IEEE | 1区SCI期刊专刊信息6条

计算机网络

IEEE Journal on Selected Areas in Communications

Millimeter-Wave Networking

全文截稿: 2019-04-30

影响因子: 7.172

CCF分类: A类

中科院JCR分区:

  • 大类 : 工程技术 - 1区

• 小类 : 工程：电子与电气 - 1区

  • 小类 : 电信学 - 1区

网址: http://www.comsoc.org/jsac

The increasing density of wireless devices, ever-growing demands for extremely high-data rates, and the spectrum scarcity at the sub-6 GHz bands are making the use of spectrum-rich millimeter-wave (mmWave) frequencies among the most important components of future wireless networks. The major commercial potential of mmWave networks has led to mmWave being considered as a key element of 5G-and-beyond mobile cellular networks, as well as for emerging Gbps-speed Wi-Fi networks based on the IEEE 802.11ad and draft IEEE 802.11ay standards. Despite this intense interest in mmWave from both the research community and industry, much fundamental research is still needed, especially at the networking layers.

Compared to traditional wireless communication systems, the special propagation features and hardware constraints of mmWave systems introduce many new challenges in the design of efficient and robust medium access control (MAC), routing, and transport protocols. Communication at such high frequencies suffers from high attenuation and signal absorption as well as high penetration loss, requiring the use of highly directional antennas. This move from conventional omnidirectional networks to directional, blockage-prone mmWave connectivity marks a true paradigm shift for mmWave networking, affecting the design of all aspects of network control and resource management. The extremely high data rates achievable at mmWave come at the price of high coordination overhead. This in turn requires a radical rethinking of the design of all aspects of network coordination and resource management, including cell-discovery and initial access, mobility management, routing, coordination, scheduling, user association, resource allocation, and network planning.  

Presently, there is arguably an adequate understanding of physical layer issues, which have been the object of much prior work, including special issues of JSAC. By contrast, the upper layers of the protocol stack are still largely unexplored in the context of efficient and robust mmWave networking. The severe channel attenuation, vulnerability to blockage by mobile and environmental obstacles, inherent directionality of mmWave links, the reduced interference footprint, complicated channel establishment and mobility management, and high signaling overhead of mmWave networks demand a thorough reconsideration of traditional protocols and design principles at the MAC as well as the higher layers.  

This special issue seeks new ideas to address networking challenges of future mmWave networks. The purpose is not only to serve as a collection of recent developments of mmWave communications, but also to inspire readers/researchers to contribute in this exciting and promising field. Prospective authors are invited to submit high-quality original manuscripts on topics including, but not limited to:
- Network planning, optimization and learning theories for mmWave networks
- Performance analysis, optimization, and fundamental limits
- Signal processing for effective networking
- Measurement studies and deployment experiences of mmWave networks
- Routing, scheduling, resource allocation, and interference management
- Initial access and link establishment for mmWave networks
- Mobility management and seamless handover for mmWave networks  
- Association and coordination among access points/base stations and terminals/mobile equipment
- Spectrum sharing (bandwidth or infrastructure) for mmWave networks
- Beam-steering and tracking algorithms for mmWave networks
- Scalable wireless access algorithms for large numbers of connected devices
- Novel transport protocols for mmWave networks
- Cross-layer adaptation for mmWave networks
- Coexistence/integration with e.g. microwave technologies in heterogeneous networks

计算机网络

IEEE Journal on Selected Areas in Communications

Communications and Data Analytics in Smart Grid

全文截稿: 2019-06-01

影响因子: 7.172

CCF分类: A类

中科院JCR分区:

  • 大类 : 工程技术 - 1区

• 小类 : 工程：电子与电气 - 1区

  • 小类 : 电信学 - 1区

网址: http://www.comsoc.org/jsac

The electric grid is an ecosystem of asset owners, manufacturers, service providers, and system operators, all working together to run one of the most reliable systems in the world. The Smart Grid represents an unprecedented opportunity to move the electric grid into a new era of reliability, availability, and efficiency that will contribute to our economic and environmental health. The ongoing digitalization, e.g. deployment of advanced metering infrastructure (AMI) and phasor measurement units (PMUs) as well as intelligent automation systems is drastically increasing the amount, quality, and variety of data that utilities and grid operators are collecting on supply, transmission, distribution, and demand. Sensing, computing, and disseminating these data are essential for the modernization of the grid. As such, communication and data analytics are two fundamental pillars of the Smart Grid.

This special issue solicits original research papers focused on Communications and Data Analytics for
- Smart Metering, Demand Response and Dynamic Pricing
- Energy Storage and Electric Vehicles
- Active Distribution Grids and Microgrids
- Cyber Security, Privacy, and Resilience for Smart Grids
- Transactive Energy
- Energy Internet and Energy System Integration
- Distribution and Transmission Synchrophasor Networks

计算机网络

IEEE Journal on Selected Areas in Communications

Advances in Quantum Communications, Computing, Cryptography and Sensing

全文截稿: 2019-07-01

影响因子: 7.172

CCF分类: A类

中科院JCR分区:

  • 大类 : 工程技术 - 1区

• 小类 : 工程：电子与电气 - 1区

  • 小类 : 电信学 - 1区

网址: http://www.comsoc.org/jsac

The celebrated Moore’s law is beginning to hit physical limits where the ever-shrinking transistor size is making it necessary to account for quantum effects. Concurrently, the growing demand for high-rate processing is imposing unsustainable power and heat dissipation requirements. Thus, there is an urgent need to develop quantum information processing systems that can circumvent the limitations of existing technology. Quantum computing paradigms have been investigated since the 1980s and foundational advances have shown that harnessing the unique quantum mechanical concepts of superposition and entanglement can lead to capabilities that are beyond the reach of classical systems. Several physical platforms for realizing quantum bits, or qubits, have been explored. One of the most promising technologies relies on superconducting qubits under investigation by D-Wave, IBM and Google, while another is based on trapped ions explored by other groups and startups. A chip with 1024 qubits, suitable for quantum annealing algorithms, is commercially available from D-Wave, while IBM and Google recently announced their gate-based architectures with 50-100 qubits. Furthermore, the recent launch of the Micius quantum-enabled satellite heralds a major advance in long-range secure quantum communication. Yet other efforts are aimed at exploiting quantum effects for sensing with unprecedented resolution and sensitivity. These advances also underscore the daunting technical challenges that have to be overcome to realize the full potential of quantum information science and engineering. Given the cross-disciplinary nature of challenges in quantum information technology, and the worldwide attention it is enjoying, this is a unique and timely opportunity for the signal processing, communications, information science, and networking communities to get engaged in this emerging research frontier. This special issue is aimed at promoting foundational, algorithmic, and experimental advances in quantum information science and engineering spanning communications, cryptography, computing, and sensing, as well as fostering new avenues for cross-disciplinary research. The topics of interest include, but are not limited to:

- Quantum communications
- Quantum information theory
- Quantum error correction & modulation
- Quantum algorithms and applications
- Quantum key distribution
- Entanglement distillation and purification
- Experimental results and demonstrations
- Prototypes and testbeds
- Quantum state preparation
- Quantum networks and architectures
- Quantum secure direct communication
- Modeling and simulation of quantum information processing systems
- Quantum detection and estimation
- Role of entanglement in encoding and decoding of information
- Quantum sensing and measurements

计算机网络

IEEE Journal on Selected Areas in Communications

Smart Data Pricing for Next Generation Networks

全文截稿: 2019-08-01

影响因子: 7.172

CCF分类: A类

中科院JCR分区:

  • 大类 : 工程技术 - 1区

• 小类 : 工程：电子与电气 - 1区

  • 小类 : 电信学 - 1区

网址: http://www.comsoc.org/jsac

The growing demand for data and the evolution of next generation networks, particularly 5G networks, has called for new approaches to pricing and managing the limited capacity of existing network resources and infrastructures. Several recent policy changes and regulatory initiatives have been proposed to address the shift in demands due to next generation networks and technologies. These include the FCC's “5G Fast Plan,” which outlines strategies for modifying spectrum policies, infrastructure policies, and existing regulations, in light of emerging 5G technologies. This plan has included the rollback of net neutrality rules in June 2018, allowing broadband providers to offer a wider variety of service options.  

In addition to these regulatory initiatives, several technologies are needed to enable next generation 5G networks, including the use of new spectrum frequencies, dense infrastructure deployments, and fog/edge computing. Adopting these technologies presents new challenges for innovative smart pricing strategies and business opportunities. Examples include pricing and incentive mechanisms for pooling of distributed resources, including IoT pricing, revenue models and incentives for infrastructure sharing, new data plans for bundling 4G and 5G services, and novel spectrum auction formats.  

These recent developments, from both the regulatory and technological standpoints, open up a range of opportunities for academic and industry research to explore new directions in pricing innovation and policy issues for next generation networks. This special issue solicits original state-of-the-art analytical and empirical research papers that address the following non-exhaustive list of topics:

- Theories, models, and analyses of access pricing/resource monetization
- Fogonomics: Innovation and pricing for IoT and edge/fog networks
- Pricing of IoT services and M2M traffic
- Revenue models for infrastructure sharing, network sharing in competitive markets
- Spectrum resale markets, FCC auctions, spectrum sharing
- Two-sided pricing, reverse billing, and sponsored content
- Role of data analytics in designing/enabling new pricing schemes
- Regulatory challenges, effects of recent policies on data pricing and infrastructure investments

计算机网络

IEEE Journal on Selected Areas in Communications

Multiple Antenna Technologies for Beyond 5G

全文截稿: 2019-09-01

影响因子: 7.172

CCF分类: A类

中科院JCR分区:

  • 大类 : 工程技术 - 1区

• 小类 : 工程：电子与电气 - 1区

  • 小类 : 电信学 - 1区

网址: http://www.comsoc.org/jsac

Recently, the first version of 5G NR with Massive MIMO has been finished by 3GPP and early deployments took place in 2018. However, there are many future requirements that cannot be addressed by 5G, such as exceptionally high bit rates, super low latencies, great energy efficiency, robustness against blocking, and wireless charging. It is time to analyse what lies beyond 5G, or rather what the current Multiple Antenna Technologies can be evolved into beyond what is currently envisaged. Potential paradigm shifts in wireless network design for beyond 5G are cell-free Massive MIMO, lens antenna arrays (beamspace massive MIMO), and large intelligent surfaces (LIS). Cell-free Massive MIMO consists of a large number of distributed access points (APs) that jointly serve all the users in a coordinated fashion, using only local channel state information at each AP. While the performance of cell-free Massive MIMO can be analysed using similar methodology as in cellular Massive MIMO, the fundamental limits, signal processing, and resource allocation are substantially different. In order to reduce the hardware cost and energy consumption in mmWave Massive MIMO systems, the beamspace MIMO has been proposed to significantly reduce the number of required RF chains by using lens antenna arrays or phase shifters. Alternatively, the LIS concept utilizes electromagnetically controllable surfaces that can be integrated into the existing infrastructure, for example, along the walls of buildings in a mega-city, in airports and large shopping malls, or along the structure of a stadium. There are active and partially passive forms of LIS, and variants with either large antenna spacing or continuous aperture. There are also some substantial differences between the new multiple antenna technologies and traditional MIMO systems, such as transceiver design and propagation models.

This special issue has the objective of bringing the above concepts, and other related concepts, closer to reality and will provide a comprehensive overview of the state-of-the-art on multiple antenna communications for beyond 5G. Prospective authors are invited to submit original manuscripts on these topics including, but not limited to:

- Information-theoretic analysis
- Channel and propagation modelling
- Channel estimation and feedback designs
- Precoding/decoding and power control
- Wireless resource management
- Backhaul and fronthaul designs
- Machine learning and Artificial Intelligence-based designs
- Antenna and beamforming designs
- mmWave and THz communications
- Wirelessly powered communications
- Physical layer security
- Novel waveform design and multiple access methods
- Cost efficient transceiver designs
- Transceiver hardware impairment analysis
- Distributed and cell-free network architectures
- Large intelligent surfaces, reflectarrays, and holographic beamforming
- Lens antenna arrays and beamspace MIMO processing
- Practical super directive arrays
- Prototyping, measurements and experimentation

计算机网络

IEEE Journal on Selected Areas in Communications

Advances in Artificial Intelligence and Machine Learning for Networking

全文截稿: 2019-10-01

影响因子: 7.172

CCF分类: A类

中科院JCR分区:

  • 大类 : 工程技术 - 1区

• 小类 : 工程：电子与电气 - 1区

  • 小类 : 电信学 - 1区

网址: http://www.comsoc.org/jsac

Artificial Intelligence (AI) and Machine Learning (ML) approaches have emerged in the networking domain with great promise. They can be clustered into AI/ML techniques for network engineering and management, network design for AI/ML applications, and system aspects. AI/ML techniques for network management, operations and automation improve the way we address networking today. They support efficient, rapid, and trustworthy management operations. The current interest in softwarization and network programmability fuels the need for improved network automation in agile infrastructures, including edge and fog environments. Network design and optimization for AI/ML applications address the complementary topic of supporting AI/ML-based systems through novel networking techniques, including new architectures and performance models. A third topic area is system implementation and open-source software development.

This special issue will focus on networking aspects (mostly, network layer and above). Work with primary contribution to physical layer concepts or wireless access should be submitted to other venues. Prospective authors are invited to submit high-quality, original manuscripts on topics including, but not limited to:

1. Fundamental Frameworks

- Network theory inspired by machine learning
- Transfer learning and reinforcement learning for networking
- Big data analytic frameworks for networking data

2. Network analytics

- Machine learning, data mining and big data analytics for networking
- Representation learning on operational data
- Data mining, statistical modeling, and machine learning for network management
- User experience-driven network planning
- Learning algorithms and tools for network diagnostics and root cause analysis

3. Network decision making and optimization

- Protocol design and optimization using machine learning
- Network architecture and optimization for AI/ML applications at scale
- Resource allocation for shared/virtualized networks using machine learning
- Energy-efficient network operations based on AI/ML algorithms
- AI/ML Algorithms for network security
- Network Reliability, robustness and safety based on AI/ML concepts
- Security for networks optimized and operated based on AI/ML concepts

4. Network automation

- Self-driving networks
- Self-Learning and adaptive networking protocols and algorithms
- Deep learning and reinforcement learning in network control & management
- Predictive or self-aware networking maintenance
- Open-source AI software for networking or networked applications

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