Energy Aware Data Collection with Route Planning for 6G Enabled UAV Communication

With technological advancements in 6G and Internet of Things (IoT), the incorporation of Unmanned Aerial Vehicles (UAVs) and cellular networks has become a hot research topic. At present, the proficient evolution of 6G networks allows the UAVs to offer cost-effective and timely solutions for real-time applications such as medicine, tracking, surveillance, etc. Energy efficiency, data collection, and route planning are crucial processes to improve the network communication. These processes are highly difficult owing to high mobility, presence of non-stationary links, dynamic topology, and energy-restricted UAVs. With this motivation, the current research paper presents a novel Energy Aware Data Collection with Routing Planning for 6G-enabled UAV communication (EADCRP-6G) technique. The goal of the proposed EADCRP-6G technique is to conduct energy-efficient cluster-based data collection and optimal route planning for 6G-enabled UAV networks. EADCRP-6G technique deploys Improved Red Deer Algorithm-based Clustering (IRDAC) technique to elect an optimal set of Cluster Heads (CH) and organize these clusters. Besides, Artificial Fish Swarm-based Route Planning (AFSRP) technique is applied to choose an optimum set of routes for UAV communication in 6G networks. In order to validated whether the proposed EADCRP-6G technique enhances the performance, a series of simulations was performed and the outcomes were investigated under different dimensions. The experimental results showcase that the proposed model outperformed all other existing models under different evaluation parameters.     

DQN-Based Proactive Trajectory Planning of UAVs in Multi-Access Edge Computing

The main aim of future mobile networks is to provide secure, reliable, intelligent, and seamless connectivity. It also enables mobile network operators to ensure their customer’s a better quality of service (QoS). Nowadays, Unmanned Aerial Vehicles (UAVs) are a significant part of the mobile network due to their continuously growing use in various applications. For better coverage, cost-effective, and seamless service connectivity and provisioning, UAVs have emerged as the best choice for telco operators. UAVs can be used as flying base stations, edge servers, and relay nodes in mobile networks. On the other side, Multi-access Edge Computing (MEC) technology also emerged in the 5G network to provide a better quality of experience (QoE) to users with different QoS requirements. However, UAVs in a mobile network for coverage enhancement and better QoS face several challenges such as trajectory designing, path planning, optimization, QoS assurance, mobility management, etc. The efficient and proactive path planning and optimization in a highly dynamic environment containing buildings and obstacles are challenging. So, an automated Artificial Intelligence (AI) enabled QoS-aware solution is needed for trajectory planning and optimization. Therefore, this work introduces a well-designed AI and MEC-enabled architecture for a UAVs-assisted future network. It has an efficient Deep Reinforcement Learning (DRL) algorithm for real-time and proactive trajectory planning and optimization. It also fulfills QoS-aware service provisioning. A greedy-policy approach is used to maximize the long-term reward for serving more users with QoS. Simulation results reveal the superiority of the proposed DRL mechanism for energy-efficient and QoS-aware trajectory planning over the existing models.     

Performance of a laser microsatellite network with an optical preamplifier

Laser satellite communication (LSC) uses free space as a propagation medium for various applications, such as intersatellite communication or satellite networking. An LSC system includes a laser transmitter and an optical receiver. For communication to occur, the line of sight of the transmitter and the receiver must be aligned. However, mechanical vibration and electronic noise in the control system reduce alignment between the transmitter laser beam and the receiver field of view (FOV), which results in pointing errors. The outcome of pointing errors is fading of the received signal, which leads to impaired link performance. An LSC system is considered in which the optical preamplifier is incorporated into the receiver, and a bit error probability (BEP) model is derived that takes into account the statistics of the pointing error as well as the optical amplifier and communication system parameters. The model and the numerical calculation results indicate that random pointing errors of sigma(chi)2G > 0.05 penalize communication performance dramatically for all combinations of optical amplifier gains and noise figures that were calculated.     

Green5G: Enhancing Capacity and Coverage in Device-to-Device Communication

With the popularity of green computing and the huge usage of networks, there is an acute need for expansion of the 5G network. 5G is used where energy efficiency is the highest priority, and it can play a pinnacle role in helping every industry to hit sustainability. While in the 5G network, conventional performance guides, such as network capacity and coverage are still major issues and need improvements. Device to Device communication (D2D) communication technology plays an important role to improve the capacity and coverage of 5G technology using different techniques. The issue of energy utilization in the IoT based system is a significant exploration center. Energy optimization in D2D communication is an important point. We need to resolve this issue for increasing system performance. Green IoT speaks to the issue of lessening energy utilization of IoT gadgets which accomplishes a supportable climate for IoT systems. In this paper, we improve the capacity and coverage of 5G technology using Multiple Inputs Multiple Outputs (MU-MIMO). MU-MIMO increases the capacity of 5G in D2D communication. We also present all the problems faced by 5G technology and proposed architecture to enhance system performance.     

Resource Management in UAV Enabled MEC Networks

Mobile edge cloud networks can be used to offload computationally intensive tasks from Internet of Things (IoT) devices to nearby mobile edge servers, thereby lowering energy consumption and response time for ground mobile users or IoT devices. Integration of Unmanned Aerial Vehicles (UAVs) and the mobile edge computing (MEC) server will significantly benefit small, battery-powered, and energy-constrained devices in 5G and future wireless networks. We address the problem of maximising computation efficiency in U-MEC networks by optimising the user association and offloading indicator (OI), the computational capacity (CC), the power consumption, the time duration, and the optimal location planning simultaneously. It is possible to assign some heavy tasks to the UAV for faster processing and small ones to the mobile users (MUs) locally. This paper utilizes the k-means clustering algorithm, the interior point method, and the conjugate gradient method to iteratively solve the non-convex multi-objective resource allocation problem. According to simulation results, both local and offloading schemes give optimal solution.     

Power Optimized Multiple-UAV Error-Free Network in Cognitive Environment

Many extensive UAV communication networks have used UAV cooperative control. Wireless networking services can be offered using unmanned aerial vehicles (UAVs) as aerial base stations. Not only is coverage maximization, but also better connectivity, a fundamental design challenge that must be solved. The number of applications for unmanned aerial vehicles (UAVs) operating in unlicensed bands is fast expanding as the Internet of Things (IoT) develops. Those bands, however, have become overcrowded as the number of systems that use them grows. Cognitive Radio (CR) and spectrum allocation approaches have emerged as a potential approach for resolving spectrum scarcity in wireless networks, and hence as technological solutions for future generations, from this perspective. As a result, combining CR with UAVs has the potential to give significant benefits for large-scale UAV deployment. The paper examines existing research on the subject of UAV covering and proposes a multi-UAV cognitive-based error-free model for energy-efficient communication. Coverage maximization, power control, and enhanced connection quality are the three steps of the proposed model. To satisfy the desired signal-to-noise ratio, the covering zone efficacy is investigated as a function of the distance among UAVs stationed in a specific geographic region depending on multiple deployment configurations like as rural, suburban, and urban macro deployment scenarios of the ITU-R M.2135 standard (SNR).     

On the performance of time division multiple access-based multihop fixed cellular networks with respect to available frequency carriers

In multihop cellular networks (MCN), the user nodes can act as relays and forward other nodes' traffic to/from base stations. There are several advantages of MCN such as the improved signal quality and higher coverage. However, it is known that multihop relaying networks require extra radio resources. Therefore the performance of MCN depends to a great extent on the availability of adequate radio resources. The performance of a time division multiple access (TDMA)-based multihop fixed cellular network is analysed with highlighting the dependence of the system performance on the amount of available radio resources, namely, the number of frequency carriers. Results show that in a fixed cellular network, the multihop architecture significantly outperforms the traditional single-hop architecture in terms of the outage probability and throughput if an adequate amount of frequency carriers is available in the network. Otherwise, the multihop fixed cellular networks architecture loses its superiority and might even lead to performance degradation, particularly at high loading levels.     

Joint Spectrum Partition and Performance Analysis of Full-Duplex D2D Communications in Multi-Tier Wireless Networks

Full-duplex (FD) has been recognized as a promising technology for future 5G networks to improve the spectrum efficiency. However, the biggest practical impediments of realizing full-duplex communications are the presence of self-interference, especially in complex cellular networks. With the current development of self-interference cancellation techniques, full-duplex has been considered to be more suitable for device-to-device (D2D) and small cell communications which have small transmission range and low transmit power. In this paper, we consider the full-duplex D2D communications in multi-tier wireless networks and present an analytical model which jointly considers mode selection, resource allocation, and power control. Specifically, we consider a distance based mode selection scheme. The performance analysis of different D2D communications modes are performed based on stochastic geometry, and tractable analytical solutions are obtained. Then we investigate the optimal resource partitions between dedicated D2D mode and cellular mode. Numerical results validate the theoretical anlaysis and indicate that with appropriate proportions of users operated in different transmission modes and optimal partitioning of spectrum, the performance gain of FD-D2D communication can be achieved.     

Power control for interference mitigation by evolutionary game theory in uplink NOMA for 5G networks

The demand for mobile uplink traffic has increased significantly in the past few decades with the development of the Internet of Things (IoT) and mobile Internet. This has subsequently imposed challenges on 5G networks to provide high spectral efficiency and low-power massive connectivity. Non-orthogonal multiple access (NOMA) is a viable alternative to the current state-of-the-art orthogonal multiple access (OMA) techniques to address the challenges in 5G systems. In addition, a power control (PC) mechanism to mitigate the effect of interference between users can be accommodated to improve network performance. In this paper, we discuss the basic principles, key features, and strengths/weaknesses of the various power domain NOMA schemes. Moreover, we propose an uplink PC scheme for the users of a power domain NOMA network. The proposed PC method makes use of the evolutionary game theory (EGT) model to adaptively adjust the transmitted power level of the users which helps in mitigating user interference. A successive interference cancellation (SIC) receiver is applied at a base station (BS) in order to separate the users’ signals. By performing simulations, we show that the proposed EGT-based PC scheme achieves higher network efficiency, spectral efficiency, and energy efficiency.     

A Robust Resource Allocation Scheme for Device-to-Device Communications Based on Q-Learning

One of the most effective technology for the 5G mobile communications is Device-to-device (D2D) communication which is also called terminal pass-through technology. It can directly communicate between devices under the control of a base station and does not require a base station to forward it. The advantages of applying D2D communication technology to cellular networks are: It can increase the communication system capacity, improve the system spectrum efficiency, increase the data transmission rate, and reduce the base station load. Aiming at the problem of co-channel interference between the D2D and cellular users, this paper proposes an efficient algorithm for resource allocation based on the idea of Q-learning, which creates multi-agent learners from multiple D2D users, and the system throughput is determined from the corresponding state-learning of the Q value list and the maximum Q action is obtained through dynamic power for control for D2D users. The mutual interference between the D2D users and base stations and exact channel state information is not required during the Q-learning process and symmetric data transmission mechanism is adopted. The proposed algorithm maximizes the system throughput by controlling the power of D2D users while guaranteeing the quality-of-service of the cellular users. Simulation results show that the proposed algorithm effectively improves system performance as compared with existing algorithms.     

Coverage probability analysis of IEEE 802.16 system with smart antenna system over stanford university interim fading channels

The IEEE 802.16 system, a promising wireless communication system, has a maximum transmission range of 50 km according to the IEEE 802.16 standard. In reality, the transmission range and coverage probability of an IEEE 802.16 system vary for different wireless scenarios. Evaluating the transmission range and coverage probability of an IEEE 802.16 system prior to implementation is important. Hence, the Stanford University Interim (SUI) channel model in IEEE 802.16 specifications is suitable for evaluating the performance of IEEE 802.16 systems. To generate an effective method for predicting coverage, this study uses the SUI channel model to analyse the coverage probability of an IEEE 802.16 system. Furthermore, this study utilises a smart antenna system (SAS) to enhance IEEE 802.16 system performance. In terms of different antenna heights of the Base Transceiver Station and Customer Premises Equipment, the performance evaluation results show that the cell radius with the SAS is at least 30% more than that without the SAS.     

Metaheuristic Resource Allocation Strategy for Cluster Based 6G Industrial Applications

The emergence of Beyond 5G (B5G) and 6G networks translated personal and industrial operations highly effective, reliable, and gainful by speeding up the growth of next generation Internet of Things (IoT). Industrial equipment in 6G encompasses a huge number of wireless sensors, responsible for collecting massive quantities of data. At the same time, 6G network can take real-world intelligent decisions and implement automated equipment operations. But the inclusion of different technologies into the system increased its energy consumption for which appropriate measures need to be taken. This has become mandatory for optimal resource allocation in 6G-enabled industrial applications. In this scenario, the current research paper introduces a new metaheuristic resource allocation strategy for cluster-based 6G industrial applications, named MRAS-CBIA technique. MRAS-CBIA technique aims at accomplishing energy efficiency and optimal resource allocation in 6G-enabled industrial applications. The proposed MRAS-CBIR technique involves three major processes. Firstly, Weighted Clustering Technique (WCT) is employed to elect the optimal Cluster Heads (CHs) or coordinating agents with the help of three parameters namely, residual energy, distance, and node degree. Secondly, Decision Tree-based Location Prediction (DTLP) mechanism is applied to determine the exact location of Management Agent (MA). Finally, Fuzzy C-means with Tunicate Swarm Algorithm (FCM-TSA) is used for optimal resource allocation in 6G industrial applications. The performance of the proposed MRAS-CBIA technique was validated and the results were examined under different dimensions. The resultant experimental values highlighted the superior performance of MRAS-CBIR technique over existing state-of-the-art methods.     

Effects of fabrication errors on the performance of cylindrical diffractive lenses: rigorous boundary-element method and scalar approximation

The effects of fabrication errors on the performance of collimating finite-thickness cylindrical diffractive lenses with eight discrete levels are investigated with a rigorous boundary-element method and a scalar approach. The photolithographic fabrication errors considered are mask alignment errors, exposure errors (that result in linewidth errors), and etch-depth errors. A cylindrical Gaussian beam of TE or TM polarization is incident upon the resulting lenses. Lenses of F/4, F/2, and F/1.4 are examined. The diffraction efficiencies of the lenses with fabrication errors are generally lower than the error-free lenses with the most severe performance degradation occurring for mask misalignment and exposure errors.     

Improving the performance of mobile broadcasting systems using multiple base stations and distributed space?time codes

Error performance of distributed space-time codes used for mobile broadcasting systems consisting of multiple base stations is analysed. The base stations are assumed to operate in a simulcast mode, that is they simultaneously transmit the same message using the same carrier frequency. Mobile users within the intersection of the coverage areas, thus, enjoy a small probability of shadowing and a high probability of at least one line-of-sight (LOS) link. In effect, the base stations establish a virtual multiple-antenna system. The use of a distributed space-time code offers an additional spatial diversity gain. Considering a single user with a fixed (random) position, the impact of shadowing and LOS components on the error performance of the system is analysed and compared with a conventional multiple-antenna system with co-located transmit antennas. Specifically, the analysis shows that already a single LOS link significantly improves the overall system performance. In the case of shadowing, huge diversity gains are obtained in the distributed system. In a system with co-located transmitters, however, the performance improvements compared with a single-antenna system are rather small, when shadowing is taken into account. Altogether, it can be concluded that systems with distributed transmitters are typically superior to conventional multiple-antenna systems (due to macroscopic diversity), and that distributed space-time codes are superior to conventional simulcasting (due to microscopic diversity)     

Kinetic and equilibrium binding analysis of protein-ligand interactions at poly(amidoamine) dendrimer monolayers

The interaction of streptavidin (SA) with a biotinylated surface has been of great interest in the development of an interfacial layer for protein immobilization based on self-assembled monolayers (SAMs) and polymeric layers. Here, we demonstrate the unique characteristics of protein-ligand interactions on dendrimer monolayers based on kinetic and equilibrium binding analyses. With amine-ended poly(amidoamine) dendrimers from the first (G1) to fourth (G4) generation, the formation of even, compact dendrimer monolayers on gold was confirmed using FT-IR spectroscopy and ellipsometry. For the SA-biotin interaction, quantitative analysis of bound SA using surface plasmon resonance showed that the saturation binding level of SA was fairly higher in all dendrimer layers when compared to other tested systems of 11-mercaptoundecylamine SAMs and a poly(L-lysine) layer. Kinetic studies revealed that the initial binding rate of SA up to the saturation level was 2-fold higher in all dendrimer layers than in the SAMs regardless of the surface density of functionalized biotin. Concurrently, the dendrimer layers led to much higher values of sticking probability, which is defined as the probability that the SA molecule adsorbs upon collision with a biotinylated surface, at a fixed SA coverage, and prolonged the significant levels around the maximum probability with increasing SA coverage. Plots of the saturation coverage of SA versus the SA concentration in solution showed that SA binding onto the biotinylated G1 and G3 layers fit to a Langmuir isotherm model. Taken together, faster binding of SA and highly ordered packing of the molecules seems to be achieved through typical properties of the dendrimer monolayers such as surface distribution of functionalized biotin, surface corrugation, and flexibility of highly branched larger dendrimers, which provides a guideline for the construction and analysis of an interfacial layer in biosensing applications.     

Towards Fully Secure 5G Ultra-Low Latency Communications: ACost-Security Functions Analysis

Future components to enhance the basic, native security of 5G networks are either complex mechanisms whose impact in the requiring 5G communications are not considered, or lightweight solutions adapted to ultra-reliable low-latency communications (URLLC) but whose security properties remain under discussion. Although different 5G network slices may have different requirements, in general, both visions seem to fall short at provisioning secure URLLC in the future. In this work we address this challenge, by introducing cost-security functions as a method to evaluate the performance and adequacy of most developed and employed non-native enhanced security mechanisms in 5G networks. We categorize those new security components into different groups according to their purpose and deployment scope. We propose to analyze them in the context of existing 5G architectures using two different approaches. First, using model checking techniques, we will evaluate the probability of an attacker to be successful against each security solution. Second, using analytical models, we will analyze the impact of these security mechanisms in terms of delay, throughput consumption, and reliability. Finally, we will combine both approaches using stochastic cost-security functions and the PRISM model checker to create a global picture. Our results are first evidence of how a 5G network that covers and strengthened all security areas through enhanced, dedicated non-native mechanisms could only guarantee secure URLLC with a probability of ∼55%.     

A two-user approximation-based transmit beamforming for physical-layer multicasting in mobile cellular downlink systems

Multicast has been known as an efficient transmission technique for group-oriented applications such as multi-party video conferencing, video streaming for paid users, online gaming, and social networking. In this paper, we investigate physical-layer multicasting in mobile cellular downlink systems, where the antennas at base station are employed to transmit common signals to multiple users simultaneously. A central design problem of downlink physical-layer multicasting is the search for the optimal beamforming vector that maximizes the multicast rate. Traditionally, the problem has been formulated as a quadratically constrained quadratic programming problem and shown to be NP-hard in general. In this paper, starting from examining the Karush–Kuhn–Tucker stationary conditions, a new method based on two-user approximation is proposed for the search for the optimal beamforming vector. The method is able to achieve a much higher multicast rate than the existing methods and provides an attractive trade-off between performance and complexity, especially for the case of using a large number of antennas. Using a large number of antennas at base station, also known as the large-scale multiple-input and multiple-output technique, has been regarded widely as one of the most promising technologies to increase system capacity, coverage, and user throughput for future generations of mobile cellular systems.     

An Attention-Based Friend Recommendation Model in Social Network

In social networks, user attention affects the user’s decision-making, resulting in a performance alteration of the recommendation systems. Existing systems make recommendations mainly according to users’ preferences with a particular focus on items. However, the significance of users’ attention and the difference in the influence of different users and items are often ignored. Thus, this paper proposes an attention-based multi-layer friend recommendation model to mitigate information overload in social networks. We first constructed the basic user and item matrix via convolutional neural networks (CNN). Then, we obtained user preferences by using the relationships between users and items, which were later inputted into our model to learn the preferences between friends. The error performance of the proposed method was compared with the traditional solutions based on collaborative filtering. A comprehensive performance evaluation was also conducted using large-scale real-world datasets collected from three popular location-based social networks. The experimental results revealed that our proposal outperforms the traditional methods in terms of recommendation performance.     

Cooperative Relay Selection Mechanism in Multi-Hop Networks

In this paper, we consider a three-hop relay system based on interference cancellation technique in Underlay cognitive radio (CR) network. Although underlay CR has been shown as a promising technique to better utilize the source of primary users (PUs), its secondary performance will be severely degraded. On one hand, by adapting the Underlay spectrum sharing pattern, secondary users (SUs) would observe the strict power constraints and be interfered by primary users. On the other hand, limited transmit power results in limited transmission range, which greatly degrade the secondary transmission capacity. To solve the problems above, we propose an interference cancellation protocol for multi-hop wireless communication networks in underlay CR, which could develop the long-distance transmission performance and improve the transmission efficiency significantly. As simulation results shows, proposed scheme significantly reduce the secondary outage probability and increase the secondary diversity than the traditional cases.     

抑制运动强干扰的稳健波束域目标方位估计方法

为了减小来自旁瓣区快速运动的强干扰对波束域高分辨方位估计方法的影响,提出一种稳健的波束域高分辨方位估计方法。该方法在形成多波束时,将稳健自适应波束形成与零陷扩宽技术相结合,有效地抑制了运动强干扰所造成的快拍失效和扫描方向误差引起的自适应波束图畸变,从而保证波束域方法能准确地估计目标方位。仿真结果验证了该方法的有效性。