IEEE Transactions on Cognitive Communications and Networking最新文献_第2页

Connectivity of Intelligent Reflecting Surface Assisted Network via Percolation Theory 通过渗流理论实现智能反射面辅助网络的连通性

IF 8.6 1区计算机科学

IEEE Transactions on Cognitive Communications and Networking Pub Date : 2023-08-25 DOI: 10.1109/TCCN.2023.3308782

Qi Wu;Xiaoyang He;Xiaoxia Huang

{"title":"Connectivity of Intelligent Reflecting Surface Assisted Network via Percolation Theory","authors":"Qi Wu;Xiaoyang He;Xiaoxia Huang","doi":"10.1109/TCCN.2023.3308782","DOIUrl":"10.1109/TCCN.2023.3308782","url":null,"abstract":"The integrated access and backhaul (IAB) technology enables cost-effective ultradense network deployment, by replacing wired transmission infrastructure. However, IAB is hindered by high energy consumption, fragile wireless links, and coverage holes. With the capability of enhancing coverage and smart configuration of the radio environment at a low cost, intelligent reflecting surfaces (IRSs) can well address the challenges in IAB. This paper focuses on forming heterogeneous communication between base station (BS) and IRS in large-scale IAB networks, achieving percolation-based connectivity of BSs to ensure further cost-effective BSs deployment and successful data packet delivery between users. The dependency between the two node processes and the asymmetry of communication links introduce new challenges for connectivity in IRS-assisted IAB networks. To address these challenges, continuum percolation theory is applied to scrutinize the topological and analytical properties of the IRS-assisted wireless network. Specifically, the analysis demonstrates the uniqueness of the infinite connected component and proves the topological connectivity within the corresponding connectivity region. Additionally, the paper establishes the necessary and sufficient conditions for achieving network connectivity manifested in the critical densities of both BS and IRS nodes. The theoretical analysis is validated through simulations, confirming alignment between derived bounds and Monte Carlo results.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"9 6","pages":"1625-1640"},"PeriodicalIF":8.6,"publicationDate":"2023-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62525271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Inter-Cell Interference Mitigation for Cellular-Connected UAVs Using MOSDS-DQN 利用 MOSDS-DQN 缓解蜂窝连接无人机的蜂窝间干扰

IF 8.6 1区计算机科学

IEEE Transactions on Cognitive Communications and Networking Pub Date : 2023-08-23 DOI: 10.1109/TCCN.2023.3307940

Liyana Adilla Binti Burhanuddin;Xiaonan Liu;Yansha Deng;Maged Elkashlan;Arumugam Nallanathan

{"title":"Inter-Cell Interference Mitigation for Cellular-Connected UAVs Using MOSDS-DQN","authors":"Liyana Adilla Binti Burhanuddin;Xiaonan Liu;Yansha Deng;Maged Elkashlan;Arumugam Nallanathan","doi":"10.1109/TCCN.2023.3307940","DOIUrl":"10.1109/TCCN.2023.3307940","url":null,"abstract":"In 5G and beyond, UAVs are integrated into cellular networks as new aerial mobile users to support many applications and provide higher probability of line-of-sight (LoS) transmission to base stations (BSs). Nevertheless, due to limited frequency bandwidth and spectrum resource reuse when BSs serving terrestrial users (TUEs) and UAVs, it causes severe downlink interference to TUEs, especially when the network has a heavy load. Thus, in this paper, we study the performance of radio connectivity of UAVs and TUEs in an urban area and introduce a downlink inter-cell interference coordination mechanism. Then, we propose adaptive cell muting interference and resource allocation scheduling schemes. A value function approximation solution (VFA), Tabular-Q, and Deep-Q Network (DQN) are proposed to maximize the long-term network throughput of TUEs while guaranteeing the data rate requirements of UAVs. With increasing number of UAVs and TUEs and dynamic wireless environment, we further propose a Muting Optimization Scheme and Dynamic time-frequency Scheduling (MOSDS) algorithm to increase throughput and satisfactory level for both UAVs and TUEs. Simulation results show that the proposed algorithms achieve 80% performance improvement of throughput of UAV and TUE networks and mitigate the interference among them. Also, the proposed MOSDS-DQN shows 18% improvement compared to the DQN algorithm.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"9 6","pages":"1596-1609"},"PeriodicalIF":8.6,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62525238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cell-Level RSRP Estimation With the Image-to-Image Wireless Propagation Model Based on Measured Data 利用基于测量数据的图像到图像无线传播模型进行细胞级 RSRP 估算

IF 8.6 1区计算机科学

IEEE Transactions on Cognitive Communications and Networking Pub Date : 2023-08-23 DOI: 10.1109/TCCN.2023.3307945

Yi Zheng;Ji Wang;Xingwang Li;Jiping Li;Shouyin Liu

{"title":"Cell-Level RSRP Estimation With the Image-to-Image Wireless Propagation Model Based on Measured Data","authors":"Yi Zheng;Ji Wang;Xingwang Li;Jiping Li;Shouyin Liu","doi":"10.1109/TCCN.2023.3307945","DOIUrl":"10.1109/TCCN.2023.3307945","url":null,"abstract":"Wireless propagation models play a significant role in the deployments of base stations that are used to the reference signal receiving power (RSRP) of signal receivers in a cell. However, the existing models predict the RSRP of one receiver point in a cell at a time, which cannot be generalized to other cells. Motivated by this, a cell-level RSRP estimation method is proposed to directly predict the whole-cell RSRP by converting the RSRP estimation into an image-to-image translation. First, an environment map of each cell and measured RSRP for each cell is transformed into an image. Second, a cell-level image-to-image wireless propagation model based on conditional generative adversarial networks is proposed, which can directly predict the whole-cell RSRP at a time. In particular, a residual estimation method is proposed for the measurement RSRP data in the real world. The proposed method employs an empirical model to reveal the wireless propagation law as a priori knowledge and guide the training steps of the deep learning model. Finally, the experimental results verify the accuracy and generalization performance of the proposed image-to-image wireless propagation model.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"9 6","pages":"1412-1423"},"PeriodicalIF":8.6,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62525708","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adapting to Nonlinear Transmitters With Hybrid Model Training for Neural Receivers 利用神经接收器的混合模型训练适应非线性发射机

IF 8.6 1区计算机科学

IEEE Transactions on Cognitive Communications and Networking Pub Date : 2023-08-23 DOI: 10.1109/TCCN.2023.3307948

Martin Hedegaard Nielsen;Elisabeth De Carvalho;Ming Shen

{"title":"Adapting to Nonlinear Transmitters With Hybrid Model Training for Neural Receivers","authors":"Martin Hedegaard Nielsen;Elisabeth De Carvalho;Ming Shen","doi":"10.1109/TCCN.2023.3307948","DOIUrl":"10.1109/TCCN.2023.3307948","url":null,"abstract":"This paper proposes a novel hybrid model transfer learning approach designed for end-to-end OFDM neural receivers that effectively manage multiple channels and nonlinear transmitters. The hybrid model transfer learning method uses mixed Rayleigh channels and other obscured front-end models. This two-step process compensates for nonlinear front-end realizations and different channels, training a robust neural receiver. The neural receiver used is a deep complex convoluted network (DCCN), which replaces the conventional communication blocks with trainable layers that can correct the transmitter’s nonlinear performance and other imperfections in the physical layer. This training approach improves the DCCN by 35% for bit error rate (BER), and training time can be reduced by 19% compared to other training approaches for the same tasks while adapting to different fading channels and being robust to noise in power amplifier models. Measurements on both a 28 GHz active phased array in package (AiP) and a GaN Hemt PA show that the trained DCCN can adapt to nonlinear behavior without sacrificing BER. This work demonstrates how training for multiple device operation states and channels helps develop a robust deep neural network capable of demodulating OFDM symbols subject to nonlinear distortions in multiple channel environments without retraining.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"9 6","pages":"1657-1665"},"PeriodicalIF":8.6,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62525723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Message Passing Neural Network Versus Message Passing Algorithm for Cooperative Positioning 用于合作定位的信息传递神经网络与信息传递算法

IF 8.6 1区计算机科学

IEEE Transactions on Cognitive Communications and Networking Pub Date : 2023-08-23 DOI: 10.1109/TCCN.2023.3307953

Bernardo Camajori Tedeschini;Mattia Brambilla;Monica Nicoli

{"title":"Message Passing Neural Network Versus Message Passing Algorithm for Cooperative Positioning","authors":"Bernardo Camajori Tedeschini;Mattia Brambilla;Monica Nicoli","doi":"10.1109/TCCN.2023.3307953","DOIUrl":"10.1109/TCCN.2023.3307953","url":null,"abstract":"Cooperative Positioning (CP) relies on a network of connected agents equipped with sensing and communication technologies to improve the positioning performance of standalone solutions. In this paper, we develop a completely data-driven model combining Long Short-Term Memory (LSTM) and Message Passing Neural Network (MPNN) for CP, where agents estimate their states from inter-agent and ego-agent measurements. The proposed LSTM-MPNN model is derived by exploiting the analogy with the probability-based Message Passing Algorithm (MPA), from which the graph-based structure of the problem and message passing scheme are inherited. In our solution, the LSTM block predicts the motion of the agents, while the MPNN elaborates the node and edge embeddings for an effective inference of the agents’ state. We present numerical evidence that our approach can enhance position estimation, while being at the same time an order of magnitude less complex than typical particle-based implementations of MPA for non-linear problems. In particular, the presented LSTM-MPNN model can reduce the error on agents’ positioning to one third compared to MPA-based CP, it holds a higher convergence speed and better exploits cooperation among agents.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"9 6","pages":"1666-1676"},"PeriodicalIF":8.6,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10227084","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62525264","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Traffic Priority-Aware Multi-User Distributed Dynamic Spectrum Access: A Multi-Agent Deep RL Approach 流量优先感知的多用户分布式动态频谱接入：多代理深度 RL 方法

IF 8.6 1区计算机科学

IEEE Transactions on Cognitive Communications and Networking Pub Date : 2023-08-23 DOI: 10.1109/TCCN.2023.3307944

Shuying Zhang;Zuyao Ni;Linling Kuang;Chunxiao Jiang;Xiaohui Zhao

{"title":"Traffic Priority-Aware Multi-User Distributed Dynamic Spectrum Access: A Multi-Agent Deep RL Approach","authors":"Shuying Zhang;Zuyao Ni;Linling Kuang;Chunxiao Jiang;Xiaohui Zhao","doi":"10.1109/TCCN.2023.3307944","DOIUrl":"10.1109/TCCN.2023.3307944","url":null,"abstract":"Real-time information exchange on traffic and channel selection results among users in dynamic spectrum access (DSA) system consumes scarce spectrum resources. However, it is difficult to avoid collision and improve system-wide global utility simultaneously without assistance of these information in a distributed way. To solve this problem, we propose a multi-agent deep reinforcement learning (RL) based traffic priority-aware multi-user distributed DSA scheme for a multiple orthogonal channels scenario. Different from the conventional approaches for throughput sum maximization, we maximize a total network utility parameterized by the situation of each user’s traffic buffer queue. This scheme includes off-line centralized training and distributed execution. The deep Q-learning neural network (DQN) of each user is trained by an offline simulator with global information to learn near-optimal channel selection policies from the transition history. The input of DQN requires only user’s local observation to ensure that the scheme based on the trained DQNs can be executed in a distributed way. Simulation results show that the proposed scheme compared with benchmark algorithms can achieve about 90% or more of performance of Genie-aided algorithm based on global information, and is much better than random-type algorithms.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"9 6","pages":"1454-1471"},"PeriodicalIF":8.6,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62525522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Learning-Based MIMO Detection With Dynamic Spatial Modulation 基于学习的多输入多输出检测与动态空间调制

IF 8.6 1区计算机科学

IEEE Transactions on Cognitive Communications and Networking Pub Date : 2023-08-21 DOI: 10.1109/TCCN.2023.3306853

Le He;Lisheng Fan;Xianfu Lei;Xiaohu Tang;Pingzhi Fan;Arumugam Nallanathan

{"title":"Learning-Based MIMO Detection With Dynamic Spatial Modulation","authors":"Le He;Lisheng Fan;Xianfu Lei;Xiaohu Tang;Pingzhi Fan;Arumugam Nallanathan","doi":"10.1109/TCCN.2023.3306853","DOIUrl":"10.1109/TCCN.2023.3306853","url":null,"abstract":"In this paper, we investigate signal detection in emerging dynamic spatial modulation (DSM) based MIMO systems, where the existing mapping and detection methods do not work efficiently. In order to address this issue, we begin by proposing a combinatorial mapping-based DSM (CM-DSM) scheme in this paper. The proposed CM-DSM scheme employs a combinatorial 3D mapping to address the detection ambiguity by leveraging the combinatorial nature of DSM. Additionally, this mapping helps construct an appropriate decision tree for the optimal signal detection. By leveraging the resulting tree, we further propose a memory-bounded tree search (METS) algorithm, which efficiently finds the maximum likelihood (ML) estimate. To further enhance detection efficiency, we propose a deep learning boosted version of METS (DL-METS), which efficiently reduces the computational complexity via estimating the optimal heuristic function. Simulation results show that both the proposed METS and DL-METS work well in the considered system. In particular, the proposed DL-METS achieves nearly optimal detection performance while maintaining almost the lowest expected computational complexity, which strongly validates the effectiveness of the proposed algorithm.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"9 6","pages":"1489-1502"},"PeriodicalIF":8.6,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62525682","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 19

Deep Joint Source-Channel Coding for Image Transmission With Visual Protection 用于图像传输与视觉保护的深度源-信道联合编码

IF 8.6 1区计算机科学

IEEE Transactions on Cognitive Communications and Networking Pub Date : 2023-08-21 DOI: 10.1109/TCCN.2023.3306851

Jialong Xu;Bo Ai;Wei Chen;Ning Wang;Miguel Rodrigues

{"title":"Deep Joint Source-Channel Coding for Image Transmission With Visual Protection","authors":"Jialong Xu;Bo Ai;Wei Chen;Ning Wang;Miguel Rodrigues","doi":"10.1109/TCCN.2023.3306851","DOIUrl":"10.1109/TCCN.2023.3306851","url":null,"abstract":"Joint source-channel coding (JSCC) has achieved great success due to the introduction of deep learning (DL). Compared to traditional separate source-channel coding (SSCC) schemes, the advantages of DL-based JSCC (DJSCC) include high spectrum efficiency, high reconstruction quality, and relief of “cliff effect”. However, it is difficult to couple existing secure communication mechanisms (e.g., encryption-decryption mechanism) with DJSCC in contrast with traditional SSCC schemes, which hinders the practical usage of this emerging technology. To this end, our paper proposes a novel method called DL-based joint protection and source-channel coding (DJPSCC) for images that can successfully protect the visual content of the plain image without significantly sacrificing image reconstruction performance. The idea of the design is to use a neural network to conduct visual protection, which converts the plain image to a visually protected one with the consideration of its interaction with DJSCC. During the training stage, the proposed DJPSCC method learns: 1) deep neural networks for image protection and image deprotection, and 2) an effective DJSCC network for image transmission in the protected domain. Compared to existing source protection methods applied with DJSCC transmission, the DJPSCC method achieves much better reconstruction performance.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"9 6","pages":"1399-1411"},"PeriodicalIF":8.6,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62525643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A Cognitive Multi-Carrier Radar for Communication Interference Avoidance via Deep Reinforcement Learning 通过深度强化学习规避通信干扰的认知多载波雷达

IF 8.6 1区计算机科学

IEEE Transactions on Cognitive Communications and Networking Pub Date : 2023-08-21 DOI: 10.1109/TCCN.2023.3306854

Zhao Shan;Pengfei Liu;Lei Wang;Yimin Liu

{"title":"A Cognitive Multi-Carrier Radar for Communication Interference Avoidance via Deep Reinforcement Learning","authors":"Zhao Shan;Pengfei Liu;Lei Wang;Yimin Liu","doi":"10.1109/TCCN.2023.3306854","DOIUrl":"10.1109/TCCN.2023.3306854","url":null,"abstract":"Spectrum sharing between the radar and communication systems has become increasingly prevalent in recent years, therefore reducing the communication interference is a critical issue for radar. Deep reinforcement learning (DRL) based frequency allocation is a popular approach to solving the problem, especially in the highly dynamic spectrum. However, most DRL based methods suffer from low training efficiency due to the limited channel state information (CSI). To address the challenge, we propose a cognitive multi-carrier radar (CMCR), which acquires more CSI in one transmission and thus can learn the spectrum evolution faster. The frequency allocation problem for the CMCR is formulated as a partially observable Markov decision process which is hard to solve due to the combinatorial action space. To this end, we use the Iteratively Selecting approach along with the Proximal Policy Optimization (ISPPO) to solve it. To further enhance the performance of the CMCR in a short-term task, we pre-train the policy with model agnostic meta learning (MAML). Simulation results show that the CMCR learns fast and achieves an excellent detection ability in a congested spectrum on the basis of the ISPPO method. Besides, we also illustrate the efficiency of the MAML pre-training.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"9 6","pages":"1561-1578"},"PeriodicalIF":8.6,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62525693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Lightweight Automatic Modulation Classification via Progressive Differentiable Architecture Search 通过渐进式可微分架构搜索实现轻量级自动调制分类

IF 8.6 1区计算机科学

IEEE Transactions on Cognitive Communications and Networking Pub Date : 2023-08-18 DOI: 10.1109/TCCN.2023.3306391

Xixi Zhang;Xiaofeng Chen;Yu Wang;Guan Gui;Bamidele Adebisi;Hikmet Sari;Fumiyuki Adachi

{"title":"Lightweight Automatic Modulation Classification via Progressive Differentiable Architecture Search","authors":"Xixi Zhang;Xiaofeng Chen;Yu Wang;Guan Gui;Bamidele Adebisi;Hikmet Sari;Fumiyuki Adachi","doi":"10.1109/TCCN.2023.3306391","DOIUrl":"10.1109/TCCN.2023.3306391","url":null,"abstract":"Automatic modulation classification (AMC) is a key step of signal demodulation that determines whether the receiver can correctly receive the transmitted signal without prior knowledge of the modulation type. Deep learning (DL) based AMC methods have been proven to achieve excellent performances. However, these DL-based methods rely heavily on expert experience to design neural network structures. These hand-designed networks have fixed architectures and lack flexibility, which often leads to insufficient model generalization. Neural architecture search (NAS) is a vital direction for automatic machine learning (AutoML), which can solve the shortcomings of hand-designed network architectures. In this paper, according to the specific modulation classification task, we propose a lightweight progressive differentiable architecture search-based AMC (PDARTS-AMC) method to search for a very lightweight network with great performance. In addition, the optimal architecture searched on dataset simulated by MATLAB is transferred to the RadioML2016.10B task, to verify the robustness and generalization of the proposed method. Experimental results show that the proposed PDARTS-AMC method both improves the classification accuracy and reduces the computational cost when compared with existing classical AMC methods.","PeriodicalId":13069,"journal":{"name":"IEEE Transactions on Cognitive Communications and Networking","volume":"9 6","pages":"1519-1530"},"PeriodicalIF":8.6,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"62525629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0