation schemes resilient to doubly dispersive channels (e.g., OTFS, ODDM, OCDM, AFDM)
- Novel spectrum and energy-efficient modulation schemes for 6G (e.g., index modulation, backscatter communications)
- RIS/metasurface-enabled modulation methodologies
- Advanced ISAC waveform design for 6G
- New waveform design for THz/optical wireless communications
- Modulation schemes for nano-scale communications (e.g., molecular communications)
- Physical-layer security for modulation schemes within 6G
- Theoretical analysis for modulation schemes towards 6G
- AI-based transceiver design for 6G-oriented modulation schemes
- Low-complexity transceiver design for modulation schemes towards 6G
- Hardware implementation, field trials, and standardization of modulation schemes for 6G