Journal Information
Physical Communication
Impact Factor:

Call For Papers
PHYCOM: Physical Communication is an international and archival journal providing complete coverage of all topics of interest to those involved in all aspects of physical layer communications. Theoretical research contributions presenting new techniques, concepts or analyses, applied contributions reporting on experiences and experiments, and tutorials are published.

Topics of interest include but are not limited to:

Physical layer issues of Wireless Local Area Networks, WiMAX, Wireless Mesh Networks, Sensor and Ad Hoc Networks, PCS Systems; Radio access protocols and algorithms for the physical layer; Spread Spectrum Communications; Channel Modeling; Detection and Estimation; Modulation and Coding; Multiplexing and Carrier Techniques; Broadband Wireless Communications; Wireless Personal Communications; Multi-user Detection; Signal Separation and Interference rejection: Multimedia Communications over Wireless; DSP Applications to Wireless Systems; Experimental and Prototype Results; Multiple Access Techniques; Space-time Processing; Synchronization Techniques; Error Control Techniques; Cryptography; Software Radios; Tracking; Resource Allocation and Inference Management; Multi-rate and Multi-carrier Communications; Cross layer Design and Optimization; Propagation and Channel Characterization; OFDM Systems; MIMO Systems; Ultra-Wideband Communications; Cognitive Radio System Architectures; Platforms and Hardware Implementations for the Support of Cognitive, Radio Systems; Cognitive Radio Resource Management and Dynamic Spectrum Sharing.
Last updated by Dou Sun in 2019-12-08
Special Issues
Special Issue on Deep Learning Methods for Physical-Layer Wireless Communications- Recent Advances and Future Trends
Submission Date: 2020-02-29

Deep Learning (DL) and deep reinforcement learning (DRL) methods, well known from computer science (CS) disciplines, are beginning to emerge in wireless communications. These approaches were first widely applied to the upper layers of wireless communication systems for various purposes, such as routing establishment/optimization, and deployment of cognitive radio and communication network. These system models and algorithms designed with DL technology greatly improve the performance of communication systems based on traditional methods. New features of future communications, such as complex scenarios with unknown channel models, high speed and accurate processing requirements, make traditional methods no longer suitable, and provides many more potential applications of DL. DL technology has become a new hotspot in the research of physical-layer wireless communications and challenges conventional communication theories. Currently, DL-based ‘black-box’ methods show promising performance improvements but have certain limitations, such as the lack of solid analytical tools and the use of architectures specifically designed for communication and implementation research. With the development of DL technology, in addition to the traditional neural network-based data-driven model, the model-driven deep network model and the DRL model (i.e. DQN) which combined DL with reinforcement learning, are more suitable for dealing with future complex communication systems. As in most cases of wireless resource allocation, there are no definite samples to train the model, hence DRL, which trains the model by maximizing the reward associated with different actions, can be adopted. This Special Issue focuses on the application of DL/DRL methods to physical-layer wireless communications to make future communications more intelligent. We invite submissions of high-quality original technical and survey articles, which have not been published previously, on DL/DRL techniques and their applications for wireless communication and signal processing. The topics of interests include, but are not limited to: · Deep Learning based 5G wireless technologies · Deep Learning based beamforming in mmWave massive MIMO · Deep Learning based hybrid precoding in massive MIMO system, mmWave system · Deep Learning based non-orthogonal multiple access (NOMA) techniques · Deep Learning based MIMO-NOMA frameworks · Deep Learning based sparse channel estimation · Deep Learning based communication frameworks · Deep Learning based multiuser detection · Deep Learning based modulation and coding · Deep Learning based direction-of-arrival estimation · Deep Learning based channel modeling · Deep Learning based signal classification · Deep Learning based unmanned aerial vehicles (UAVs) techniques · Deep Learning based energy-efficient network operations · Deep Learning based ultra-dense cell communication · Deep Learning based testbeds and experimental evaluations
Last updated by Dou Sun in 2019-12-08
Special Issue on Advanced Technologies for Multi-access Edge Computing
Submission Date: 2020-09-20

With the exponential growth of data traffic due to ubiquitous portable devices, machine to machine communications, and novel user-centric applications, such as augmented/virtual reality, the demands for a new computing paradigm are increasing. The large pool of traditional cloud resources and services has contributed to the development of cloud computing. However, state-of-the-art cloud computing turns into a problem for communication-intensive applications, which need to meet the stringent delay requirements. The problem becomes more severe with the introduction of new data-intensive applications in the Internet of Things era. Multi-access Edge Computing which processes data at the nearest available nodes is emerging as a promising computation architecture to handle these ever-increasing demands. It aims to enable flexible and rapid deployment of new applications and services for customers at the cellular base stations or other edge nodes. It allows cellular operators to open their radio access network to authorized third parties, and moves the computing of traffic and services to the edge of the network to achieve low latency. However, there are several challenges that need to be addressed before we can enjoy the benefits of the advanced Multi-access Edge Computing architecture. For example, these problems include the heterogeneity of different kinds of devices, the deployment of services and applications, and the task allocation and offloading, as well as resource management. This Special Issue will bring together leading researchers and developers to explore recent advanced technologies for Multi-access Edge Computing, especially in the wireless personal communications field, in terms of system, service and application. Relevant topics of interest to this special section include (but are not limited to): Advanced communication network planning and operation in Multi-access Edge Computing Autonomous mobile edge system in wireless personal communication Operating system and framework for Multi-access Edge Computing Smart mobile edge services and application Testbed, experiments, and simulations of advanced mobile edge system Advanced workload allocation and offloading mechanisms Green and sustainable resource management Advanced QoS adaptive design and algorithms in Multi-access Edge Computing Performance optimization in Multi-access Edge Computing Artificial intelligence algorithm design and application in Multi-access Edge Computing Advanced security and privacy in Multi-access Edge Computing Tools and benchmarks for intelligent Multi-access Edge Computing system
Last updated by Dou Sun in 2020-02-23
Special Issue on Advanced Signal Processing (ASP) and Its Applications in B5G Wireless Communication Networks
Submission Date: 2020-10-30

The fifth generation (5G) wireless communication networks are actively being developed and will be rolled out around 2020, after which we are going to enter the beyond 5G (B5G) wireless communications era. With respect to the development law of wireless communication traffic "1000 times in 10 years", B5G wireless communication networks should achieve greater system capacity (> 1000 times) in terms of data rate (terabits per second) and user density (the internet of things). Yet, they are also expected to provide lower latency, high reliability, better security and more intelligence, etc.. To achieve these requirements, we cannot only rely on a simple technology; in fact, we need to integrate multiple key technologies that include extreme densification of infrastructure, large quantities of new bandwidth, and a larger number of antennas, etc., just tp mention a few. This renders traditional signal processing and methods not applicable for B5G communication networks. Therefore, there is great immediacy to develop advanced signal processing techniques, methods and algorithms to characterize the performance of B5G networks. Moreover, faced with the coming B5G era, the intelligence applications, such as autonomous driving, smart cities and telehealth, that require lower latency and high reliability should be also fully explored, The objective of this special issue is to solicit relevant contributions from both academia and industry, to motivate discussions on advanced signal processing theories and methods for B5G wireless communication networks, which span array signal processing, wireless signal transmission, information processing, practical applications and realistic scenarios, etc.. Both theoretical contributions and application validations are welcomed. Potential topics include but are not limited to: l New architectures for B5G communication with ASP l New waveforms and coding for B5G communication with ASP l Intelligence applications to B5G communication with ASP l Resource allocation and management schemes for B5G communication with ASP l Mobile edge computing for B5G communication with ASP l Cloud computing and big data for B5G communication with ASP l Higher frequency band (millimeter wave, and terahertz) operation for B5G communication with ASP l Satellite, unmanned aerial vehicle (UAV), and ocean communications for B5G communication with ASP l Smart antenna and array antenna design for B5G communication with ASP l Data reliability, privacy and security for B5G communication with ASP l Network infrastructure, backhaul & core network issues for B5G communication with ASP l Vehicle to vehicle (V2V) communications for B5G communication with ASP l Energy harvesting, storage, and recycling for B5G communication with ASP l New applications and scenarios of B5G communication with ASP l Other emerging techniques for B5G communication with ASP
Last updated by Dou Sun in 2019-12-08
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