In this paper, a 3D geometrical cluster-based channel model is developed for reconfigurable intelligent surface (RIS)-assisted multi-user communication systems. Herein, spatial consistency of multi-user channels is considered to guarantee smooth evolution of large-scale (i.e. Ricean K-factor, shadow fading, and number of clusters) and small-scale (i.e. distance and angle of multipath components) channel parameters for base station (BS)-receiver (Rx) and BS-RIS-Rx links of different users. Two different optimization objectives for multi-user channels are presented, namely, maximizing sum of channel gains (O1) and maximizing channel capacity (O2). Then, projected gradient ascent algorithm is applied to solve RIS phase shift matrix under both optimization objectives. Furthermore, some channel statistical characteristics, namely, correlation coefficient, singular value spread, channel capacity, and root mean square (RMS) delay spread are derived and analyzed. At the same time, impacts of solved RIS phase matrices under different optimization objectives, as well as locations of RIS and users, on channel characteristics are compared and investigated. Simulation results demonstrate that the role of RIS under O1 is to align path phases of BS-RIS-Rx link with those of BS-Rx link as much as possible, whereas under O2, the RIS aims to enhance power while reducing user channel correlation. In addition, solved RIS phase under O2 can increase channel capacity and reduce channel correlation between different BS antennas, whereas O1 leads to a reduction in channel capacity. Location of RIS is also found to affect channel response impulse amplitude of different users. Furthermore, accuracy of the proposed model is verified by comparing RMS delay spread with measured data. These observations will provide a foundation for developing reconstructed wireless propagation environment of future multi-user communication systems.

In this paper, a 3D geometry-based stochastic channel model (GBSM) is proposed for RIS-assisted multi-user communications. The proposed GBSM is divided into two sub-channels, that is, BS-RIS and RIS-Rx links, and propagation distances and angles of multipath components are derived to describe multi-user channels. In addition, optimization objective for multi-user channel is proposed, and deep reinforcement learning is introduced to solve high-dimensional RIS phase problem. Based on the proposed model and solved RIS phase, channel capacity and root mean square delay spread are derived. The simulation results show that RIS optimization parameters and channel parameters have major impact on channel characteristics. The conclusions can provide a reference for designing and developing of RIS-assisted multi-user systems.

Utilizing unmanned aerial vehicles (UAVs) to provide reliable and effective wireless communication services for ground users has become a promising solution in emergency scenarios. However, energy consumption limitations lead to new challenges to timeliness of UAV communications. In this article, we propose a service time optimization strategy using UAV as an aerial base station. Total service time of UAV, including hover time and flight time, is minimized while satisfying user load demand. First, the K-means algorithm is used to complete area division. Under the constraints of UAV coverage and transmit power, hover positions of UAV in each partition are optimized to obtain the shortest hover time. Subsequently, the shortest flight time is obtained by solving the traveling salesman problem. Finally, the above process is integrated into a complete service time optimization problem. Simulation results show that the proposed strategy successfully achieves full-area coverage with the shortest service time, which significantly outperforms other existing algorithms, and there exists an optimal altitude to minimize the total service time.

To meet increasing applications of Internet of Things (IoT), the 3rd generation partnership project has specified Narrow Band Internet of Things (NB-IoT) standard. However, collisions in NB-IoT physical random access channel (NPRACH) can be severe due to mismatch between frequent random access requests of a huge number of devices and limited available preambles. In this paper, we propose to use non-orthogonal frequency hopping between preambles and introduce controllable partial collisions to effectively avoid full preamble collisions. This will increase available preambles and thus reduce preamble collision significantly. The proposed hopping pattern maintains compatibility with the current standard NB-IoT system, by keeping NPRACH structure in standard with minimal change. Simulation results show that the proposed frequency hopping pattern can greatly increase accessing devices due to collision avoidance while slightly reducing detection probability.

Wireless channel modeling is widely considered as foundation of wireless communication system design. Sufficient and diverse channel data provides strong support for wireless channel characterization and modeling. However, channel data from real measurement is usually limited considering complexity of channel measurements for different scenarios and frequency bands. In this work, a deep learning-based cross-frequency channel generation and modeling framework is proposed. Without requiring a traditional parametric channel model, the proposed framework can generate realistic cross-frequency channels by employing generative adversarial networks. Based on vehicular channel measurement data, cross-frequency reconstruction performance of the proposed framework is validated by comparing characteristics of measured and reconstructed channels. It is also found that channel non-stationary characteristics can be well embodied in the reconstructed channels.

Recently, unmanned aerial vehicle (UAV)-assisted ground targets localization is widely used in search and rescue (SAR) scenes. In this article, we propose a UAV search and localization strategy, which can search and locate unknown number of victims. The proposed strategy uses the UAV as a mobile anchor to measure time of arrival (TOA) and can effectively mitigate localization errors caused by non-line-of-sight (NLOS) propagation. Generally speaking, the strategy is divided into two parts: 1) trajectory planning and 2) localization. By selecting waypoints and planning a suitable search trajectory, UAV can obtain all the measurement information in deployment area and ensure that each target has at least three anchors for localization even in the NLOS propagation environment. In the localization stage, a new estimator is proposed based on maximum-likelihood estimation (MLE), which estimates average NLOS bias together with the coordinates of target and can be well solved by the particle swarm algorithm. The simulation results show that the proposed strategy outperforms for mitigating NLOS error compared with other methods, and can effectively ensure high localization accuracy in the NLOS propagation environment.

The user behavior is one of the key factors to impact the communication performance of vehicular ad hoc networks. Generally, more active vehicles can enhance the connectivity performance. Nevertheless, an increasing number of active vehicles may bring about significant interference and thus negatively affect the connectivity. In this paper, a highway model is presented to study the effects of active vehicle ratio on connectivity performance in the presence of co-channel interference. A closed form expression of the inter-vehicle connectivity probability is also derived to evaluate the impacts of active ratio, traffic flow, and fading factors under Nakagami fading channel. Analytical results of inter-vehicle connectivity probability are shown to be consistent with the simulation results, and can be applied to estimate connectivity performance with interference in the network design for highway vehicular scenarios.

Wireless communication link quality can be determined by the transmission protocol design, the interference level, the channel fading properties, and the mobility characteristics etc. As one of the most essential features for vehicular networks, high mobility brings in intermittent connectivity and relative short link lifetime. Therefore, an analytical model on link lifetime can be of great help for an efficient and effective vehicular communication protocol design. In this paper, the impact of mobility on link lifetime in the highway environment is modeled and analyzed by utilizing the discrete-time Markov chain (DTMC). Specifically, two link lifetime theoretical models, the first-order Markov model and the second-order Markov model, are studied and analyzed for effectively predicting the proprieties of a vehicular communication link. It is shown that our proposed models, especially the second-order Markov model, can provide more accuracy in performance prediction. Additionally, extensive simulations are carried out to verify our analytical results on link lifetime.

The high-speed railway (HSR) propagation channel has a significant impact on the design and performance analysis of wireless railway control systems. This paper derives a stochastic model for the HSR wireless channel at 930 MHz. The model is based on a large number of measurements in 100 cells using a practically deployed and operative communication system. We use the Akaike information criterion to select the distribution of the parameter distributions, including the variations from cell to cell. The model incorporates the impact of directional base station (BS) antennas, includes several previously investigated HSR deployment scenarios as special cases, and is parameterized for practical HSR cell sizes, which can be several kilometers. The proposed model provides a consistent prediction of the propagation in HSR environments and allows a straightforward and time-saving implementation for simulation.

Vehicle-to-Vehicle (V2V) communication is widely used to improve traffic safety. However, the V2V link is easily blocked due to the low antenna height. The vehicular obstructions also lead to strong shadowing effect, which has been ignored in the V2V system performance evaluation. By incorporating channel state transition with a measurement-based dual-slope path loss model, a light-of-sight (LOS) blocked model is presented for the V2V highway scenarios. The joint effects of radio environment and traffic flow on link connectivity are investigated to represent the obstruction probability and inter-vehicle connectivity probability. All the analytical results are validated by extensive simulations under a measurement-based propagation model. The results can be used for the system design and development of a channel simulator for V2V communications.

The high-speed railway propagation channel has significant effect on the design and performance analysis of wireless railway control systems. An important feature of the high-speed railway communications is the usage of directional transmitting antennas, due to which the receiver may experience strong attenuation of the line-of-sight (LOS) path under the base station (BS). This leads to a drop, and strong variations, of the signal strength under the BS. While the physical origin of the signal variations is different from conventional shadowing, it can be described by similar statistical methods. However, the effect has been largely neglected in the literature. In this paper we first define the region of the bottom of the BS, and then present a simple shadow fading model based on the measurements performed in high-speed railways at 930 MHz. It is found that the bottom area of the BS has a range of 400 m – 800 m; the standard deviation of the shadowing also follows a Gaussian distribution; the double exponential model fits the autocovariance of the shadow fading very well. We find that the directivity of the transmitting antenna leads to a higher standard deviation of shadowing and a smaller decorrelation distance under the BS compared to the region away from the BS.

The non-saturated performance of the IEEE 802.11 broadcast scheme in the two-dimensional (2-D) mobile ad hoc networks is studied in this paper. A simplified yet reasonable system model is built to derive the closed-form expressions of average packet reception probability, non-saturated throughput, and average queuing delay. Compared with the existing work in the literature, the proposed system model takes into account the effect of arbitrary transmission queue length. Moreover, our analysis considers both the impact of concurrent transmissions caused by identical backoff interval selection and that of hidden nodes caused by limited sensing range on an observed transmitter. All the analysis results are validated by extensive simulations.

This paper evaluates the conditional connectivity performance for vehicular ad hoc networks. Conditional connectivity is the probability that a communication pair can stay connected during a whole data transmission conditioned on the connection is successfully built up when the transmission is initialized. To analyze the conditional connectivity, a simplified but reasonable unidirectional highway model is proposed in this paper. By combining the probability density function of inter-vehicle initial distance and the distribution of vehicles' relative speed, which are both derived in this paper, the closed-form of conditional connectivity is obtained. All the analysis results are validated by extensive simulations.

Precise location information is important in sensor networks to support a great many functions like location aided routing, tracking and other specific applications. This paper proposes a Cluster based Iterative GPS-Free Localization(CIGL) algorithm for wireless sensor network, which only depends upon the distances between the sensor nodes and their neighbors obtained by measurements like TOA. The proposed algorithm firstly selects a subset of nodes to establish Local Coordinate Systems (LCSs) on the basis of clustering results. By rotation and reflection transformation, all the LCSs converge to form the global coordinate system and complete the nodes localization in succession. The located nodes are nominated as new beacons to re-locate the remaining unknown nodes, namely expand localization coverage by iteration. Comparing with the previous approach, simulation results indicate that the proposed algorithm achieves considerable performance in terms of localization accuracy and coverage, especially when the communication range is small and the network deployment is sparse.