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    PHYSICAL AND FUNDAMENTALS
  • PHYSICAL AND FUNDAMENTALS
    A New HARQ Scheme for 5G Systems via Interleaved Superposition Retransmission
    2023, 20(4): 1-11. https://doi.org/10.23919/JCC.fa.2022-0670.202304
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    Within the framework of the 5G new radio (NR), we propose a new hybrid automatic repeat request (HARQ) scheme to improve the throughput performance. The difference between the proposed scheme and the conventional one lies in the first retransmission, where the erroneous coded block group is interleaved and superimposed (XORed) onto a fresh coded block group. At the receiver, an iterative message-passing decoding algorithm can be employed to recover the target erroneous code block group (CBG). Only when the superposed retransmission fails, the conventional incremental redundancy (IR) or repetition redundancy (RR) retransmission is initiated. In any case, since the first retransmission is along with but has negligible effect on the fresh CBG, it costs neither transmitted power nor bandwidth. Monte-Carlo simulation results reveal that the presented HARQ schemes can achieve throughput improvements up to 10% over block fading channels and up to 50% over fast fading channels in comparison with the original 5G CBG-level HARQ scheme but without excessively increasing the implementation complexity.
  • PHYSICAL AND FUNDAMENTALS
    Multiple Vandermonde Decomposition Based Channel Estimation for MmWave MIMO High-Mobility Communication in 5G and Beyond
    2023, 20(4): 12-25. https://doi.org/10.23919/JCC.fa.2022-0540.202304
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    Millimeter wave (mmWave) massive massive multiple input multiple output (MIMO) technique has been regarded as the viable solution for vehicular communications in 5G and beyond. To achieve the substantial increase in date rates, it is important to take an effective channel state information (CSI). However, existing channel estimation strategies are unavailable since the users high-mobility. To solve above issues, in this paper, inspired by a specific antenna structure, we propose a novel approach for fast time-varying channel estimation. Specifically, by considering the vehicle scenario with high-mobility, a corresponding mathematical model is firstly established. Then, based on the special structural of the sparse array, the switch network is used to replace the convention phase shifter of mmWave hybrid system, which can effectively reduce the number of radio-frequency (RF) chains and antennas. Furthermore, by solving the semidefinite programming (SDP) duality problem, the Doppler frequency and path parameters are effectively estimated. Simulation results are shown that the computational complexity and estimation accuracy of the proposed algorithm is superior than that of the traditional schemes.
  • PHYSICAL AND FUNDAMENTALS
    Dynamic MmWave Channel Reconstruction of MIMO OTA Testing for Handover Scenarios
    2023, 20(4): 26-40. https://doi.org/10.23919/JCC.fa.2022-0610.202304
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    The evaluation of handover performance is essential for ensuring seamless user experience under innovative application scenarios in the fifth generation (5G) and beyond era, including autonomous driving, mobile augmented and virtual reality. However, due to the hardware constrains of a sectored multiprobe anechoic chamber (SMPAC), switching among multiple channel models is of low precision with a high cost in traditional over-the-air (OTA) test solutions. In this paper, we present an efficient and repeatable emulation strategy to reconstruct dynamic millimeter-wave (mmWave) channels in laboratories for multiple-input multiple-output (MIMO) mobile devices. Firstly, we propose a novel evaluation metric, called average power angular spectrum similarity percentage (APSP), which minimizes the unexpected impact induced by the indefinite condition of adaptive antenna arrays in mmWave terminals during handover process. Moreover, we propose a partitioned probe configuration strategy by designing a beam directivity-based switching circuit, which enables quick changes of probe configurations in SMPAC. Simulation results demonstrate the effectiveness of the proposed algorithms, thus providing a guideline for the reconstruction of the dynamic channel in different scenarios with resource limitation.
  • PHYSICAL AND FUNDAMENTALS
    On Dynamic Channel Emulation in Sector MPAC for Over-the-Air Testing of Beamformed Massive MIMO Devices
    2023, 20(4): 41-56. https://doi.org/10.23919/JCC.fa.2022-0644.202304
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    In this article, novel emulation strategies for the sectored multiple probe anechoic chamber (SMPAC) are proposed to enable the reliable evaluation of the massive multiple-input multiple-output (MIMO) device operating at beamforming mode, which requires a realistic non-stationary channel environment. For the dynamic propagation emulation, an efficient closed-form probe weighting strategy minimizing the power angular spectrum (PAS) emulation errors is derived, substantially reducing the associated computational complexity. On the other hand, a novel probe selection algorithm is proposed to reproduce a more accurate fading environment. Various standard channel models and setup configurations are comprehensively simulated to validate the capacity of the proposed methods. The simulation results show that more competent active probes are selected with the proposed method compared to the conventional algorithms. Furthermore, the derived closed-form probe weighting strategy offers identical accuracy to that obtained with complicated numerical optimization. Moreover, a realistic dynamic channel measured in an indoor environment is reconstructed with the developed methodologies, and 95.6% PAS similarity can be achieved with 6 active probes. The satisfactory results demonstrate that the proposed algorithms are suitable for arbitrary channel emulation.
  • PHYSICAL AND FUNDAMENTALS
    Performance Analysis for Orthogonal Time Frequency Space Modulation Systems with Generalized Waveform
    2023, 20(4): 57-72. https://doi.org/10.23919/JCC.fa.2022-0452.202304
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    Orthogonal time-frequency space (OTFS), which exhibits beneficial advantages in high-mobility scenarios, has been considered as a promising technology in future wireless communication systems. In this paper, a universal model for OTFS systems with generalized waveform has been developed. Furthermore, the average bit error probability (ABEP) upper bounds of the optimal maximum likelihood (ML) detector are first derived for OTFS systems with generalized waveforms. Specifically, for OTFS systems with the ideal waveform, we elicit the ABEP bound by recombining the transmitted signal and the received signal. For OTFS systems with practical waveforms, a universal ABEP upper bound expression is derived using moment-generating function (MGF), which is further extended to MIMO-OTFS systems. Numerical results validate that our theoretical ABEP upper bounds are concur with the simulation performance achieved by ML detectors.
  • PHYSICAL AND FUNDAMENTALS
    Joint Flexible Duplexing and Power Allocation with Deep Reinforcement Learning in Cell-Free Massive MIMO System
    2023, 20(4): 73-85. https://doi.org/10.23919/JCC.fa.2022-0520.202304
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    Network-assisted full duplex (NAFD) cell-free (CF) massive MIMO has drawn increasing attention in 6G evolvement. In this paper, we build an NAFD CF system in which the users and access points (APs) can flexibly select their duplex modes to increase the link spectral efficiency. Then we formulate a joint flexible duplexing and power allocation problem to balance the user fairness and system spectral efficiency. We further transform the problem into a probability optimization to accommodate the short-term communications. In contrast with the instant performance optimization, the probability optimization belongs to a sequential decision making problem, and thus we reformulate it as a Markov Decision Process (MDP). We utilizes deep reinforcement learning (DRL) algorithm to search the solution from a large state-action space, and propose an asynchronous advantage actor-critic (A3C)-based scheme to reduce the chance of converging to the suboptimal policy. Simulation results demonstrate that the A3C-based scheme is superior to the baseline schemes in term of the complexity, accumulated log spectral efficiency, and stability.
  • PHYSICAL AND FUNDAMENTALS
    Mathematical Modeling and Design of RIS Deployment in a RIS-Assisted Metal Cuboid for WAIC Systems
    2023, 20(4): 86-101. https://doi.org/10.23919/JCC.fa.2022-0652.202304
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    Wireless avionics intra-communications (WAIC) is an emergent research topic, since it can improve fuel efficiency and enhance aircraft safety significantly. However, there are numerous baffles in an aircraft, e.g., seats and cabin bulkheads, resulting in serious blockage and even destroying wireless communications. Thus, this paper focuses on the reconfigurable intelligent surface (RIS) deployment issue of RIS-assisted WAIC systems, to solve the blockage problem caused by baffles. We first propose the mirror-symmetric imaging principle for mathematically analyzing electromagnetic (EM) wave propagation in a metal cuboid, which is a typical structure of WAIC systems. Based on the mirror-symmetric imaging principle, the mathematical channel model in a metal cuboid is deduced in detail. In addition, we develop an objective function of RIS's location and deduce the optimal RIS deployment location based on the geometric center optimization lemma. A two-dimensional gravity center search algorithm is then presented. Simulation results show that the designed RIS deployment can greatly increase the received power and efficiently solve the blockage problem in the aircraft.
  • PHYSICAL AND FUNDAMENTALS
    DOA and Subarray-Interval Estimation for Arbitrarily Distributed UAV Swarm System
    2023, 20(4): 102-117. https://doi.org/10.23919/JCC.fa.2022-0533.202304
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    In this paper, DOA and subarray-interval estimation are considered and applied to arbitrarily distributed unmanned aerial vehicle (UAV) swarm system, in which multiple small UAVs containing uniform linear array (ULA) are divided by unknown intervals because of dynamic moving. Three parameters are taken to indicate the steering vector, namely, the direction of arrivals (DOAs) of target users, the intervals of UAVs, and the orientation angles of UAVs. The orientation angles are first estimated with an auxiliary user and the DOAs are obtained through a search free rooting method, despite the intervals among the UAVs. Afterwards, the intervals among UAVs can also be calculated via exhaustive when the number of target users are no less than three. We further develop a low-complex method to reduce the computational complexity during subarray-interval estimation. The deterministic Cramér-Rao bound (CRB) of the DOA, orientation angle and subarray-interval can be inferred in a closed form. Eventually, numerical instances are cited to verify the research results.
  • PHYSICAL AND FUNDAMENTALS
    Measurement-Based Channel Characterization in a Large Hall Scenario at 300 GHz
    2023, 20(4): 118-131. https://doi.org/10.23919/JCC.fa.2022-0286.202304
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    Sub-terahertz (Sub-THz), defined as the frequency bands in $100$-$300$ GHz, is promising for future generation communications and sensing applications. Accurate channel measurement and modeling are essential for development and performance evaluation of the future communication systems. Accurate channel modeling relies on realistic channel data, which should be collected by high-fidelity channel sounder. This paper presents the measurement-based channel characterization in a large indoor scenario at $299$-$301$ GHz. We firstly review the state-of-the-art channel measurements at sub-THz frequency bands. We then presented a VNA-based channel sounder for long-range measurements, which uses the radio-over-fiber techniques. Channel measurements using this channel sounder are conducted in a large hall scenario. Based on the measurement data, we calculated and analyzed key propagation channel parameters, e.g., path loss, delay spread, and angular spread. The results are also analyzed both in the line-of-sight (LoS) and none-LoS (NLoS) cases. The large delay components in the measurements demonstrate the possibility of the long-range channel measurement campaign at $300$ GHz.
  • PHYSICAL AND FUNDAMENTALS
    Modular Extremely Large-Scale Array Communication: Near-Field Modelling and Performance Analysis
    2023, 20(4): 132-152. https://doi.org/10.23919/JCC.fa.2022-0715.202304
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    This paper investigates the wireless communication with a novel architecture of antenna arrays, termed modular extremely large-scale array (XL-array), where array elements of an extremely large number/size are regularly mounted on a shared platform with both horizontally and vertically interlaced modules. Each module consists of a moderate/flexible number of array elements with the inter-element distance typically in the order of the signal wavelength, while different modules are separated by the relatively large inter-module distance for convenience of practical deployment. By accurately modelling the signal amplitudes and phases, as well as projected apertures across all modular elements, we analyse the near-field signal-to-noise ratio (SNR) performance for modular XL-array communications. Based on the non-uniform spherical wave (NUSW) modelling, the closed-form SNR expression is derived in terms of key system parameters, such as the overall modular array size, distances of adjacent modules along all dimensions, and the user's three-dimensional (3D) location. In addition, with the number of modules in different dimensions increasing infinitely, the asymptotic SNR scaling laws are revealed. Furthermore, we show that our proposed near-field modelling and performance analysis include the results for existing array architectures/modelling as special cases, e.g., the collocated XL-array architecture, the uniform plane wave (UPW) based far-field modelling, and the modular extremely large-scale uniform linear array (XL-ULA) of one-dimension. Extensive simulation results are presented to validate our findings.
  • PHYSICAL AND FUNDAMENTALS
    Physical-Layer Key Generation Based on Multipath Channel Diversity Using Dynamic Metasurface Antennas
    2023, 20(4): 153-166. https://doi.org/10.23919/JCC.fa.2022-0637.202304
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    Physical layer key generation (PKG) technology leverages the reciprocal channel randomness to generate the shared secret keys. The low secret key capacity of the existing PKG schemes is due to the reduction in degree-of-freedom from multipath fading channels to multipath combined channels. To improve the wireless key generation rate, we propose a multipath channel diversity-based PKG scheme. Assisted by dynamic metasurface antennas (DMA), a two-stage multipath channel parameter estimation algorithm is proposed to efficiently realize super-resolution multipath parameter estimation. The proposed algorithm first estimates the angle of arrival (AOA) based on the reconfigurable radiation pattern of DMA, and then utilizes the results to design the training beamforming and receive beamforming to improve the estimation accuracy of the path gain. After multipath separation and parameter estimation, multi-dimensional independent path gains are utilized for generating secret keys. Finally, we analyze the security and complexity of the proposed scheme and give an upper bound on the secret key capacity in the high signal-to-noise ratio (SNR) region. The simulation results demonstrate that the proposed scheme can greatly improve the secret key capacity compared with the existing schemes.
  • PHYSICAL AND FUNDAMENTALS
    Novel E2E-QoE Metric for PHY Optimization: A Cross-Layered Framework
    2023, 20(4): 167-179. https://doi.org/10.23919/JCC.fa.2022-0658.202304
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    Existing systems use key performance indicators (KPIs) as metrics for physical layer (PHY) optimization, which suffers from the problem of over-optimization, because some unnecessary PHY enhancements are imperceptible to terminal users and thus induce additional cost and energy waste. Therefore, it is necessary to utilize directly the quality of experience (QoE) of user as a metric of optimization, which can achieve the global optimum of QoE under cost and energy constraints. However, QoE is still a metric of application layer that cannot be easily used to design and optimize the PHY. To address this problem, we in this paper propose a novel end-to-end QoE (E2E-QoE) based optimization architecture at the user-side for the first time. Specifically, a cross-layer parameterized model is proposed to establish the relationship between PHY and E2E-QoE. Based on this, an E2E-QoE oriented PHY anomaly diagnosis method is further designed to locate the time and root cause of anomalies. Finally, we investigate to optimize the PHY algorithm directly based on the E2E-QoE. The proposed frameworks and algorithms are all validated using the data from real fifth-generation (5G) mobile system, which show that using E2E-QoE as the metric of PHY optimization is feasible and can outperform existing schemes.
  • PHYSICAL AND FUNDAMENTALS
    HY-PC: Enabling Consistent Positioning and Communication Using Visible Light
    2023, 20(4): 180-194. https://doi.org/10.23919/JCC.fa.2022-0731.202304
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    Visible light (VL) plays an important role in achieving high-precision positioning and low bit error radio (BER) data communication. However, most VL-based systems can not achieve positioning and communication, simultaneously. There are two problems: 1) the hybrid systems are difficult to extract distinguishable positioning beacon features without affecting communication performance, 2) in the hybrid systems, the lost data bits in the inter-frame gap (IFG) are hard to recover, which affects positioning and communication performance. Therefore, in this article, we propose a novel VL-based hybrid positioning and communication system, named HY-PC system, to solve the above problems. First, we propose the robust T-W mapping for recognizing specific Light Emitting Diodes (LEDs), which can provide stable LED recognition accuracy without adding extra beacon data and does not decrease the communication rate. Furthermore, we also propose the novel linear block coding and bit interleaving mechanism, which can recover the lost data bits in the IFG and improve data communication performance. Finally, we use commercial off-the-shelf devices to implement our HY-PC system, extensive experimental results show that our HY-PC system can achieve consistent high-precision positioning and low-BER data communication, simultaneously.
  • PHYSICAL AND FUNDAMENTALS
    Covert Communication with a Spectrum Sharing Relay in the Finite Blocklength Regime
    2023, 20(4): 195-211. https://doi.org/10.23919/JCC.fa.2022-0490.202304
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    In this paper, we investigate the feasibility and performance of the covert communication with a spectrum sharing relay in the finite blocklength regime. Specifically, the relay opportunistically forwards the source's messages to the primary receiver or conveys the covert messages to its own receiver via the sharing spectrum, while the warden attempts to detect the transmission. First, we derive a lower bound on the covertness constraint, and the analytical expressions of both the primary average effective covert throughput (AECT) and sum AECT are presented by considering the overall decoding error performance. Then, we formulate two optimization problems to maximize the primary and sum AECT respectively by optimizing the blocklength and the transmit power at the source and the relay. Our examinations show that there exists an optimal blocklength to maximize the primary and sum AECT. Besides, it is revealed that, to maximize the primary AECT, the optimal transmit power of each hop increases as its channel quality deteriorates. Furthermore, in the optimization for maximizing the sum AECT, the optimal transmit power at the source equals to zero when the channel quality from relay to the secondary receiver is not weaker than that from relay to the primary receiver.
  • PHYSICAL AND FUNDAMENTALS
    A Trusted Multi-Task Distribution Mechanism for Internet of Vehicles Based on Smart Contract
    2023, 20(4): 212-226. https://doi.org/10.23919/JCC.fa.2022-0622.202304
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    In this paper, a trusted multi-task distribution mechanism for Internet of Vehicles based on smart contract is proposed to improve the security and efficiency for the task distribution in Internet of Vehicles. Firstly, a three-tier trusted multi-task distribution framework is presented based on smart contract. The smart contract will be triggered by the task request. As the important part of the smart contract, the task distribution algorithm is stored on the blockchain and run automatically. In the process of the task distribution, the cost of the task distribution and the system stability play a critical role. Therefore, the task distribution problem is formulated to minimize the cost of the task distribution whilst maintaining the stability of the system based on Lyapunov theorem. Unfortunately, this problem is a mixed integer nonlinear programming problem with NP-hard characteristics. To tackle this, the optimization problem is decomposed into two sub problems of computing resource allocation and task distribution decision, and an effective task distribution algorithm is proposed. Simulation results show that the proposed algorithm can effectively improves system performance.
  • PHYSICAL AND FUNDAMENTALS
    NOMA-Based Spectrum Sensing for Satellite-Terrestrial Communication
    2023, 20(4): 227-242. https://doi.org/10.23919/JCC.fa.2022-0571.202304
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    With the continuous development of wireless communication technology, the number of access devices continues to soar, which poses a grate challenge to the already scarce spectrum resources. Meanwhile, 6G will be an era of air-space-terrestrial-sea integration, and satellite spectrum resources are also very tight in the context of giant constellations. In this paper, we propose a Non-Orthogonal Multiple Access (NOMA) based spectrum sensing scheme for the future satellite-terrestrial communication scenarios, and design the transceiver from uplink and downlink scenarios, respectively. In order to better identify the user's transmission status, we obtain the feature values of each user through feature detection to make decision. We combine these two technologies to design the transceiver architecture and deduce the threshold value of feature detection in the satellite-terrestrial communication scenario. Simulations are performed in each scenario, and the results illustrate that the proposed scheme combining NOMA and spectrum sensing can greatly improve the throughput with a similar detection probability as Orthogonal Multiple Access (OMA).
    • EMERGING TECHNOLOGIES AND SERVICES
  • EMERGING TECHNOLOGIES AND SERVICES
    Multi-Sinusoidal Waveform Shaping for Integrated Data and Energy Transfer in Aging Channels
    2023, 20(4): 243-258. https://doi.org/10.23919/JCC.fa.2022-0419.202304
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    Integrated data and energy transfer (IDET) is capable of simultaneously delivering on-demand data and energy to low-power Internet of Everything (IoE) devices. We propose a multi-carrier IDET transceiver relying on superposition waveforms consisting of multi-sinusoidal signals for wireless energy transfer (WET) and orthogonal-frequency-division-multiplexing (OFDM) signals for wireless data transfer (WDT). The outdated channel state information (CSI) in aging channels is employed by the transmitter to shape IDET waveforms. With the constraints of transmission power and WDT requirement, the amplitudes and phases of the IDET waveform at the transmitter and the power splitter at the receiver are jointly optimised for maximising the average direct-current (DC) among a limited number of transmission frames with the existence of carrier-frequency-offset (CFO). For the amplitude optimisation, the original non-convex problem can be transformed into a reversed geometric programming problem, then it can be effectively solved with existing tools. As for the phase optimisation, the artificial bee colony (ABC) algorithm is invoked in order to deal with the non-convexity. Iteration between the amplitude optimisation and phase optimisation yields our joint design. Numerical results demonstrate the advantage of our joint design for the IDET waveform shaping with the existence of the CFO and the outdated CSI.
  • EMERGING TECHNOLOGIES AND SERVICES
    Deep Reinforcement Learning Based Power Minimization for RIS-Assisted MISO-OFDM Systems
    2023, 20(4): 259-269. https://doi.org/10.23919/JCC.fa.2022-0421.202304
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    In this paper, we investigate the downlink orthogonal frequency division multiplexing (OFDM) transmission system assisted by reconfigurable intelligent surfaces (RISs). Considering multiple antennas at the base station (BS) and multiple single-antenna users, the joint optimization of precoder at the BS and the phase shift design at the RIS is studied to minimize the transmit power under the constraint of the certain quality-of-service. A deep reinforcement learning (DRL) based algorithm is proposed, in which maximum ratio transmission (MRT) precoding is utilized at the BS and the twin delayed deep deterministic policy gradient (TD3) method is utilized for RIS phase shift optimization. Numerical results demonstrate that the proposed DRL based algorithm can achieve a transmit power almost the same with the lower bound achieved by manifold optimization (MO) algorithm while has much less computation delay.
  • EMERGING TECHNOLOGIES AND SERVICES
    PGSLM: Edge-Enabled Probabilistic Graph Structure Learning Model for Traffic Forecasting in Internet of Vehicles
    2023, 20(4): 270-286. https://doi.org/10.23919/JCC.fa.2022-0489.202304
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    With the rapid development of the 5G communications, the edge intelligence enables Internet of Vehicles (IoV) to provide traffic forecasting to alleviate traffic congestion and improve quality of experience of users simultaneously. To enhance the forecasting performance, a novel edge-enabled probabilistic graph structure learning model (PGSLM) is proposed, which learns the graph structure and parameters by the edge sensing information and discrete probability distribution on the edges of the traffic road network. To obtain the spatio-temporal dependencies of traffic data, the learned dynamic graphs are combined with a predefined static graph to generate the graph convolution part of the recurrent graph convolution module. During the training process, a new graph training loss is introduced, which is composed of the K nearest neighbor (KNN) graph constructed by the traffic feature tensors and the graph structure. Detailed experimental results show that, compared with existing models, the proposed PGSLM improves the traffic prediction performance in terms of average absolute error and root mean square error in IoV.
  • EMERGING TECHNOLOGIES AND SERVICES
    Minimizing Age of Information in the Uplink Multi-User Networks via Dynamic Bandwidth Allocation
    2023, 20(4): 287-301. https://doi.org/10.23919/JCC.fa.2022-0586.202304
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    Improving the information freshness is critical for the monitoring and controlling applications in the cellular Internet of Things (IoT). In this paper, we are interested in optimizing the bandwidth allocation dynamically to improve the information freshness of the short packet based uplink status updates, which is characterized by a recently proposed metric, age of information (AoI). We first design a status update scheme with channel distribution information (CDI). By relaxing the hard bandwidth constraint and introducing a Lagrangian multiplier, we first decouple the multi-MTCD bandwidth allocation problem into a single MTCD Markov decision process (MDP). Under the MDP framework, after variable substitution, we obtain the single-MTCD status update scheme by solving a linear programming problem. Then, we adjust the Lagrangian multiplier to make the obtained scheme satisfy the relaxed bandwidth constraint. Finally, a greedy policy is built on the proposed scheme to adjust the bandwidth allocation in each slot to satisfy the hard bandwidth constraint. In the unknown environment without CDI, we further design a bandwidth allocation scheme which only maximizes the expected sum AoI drop within each time slot. Simulation results show that in terms of AoI, the proposed schemes outperform the benchmark schemes.
  • EMERGING TECHNOLOGIES AND SERVICES
    Detecting While Accessing: A Semi-Supervised Learning-Based Approach for Malicious Traffic Detection in Internet of Things
    2023, 20(4): 302-314. https://doi.org/10.23919/JCC.fa.2022-0783.202304
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    In the upcoming large-scale Internet of Things (IoT), it is increasingly challenging to defend against malicious traffic, due to the heterogeneity of IoT devices and the diversity of IoT communication protocols. In this paper, we propose a semi-supervised learning-based approach to detect malicious traffic at the access side. It overcomes the resource-bottleneck problem of traditional malicious traffic defenders which are deployed at the victim side, and also is free of labeled traffic data in model training. Specifically, we design a coarse-grained behavior model of IoT devices by self-supervised learning with unlabeled traffic data. Then, we fine-tune this model to improve its accuracy in malicious traffic detection by adopting a transfer learning method using a small amount of labeled data. Experimental results show that our method can achieve the accuracy of 99.52% and the F1-score of 99.52% with only 1% of the labeled training data based on the CICDDoS2019 dataset. Moreover, our method outperforms the state-of-the-art supervised learning-based methods in terms of accuracy, precision, recall and F1-score with 1% of the training data.
    • MAC AND NETWORKS
  • MAC AND NETWORKS
    From Earth to Space: A First Deployment of 5G Core Network on Satellite
    2023, 20(4): 315-325. https://doi.org/10.23919/JCC.2023.00.001
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    Recent developments in the aerospace industry have led to a dramatic reduction in the manufacturing and launch costs of low Earth orbit satellites. The new trend enables the paradigm shift of satellite-terrestrial integrated networks with global coverage. In particular, the integration of 5G communication systems and satellites has the potential to restructure next-generation mobile networks. By leveraging the network function virtualization and network slicing, the satellite 5G core networks will facilitate the coordination and management of network functions in satellite-terrestrial integrated networks. We are the first to deploy a 5G core network on a real-world satellite to investigate its feasibility. We conducted experiments to validate the satellite 5G core network functions. The validated procedures include registration and session setup procedures. The results show that the satellite 5G core network can function normally and generate correct signaling.
  • MAC AND NETWORKS
    Task Offloading and Resource Allocation for Edge-Enabled Mobile Learning
    2023, 20(4): 326-339. https://doi.org/10.23919/JCC.fa.2022-0521.202304
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    Mobile learning has evolved into a new format of education based on communication and computer technology that is favored by an increasing number of learning users thanks to the development of wireless communication networks, mobile edge computing, artificial intelligence, and mobile devices. However, due to the constrained data processing capacity of mobile devices, efficient and effective interactive mobile learning is a challenge. Therefore, for mobile learning, we propose a "Cloud, Edge and End" fusion system architecture. Through task offloading and resource allocation for edge-enabled mobile learning to reduce the time and energy consumption of user equipment. Then, we present the proposed solutions that uses the minimum cost maximum flow (MCMF) algorithm to deal with the offloading problem and the deep Q network (DQN) algorithm to deal with the resource allocation problem respectively. Finally, the performance evaluation shows that the proposed offloading and resource allocation scheme can improve system performance, save energy, and satisfy the needs of learning users.
  • MAC AND NETWORKS
    Dynamic QoS Mapping and Adaptive Semi-Persistent Scheduling in 5G-TSN Integrated Networks
    2023, 20(4): 340-355. https://doi.org/10.23919/JCC.fa.2022-0548.202304
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    The ubiquitous and deterministic communication systems are becoming indispensable for future vertical applications such as industrial automation systems and smart grids. 5G-TSN (Time-Sensitive Networking) integrated networks with the 5G system (5GS) as a TSN bridge are promising to provide the required communication service. To guarantee the end-to-end (E2E) QoS (Quality of Service) performance of traffic is a great challenge in 5G-TSN integrated networks. A dynamic QoS mapping method is proposed in this paper. It is based on the improved K-means clustering algorithm and the rough set theory (IKC-RQM). The IKC-RQM designs a dynamic and load-aware QoS mapping algorithm to improve its flexibility. An adaptive semi-persistent scheduling (ASPS) mechanism is proposed to solve the challenging deterministic scheduling in 5GS. It includes two parts: one part is the persistent resource allocation for time-sensitive flows, and the other part is the dynamic resource allocation based on the max-min fair share algorithm. Simulation results show that the proposed IKC-RQM algorithm achieves flexible and appropriate QoS mapping, and the ASPS performs corresponding resource allocations to guarantee the deterministic transmissions of time-sensitive flows in 5G-TSN integrated networks.
  • MAC AND NETWORKS
    AUV-Aided Data Collection Considering Adaptive Ocean Currents for Underwater Wireless Sensor Networks
    2023, 20(4): 356-367. https://doi.org/10.23919/JCC.fa.2022-0608.202304
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    Autonomous underwater vehicle (AUV)-assisted data collection is an efficient approach to implementing smart ocean. However, the data collection in time-varying ocean currents is plagued by two critical issues: AUV yaw and sensor node movement. We propose an adaptive AUV-assisted data collection strategy for ocean currents to address these issues. First, we consider the energy consumption of an AUV in conjunction with the value of information (VoI) over the sensor nodes and formulate an optimization problem to maximize the VoI-energy ratio. The AUV yaw problem is then solved by deriving the AUV's reachable region in different ocean current environments and the optimal cruising direction to the target nodes. Finally, using the predicted VoI-energy ratio, we sequentially design a distributed path planning algorithm to select the next target node for AUV. The simulation results indicate that the proposed strategy can utilize ocean currents to aid AUV navigation, thereby reducing the AUV's energy consumption and ensuring timely data collection.
  • MAC AND NETWORKS
    Online Intrusion Detection Mechanism Based on Model Migration in Intelligent Pumped Storage Power Stations
    2023, 20(4): 368-381. https://doi.org/10.23919/JCC.fa.2022-0690.202304
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    With the continuous integration of new energy into the power grid, various new attacks continue to emerge and the feature distributions are constantly changing during the deployment of intelligent pumped storage power stations. %Therefore, the intrusion detection model trained from previous data is difficult to effectively identify new attacks. Moreover, obtaining large amounts of labeled data is laborious and time consuming. As such, it is difficult to establish a well-performing intrusion detection model due to the lack of new labeled data, and the model cannot be updated in time when the data arrives online. The intrusion detection model trained on the old data is hard to effectively identify new attacks, and it is difficult to update the intrusion detection model in time when lacking data. To solve this issue, by using model-based transfer learning methods, in this paper we propose a convolutional neural network (CNN) based transfer online sequential extreme learning machine (TOS-ELM) scheme to enable the online intrusion detection, which is called CNN-TOSELM in this paper. %CNN-TOSELM combines the feature extraction ability of the pre-trained CNN and the online migration function of TOS-ELM, which can utilize the source domain model knowledge and realize the real-time update and detection of the attacks. In our proposed scheme, we use pre-trained CNN to extract the characteristics of the target domain data as input, and then build online learning classifier TOS-ELM to transfer the parameter of the ELM classifier of the source domain. Experimental results show the proposed CNN-TOSELM scheme can achieve better detection performance and extremely short model update time for intelligent pumped storage power stations.
  • MAC AND NETWORKS
    Wireless Interference Classification with Low Complexity Multi-Branch Networks
    2023, 20(4): 382-394. https://doi.org/10.23919/JCC.fa.2022-0692.202304
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    In non-cooperative communication systems, wireless interference classification (WIC) is one of the most essential technologies. Recently, deep learning (DL) based WIC methods have been proposed. However, conventional DL-based WIC methods have high computational complexity and unsatisfactory accuracy, especially when the interference-to-noise ratio (INR) is low. To this end, we propose three effective approaches. Firstly, we introduce multi-branch convolutional neural networks (CNNs) for interference recognition. The multi-branch CNN is constructed by repeating a layer that aggregates several transformations with the same topology, and it notably improves the recognition ability for WIC. Our design avoids the carefully crafted selection of each transformation. Unfortunately, multi-branch CNNs are computationally expensive and memory-inefficient. To this end, we further propose Low complexity multi-branch networks (LCMN), which are mathematically equivalent to multi-branch CNNs but maintain low computing costs and efficient inference. Thirdly, we present novel loss function, which encourages networks to have consistent prediction probabilities for samples with high visual similarities, resulting in increasing recognition accuracy of LCMN. Experimental results demonstrate the proposed methods consistently boost the classification performance of WIC without substantially increasing computational overhead compared to traditional DL-based methods.