Multilayer satellite networks (MLSNs) incorporating non-orthogonal multiple access (NOMA) offer a wide range of applications and enable seamless connectivity for various user demands. However, the effects of co-channel interference, complicated fading facts, and the dynamics of satellite networks limit further practical applications of NOMA in MLSNs. In this paper, we propose a NOMA-based MLSN system with an adaptive power allocation scheme. In order to accurately model the mobility of satellites, we derive a closed-form expression for the probability density function of the elevation angle observed by the terrestrial users for a low-Earth-orbit satellite, based on which detailed theoretical discussions are given about the statistical properties and channel capacities of satellite-terrestrial and inter-satellite links. Finally, we establish a novel adaptive power allocation scheme for the NOMA-based MLSNs to enhance user fairness by applying a particle swarm optimization (PSO) algorithm. The simulation results confirm the correctness of our theoretical results and the superiority of the adaptive power allocation scheme for user fairness of the NOMA-based dynamic MLSNs considered in this work.

The increasing demand for online high-quality video streaming has brought huge challenges to the traditional client-server video streaming systems due to the high feedback delay, rigorous bandwidth requirement, and the lack of a mechanism of centralized resource management between users. In this work, we propose AMIS-MU, an edge computing-based mobile video streaming system that optimizes the watching experience of users via playback adaptation and channel resource allocation. AMIS-MU fully explores the power of edge servers from three perspectives. First, by pre-caching videos from the cloud, AMIS-MU analyzes video contents at the edge, and achieves a nearly imperceptible content-based playback speed adaptation. Second, as the edge server controls the channel resources of users in a centralized fashion, AMIS-MU adaptively updates the channel configuration to optimize the overall watching experience. Last, the plenty of computational power available at the edge enables a more intelligent playback control by using deep reinforcement learning (DRL). We propose a novel usage of DRL which significantly reduces the complexity of the cross-layer joint optimization problem and solve the non-convex channel resource allocation problem by Lyapunov optimization. Experiments show that AMIS-MU outperforms other existing algorithms in terms of average QoE and fairness.

Digital Mobile Radio (DMR) is widely used in mission-critical communication due to its cost-effectiveness. However DMR only provide voice service for users in a small range. To address these limitations, a protocol conversion gateway named DoIP was designed and implemented to allow DMR devices to access a variety of communication services over long distances using the internet. In our proposed hierarchical model, DoIP works in an add-on mode. To meet the requirements of real-time, reliable, and multimedia applications, we designed the DMR frame structure, SPI packet structure, and Internet Protocol (IP) packet structure for inter-layer transmission. We also proposed the mapping rules between different protocols and achieved the conversion of DMR frames to IP packets. Finally, we implemented the DoIP on a commodity DMR repeater and evaluated its performance. The comprehensive evaluation revealed that DoIP successfully realized protocol conversion between DMR and TCP/IP, with a conversion delay of 9.10 ms, a packet loss rate of 0.3%, and an average jitter of 1.90 ms.

Mobile Edge Computing (MEC), as a promising key technology, provides tremendous support for latency-sensitive applications in Internet of Vehicles (IoV). In this paper, we focus on the MEC-assisted computation offloading problem for mixed traffic scenarios that autonomous and human-driven connected vehicles coexist. With the objective of minimizing system average latency, a priority-based serial and parallel joint offloading scheme is designed and formulate the optimization problem as a Markov decision process (MDP). Then, we propose an adaptive offloading strategy based on deep reinforcement learning. Simulation results compared to contrast algorithm and baseline schemes demonstrate the superiority of the proposed priority-based offloading scheme, effectively reducing the system average latency and ensuring the latency requirements of latency-sensitive tasks.

Passive localization is commonly achieved through the direction finding and positioning technique, which uses a airborne or ground multi-station angle measuring system to intersect pointing lines for fast and omnidirectional positioning. However, as the number of targets increases, so does the occurrence of false points. This poses a challenge to the positioning performance of system, requiring the prompt elimination of false points. To address the issue, we propose a high detection rate passive localization method based on density peak clustering (DPC). In this method, a suitable non-ideal location model is established, and improved density peak clustering is utilized to achieve data association and target localization. Simulation results confirm the proposed positioning method’s superior performance and adaptation to the non-ideal conditions of multi-target localization.

The optimization of power control for base station (BS) and user equipment (UE) is crucial to enhance network performance in the dynamic landscape of wireless communication. With the advent of Sixth Generation (6G) communication systems, the demand for real-time communication has surged, leading to a pressing need to develop power optimization strategies that can tackle network latency. We propose a joint power optimization method for both BS and user UE based on the Age of Information (AoI), and address the challenge of power allocation in multi-user communication systems. We begin by modeling a single BS and studying the impact of its power on the AoI. Next, we design a UE power allocation algorithm that considers limited conditions. To achieve this, we transform the problem into a Markov decision problem and solve for the optimal solution using a deep learning algorithm. Our proposed method provides an effective approach to optimize power allocation in multi-user communication systems. The simulation results demonstrate that our proposed algorithm can effectively minimize AoI and achieve on-demand power allocation. Compared to the mean algorithm for power allocation, our proposed algorithm has better performance.

Due to increasing demands of seamless connection and massive information exchange across the world, the integrated satellite-terrestrial communication systems develop rapidly. To shed lights on the design of this system, we consider an uplink communication model consisting of a single satellite, a single terrestrial station and multiple ground users. The terrestrial station uses decode-and-forward (DF) to facilitate the communication between ground users and the satellite. The channel between the satellite and the terrestrial station is assumed to be a quasi-static shadowed Rician fading channel, while the channels between the terrestrial station and ground users are assumed to experience independent quasi-static Rayleigh fading. We consider two cases of channel state information (CSI) availability. When perfect CSI is available, we derive the instantaneous achievable sum rate of all ground users and formulate an optimization problem to maximize the sum rate. When only channel distribution information (CDI) is available, we derive a closed-form expression for the outage probability and formulate another optimization problem to minimize the outage probability. Both optimization problems correspond to scheduling algorithms for ground users. For both cases, we propose low-complexity user scheduling algorithms and demonstrate the efficiency of our scheduling algorithms via numerical simulations.

Real-time fault diagnosis on vehicles can effectively avoid potential accidents, which, however, is difficult and challenging to be widely deployed due to the low computational capability and limited data storage of electric vehicles (EVs). To address this issue, we propose a vehicle-mounted fault diagnosis system with low computational complexity and small data storage, for achieving real-time monitoring of vehicle status. To facilitate the accurate and optimized feature selection, we had been collecting 6.52-GB real data from three EVs in 12 months. Motivated by those data, we first propose a multilabel feature selection algorithm to obtain the feature weights, based on which the optimal number of features is then calculated through the backpropagation neural network (BPNN), thus minimizing the computational cost of real-time fault diagnosis regarding sample dimensions. To further simplify the fault diagnosis system, i.e., reducing the minimum required capacity of data storage, we design a real-time diagnosis sliding window (RDSW) where the window moves forward as new samples arrive and the stale data outside the window are discarded. In particular, we calculate the optimal size of RDSW, which controls the minimum required number of samples to guarantee the accuracy of real-time fault diagnosis. Owing to the mechanism of RDSW, vehicles no longer need to store massive data to guarantee the accuracy of real-time fault diagnosis. In addition, the results of real-time fault diagnosis at each vehicle can be shared with other vehicles in cooperative intelligent transportation systems (C-ITS). Finally, comprehensive simulation is conducted to validate the effectiveness of the proposed diagnosis system in terms of accuracy, complexity and storage capacity.

Many scenarios require continuous positioning for mobile nodes, such as mobile wireless sensor networks (MWSN), the Internet of Things (IoT), and the Internet of Vehicles (IoV). However, because the position of a mobile node is constantly changing, a lag is caused in the position information during the process of collecting position-related information. Then, a huge challenge is brought to fast and continuous positioning. To address the issue, we propose a position monitoring system (PMS) based on hierarchical clustering. First, the cross location algorithm is used as the initial positioning method, and the possible positions of targets are recorded. Then, to overcome the problem of information lag, a hierarchical clustering based PMS is proposed to predict the positions through performing information fusion and redundancy removal on existing complete data including the possible positions. Finally, a comprehensive simulation of the proposed method is carried out. The simulation results prove that our method is effective.

This work proposes AMIS, an edge computing-based adaptive video streaming system. AMIS explores the power of edge computing in three aspects. First, with video contents pre-cached in the local buffer, AMIS is content-aware which adapts the video playout strategy based on the scene features of video contents and quality of experience (QoE) of users. Second, AMIS is channel-aware which measures the channel conditions in real-time and estimates the wireless bandwidth. Third, by integrating the content features and channel estimation, AMIS applies the deep reinforcement learning model to optimize the playout strategy towards the best QoE. Therefore, AMIS is an intelligent content- and channel-aware scheme which fully explores the intelligence of edge computing and adapts to general environments and QoE requirements. Using trace-driven simulations, we show that AMIS can succeed in improving the average QoE by 14%-46% as compared to the state-of-the-art adaptive bitrate algorithms.

Neighbor information perception with high accuracy and low overhead is quite essential for vehicular networks, which is accomplished by the neighbor discovery scheme. Following the scheme, nodes exchange short discovery messages for advertising their existence and sensing neighboring vehicles. To combat high vehicle mobility and severe channel fading, the discovery message is always exchanged frequently in vehicular networks. This, however, introduces superabundant communication overhead. In this article, a novel neighbor discovery method with mobility awareness and channel randomness adaptability is proposed for investigating the aforementioned issue. First, a closed-form expression is derived, which captures the quantitive relation of the neighbor discovery performance to the vehicle mobility and channel randomness. Guided by our theoretical analysis, the optimal neighbor discovery scheme is developed to adjust the discovery frequency adaptively based on mobility and channel. Thus, an optimal tradeoff between the discovery accuracy and overhead is achieved in vehicular networks. Simulation results coincide with our analysis results, which further demonstrates that the proposed discovery scheme outperforms the periodic and existing adaptive methods in terms of discovery accuracy and overhead.

Implementing real-time and onboard fault diagnosis on electric vehicles can effectively avoid potential dangers. However, the low calculating ability and limited storage capacity of electric vehicles hamper the development of real-time and onboard fault diagnosis. To address the issue, combining neural network and fuzzy logic, we propose a low complexity onboard vehicle fault diagnosis method to monitor the vehicle status and give early warning of accidents. In twelve months, we first utilize three electric vehicles and collect 6. 52GB real data related to vehicle components. Motivated by those data, we conducted an in-depth research on the major vehicle faults, and divided them into four types which are no fault, battery fault, sensor fault, and module fault. Furthermore, we propose a BP neural network based multiple training method to define the correlation between data types and fault types. Then, applying the correlation and data, a fuzzy logic based classification method is proposed to evaluate the vehicle status and give early warning. Finally, a comprehensive simulation is conducted, which indicates that the accuracy is 88%.

Non-orthogonal multiple access (NOMA) is one of the potential multiuser supporting techniques in the fifth generation (5G) cellular systems due to its higher spectrum efficiency (SE) and cell-edge throughput. Vector-perturbation (VP) is widely known as one of the nonlinear precoding schemes that achieves near-capacity performance in practical wireless multi-input-multi-output (MIMO) communication systems. In this paper, we propose a hybrid transmission strategy based on VP and NOMA (VP-NOMA) by designing a beamforming matrix with the power allocation strategy to minimize total transmit power for certain quality of service (QoS) requirements. Rather than searching for the optimal beamforming matrix, we propose a more intuitive sub-optimal algorithm, called iteration beamforming for VP-NOMA systems (IBVP-NOMA), to find beamforming vectors. Further, different user clustering strategies are considered and compared to enhance the performance of the VP-NOMA systems. The simulation results demonstrate that the proposed method requires lower transmit power than the NOMA system without VP.

In vehicular ad hoc networks (VANETs), three-dimensional scenarios are always ignored, even though they are attractive for their effectiveness in land use. Focusing on those scenarios, we propose and prove their severe impacts on the performance of a vehicular network. We first conduct a transmission experiment. The results prove that the existence of those scenarios induces the inter-layer communication, and then significantly reduces the transmission range. Furthermore, we demonstrate that the variation of the transmission range makes an enormous difference in the neighbor number, which severely affects the network performance. At last, our extensive simulations show that the aforementioned analysis are in fact quite accurate.

© 2018 IEEE. Neighbor discovery, providing the neighbor information by broadcasting discovery messages, is a promising solution for cooperative perception of Intelligent Vehicles (IVs). However, the high vehicle mobility and severe channel randomness of IV environments call for a frequent discovery, which results in a superabundant overhead. In this paper, we propose a new framework for cooperative perception of IVs by novelly introducing an improved neighbor discovery method. We first establish an analytical framework to capture the quantitive relation between the hitting probability of neighbor discovery with the vehicle mobility and channel randomness using a closed-form expression. Based on the analysis, an adaptive neighbor discovery method is developed to adaptively make tradeoff between the discovery accuracy and overhead at varying driving status of IVs. Applying the improved neighbor discovery, the process of cooperative perception is discussed. Accordingly, simulations in three IV scenarios are conducted whose results are consistent with our analysis.

MmWave systems with most prior work focused on its narrowband hybrid analog/digital precoding, however, will likely operate on wideband channels with frequency selectivity. Therefore, in this paper we investigate wideband angular beamforming schemes for mmWave massive MIMO-OFDM systems. First, for the RF analog beamforming, the optimal beamforming of an unconstrained antenna array (UAA) is given as the performance benchmark. Then, the optimal angular beamforming (OAB) and a simple dominant angular beamforming (DAB) of a shared antenna array (SAA) are compared in received SNR and implementation cost. Second, for the baseband digital precoding, the space-frequency vector perturbation (SFVP) precoding is proposed to collect both spatial and multi-path diversity. Finally, analytical and simulation results show that: a) DAB is a cost- effective RF beamforming scheme under LOS channel environment; b) the proposed hybrid DAB-SFVP beamforming scheme achieves the array gain equaling the number of transmit antennas Ntand diversity gain equaling the product of the number of RF chains K and the number of temporal resolvable clusters ℒ.

Effective file transfer between vehicles is fundamental to many emerging vehicular infotainment applications in the highway Vehicular Ad Hoc Networks (VANETs), such as content distribution and social networking. However, due to fast mobility, the connection between vehicles tends to be short-lived and lossy, which makes intact file transfer extremely challenging. To tackle this problem, we presents a novel Cluster-based File Transfer (CFT) scheme for highway VANETs in this paper. With CFT, when a vehicle requests a file, the transmission capacity between the resource vehicle and the destination vehicle is evaluated. If the requested file can be successfully transferred over the direct Vehicular-to-Vehicular (V2V) connection, the file transfer will be completed by the resource and the destination themselves. Otherwise, a cluster will be formed to help the file transfer. As a fully-distributed scheme that relies on the collaboration of cluster members, CFT does not require any assistance from roadside units or access points. Our experimental results indicate that CFT outperforms the existing file transfer schemes for highway VANETs.

The Intelligent Transportation System (ITS) targets to a coordinated traffic system by applying the advanced wireless communication technologies for road traffic scheduling. Towards an accurate road traffic control, the short-term traffic forecasting which predicts the road traffic at the particular site in a short period is often useful and important. In existing works, Seasonal Autoregressive Integrated Moving Average (SARIMA) model is a popular approach. The scheme however encounters two challenges: (1) the analysis on related data is insufficient whereas some important features of data may be neglected; and (2) with data presenting different features, it is unlikely to have one predictive model that can fit all situations. To tackle above issues, in this work, we develop a hybrid model to improve accuracy of SARIMA. In specific, we first explore the autocorrelation and distribution features existed in traffic flow to amend structure of the time series model. Based on the Gaussian distribution of traffic flow, a hybrid model with a Bayesian learning algorithm is developed which can effectively expand the application scenarios of SARIMA. We show the efficiency and accuracy of our proposal using both analysis and experimental studies. Using the real-world trace data, we show that the proposed predicting approach can achieve satisfactory performance in practice.

To enhance the spectral efficiency of geostationary Earth orbit (GEO) satellite communication systems with scarce GEO resources, cooperative transmission is widely used in GEO satellite collocation (GEOSC) systems. Current analysis on GEOSC channels is usually based on the hypothesis that channels are line‐of‐sight (LOS) ones, while multipath components are ignored. In this study, a more realistic cooperative transmission method is studied for GEOSC systems over Rician fading channels, where multipath components are taken into consideration in conjunction with LOS components. On the basis of this model, a practical user selection strategy with opportunistic beamforming is studied to optimise the capacity of GEOSC systems. Simulation results show that the GEOSC system using the techniques developed in this study has better performance comparing with the ones using existing approaches.

Resource management is a crucial task in vehicular ad hoc networks (VANETs) due to the existence of various resources, such as text, audio and video. However, the highly dynamic network feature and the limited memory of the local server pose challenges to resource management, which not only lead to the failure of resource presentations to users, but the transmission of the invalid fragment data would also result in the significant waste of precious bandwidth and memory. To address the issue, our paper proposes a Fuzzy Logic based Resource Management scheme (FLRM) under fog computing platform in VANETs. In the scheme, we first gather and record the request time and download time for each resource by the designed Vehicle to Infrastructure (V2I) communication mode. Depending on the above information, we define a survival time for each stored resource by the proposed fuzzy logic based popularity evaluation algorithm. Motivated by the defined survival time, the local server can update the resource list in real time. In the end, we conduct simulations to verify the performance of FLRM. Results demonstrate that the proposed scheme performs well in terms of throughput, which increases the user experience with fresh resources.

Mobility models play a vital role in Vehicular Ad Hoc Networks (VANETs) simulations. Moreover, mobility models with higher extent of reality to fit into the characteristics of VANETs better make simulations more credible and accurate. However, most of traditional mobility models only devote to planar and ideal scenarios. It is rare for them to reflect more realistic environments, such as ubiquitous 3D scenarios where accidents occur frequently because of the complex vehicle motion and human factors. To address these issues, a Three-dimensional Accident Driver Model (T-ADM) is proposed for VANETs in this paper to reflect the real world more realistically. With characteristics based on the combination of plane and space in VANETs, T-ADM can produce 3D scenarios like viaducts in VANETs. Furthermore, T-ADM can mimic the non-standard driver behavior and generate accident scenarios. Finally, a comparison among T-ADM and other mobility models is generated by Matlab and VanetMobiSim. It demonstrates that T-ADM is able to not only reflect the corresponding realistic characteristics mentioned above but also give an objective description of vehicle density and velocity in VANETs.

The shortest path problem has become an important issue in the increasingly complex road networks nowadays, especially for these applications which are strict with high timeliness. However, searching the shortest path is difficult as road traffic flows are time-varying. An important issue in searching the shortest path is how to obtain the time expired on each segment at the given time. To this purpose, we propose a hybrid speed model to calculate the travel time in this paper, which considers the difference between the speed in congested and uncongested road networks. And analysis of speed in both conditions are given, respectively. Subsequently, a metric is also proposed to distinguish between congested and uncongested networks. Our work also utilizes the huge traffic data to reflect and analyze the real scenario. Compared to the previous work, this paper considers a more complex urban traffic scenario and some verifications are made with our data. Finally, a numerical study is carried out in the urban road network in Kaohsiung, Taiwan. The results show the hybrid speed model can give travel time prediction in an accurate way and can provide useful information for road designers.

Even the greedy routing is widely used in wireless networks, its theoretical study is still limited in vehicle environments. In this paper, we theoretically analyze the performance of the greedy routing under three typical vehicle scenarios, i.e., the single-lane road, the multilane road, and the multilevel road. We first propose the analytical model by analyzing characteristics of traffic environments, which contain the width and multilevel features of roads. Specifically, we prove that the road-width is ignorable under certain conditions, whereas the data measured in an outdoor experiment reveal that the multilevel feature is non-ignorable because its existence dramatically degrades the transmission range. Based on the model, we analyze the routing length of the greedy routing for all scenarios in the following three aspects. 1) We derive the distribution function for the first one-hop progress. 2) We prove that routing increments are history-dependent and give one sufficient condition that ensures these increments are approximately i.i.d. 3) We calculate the routing length described by the$h$-hop coverage and hop count using the renewal theory. Finally, simulations are conducted to verify the accuracy of our analysis.

As a technical method over VANETs, video delivery has the potential power to enhance the application experience associated with traffic safety, management and infotainment. The experience of user is seriously affected by the quality of the video display. Therefore, Quality of Experience (QoE) enhancement for video delivery in VANETs is an important issue. However, the high mobility and dynamic nature of VANETs cause the dynamic topology which poses a significant challenge for video delivery. In this paper, we propose a user-oriented cluster-based solution called CDOV (Cluster and Dynamic Overlay based video delivery over VANETs), which combine the novel clustering algorithm and the dynamic overlay structure into a novel structure. Simulation results show that CDOV scheme for video delivery over VANETs significantly reduces the startup delay and increases the delivery rate compared with the gossiping-based scheme.

In realistic Vehicular Ad hoc NETworks (VANETs), due to the existence of three-dimensional (3D) scenarios, such as the viaduct, tunnel and ramp, the distribution of vehicles is non-planar. However, existing routing protocols in VANETs are mainly analyzed and designed based on ideal plane scenarios. We call them plane-based routing protocols. In this paper, we focus on routing issues in 3D scenarios of VANETs. Through analysis, we demonstrate that applied in 3D scenarios, the plane-based routing protocols suffer a series of severe problems, i.e., hop count increases and delivery ratio decreases. To address the issues, we propose a Three-dimensional scenario oriented Routing (TDR) protocol for VANETs. Utilizing three-dimensional information, TDR establishes a route hop by hop and transmits packets as far as possible to the optimal immediate neighbor node which is located on the same plane with the current forwarding node. In the end, a comparison between TDR and the existing protocol GPSR is conducted on the network simulator NS2. The results show that TDR has higher delivery ratio but lower end-to-end delay and average hops.

Vehicular ad hoc network (VANET) is suffering from intermittent connectivity problems due to vehicles mobility, which challenge routing protocols. To address the issue, we propose a novel strategy called Reactive Pseudo-suboptimal-path Selection routing protocol (RPS). It is different from existing solutions which rely on vehicles physical movement to carry packets in intermittent connectivity scenarios. RPS gives the recently passed intersection a chance to select a new path from suboptimal-path unilaterally determined by local knowledge. Thus it improves the probability of transmission through wireless channels. A comparison between RPS and current protocols is presented and results show that the proposed RPS has higher packet delivery ratio and lower end-to-end delay.

Predicting the propagation path of a network worm is highly beneficial for taking appropriate countermeasures in advance. Traditional worm propagation models mainly deal with the total number of infected hosts during a period of time, which cannot indicate a worm's track. We choose the worm using random scanning to study, as it was the basic type and all the others were derived from it. A novel model proposed in this paper locates the subnets going to be infected at a given time based on the infection measurement of the subnet. The time and frequency for victims in the subnet to increase were calculated according to common characteristics of worm diffusion and the relationship between malicious traffic and bandwidth usage. Taking the two factors above as input, fuzzy reasoning was adopted to deduce the real-time infection situation for each subnet. The bigger the value of infection situation, the more likely the corresponding subnet would be attacked in a short time. Simulation experimental results show that the model estimates the worm's track dynamically with acceptable accuracy. Furthermore, the increase interval of victims in subnet is much longer for worm with slower spread speed, which provides sufficient time to carry out pertinent response.

Predicting or discovering the possible propagation direction of spreading network worms can efficiently benefit the enforcement of network security countermeasures like blocking them in real-time way. Most worms exhaust all of the network bandwidth maliciously in very short time. This paper proposed a model on predicting the propagation direction between areas based on two key indexes including area-infected-time (AIT) and area-infected-probability (AIP), and calculates alert level for each area by fuzzy reasoning. The higher alert level is, the more likely that the corresponding area is infected by worm in short time, and this area is the propagation direction of worm at the moment. Simulation experimental results show that the early warning model proposed in this paper can deduce area-alert-level (AAL) correctly and predict the propagation direction of network worm dynamically.