Effects Of Different Network Parameters Research Articles

Switching in quantum networks: an optimization investigation

Quantum key distribution (QKD) promises information theoretic security. However, the distances over which complete security can be achieved are fundamentally limited in the absence of quantum repeaters. Thus, a key question is how to build a quantum network (QN) given this restriction. One paradigm that has been considered is trusted node (TN) quantum networks where intermediate trusted nodes are used as relays of quantum keys. Another paradigm is to route key channels through intermediate nodes optically, either through wavelength or fiber switching, thus avoiding the use of TNs. In both of these paradigms, a QKD receiver or transmitter at a specific node can be shared between multiple QKD transmitters or receivers at different nodes in order to reduce the overall costs; this sharing can be enabled via an optical switch. In this paper, we investigate the two paradigms for designing QNs. In the TN model we assume the Decoy BB84 protocol, whereas in the non-TN model, we employ twin-field QKD (TF-QKD) due to the increased single hop distances. We present mixed integer linear program models to optimize network design in both of these paradigms and use these to investigate the viability of switching in the network models as a method of sharing devices. We show that sharing of devices can provide cost reduction in QNs up to a certain transmission requirement rate between users in the TN model, while also providing benefits even at significantly higher transmission requirements in the TF-QKD model. The specific value of this rate is dependent on the network graph; however, for mesh topology TN networks this is expected to occur at average key transmission requirements of ∼1000−5000bits/s. We further use the models to investigate the effects of different network parameters, such as cooling costs, switch frequency, and device costs. We show that cooled detectors are useful in large TF-QKD networks, despite higher costs, but are only useful in TN networks when transmission requirements are very high or cooling is cheap. We also investigate how network costs vary with switching frequency and switch loss, showing that compromising for slightly faster switching times and higher loss switches does not significantly increase network costs; thus a significant effort in improving switch loss may not be necessary. Finally, we look at how the benefits of sharing devices change as the cost of devices changes, showing that for any non-negligible device cost, device sharing is always beneficial at low transmission requirements.

Computation Efficiency Maximization in Multi-UAV-Enabled Mobile Edge Computing Systems Based on 3D Deployment Optimization

Unmanned aerial vehicles (UAVs) have been widely devoted to mobile edge computing (MEC) systems that have limited resources to provide high-quality computing and communication services for Internet of Things (IoT) terminals. Energy-efficient computation and resource allocation are key issues for the sustainable operation of the above-mentioned systems. The 3D deployment optimization of multi-UAVs is also crucial to maximizing the system's computation efficiency. In this paper, we discuss a multi-UAV-enabled MEC system. To maximize the computation efficiency of the terminal system, we consider jointly optimizing the terminal's CPU frequency, transmission power, offloading correlation decision, and the 3D position and beamwidth of the UAV. Since the original problem is a mixed-integer nonlinear programming (MINLP) problem with a fractional structure, which is difficult to solve directly. Based on Dinkelbach's method, convex optimization theory, and greedy strategy, we simplify the mathematical model and propose a four-stage alternating iterative computation efficiency maximization algorithm(FICEM) to solve the problem. The simulation results indicate that the algorithm converges fast in various network scenarios and that its computation efficiency is better than that of the baseline algorithm. In addition, the simulation results also manifest the effect of different network parameters on the computation efficiency of the terminal system.

Adaptive–Persistent Nonorthogonal Random Access Scheme for URLL Massive IoT Networks

Critical massive Internet of Things networks are emerging technologies that face new challenges in designing transmission protocols for beyond 5G communication systems. Conventional transmission schemes are ill-suited to provide ultrareliable, lowlatency, and scalability requirements of IoT networks with the massive number of nodes having sporadic data traffic behavior. This article overcomes such challenges with proposing a random access transmission scheme that exploits nonorthogonal multiple access (NOMA) with short-packet transmissions and automatic request and repeat (ARQ) strategy with the limited number of retransmissions. To utilize the spectrum further and to meet ultrareliable low-latency requirement, an adaptive–persistent technique is proposed in which each node distributively controls its transmission based on the number of active devices without extra signaling. Since the nodes’ data traffic behavior is assumed sporadic, NOMA-based clustering is performed dynamically at each frame, avoiding additional signaling overhead. Network metrics, such as reliability, effective sum rate, and the distribution of packet latency, are analytically derived. Furthermore, the effects of different network parameters, such as blocklength, the maximum number of packet replicas, number of nodes, and number of resource blocks on network metrics, are investigated and compared with S-ALOHA-ARQ. The analysis show that the proposed scheme outperforms conventional schemes in terms of effective sum rate, reliability, and average packet latency.

An Optimized Convolutional Neural Network for the 3D Point-Cloud Compression.

Due to the tremendous volume taken by the 3D point-cloud models, knowing how to achieve the balance between a high compression ratio, a low distortion rate, and computing cost in point-cloud compression is a significant issue in the field of virtual reality (VR). Convolutional neural networks have been used in numerous point-cloud compression research approaches during the past few years in an effort to progress the research state. In this work, we have evaluated the effects of different network parameters, including neural network depth, stride, and activation function on point-cloud compression, resulting in an optimized convolutional neural network for compression. We first have analyzed earlier research on point-cloud compression based on convolutional neural networks before designing our own convolutional neural network. Then, we have modified our model parameters using the experimental data to further enhance the effect of point-cloud compression. Based on the experimental results, we have found that the neural network with the 4 layers and 2 strides parameter configuration using the Sigmoid activation function outperforms the default configuration by 208% in terms of the compression-distortion rate. The experimental results show that our findings are effective and universal and make a great contribution to the research of point-cloud compression using convolutional neural networks.

Joint QoS Aware Admission Control and Power Allocation in NOMA Downlink Networks

In this work, we talk about the problem of joint power allocation and user association based on quality-of-service for non-orthogonal multiple access (NOMA) to downlink networks. The problem is especially difficult due to its non-convex form and the large number of optimization variables, which are solved using two different nature-inspired algorithms with low complexity. We investigate the effect of different network parameters on increasing users. Numerical results show that, for a growing number of users, the problem is becoming increasingly difficult, which indicates the increasing network resources required to solve it. The results of the simulations show that using evolutionary algorithms is a fast and effective way to solve this kind of problem. Moreover, the NOMA advantage over OMA becomes clear as the number of users increases. Evolutionary techniques outperform randomly generated solutions, as expected.

Joint Optimization of Fractional Frequency Reuse and Cell Clustering for Dynamic TDD Small Cell Networks

In dense small cell networks, dynamic time-division duplex (D-TDD) technology has emerged as a promising solution to accommodate the fast variants of volatile traffic conditions because it allows each cell to dynamically configure the uplink and downlink transmission directions. However, the flexibility of traffic configuration introduces additional inter-cell interference, which largely deteriorates network throughput. This paper proposes an interference coordination technology for D-TDD small cell networks by integrating fractional frequency reuse (FFR) with cell clustering. To evaluate the system performance, we develop a theoretical framework to analytically characterize the mean packet throughput (MPT) performance by considering the impact of spatio-temporal traffic. The analytical model can be extended to further study the FFR-based D-TDD, clustered D-TDD, and traditional D-TDD networks. We verify the accuracy of our analysis through simulations and whereby explore the effect of different network parameters. Numerical results demonstrate that the proposed scheme outperforms clustered D-TDD and traditional D-TDD for both the downlink and uplink spatially averaged MPT, and can significantly improve the performance in uplink while slightly decreasing that in downlink compared with FFR-based D-TDD. Furthermore, by jointly optimizing network parameters, the spatially averaged MPT can be maximized while enduring MPT per user.

Efficient physical intrusion detection in Internet of Things: A Node deployment approach

In Internet of Things (IoT), intrusion detection plays an important role in many applications for detecting malicious intruders. The intruder can be, an unexpected physically moving entity, invading an area under surveillance, or an adversary in a battlefield. Node deployment strategy plays a crucial role in determining the intrusion detection capability of an IoT network. With uniform deployment, the detection probability is the same for any location in the network area. Nevertheless, different applications may need diverse levels of detection probability at key areas within the network. For example, a battlefield surveillance application needs improved detection probability around the headquarter. On the contrary, a Gaussian deployment strategy provides improved detection probability to the key areas due to differentiated node density. However, it is neither energy-efficient nor provides a quick detection of the physical intruder.In this work, we introduce a novel deployment strategy to overcome the above said limitations of both uniform and Gaussian deployments for energy-efficient and quick detection. Initially, we investigate the problem of physical intrusion detection in our introduced deployment strategy considering a realistic sensing model. Furthermore, we examine the effects of different network parameters on the detection probability in details. We also derive the relationship between the different network parameters and connectivity to ensure fast detection. We perform exhaustive experiments on real datasets, primarily, in order to validate the correctness of modeling and analyses. Next, we examine the effects of different network parameters on the detection probability. The results clearly demonstrate that our approach improves the detection probability by more than 25% when compared to two well-known deployment strategies under various network parameters.

Optimal throughput performance in full-duplex relay assisted cognitive networks

In this paper, we study a full-duplex cooperative cognitive radio network with multiple full-duplex secondary users acting as potential relays for transmitting the packets of a primary user. In addition to having full-duplex capability, the receivers also have multi-packet reception capability allowing them to simultaneously decode packets incoming from different senders. Our objective is to maximize the sum throughput of the secondary users while stabilizing the primary and relay queues. Towards this objective, we characterize the optimal scheduling of primary and relay packets at the full-duplex secondary users. The resulting problem is non-convex, and thus, we transform it into a linear fractional problem by using the dominant system approach. This, in turn, facilitates an efficient numerical solution by the bisection method. We analyze the effects of different network parameters on the optimal solution numerically for a number of possible scenarios. Our numerical results demonstrate how the multi-packet reception and full-duplex capabilities, as well as the partial relaying and number of secondary users affect the primary and secondary users’ stable throughput revealing new insights into the performance of overlay cognitive networks. In particular, we demonstrate that full-duplex capability of secondary users together with multi-packet reception capability of the primary destination is the key in reaping the benefits of full-duplex cooperative cognitive communications.

On User Association in Multi-Tier Full-Duplex Cellular Networks

We address the user association problem in multi-tier in-band full-duplex (FD) networks. Specifically, we consider the case of decoupled user association (DUA), in which users (UEs) are not necessarily served by the same base station (BS) for uplink (UL) and downlink (DL) transmissions. Instead, UEs can simultaneously associate to different BSs based on two independent weighted path-loss user association criteria for UL and DL. We use stochastic geometry to develop a comprehensive modeling framework for the proposed system model, where BSs and UEs are spatially distributed according to independent point processes. We derive closed-form expressions for the mean rate utility in FD, half-duplex (HD) DL, and HD UL networks as well as the mean rate utility of legacy nodes with only HD capabilities in a multi-tier FD network. We formulate and solve an optimization problem that aims at maximizing the mean rate utility of the FD network by optimizing the DL and UL user association criteria. We investigate the effects of different network parameters, including the spatial density of BSs and power control parameter. We also investigate the effect of imperfect self-interference cancellation (SIC) and show that it is more severe at UL, where there exist minimum required SIC capabilities for BSs and UEs, for which FD networks are preferable to HD networks; otherwise, HD networks are preferable. In addition, we discuss several special cases and provide guidelines on the possible extensions of the proposed framework. We conclude that DUA outperforms coupled user association, in which UEs associate to the same BS for both UL and DL transmissions.

Modeling and analysis of simultaneous information and energy transfer in Internet of Things

AbstractThe Internet of Things (IoT) will allow unprecedented connectivity among devices and is predicted to make our world smarter. However, due to battery constraints, energy efficiency of IoT with numerous smart devices is a key challenge. Simultaneous information and energy transfer (SIET) is a promising method to provide green energy for energy‐constrained wireless networks. This paper, for the first time, investigates the modeling and analysis of SIET in a network where IoT nodes are powered via IoT gateways. Specifically, a Ginibre point process that captures the repulsion between spatially distributed nodes is used to model the IoT gateway network. The goal of our work is to uncover a strategy to balance the trade‐off between power outage probability and transmission outage probability by a careful design of the power split ratio of SIET. To achieve that, we derive closed‐form expressions for these 2 probabilities in IoT for a practical case. The simulation results verify our theoretical results and illustrate the effects of different network parameters (eg, transmit power and density of gateways and signal‐to‐interference‐plus‐noise ratio threshold) on SIET.

An intelligent approach for resource allocation in the cognitive radio vehicular ad hoc network

AbstractWith the overwhelming demand for wireless devices, cognitive radios (CRs) have been envisioned to be applied in the vehicular communications in the near future. In the moving vehicular scenario, reliable detection of the primary user and the interference‐aware power allocation to the vehicular secondary users (VSUs) are amongst the main challenging issues in CR. So, this paper proposes techniques to evaluate the false alarm probability and detection probability on the basis of the spatial correlation among the vehicular users in a soft decision fusion‐based CR vehicular ad hoc network. Further, an attempt has been made to optimize the power allocation to the VSUs. The energy efficiency maximization problem is formulated under the constraints of maximum transmission power limit, interference to the primary receiver and the minimum achievable data rate. For the nonlinear and nonconvex optimization problem to be solved, the parametric transformation is used. The power allocation to the VSUs satisfying the nonlinear constraints is solved by our proposed adaptive scheme on the basis of normalized least mean square algorithm. The relevant simulation results provided show the efficacy of our proposed scheme, and the effect of different network parameters is also studied.

Gaussian versus Uniform Distribution for Intrusion Detection in Wireless Sensor Networks

In a Wireless Sensor Network (WSN), intrusion detection is of significant importance in many applications in detecting malicious or unexpected intruder(s). The intruder can be an enemy in a battlefield, or a malicious moving object in the area of interest. With uniform sensor deployment, the detection probability is the same for any point in a WSN. However, some applications may require different degrees of detection probability at different locations. For example, an intrusion detection application may need improved detection probability around important entities. Gaussian-distributed WSNs can provide differentiated detection capabilities at different locations but related work is limited. This paper analyzes the problem of intrusion detection in a Gaussian-distributed WSN by characterizing the detection probability with respect to the application requirements and the network parameters under both single-sensing detection and multiple-sensing detection scenarios. Effects of different network parameters on the detection probability are examined in detail. Furthermore, performance of Gaussian-distributed WSNs is compared with uniformly distributed WSNs. This work allows us to analytically formulate detection probability in a random WSN and provides guidelines in selecting an appropriate deployment strategy and determining critical network parameters.

Performance analysis of distributed transmission schemes in cooperative random networks

In this paper, two distributed transmission schemes, namely Distributed Transmitter Maximal Ratio Combining (D-TxMRC) and Distributed Space Time Block Coding (D-STBC) are analysed. The array and diversity gain inherent to the underlying Multiple Input Multiple Output (MIMO) technique are studied, both under independent and correlated fading scenario. It is shown that D-TxMRC is capable of achieving high array gain at the expense of complexity, while only limited array gain is attained using D-STBC scheme. As far as diversity gain is concerned, both schemes achieve full diversity in the order of total number of cooperating nodes under independent fading. The overall performance of the schemes under random network scenario is analysed using average outage probability as a performance metric. The averaging encompasses channel variation as well as network dependent parameters. Effects of different network parameters towards system performance are also analysed. Finally, the performance of cooperative schemes in a more practical network with regular node placements are also studied.

Application of neural networks to prediction of phase transport characteristics in high-pressure two-phase turbulent bubbly flows

The phase transport phenomenon of the high-pressure two-phase turbulent bubbly flow involves complicated interfacial interactions of the mass, momentum, and energy transfer processes between phases, revealing that an enormous effort is required in characterizing the liquid–gas flow behavior. Nonetheless, the instantaneous information of bubbly flow properties is often desired for many industrial applications. This investigation aims to demonstrate the successful use of neural networks in the real-time determination of two-phase flow properties at elevated pressures. Three back-propagation neural networks, trained with the simulation results of a comprehensive theoretical model, are established to predict the transport characteristics (specifically the distributions of void-fraction and axial liquid–gas velocities) of upward turbulent bubbly pipe flows at pressures covering 3.5–7.0 MPa. Comparisons of the predictions with the test target vectors indicate that the averaged root-mean-squared (RMS) error for each one of three back-propagation neural networks is within 4.59%. In addition, this study appraises the effects of different network parameters, including the number of hidden nodes, the type of transfer function, the number of training pairs, the learning rate-increasing ratio, the learning rate-decreasing ratio, and the momentum value, on the training quality of neural networks.