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Echo State Network Based on Improved Knowledge Distillation for Edge Intelligence

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Chinese Journal of Electronics 2024年第1期33卷 101-111页

作者： Jian ZHOU Yuwen JIANG Lijie XU Lu ZHAO Fu XIAO College of Computer Nanjing University of Posts and Telecommunications Jiangsu high Technology Research Key laboratory for Wireless Sensor networks

Echo state network(ESN) as a novel artificial neural network has drawn much attention from time series prediction in edge intelligence. ESN is slightly insufficient in long-term memory, thereby impacting the prediction performance. It suffers from a higher computational overhead when deploying on edge devices. We firstly introduce the knowledge distillation into the reservoir structure optimization, and then propose the echo state network based on improved knowledge distillation(ESN-IKD) for edge intelligence to improve the prediction performance and reduce the computational overhead. The model of ESN-IKD is constructed with the classic ESN as a student network, the long and short-term memory network as a teacher network, and the ESN with double loop reservoir structure as an assistant network. The student network learns the long-term memory capability of the teacher network with the help of the assistant network. The training algorithm of ESN-IKD is proposed to correct the learning direction through the assistant network and eliminate the redundant knowledge through the iterative pruning. It can solve the problems of error learning and redundant learning in the traditional knowledge distillation process. Extensive experimental simulation shows that ESN-IKD has a good time series prediction performance in both long-term and short-term memory, and achieves a lower computational overhead.

关键词： Knowledge engineering Training Performance evaluation Computational modeling Time series analysis Artificial neural networks Reservoirs

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Cognitive Jammers Assisted Covert Communication in Cognitive Radio networks

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China Communications 2024年第9期21卷 75-89页

作者： Liu Changjun Shi Jia Tie Zhuangzhuang Wang Yongchao The State Key laboratory of Integrated Services networks Xidian UniversityXi’an 710071China

In this work,we investigate the covert communication in cognitive radio(CR)networks with the existence of multiple cognitive jammers(CJs).Specifically,the secondary transmitter(ST)helps the primary transmitter(PT)to relay information to primary receiver(PR),as a reward,the ST can use PT's spectrum to transmit private information against the eavesdropper(Eve)under the help of one selected cognitive jammer(CJ).Meanwhile,we propose three jammer-selection schemes,namely,link-oriented jammer selection(LJS),min-max jammer selection(MMJS)and random jammer selection(RJS).For each scheme,we analyze the average covert throughput(ACT)and covert outage probability(COP).Our simulation results show that CJ is helpful to ST's covert communication,the expected minimum detection error probability and ACT can be significantly improved with the increase of false alarm of ***,the LJS scheme achieves best performance in ACT and COP,followed by RJS scheme,and MMJS scheme shows the worst performance.

关键词： cognitive jammer cognitive radio covert communication performance analysis

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Age of Information Based User Scheduling and Data Assignment in Multi-User Mobile Edge Computing networks:An Online Algorithm

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China Communications 2024年第5期21卷 153-165页

作者： Ge Yiyang Xiong Ke Dong Rui Lu Yang Fan Pingyi Qu Gang School of Computer and Information Technology Beijing Jiaotong UniversityBeijing 100044China Engineering Research Center of Network Management Technology for high speed Railway of Ministry of Education Beijing 100044China Beijing Engineering Research Center of high-speed Railway Broadband Mobile Communications Beijing 100044China Department of Electronic Engineering Tsinghua UniversityBeijing 100084China Beijing National Research Center for Information Science and Technology Tsinghua UniversityBeijing 100084China Department of Electrical and Computer Engineering College ParkMarylandUSA

This paper investigates the age of information(AoI)-based multi-user mobile edge computing(MEC)network with partial offloading *** weighted sum AoI(WSA)is first analyzed and derived,and then a WSA minimization problem is formulated by jointly optimizing the user scheduling and data *** to the non-analytic expression of the WSA *** optimization variables and the unknowability of future network information,the problem cannot be solved with known solution ***,an online Joint Partial Offloading and User Scheduling Optimization(JPOUSO)algorithm is proposed by transforming the original problem into a single-slot data assignment subproblem and a single-slot user scheduling sub-problem and solving the two sub-problems *** analyze the computational complexity of the presented JPO-USO algorithm,which is of O(N),with N being the number of *** results show that the proposed JPO-USO algorithm is able to achieve better AoI performance compared with various baseline *** is shown that both the user’s data assignment and the user’s AoI should be jointly taken into account to decrease the system WSA when scheduling users.

关键词： age of information(aoi) mobile edge computing(mec) user scheduling

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Resource Allocation in Multi-User Cellular networks:A Transformer-Based Deep Reinforcement Learning Approach

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China Communications 2024年第5期21卷 77-96页

作者： Zhao Di Zheng Zhong Qin Pengfei Qin Hao Song Bin State Key laboratory of Integrated Services networks Xidian University710071China China Academy of Space Technology Beijing 100081China

To meet the communication services with diverse requirements,dynamic resource allocation has shown increasing *** this paper,we consider the multi-slot and multi-user resource allocation(MSMU-RA)in a downlink cellular scenario with the aim of maximizing system spectral efficiency while guaranteeing user *** first model the MSMURA problem as a dual-sequence decision-making process,and then solve it by a novel Transformerbased deep reinforcement learning(TDRL)***,the proposed TDRL approach can be achieved based on two aspects:1)To adapt to the dynamic wireless environment,the proximal policy optimization(PPO)algorithm is used to optimize the multi-slot RA strategy.2)To avoid co-channel interference,the Transformer-based PPO algorithm is presented to obtain the optimal multi-user RA scheme by exploring the mapping between user sequence and resource *** results show that:i)the proposed approach outperforms both the traditional and DRL methods in spectral efficiency and user fairness,ii)the proposed algorithm is superior to DRL approaches in terms of convergence speed and generalization performance.

关键词： dynamic resource allocation multi-user cellular network spectrum efficiency user fairness

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A credible traffic prediction method based on self-supervised causal discovery

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Science China(Information Sciences) 2024年第5期67卷 244-258页

作者： Dan WANG Yingjie LIU Bin SONG State Key laboratory of Integrated Services networks Xidian University

Next-generation wireless network aims to support low-latency, high-speed data transmission services by incorporating artificial intelligence(AI) technologies. To fulfill this promise, AI-based network traffic prediction is essential for pre-allocating resources, such as bandwidth and computing power. This can help reduce network congestion and improve the quality of service(QoS) for users. Most studies achieve future traffic prediction by exploiting deep learning and reinforcement learning, to mine spatio-temporal correlated variables. Nevertheless, the prediction results obtained only by the spatio-temporal correlated variables cannot reflect real traffic changes. This phenomenon prevents the true prediction variables from being inferred, making the prediction algorithm perform poorly. Inspired by causal science, we propose a novel network traffic prediction method based on self-supervised spatio-temporal causal discovery(SSTCD). We first introduce the Granger causal discovery algorithm to build a causal graph among prediction variables and obtain spatio-temporal causality in the observed data, which reflects the real reasons affecting traffic ***, a graph neural network(GNN) is adopted to incorporate causality for traffic prediction. Furthermore,we propose a self-supervised method to implement causal discovery to to address the challenge of lacking ground-truth causal graphs in the observed data. Experimental results demonstrate the effectiveness of the SSTCD method.

关键词： wireless network traffic prediction causal discovery self-supervised

Conversion of a single-layer ANN to photonic SNN for pattern recognition

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Science China(Information Sciences) 2024年第1期67卷 261-270页

作者： Yanan HAN Shuiying XIANG Tianrui ZHANG Yahui ZHANG Xingxing GUO Yuechun SHI State Key laboratory of Integrated Service networks State Key Discipline Laboratory of Wide Bandgap Semiconductor TechnologyXidian University Yongjiang laboratory

This work presents a complete conversion scheme for photonic spiking neural networks(SNNs).We verified that the output of an artificial neural network(ANN) trained with the simulated optical activation function can be directly converted into the spike rate of a photonic spiking neuron *** reveal the feasibility of hardware implementation,we considered the effects of different bit precisions of data and weight,noise level,and bias current mismatch on the converted *** proposed scheme was evaluated using the Deterding vowel,IRIS,TIDIGITS,and MNIST datasets for pattern recognition,and achieved mean accuracies of 95.80%,98.67%,96.19%,and 92.33%,*** proposed scheme can convert an ANN into a photonic SNN with almost no precision loss,and the performance was comparable to that of an ANN trained with the rectified linear unit *** proposed scheme can enable the high-performance implementation of photonic SNNs.

关键词： photonic SNN conversion optical computing pattern recognition artificial neural network

Mitigate noisy data for smart IoT via GAN based machine unlearning

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Science China(Information Sciences) 2024年第3期67卷 62-78页

作者： Zhuo MA Yilong YANG Yang LIU Xinjing LIU Jianfeng MA School of Cyber Engineering Xidian University State Key laboratory of Integrated Services networks (ISN)

With the development of IoT applications, machine learning dramatically improves the utility of variable IoT systems such as autonomous driving. Although the pretrain-finetune framework can cope well with data heterogeneity in complex IoT scenarios, the data collected by sensors often contain unexpected noisy data, e.g., out-of-distribution(OOD) data, which leads to the reduced performance of fine-tuned models. To resolve the problem, this paper proposes Mu GAN, a method that can mitigate the side-effect of OOD data via the generative adversarial network(GAN)-based machine unlearning. Mu GAN follows a straightforward but effective idea to mitigate the performance loss caused by OOD data, i.e., “flashbacking” the model to the condition where OOD data are excluded from model training. To achieve the goal, we design an adversarial game, where a discriminator is trained to identify whether a sample belongs to the training set by observing the confidence score. Meanwhile, a generator(i.e., the target model) is updated to fool the discriminator into believing that the OOD data are not included in the training set but others do. The experimental results show that benefiting from the high unlearning rate(more than 90%) and retention rate(99%), Mu GAN succeeds in lowering the model performance degradation caused by OOD data from 5.88% to 0.8%.

关键词： machine unlearning generative adversarial network out of distribution data Internet of Thing neural network

Experimental demonstration of a photonic spiking neuron based on a DFB laser subject to side-mode optical pulse injection

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Science China(Information Sciences) 2024年第3期67卷 271-280页

作者： Shuiying XIANG Shuang GAO Yuechun SHI Yuna ZHANG Ziwei SONG Xingxing GUO Yahui ZHANG Yuxin MA Xiangfei CHEN State Key laboratory of Integrated Service networks Xidian University Yongjiang laboratory 3. College of Engineering and Applied Sciences Nanjing University

We proposed and experimentally demonstrated a simple and novel photonic spiking neuron based on a distributed feedback(DFB) laser subject to side-mode optical pulse injection(SMOPI). The DFB laser chip is designed and fabricated based on asymmetric equivalent π phase shift(π-EPS) with the reconstruction-equivalent-chirp(REC) technique. Under side-mode continuous-wave(CW) optical injection,excitability pulse was experimentally observed during the dominant mode switching process due to the injection-locked effect. Based on the transition between the excitability regime and the side-mode injection locking effect, the controllable and repeatable neuron-like spiking response can be realized when external stimulus pulses are electro-optically modulated on the CW optical carrier. The experimental results show that the spike threshold, temporal integration, and refractory period, which are important spike processing mechanisms in biological neurons, can all be achieved in the optically-injected DFB laser. The experimental findings are also verified numerically with a rate equation model that considers the SMOPI. To the best of our knowledge, this is the first experimental demonstration of a photonic spiking neuron based on a DFB laser subject to SMOPI, which holds promise for realizing large-scale photonic spiking neuron arrays for hardware photonic spiking neural network chips.

关键词： neuromorphic photonics photonic spiking neuron DFB laser side-mode optical pulse injections

Joint position optimization,user association,and resource allocation for load balancing in UAV-assisted wireless networks

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Digital Communications and networks 2024年第1期10卷 25-37页

作者： Daosen Zhai Huan Li Xiao Tang Ruonan Zhang Haotong Cao School of Electronics and Information Northwestern Polytechnical UniversityXian710072China State Key laboratory of Integrated Services networks Xidian UniversityXian710071China Department of Computing Hong Kong Polytechnic UniversityHong Kong SARChina

Unbalanced traffic distribution in cellular networks results in congestion and degrades spectrum *** tackle this problem,we propose an Unmanned Aerial Vehicle(UAV)-assisted wireless network in which the UAV acts as an aerial relay to divert some traffic from the overloaded cell to its adjacent underloaded *** fully exploit its potential,we jointly optimize the UAV position,user association,spectrum allocation,and power allocation to maximize the sum-log-rate of all users in two adjacent *** tackle the complicated joint optimization problem,we first design a genetic-based algorithm to optimize the UAV ***,we simplify the problem by theoretical analysis and devise a low-complexity algorithm according to the branch-and-bound method,so as to obtain the optimal user association and spectrum allocation *** further propose an iterative power allocation algorithm based on the sequential convex approximation *** simulation results indicate that the proposed UAV-assisted wireless network is superior to the terrestrial network in both utility and throughput,and the proposed algorithms can substantially improve the network performance in comparison with the other schemes.

关键词： Load balance Unmanned aerial vehicle Userassociation Resource management