ResInNet: A Novel Deep Neural Network With Feature Reuse for Internet of Things

Sun, Xiaochuan; Gui, Guan; Li, Yingqi; Liu, Ren Ping; An, Yongli

doi:10.1109/jiot.2018.2853663

Cited by 79 publications

(43 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Equation (15) and (16) are both sequential decision making problems. We will present optimal solution in next section and introduce a QL method for solving problem (15) and a DQN method for problem (16).…”

Section: Rl Formulationmentioning

confidence: 99%

“…With the great progress of machine learning (ML) recent years, increasing research have adopted ML to solve complicated communication problems, for example, a new deep learning based Non-Orthogonal Multiple Access scheme which can detect the channel characteristics automatically [14], IoT feature extraction and reuse [15], caching file selections [16]. Especially the branch of deep learning is suitable to deal with some non-convex optimization problems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Energy Minimization in D2D-Assisted Cache-Enabled Internet of Things: A Deep Reinforcement Learning Approach

Tang

Zhang

et al. 2020

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

Mobile edge caching (MEC) and device to device (D2D) communications are two potential technologies to resolve traffic overload problems in internet of things (IoT). Previous works usually investigate them separately with MEC for traffic offloading and D2D for information transmission. In this paper, a joint framework consisting of MEC and cache-enabled D2D communications is proposed to minimize the energy cost of systematic traffic transmission, where file popularity and user preference are the critical criteria for small base stations (SBSs) and user devices, respectively. Under this framework, we propose a novel caching strategy where Markov decision process (MDP) is applied to model the requesting behaviours. A novel scheme based on reinforcement learning (RL) is proposed to reveal the popularity of files as well as users' preference. In particular, Q-learning (QL) algorithm and deep Q-network (DQN) algorithm are respectively applied to user devices and SBS due to different complexities of status. To save the energy cost of systematic traffic transmission, users acquire partial traffic through D2D communications based on the cached contents and user distribution. Taking the memory limits, D2D available files and status changing into consideration, the proposed RL algorithm enables user devices and SBS to prefetch the optimal files while learning, which can reduce the energy cost significantly. Simulation results demonstrate the superior energy saving performance of the proposed RL-based algorithm over other existing methods under various conditions.

show abstract

Section: Rl Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy Minimization in D2D-Assisted Cache-Enabled Internet of Things: A Deep Reinforcement Learning Approach

Tang

Zhang

et al. 2020

IEEE Trans. Ind. Inf.

View full text Add to dashboard Cite

show abstract

“…In the perspective of data analysis, DL is expert in automatic feature extraction from big data, instead of the complex and difficult design of manmade features [21], [22]. Motivated by the advances, DL has been successfully applied in network traffic prediction [23]- [25], wireless communications [26]- [31], and internet of things [32]- [37].…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-based Automatic Modulation Recognition Method in the Presence of Phase Offset

Shi¹,

Song²,

Cai³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Automatic modulation recognition (AMR) plays an important role in various communications systems. It has the ability of adaptive modulation and can adapt to various complex environments. Automatic modulation recognition is also widely used in orthogonal frequency division multiplexing (OFDM) systems. However, because the recognition accuracy of traditional methods to extract the features of OFDM signals is very limited. In order to solve these problems, many deep learning based AMR methods have been proposed to improve the recognition performance. However, most of these AMR methods neglect the harmful effect by carrier phase offset (PO) which often appears in real communications systems. Hence it is required to consider the PO effect for designing the OFDM system. Unlike conventional methods, we propose a convolutional neural network (CNN) based AMR method for considering PO in the OFDM system. The proposed method is used to eliminate the PO to achieve the high classification accuracy. Experiment results are provided to confirm the proposed method when comparing to conventional methods.

show abstract

“…In recent years, deep learning (DL) is considered as a powerful tool, because it is expert in automatic feature extraction from huge amounts of data, instead of the complex and difficult design of manmade features [10], [11]. For this reason, DL has been successfully applied in wireless communications [12]- [16] and Internet-of-Things [18]- [24].…”

Section: Introductionmentioning

confidence: 99%

Generalized Automatic Modulation Classification Under Non-Gaussian Noise with Varying SNR Conditions: A CNN Enable Method

Wang¹,

Gui²,

Ohtsuki³

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Automatic modulation classification (AMC) is an critical step to identify signal modulation types so as to enable more accurate demodulation in the non-cooperative scenarios. Convolutional neural network (CNN)-based AMC is believed as one of the most promising methods with great classification accuracy. However, the conventional CNN-based methods are lack of generality capabilities under time-varying signal-to-noise ratio (SNR) conditions, because these methods are merely trained on specific datasets and can only work at the corresponding condition. In this paper, a novel CNN-based generalized AMC method is proposed, and a more realistic scenario is considered, including white non-Gaussian noise and synchronization error. Its generalization capability stems from the mixed datasets under varying noise scenarios, and the CNN can extract common features from these datasets. Simulation results show that our proposed architecture can achieve higher robustness and generalization than the conventional ones.

show abstract

ResInNet: A Novel Deep Neural Network With Feature Reuse for Internet of Things

Cited by 79 publications

References 46 publications

Energy Minimization in D2D-Assisted Cache-Enabled Internet of Things: A Deep Reinforcement Learning Approach

Energy Minimization in D2D-Assisted Cache-Enabled Internet of Things: A Deep Reinforcement Learning Approach

Deep Learning-based Automatic Modulation Recognition Method in the Presence of Phase Offset

Generalized Automatic Modulation Classification Under Non-Gaussian Noise with Varying SNR Conditions: A CNN Enable Method

Contact Info

Product

Resources

About