Journal of Communications and Information Networks
Print ISSN 2096-1081 CN 10-1375/TN
Editor-in-Chief: Wei Zhang
The 5G network connecting billions of Internet of things (IoT) devices will make it possible to harvest an enormous amount of real-time mobile data. Furthermore, the 5G virtualization architecture will enable cloud computing at the (network) edge. The availability of both rich data and computation power at the edge has motivated Internet companies to deploy artificial intelligence (AI) there, creating the hot area of edge-AI. Edge learning, the theme of this project, concerns training edge-AI models, which endow on IoT devices intelligence for responding to real-time events. However, the transmission of high-dimensional data from many edge devices to servers can result in excessive communication latency, creating a bottleneck for edge learning. Traditional wireless techniques deigned for only radio access are ineffective in tackling the challenge. Attempts to overcome the communication bottleneck has led to the development of a new class of techniques for intelligent radio resource management (RRM), called data-importance aware RRM. Their designs feature the interplay of active machine learning and wireless communication. Specifically, the metrics that measure data importance in active learning (e. g. , classification uncertainty and data diversity) are applied to RRM for efficient acquisition of distributed data in wireless networks to train AI models at servers. This article aims at providing an introduction to the emerging area of importance-aware RRM. To this end, we will introduce the design principles, survey recent advancements in the area, discuss some design examples, and suggest some promising research opportunities.
While the commercial deployment and promotion of 5G is ongoing, mobile communication networks are still facing three fundamental challenges, i. e. , spectrum resource scarcity, especially for low-frequency spectrum, exacerbated by fragmented spectrum allocation, user-centric network service provision when facing billions of personalized user demands in the era of Internet of everything (IoE), and proliferating operation costs mainly due to huge energy consumption of network infrastructure. To address these issues, it is imperative to consider and develop disruptive technologies in the next generation mobile communication networks, namely 6G. In this paper, by studying brain neurons and the neurotransmission, we propose the fully-decoupled radio access network(FD-RAN). In the FD-RAN, base stations (BSs) are physically decoupled into control BSs and data BSs, and the data BSs are further physically split into uplink BSs and downlink BSs. We first review the fundamentals of neurotransmission and then propose the 6G design principles inspired by the neurotransmission. Based on the principles, we propose the FD-RAN architecture, elastic resource cooperation in FD-RAN, and improved transport service layer design. The proposed fully decoupled and flexible architecture can profoundly facilitate resource cooperation to enhance the spectrum utilization, reduce the network energy consumption and improve the quality of user experience. Future research topics in this direction are envisioned and discussed.
Aerial platforms and edge servers have been recognized as two promising building blocks to improve the quality of service(QoS)in space-air-ground integrated vehicular networks(SAGIN). Communication intensive tasks can be offloaded to aerial platforms via broadcasting, while computation intensive tasks can be offloaded to ground edge servers. However, the key issues including how to allocate radio resources and how to determine the task offloading strategy for the two types of tasks, are yet to be solved. In this paper, the joint optimization of radio resource allocation and bidirectional offloading configuration is investigated. To deal with the non-convex nature of the original problem, we decouple it into a two-step optimization problem. In the first step, we optimize the bidirectional offloading configuration in the case of the radio resource allocation known in advance, which is proved to be a convex optimization problem. In the second step, we optimize the radio resource allocation through a brute-force search method. We use queuing theories to analyze the average delay of the two tasks with respect to the broadcasting capacity and task arrival rate. The offloading strategies with closed-form expressions of communication intensive tasks are proposed. We then propose a heuristic algorithm which is shown to perform better than interior point algorithm in simulations. The numerical results also demonstrate that the aerial platforms and edge servers can significantly reduce the average delay of the tasks under different network conditions.
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.
The Internet of things(IoT), as an extension of the Internet, has become a trend of network development nowadays. In order to protect the integrity and authenticity of the information in the IoT, an identity authentication protocol applied to the networked devices is designed in this paper, using the physical unclonable function (PUF) to extract the uniqueness and tamper resistance of the randomness in the manufacturing process of the physical device. We propose the protocol including the database, accessed devices, access devices and users in the specific network environment. Relying on the unique identification information generated by the PUF embedded in devices and passwords set by users, devices and users identities could be verified through zero-knowledge proofs. The performance analysis and the experiment at the end of this work show that our protocol provides users with a strong security guarantee for IoT devices.
Enabling cellular access for unmanned aerial vehicles (UAVs) is a practically appealing solution to realize their high-quality communications with the ground for ensuring safe and efficient operations. In this paper, we study the trajectory design for a cellularconnected UAV that needs to fly from given initial to final locations, while communicating with the ground base stations (GBSs) subject to a minimum signal-to-noise ratio(SNR)requirement along its flight. However, due to various practical considerations such as GBSs’ locations and coverage range as well as UAV’s trajectory and mobility constraints, the SNR target may not be met at certain time periods during the flight, each termed as an outage duration. In this paper, we first propose a general outage cost function in terms of outage durations in the flight, which includes the two commonly used metrics, namely total outage duration and maximum outage duration as special cases. Based on it, we formulate a UAV trajectory optimization problem to minimize its mission completion time, subject to a constraint on the maximum tolerable outage cost. To tackle this challenging (non-convex) optimization problem, we first transform it into a tractable form and thereby reveal some useful properties of the optimal trajectory solution. Based on these properties, we further simplify the problem and propose efficient algorithms to check its feasibility and obtain optimal as well as low-complexity suboptimal solutions for it by leveraging graph theory and convex optimization techniques. Numerical results show that our proposed trajectory designs outperform that by the conventional method of dynamic programming, in terms of both performance and complexity.
This paper studies a wireless powered mobile edge computing(MEC)system with device-to-device (D2D)-enabled task offloading. In this system, a set of distributed multi-antenna energy transmitters (ETs) use collaborative energy beamforming to wirelessly charge multiple users. By using the harvested energy, the actively computing user nodes can offload their computation tasks to nearby idle users (as helper nodes) via D2D communication links for self-sustainable remote computing. We consider the frequency division multiple access (FDMA) protocol, such that the D2D communications of different user-helper pairs are implemented over orthogonal frequency bands. Furthermore, we focus on a particular time block for task execution, which is divided into three slots for computation task offloading, remote computing, and result downloading, respectively, at different user-helper pairs. Under this setup, we jointly optimize the collaborative energy beamforming at ETs, the communication and computation resource allocation at users and helpers, and the user-helper pairing, so as to maximize the sum computation rate (i. e. , the number of task input-bits executed over this block) of the users, subject to individual energy neutrality constraints at both users and helpers. First, we consider the computation rate maximization problem under any given user-helper pairs, for which an efficient solution is proposed by using the techniques of alternating optimization and convex optimization. Next, we develop the optimal user-helper pairing scheme based on exhaustive search and a lowcomplexity scheme based on greedy selection. Numerical results show that the proposed design significantly improves the sum computation rate at users, as compared to benchmark schemes without such joint optimization.
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.
In this paper, we combine the circular polarization technology and orbital angular momentum (OAM) into the array antenna design for the first time, achieve free switch of the different topological charges in the array by using high-speed radio frequency (RF) switch technology. We arrange microstrip patch antenna elements equidistantly along the circumference to form eight elements multi-modal OAM vortex electromagnetic wave microstrip array antenna. It can generate electromagnetic waves with dual characteristics of circular polarization and multi-modal vortex OAM (where OAM mode values are l=0, l=±1, l=±2, l=±3). Through simulation, we find mutual coupling between the radiating elements is small relatively, and increasing the number of array elements can not only improve the beam quality, but also generate electromagnetic waves with a higher order of OAM modes. Antenna model can meet the basic demands of ordinary array antenna, and also confirm the practicality of this circular polarized microstrip antenna array model.
In this paper, a resource mobility aware two-stage hybrid task planning algorithm is proposed to reduce the resource conflict between emergency tasks and the common tasks, so as to improve the overall performance of space information networks. Specifically, in the common task planning stage, a resource fragment avoidance task planning algorithm is proposed, which reduces the contention between emergency tasks and the planned common tasks in the next stage by avoiding the generation of resource fragments. For emergency tasks, we design a metric to quantify the revenue of the candidate resource combination of emergency tasks, which considers both the priority of the tasks and the impact on the planned common tasks. Based on this, a resource mobility aware emergency task planning algorithm is proposed, which strikes a good balance between improving the sum priority and avoiding disturbing the planned common tasks. Finally, simulation results show that the proposed algorithm is superior to the existing algorithms in both the sum task priority and the task completion rate.
