Journal of Communications and Information Networks

In the 1940s,Claude Shannon developed the information theory focusing on quantifying the maximum data rate that can be supported by a communication channel. Guided by this fundamental work, the main theme of wireless system design up until the fifth generation(5G)was the data rate maximization. In Shannon’s theory, the semantic aspect and meaning of messages were treated as largely irrelevant to communication. The classic theory started to reveal its limitations in the modern era of machine intelligence, consisting of the synergy between Internet-of-things (IoT) and artificial intelligence (AI). By broadening the scope of the classic communication-theoretic framework, in this article, we present a view of semantic communication (SemCom) and conveying meaning through the communication systems. We address three communication modalities:human-to-human(H2H),human-to-machine(H2M),and machine-to-machine(M2M)communications. The latter two represent the paradigm shift in communication and computing, and define the main theme of this article. H2M SemCom refers to semantic techniques for conveying meanings understandable not only by humans but also by machines so that they can have interaction and“dialogue”. On the other hand, M2M SemCom refers to effective techniques for efficiently connecting multiple machines such that they can effectively execute a specific computation task in a wireless network. The first part of this article focuses on introducing the SemCom principles including encoding, layered system architecture, and two design approaches: 1) layer-coupling design; and 2) end-to-end design using a neural network. The second part focuses on the discussion of specific techniques for different application areas of H2M SemCom [including human and AI symbiosis,recommendation,human sensing and care, and virtual reality(VR)/augmented reality (AR)]and M2M SemCom(including distributed learning, split inference,distributed consensus,and machine-vision cameras). Finally,we discuss the approach for designing SemCom systems based on knowledge graphs.We believe that this comprehensive introduction will provide a useful guide into the emerging area of SemCom that is expected to play an important role in sixth generation (6G) featuring connected intelligence and integrated sensing, computing,communication,and control.

As unmanned aerial vehicles (UAV) deployment is shifting from military to civilian sectors, and in particular as a means of facilitating more flexible and versatile wireless communications, the UAV channels that were previously not well understood are attracting increasing research and investigation. As the key component facilitating the UAV-aided communications, UAV channel characteristics and modeling are of critical importance to the designed UAV system to achieve satisfactory performance. In this article, we will provide a comprehensive overview and the future perspective of envisioned UAV communication system benefits, the existing and needed UAV channel measurements and modeling approaches, together with the new viewpoint of UAV channel applications in different communication and networking layers. Both existing work and future directions are extensively covered.

With the development of fishery industry, accurate estimation of the number of fish in aquaculture waters is of great importance to fish behavior analysis, bait feeding and fishery resource investigation. In this paper, we propose a method for fish density estimation based on the multi-scale context enhanced convolutional network, which could map a fish school image taken at any angle to a density map, and calculate the number of fish in the image finally. In order to eliminate the influence of camera perspective effect and image resolution on density estimation, multi-scale filters are utilized in a convolutional neural network to process fish image in parallel. And then, the context enhancement module is merged in the network structure to help the network understand the global context information of the image. Finally, different feature maps are merged together to construct the density map of fish school images, and finally get the number of fish in the image. In order to make the effectiveness of our method valid, we test the proposed method on DlouDataset. The results show that the proposed method has lower mean square error and mean absolute error, which is helpful to improve the accuracy of the fish counting in dense fish school images.

Orthogonal time frequency space (OTFS) modulation, collaborated with millimeter-wave (mmWave) massive multiple-input-multiple-output (MIMO), is a promising technology for next generation wireless communications in high mobility scenarios. However, one of the main challenges for mmWave massive MIMO-OTFS systems is the enormous computational complexity of channel estimation incurred by the huge OTFS symbol size and the large number of antennas. To address this issue, in this paper, a tensor-based orthogonal matching pursuit(OMP)channel estimation algorithm is proposed by exploiting the channel sparsity in the delayDoppler-angle domain. In particular, we firstly propose a novel pilot design for the OTFS symbol structure in the frequency-time domain. Then, based on the proposed pilot structure, we formulate the channel estimation as a sparse signal recovery problem, and the tensor decomposition and parallel support detection are introduced into the tensor-based OMP algorithm to reduce the signal processing dimension significantly. Numerical simulations are performed to verify the superiority and the robustness of the proposed tensor-based OMP algorithm.

This paper studies a federated edge learning system, in which an edge server coordinates a set of edge devices to train a shared machine learning (ML) model based on their locally distributed data samples. During the distributed training, we exploit the joint communication and computation design for improving the system energy efficiency, in which both the communication resource allocation for global ML-parameters aggregation and the computation resource allocation for locally updating ML-parameters are jointly optimized. In particular,we consider two transmission protocols for edge devices to upload ML-parameters to edge server, based on the non-orthogonal multiple access (NOMA) and time division multiple access (TDMA), respectively. Under both protocols, we minimize the total energy consumption at all edge devices over a particular finite training duration subject to a given training accuracy,by jointly optimizing the transmission power and rates at edge devices for uploading ML-parameters and their central processing unit (CPU) frequencies for local update. We propose efficient algorithms to solve the formulated energy minimization problems by using the techniques from convex optimization. Numerical results show that as compared to other benchmark schemes,our proposed joint communication and computation design significantly can improve the energy efficiency of the federated edge learning system,by properly balancing the energy tradeoff between communication and computation.

Constrained by orbital configuration and spectrum sharing, non-geostationary orbit (NGEO) satellites may be interfered when they are in the beam range of geostationary orbit(GEO)satellite. However, it is difficult for NGEO operators to determine the signal source. Herein, we propose a method to locate the GEO signal source and estimate beam features, including beam pointing azimuth, elevation, and beamwidth, by the beam edge positions. We transform this estimation problem into two optimization problems by minimizing the estimation error, and solve both of them through a multi-variable joint iteration method with acceptable computation complexity. Numerical results show that when NGEO satellites pass through the beam twice, the longitude estimation error is about 0.01 degree, and the estimation results will be more and more accurate as the number of passing times increases. Besides, the proposed method is also effective when there are kilometer-level errors in beam edge positions.

Atmospheric ducting has a significant impact on electromagnetic wave propagation. Radio signals that are trapped and guided by the atmospheric duct can travel a much longer distance over the horizon with lower attenuation since the signal power does not spread isotropically through the atmosphere.Atmospheric ducting brings both challenges and opportunities to wireless communications. On one hand, the signals propagating in the atmospheric duct may interfere with a receiver far away as remote co-channel interference. On the other hand, a point-to-point link can be established directly through the atmospheric duct to enable beyond line-of-sight communications. In this article, the formation of the atmospheric duct and its effects on radio wave propagation are first overviewed. Then solutions and standardization activities in the 3rd Generation Partnership Project (3GPP) to mitigate atmospheric duct induced remote interference are presented. Finally, the applications and design challenges of atmospheric duct enabled beyond line-of-sight communications are reviewed and future research directions are suggested.

Massive machine type communication (mMTC) is anticipated to be an essential part of fifth generation (5G) networks. The main challenge for mMTC devices (mMTCDs) is the design of an access authentication scheme that can fulfill the security and privacy requirements of 5G applications, which have specific conditions, including rigorous latency and simultaneous access. Thus, a novel 5G authentication and key agreement (5G-AKA) protocol was introduced by the 3rd generation partnership project (3GPP) to achieve mMTCD access authentication. However, 5GAKA protocol comes with some security vulnerabilities and significant delay for real-time mMTC applications, particularly when mMTCDs concurrently roam into new networks. In order to address the real-time secure and efficient access issues of multiple mMTCDs, this paper proposes a lightweight and efficient group authentication protocol for mMTC in 5G wireless networks. The proposed protocol, which integrates bilinear maps and an aggregate certificateless signature mechanism, can achieve several security goals, including avoidance of signaling congestion in the authentication process, mutual authentication, session key agreement, perfect forward/backward secrecy, and masked attack and key escrow resistance. Compared to existing conventional protocols, the proposed protocol demonstrates robust security and improved performance in terms of signaling cost authentication, bandwidth consumption and computational cost.

The performance of a device-to-device (D2D) underlay communication system is limited by the co-channel interference between cellular users (CUs) and D2D devices. To address this challenge, a reconfigurable intelligent surface (RIS) aided D2D underlay system is studied in this paper. A two-timescale optimization scheme is proposed to reduce the required channel training and feedback overhead, where transmit beamforming at the base station(BS)and power control at the D2D transmitters are adapted to instantaneous effective channel state information (CSI); and the RIS phase shifts are adapted to slow-varying channel mean. Based on the two-timescale optimization scheme, we aim to maximize the D2D ergodic weighted sum-rate (WSR)subject to a given outage probability constrained signal-to-interference-plus-noise ratio (SINR) target for each CU. The two-timescale problem is decoupled into two sub-problems, and the two sub-problems are solved iteratively. Numerical results verified that the two-timescale based optimization performs better than two baselines, and also demonstrated a favourable trade-off between system performance and CSI overhead.

Software defined radio (SDR) is a wireless communication technology that uses modern software to control the traditional“pure hardware circuit”. It can provide an effective and secure solution to the problem of building multi-mode, multi-frequency and multifunction wireless communication equipment.Although the concept and application of SDR have been studied a lot, there is little discussion about the operating efficiency of the established system. For the purpose of shortening the delay of mapping and reducing the high computing load in the cloud, a radio monitoring system based on edge computing is developed to achieve the flexible,extensible and real-time monitoring of high-performance SDR applications. To promote the edge intelligence of deep learning (DL) service deployment through edge computing(EC),we developed an edge intelligence algorithm of convolutional neural network (CNN) based on attention mechanism to carry out modulation recognition (MR) of the edge signal and make MR closer to the antenna terminal. Through the experiment of the system and the edge algorithm,this thesis verifies the effectiveness of the developed multifunction radio signal monitoring system.

In this paper, we investigate the resource slicing and scheduling problem in the space-terrestrial integrated vehicular networks to support both delaysensitive services (DSSs) and delay-tolerant services (DTSs). Resource slicing and scheduling are to allocate spectrum resources to different slices and determine user association and bandwidth allocation for individual vehicles. To accommodate the dynamic network conditions, we first formulate a joint resource slicing and scheduling (JRSS) problem to minimize the long-term system cost, including the DSS requirement violation cost,DTS delay cost,and slice reconfiguration cost. Since resource slicing and scheduling decisions are interdependent with different timescales, we decompose the JRSS problem into a large-timescale resource slicing subproblem and a smalltimescale resource scheduling subproblem. We propose a two-layered reinforcement learning (RL)-based JRSS scheme to find the solutions to the subproblems.In the resource slicing layer,spectrum resources are pre-allocated to different slices via a proximal policy optimization-based RL algorithm. In the resource scheduling layer,spectrum resources in each slice are scheduled to individual vehicles based on dynamic network conditions and service requirements via matching-based algorithms.We conduct extensive trace-driven experiments to demonstrate that the proposed scheme can effectively reduce the system cost while satisfying service quality requirements.

Considering the global demands on Internet of things (IoT), and the limitation of constructing base stations for the terrestrial IoT, the satellite IoT approach is a realizable and powerful supplement to the terrestrial IoT. Meanwhile, in order to dynamically access the available terrestrial and satellite networks, IoT terminals may have the ability of accessing both the terrestrial IoT and the satellite IoT, leading to great challenges on the access-control of the IoT.In this paper, we design a satellite-terrestrial integrated architecture for the IoT relying on the software defined network (SDN). Moreover, based on this architecture, we further propose a dynamic channel resource allocation algorithm to control the access of the IoT terminals with different priorities. Simulation results show that the demands on the probabilities of successful access of IoT terminals with various priorities can be simultaneously met if the access of the IoT terminals are well controlled.

With the rapid growth of wireless data demand and the shortage of global bandwidth,the use of millimeter-wave (mmWave) frequency band for wireless cellular networks has become the core content of the fifth generation cellular network. Because mmWave communication has different characteristics from microwave communication,using traditional optimization techniques to manage the resource of mmWave communication networks is inappropriate. In this paper, we propose a neural network-based algorithm to solve the joint user association and resource allocation for mmWave communication system with multi-connectivity(MC)and integrated access backhaul(IAB).The resource allocation problem is formulated as a mixed-integer quadratically constrained quadratic programming(MIQCQP),which is very difficult to solve. First,we decompose the MIQCQP into two sub-problems, i.e., binary associated matrix sub-problem and continuous IAB ratio sub-problem. Then we propose a neural network to solve the binary associated matrix inference problem and a resource allocation algorithm to find the sub-optimal IAB ratio. Simulation results show that the proposed algorithm can achieve good performance with a fast inference speed.