In high-speed railway (HSR) scenarios, it is essential to ensure reliable handover for sustaining always-online communications of trains. However, this reliability is challenged by limited wireless coverage at cell edges and frequent handovers. To tackle these challenges, this paper explores the potential of dynamic beamforming to simultaneously improve the probability of successful handover and mitigate communication disruptions. First, we establish a beamforming-based transmission model for trains during handovers. Based on this model, we derive the impact of the beam directions of the serving and target cells on communication performance. Second, we formulate an optimization problem aiming at maximizing the conditional data rate of the train within handover regions, where the impacts of handover failure, rapid mobility, and beamforming overhead are considered. Third, to solve this optimization problem, we propose a dynamic beam direction adjustment algorithm by leveraging the property of deep reinforcement learning. The algorithm efficiently determines the optimal beam direction adjustment strategy based on the dynamic channel conditions. Finally, compared to state-of-the-art deep learning methods and beamforming strategies, simulation results demonstrate that the proposed method achieves superiority in communication quality at cell edges and handover performance, providing an efficient and reliable technical solution for HSR communications.

Air-ground Integrated Mobility (AIM), as a disruptive mode of travel, has the tremendous potential to alleviate ground traffic congestion issues substantially. However, the primary challenge in achieving this leapfrog development lies in ensuring driving safety. Receiving collision warnings in time within a limited distance can significantly reduce collision risks, which is crucial for ensuring driving safety in AIM. However, due to challenges in aerial network coverage, ensuring the communication quality of aerial Personal Aerial Vehicles (PAVs) remains difficult, thereby affecting the effective transmission of messages. Furthermore, the integration of ground Connected and Automated Vehicles (CAVs) with aerial PAVs in AIM results in significant differences in user resource requirements. Given the complexity of the AIM environment and the high mobility of PAVs, it is challenging to rapidly and accurately capture user communication quality. Therefore, addressing the differential resource requirements of users in this environment is particularly challenging. To this end, we propose a personalized resource allocation strategy assisted by a Channel Knowledge Map (CKM) in AIM. This strategy aims to meet the personalized resource requirements of users while maintaining the maximum Perception Response Time (PRT), thereby ensuring driving safety. Specifically, the CKM in AIM is constructed to obtain channel states through environment-aware communication. Next, a 3D collision warning system is designed to analyze rigorously the maximum PRT of vehicles under different motion states in avoiding collisions. On this basis, with the help of CKM, the channel knowledge of the user’s location is obtained to quantify the communication and computing resources required by each user to maintain the maximum PRT. Finally, we establish the PRT-driven resource optimization problem and employ Deep Reinforcement Learning (DRL) to seek the optimal resource allocation strategy. Simulation results indicate that the proposed method effectively enhances safety and resource utilization in AIM under resource constraints and uneven distribution.

Urban air mobility (UAM) provides a new solution to relieve urban transportation pressure by expanding transportation resources of near-ground space. The vigorous development of emerging technologies such as artificial intelligence, intelligent transportation, and sixth-generation (6G) communication technologies have greatly promoted the progress of UAM. However, UAM also increases traffic safety hazards while introducing vertical dimension transportation resources. Traditional collision avoidance is not suitable for three-dimensional (3-D) air-ground integrated mobility scenario, which considers safety hazards in vertical dimensions as well as the resource supply and demand conflict due to the combined effect of directional antenna angle and limited communication distance. Therefore, a safety-oriented on-demand resource allocation strategy for air-ground integrated mobility is proposed. Specifically, we first model the 3-D safety distance model in the air-ground integrated mobility scenario and construct its quantitative relationship with communication and computing resources. Secondly, a 3-D safety distance optimization model is proposed with joint consideration of safety-oriented resource requirements and resource distribution, which can allocate resources in the scenario. Furthermore, a 3-D safety distance optimization algorithm based on deep reinforcement learning (DRL) is designed for solving the optimization model, which implements a safety-oriented resource allocation. Simulation results show that the proposed safety control strategy can effectively improve the safety of air-ground integrated mobility and alleviate the contradiction between the supply and demand of resources.

The fifth-generation network (5G) has made great progress. With the continuous development of communication technology, by analyzing the characteristics of 5G scenarios, the sixth-generation network (6G) technology combined with multiple scenarios provides effective solutions for the implementation of emerging services with stringent requirements. It is worth noting that the vigorous development of emerging services has been weakened due to the limited resources provided by a single scenario, cross-scenario technologies are urgently needed to enable emerging services in the 6G stage. However, most of the existing work only focuses on a single scenario, which leads to emerging services with complex requirements still difficult to achieve in practice. Therefore, we propose an efficient representation and scheduling framework to achieve the unification of cross-scenario resources, aiming to solve the problem of resource scheduling in cross-scenario. In the above framework, first of all, considering the strict resource requirements of emerging services, we establish a unified resource representation model based on the Time-Expanded Graph (TEG). Secondly, to maximize resource utilization, based on the representation model, a cross-scenario resource scheduling model is proposed. Then, considering the complexity of solving the scheduling model, a resource utilization maximization strategy is presented through the primal decomposition. Simulation results show that the unified framework can effectively improve resource allocation efficiency in complex 6G scenarios.

This paper presents a new approach to classify human motions using a Doppler radar for applications in security and surveillance. Traditionally, the Doppler radar is an effective tool for detecting the position and velocity of a moving target, even in adverse weather conditions and from a long range. In this paper, we are interested in using the Doppler radar to recognize the micro-motions exhibited by people. In the proposed approach, a frequency modulated continuous wave radar is applied to scan the target, and the short-time Fourier transform is used to convert the radar signal into spectrogram. Then, the new two-directional, two-dimensional principal component analysis and linear discriminant analysis are performed to obtain the feature vectors. This approach is more computationally efficient than the traditional principal component analysis. Finally, support vector machines are applied to classify feature vectors into different human motions. Evaluated on a radar data set with three types of motions, the proposed approach has a classification rate of 91.9%.

Acquiring and securing position information is of fundamental importance in vehicular ad hoc networks and their applications. Recently, it has been shown that securing position information can be achieved by enlisting the help of on-board radar devices that have seen a phenomenal penetration of the vehicular market. However, most on-board radar devices have a static range and this can cause a number of insecurities and can add to communication delay. The main contribution of this work is a novel low-cost radar model which can dynamically tune its range by changing the signal sample size. As it turns out, the dynamically-tunable radar considerably enhances position security.

Co-channel interference (CCI) has become an important problem with the increasing deployment of wireless networks in the unlicensed frequency band. The existing Bluetooth scheme avoids collisions by modifying its hop sequences in the presence of WLAN. We propose a frequency diversity technique, namely dual channel transmission (DCT), which reduces packet error rate (PER) due to CCI when multiple Bluetooth piconets coexist with or without WLAN interference. The idea of DCT is to transmit the same packet on two distinct frequency hopped channels simultaneously and the power used in each channel is half of what would be used in single channel transmission (SCT). Since a packet is successfully received if at least one channel survives, the PER is reduced even when multiple piconets coexist. Further, the two channels of DCT are separated by at least 22 MHz to ensure robustness to WLAN interference. Theoretic analysis and numerical simulations on key metrics - PER, throughput, and transmission time are presented to validate the proposed approach and quantify its advantages. Comparisons to other coexistence mechanisms also demonstrate the effectiveness of DCT.

Co-channel interference has become an important problem with the increasing deployment of wireless networks in the unlicensed frequency band. Most existing schemes aim to avoid collisions on Bluetooth devices by modifying their hop sequences in the presence of WLAN interference. We propose a frequency diversity technique for Bluetooth, namely dual channel transmission (DCT), which reduces packet error rate (PER) due to co-channel interference when multiple bluetooth piconets coexist with or without WLAN interference. The idea of DCT is to transmit the same packet on two distinct frequency hopped channels simultaneously and the power used in each channel is half of what would be used in single channel transmission (SCT). Since a packet is successfully received if at least one channel survives, the PER is reduced when multiple bluetooth piconets coexist. Meanwhile, the two channels of DCT are separated by at least 22 MHz so that bluetooth is also robust to WLAN interference. Theoretic analysis and numerical simulations show that PER for bluetooth with DCT is significantly lower than that with SCT, when a small number of piconets coexist in the presence of WLAN interference. Comparisons to other collision avoidance mechanisms also demonstrate the effectiveness of DCT.

The paper proposed a frequency diversity technique for Bluetooth, namely dual channel transmission (DCT), to reduce the packet error rate (PER) due to inter-piconet interference when multiple Bluetooth piconets coexist. With DCT, the same packet is transmitted on two distinct frequency hopped channels simultaneously, and the power used in each channel is half of what would be used in single channel transmission (SCT). A packet is successfully received if at least one channel survives. There is a tradeoff between the increased reliability due to the diversity offered by DCT, and the potentially increased interference due to the use of extra channel. The authors evaluate the performance of DCT when co-channel and adjacent channel interference, as well as wireless propagation characteristics are considered. It is shown that with DCT, the PER is significantly lower than that of SCT when a small number of piconets coexist. Numerical simulations validate the analysis and demonstrate the effectiveness of DCT.

Frequency collision caused by coexistence has become a critical issue with the increasing deployment of wireless networks in the unlicensed frequency band. Most collision avoidance schemes developed for the coexistence of Bluetooth and WLAN are not applicable to multiple coexisting Bluetooth piconets due to constant frequency hopping in Bluetooth. Relying on a blind hop timing and frequency estimation algorithm, in this paper we propose a collision resolution technique that resolves collided data packets without retransmission, thus enabling robust coexistence of multiple Bluetooth piconets. Simulation results demonstrate the effectiveness of this technique.