IEEE Transactions on Machine Learning in Communications and Networking最新文献

{"title":"Reinforcement-Learning-Based Trajectory Design and Phase-Shift Control in UAV-Mounted-RIS Communications","authors":"Tianjiao Sun;Sixing Yin;Li Deng;F. Richard Yu","doi":"10.1109/TMLCN.2024.3502576","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3502576","url":null,"abstract":"Taking advantages of both unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs), UAV-mounted-RIS systems are expected to enhance transmission performance in complicated wireless environments. In this paper, we focus on system design for a UAV-mounted-RIS system and investigate joint optimization for the RIS’s phase shift and the UAV’s trajectory. To cope with the practical issue of inaccessible information on the user terminals’ (UTs) location and channel state, a reinforcement learning (RL)-based solution is proposed to find the optimal policy with finite steps of “trial-and-error”. As the action space is continuous, the deep deterministic policy gradient (DDPG) algorithm is applied to train the RL model. However, the online interaction between the agent and environment may lead to instability during the training and the assumption of (first-order) Markovian state transition could be impractical in real-world problems. Therefore, the decision transformer (DT) algorithm is employed as an alternative for RL model training to adapt to more general situations of state transition. Experimental results demonstrate that the proposed RL solutions are highly efficient in model training along with acceptable performance close to the benchmark, which relies on conventional optimization algorithms with the UT’s locations and channel parameters explicitly known beforehand.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"163-175"},"PeriodicalIF":0.0,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10758222","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A2PC: Augmented Advantage Pointer-Critic Model for Low Latency on Mobile IoT With Edge Computing","authors":"Rodrigo Carvalho;Faroq Al-Tam;Noélia Correia","doi":"10.1109/TMLCN.2024.3501217","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3501217","url":null,"abstract":"As a growing trend, edge computing infrastructures are starting to be integrated with Internet of Things (IoT) systems to facilitate time-critical applications. These systems often require the processing of data with limited usefulness in time, so the edge becomes vital in the development of such reactive IoT applications with real-time requirements. Although different architectural designs will always have advantages and disadvantages, mobile gateways appear to be particularly relevant in enabling this integration with the edge, particularly in the context of wide area networks with occasional data generation. In these scenarios, mobility planning is necessary, as aspects of the technology need to be aligned with the temporal needs of an application. The nature of this planning problem makes cutting-edge deep reinforcement learning (DRL) techniques useful in solving pertinent issues, such as having to deal with multiple dimensions in the action space while aiming for optimum levels of system performance. This article presents a novel scalable DRL model that incorporates a pointer-network (Ptr-Net) and an actor-critic algorithm to handle complex action spaces. The model synchronously determines the gateway location and visit time. Ultimately, the gateways are able to attain high-quality trajectory planning with reduced latency.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"3 ","pages":"1-16"},"PeriodicalIF":0.0,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10755120","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142821217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing Power Allocation in HAPs Assisted LEO Satellite Communications","authors":"Zain Ali;Zouheir Rezki;Mohamed-Slim Alouini","doi":"10.1109/TMLCN.2024.3491054","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3491054","url":null,"abstract":"The next generation of communication devices will require robust connectivity for millions of ground devices such as sensors or mobile devices in remote or disaster-stricken areas to be connected to the network. Non-terrestrial network (NTN) nodes can play a vital role in fulfilling these requirements. Specifically, low-earth orbiting (LEO) satellites have emerged as an efficient and cost-effective technique to connect devices over long distances through space. However, due to their low power and environmental limitations, LEO satellites may require assistance from aerial devices such as high-altitude platforms (HAPs) or unmanned aerial vehicles to forward their data to the ground devices. Moreover, the limited power available at the NTNs makes it crucial to utilize available resources efficiently. In this paper, we present a model in which a LEO satellite communicates with multiple ground devices with the help of HAPs that relay LEO data to the ground devices. We formulate the problem of optimizing power allocation at the LEO satellite and all the HAPs to maximize the sum-rate of the system. To take advantage of the benefits of free-space optical (FSO) communication in satellites, we consider the LEO transmitting data to the HAPs on FSO links, which are then broadcast to the connected ground devices on radio frequency channels. We transform the complex non-convex problem into a convex form and compute the Karush-Kuhn-Tucker (KKT) conditions-based solution of the problem for power allocation at the LEO satellite and HAPs. Then, to reduce computation time, we propose a soft actor-critic (SAC) reinforcement learning (RL) framework that provides the solution in significantly less time while delivering comparable performance to the KKT scheme. Our simulation results demonstrate that the proposed solutions provide excellent performance and are scalable to any number of HAPs and ground devices in the system.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1661-1677"},"PeriodicalIF":0.0,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10741546","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Attention-Aided Outdoor Localization in Commercial 5G NR Systems","authors":"Guoda Tian;Dino Pjanić;Xuesong Cai;Bo Bernhardsson;Fredrik Tufvesson","doi":"10.1109/TMLCN.2024.3490496","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3490496","url":null,"abstract":"The integration of high-precision cellular localization and machine learning (ML) is considered a cornerstone technique in future cellular navigation systems, offering unparalleled accuracy and functionality. This study focuses on localization based on uplink channel measurements in a fifth-generation (5G) new radio (NR) system. An attention-aided ML-based single-snapshot localization pipeline is presented, which consists of several cascaded blocks, namely a signal processing block, an attention-aided block, and an uncertainty estimation block. Specifically, the signal processing block generates an impulse response beam matrix for all beams. The attention-aided block trains on the channel impulse responses using an attention-aided network, which captures the correlation between impulse responses for different beams. The uncertainty estimation block predicts the probability density function of the user equipment (UE) position, thereby also indicating the confidence level of the localization result. Two representative uncertainty estimation techniques, the negative log-likelihood and the regression-by-classification techniques, are applied and compared. Furthermore, for dynamic measurements with multiple snapshots available, we combine the proposed pipeline with a Kalman filter to enhance localization accuracy. To evaluate our approach, we extract channel impulse responses for different beams from a commercial base station. The outdoor measurement campaign covers Line-of-Sight (LoS), Non Line-of-Sight (NLoS), and a mix of LoS and NLoS scenarios. The results show that sub-meter localization accuracy can be achieved.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1678-1692"},"PeriodicalIF":0.0,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10741343","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142694615","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Information Bottleneck-Based Domain Adaptation for Hybrid Deep Learning in Scalable Network Slicing","authors":"Tianlun Hu;Qi Liao;Qiang Liu;Georg Carle","doi":"10.1109/TMLCN.2024.3485520","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3485520","url":null,"abstract":"Network slicing enables operators to efficiently support diverse applications on a shared infrastructure. However, the evolving complexity of networks, compounded by inter-cell interference, necessitates agile and adaptable resource management. While deep learning offers solutions for coping with complexity, its adaptability to dynamic configurations remains limited. In this paper, we propose a novel hybrid deep learning algorithm called IDLA (integrated deep learning with the Lagrangian method). This integrated approach aims to enhance the scalability, flexibility, and robustness of slicing resource allocation solutions by harnessing the high approximation capability of deep learning and the strong generalization of classical non-linear optimization methods. Then, we introduce a variational information bottleneck (VIB)-assisted domain adaptation (DA) approach to enhance integrated deep learning and Lagrangian method (IDLA)’s adaptability across diverse network environments and conditions. We propose pre-training a variational information bottleneck (VIB)-based Quality of Service (QoS) estimator, using slice-specific inputs shared across all source domain slices. Each target domain slice can deploy this estimator to predict its QoS and optimize slice resource allocation using the IDLA algorithm. This VIB-based estimator is continuously fine-tuned with a mixture of samples from both the source and target domains until convergence. Evaluating on a multi-cell network with time-varying slice configurations, the VIB-enhanced IDLA algorithm outperforms baselines such as heuristic and deep reinforcement learning-based solutions, achieving twice the convergence speed and 16.52% higher asymptotic performance after slicing configuration changes. Transferability assessment demonstrates a 25.66% improvement in estimation accuracy with VIB, especially in scenarios with significant domain gaps, highlighting its robustness and effectiveness across diverse domains.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1642-1660"},"PeriodicalIF":0.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10734592","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Polarization-Aware Channel State Prediction Using Phasor Quaternion Neural Networks","authors":"Anzhe Ye;Haotian Chen;Ryo Natsuaki;Akira Hirose","doi":"10.1109/TMLCN.2024.3485521","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3485521","url":null,"abstract":"The performance of a wireless communication system depends to a large extent on the wireless channel. Due to the multipath fading environment during the radio wave propagation, channel prediction plays a vital role to enable adaptive transmission for wireless communication systems. Predicting various channel characteristics by using neural networks can help address more complex communication environments. However, achieving this goal typically requires the simultaneous use of multiple distinct neural models, which is undoubtedly unaffordable for mobile communications. Therefore, it is necessary to enable a simpler structure to simultaneously predict multiple channel characteristics. In this paper, we propose a fading channel prediction method using phasor quaternion neural networks (PQNNs) to predict the polarization states, with phase information involved to enhance the channel compensation ability. We evaluate the performance of the proposed PQNN method in two different fading situations in an actual environment, and we find that the proposed scheme provides 2.8 dB and 4.0 dB improvements at bit error rate (BER) of \u0000<inline-formula> <tex-math>$10^{-4}$ </tex-math></inline-formula>\u0000, showing better BER performance in light and serious fading situations, respectively. This work also reveals that by treating polarization information and phase information as a single entity, the model can leverage their physical correlation to achieve improved performance.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1628-1641"},"PeriodicalIF":0.0,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10731896","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142579173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TWIRLD: Transformer Generated Terahertz Waveform for Improved Radio Link Distance","authors":"Shuvam Chakraborty;Claire Parisi;Dola Saha;Ngwe Thawdar","doi":"10.1109/TMLCN.2024.3483111","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3483111","url":null,"abstract":"terahertz (THz) band communication is envisioned as one of the leading technologies to meet the exponentially growing data rate requirements of emerging and future wireless communication networks. Utilizing the contiguous bandwidth available at THz frequencies requires a transceiver design tailored to tackle issues existing at these frequencies such as strong propagation and absorption loss, small scale fading (e.g. scattering, reflection, refraction), hardware non-linearity, etc. In prior works, multicarrier waveforms (e.g., Orthogonal Frequency Division Multiplexing (OFDM)) are shown to be efficient in tackling some of these issues. However, OFDM introduces a drawback in the form of peak-to-average power ratio (PAPR) which, compounded with strong propagation and absorption loss and high noise power due to large bandwidth at THz and sub-THz frequencies, severely limits link distances and, in turn, capacity, preventing efficient bandwidth usage. In this work, we propose \u0000<monospace>TWIRLD</monospace>\u0000 - a deep learning (DL)-based joint optimization method, modeled and implemented as components of end-to-end transceiver chain. \u0000<monospace>TWIRLD</monospace>\u0000 performs a symbol remapping at baseband of OFDM signals, which increases average transmit power while also optimizing the bit error rate (BER). We provide theoretical analysis, statistical equivalence of \u0000<monospace>TWIRLD</monospace>\u0000 to the ideal receiver, and comprehensive complexity and footprint estimates. We validate \u0000<monospace>TWIRLD</monospace>\u0000 in simulation showing link distance improvement of up to 91% and compare the results with legacy and state of the art methods and their enhanced versions. Finally, \u0000<monospace>TWIRLD</monospace>\u0000 is validated with over the air (OTA) communication using a state-of-the-art testbed at 140 GHz up to a bandwidth of 5 GHz where we observe improvement of up to 79% in link distance accommodating for practical channel and other transmission losses.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1595-1614"},"PeriodicalIF":0.0,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10720922","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142550544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Recursive GNNs for Learning Precoding Policies With Size-Generalizability","authors":"Jia Guo;Chenyang Yang","doi":"10.1109/TMLCN.2024.3480044","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3480044","url":null,"abstract":"Graph neural networks (GNNs) have been shown promising in optimizing power allocation and link scheduling with good size generalizability and low training complexity. These merits are important for learning wireless policies under dynamic environments, which partially come from the matched permutation equivariance (PE) properties of the GNNs to the policies to be learned. Nonetheless, it has been noticed in literature that only satisfying the PE property of a precoding policy in multi-antenna systems cannot ensure a GNN for learning precoding to be generalizable to the unseen problem scales. Incorporating models with GNNs helps improve size generalizability, which however is only applicable to specific problems, settings, and algorithms. In this paper, we propose a framework of size generalizable GNNs for learning precoding policies that are purely data-driven and can learn wireless policies including but not limited to baseband and hybrid precoding in multi-user multi-antenna systems. To this end, we first find a special structure of each iteration of several numerical algorithms for optimizing precoding, from which we identify the key characteristics of a GNN that affect its size generalizability. Then, we design size-generalizable GNNs that are with these key characteristics and satisfy the PE properties of precoding policies in a recursive manner. Simulation results show that the proposed GNNs can be well-generalized to the number of users for learning baseband and hybrid precoding policies, require much fewer samples than existing GNNs and shorter inference time than numerical algorithms to achieve the same performance.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1558-1579"},"PeriodicalIF":0.0,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10716720","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"NeIL: Intelligent Replica Selection for Distributed Applications","authors":"Faraz Ahmed;Lianjie Cao;Ayush Goel;Puneet Sharma","doi":"10.1109/TMLCN.2024.3479109","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3479109","url":null,"abstract":"Distributed applications such as cloud gaming, streaming, etc., are increasingly using edge-to-cloud infrastructure for high availability and performance. While edge infrastructure brings services closer to the end-user, the number of sites on which the services need to be replicated has also increased. This makes replica selection challenging for clients of the replicated services. Traditional replica selection methods including anycast based methods and DNS re-directions are performance agnostic, and clients experience degraded network performance when network performance dynamics are not considered in replica selection. In this work, we present a client-side replica selection framework NeIL, that enables network performance aware replica selection. We propose to use bandits with experts based Multi-Armed Bandit (MAB) algorithms and adapt these algorithms for replica selection at individual clients without centralized coordination. We evaluate our approach using three different setups including a distributed Mininet setup where we use publicly available network performance data from the Measurement Lab (M-Lab) to emulate network conditions, a setup where we deploy replica servers on AWS, and finally we present results from a global enterprise deployment. Our experimental results show that in comparison to greedy selection, NeIL performs better than greedy for 45% of the time and better than or equal to greedy selection for 80% of the time resulting in a net gain in end-to-end network performance. On AWS, we see similar results where NeIL performs better than or equal to greedy for 75% of the time. We have successfully deployed NeIL in a global enterprise remote device management service with over 4000 client devices and our analysis shows that NeIL achieves significantly better tail service quality by cutting the \u0000<inline-formula> <tex-math>$99th$ </tex-math></inline-formula>\u0000 percentile tail latency from 5.6 seconds to 1.7 seconds.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1580-1594"},"PeriodicalIF":0.0,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10714467","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142540425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Intelligent and Programmable Data Plane for QoS-Aware Packet Processing","authors":"Muhammad Saqib;Halime Elbiaze;Roch H. Glitho;Yacine Ghamri-Doudane","doi":"10.1109/TMLCN.2024.3475968","DOIUrl":"https://doi.org/10.1109/TMLCN.2024.3475968","url":null,"abstract":"One of the main features of data plane programmability is that it allows the easy deployment of a programmable network traffic management framework. One can build an early-stage Internet traffic classifier to facilitate effective Quality of Service (QoS) provisioning. However, maintaining accuracy and efficiency (i.e., processing delay/pipeline latency) in early-stage traffic classification is challenging due to memory and operational constraints in the network data plane. Additionally, deploying network-wide flow-specific rules for QoS leads to significant memory usage and overheads. To address these challenges, we propose new architectural components encompassing efficient processing logic into the programmable traffic management framework. In particular, we propose a single feature-based traffic classification algorithm and a stateless QoS-aware packet scheduling mechanism. Our approach first focuses on maintaining accuracy and processing efficiency in early-stage traffic classification by leveraging a single input feature - sequential packet size information. We then use the classifier to embed the Service Level Objective (SLO) into the header of the packets. Carrying SLOs inside the packet allows QoS-aware packet processing through admission control-enabled priority queuing. The results show that most flows are properly classified with the first four packets. Furthermore, using the SLO-enabled admission control mechanism on top of the priority queues enables stateless QoS provisioning. Our approach outperforms the classical and objective-based priority queuing in managing heterogeneous traffic demands by improving network resource utilization.","PeriodicalId":100641,"journal":{"name":"IEEE Transactions on Machine Learning in Communications and Networking","volume":"2 ","pages":"1540-1557"},"PeriodicalIF":0.0,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10706883","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142443062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}