Ultra-reliable MU-MIMO detector based on deep learning for 5G/B5G-enabled IoT

He, Ke; Wang, Zizhi; Liu, Dong; Zhu, Fusheng; Fan, Lisheng

doi:10.1016/j.phycom.2020.101181

Cited by 55 publications

(31 citation statements)

References 25 publications

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“…Finally, some simulations under Matlab platform were demonstrated to show that the proposed algorithm can effectively improve the secrecy data rate and reduce the whole system latency. In future works, we will incorporate some other wireless transmission techniques, such as UAV [41], massive MIMO [42], and deep learning technique [43], [44] into the considered MEC networks, in order to further reduce the system latency and energy consumption.…”

Section: Discussionmentioning

confidence: 99%

Mobile Cooperative Sensing Based Secure Communication Strategy of Edge Computational Networks for Smart Cities

Sun

Zhao

et al. 2020

IEEE Access

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With the development of smart cities, lots of mobile cooperative sensing based nodes have emerged. However, due to the open nature of wireless transmission, attackers in the networks can use some intelligent radio devices to deteriorate the secure transmission, which imposes a severe issue of information leakage. In this paper, we consider the transmitter has some computational tasks to be computed, under the environments of intelligent attacker. Due to the limited computational capability, the sender needs to offload some tasks to the receiver. To address this problem, we propose a power allocation algorithm based on combining the technology of reinforcement learning and game theory, in order to achieve an optimal secure data rate and meanwhile reduce the whole task latency of the transmission and computation with Q learning and Nash equilibrium. Then, the Nash equilibrium and its existence conditions are derived and proven mathematically. Finally, we perform some simulations under Matlab platform, and the results show that the proposed algorithm can effectively improve the secrecy data rate and reduce the whole system latency.INDEX TERMS Mobile cooperative sensing, secure communication, smart environments.

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Section: Discussionmentioning

confidence: 99%

Mobile Cooperative Sensing Based Secure Communication Strategy of Edge Computational Networks for Smart Cities

Sun

Zhao

et al. 2020

IEEE Access

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“…To solve these problems, artificial intelligence-based algorithms have been proposed to exploit the system resources of the communication networks. In this area, the authors in [24,25] considered the correlated interference and utilized the deep convolutional neural network (DCNN) to improve the signal detection performance. For the non-Gaussian noise, a deep learning-based algorithm was proposed to approximate the probability density function of noise under impulsive noise environments [26].…”

Section: The Impact Of Interference On Wireless Transmissionmentioning

confidence: 99%

A Survey on the AI and Spectrum Management for Cache‐Enabled Internet of Things in Smart Cities

Chen

Kuang

Zhu³

et al. 2021

Wireless Communications and Mobile Computing

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In this paper, we provide a comprehensive survey for the artificial intelligence and spectrum management, which are used for cache-enabled Internet of Things (IoT) in smart cities. In smart cities, there emerge a lot of new applications such as data collection and communication, environment monitoring, and real-time processing, which cannot be supported by the conventional wireless transmission techniques. Hence, some new wireless transmission techniques should be developed to support the emerging applications in smart cities. In this survey, we focus on the artificial intelligence, spectrum management, and caching techniques, where the interference arises due to the limited spectrum resources. In particular, we first review the current research status of these new techniques and, then, give some challenges on the system design. We further provide several feasible solutions on these challenges, in order to implement the IoT networks in smart cities. Finally, we conclude the work in the part of conclusions and give some discussions on the future works.

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“…For example, in application scenarios such as smart monitoring systems, smart transportation systems, and Internet of Vehicles, mobile devices such as smart phones or smart cars need to continuously receive data and process computational tasks. However, the computational capability of mobile devices is very limited, and processing large-scale data will cause a lot of system latency, which will seriously reduce the quality of user services [12]- [14]. To deal with this problem, researchers have proposed cloud computing solutions, where the computational tasks of mobile device can be offloaded to the cloud server for the assistance of computation, and then the results will be fedback to the mobile devices.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Mobile Edge Computing Networks for Internet of Things

Chen

Kuang

Zhu³

et al. 2021

IEEE Access

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In this work, an intelligent mobile edge computing (MEC) network is studied for Internet of Things (IoT) in the presence of eavesdropping environments, where there are multiple users who can offload their confidential tasks to the computational access point (CAP) for the assistance of computation. One unmanned aerial vehicle (UAV) attacker exists in the system and it can listen to the confidential data transmission from the users to the CAP. We optimize the system design of the intelligent MEC network, by adaptively allocating the offloading ratio and wireless bandwidth, to reduce the linearly weighted cost of the latency as well as energy consumption (EnC). Specifically, starting from the deep reinforcement learning, we devise a deep Q-network (DQN) network to adjust the offloading ratio and transmission bandwidth, which can help calculate the computational tasks and suppress the eavesdropping from the UAV efficiently. We finally provide some simulation results to validate the proposed offloading strategy. In particular, the proposed offloading strategy can achieve a much lower cost compared to the conventional ones, in the terms of latency and EnC.INDEX TERMS Deep reinforcement learning, Internet of Things, mobile edge computing, task offloading, unmanned aerial vehicles.

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Ultra-reliable MU-MIMO detector based on deep learning for 5G/B5G-enabled IoT

Cited by 55 publications

References 25 publications

Mobile Cooperative Sensing Based Secure Communication Strategy of Edge Computational Networks for Smart Cities

Mobile Cooperative Sensing Based Secure Communication Strategy of Edge Computational Networks for Smart Cities

A Survey on the AI and Spectrum Management for Cache‐Enabled Internet of Things in Smart Cities

Intelligent Mobile Edge Computing Networks for Internet of Things

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