Journal of Communications and Information Networks
Print ISSN 2096-1081 CN 10-1375/TN
Editor-in-Chief: Wei Zhang
Network slicing has gained popularity as a result of the advances in the fifth generation (5G) mobile network. Network slicing facilitates the support of different service types with varying requirements, which brings into light the slicing-aware next generation mobile network architecture. While allowing resource sharing among multiple stakeholders,there is a long list of administrative negotiations among parties that have not established mutual trust. Distributed ledger technology may be a solution to mitigate the above issues by taking its decentralized yet immutable and auditable ledger, which may help to ease administrative negotiations and build mutual trust among multi-stakeholders.There have been many research interests in this direction which focus on handling various problems in network slicing. This paper aims at constructing this area of knowledge by introducing network slice from a standardization point of view to start with, and presenting security, privacy, and trust challenges of network slicing in 5G and beyond networks. Furthermore, this paper covers distributed ledger technologies basics and related approaches that tackle security,privacy,and trust threats in network slicing for 5G and beyond networks. The various proposals proposed in the literature are compared and presented. Lastly, limitations of current work and open challenges are illustrated as well.
Ultra-dense low earth orbit (LEO) integrated satellite-terrestrial network(ULISTN)has become an emerging paradigm to support massive access of Internet of things(IoT)in beyond fifth generation mobile networks (B5G) . In ULISTN, there are two communication modes: cellular mode and satellite mode, where IoT users assessing terrestrial small base stations(TSBSs) and terrestrial-satellite terminals (TSTs) respectively. However, how to optimize the network performance and guarantee self-interests of the operator and IoT users in ULISTN is a challenging issue. In this paper, we propose a cybertwin-assisted joint mode selection and dynamic pricing (JMSDP) scheme for effective network management in ULISTN, where cybertwin serves as the intelligent agent. In JMSDP, the operator determines optimal access prices of TSBSs and TSTs, while each user selects the access mode according to access prices. Specifically, the operator conducts the Stackelberg game aiming at maximizing average throughput depending on the mode selection results of IoT users. Meanwhile, IoT users as followers adopt the evolutionary game to choose an access mode based on the access prices provided by the operator. Simulation results show that the proposed JMSDP can improve the average throughput and reduce the delay effectively,comparing with random access(RA) and maximum rate access.
Nowadays, the emerging paradigm of semantic communications seems to offer an attractive opportunity to improve the transmission reliability and efficiency in new generation communication systems. In particular, focusing on spectrum scarcity, expected to afflict the upcoming sixth generation(6G)networks,this paper analyses the semantic communications behavior in the context of a cell-dense scenario, in which users belonging to different small base station areas may be allocated on a same channel giving rise to a non-negligible interference that severely affects the communications reliability. In such a context, artificial intelligence methodologies are of paramount importance in order to speed up the switch from traditional communication to the novel semantic communication paradigm. As a consequence, a deep-convolution neural networks based encoder-decoder architecture has been exploited here in the definition of the proposed semantic communications framework.Finally,extensive numerical simulations have been performed to test the advantages of the proposed framework in different interfering scenarios and in comparison with different traditional or semantic alternatives.
In this paper, an indoor layout sensing and localization system with testbed in the 60-GHz millimeter wave(mmWave)band, named mmReality, is elaborated. The mmReality system consists of one transmitter and one mobile receiver,both with a phased array and a single radio frequency (RF) chain. To reconstruct the room layout, the pilot signal is delivered from the transmitter to the receiver via different pairs of transmission and receiving beams, so that multipath signals in all directions can be captured. Then spatial smoothing and the two-dimensional multiple signal classification (MUSIC) algorithm are applied to detect the angle-of-departures (AoDs) and angle-of-arrivals (AoAs) of propagation paths. Moreover, the technique of multi-carrier ranging is adopted to measure the path lengths. Therefore, with the measurements of the receiver in different locations of the room,the receiver and virtual transmitters can be pinpointed to reconstruct the room layout. Experiments show that the reconstructed room layout can be utilized to localize a mobile device via the AoA spectrum.
The large-scale deployment of intelligent Internet of things (IoT) devices have brought increasing needs for computation support in wireless access networks. Applying machine learning (ML) algorithms at the network edge, i.e., edge learning, requires efficient training, in order to adapt themselves to the varying environment. However, the transmission of the training data collected by devices requires huge wireless resources. To address this issue, we exploit the fact that data samples have different importance for training, and use an influence function to represent the importance. Based on the importance metric,we propose a data pre-processing scheme combining data filtering that reduces the size of dataset and data compression that removes redundant information. As a result, the number of data samples as well as the size of every data sample to be transmitted can be substantially reduced while keeping the training accuracy.Furthermore,we propose device scheduling policies, including rate-based and Monte-Carlo-based policies, for multi-device multi-channel systems, maximizing the summation of data importance of scheduled devices. Experiments show that the proposed device scheduling policies bring more than 2% improvement in training accuracy.
The ability to effortlessly construct and broadcast false messages makes IEEE 802.11 wireless networks particularly vulnerable to attack. False frame generation allows rogue devices to impersonate an authorized user and issue commands that impact the user’s network connection or possibly the entire network’s security. Unfortunately,the current device impersonation detection methods are unsuitable for small devices or real-time applications.Our contribution is to demonstrate that a rule-based learning classifier using several random forest(RF)features from an IEEE 802.11 frame can determine the probability that an impersonating device has generated that frame in real time.Our main innovation is a processing pipeline,and the algorithm that implements concurrent one-class classifiers on a per device basis yet is lightweight enough to run directly on a wireless access point(WAP)and produce real-time outputs.
The space-ground integration network (SGIN) with large-scale constellations has become an important topic of the next generation mobile communication technology, in which the handover technology between the satellite and the ground is the key technology to ensure the continuity of user service. However, compared with the ground base station’s coverage of users, satellites have larger coverage and propagation delay,and large-scale constellations make multiple selectable service satellites above the same user. These phenomena bring great challenges to the handover algorithm. This paper designs a reinforcement learning-based multi-attribute satellite-ground handover strategy(RLMSGHS)for SGIN with large-scale constellations.Firstly,users are clustered with the attributes of location, speed, and bandwidth demand. Then,the handover decision can be made based on the proposed RLMSGHS according to the attributes of received signal strength(RSS),speed,network bandwidth utilization and, handover cost. Finally, the simulation results demonstrate that the heavy decision-making burden caused by the large-scale growth of users in the SGIN is significantly reduced. The multi-attribute handover decision in the SGIN is realized,which reduces the handover demand of users and improves the resource utilization rate of the SGIN.
For trapped users in disaster areas, the available energy of affected user equipment (UE) is limited due to the breakdown of the ground power system. When complex geographical condition prevents ground emergency vehicles from reaching disaster-stricken areas, unmanned aerial vehicle (UAV) can effectively work as a temporary aerial base station for serving terrestrial trapped users. Simultaneous wireless information and power transfer (SWIPT) system is intriguing for distributed batteryless users (BUs) by transferring data and energy simultaneously. However,how to achieve the maximum energy efficiency (EE) and energy transfer efficiency (ETE) for distributed BUs in UAV-enabled SWIPT systems is not very clear.In this paper,we develop three novel reconfigurable intelligent surface(RIS)-based SWIPT algorithms to solve this nonconvex joint optimization problem using deep reinforcement learning (RL) algorithms. Through the deployment of RIS-assisted UAVs,we aim to maximize the EE along with the ETE via jointly designing the UAV trajectory, the phase matrix, and the power splitting ratio within strict time and energy constraints.The obtained numerical results show that our developed RL-based algorithms can effectively improve the cost time,the average charging rate,data rate,and the EE/ETE performance of the RIS-assisted SWIPT systems as compared with benchmark solutions.
Integrated sensing and communication (ISAC)is a spectrum and energy efficient approach to realizing dual functions by a unified hardware platform. In this paper, we consider a multiple-input multiple-output (MIMO) ISAC system, where the transmitted waveform consisting of communication signals and dedicated sensing signal is optimized for dual purposes of estimating targets and serving downlink single-antenna users. Specifically, the sensing interference and multi-user interference are exploited,rather than suppressed,by the waveform design scheme.The joint waveform design problem is formulated by maximizing the constructive interference (CI) while ensuring the power budget and waveform similarity error with the benchmark signal, which limits the sensing estimation accuracy. To obtain the benchmark signal which achieves the optimal estimation performance, we propose a semidefinite relaxation based algorithm to solve the optimization problem. For clarity, we derive the real representation of the complex joint waveform design problem and prove its convexity.Numerical results verify the superiority of the proposed CI-based waveform design when the interference was efficiently exploited as a useful signal source achieving favorable symbol error ratio performance.Moreover,the dedicated sensing signal provides more degree of freedom for waveform design.
In this paper, an acoustic velocityindependent two-dimensional direction of arrival (2-D DOA)estimation for underwater application is presented to eliminate the effect of the inaccurate acoustic velocity estimation. According to the geometric relationship between the linear arrays,the proposed method employs the cross correlation matrix (CCM) of the data received by three crossed linear arrays to remove the acoustic velocity factor. The simulation results demonstrate that the proposed method is not susceptible to the acoustic velocity. For a single source, the proposed method outperforms the conventional method in all conditions. For multiple sources,there is a little performance degradation for the proposed method compared with the conventional method. However,the proposed method displays a better performance than the conventional method in situations where the signal to noise ratio(SNR)is extremely low or the acoustic velocity estimation error is non-negligible. Furthermore, the computational complexity of the proposed method is lower than that of the conventional method using the same amount of sensors in total, while the performance is still acceptable.
