Journal of Communications and Information Networks
Print ISSN 2096-1081 CN 10-1375/TN
Editor-in-Chief: Wei Zhang
Recently, holographic multiple-input multiple-output (HMIMO) has motivated its potential use to support high-capacity data transmission with spatially quasi-continuous apertures. As a practical instance of HMIMO, reconfigurable refractive surfaces (RRSs) equipped with numerous metamaterial elements are utilized as antennas by refracting incident signals from signal sources.In this paper,we investigate a multi-user communication system with an RRS deployed as the base station (BS)’s transmit antenna. To mitigate the high overhead of accurate channel state information (CSI) acquisition, the codebook design and beam training are employed to perform beamforming. Given the large scale of RRS, users are likely to be randomly distributed in both the near and far fields around the BS, which is unknown in advance. By considering radiation characteristics in both fields, a near-far field codebook is designed to be applicable to all users, regardless of their locations. To reduce overhead, a multi-user beam training is proposed to serve all users simultaneously by enhancing each codeword capable of covering multiple areas. Considering a general case that includes users in both fields,simulation results indicate that,without prior knowledge of user distribution, the proposed scheme outperforms state-of-the-art ones in terms of sum rate and overhead.
Domain name system (DNS) amplification distributed denial of service(DDoS)attacks are one of the popular types of intrusions that involve accessing DNS servers on behalf of the victim. In this case, the size of the response is many times greater than the size of the request, in which the source of the request is substituted for the address of the victim. This paper presents an original method for countering DNS amplification DDoS attacks. The novelty of our approach lies in the analysis of outgoing traffic from the victim’s server. DNS servers used for amplification attacks are easily detected in Internet control message protocol(ICMP)packet headers (type 3,code 3)in outgoing traffic. ICMP packets of this type are generated when accessing closed user datagram protocol (UDP) ports of the victim, which are randomly assigned by the Saddam attack tool. To prevent such attacks, we used a Linux utility and a software-defined network (SDN) module that we previously developed to protect against port scanning. The Linux utility showed the highest efficiency of 99.8%, i.e., only two attack packets out of a thousand reached the victim server.
This paper investigates the secure transmission for reconfigurable intelligent surface (RIS)-assisted wireless communication systems. In the studied model, one user connects to the access point via a RIS while an eavesdropper eavesdrops on the signal sent from the user to the access point. Therefore, it is necessary to design an appropriate RIS reflection vector to solve the eavesdropping problem. This problem is formulated as an optimization problem whose goal is to maximize the secure energy efficiency which is defined as the ratio of the secure rate to the total energy consumption of the system via jointly optimizing the RIS reflection reflector as well as the number of RIS elements, which results in a non-convex optimization problem that is intractable to solve by traditional methods. To tackle this issue, a new algorithm by leveraging the advance of the established deep learning (DL) technique is proposed so as to find the optimal RIS reflection vector and determine the optimal number of RIS reflection elements. Simulation results show that the proposed method reaches 96% of the optimal secure energy efficiency of the genie-aided algorithm.
Intelligent reflecting surface (IRS)-assisted simultaneous wireless information and power transfer (SWIPT) is a promising technology for prolonging the lifetime of the users and improving system performance. However, it is challenging to obtain the perfect channel state information (CSI) at the base station (BS) due to the lack of radio frequency(RF)chains at the IRS,which becomes even harder when eavesdroppers (Eves) exist. In this paper,we study the power issues in the IRS-aided SWIPT system under the imperfect CSI of BS-IRS-Eves links. We aim to minimize the transmitting power at the BS by jointly designing transmit beamforming and reflective beamforming with subject to the rate outage probability constraints of the Eves,the energy harvesting constraints of the energy receivers (ERs), and the minimum rate constraints of the information receivers (IRs). We use Bernstein-type inequality to solve the rate outage probability constraints. Afterward, the formulated nonconvex problem can be efficiently tackled by employing alternating optimization(AO)combined with semidefinite relaxation (SDR) and penalty convex-concave procedure (CCP).Numerical results demonstrate the effectiveness of the proposed scheme.
This paper considers the problem of joint beamformer design for a two-tier wireless network, whereby a set of cache-enabled access points (APs) are connected to the base station (BS) via wireless backhaul links. The APs can prefetch and store the files requested by users,to serve users directly in the access links. Thus low-latency transmissions are enabled as the transmission in the backhaul links is saved.However,due to the limited cache capacity,not all requested files can be stored in the APs,some of the non-cached APs then should be utilized as long as their transmission delays in the access and backhaul links can be well addressed. Two delay optimal beamformer design(DOBD)problems are formulated to minimize the overall delay incurred in the backhaul and access link transmissions via a joint optimization of the beamformer at the BS and APs. We consider the DOBD problem under non-fragment and fragment caching policies, both involving nonconvex link rate constraints. The semi-definite relaxation(SDR)and sequential convex approximation (SCA) schemes are adopted to approximate the nonconvex problems into convex ones,which are then iteratively solved. Numerical results demonstrate the convergence and improved transmission delay performance of the proposed scheme under various network settings.
The existing radio access network (RAN) is facing many challenges to meet the very strict speed and latency requirements by different mobile applications in addition to the increasing pressure to reduce operating cost. Innovation and development in RAN have been accelerated to tackle these challenges and to define how next generation mobile networks should look like. The role of machine learning (ML) and artificial intelligence (AI) driven innovations within the RAN domain is strengthening and attracting lots of attention to tackle many of the challenging problems. In this paper we surveyed RAN network base stations(BSs)clustering and its applications in the literature. The paper also demonstrates how to leverage community detection algorithms to understand underlying community structures within RAN. Tracking areas(TAs)novel framework was developed by adapting existing community detection algorithm to solve the problem of statically partitioning a set of BSs into TA according to mobility patterns. Finally, live network dataset in dense urban part of Cairo is used to assess how the developed framework is used to partition this part of the network more efficiently compared to other clustering techniques. Results obtained showed that the new methodology saved up to 34.6% of inter TA signaling overhead and surpassing other conventional clustering algorithms.
In this work, evolutionary algorithms are applied for the first time to achieve better radiation characteristics over the conventional beamforming algorithm in the concentric hexagonal antenna array (CHAA), which improves the performance of wireless communication. Multiple signal classification(MUSIC)algorithm is employed for direction of arrival (DoA) estimation. The conventional adaptive beam steering algorithm, least mean-square (LMS) algorithm, is used to steer the beam. Further, the proposed approach is employed by novel particle swarm optimization (NPSO) to reduce sidelobe level(SLL)even further. A six-ring CHAA with 126 elements for DoA estimation and beam steering is simulated. The simulation results of the MUSIC, LMS, NPSO,and particle swarm optimization(PSO)algorithms are provided for various DoAs.
Maritime communication network is affected by many factors such as meteorological environment,resource constraints, communication distance, and so on, resulting in task packet loss, failure, and node overload when the task is offloaded and executed. In this paper, in order to improve quality of service(QoS)of maritime edge computing networks, a task offloading scheme is proposed. Considering the characteristic of the ship, we analyze ship position and network connectivity, and the ship motion model is constructed. The task offloading link formed with ship as node. With delay and energy consumption as constraints,the revenue function for task offloading path is designed.The function comprehensively considers network resources, channel transmission and node execution ability,thereby achieving the reliable task offloading. Simulation results prove that the proposed scheme can effectively improve the success rate of task execution,reduce the network packet loss rate and ensure the network resources load balancing, which effectively improve the QoS of maritime edge computing networks.
