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13 June 2023, Volume 21 Issue 2

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The whole issue of ZTE Communications June 2023, Vol. 21 No. 2

2023, 21(2):  0. 

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Special Topic

Special Topic on Evolution of AI Enabled Wireless Networks

WANG Ling, GAO Yin

2023, 21(2):  1-2.  doi:10.12142/ZTECOM.202302001

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Intelligent 6G Wireless Network with Multi-Dimensional Information Perception

YANG Bei, LIANG Xin, LIU Shengnan, JIANG Zheng, ZHU Jianchi, SHE Xiaoming

2023, 21(2):  3-10.  doi:10.12142/ZTECOM.202302002

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Intelligence and perception are two operative technologies in 6G scenarios. The intelligent wireless network and information perception require a deep fusion of artificial intelligence (AI) and wireless communications in 6G systems. Therefore, fusion is becoming a typical feature and key challenge of 6G wireless communication systems. In this paper, we focus on the critical issues and propose three application scenarios in 6G wireless systems. Specifically, we first discuss the fusion of AI and 6G networks for the enhancement of 5G-advanced technology and future wireless communication systems. Then, we introduce the wireless AI technology architecture with 6G multi-dimensional information perception, which includes the physical layer technology of multi-dimensional feature information perception, full spectrum fusion technology, and intelligent wireless resource management. The discussion of key technologies for intelligent 6G wireless network networks is expected to provide a guideline for future research.

Deep Learning-Based Semantic Feature Extraction: A Literature Review and Future Directions

DENG Letian, ZHAO Yanru

2023, 21(2):  11-17.  doi:10.12142/ZTECOM.202302003

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Semantic communication, as a critical component of artificial intelligence (AI), has gained increasing attention in recent years due to its significant impact on various fields. In this paper, we focus on the applications of semantic feature extraction, a key step in the semantic communication, in several areas of artificial intelligence, including natural language processing, medical imaging, remote sensing, autonomous driving, and other image-related applications. Specifically, we discuss how semantic feature extraction can enhance the accuracy and efficiency of natural language processing tasks, such as text classification, sentiment analysis, and topic modeling. In the medical imaging field, we explore how semantic feature extraction can be used for disease diagnosis, drug development, and treatment planning. In addition, we investigate the applications of semantic feature extraction in remote sensing and autonomous driving, where it can facilitate object detection, scene understanding, and other tasks. By providing an overview of the applications of semantic feature extraction in various fields, this paper aims to provide insights into the potential of this technology to advance the development of artificial intelligence.

Content Popularity Prediction via Federated Learning in Cache-Enabled Wireless Networks

YAN Yuna, LIU Ying, NI Tao, LIN Wensheng, LI Lixin

2023, 21(2):  18-24.  doi:10.12142/ZTECOM.202302004

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With the rapid development of networks, users are increasingly seeking richer and high-quality content experience, and there is an urgent need to develop efficient content caching strategies to improve the content distribution efficiency of caching. Therefore, it will be an effective solution to combine content popularity prediction based on machine learning (ML) and content caching to enable the network to predict and analyze popular content. However, the data sets which contain users’ private data cause the risk of privacy leakage. In this paper, to address this challenge, we propose a privacy-preserving algorithm based on federated learning (FL) and long short-term memory (LSTM), which is referred to as FL-LSTM, to predict content popularity. Simulation results demonstrate that the performance of the proposed algorithm is close to the centralized LSTM and better than other benchmark algorithms in terms of privacy protection. Meanwhile, the caching policy in this paper raises about 14.3% of the content hit rate.

Federated Learning for 6G: A Survey From Perspective of Integrated Sensing, Communication and Computation

ZHAO Moke, HUANG Yansong, LI Xuan

2023, 21(2):  25-33.  doi:10.12142/ZTECOM.202302005

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With the rapid advancements in edge computing and artificial intelligence, federated learning (FL) has gained momentum as a promising approach to collaborative data utilization across organizations and devices, while ensuring data privacy and information security. In order to further harness the energy efficiency of wireless networks, an integrated sensing, communication and computation (ISCC) framework has been proposed, which is anticipated to be a key enabler in the era of 6G networks. Although the advantages of pushing intelligence to edge devices are multi-fold, some challenges arise when incorporating FL into wireless networks under the umbrella of ISCC. This paper provides a comprehensive survey of FL, with special emphasis on the design and optimization of ISCC. We commence by introducing the background and fundamentals of FL and the ISCC framework. Subsequently, the aforementioned challenges are highlighted and the state of the art in potential solutions is reviewed. Finally, design guidelines are provided for the incorporation of FL and ISCC. Overall, this paper aims to contribute to the understanding of FL in the context of wireless networks, with a focus on the ISCC framework, and provide insights into addressing the challenges and optimizing the design for the integration of FL into future 6G networks.

Future Vision on Artificial Intelligence Assisted Green Energy Efficiency Network

CHEN Jiajun, GAO Yin, LIU Zhuang, LI Dapeng

2023, 21(2):  34-39.  doi:10.12142/ZTECOM.202302006

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To meet the key performance requirement of the 5G network and the demand of the growing number of mobile subscribers, millions of base stations are being constructed. 5G New Radio is designed to enable denser network deployments, which raises significant concerns about network energy consumption. Machine learning (ML), as a kind of artificial intelligence (AI) technologies, can enhance network optimization performance and energy efficiency. In this paper, we propose AI/ML-assisted energy-saving strategies to achieve optimal performance in terms of cell shutdown duration and energy efficiency. To realize network intelligence, we put forward the concept of intrinsic AI, which integrates AI into every aspect of wireless communication networks.

Machine Learning Driven Latency Optimization for Internet of Things Applications in Edge Computing

AWADA Uchechukwu, ZHANG Jiankang, CHEN Sheng, LI Shuangzhi, YANG Shouyi

2023, 21(2):  40-52.  doi:10.12142/ZTECOM.202302007

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Emerging Internet of Things (IoT) applications require faster execution time and response time to achieve optimal performance. However, most IoT devices have limited or no computing capability to achieve such stringent application requirements. To this end, computation offloading in edge computing has been used for IoT systems to achieve the desired performance. Nevertheless, randomly offloading applications to any available edge without considering their resource demands, inter-application dependencies and edge resource availability may eventually result in execution delay and performance degradation. We introduce Edge-IoT, a machine learning-enabled orchestration framework in this paper, which utilizes the states of edge resources and application resource requirements to facilitate a resource-aware offloading scheme for minimizing the average latency. We further propose a variant bin-packing optimization model that co-locates applications firmly on edge resources to fully utilize available resources. Extensive experiments show the effectiveness and resource efficiency of the proposed approach.

Multi-User MmWave Beam Tracking via Multi-Agent Deep Q-Learning

MENG Fan, HUANG Yongming, LU Zhaohua, XIAO Huahua

2023, 21(2):  53-60.  doi:10.12142/ZTECOM.202302008

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Beamforming is significant for millimeter wave multi-user massive multi-input multi-output systems. In the meanwhile, the overhead cost of channel state information and beam training is considerable， especially in dynamic environments. To reduce the overhead cost, we propose a multi-user beam tracking algorithm using a distributed deep Q-learning method. With online learning of users’ moving trajectories, the proposed algorithm learns to scan a beam subspace to maximize the average effective sum rate. Considering practical implementation, we model the continuous beam tracking problem as a non-Markov decision process and thus develop a simplified training scheme of deep Q-learning to reduce the training complexity. Furthermore, we propose a scalable state-action-reward design for scenarios with different users and antenna numbers. Simulation results verify the effectiveness of the designed method.

RIS-Assisted UAV-D2D Communications Exploiting Deep Reinforcement Learning

YOU Qian, XU Qian, YANG Xin, ZHANG Tao, CHEN Ming

2023, 21(2):  61-69.  doi:10.12142/ZTECOM.202302009

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Device-to-device (D2D) communications underlying cellular networks enabled by unmanned aerial vehicles (UAV) have been regarded as promising techniques for next-generation communications. To mitigate the strong interference caused by the line-of-sight (LoS) air-to-ground channels, we deploy a reconfigurable intelligent surface (RIS) to rebuild the wireless channels. A joint optimization problem of the transmit power of UAV, the transmit power of D2D users and the RIS phase configuration are investigated to maximize the achievable rate of D2D users while satisfying the quality of service (QoS) requirement of cellular users. Due to the high channel dynamics and the coupling among cellular users, the RIS, and the D2D users, it is challenging to find a proper solution. Thus, a RIS softmax deep double deterministic (RIS-SD3) policy gradient method is proposed, which can smooth the optimization space as well as reduce the number of local optimizations. Specifically, the SD3 algorithm maximizes the reward of the agent by training the agent to maximize the value function after the softmax operator is introduced. Simulation results show that the proposed RIS-SD3 algorithm can significantly improve the rate of the D2D users while controlling the interference to the cellular user. Moreover, the proposed RIS-SD3 algorithm has better robustness than the twin delayed deep deterministic (TD3) policy gradient algorithm in a dynamic environment.

SST-V: A Scalable Semantic Transmission Framework for Video

LIU Chenyao, GUO Jiejie, ZHANG Yimeng, XU Wenjun, LIU Yiming

2023, 21(2):  70-79.  doi:10.12142/ZTECOM.202302010

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The emerging new services in the sixth generation (6G) communication system impose increasingly stringent requirements and challenges on video transmission. Semantic communications are envisioned as a promising solution to these challenges. This paper provides a highly-efficient solution to video transmission by proposing a scalable semantic transmission algorithm, named scalable semantic transmission framework for video (SST-V), which jointly considers the semantic importance and channel conditions. Specifically, a semantic importance evaluation module is designed to extract more informative semantic features according to the estimated importance level, facilitating high-efficiency semantic coding. By further considering the channel condition, a cascaded learning based scalable joint semantic-channel coding algorithm is proposed, which autonomously adapts the semantic coding and channel coding strategies to the specific signal-to-noise ratio (SNR). Simulation results show that SST-V achieves better video reconstruction performance, while significantly reducing the transmission overhead.

UAV Autonomous Navigation for Wireless Powered Data Collection with Onboard Deep Q-Network

LI Yuting, DING Yi, GAO Jiangchuan, LIU Yusha, HU Jie, YANG Kun

2023, 21(2):  80-87.  doi:10.12142/ZTECOM.202302011

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In a rechargeable wireless sensor network, utilizing the unmanned aerial vehicle (UAV) as a mobile base station (BS) to charge sensors and collect data effectively prolongs the network’s lifetime. In this paper, we jointly optimize the UAV’s flight trajectory and the sensor selection and operation modes to maximize the average data traffic of all sensors within a wireless sensor network (WSN) during finite UAV’s flight time, while ensuring the energy required for each sensor by wireless power transfer (WPT). We consider a practical scenario, where the UAV has no prior knowledge of sensor locations. The UAV performs autonomous navigation based on the status information obtained within the coverage area, which is modeled as a Markov decision process (MDP). The deep Q-network (DQN) is employed to execute the navigation based on the UAV position, the battery level state, channel conditions and current data traffic of sensors within the UAV’s coverage area. Our simulation results demonstrate that the DQN algorithm significantly improves the network performance in terms of the average data traffic and trajectory design.

Review

Research Towards Terahertz Power Amplifiers in Silicon-Based Process

CHEN Jixin, ZHOU Peigen, YU Jiayang, LI Zekun, LI Huanbo, PENG Lin

2023, 21(2):  88-94.  doi:10.12142/ZTECOM.202302012

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In view of the existing design challenges for Terahertz (THz) power amplifiers (PAs), the common design methods and the efforts of the State Key Laboratory of Millimeter Wave, Southeast University, China in the development of silicon-based THz PAs, mainly including silicon-based PAs with operating frequencies covering 100–300 GHz, are summarized in this paper. Particularly, we design an LC-balun-based two-stage differential cascode PA with a center frequency of 150 GHz and an output power of 14 dBm. Based on a Marchand balun, we report a 220 GHz three-stage differential cascode PA with a saturated output power of 9.5 dBm. To further increase the output power of THz PA, based on a four-way differential power combining technique, we report a 211–263 GHz dual-LC-tank-based broadband PA with a recorded 14.7 dBm Psat and 16.4 dB peak gain. All the above circuits are designed in a standard 130 nm silicon germanium (SiGe) BiCMOS process.