Mobile Networks and Applications

A tunable N-path band-pass filter is presented in this paper. Switched capacitor circuit and 6th topology are adopted to realize high-order harmonic suppression and tunability. the transfer function of N-path filter with the non-ideal factors such as parasitic capacitance and switching resistance is deduced. The N-path filter circuit is designed and simulation, including six-phase non-overlapping clock generation module, N-path switched capacitor unit with harmonic suppression, active gyrator and low-pass filter for suppressing high-frequency harmonics. The circuit can achieve adjustable center frequency of 50-100 MHz, adjustable bandwidth of 1.5-45 MHz.

In recent years, the use of edge computing to solve the problem of limited resources in the IoV has attracted more and more attention. Vehicles can upload tasks to the edge servers within their radio range for computing cooperation and offloading. However, due to shared resources among edge servers, the uneven distribution of vehicles may lead to many problems such as uneven tasks distribution, unbalanced load, and low computing efficiency on different edge servers. On the other hand, most of the existing works are proposed to address the load unbalance issue of edge servers with the help of remote cloud or vehicle cloud, but still leading to resource wasting problems especially for edge servers with a light load. To address this issue, a task transfer scheme has been proposed among different edge servers in this paper. First, we designed a partitioned and hierarchical software-defined network architecture for IoV. After that, a load balancing model is proposed under this network architecture, in which the global controller can manage and schedule the local tasks from each edge server. Next, we proved that the task allocation problem for our load balancing model under the proposed network architecture is an NP-hard problem. To solve this problem, a Deep Q-Network (DQN) model is proposed to minimize the mean square deviation of loads among different edge servers. Numerical results show that our proposed load balancing model can significantly improve the resource utilization of edge servers as well as reduce the computational latency of vehicular tasks.

In order to improve the quality of online distance education and students' online learning, an intelligent online distance education decision-making method based on cloud computing is proposed. Using computing, we provide decision-making resources for online education decision-making through the resource layer; adopt the G1 deviation maximization method to calculate the combination weight, and determine the optimal decision-making scheme in combination with bipolar binary semantics and cloud model; moreover, we provide human–computer interaction windows to view the decision-making scheme at the application layer; and complete the optimal decision-making for intelligent online education by providing the management function of cloud computing services. Experimental results show that this method can effectively obtain a decision-making scheme for network education. After the application of this method, the students' learning ability and academic performance have been significantly improved.

The dynamic packet reservation multiple access (DPRMA) scheme, a medium access control protocol for wireless multimedia applications, is proposed and investigated. DPRMA allows the integration of multiple traffic types through a single access control mechanism that permits users to specify their immediate bandwidth requirements. The primary feature of DPRMA is the dynamic matching of the traffic source generation rates with the assigned portion of the channel capacity. This is accomplished by a control algorithm that regulates the actual amount of channel capacity assigned to users. To support multimedia communication, channel capacity assignments are prioritized by traffic type. The performance of the scheme is evaluated and the scheme is shown to perform well in a system with voice, video conferencing, and data users present. It is also shown to provide improved performance over a system with a modified version of the packet reservation multiple access (PRMA) scheme. Furthermore, several system parameters are studied and optimized.

We propose a distributed channel allocation algorithm based on a threshold scheme, called D-CAT, for cellular mobile networks. The algorithm employs two thresholds: (i) a heavy threshold for determining whether a cell is heavy, or overloaded, and for triggering the channel allocation algorithm; and (ii) a target threshold for indicating the target number of free channels that a heavy cell intends to acquire. Based on the two-threshold scheme, the D-CAT algorithm can determine the optimal number of free channels as well as the cell(s) from where a heavy cell should import channels in order to satisfy the required channel demand. Simulation experiments and analyses show that the proposed algorithm incurs lower overhead for channel allocation and is more efficient in terms of channel utilization than other distributed channel allocation algorithms. It also outperforms other centralized and distributed algorithms in terms of call blocking probability.

The use of deep learning (DL) techniques for side-channel analysis (SCA) has become increasingly popular recently. This paper assesses the application of DL to non-profiled SCA attacks on AES-128 encryption, taking into consideration various challenges, including high-dimensional data, imbalanced classes, and countermeasures. The paper proposes using a multi-layer perceptron (MLP) and a convolutional neural network (CNN) to tackle hiding protection methods, such as noise generation and de-synchronization. The paper also introduces a technique called significant Hamming weight (SHW) labeling and a dataset reconstruction approach to handle imbalanced datasets, resulting in a reduction of 30% in the number of measurements required for training. The experimental results on reconstructed dataset demonstrate improved performance in DL-based SCA compared to binary labeling techniques, especially in the face of hiding countermeasures. This leads to better results for non-profiled attacks on different targets, such as ASCAD and RISC-V microcontrollers.

The main motivation for the present work stems from the wide gap which exists between the research efforts devoted to developing formal descriptions for communication protocols and the effective development methodologies used in industrial implementations. We apply Object-Oriented (OO) modelling principles to networking protocols, exploring the potential for producing re-useable software modules by discovering the underlying generic class structures and behaviour. Petri Nets (PNs) are used to derive re-useable model elements and a slightly modified TTCN notation is used for message sequence encoding. This demonstrates a formal, practical approach to the development of a protocol implementation through OO modelling. Our utilisation of PNs in the context of object based modelling allows for isolation of the behavioural characterisation of objects into a separate design plane, treated as a meta-level object control. This separation permits greater execution flexibility of the underlying object models. It is that very aspect of our modelling approach which can be utilised in software implementations where dynamically determined ‘re-programming’ (i.e., change of procedures) is needed. For example, one of the requirements in wireless networking software is the ability to cope with ever-changing transmission/reception conditions and that, in turn, creates greatly varying error rates. Similarly, handoff procedures create situations where dynamically determined change of operational modes is required. To illustrate the modelling concepts, the paper addresses the problem of inter-layer communication among multiple protocol entities (PEs), assuming the standard ISO/OSI Reference Model. A generalised model called the Inter-Layer Communication (ILC) Model is proposed. An example of a PE based on the Alternating-Bit Protocol (ABP) is also discussed. The final example demonstrates how meta-level object control (PNs) allows for the dynamic selection of different ARQ based algorithms.