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    THEORIES & SYSTEMS
  • THEORIES & SYSTEMS
    Capacity and Flexibility Improvement of Traffic Aggregation for Fixed 5G: Key Enabling Technologies, Challenges and Trends
    Jing Zhang, Tianhai Chang, Zhuqiang Zhong, Xianqing Jin, Shaohua Hu, Taowei Jin, Jiahao Zhou, Mingyue Zhu, Yi Yu, Jianming Tang, Liangchuan Li, Kun Qiu
    2022, 19(12): 1-13.
    Abstract ( ) Download PDF ( )   Knowledge map   Save
    As the emergence of various high-bandwidth services and the requirements to support 5G/Wi-Fi 6 wireless networks, the next generation fixed networks, i.e. F5G, are expected to be realized in the 5G era. F5G is endowed with new characteristics, including ultra-high bandwidth, all-optical connections and optimal service experience. With the prospect of optical-to-everywhere, optical technologies are used for mobile front-haul, mid-haul, and back-haul. Optical access networks would play an important role in F5G to support radio access network and fixed access network. Low-latency PON is a key for cost effective-haul traffic aggregation. In terms of signal transmission, intensity modulation direct-detection (IM-DD) is a promising scheme due to its simple architecture. The fundamental challenge associated with direct-detection is the disappearance of the transmitted signal’s phase. In access network, the flexibility and low latency are the two key factors affecting service experience. In this article, we review the evolution of PONs and the challenges of current PONs in detail. We analyze key enabling digital signal processing (DSP) techniques, including detection linearization for direct-detection and simplified coherent detection, adaptive equalizers, digital filer enabled flexible access network and low-latency inter-ONU communications. Finally, we discuss the developing trends of future optical access networks.
  • THEORIES & SYSTEMS
    Multi-Hop Transmission Method of 5G Millimeter Wave Relay Based on Network Coding
    Yilin Wang, Weisheng He, Xuwei Fan, Lianfen Huang, Jie Yang, Yuliang Tang
    2022, 19(12): 14-26.
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    With the rapid development of the Internet technology, millimeter wave (mmWave) will be used as a supplement to 5G low frequency bands to meet the extremely high system capacity requirements of 5G in hot spots. Although 5G mmWave communication can adapt to the needs of 5G network and carry a large amount of transmitted data, transmission stability has become one of the key technical issues of 5G network mmWave communication due to problems such as strong attenuation and poor penetration of mmWave. In order to improve the efficiency of the mmWave multi-hop transmission, we propose a 5G mmWave multi-hop transmission method based on network coding, which can adapt to the current wireless network environment, improve spectrum efficiency and increase network throughput. Based on MATLAB simulation experiments, it is verified that the proposed method can greatly improve the transmission efficiency and reduce the signal loss under the premise of ensuring the accurate signal transmission.
  • THEORIES & SYSTEMS
    Wireless Communication Based on Microwave Photon-Level Detection with Superconducting Devices: Achievable Rate Prediction
    Junyu Zhang, Chen Gong, Shangbin Li, Rui Ni, Chengjie Zuo, Jinkang Zhu, Ming Zhao, Zhengyuan Xu
    2022, 19(12): 27-40.
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    Future wireless communication system embraces physical-layer signal detection with high sensitivity, especially in the microwave photon level. Currently, the receiver primarily adopts the signal detection based on semi-conductor devices for signal detection, while this paper introduces high-sensitivity photon-level microwave detection based on superconducting structure. We first overview existing works on the photon-level communication in the optical spectrum as well as the microwave photon-level sensing based on superconducting structure in both theoretical and experimental perspectives, including microwave detection circuit model based on Josephson junction, microwave photon counter based on Josephson junction, and two reconstruction approaches under background noise.In addition, we characterize channel modeling based on two different microwave photon detection approaches, including the absorption barrier and the dual-path Handury Brown-Twiss (HBT) experiments, and predict the corresponding achievable rates. According to the performance prediction, it is seen that the microwave photon-level signal detection can increase the receiver sensitivity compared with the state-of-the-art standardized communication system with waveform signal reception, with gain over 10 dB.
  • THEORIES & SYSTEMS
    RIS-Assisted Communications in Correlated Rayleigh Channels: Comparison of Training Overhead Reduction Schemes
    Jue Wang, Shuaifeng Lu, JingWang, Zhenyu Jiang, Rui Tang, Ruifeng Gao, Yingdong Hu, Ye Li
    2022, 19(12): 41-53.
    Abstract ( )   Knowledge map   Save
    Channel training in reconfigurable intelligent surface (RIS)-assisted communications is usually conducted in an on-off manner, resulting in unaffordable training time overhead when the number of RIS elements is large. In this paper, for correlated Rayleigh channels, we compare three typical training overhead reduction schemes, namely RIS element selection (Scheme 1), element grouping (Scheme 2), and statistical CSI-based phase shifts design (Scheme 3). For Scheme 1 and Scheme 2, we propose two algorithms to select RIS elements (or form element groups) and determine the optimal number of activated elements (or formed groups), based on the channel correlation information only; for Scheme 3, we consider a semi-definite programming-based approach in the literature, and propose an alternative dominant eigenvector-based method for determining the RIS phase shifts vector. Via extensive simulations, we compare the achievable ergodic rates of these schemes versus the signal-to-noise ratio, the channel correlation level, and the element number-to-coherent time ratio, respectively, and discuss possible switching of the three schemes over these system parameters. At last, operation regions of the considered training overhead reduction schemes are shown in the plane characterized by the system parameters, which provides useful guidelines for practical scheme determination.
  • THEORIES & SYSTEMS
    A DTN Oriented Adaptive Routing Algorithm Based on Node Load
    Hongyan Cui, Diyue Chen, Roy E.Welsch
    2022, 19(12): 54-63.
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    In recent years, Delay Tolerant Networks (DTN) have received more and more attention. At the same time, several existing DTN routing algorithms generally have disadvantages such as poor scalability and inability to perceive changes in the network environment. This paper proposes an AdaptiveSpray routing algorithm. The algorithm can dynamically control the initial maximum message copy number according to the cache occupancy rate of the node itself, and the cache occupancy rate is added as an impact factor to the calculation of the probability of each node meeting the destination node. In the forwarding phase, the node will first compare the meeting probability of itself and the meeting node to the destination node, and then choose different forwarding strategies. The simulation shows that the AdaptiveSpray algorithm proposed in this paper has obvious advantages compared with the existing routing algorithms in terms of message delivery rate and average delay.
  • THEORIES & SYSTEMS
    A Novel Method for Identifying Recursive Systematic Convolutional Encoders Based on the Cuckoo Search Algorithm
    Shunan Han, Peng Liu, Guang Huang
    2022, 19(12): 64-72.
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    The existing methods for identifying recursive systematic convolutional encoders with high robustness require to test all the candidate generator matrixes in the search space exhaustively. With the increase of the codeword length and constraint length, the search space expands exponentially, and thus it limits the application of these methods in practice. To overcome the limitation, a novel identification method, which gets rid of exhaustive test, is proposed based on the cuckoo search algorithm by using soft-decision data. Firstly, by using soft-decision data, the probability that a parity check equation holds is derived. Thus, solving the parity check equations is converted to maximize the joint probability that parity check equations hold. Secondly, based on the standard cuckoo search algorithm, the established cost function is optimized. According to the final solution of the optimization problem, the generator matrix of recursive systematic convolutional code is estimated. Compared with the existing methods, our proposed method does not need to search for the generator matrix exhaustively and has high robustness. Additionally, it does not require the prior knowledge of the constraint length and is applicable in any modulation type.
  • THEORIES & SYSTEMS
    ADC-DL: Communication-Efficient Distributed Learning with Hierarchical Clustering and Adaptive Dataset Condensation
    Zhipeng Gao, Yan Yang, Chen Zhao, Zijia Mo
    2022, 19(12): 73-85.
    Abstract ( )   Knowledge map   Save
    The rapid growth of modern mobile devices leads to a large number of distributed data, which is extremely valuable for learning models. Unfortunately, model training by collecting all these original data to a centralized cloud server is not applicable due to data privacy and communication costs concerns, hindering artificial intelligence from empowering mobile devices. Moreover, these data are not identically and independently distributed (Non-IID) caused by their different context, which will deteriorate the performance of the model. To address these issues, we propose a novel Distributed Learning algorithm based on hierarchical clustering and Adaptive Dataset Condensation, named ADC-DL, which learns a shared model by collecting the synthetic samples generated on each device. To tackle the heterogeneity of data distribution, we propose an entropy topsis comprehensive tiering model for hierarchical clustering, which distinguishes clients in terms of their data characteristics. Subsequently, synthetic dummy samples are generated based on the hierarchical structure utilizing adaptive dataset condensation. The procedure of dataset condensation can be adjusted adaptively according to the tier of the client. Extensive experiments demonstrate that the performance of our ADC-DL is more outstanding in prediction accuracy and communication costs compared with existing algorithms.
  • THEORIES & SYSTEMS
    Formulation of Optimization Problems with Radiation Field Phases as Design Variables for Pattern Synthesis of Linear Antenna Arrays
    Fei Zhao, Jun He, Sanyou Zeng, Changhe Li, Qinghui Xu, Zhigao Zeng
    2022, 19(12): 86-100.
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    Pattern synthesise of antenna arrays is usually complicated optimization problems, while evolutionary algorithms (EAs) are promising in solving these problems. This paper does not propose a new EA, but does construct a new form of optimization problems. The new optimization formulation has two differences from the common ones. One is the objective function is the field error between the desired and the designed, not the usual amplitude error between the desired and the designed. %approach the given radiation field with both information of magnitudes and phases, instead of that with only the information of magnitudes. This difference is beneficial to decrease complexity in some sense. The second difference is that the design variables are changed as phases of desired radiation field within shaped-region, instead of excitation parameters. This difference leads to the reduction of the number of design variables. A series of synthesis experiments including equally and unequally spaced linear arrays with different pattern shape requirements are applied, and the effectiveness and advantages of the proposed new optimization problems are validated. The results show that the proposing a new optimization formulation with less complexity is as significant as proposing a new algorithm.
    • NETWORKS & SECURITY
  • NETWORKS & SECURITY
    Plume: Lightweight and Generalized Congestion Control with Deep Reinforcement Learning
    DehuiWei, Jiao Zhang, Xuan Zhang, Chengyuan Huang
    2022, 19(12): 101-117.
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    Congestion control (CC) is always an important issue in the field of networking, and the enthusiasm for its research has never diminished in both academia and industry. In current years, due to the rapid development of machine learning (ML), the combination of reinforcement learning (RL) and CC has a striking effect. However, These complicated schemes lack generalization and are too heavyweight in storage and computing to be directly implemented in mobile devices. In order to address these problems, we propose Plume, a high-performance, lightweight and generalized RL-CC scheme. Plume proposes a lightweight framework to reduce the overheads while preserving the original performance. Besides, Plume innovatively modifies the framework parameters of the reward function during the retraining process, so that the algorithm can be applied to a variety of scenarios. Evaluation results show that Plume can retain almost all the performance of the original model but the size and decision latency can be reduced by more than 50\% and 20\%, respectively. Moreover, Plume has better performances in some special scenes.
  • NETWORKS & SECURITY
    A Successful Transmission Probability Based AP Clustering Scheme in User-Centric Cell-Free Networks
    Xiaokang Zhou, Huiyun Xia, ShaochuanWu
    2022, 19(12): 118-128.
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    Cell-free network is a promising architecture with numerous merits in energy efficiency and macro diversity, which is easy and flexible to integrate with other communication technologies. However, its current network topology where access points (APs) are connected to a central processing unit (CPU) to jointly serve the users, causes huge burden to the fronthaul network. To deal with this problem, in this paper, we first combine thoughts in user-centric (UC) network where users are served by selected subset of APs. Then, we propose a successful transmission probability (STP) based AP clustering scheme to reduce the fronthaul capacity requirement (FCR). By using stochastic geometry and proper approximation methods, the approximated STP calculation expression is derived. Numerical simulations demonstrate that the obtained STP expression can provide a tight approximation compared to Monte Carlo simulation results under different system parameters while keeping the computation tractable. Furthermore, the relationship between the FCR and the STP threshold is formulated as a clustering optimization problem, which gives insights on clustering design in UC-CF network systems. We show by simulation results that the proposed scheme requires less fronthaul capacity than the original CF approach while ensuring the STP performance.
  • NETWORKS & SECURITY
    Unlicensed Band Sharing Based on Bargaining Game for Heterogeneous Networks
    Haixia Cui, Shujie Zou
    2022, 19(12): 129-141.
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    Currently, limited licensed frequency bands cannot meet the increasing demands for various wireless communication applications any more. It is necessary to extend wireless communication networks to unlicensed spectrum. In this paper, we propose a new bargaining framework for unlicensed band access to achieve high spectrum efficiency, where one radio access technology (RAT) (such as macro cellular network) ``competes'' the unlicensed bands with multiple other RATs (such as small cellular networks or Wi-Fi) virtually. Considering that macro cell can share unlicensed frequencies with multiple small cells which are in the same coverage area for more freedom, we use bargaining game theory to fairly and effectively share the unlicensed spectrum between macro and multiple heterogeneous small cell networks, where bargaining loss and time dissipation loss for virtual ``price'' of unlicensed bands are mainly considered. In the one-to-many bargaining process, we also develop a multiple RAT alliance game strategy to reduce transmission loss in a joint manner. Simulation results show that the proposed unlicensed band sharing algorithm significantly improves the spectrum efficiency performance compared with the other practical schemes for heterogeneous networks.
  • NETWORKS & SECURITY
    Joint Optimization of Task Caching, Computation Offloading and Resource Allocation for Mobile Edge Computing
    Zhixiong Chen, Zhengchuan Chen, Zhi Ren, Liang Liang, WanliWen, Yunjian Jia
    2022, 19(12): 142-159.
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    Applications with sensitive delay and sizeable data volumes, such as interactive gaming and augmented reality, have become popular in recent years. These applications pose a huge challenge for mobile users with limited resources. Computation offloading is a mainstream technique to reduce execution delay and save energy for mobile users. However, computation offloading requires communication between mobile users and mobile edge computing (MEC) servers. Such a mechanism would difficultly meet users' demand in some data-hungry and computation-intensive applications because the energy consumption and delay caused by transmissions are considerable expenses for users. Caching task data can effectively reduce the data transmissions when users offload their tasks to the MEC server. The limited caching space at the MEC server calls for judiciously decide which tasks should be cached. Motivated by this, we consider the joint optimization of computation offloading and task caching in a cellular network. In particular, it allows users to proactively cache or offload their tasks at the MEC server. The objective of this paper is to minimize the system cost, which is defined as the weighted sum of task execution delay and energy consumption for all users. Aiming at establishing optimal performance bound for the system design, we formulate an optimization problem by jointly optimizing the task caching, computation offloading, and resource allocation. The problem is a challenging mixed-integer non-linear programming problem and is NP-hard in general. To solve it efficiently, by using convex optimization, Karmarkar's algorithm and the proposed fast search algorithm, we obtain an optimal solution of the formulated problem with manageable computational complexity. Extensive simulation results show that in comparison to some representative benchmark methods, the proposed solution can effectively reduce the system cost.
  • NETWORKS & SECURITY
    A Resource-Based Pricing Collaborative Approach for Mitigating DDoS Attack in Mobile Edge Computing
    Lu Chen, Hongbo Tang, Wei You, Yi Bai
    2022, 19(12): 160-175.
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    Resource-constrainted and located closer to users, edge servers are more vulnerable to Distributed Denial of Service (DDoS) attacks. In order to mitigate the impact of DDoS attacks on benign users, this paper designed a Resource-based Pricing Collaborative approach (RPC) in mobile edge computing. By introducing the influence of resource prices on requester in economics, a collaboration model based on resource pricing was established, and the allocation of user request was regarded as a game strategy to obtain the overall minimum offloading cost of the user in network. The article theoretically proved the existence and rationality of the Nash equilibrium. Finally, simulation results verified the effectiveness and feasibility of the proposed approach in two experimental scenes. Experimental results shows that RPC can effectively improve the network ability to mitigate DDoS attacks, and alleviate the adverse effects of server attacks under delay constraints.
    • EMERGING TECHNOLOGIES & APPLICATIONS
  • EMERGING TECHNOLOGIES & APPLICATIONS
    PS-G-GERT Effectiveness Evaluation Model of Link-Based GEO Satellite Communication Constellation under Poor Information Background
    Ruirui Shao, Zhigeng Fang, Su Gao, Sifeng Liu, Weiqing You
    2022, 19(12): 176-196.
    Abstract ( )   Knowledge map   Save
    The effectiveness evaluation of GEO satellite communication constellation not only characterizes the communication capability of the constellation but also provides a basis for optimizing the constellation structure. Whether due to information poverty or the complexity of the system, the impact of uncertain information on the effectiveness evaluation needs to be considered to ensure the accuracy of the evaluation results. To address this issue, this paper develops a model for evaluating the GEO satellite communication constellation's effectiveness in the context of poor information. Firstly, it analyses the GEO satellite communication constellation plus system based on communication links with an in-depth analysis of the constellation structure. Secondly, an equivalent transfer function algorithm based on the characteristic function and transfer probability is proposed with the help of graphical evaluation and review technique. Then, by analyzing the communication link importance connotation, the algorithm formula of communication link effectiveness and its importance is derived, and the constellation effectiveness and variance are found. Finally, the model constructed in this paper is used to evaluate the effectiveness of a GEO satellite communication constellation, further verifying the accuracy and validity of the model. Through comparative analysis, it is shown that the model can not only solving the effectiveness of the constellation but also analyzing the variation of its effectiveness. It lays a theoretical foundation for the analysis and optimization of the GEO satellite communication constellation effectiveness.
  • EMERGING TECHNOLOGIES & APPLICATIONS
    Selective PNC in Satellite-Terrestrial Network over Non-Identical Fading Channels
    Zhen Li, Mingchuan Yang, GangWang, Donglai Zhao
    2022, 19(12): 197-206.
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    We analyze the performance of a two-way satellite-terrestrial decode-and-forward (DF) relay network over non-identical fading channels. In particular, selective physical-layer network coding (SPNC) is employed in the proposed network to improve the average end-to-end throughput performance. More specifically, by assuming that the DF relay performs instantaneous throughput comparisons before performing corresponding protocols, we derive the expressions of system instantaneous bit-error-rate (BER), instantaneous end-to-end throughput, average end-to-end throughput, single node detection (SND) occurrence probability and average end-to-end BER over non-identical fading channels. Finally, theoretical analyses and Monte Carlo simulation results are presented. Evaluations show that: 1) SPNC protocol outperforms the conventional physical-layer network coding (PNC) protocol in infrequent light shadowing (ILS), average shadowing (AS) and frequent heavy shadowing (FHS) Shadowed-Rician fading channels. 2) As the satellite-relay channel fading gets more severe, SPNC protocol can achieve more performance improvement than PNC protocol and the occurrence probability of SND protocol increases progressively. 3) The occurrence probability increase of SND has a beneficial effect on the average end-to-end throughput in low signal-to-noise ratio (SNR) regime, while the occurrence probability decrease of SND has a beneficial effect on the average end-to-end BER in high-SNR regime.
  • EMERGING TECHNOLOGIES & APPLICATIONS
    LDM-Satellite: A New Scheme for Packet Loss Classification over LEO Satellite Network
    Ning Li, Qiaodi Zhu, Zhongliang Deng
    2022, 19(12): 207-215.
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    The packet loss classification has always been a hot and difficult issue in TCP congestion control research. Compared with the terrestrial network, the probability of packet loss in LEO satellite network increases dramatically. What’s more, the problem of concept drifting is also more serious, which greatly affects the accuracy of the loss classification model. In this paper, we propose a new loss classification scheme based on concept drift detection and hybrid integration learning for LEO satellite networks, named LDM-Satellite, which consists of three modules: concept drift detection, lost packet cache and hybrid integration classification. As far, this is the first paper to consider the influence of concept drift on the loss classification model in satellite networks. We also innovatively use multiple base classifiers and a naive Bayes classifier as the final hybrid classifier. And a new weight algorithm for these classifiers is given. In ns-2 simulation, LDM-Satellite has a better AUC (0.9885) than the single-model machine learning classification algorithms. The accuracy of loss classification even exceeds 98\%, higher than traditional TCP protocols. Moreover, compared with the existing protocols used for satellite networks, LDM-Satellite not only improves the throughput rate but also has good fairness.
  • EMERGING TECHNOLOGIES & APPLICATIONS
    Energy-Efficient Full-Duplex UAV Relaying with Trajectory Optimization and Power Control in Maritime Communication Environments
    Lili Guo, Xiaodong Ji, Shibing Zhang
    2022, 19(12): 216-231.
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    This paper solves an energy-efficient optimization problem of a fixed-wing unmanned aerial vehicle (UAV) assisted full-duplex mobile relaying in maritime communication environments. Taking the speed and the acceleration of the UAV and the information-causality constraints into consideration, the energy-efficiency of the system under investigation is maximized by jointly optimizing the UAV’s trajectory and the individual transmit power levels of the source and the UAV relay nodes. The optimization problem is non-convex and thus cannot be solved directly. Therefore, it is decoupled into two sub-problems. One sub-problem is for the transmit power control at the source and the UAV relay nodes, and the other aims at optimizing the UAV’s flight trajectory. By using the Lagrangian dual and Dinkelbach methods, the two sub-problems are solved, leading to an iterative algorithm for the joint design of transmit power control and trajectory optimization. Computer simulations demonstrated that by conducting the proposed algorithm, the flight trajectory of the UAV and the individual transmit power levels of the nodes can be flexibly adjusted according to the system conditions, and the proposed algorithm can achieve significantly higher energy efficiency as compared with the other benchmark schemes.