Journal of Communications and Information Networks
Print ISSN 2096-1081 CN 10-1375/TN
Editor-in-Chief: Wei Zhang
Intelligent reflecting surface (IRS) is a revolutionizing and promising technology to improve the high transmission rate of the wireless communication systems.Specifically,an IRS consists of a great amount of low-cost passive reflecting elements, which reflect the incident signals to the desired user by collaboratively using passive beamforming. This paper introduces IRSs into a device-to-device (D2D) underlying cellular system to enhance transmission rate performance of the D2D pairs. We formulate an optimization problem of maximizing the transmission rate of the D2D pairs while satisfying the minimum required rate of the cellular users. We address this problem by jointly optimizing the reuse indicator, received beamforming, power allocation, and phase shift matrices. Block coordinate descent (BCD) algorithm is adopted to decouple the original problem into four subproblems. Closed form solutions are obtained by solving the sub-problems of optimizing the received beamforming and power allocation. Then, Kuhn-Munkres(KM)algorithm and minimization-majorization (MM) algorithm are adopted to solve the sub-problems of optimizing the reuse indicator and phase shift matrices, respectively. Simulation results demonstrate that IRSs can effectively improve the transmission rate of the D2D pairs and our proposed distributed IRSs scheme outperforms the other benchmark schemes.
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.
With the rapid development of vehicular access network, three different communication modes have emerged as the fundamental technologies in cellular V2X,i.e.,cellular mode,reuse mode,and dedicated mode, through different spectrum sharing strategies. However, how to conduct the multi-mode access management for cellular V2X is challenging considering the heterogeneity of network resource and the diverse vehicular services demands. In this paper, we investigate a software defined multi-mode access management framework in cellular V2X, which is capable of providing flexibility and programmability for dynamic spectrum sharing in vehicular access. We first conduct the performance analysis of multi-mode communications in cellular V2X. Then, we design an analytic hierarchy process (AHP) based evolutionary game to optimize the multi-mode access management solution, in which four performance indicators are jointly considered, including transmission rate, interference, transmission delay, and energy consumption. Extensive experimental simulations have validated the superiority of the proposed algorithm, and illustrated the impact of different vehicle access schemes, signal-to-interference-plus-noise ratio (SINR) threshold and other parameters on the performance of cellular V2X.
In this paper, we study the joint bandwidth allocation and path selection problem, which is an extension of the well-known network utility maximization (NUM) problem, via solving a multi-objective minimization problem under path cardinality constraints. Specifically, such a problem formulation captures various types of objectives including proportional fairness, average delay, as well as load balancing. In addition, in order to handle the“unsplittable flows”, path cardinality constraints are added,making the resulting optimization problem quite challenging to solve due to intrinsic nonsmoothness and nonconvexity. Almost all existing works deal with such a problem using relaxation techniques to transform it into a convex optimization problem. However,we provide a novel solution framework based on the linearized alternating direction method of multipliers (LADMM) to split the original problem with coupling terms into several subproblems.We then derive that these subproblems, albeit nonconvex nonsmooth, are actually simple to solve and easy to implement, which can be of independent interest. Under some mild assumptions, we prove that any limiting point of the generated sequence of the proposed algorithm is a stationary point. Numerical simulations are performed to demonstrate the advantages of our proposed algorithm compared with various baselines.
As one of the most important Internet infrastructures,domain name system(DNS)is vulnerable to various attacks, and the issue of DNS security has received critical attention. However, most of the existing DNS security enhancements have encountered great difficulties in the process of popularization. The main reason is that these enhancement measures usually focus on the server side,thus requiring changes to existing DNS protocol or architecture, while modifying the Internet infrastructure is inherently hard. Noticing that the range of domain name frequently visited by a single user is much smaller than the entire domain system, in this paper we propose the idea of personal DNS agent (P-DNS), which migrates DNS security from servers to user terminals and can be applied without changing the current DNS infrastructure. P-DNS takes advantage of static and dynamic redundancy to enhance DNS security. Specifically,in the static redundancy phase, P-DNS improves the resolution efficiency by utilizing resolution results cached in LDAP. While in the dynamic redundancy stage,P-DNS improves the reliability of resolution results by querying multiple recursive name servers (RNSs). Simulation results show that our proposed architecture can effectively improve DNS security performance and greatly reduce the additional delay caused by redundancy.
Nowadays, video streaming counts for the major part of network traffic over the Internet. However, on account of the host-to-host mechanism of the traditional IP network, video distribution over IP-based Internet encounters bottlenecks. Fortunately, a new proposed future Internet architecture, named data networking (NDN) can improve the performance of video distribution by its features such as in-network storage, multi-path forwarding, etc. In this paper, we design an adaptive bitrate algorithm based on Lyapunov optimization theory over NDN to optimize the long-term quality-of-experience (QoE) of video distribution while ensuring the stability of the whole system. When the network condition is abundant and stable, the problem can be simplified by approximating to a fixed-slot queuing model, but the theoretical performance will degrade when the network status is poor and fluctuate fiercely. Therefore,we divide the problem into two models of fixed time slot and non-fixed time slot and design two Lyapunov optimization algorithms to adapt different network scenarios. The proposed algorithms do not require prior knowledge of the network bandwidth and are capable of running online with the client’s available information. Simulation and realistic experiment results demonstrate that our algorithms perform better than others in NDN.
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.
With the continuous development of satellite communication and Internet of things technology, more and more devices can access space information networks (SIN)and enjoy satellite services everywhere in the world. However, due to the openness of the air-to-ground channel, the device will face a series of security threats when accessing SIN, such as replay attacks, eavesdropping attacks, impersonation attacks, and man-in-the-middle attacks. These security threats will lead to illegal entity access and further endanger the reliability and availability of the system. Although scholars have proposed many enhanced security access authentication protocols, most of them have been proved to have security vulnerabilities. In addition,with the development of quantum computing, the previous authentication protocols based on some asymmetric cryptographic mechanisms such as discrete logarithm and elliptic curve cryptographic mechanisms may face new security challenges. In this context, this paper proposes a novel anti-quantum access authentication protocol based on ring learning with errors(RLWE), which meets more security requirements and greatly reduces the authentication delay through pre-negotiation. Through the security analysis and performance analysis, it is shown that our protocol can be more suitable for delay-sensitive IoT devices to access SIN while ensuring higher security.
Aerostat platforms could serve as potential aerial platforms because of their low cost, long flight times, and easy load recovery. They are suitable for emergency communication,early warnings,Earth observations,and other fields. The air-ground communication link between an aerostat platform and the ground is the basic guarantee for aerostat measurement control and service transmission. In traditional aerostat applications, platform measurement control and service transmission usually use different links. The load and ground equipment are complex, and the energy, load, and size of the platform are greatly occupied. In this paper,we propose a Beidou-assisted air-ground communication ground for a near-space platform that uses Beidou’s positioning and communication functions to provide tracking and guidance information for the ground station. An air-ground wireless communication mode integrating measurement control and service transmission is achieved. The flight test shows that the system can effectively meet the practical application requirements.
