Search

The integration of antennas into disposable paper drones using inkjet printing technology is presented. These drones or unmanned aerial vehicles (UAVs) are developed using origami folding structures. Two vertical monopole antennas based on the same design concept are proposed and their performance assessed for two different conditions. The conditions relate to the placement of the other electronic components and circuits on the origami drones as reported in the literature. The first is when the electromechanical components and corresponding metallic layers are located in the wings. In this case, the effect of the possible location of the antenna as well as the deformation of the wings on S11 is discussed. The second is a more general case scenario which includes when the components and motors are placed at the tail and lower part of the body of the drone. The antenna elements are directly printed onto a photo paper substrate using silver nanoparticle conductive ink. Subsequently, the substrate is folded to create a paper drone. Low-cost desktop inkjet printing equipment is used to deposit the metallic tracks of the antenna. The designs target the current frequency bands employed in the control and wireless communication of commercial drones (2.4 GHz and 5 GHz bands). The purpose of this work is to investigate potential antenna scenarios for disposable drones which may one day be fully fabricated using inkjet printing technology. All antenna designs and studies have been simulated using CST Microwave Studio and compared well with experimental results. [ABSTRACT FROM AUTHOR]

To exploit richer spectrum resources for even better service quality, millimeter wave (mmWave) communication has been considered for high-speed railway (HSR) communication systems. In this paper, we focus on scheduling as many flows as possible while satisfying their QoS requirements. Due to interference, eavesdropping, or other problems, some flows may not be directly transmitted from the track-side BS. In this paper, we propose an UAV-assisted scheduling scheme which utilizes a UAV to serve as relay for such flows. The proposed scheme also utilize two mmWave bands, one for the BS links and the other for the UAV links. The proposed algorithm aims to maximize the number of flows with their QoS requirements satisfied. Simulations demonstrate that the proposed scheme achieves a superior performance on the number of completed flows and the system throughput over two baseline schemes. [ABSTRACT FROM AUTHOR]

The explosive growth of Internet-of-Things (IoT) applications such as smart cities and Industry 4.0 have led to drastic increase in demand for wireless bandwidth, hence motivating the rapid development of new techniques for enhancing spectrum utilization needed by new generation wireless communication technologies. Among others, dynamic spectrum access (DSA) is one of the most widely accepted approaches. In this paper, as an enhancement of existing works, we take into consideration of inter-node collaborations in a dynamic spectrum environment. Typically, in such distributed circumstances, intelligent dynamic spectrum access almost invariably relies on self-learning to achieve dynamic spectrum access improvement. Whereas, this paper proposes a DSA scheme based on deep reinforcement learning to enhance spectrum and access efficiency. Unlike traditional Q-learning-based DSA, we introduce the following to enhance the spectrum efficiency in dynamic IoT spectrum environments. First, deep double Q-learning is adopted to perform local self-spectrum-learning for IoT terminals in order to achieve better dynamic access accuracy. Second, to accelerate learning convergence, federated learning (FL) in edge nodes is used to improve the self-learning. Third, multiple secondary users, who do not interfere with each other and have similar operation condition, are clustered for federated learning to enhance the efficiency of deep reinforcement learning. Comparing with the traditional distributed DSA with deep learning, the proposed scheme has faster access convergence speed due to the characteristic of global optimization for federated learning. Based on this, a framework of federated deep reinforcement learning (FDRL) for DSA is proposed. Furthermore, this scheme preserves privacy of IoT users in that FDRL only requires model parameters to be uploaded to edge servers. Simulations are performed to show the effectiveness of theproposed FDRL-based DSA framework. [ABSTRACT FROM AUTHOR]

In this paper, we consider a cooperative simultaneous wireless information and power transfer (SWIPT) network with one source–destination pair and multiple energy harvesting relays. How to efficiently use these relays has been addressed in conventional cooperative networks, and this paper investigates the impact of SWIPT on the performance of such multirelay cooperative networks. In particular, the main contribution of this paper is that a closed-form expression of the outage probability achieved by the multirelay cooperative protocol is obtained, as well as its approximation at high SNR. In addition, the performance of the cooperative network with the SWIPT relays is compared with that in conventional systems with self-powered relays. [ABSTRACT FROM PUBLISHER]

In concurrent spectrum sharing scenarios between radars and wireless communication systems, one strict approach to protect the radar from undesired interference is locating the base stations outside a guard (or exclusion) zone of the radar. As a result, users inside that zone experience bad communication coverage. This paper uses Reconfigurable Intelligent Surfaces (RISs) to improve the base station coverage of users in the radar exclusion zone. The paper shows, through analysis and simulation, that joint optimization of base station and RIS locations improves the probability of coverage in the exclusion zone over the shared spectrum without disturbing the radar operation. [ABSTRACT FROM AUTHOR]

Fixed Satellite Services (FSS) used to be alone at the C-Band spectrum in most countries. Since the deployment of 5G in many countries (i.e., 3.3–3.6 GHz), FSS is not the exclusive system in the C-Band anymore. In order to minimize the detrimental interference for the FSS to allowable levels, regional exclusion zones of maximum radiated power in 5G base stations (BS) are proposed and evaluated. In this paper, a measurement campaign has been carried out, and an analysis of the interference has been studied. A filtering model, namely Filter to Remove Broadband Interference 5G (FIREBRING), is proposed and analyzed concerning the carrier-to-noise ratio (C/N). Moreover, this paper focuses on the evaluation of the 5G interference into the FSS. The proposed solution deployed an Low-Noise Block (LNB) with a band frequency of 3.7 to 4.2 GHz to test the satellite down-conversion signal at the receiver. The paper offered a complete analysis of the 5G signal, taking into account the implications of out-of-band emissions, potentially LNB saturation into FSS receiver, and the repercussions of the deployment of the 5G BS active antenna systems. With the LNB and down-converter in place, it can be found that the signal interference between 1.450 GHz and 1.550 GHz, is nearly 18 dB. [ABSTRACT FROM AUTHOR]

As more and more people choose high-speed rail (HSR) as a means of transportation for short trips, there is ever growing demand of high quality of multimedia services. With its rich spectrum resources, millimeter wave (mm-wave) communications can satisfy the high network capacity requirements for HSR. Also, it is possible for receivers (RXs) to be equipped with antenna arrays in mm-wave communication systems due to its short wavelength. However, as HSRs run with high speed, the received signal power (RSP) varies rapidly over a cell and it is the lowest at the edge of the cell compared to other locations. Consequently, it is necessary to conduct research on RX beamforming for HSR in mm-wave band to improve the quality of the received signal. In this paper, we focus on RX beamforming for a mm-wave train-ground communication system. To improve the RSP, we propose an effective RX beamforming scheme based on deep reinforcement learning (DRL), and develop a deep Q-network (DQN) algorithm to train and determine the optimal RX beam direction with the purpose of maximizing average RSP. Through extensive simulations, we demonstrate that the proposed scheme has better performance than the four baseline schemes in terms of average RSP at most positions on the railway. [ABSTRACT FROM AUTHOR]

In this paper, we study the average throughput performance of energy beamforming in a multi-antenna wireless-powered communication network (WPCN). The considered network consists of one hybrid access point (AP) with multiple antennas and a single-antenna user. The user has no constant power supply and, thus, needs to harvest energy from the signals broadcast by the AP in the downlink (DL) before sending its data back to the AP with the harvested energy in the uplink (UL). We derive closed-form expressions of the average throughput and their asymptotic expressions at a high SNR for both delay-limited and delay-tolerant transmission modes. The optimal DL energy harvesting time, which maximizes the system throughput, is then obtained for high SNR. All analytical expressions are validated by numerical simulations. The impact of various parameters, such as the AP transmit power, the energy harvesting time, and the number of antennas, on the system throughput is also investigated. [ABSTRACT FROM AUTHOR]

In this paper, we investigate device-to-device (D2D) communication networks which are one of the key technologies for next-generation mobile communication networks and many other applications such as unmanned aerial vehicles (UAVs), vehicle-to-vehicle (V2V), and Internet of things (IoT). The overlay D2D communication networks that are considered in our study use dedicated radio resources separate from what cellular networks use and there exists co-channel interference in D2D networks without cross-channel interference between two networks. We propose a new transmission scheme for overlay D2D networks that uses a dueling deep reinforcement learning (DRL) architecture. The DRL is especially effective in environments where actions do not affect subsequent states as in wireless communication networks. The main contribution of this paper is that the proposed architecture is designed to utilize only information that each D2D devices can easily obtain by measuring channels. The proposed scheme thus enables D2D devices to train their own neural networks and to decide autonomously whether to transmit data without any intervention from infrastructures. The performance of the proposed scheme is analyzed in terms of average sum-rates and is compared to three baseline schemes. Simulation results show that the proposed scheme can achieve almost optimal sum-rates with low signal-to-noise (SNR) values without any intervention from infrastructure. [ABSTRACT FROM AUTHOR]

In this paper, we explore the different per-user bandwidths available for each tier of heterogeneous networks (HetNets) and the corresponding downlink range expansion (RE, a key measure to boost small cell association). Based on the practical case of tier-specific path loss exponents, we derive the local delay of HetNets under the random discontinuous transmission scheme (DTX, employed to manage the interference and reduce energy consumption). Using such a new expression for local delay, we then analyze the corresponding energy efficiency (EE). These delay and EE expressions reveal that there exists an optimal value for both the RE bias factor and for the DTX mute probability. A closed-form optimum solution for the RE bias factor is also derived for the low-rate regime. [ABSTRACT FROM AUTHOR]

The heterogeneous fog radio access networks (Fog-RAN), the integration of fog computing, and traditional heterogeneous radio access networks can be implemented through the next-generation wireless communication networks. However, most of the solutions are limited to the spectrum efficiency optimization, and cross-tier interference existing in the fog access points (F-APs) could affect the network performance seriously. In this paper, the user association, resource allocation (including bandwidth and power), and caching deployment are investigated in the heterogeneous Fog-RAN to consider energy efficiency and cross-tier interference mitigation. Specifically, the user association, resource allocation, and caching strategy are formulated as a non-convex optimization problem and then transformed into a convex problem, which can be solved by a proposed algorithm based on the concept of the alternating direction method of multipliers (ADMM). Then an ADMM-based algorithm is proposed to enhance the energy efficiency of the Fog-RAN. Compared with the current solutions, simulation results illustrate the proposed algorithm’s convergence and effectiveness. [ABSTRACT FROM AUTHOR]

This paper considers the $\mathscr{L}_{2}$ string stability analysis and controller synthesis for vehicle platooning under jamming attacks and interferences from other communication channels. Jamming attacks and interferences for vehicle platooning systems are modeled as periodic denial of service attacks. Further, the vehicle platooning with denial of service attacks is modeled as a distributed switching system where an average dwell time approach is adopted for the stability analysis. Under the distributed switching system framework, this paper presents a co-design paradigm for the vehicle platooning. The distributed event-triggered method (ETM) and controllers are presented to guarantee $\mathscr{L}_{2}$ string stability, which can ensure that the output cannot be amplified along the vehicle platooning. Sufficient conditions for $\mathscr{L}_{2}$ string stability are then provided to guide the co-design of the ETM and controllers, which can be efficiently obtained by solving LMIs. Simulation results are presented to demonstrate the efficacy of the proposed co-design approach in addressing the denial of service attacks for vehicle platooning systems. [ABSTRACT FROM AUTHOR]

This paper provides radio frequency (RF) energy harvesting and coverage probability analysis using stochastic geometry in a two-hop communication network. In the first hop, terrestrial base stations (TBSs) transmit RF energy and data to the aerial base stations (ABSs) and in the second hop, ABSs forward the data to the user. Numerical results show that the energy harvesting and coverage probabilities can be maximized at almost the same altitude of the ABSs with respect to the TBSs height for low TBSs densities. However, as the density of the backhaul nodes and the energy harvesting sub-timeslot increases, energy harvesting probability improves while coverage probability monotonically decreases by increasing the ABSs height. [ABSTRACT FROM AUTHOR]

In this paper, performance is investigated for a random access packet composed of a preamble and message. To minimize the error probability of a packet with channel-adaptive random access, the optimum energy allocation is obtained between the preamble and the message. The performances with the optimum and sub-optimum energy allocations are compared by simulation. [ABSTRACT FROM AUTHOR]

In this paper, we study how to achieve network utility maximization (NUM) in flow-level wireless networks, which can be characterized by the random arrivals and departures of flow-level traffic in such networks. Existing work in this area has mainly concentrated on finding the stability region in such networks while achieving the NUM objective. In this paper, we propose a fully distributed adaptive CSMA based flow-level cross-layer resource allocation algorithm (FLRA), which achieves the NUM goal while satisfying desired delay requirement via optimized link scheduling at the MAC layer and optimal flow-level admission control at the transport layer. Detailed algorithm design is presented. We analyze the convergence property of FLRA and its optimality in terms of network utility while meeting desired delay requirement. We further reveal the relationship among maximum network utility, desired delay, and number of active flows when a network reaches stability. We conduct extensive simulations and the results validate the effectiveness of our analytical results. [ABSTRACT FROM AUTHOR]

One of the goals of this paper is to contribute to finding distributed power control strategies, which exploit efficiently the information available about the global channel state; it may be local or noisy. A suited way of measuring the global efficiency of a distributed power control scheme is to use the long-term utility region. First, we provide the utility region characterization for general utility functions when the channel state obeys an independent block fading law and the observation structure is memoryless. Second, the corresponding theorem is exploited to construct an iterative algorithm, which provides one-shot power control strategies. The performance of the proposed algorithm is assessed for energy-efficient and spectrally efficient communications and shown to perform much better than state-of-the-art techniques, with the additional advantage of being applicable even in the presence of arbitrary observation structures such as those corresponding to noisy channel gain estimates. [ABSTRACT FROM AUTHOR]

There has been much increased interest in the academic and industrial research communities on vehicular ad-hoc networks (VANETs). In this paper, we present an analytical model to study the end-to-end delay in a one-way VANET. This paper proposes an analytical formula for the end-to-end delay probability distribution. Using the derived probability distribution, the probability that the end-to-end delay is lower than a given threshold may be calculated. In addition, one can straightforwardly study the impact of parameters such as wireless communication range, vehicular densities, distance between source the destination, and minimum and maximum vehicle speeds on the end-to-end delay. This can help to better understand data dissemination in VANETs. Moreover, closed forms for lower and upper bounds on the end-to-end delay probability distribution are obtained. Extensive simulation results demonstrate the accuracy of our analysis. [ABSTRACT FROM AUTHOR]

Rail transportation, especially, high-speed rails (HSR), is an important infrastructure for the development of national economy and the promotion of passenger experience. Due to the large bandwidth, millimeter wave (mmWave) communication is regarded as a promising technology to meet the demand of high data rates. However, since mmWave communication has the characteristic of high attenuation, mobile relay (MR) is considered in this paper. Also, full-duplex (FD) communications have been proposed to improve the spectral efficiency. However, because of the high speed, as well as the problem of penetration loss, passengers on the train have a poor quality of service. Consequently, an effective user association scheme for HSR in mmWave band is necessary. In this paper, we investigate the user association optimization problem in mmWave mobile relay systems where the MRs operate in the FD mode. To maximize the system capacity, we propose a cooperative user association approach based on coalition formation game, and develop a coalition formation algorithm to solve the challenging NP-hard problem. We also prove the convergence and Nash-stable property of the proposed algorithm. Extensive simulations are done to show the system performance of the proposed scheme under various network settings. It is demonstrated that the proposed distributed low complexity scheme achieves a near-optimal performance and outperforms two baseline schemes in terms of average system throughput. [ABSTRACT FROM AUTHOR]

In underwater acoustic (UWA) adaptive communication system, due to time-varying channel, the transmitter often has outdated channel state information (CSI), which results in low efficiency. UWA channels are much more difficult to estimate and predict than terrestrial wireless channels, given the more severe multipath environments with varying propagation speeds in different locations, non-linear propagation paths, several-order higher propagation latency, mobile transceiver and obstacles in the sea, etc. To handle the complexity, this paper proposes an efficient online CSI prediction model for UWA CSI prediction considering the complicated correlationship of UWA channels in both the time and frequency domains. This paper designs a learning model called CsiPreNet, which is an integration of a one-dimensional convolutional neural network (CNN) and a long short term memory (LSTM) network. The performance is compared with the widely used recursive least squares (RLS) predictor, the approximate linear dependency recursive kernel least-squares (ALD-KRLS), and two common conventional deep neural networks (DNN) predictors, i.e., back propagation neural network (BPNN) and LSTM network using the measured data recorded in the South China Sea. To validate the efficacy of prediction, we investigate the prediction of CSI in simulated downlink UWA orthogonal frequency division multiple access (OFDMA) systems. Specifically, the measured UWA channel is used in the OFDMA system. A joint subcarrier-bit-power adaptive allocation scheme is used for resource allocation. To further improve the performance, we develop an offline-online prediction scheme, enabling the prediction results to be more stable. Simulation and experimental results show that the CsiPreNet has superior performance than the existing solutions, thanks to its capability in capturing both the temporal and frequency correlation of the UWA CSIs. [ABSTRACT FROM AUTHOR]

This paper studies the allocation of shared resources between vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) links in vehicle-to-everything (V2X) communications. In existing algorithms, dynamic vehicular environments and quantization of continuous power become the bottlenecks for providing an effective and timely resource allocation policy. In this paper, we develop two algorithms to deal with these difficulties. First, we propose a deep reinforcement learning (DRL)-based resource allocation algorithm to improve the performance of both V2I and V2V links. Specifically, the algorithm uses deep Q-network (DQN) to solve the sub-band assignment and deep deterministic policy-gradient (DDPG) to solve the continuous power allocation problem. Second, we propose a meta-based DRL algorithm to enhance the fast adaptability of the resource allocation policy in the dynamic environment. Numerical results demonstrate that the proposed DRL-based algorithm can significantly improve the performance compared to the DQN-based algorithm that quantizes continuous power. In addition, the proposed meta-based DRL algorithm can achieve the required fast adaptation in the new environment with limited experiences. [ABSTRACT FROM AUTHOR]

The densification of wireless infrastructure to meet ever-increasing quality of service (QoS) demands, and the ever-growing number of wireless devices may lead to higher levels of electromagnetic field (EMF) exposure in the environment, in the 5G era. The possible long term health effects related to the EMF radiation are still an open debate and requires attention. Therefore, in this paper, we propose a novel EMF-aware resource allocation scheme based on the power domain non-orthogonal multiple access (PD-NOMA) and machine learning (ML) technologies for reducing the EMF exposure in the uplink of cellular systems. More specifically, we use the K-means approach (an unsupervised ML approach) to create clusters of users to be allocated together and to then strategically group and assign them on the subcarriers, based on their associated channel properties. Finding the best number of clusters in the PD-NOMA environment is a key challenge, and in this paper, we have used the elbow method in conjunction with the F-test method to effectively control the maximum number of users to be allocated at the same time per subcarrier. We have also derived an EMF-aware power allocation by formulating and solving a convex optimization problem. Based on the simulation results, our proposed ML-based strategy effectively reduces the EMF exposure, in comparison with the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Digital twin (DT) has emerged as a promising technology to create digital replicas of physical vehicles to facilitate driving in Internet of Vehicles (IoV). However, due to the uneven distribution of vehicles in the networks and the dynamics of heterogeneous wireless links, how to optimize the connection along the vehicle's trip between the vehicle and its DT to achieve the lowest cost (i.e., delay and energy consumption) is a fundamental challenge. In this paper, we investigate the joint network selection and power level allocation problem in the data synchronization issue between vehicles and DTs in the heterogeneous access networks. In order to better use the information shared by DT to facilitate the data synchronization process, we propose a learning-based heterogeneous network selection scheme in DT empowered IoV. First, we consider the data divided into blocks and model the data synchronization problem as a Markov Decision Process (MDP) to minimize the long-term cost in terms of the delay and energy consumption. Based on the information obtained from the digital twin communications, we develop an actor-critic learning approach to solve the MDP problem with the block-level state method, and obtain the optimal network selection strategy for the vehicles. In addition, we explore the capability of digital twins by applying transfer learning. The vehicles are divided into two types, i.e., learner vehicles and expert vehicles, so that the knowledge can be transferred from the expert vehicles to the learner vehicles to effectively improve the learning efficiency and decrease the data synchronization latency. Extensive experiments demonstrate that our scheme can effectively find the optimal network selection policy and achieve the fast convergence and better performance compared with existing related approaches. [ABSTRACT FROM AUTHOR]

The new Fifth generation (5G) of wireless communications and beyond 5G (B5G) networks are required to provide connectivity for massive number of devices at high speed and low latency. Within this context, non-orthogonal multiple access (NOMA) has been involved and emerged in recent research since it is considered as an enabling technology for 5G and B5G networks. NOMA allows several users to share the same resources of time and frequency, making it an excellent candidate to provide impressively large connectivity with high spectral efficiency. In this paper, we study the performance of NOMA systems assuming independent but not necessarily to be identical $N$ -Nakagami-m multipath fading channels. This channel model subsumes the double Nakagami-m, which was investigated in the context of intervehicular communications and keyhole multiple input multiple output systems, as special cases. To this end, we derive exact analytical and asymptotic expressions for the probability density function of the overall signal to noise ratio, system capacity, and the pairwise error probability (PEP) for each user. Numerical results are also provided herein to show the effects of fading parameters and number of users on the capacity and the PEP of each user. [ABSTRACT FROM AUTHOR]

In this paper, a pervasive wireless channel modeling theory is first proposed, which uses a unified channel modeling method and a unified equation of channel impulse response (CIR), and can integrate important channel characteristics at different frequency bands and scenarios. Then, we apply the proposed theory to a three dimensional (3D) space-time-frequency (STF) non-stationary geometry-based stochastic model (GBSM) for the sixth generation (6G) wireless communication systems. The proposed 6G pervasive channel model (6GPCM) can characterize statistical properties of channels at all frequency bands from sub-6 GHz to visible light communication (VLC) bands and all scenarios such as unmanned aerial vehicle (UAV), maritime, (ultra-)massive multiple-input multiple-output (MIMO), reconfigurable intelligent surface (RIS), and industry Internet of things (IIoT) scenarios. By adjusting channel model parameters, the 6GPCM can be reduced to various simplified channel models for specific frequency bands and scenarios. Also, it includes standard fifth generation (5G) channel models as special cases. In addition, key statistical properties of the proposed 6GPCM are derived, simulated, and verified by various channel measurement results, which clearly demonstrates its accuracy, pervasiveness, and applicability. [ABSTRACT FROM AUTHOR]

Resource-constrained end devices can offload computation to backend clouds. The stochastic wireless channel that an end device is connected to can introduce bursty computation demand to the cloud. Specifically, under good channel conditions, a device can transmit more data to the cloud, which consequently yields higher instantaneous computation demand. Conversely, poor channel conditions can result in lower instantaneous demand. The performance indicator for such a mobile cloud computing system is the average of the response time, which is the time span from the arrival of the computation demand at the backend cloud instance to the completion of its execution. The question we target in this paper is how resources should be provisioned for the backend cloud instance to address this bursty computation demand and guarantee a desired quality-of-service (QoS), namely, a user-specified average response time. To answer this question, we model the mobile cloud computing system as two tandem queues. We analyze this queueing network using the fluid flow analysis framework, and derive the analytical relationship between the required resource capacity at the backend cloud instance and the desired QoS, given the workload generation process at the end device and the wireless channel conditions. Having obtained the required resource capacity for a desired QoS, we then determine whether it is economical to provision this resource capacity by subscribing to the traditional static instance or the recently introduced burstable instance offered by public cloud providers. Finally, trace-driven simulations validate our theoretical results. [ABSTRACT FROM AUTHOR]

Recently, reconfigurable intelligent surface (RIS) has drawn much attention due to its capability of improving coverage and communication performance. The development of RIS-assisted communication technology relies on deep understanding of RIS channel characteristics. However, most of the existing investigations focus on the impact of RIS, ignoring the existence of local scatterers in the real propagation environment. The ideal propagation condition limits the accuracy and application of the developed channel model. In this paper, we aim to analyze the channel characteristics of RIS-assisted near-filed communication by considering different conditions of local scatterers. Specifically, we propose a three-dimensional (3D) RIS-assisted multiple-input multiple-output (MIMO) channel model based on a three-dimensional cylinder model, which considers line-of-sight, single-bounced at RIS (SBR) and double-bounced (DB) modes where radio waves reflect from RIS and scatterers near receiver. Based on the proposed channel model, we investigate some key statistical properties for SBR and DB propagation, including the time-frequency-space correlation function and Doppler power spectrum. From the simulation results, it is found that RIS element number, RIS horizontal and vertical locations, distribution of surrounding scatterers, and movement of Rx have significant impacts on channel characteristics. Finally, channel capacities of SBR and DB modes are calculated, where the large size and appropriate locations of RIS significantly improve the channel capacity of RIS-assisted communication. These numerical results and observations can be used for design of RIS-assisted near-field communication systems. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a two-hop simultaneous wireless information and power transfer (SWIPT) amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay communication system with an energy-limited relay node. The relay node harvests energy based on the radio-frequency (RF) signal transmitted from the source node through the time-switching (TS) protocol and fully uses the harvested energy to precode and forward the information to the destination node. The non-linear energy harvesting models are considered at the relay node. With the consideration of the channel estimation error, the joint optimization of the TS factor, source and relay precoding matrices is proposed with robustness against the channel state information (CSI) mismatch to maximize the mutual information (MI) between the source and destination nodes. The optimal structure for the source and relay precoding matrices is derived to simplify the transceiver optimization problem. Numerical simulations show that the system performance provided by the proposed algorithms with robustness is better than the non-robust algorithm. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) has been recognized as a viable technology to satisfy low-delay computation requirements for resource-constrained Internet of things (IoT) devices. Nevertheless, the broadcast feature of wireless electromagnetic communications may lead to the security threats to IoT devices. In order to enhance the task offloading security, this paper proposes a reconfigurable intelligent surface (RIS)-assisted secure MEC network framework. Furthermore, we investigate the max-min computation efficiency problem under the secure computation rate requirements, by jointly optimizing the local computing frequencies and transmission power of IoT devices, time-slot assignment, and phase beamforming of the RIS. To solve the formulated non-convex problem, we further develop an iterative algorithm, in which the Dinkelbach-type method and block coordinate descent (BCD) technique are utilized to tackle the fractional objective function and coupled optimization variables, respectively. In particular, the successive convex approximation (SCA) and penalty function-based methods are exploited to solve the transmit power control and reflecting beamforming optimization subproblems, respectively, and the closed-form expression for local computing frequencies optimization subproblem is derived. Numerical results quantify the performance gain achieved by the proposed RIS-assisted secure MEC networks, when compared to existing benchmark methods. [ABSTRACT FROM AUTHOR]

Electromagnetic spectrum resources are a very important resource. With the rapid development of radio technology, electromagnetic spectrum resources have become scarce. Abuse of spectrum resources and interference with primary user often occur. Pre-sensing the electromagnetic environment and performing dynamic spectrum access is the direction of future wireless technology development. This paper focuses on the electromagnetic spectrum information service platform. We design a spectrum detection node in combination with an embedded system. The distributed spectrum detection network is constructed by using the TV White Space as the communication frequency band of the backbone network. From the perspective of signal source, research on detection and identification methods based on radio signal characteristics improves the efficiency of electromagnetic spectrum detection. A phase extreme range estimation method based on radio frequency I/Q data is proposed to improve signal estimation accuracy in a low signal-noise-ratio environment. The interquartile range is proposed to distinguish noise and signal. The experiment results show that distributed electromagnetic spectrum information service platform based on signal characteristics can improve detection accuracy and increase system flexibility. [ABSTRACT FROM AUTHOR]

Radio-frequency (RF) energy harvesting is one of the promising technologies in wireless networks. This paper considers wireless powered communication networks (WPCNs) where one hybrid access point (H-AP) coordinates RF energy/information transmissions to/from a set of nodes. For the important type of WPCNs where the nodes have throughput requirement, we aim to minimize H-AP's energy provision (EP) via jointly optimizing the time allocation of energy/information transmissions and H-AP's transmission power while satisfying node throughput requirement. More specifically, the half-duplex WPCN is considered first. We prove that H-AP's energy transmission power must be the maximum allowed value to achieve the minimal EP, then show that the problem is convex under this condition and finally design an algorithm applying golden section search and bisection search jointly to efficiently obtain the optimal time allocation. Second, the full-duplex WPCN is considered. Its EP minimization problem is proved convex. However, due to the hardness of solving it directly, we turn to solve the feasibility problem (FP) for a given EP per unit time. We construct a convex optimization problem based on this FP and design a dual subgradient algorithm for finding its optimal solution, which indicates whether this FP is feasible. The minimal EP per unit time and its corresponding optimal solution are found by using the bisection search method. Simulation results show that, the EP-minimized schemes are able to greatly reduce H-AP's EP as compared to the baseline schemes. [ABSTRACT FROM AUTHOR]

In the last years, vehicular networking has grown up in terms of interest and transmission capability, due to the possibility of exploiting the distributed communication paradigm in a mobile scenario, where moving nodes are represented by vehicles. The different existing standards for vehicular ad-hoc networks, such as dedicate short range communication (DSRC), wireless access for vehicular environment (WAVE)/IEEE802.11p, have given to the research community the possibility of developing new medium access control (MAC) and routing schemes, in order to enhance the quality and the comfort of mobile users who are driving their vehicles. In this paper, we focus our attention on the optimization of traffic flowing in a vehicular environment with vehicle-2-roadside capability. As shown later, the proposed idea exploits the information that is gathered by road-side units to redirect traffic flows (in terms of vehicles) to less congested roads, with an overall system optimization, also in terms of carbon dioxide emissions reduction. An analytical model, as well as a set of pseudo-code instructions, have been introduced in the paper. A deep campaign of simulations has been carried out to give more effectiveness to our proposal. [ABSTRACT FROM AUTHOR]

Vehicle platooning systems are often equipped with vehicle-to-vehicle (V2V) communication technologies to improve both the road efficiency and road safety by exchanging vehicle information over wireless networks to maintain relatively small inter-vehicle distance. The road safety often relies on the quality of service delivered by the V2V communication. The V2V networks are, however, subject to channel fading and often have limited communication bandwidth. The limited bandwidth inevitably compromises the road safety as the vehicular network will easily get congested if the number of vehicles joining the network increases. To address the challenges of ensuring vehicle safety under unreliable wireless network with limited bandwidth, this paper first uses a two-state Markov chain to model the channel fading in vehicular networks. Under the Markov chain model, this paper further develops a novel distributed event triggered strategies under which the vehicle platooning system achieves stochastic $\mathcal {L}_2$ string stability while ensuring an efficient use of the limited communication bandwidth. The simulation results are provided to verify the effectiveness and advantages of the proposed methods. [ABSTRACT FROM AUTHOR]

This paper proposes a framework to construct 3-D complementary codes (3DCCs), which enable chip-level space–time coding to integrate multiantenna and multiple-access techniques in a unified platform. The 3DCCs are generated based on an extension method with the help of traditional complementary codes and well-designed matrices. The relationship between the correlation properties and flock size of the constructed 3DCCs is analyzed, and the proposed framework provides a generic way to construct different families of 3DCCs with a controllable tradeoff between their correlation properties and flock sizes. As examples, three families of 3DCCs with different characteristic features are constructed using the framework, and their correlation properties are simulated to validate their properties. The general architecture of the 3DCC-based multiple-input multiple-output (MIMO) system is also proposed in this paper. The simulated bit-error-rate (BER) performance of the corresponding system over both flat and multipath fading channels show that the constructed 3DCCs can eliminate both multiuser and multipath interferences in multiuser MIMO communications. The property to provide both transmit diversity and multiplexing with its uniquely flexible tradeoff between the two also makes it particularly well suited for futuristic wireless communications to fit to varying channel conditions and application requirements. [ABSTRACT FROM PUBLISHER]

Vehicular ad hoc networks (VANETs) experience large-scale high-speed mobility and volatile topology. VANETs may therefore experience intermittent connections and may occasionally be unable to guarantee end-to-end connections. This gives the medium access control (MAC) layer the opportunity to adapt its transmission strategy to the current unstable wireless connections to improve transmission efficiency. In this paper, we propose an adaptive frame length aggregation scheme (AFLAS) for VANETs, which is designed to improve transmission efficiency and increase data throughput. In our scheme, the incoming data packets from higher layers are queued separately in the MAC layer to wait for transmission opportunities. Suitable aggregation frame lengths are calculated according to the current wireless status and applied in the MAC layer at the onset of data transmissions. In this paper, we analyze and apply our AFLAS strategy to two current frame aggregation schemes in IEEE 802.11. We also report on the performance evaluation of our scheme. Our results exhibit significant improvement results in data throughput, retransmissions, overheads, and transmission efficiency in comparison with nonadaptive aggregation schemes. [ABSTRACT FROM PUBLISHER]

Range-based positioning using wireless signal has gained remarkable attention in past decades. Accurately extracting the distance information, i.e., estimating parameters of individual multipath, is of interest. It has been shown that diffuse multipath component (DMC) in wireless propagation channel has significant influence on channel estimation and, thus, the ranging performance. In addition to specular component (SC), DMC should be considered in order to improve the estimated accuracy of channel paths’ parameters and, thus, the ranging performance. In this paper, we extend the quasi-maximum likelihood space-alternating generalized expectation-maximization (SAGE) algorithm by incorporating an autoregressive moving average (ARMA) filter to estimate the channel parameters under the presence of DMC. The parameters of both DMC and SC are estimated in an iterative approach, where the DMC is assumed as colored noise and the ARMA-filter is used to estimate the contributions from DMC. The validation of the algorithm based on measured data shows that both SC and DMC can be accurately estimated by the proposed approach. A comparison with standard SAGE and AR-SAGE is conducted, where the results reveal that the proposed method outperforms other two approaches in terms of ranging accuracy. [ABSTRACT FROM AUTHOR]

To mitigate the impact of the dramatic growth of wireless video traffic and provide users with heterogeneous video services, a collaborative caching-transmission method in cache-enabled terahertz (THz) heterogeneous networks is studied in this paper. We propose a novel scalable video coding (SVC)-based two-tier cooperative caching strategy that can provide video services with different perceptual qualities. In addition, we propose a hierarchical hybrid transmission scheme where users obtain the requested layer files through the cooperative cellular multicast-D2D and THz transmissions. A base layer-based multicast group aggregation process is presented to improve resource utilization, while an efficient multicast transmission resource scheduling scheme can make better use of the spatial reusability. We derive the expressions for the average offloading capacity gain and average energy consumption, and formulate an efficiency function optimization problem. The problem is efficiently solved by the greedy strategy-gradient descent based caching solution and long tail distribution based heuristic caching solution. Simulation results validate the two solutions and demonstrate that the proposed caching-transmission method can achieve quasi-optimal performance compared with other contrasting schemes. [ABSTRACT FROM AUTHOR]

This paper studies the impact of unmanned aerial vehicle (UAV) wobbling on the coherence time of the wireless channel between UAVs and a ground user equipment (UE), using a Rician multi-path channel model. We consider two different scenarios for the number of UAVs: (i) single UAV scenario (SUS), and (ii) multiple UAV scenario (MUS). For each scenario, we model UAV wobbling by two random processes, i.e., the Wiener and sinusoidal processes, and characterize the channel autocorrelation function (ACF) which is then used to derive the coherence time of the channel. For the MUS, we further show that the UAV-UE channels for different UAVs are uncorrelated from each other. A key observation in this paper is that even for small UAV wobbling, the coherence time of the channel may degrade quickly, which may make it difficult to track the channel and establish a reliable communication link. [ABSTRACT FROM AUTHOR]

The performance evaluation of wireless systems is crucial for the development of future systems with more connected devices. It is essential to have an easy and relevant method for ensuring the wireless communication performance of the devices. We have recently developed a new random line-of-sight (random-LOS) measurement system for evaluating the communication performance of wireless devices, e.g., transceivers and antennas installed on a vehicle. In the measurement system, a plane wave is generated in the test zone emulating the far-field wave transmitted from a radio base station. In this paper we present both numerical simulations and actual experimental results of the random-LOS over-the-air (OTA) measurement setup operating in the 1.5–3 GHz band. The measurement accuracy is determined by the field variations within the test zone, where a smaller variation gives better measurement accuracy. In this paper the achieved accuracy expressed in terms of standard deviation (STD) was evaluated to be approximately 1 dB of the power within a cylindrical test zone of height 0.4 m and diameter 2 m. The active multiple-input multiple-output (MIMO) performance of antenna systems installed on an actual vehicle was measured and evaluated using the presented setup. A comparison to a theoretical zero forcing (ZF) receiver is also presented. [ABSTRACT FROM AUTHOR]

In the past several years, unmanned aerial vehicle (UAV) have been employed to provide enhanced coverage or relay service to mobile users in a scenario with limited or even no infrastructure since they can be deployed to almost everywhere and can be manipulated at anytime. This paper studies UAV as aerial base station (BS) enabled wireless communication system, where a UAV is dispatched to provide wireless communication service to a set of ground users with difference quality-of-experience (QoE) requirements. In real world, user requirements are randomly and unevenly distributed. In addition, UAV communication coverage and on-board energy are limited and system resources are also limited (e.g., transmission power, spectrum). In order to meet the QoE of all users with limited system resources and limited UAV energy, we jointly optimize user communication scheduling, UAV trajectory, transmit power and bandwidth allocation to maximize energy-efficiency and satisfy user QoE requirement. The formulated problem is mixes integer non-convex and non-concave so it is difficult to solve. In this paper, we solvevv the problem with two steps as follows. Firstly, we transform the objective function into a tractable form. Secondly, we divide the optimal problem into four sub-optimal problems, and then use a powerful iterative algorithm with the Dinkelbach and block coordinate descent to solve the optimal problem. That is to say, the user scheduling, UAV trajectory, transmission power and bandwidth allocation are alternately optimized in each iteration. Extensive simulation results present that our proposed method can obtain higher energy efficiency than that of baselines. Specifically, the energy efficiency obtained by our proposed method is 12.5% higher than the approach that only maximizes throughput. [ABSTRACT FROM AUTHOR]

Using a well-known wiretap model, for the first time in this paper, closed-form solutions are achieved for wireless secrecy under dual correlated Rician fading. A novel wireless secrecy and end-to-end (e2e) system analysis employing decode-and-forward (DF) opportunistic relays and an adaptive encoder with on/off transmission are reported. A signal-to-noise ratio (SNR) distribution of dual correlated Rician fading is first obtained as a function of four nested infinite summations, which (i) converges for a finite number of terms, and (ii) is employed to compute secrecy outage probability for a passive wiretapper. Using two-dimensional Laplace transform of the SNR distribution of dual correlated Rician fading, an e2e system analysis is then given by deriving compact expressions for e2e SNR distributions, and e2e ergodic capacity. Relationships between important parameters are elaborated and explained. An asymptotic analysis and proof for infinite summation convergence are also given. It is shown that there is a trade-off between transmission quality and security via line-of-sight power. Simulation results are shown to match theoretical prediction, which validates this work. [ABSTRACT FROM AUTHOR]

Wireless power transfer (WPT) is an emerging technology that can realize electric power transmission over certain distances without physical contact, offering significant benefits to modern automation systems, medical applications, consumer electronics, etc. This paper provides a comprehensive review of existing compensation topologies for the loosely coupled transformer. Compensation topologies are reviewed and evaluated based on their basic and advanced functions. Individual passive resonant networks used to achieve constant (load-independent) voltage or current output are analyzed and summarized. Popular WPT compensation topologies are given as application examples, which can be regarded as the combination of multiple blocks of resonant networks. Analyses of the input zero phase angle and soft switching are conducted as well. This paper also discusses the compensation requirements for achieving the maximum efficiency according to different WPT application areas. [ABSTRACT FROM PUBLISHER]

This paper introduces the design of a 41 GHz mmWave system dedicated for high-speed train communications between train and trackside. A novel frame structure is provided for the acquirement of channel state information as well as for the transmission of user data. Based on the proposed design principles, a system prototype has been established and employed in extensive measurement campaigns. The channel measurement results obtained from a recent measurement campaign under high-speed mobile conditions where the relative velocity between the transmitter and the receiver reaches 170 km/h are presented in this paper. Channel characteristics, including the power delay profile, RMS delay spread, fade depth, small-scale fading statistics, Ricean K factor, autocovariance function, coherence time, level crossing rate, and average fading duration are analyzed in detail. Moreover, the bit-error-rate performance for the proposed system under the channel whose characteristics are obtained from the measurements is simulated. Based on the simulation results, the key parameters for the proposed frame structure are determined to improve the system's robustness in fast varying channels and in the mean time achieve high spectrum efficiency. Besides, the throughput of the system prototype recorded during the transmission of three high-definition video streams is presented. Finally, the outage probability as a function of transmission distance and path loss exponent is estimated based on the channel measurement results. [ABSTRACT FROM AUTHOR]

Characterization and linearization of RF power amplifiers (PAs) are key issues of fifth-generation wireless communication systems, especially when high peak-to-average ratio waveforms are introduced. Recently, deep learning methods have achieved great success in numerous domains including wireless physical-layer. However, there has been limited work in using deep learning for PAs behavioral modeling and linearization. In this paper, we make a bridge between memory effects of the nonlinear PAs and memory of bidirectional long short-term memory (BiLSTM) neural networks. We then build a BiLSTM-based behavioral modeling architecture and its accompanying digital predistortion (DPD) model by reconciling a non causality concern. Next, an additional model is proposed in this paper to mitigate uncertainty of the tested PA when transforming phases. The experimental results demonstrate the effectiveness of the proposed scheme, in which the adequately trained networks are capable of characterizing the PA, and the artificial intelligence-based DPD shows promising linearization performance when considering the tested PAs inherent unpredictability. [ABSTRACT FROM AUTHOR]

The support for aerial users has become the focus of recent Third-Generation Partnership Project standardizations of 5G, due to their high maneuverability and flexibility for on-demand deployment. In this paper, probabilistic caching is studied for ultra-dense small-cell networks with terrestrial and aerial users, where a dynamic on–off architecture is adopted under a sophisticated path loss model incorporating both line-of-sight and non-line-of-sight transmissions. Generally, this paper focuses on the successful download probability (SDP) of user equipments (UEs) from small-cell base stations (SBSs) that cache the requested files under various caching strategies. To be more specific, the SDP is first analyzed using stochastic geometry theory, by considering the distribution of such two-tier UEs and SBSs as homogeneous poisson point processes. Second, an optimized caching strategy (OCS) is proposed to maximize the average SDP. Third, the performance limits of the average SDP are developed for the popular caching strategy (PCS) and the uniform caching strategy (UCS). Finally, the impacts of the key parameters, such as the SBS density, the cache size, the exponent of Zipf distribution, and the height of aerial user, are investigated on the average SDP. The analytical results indicate that the UCS outperforms the PCS if the SBSs are sufficiently dense, while the PCS is better than the UCS if the exponent of Zipf distribution is large enough. Furthermore, the proposed OCS is superior to both the UCS and PCS. [ABSTRACT FROM AUTHOR]

Providing femto access points (FAPs) with computational capabilities will allow (either total or partial) offloading of highly demanding applications from smartphones to the so-called femto-cloud. Such offloading promises to be beneficial in terms of battery savings at the mobile terminal (MT) and/or in latency reduction in the execution of applications. However, for this promise to become a reality, the energy and/or the time required for the communication process must be compensated by the energy and/or the time savings that result from the remote computation at the FAPs. For this problem, we provide in this paper a framework for the joint optimization of the radio and computational resource usage exploiting the tradeoff between energy consumption and latency. Multiple antennas are assumed to be available at the MT and the serving FAP. As a result of the optimization, the optimal communication strategy (e.g., transmission power, rate, and precoder) is obtained, as well as the optimal distribution of the computational load between the handset and the serving FAP. This paper also establishes the conditions under which total or no offloading is optimal, determines which is the minimum affordable latency in the execution of the application, and analyzes, as a particular case, the minimization of the total consumed energy without latency constraints. [ABSTRACT FROM PUBLISHER]

In this paper, the throughput maximization of millimeter-wave (mm-Wave) ultradense networks (UDN) using dynamic spectrum sharing (DSS) is considered. Most of the existing works only allow temporal-domain access and admit at most one user at each time slot, resulting in significant underutilization of spectrum resource, which will be less attractive to mm-wave UDN applications. A generalized temporal-spatial sharing scheme is proposed in this paper for UDN by exploiting the location information of incumbent devices, where multiple users are allowed to access each channel simultaneously via spatial separations. For distributed applications, the global information exchange among secondary users (SU) tends to be impractical, given the unaffordable signaling overhead and latency. Thus, a noncooperative game with fine-grained two-dimensional reuse is formulated, which leads to a more efficient access strategy. It is then proved to be an ordinary potential game (OPG), which guarantees the existence of the strategy Nash equilibrium (NE). Finally, an improved decentralized reinforcement learning algorithm is designed, with which SUs can learn from wireless environments and adapt toward an NE point, relying on the individual observation and the historical action reward (rather than the global information exchanging). The convergence efficiency of the new scheme is also rigorously proved. Numerical simulations are provided to validate the performances of the proposed schemes. [ABSTRACT FROM AUTHOR]

In this paper, we propose an efficient analytical method for admission control of wireless virtual networks (VNs), with heterogeneous traffic profiles, and various quality-of-experience (QoE) requirements, in the future software-defined radio access networks. We present a novel methodology for the admission control process that includes feedback information to the VN customers to improve their traffic profile accuracy, and consequently, their QoE. We formulate the virtual network admission control problem as a convex optimization problem which allows general multiple association between user equipment and base stations. Consequently, we propose an algorithm for solving this problem. The proposed method is applicable on heterogeneous networks with heterogeneous traffic distributions. We also propose a number of extensions to the problem including mechanisms that consider minimization of the network and service cost, and optimization according to the backhaul limitations. The simulation results show that our proposed methods are accurate and result in significant improvement in network resource utilization and customer satisfaction. [ABSTRACT FROM PUBLISHER]

Channel allocation has a direct and profound impact on the performance of vehicle-to-everything (V2X) networks. Considering the dynamic nature of vehicular environments, it is appealing to devise a blended strategy to perform effective resource sharing. In this paper, we exploit deep learning techniques predict vehicles’ mobility patterns. Then we propose an architecture consisting of centralized decision making and distributed channel allocation to maximize the spectrum efficiency of all vehicles involved. To achieve this, we leverage two deep reinforcement learning techniques, namely deep Q-network (DQN) and advantage actor-critic (A2C) techniques. In addition, given the time varying nature of the user mobility, we further incorporate the long short-term memory (LSTM) into DQN and A2C techniques. The combined system tracks user mobility, varying demands and channel conditions and adapt resource allocation dynamically. We verify the performance of the proposed methods through extensive simulations and prove the effectiveness of the proposed LSTM-DQN and LSTM-A2C algorithms using real data obtained from California state transportation department. [ABSTRACT FROM AUTHOR]

Massive Machine-Type Communication (mMTC) is proposed by ITU to provide a large scale of connectivity services for billions of machine-type communications devices (MTCDs) via cellular networks. However, MTCDs are generally energy-constrained, security capability limited and short-packet transmission dominated. It has great challenges for massive MTCDs to perform short-packet transmission simultaneously while guaranteeing the confidentiality and energy efficiency of the information transmission. In this paper, we investigate the energy-efficient secure short-packet transmission of Non-Orthogonal Multiple Access (NOMA) assisted mMTC networks, in which MTCDs aim to transmit secrecy messages to the destination base station via trusted relays with a passive eavesdropper present. To realize secure, reliable and energy-efficient transmission, we maximize the secrecy energy efficiency (SEE) by implementing joint relay and power level selection. Furthermore, we formulate a decentralized stateless Q-learning-based multi-agent reinforcement learning (MARL) algorithm for the dynamic and complex mMTC systems. The simulation results show that adopting the proposed decentralized MARL algorithm and relaying with NOMA can improve the performance of SEE while reducing information exchange overhead. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the user fairness power allocation (PA) for video transmission in a non-orthogonal multiple access (NOMA)-assisted wireless system with a pair of users. Considering that video transmission is usually delay-sensitive, the video quality can be adaptively adjusted based on the network conditions. First, a new performance metric, called the average video quality degradation probability, is proposed to describe the adaptive video quality. Then, based on the principles of NOMA and user queue model, the average video quality degradation probability of both users is derived. Next, a bisection search based power allocation (PA) algorithm is proposed to balance the video quality between the NOMA pair. Simulation results demonstrate that the proposed NOMA scheme is more effective to achieve the user fairness compared with the fixed NOMA scheme, the fractional transmit power allocation (FTPA) based NOMA scheme, the achievable rate fairness (ARF) NOMA scheme, and the optimized time division multiple access (TDMA) scheme. [ABSTRACT FROM AUTHOR]