The promptness of decision-making is of paramount importance in coordinate decision-making communication systems. In this paper, we envision a system in which a sender transmits information to a receiver for decision-making. The decision-making process encompasses the extraction of decision-related information followed by subsequent computation. To assess the impact of communication delays, randomness of decision-making and computational time on the immediacy of decisions, we introduce a novel performance metric, namely the age of decision (AoD). Specifically, AoD is defined as the time elapsed since the generation of the update upon which the most recent decision is based. Considering the stochastic nature of decision-making, we analyze the average peak AoD in systems where the arrival and service processes follow general processes or Poisson processes. To facilitate our derivations, we introduce the concept of effective decisions to denote those decisions that can lead to the system’s peak AoD. Simulation results confirm the validity of our derivations and illustrate that appropriately increasing the rate of decision-making can significantly reduce the system’s average peak AoD.

Federated Learning (FL) has been recognized as a transformative approach in vehicular networks, enabling collaborative training between vehicles and preserving data privacy. However, the high mobility and dynamic topology changes inherent in vehicular environments pose significant challenges, primarily due to the increased communication overhead associated with exchanging model parameters. To mitigate these issues, a novel framework for decentralized wireless FL with soft clustering and 1-bit compressed sensing (SC1BCS-WFL) is proposed in this article. The framework considers vehicle position, vehicle attributes, vehicle speed, and model cosine similarity when grouping vehicles. It utilizes an adaptive threshold mechanism based on 1-bit compression to reduce uplink transmission load while maintaining FL performance. In addition, an early stopping strategy is incorporated into the proposed framework to avoid unnecessary waste of computational and communication resources. Simulation results show that the SC1BCS-WFL framework can enhance the efficiency of federated learning in vehicular settings, particularly with non-independent and identically distributed data. Simulation results also validate the framework’s ability to reduce communication overhead while achieving high model accuracy, indicating its suitability for distributed Internet of Vehicles (IoV) scenarios and contributing to the development of smarter and more efficient IoV applications.

Vehicle-to-vehicle (V2V) enabled platooning is a promising application for future intelligent transportation systems. V2V communications allow real-time sharing of vehicle status information within the platoon, enabling vehicles to dynamically adjust their control strategies and maintain spacing between platoon members. As such, the platoon stability depends on the timeliness of the control information. However, the information timeliness can be affected by factors such as transmission period, co-channel interference and actuator lag. In this paper, the performance of the V2V communication is analyzed for the platooning, from an information timeliness perspective. Firstly, an Age-of-Information (AoI) based joint communication and control model is proposed to determine the requirement of the AoI to maintain the stability. Then, a large-scale V2V communication model is constructed by taking account of the V2V packet transmission and resource allocation scheme. Considering the spatio-temporal correlation among the V2V transmissions, the distribution of the average AoI and average peak AoI are investigated, by using queuing theory and stochastic geometry. The analytical results can reveal the proportions of the stable platoons in the large-scale networks. Simulation results corroborate the analytical results and demonstrate the impact of the system parameters on the platoon stability. Furthermore, simulation results showed that the requirements of the maximum transmission period and the maximum actuator lag can be analytically obtained. More importantly, it is shown that the proposed model and the analytical results could provide insightful guidelines for designing the V2V-enabled platooning systems.

With their superior maneuverability and flexible deployment options, unmanned aerial vehicles (UAVs) present a viable solution to augment cellular network capabilities by serving as mobile base stations (BSs). Yet, the three-dimensional, dynamic nature of UAV deployments, along with changing aerial conditions, often results in frequent handovers, adversely affecting communication quality. Moreover, the impacts of transitions between line-of-sight (LoS) and non-line-of-sight (NLoS) links on handover and coverage probabilities in mobile UAV networks have not been thoroughly investigated. This paper provides an in-depth analysis of how multi-tier deployment altitudes and variations in LoS link probabilities influence handover and coverage probabilities. Utilizing stochastic geometry, we consider two association strategies: distance-based and strongest average received signal strength (RSS)-based. For both strategies, we provide semi-closed form expressions for handover probability and subsequently derive network coverage probabilities. Through numerical simulations, we not only unearth an optimal configuration of UAV density and deployment altitude that maximizes coverage probability for ground users, but also uncover that the relative benefits of the RSS-based association strategy wane as UAV density escalates compared to the distance-based association strategy. Furthermore, our results underscore the potential for enhancing coverage performance by adopting a strategy of deploying UAVs at various altitudes, contrasting with the traditional approach of uniform altitude deployment.

Using traffic data to accurately predict the traffic flow at a certain time in the future can alleviate problems such as traffic congestion, which plays an important role in the healthy transportation and economic development of cities. However, current traffic flow prediction models rely on human experience and only consider the advantages of single machine learning model. Therefore, in this work, we propose a multi-layer model based on transformer and deep learning for traffic flow prediction (MTDLTFP). The MTDLTFP model first draws on the idea of transformer model, which uses multiple encoders and decoders to perform feature extraction on the initial traffic data without human experience. In addition, in the prediction stage, the MTDLTFP model using deep learning technology, which input the hidden features into the convolutional neural network (CNN) and multi-layer feedforward neural network (MFNN) to obtain the prediction score respectively. The CNN model can captures the correlation information between the hidden features, and the MFNN can captures the nonlinear relationship between the features. Finally, we use a linear model to combine the two prediction scores, which can make the final prediction value take into account the common advantages of both models. Multiple experimental results on two real datasets demonstrate the effectiveness of the MTDLTFP model. The experimental results on the$WorkDay$dataset are as follows, with the RMSE value of 0.191, MAE value of 0.165. The experimental results on the$HoliDay$dataset are as follows, with RMSE value of 0.227, MAE value of 0.192.

Platooning system with Vehicle-to-vehicle (V2V) communication can offer the capability to adapt control strategies and uphold vehicle spacing through the exchange of vehicle status information. The stability of the platoon hinges on the promptness of transmitting vehicle status and control data. Yet, the currency of these data is significantly impacted by factors such as transmission interval and interference, particularly in large-scale networks. Diverging from much of the existing literature that predominantly addresses communication delay's impact on platoon stability, this study scrutinizes the freshness of V2V communication information and its ramifications on platoon stability across large-scale regions. Concretely, our work introduces a platooning control model grounded in a constant time gap policy that factors in the age of information (AoI) inherent in V2V communication. Subsequently, we construct a V2V communication model, accounting for the V2V packet transmission scheme and wireless resource allocation strategy. Through a fusion of queuing theory tool and stochastic geometry theory, we delve into the distribution of the average peak AoI (PAoI), unearthing insights into the prevalence of stable platoons in the network. Furthermore, simulation results elucidate the interplay between system parameters and platoon stability within this interconnected communication and control system.

The use of unmanned aerial vehicles (UAVs) as base stations (BSs) is a promising solution to enhance the performance of the cellular networks. This architecture enables greater mobility for BSs, which affects both communication distance and the probability of line-of-sight (LoS) and non-line-of-sight (NLoS) links in air-to-ground channels. Consequently, handovers occur frequently, impacting communication performance. However, the analytical impact of LoS and NLoS links on handover probability in mobile UAV networks remains unknown. In this paper, we employ the random geometry theory to comprehensively investigate the impact of LoS link probability on handover while considering two association strategies based on the nearest-distance and the strongest average received signal strength. We present exact expressions for handover probability in semi-closed form under these two scenarios. Our proposed analytical results enable the exploration into how UAV-BSs density, the deployment height, and the adopted association strategy affect the handover probability.

Recently, some technological issues in network slicing have been explored. However, most works focus on the physical resource management in this research field and less on slice selection. Different from the existing studies, we explore the problem of intelligent multiple slice selection, which makes some effort to dynamically obtain better user experience in a changeable state. Herein, we consider two factors about user experience: its throughput and energy consumption. Accordingly, a distributed E‐cross learning algorithm is developed in the multiagent system where each terminal is regarded as an agent in the distributed network. Furthermore, its convergence is theoretically proven for the dynamic game model. In addition, the complexity of the proposed algorithm is discussed. A mass of simulation results are presented for the convergence and effectiveness of the proposed distributed learning algorithm. Compared with greedy algorithm, the proposed intelligent algorithm has a faster convergence speed. Besides, better user experience is attained effectively with multiple slice access.

To analyze the performance of orthogonal matching pursuit (OMP)-based compressed channel estimation (CCE) with deterministic pilot patterns, we propose a mathematical framework by defining four normalized mean square errors (NMSEs): the total NMSE (NMSET), the NMSE on dominant channel components (NMSED), the NMSE caused by “lost errors” (NMSEL), and the NMSE caused by “false alarms” (NMSEF). Then, we derive a formula with a closed form for evaluating the upper bound of NMSED in the ideal case (NMSED,UB). Using the proposed analytical framework, the main findings include: 1) the NMSED,UB is determined by the following four parameters: the deterministic pilot pattern, the maximum Doppler shift, the number of dominant multipath components, and the SNR; 2) the NMSED,UB can be viewed as an approximation of practical NMSET in the case that the probability of the successes of OMP exceeds a certain threshold, in which both NMSEL and NMSEF are neglectable; and 3) using linear regression models, the practical bit-error-rate performance also can be predicted well based on the proposed NMSED,UB. We believe that the proposed framework provides a useful tool for adaptively optimizing pilot parameters according to rapidly time-varying channel conditions when using OMP-based CCEs in mobile OFDM systems.

In orthogonal frequency division multiple access (OFDMA)-based cellular networks subject to the distancedependent path loss and shadow fading (SF), the downlink inter-cell interference (ICI) for a given user equipment (UE) is essentially a sum of several lognormal random variables (RVs). So far, no method of approximating the lognormal sum distribution is explicitly accurate when the component lognormal RVs with different logarithmic means and logarithmic variances are correlated. In this paper, the Normal Inverse Gaussian (NIG) distribution is proposed to approximate the downlink ICI for a given UE with the correlated SF. First, the downlink ICI is modelled as a sum of several correlated lognormal RVs. Then original moments of the lognormal sum in the logarithmic domain are obtained analytically. Finally the estimated parameters of the NIG distribution are computed explicitly by the mean, variance, skewness and kurtosis of the lognormal sum in the logarithmic domain through moment matching. Numerical results verify the accuracy of the NIG approximation when the correlated component lognormal RVs have different logarithmic means and logarithmic variances, and show that the NIG approximation outperforms the MGF-based lognormal approximation in various scenarios.

For guaranteeing quasi-error free delivery of realtime multicast services over wireless networks, an efficient greedy algorithm is proposed for optimizing the parameters of a HARQ scheme with rateless codes under strict delay and bandwidth constraints. We take systematic Raptor codes as an example for analyzing the optimum performance of the HARQ scheme. The results show that the optimum performance can be obtained by combining the optimum HARQ and FEC. Additionally, the simulation results show that the proposed optimization framework can be used for evaluating the tight lower bound performance of the HARQ, which indicates that it is suitable for designing optimum HARQ scheme with guaranteeing the target quasi-error free delivery requirements very well.

Although frequency domain equalization (FDE) is very efficient to resist frequency selective fading in single-carrier communications, its performance will be greatly degraded due to high Doppler impact in a fast moving environment. To enhance the system performance of single-carrier communication system with FDE (SC-FDE), the self- cancellation technique is migrated from orthogonal frequency division multiplexing system (OFDM). It can obtain significant performance gain in scenarios with high Doppler in numerical simulations.