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Multi-access edge computing (MEC) has already shown great potential in enabling mobile devices to bear the computation-intensive applications by offloading some computing jobs to a nearby access point (AP) integrated with a MEC server (MES). However, due to the varying network conditions and limited computational resources of the MES, the offloading decisions taken by a mobile device and the computational resources allocated by the MES can be formulated as a mixed-integer nonlinear programming (MINLP) problem, which may not be optimized with the lowest cost. In this paper, we propose a novel offloading framework for the multi-server MEC network where each AP is equipped with an MES assisting mobile users (MUs) in executing computation-intensive jobs via offloading. Specifically, we formulate the offloading decision problem as a multiclass classification problem and formulate the MES computational resource allocation problem as a regression problem. Then a multi-task learning based feedforward neural network (MTFNN) model is designed and trained to jointly optimize the offloading decision and computational resource allocation. Numerical results show that the proposed MTFNN outperforms the conventional optimization method in terms of inference accuracy and computational complexity.

Intelligent capabilities are of utmost importance in future wireless communication systems. For optimum resource utilization, wireless communication systems require knowledge of the prevalent situation in a frequency band through learning. To learn appropriately, it is imperative for practitioners to select the right parameters for building robust data-driven learning models as well as use the appropriate algorithms and performance evaluation methods. In this paper, we evaluate the performance of deep learning models against the performance of other machine learning methods for wireless communication systems. We explore the different wireless communication scenarios in which deep learning can be used given Radio Frequency (RF) data, and evaluate its performance in various scenarios. Furthermore, we express it as a distribution alignment problem in which deep learning models do not perform well when learning from RF data of a particular distribution and evaluating on RF data from a different distribution. We also discuss our results in the light of how signal quality affects deep learning model leveraging on the knowledge from computer vision domain. The effect of Signal-to-Noise Ratio (SNR) selection for training on the model performance as it relates to practical implementation of deep learning in communications systems is also discussed. From our analysis, we conclude that the design and use of RF spectrum learning must be tailored to each specific scenario being considered in practice.

In this paper, we propose Device Authentication Code (DAC), a novel method for authenticating IoT devices with wireless interface by exploiting their radio frequency (RF) signatures. The proposed DAC is based on RF fingerprinting, information theoretic method, feature learning, and discriminatory power of deep learning. Specifically, an autoencoder is used to automatically extract features from the RF traces, and the reconstruction error is used as the DAC and this DAC is unique to the device and the particular message of interest. Then Kolmogorov-Smirnov (K-S) test is used to match the distribution of the reconstruction error generated by the autoencoder and the received message, and the result will determine whether the device of interest belongs to an authorized user. We validate this concept on two experimentally collected RF traces from six ZigBee and five universal software defined radio peripheral (USRP) devices, respectively. The traces span a range of Signalto- Noise Ratio by varying locations and mobility of the devices and channel interference and noise to ensure robustness of the model. Experimental results demonstrate that DAC is able to prevent device impersonation by extracting salient features that are unique to any wireless device of interest and can be used to identify RF devices. Furthermore, the proposed method does not need the RF traces of the intruder during model training yet be able to identify devices not seen during training, which makes it practical.

Internet of Things (IoT) and 4G/5G wireless networks have added huge number of devices and new services, where commercial-of-the-shelf (COTS) IoT devices have been deployed extensively. To ensure secure operations of these systems with wireless transmission capabilities, Radio Frequency (RF) surveillance is important to monitor their activities in RF spectrum and detect unauthorized IoT devices. Specifically, in order to prevent an adversary from impersonating legitimate users using identical devices from the same manufacturer, unique “signatures” must be obtained for every individual device in order to uniquely identify each device. In this study, a novel intrusion detection method is proposed to detect unauthorized IoT devices using deep learning. The proposed method is based on RF fingerprinting since physical layer based features are device specific and more difficult to impersonate. RF traces are collected from six “identical” ZigBee devices via a USRP based test bed. The traces span a range of Signal-to-Noise Ratio, to ensure robustness of the model. A convolutional neural network is used to extract features from the RF traces, and dimension reduction and de-correlation are performed on the extracted features. The reduced features are then clustered to identify IoT devices. We show that the proposed method is able to identify devices that are not observed during training. The results not only highlight the benefit of deep learning based feature extraction, but also show promising prospects for being able to distinguish new devices (classes) that are not observed during training.

Multi-access edge computing (MEC) has already shown the potential in enabling mobile devices to bear the computation-intensive applications by offloading some tasks to a nearby access point (AP) integrated with a MEC server (MES). However, due to the varying network conditions and limited computation resources of the MES, the offloading decisions taken by a mobile device and the computational resources allocated by the MES may not be efficiently achieved with the lowest cost. In this paper, we propose a dynamic offloading framework for the MEC network, in which the uplink non-orthogonal multiple access (NOMA) is used to enable multiple devices to upload their tasks via the same frequency band. We formulate the offloading decision problem as a multiclass classification problem and formulate the MES computational resource allocation problem as a regression problem. Then a multi-task learning based feedforward neural network (MTFNN) model is designed to jointly optimize the offloading decision and computational resource allocation. Numerical results illustrate that the proposed MTFNN outperforms the conventional optimization method in terms of inference accuracy and computation complexity.

Ahstract- While hard sensor fusion is a highly developed discipline with vast methods, the inclusion of soft evidence continues to gain significant interest because soft sensors in the fusion process has certain merits, such as the ability to model attributes of interest (e.g., emotional level) that hard sensor may not. However, how to combine the hard/soft sensor data efficiently is a challenging problem, especially when the data set becomes large. In this study, a novel algorithm is proposed to apply the Conditional Core Theorem (CCT) in computing the Fagin-Halpern conditionals in the fusion of bodies of evidence with disparate frames of discernment. The computational complexity of the proposed algorithm is derived analytically and simulations are carried out to demonstrate the efficiency of the proposed algorithm.