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Subarray-Based Hybrid-Field Channel Estimation for Terahertz Wideband UM-MIMO Systems Without Prior Location Knowledge

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IEEE TRANSACTIONS ON VEHICULAR techNOLOGY 2024年第5期73卷 7363-7367页

作者： Sun, Yanran Yang, Chuang Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol SKL N Beijing 100876 Peoples R China

Terahertz (THz) wideband ultra-massive multiple-input multiple-output (UM-MIMO) systems are critical for the upcoming 6G. The far- and near-field regions always coexist as the hybrid-field regions in the THz wideband UM-MIMO systems, but the existing hybrid-field estimation schemes can not perform well for the wideband channel without prior knowledge of user location information. In this work, we propose an efficient subarray-based hybrid-field channel estimation algorithm for the THz wideband UM-MIMO systems without prior knowledge of user location. The estimation problem is decomposed into a series of sub-problems, each of which considers only one subarray for channel estimation in combination with the wideband sparse detection. Then, the hybrid-field channel is recovered by combining all the subarray channels. Simulation results show that the proposed algorithm is able to recover the hybrid-field wideband channel accurately at a low pilot overhead and the bandwidth has little effect on the performance of the proposed.

关键词： Terahertz (THz) channel estimation hybrid-field wideband

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Joint mmWave Beamforming and Resource Allocation in NOMA-MEC Network for Internet of Things

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IEEE TRANSACTIONS ON VEHICULAR techNOLOGY 2023年第4期72卷 4969-4980页

作者： Qi, Xiaolei Peng, Mugen Zhang, Hongming Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Mobile edge computing (MEC) technology can reduce the computational burden of Internet of Things (IoT) devices by offloading computationally intensive tasks to physically proximate edge servers. The combination of millimeter-wave (mmWave) massive multiple input multiple output (MIMO) and non-orthogonal multiple access (NOMA) technique has draw significant attention for its high spectral efficiency. This paper merges the computation offloading and mmWave massive MIMO-NOMA communication to seek energy efficient performance for IoT devices in MEC network. First, we propose an IoT device clustering algorithm to schedule all IoT devices into limited beams. Following this, we minimize the total energy consumption of computing and offloading in the NOMA-MEC network by jointly optimizing hybrid beamforming, offloading task assignment (OTA) ratio and power allocation. Then, the non-convex joint optimization problem is solved by proposing a suboptimal solution with two stages. In the first stage, a cyclic block-coordinate update algorithm is proposed for analog beamforming design, and then the digital beamforming vectors are obtained by zero-forcing method. In the second stage, an alternating minimization algorithm is proposed to optimize the OTA ratio and power allocation. Finally, simulation results are provided to validate the convergence of the proposed algorithms and the superiority of our proposed schemes over other benchmark schemes, which demonstrate the outstanding performance leveraging the massive MIMO-NOMA technique during computational offloading.

关键词： Internet of Things NOMA Task analysis Millimeter wave communication Array signal processing Resource management Minimization Hybrid beamforming massive MIMO mobile edge computing resource allocation

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Root Cause Location Based on Prophet and Kernel Density Estimation

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IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT 2023年第2期20卷 904-917页

作者： Yang, Yang Sun, Yindong Long, Yuhan Mei, Jingting Yu, Peng Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

When an online service, such as an online Web service, encounters an abnormality, operators need to analyse many abnormal monitoring indicators on the affected machine and rapidly determine the root cause indicators, which can quickly locate specific problems. Then, the abnormality is isolated until the cascading effects caused by the anomalies are eliminated. This paper proposes a root cause indicator location algorithm named ProphetKdeRCL. First, the improved Prophet algorithm detects many abnormal time-series indicators. Then, the abnormal deviation degree algorithm based on kernel density estimation is used to measure the fluctuation of each abnormal indicator for sorting. The analyses of the delay causal factor dependence are combined with the time window. Finally, a root cause location list is generated to assist operators in quick troubleshooting operations. This paper uses public datasets to evaluate the overall effectiveness of the algorithm. The results show that compared to other algorithms, the ProphetKdeRCL algorithm has higher accuracy on evaluation indicator AC@1 and superior accuracy on the other commonly used indicators, namely, AC@2 and AC@3.

关键词： Anomaly detection root cause rank root cause location Prophet kernel density estimation

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An Improved Deep Neural Network for Small-Ship Detection in SAR Imagery

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2024年 17卷 2596-2609页

作者： Hu, Boyi Miao, Hongxia Beijing Univ Posts & Telecommun Sch Informat & Commun Engn State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Ship detection by using remote-sensing images based on a synthetic aperture radar (SAR) plays an important role in managing water transportation and marine safety. However, complex background, a small-ship size, and low focus on small ships results in difficulties in feature extraction and low detection accuracy. This study proposes a new small SAR ship-detection network. First, a transformer-based dynamic sparse attention module is used to improve the focus and extraction of small-ship features. Second, the feature maps are fused with deep layers, and small target-friendly detection heads are used to improve the processing of global information in the network. Third, a more suitable fused loss function is used for small ships to ensure the multiscale detection capability. Experimental results on publicly available datasets, LS-SSDD_v1.0 and AIR-SARShip-1.0, show that the proposed method effectively improves the detection accuracy of small ships on SAR images without computational burden boost. Compared with other methods based on the convolutional neural network, the proposed method demonstrates the better multiscale detection performance.

关键词： Convolutional neural network (CNN) ship detection synthetic aperture radar (SAR) transformer

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Complementary Coded Identical Code Cyclic Shift Multiple Access Under Asynchronous Frequency-Selective Fading Channels

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IEEE TRANSACTIONS ON VEHICULAR techNOLOGY 2023年第10期72卷 13269-13280页

作者： Wang, Zhifeng Liu, Xiqing Yang, Yiran Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

A complementary coded code-division multiple-access (CC-CDMA) system is built on the direct sequence (DS) spread spectrum technique, which can suppress multipath interferences and multiple-access interferences due to the ideal correlation property of complementary codes (CCs). However, the DS spread spectrum technique will cause poor bandwidth efficiency;the CC-CDMA system suffers from serious interferences due to the ideal correlation of CCs may not be reconstructed at the receiver after passing through asynchronous frequency selective fading channels. In this article, we propose a complementary coded identical code cyclic shift multiple access (CC-ICCSMA) system under asynchronous frequency selective fading channels, which allows multiple data streams to transmit in parallel by sharing one spreading code for improving the bandwidth efficiency. Considering the impact of interferences, we propose an ICCS matched filterbank to tackle the interferences, which can decode the data under all paths and achieve a high multipath diversity gain. Simulation results show that the proposed CC-ICCSMA system can achieve higher bandwidth efficiency than that of traditional CC-CDMA and can outperform the traditional DS-CDMA schemes in terms of bit-error rates and transmission rates.

关键词： Identical code cyclic shift code complementary code interferences asynchronous frequency selective fading channels

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Computation Offloading and Resource Allocation in Unmanned Aerial Vehicle Networks

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IEEE TRANSACTIONS ON VEHICULAR techNOLOGY 2023年第4期72卷 4981-4995页

作者： Liu, Binghong Liu, Chenxi Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Deploying unmanned aerial vehicles (UAVs) as mobile edge computing (MEC) servers has been attracting significant attention, since the UAVs' inherent maneuverability and mobility can further reduce the distance between the users and computational functionalities. In the UAV-assisted MEC systems, the caching and offloading decision optimization, subject to the computation and energy constraints of the UAVs, is one of the key design issues. Against this backdrop, in this paper, we propose a novel cloud-edge framework to facilitate MEC in the UAV networks. Specifically, in our framework, the edge UAVs (EUAVs), together with the cloud, provide caching and computing services for the terrestrial users. In order to minimize the weighted sum cost of latency and energy consumption, we jointly optimize the caching and offloading decisions, the EUAV deployment, the radio and computation resource allocation, while simultaneously satisfying the UAVs' cache and computation capacity constraints, as well as the users' latency and energy consumption constraints. To solve this NP-hard problem, we propose a sequential convex programming (SCP) and sequential quadratic programming (SQP) based deep Q-learning (SS-DQN) algorithm. The proposed algorithm allows the system to adaptively adjust caching and offloading decisions with the EUAV deployment scheme and the resource allocation scheme obtained by SCP and SQP algorithms, respectively. Simulation results verify the superiority of our proposed algorithm compared to two benchmark schemes, and demonstrate how the combination of DRL and convex optimization can improve the system performance significantly.

关键词： Task analysis Cloud computing Resource management Autonomous aerial vehicles Servers Energy consumption Costs Unmanned aerial vehicles (UAVs) wireless caching computation offloading

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Self-Organized and Distributed Green Resource Allocation for Space-Air-Ground IoT Networks

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IEEE INTERNET OF THINGS JOURNAL 2023年第11期10卷 9385-9397页

作者： Yu, Peng Li, Yijing Zhang, Manjun Xiong, Ao Li, Wenjing Qiu, Xuesong Meng, Luoming Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

To deal with the explosion connections and data volume for emergency communication or hot spot capacity enhancement with massive Internet of Things (IoT) devices, deploying aerial base stations (AeBSs) on unmanned aerial vehicles (UAVs) to generate heterogeneous space-air-ground networks is considered to be a quite effective method. However, the flying AeBSs and back-hauling to existing heterogeneous networks (HetNets) lead to network energy consumption a key point. To ensure the energy-efficient operation of space-air-ground networks for smart IoT applications, we put forward the cluster-based HetNets energy-efficient resource allocation mechanism (CHERA). The scheme first divides the entire network into multiple independent BS clusters with the K-means++ algorithm for distributed energy efficiency (EE) optimization. Then, we propose a greedy BS sleeping strategy and a Lagrangian-dual-based optimal power allocation algorithm for the maximization of EE in each BS cluster. The EE optimization of space-air-ground IoT networks is implemented under the self-organizing network framework to make sure of the efficient and reliable operation of the network. Simulation results indicate that energy consumption is effectively decreased with the mechanism. It boosts the EE of space-air-ground networks by 23.8% compared with a baseline algorithm in which BSs are all in active mode with no power optimization. The result is expected to be useful for achieving future green space-air-ground networks IoT applications.

关键词： Internet of Things Resource management Optimization Monitoring Energy consumption Energy efficiency Heterogeneous networks Green communications resource allocation self-organized network smart Internet of Things (IoT) applications space-air-ground networks

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Non-Line-of-Sight Full-Duplex Ultraviolet Communications Under Self-Interference

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2023年第11期22卷 7775-7788页

作者： Wang, Zhifeng Yuan, Renzhi Peng, Mugen Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

The full-duplex optical communications can be achieved simply by separating the transmitting link and the receiving link in the space. However, it is challenging to achieve the non-line-of-sight (NLOS) full-duplex ultraviolet (UV) communication due to the serious self-interference caused by the strong multiple scattering effects of UV signals. To explore the capacity of NLOS full-duplex UV communications, in this paper, we first quantify the self-interference using an analytical channel impulse response function. Based on the quantified self-interference, we then derive the error rate and the corresponding achievable information rate (AIR) for on- off keying modulation, 4-digital-pulse-interval modulation and 4-pulse-position modulation. We further propose a self-interference cancelling (SIC) method to mitigate the impacts of self-interferences. Simulation results show that the proposed SIC method can significantly improve the error rate and AIR performances. Besides, we find that the NLOS full-duplex UV communication will gradually lose its advantage over the NLOS half-duplex UV communication as either the communication distance or the elevation angle increases. However, using the proposed SIC method, the NLOS full-duplex UV communication can hold its advantage in a wide range of system geometries.

关键词： Full-duplex ultraviolet communication non-line-of-sight self-interference self-interference cancelling

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An Intersection-Based QoS Routing for Vehicular Ad Hoc Networks With Reinforcement Learning

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第9期24卷 9068-9083页

作者： Rui, Lanlan Yan, Zhibo Tan, Zuoyan Gao, Zhipeng Yang, Yang Chen, Xingyu Liu, Huiyong Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Vehicular ad hoc networks (VANETs) have the characteristics of high mobility, frequently changing topology and uneven distribution, which made it a challenge to design an efficient and robust routing protocol with low latency and high packet delivery rate. Currently, intersection-based routing method and full-path based routing method are two popular solutions for the packet routing in VANETs. Although the intersection-based routing method has better real-time performance, it has the problem of local optimization, making the routing results not global optimal. Although the full-path based method can obtain the global optimal solution, it is weak in dealing with the dynamically changing topological network. Aiming at solving the above problems, this paper designs an intersection-based QoS routing (IQRRL) algorithm, which mainly includes two crutial steps: the next intersection selection and the next hop vehicle selection. In the selection of the next intersection, this paper uses an improved intersection-based routing protocol. In addition to considering connectivity and delay, IQRRL also considers the communication quality from the neighbor's road to the destination node while evaluating the quality of the neighbor's road, which minimizes the problem of local optimization. When the next intersection is determined, a road is then determined, and then the next hop vehicle within the road should be chosen to relay the packet forward. In the next-hop vehicle selection step, this paper adopts multi-hop evaluation technology based on reinforcement learning. In addition to using "greedy decision-making" to select the next-hop vehicle, it also comprehensively evaluates whether the next-hop vehicle is still optimal in the future, so that the stability and reliability of data forwarding are improved and local optimal problems are avoided. Besides, this article uses a simulation system to compare IQRRL with other routing algorithms. The result reveals that IQRRL outperforms in

关键词： Routing Routing protocols Vehicular ad hoc networks Roads Quality of service Delays Reliability routing protocol reinforcement learning vehicular ad hoc networks

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RTHop: Real-Time Hop-by-Hop Mobile Network Routing by Decentralized Learning With Semantic Attention

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IEEE TRANSACTIONS ON MOBILE COMPUTING 2023年第3期22卷 1731-1747页

作者： He, Bo Wang, Jingyu Qi, Qi Sun, Haifeng Liao, Jianxin Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing 100876 Peoples R China

Multi-access Edge Computing and ubiquitous smart devices help serve end-users efficiently by providing emerging edge-deployed services. On the other hand, more heavy and time-varying traffic loads are generated in mobile edge networks, so that an efficient traffic forwarding mechanism is highly required to handle the routing problem in complex and highly dynamic edge environments. Thus, Deep Reinforcement Learning (DRL) is introduced since it can work in a model-free approach. However, previous centralized DRL-based methods work in a turn-based way that mismatches the real-time property of routing. In this paper, we propose a real-time and distributed learning approach, RTHop, to adapt to the volatile environment and realize a hop-by-hop routing. The Multi-Agent Deep Reinforcement Learning (MADRL) and the Real-Time Markov Decision Process (RTMDP) are used to alleviate network congestion and maximize the utilization of network resources. By joining with the self-attention mechanism, RTHop obtains the semantics from elements of the network state to help agents learn the importance of each element on routing. Experiment results show that RTHop not only overcomes the weakness of conventional turn-based DRL methods but also achieves the increase of delivered packet ratios and effective throughput compared with other routing methods.

关键词： Routing Real-time systems Delays Adaptation models Neural networks Edge computing Semantics Mobile edge network real-time network distributed routing multi-agent reinforcement learning self-attention

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