In the realm of wireless communication, stochastic modeling of channels is instrumental for the assessment and design of operational systems. Deep learning neural networks (DLNN), including generative adversarial networks (GANs), are being used to approximate wireless Orthogonal frequency-division multiplexing (OFDM) channels with fading and noise, using real measurement data. These models primarily focus on channel output distribution given input, limiting their application scope. DLNN channel models have been tested predominantly on simple simulated channels. In this paper, we build both GANs and feedforward neural networks (FNN) to approximate a more general channel model, which is represented by a conditional probability density function (PDF) of receiving signal or power of node receiving power, where d is communication distance. The stochastic models are trained and tested for the impact of fading channels on transmissions of OFDM QAM modulated signal and transmissions of general signal regardless of modulations. New metrics are proposed for evaluation of modeling accuracy and comparisons of the GAN-based model with the FNN-based model. Extensive experiments on Nakagami fading channel show accuracy and the effectiveness of the approaches.

The real-time quantification of the effect of a wireless channel on the transmitting signal is crucial for the analysis and the intelligent design of wireless communication systems for various services. Recent mechanisms to model channel characteristics independent of coding, modulation, signal processing, etc., using deep learning neural networks are promising solutions. However, the current approaches are neither statistically accurate nor able to adapt to the changing environment. In this paper, we propose a new approach that combines a deep learning neural network with a mixture density network model to derive the conditional probability density function (PDF) of receiving power given a communication distance in general wireless communication systems. Furthermore, a deep transfer learning scheme is designed and implemented to allow the channel model to dynamically adapt to changes in communication environments. Extensive experiments on Nakagami fading channel model and Lognormal shadowing channel model with path loss and noise show that the new approach is more statistically accurate, faster, and more robust than the previous deep learning-based channel models.

It is anticipated that wireless vehicular ad hoc networks (VANETs) can further enhance the safety of autonomous vehicles. Recently, two major standards for the next generation of VANET technologies have been suggested and tested: IEEE 802.11p/bd and 3GPP NR-V2X. Compared with human driving VANET, autonomous driving VANET poses big challenges for more stringent requirements on reliability and delay of message transmissions. Up to date, the quality of service (QoS) requirements of VANETs for the safety services of autonomous driving vehicles have not been investigated systematically. Whether or not the current proposed communication systems can meet such high QoS requirements is unclear. This paper reviews the communication requirements for safety use cases, and the QoS requirements for level 5 autonomous driving VANETs are deduced. Then, the QoS of IEEE 802.11-based VANETs, including 802.11p, 802.11bd, and other 802.11 cutting-edge versions, are evaluated by simulations and analyses on a few selected safety use cases and benchmarked against the corresponding QoS requirements. The numerical results reveal that the current IEEE 802.11p/bd cannot meet the QoS requirements for some selected safety applications in autonomous driving vehicles. However, IEEE 802.11 systems with new enhancements can potentially support safety services with assured QoS.

IEEE 802.11 communication systems have been extensively investigated for improving vehicular road safety. However, dynamic vehicular environments and various safety applications with different quality of service (QoS) requirements cannot be accommodated by a fixed set of communication parameters and network configuration. This paper proposes and exploits a real-time constrained optimization platform that leverages the combination of a fast stochastic model with a carefully configured regression deep learning neural network (DLNN) to achieve an optimal balance between the QoS and the channel spectrum efficiency. The stochastic model is utilized to predict the QoS of IEEE 802.11 broadcast vehicular ad hoc networks given a selected group of communication parameters and analytical equations or measured data about the communication channels. The data provided by the stochastic model is sorted based on the QoS requirements for a given safety service and is preprocessed to keep enough distance between the training data patterns. The DLNN is trained by a randomly sampled data set to accomplish the inverse mapping (from the QoS to the corresponding parameter sets) to facilitate real-time optimization. In the process of optimization, by working in tandem, the DLNN and stochastic model synergistically identify the optimal parameter set that maximizes channel efficiency while adhering to QoS constraints in a fast and precise manner. The computation complexity of the optimization is analyzed and estimated. The effectiveness and robustness of the proposed optimization system have been demonstrated through experiments conducted on Google Colab using TensorFlow and Python, showcasing its superiority over the alternative optimization algorithms.

In this study, the channel data related to fire point identification of Advanced Geostationary Radiation Imager on Fengyun-4B (FY-4B/AGRI) and Advanced Meteorological Imager on GEO-KOMPSAT 2A (GK-2A/AMI) were cross-compared. A total of 267 sampling points in China (Guangdong, Guangxi, Guizhou, Yunnan, Hainan) were selected to carry out fine positioning correction on the data in different elevation intervals. Then, a fire monitoring algorithm based on FY-4B was proposed, in which the self-adaptive threshold adjustment of the underlying surface parameters and the reprocessing module of fire point identification were added. The algorithm can realize the high-precision and stable monitoring of fire. The continuous dynamic monitoring was carried out using the grassland fire Mongolia from April 18 to 20, 2022 as an example. The results showed that the parameters of FY-4B/AGRI and GK2A/AMI channels have high consistency. The root mean square error (RMSE) of reflection channel was 1.33%, and the maximum RMSE of brightness temperature channel was less than 1.3 Kelvin (K). Through the positioning analysis in different elevation intervals, the average longitude offset of FY-4B satellite data was -0.5°–0° and the average latitude offset was -0.9°–0.6°. Overall, these findings indicate that the high-frequency observations of FY-4B can be fully utilized to monitor forest and grassland fires, which can continuously track the dynamic evolution of fire and can distinguish the spatial distribution of different fire intensities in large-scale fire field.

IEEE 802.11p/bd driven Vehicular Ad Hoc Networks (VANETs) have been investigated for safety-critical applications with high reliability and low transmission latency. However, due to dynamic vehicular environment and various safety applications requiring different quality of service (QoS), a fixed configuration of the communication network parameters performs poorly in terms of balance between QoS and channel spectrum efficiency. This paper proposes a new real-time optimization scheme based on a designated deep learning neural network (DLNN) working with a stochastic model. In the scheme, the stochastic model is adopted to predict the QoS of VANET given a set of communication parameters. The DLNN is trained to approach the inverse maps from the parameter sets to the corresponding QoS by a sampled data set from running the stochastic model. In the process of optimization, for a given safety service, the DLNN and the stochastic model complement each other to find an optimal solution of the parameters that maximize the channel efficiency under the constraints of QoS requirements in a fast and precise way. The experiments on Google Colab with TensorFlow and Python demonstrate the effectiveness of the scheme.

Taxi demand prediction plays a significant role in assisting the pre-allocation of taxi resources to avoid mismatches between demand and service, particularly in the era of the sharing economy and autonomous driving. However, most studies have only tried to figure out the complex spatial-temporal pattern of taxi demand from historical taxi demand series, neglecting the intrinsic influences of regional functions, and failing to effectively capture the dynamic long-term periodicity. In this paper, we make two important observations: (1) taxi demand pattern varies significantly between different functional regions; and (2) taxi demand follows a dynamic daily and weekly pattern. To address these two issues, we adopt Points of Interest (POIs) to identify regional functions, and propose a novel BERT-based Deep Spatial-Temporal Network (BDSTN) to model the complex spatial-temporal relations from heterogeneous local and global features. In BDSTN, a Spatiotemporal Pattern Matching module is introduced to capture the complex spatiotemporal pattern of taxi demand while considering its dynamic temporal periodicity, and a Functional Similarity Embedding module is adopted to learn the functional similarity among all regions via POIs. To the best of our knowledge, this is the first work to use BERT-based architecture to learn taxi demand patterns, and is also the first to take functional similarity represented by POIs into consideration. Our experimental results on real-world traffic datasets in New York City demonstrate that the effectiveness of the proposed method outperforms the state-of-the-art methods, and that the efficiency of our proposed model is higher than other deep learning methods.

The safety-critical applications of Vehicular Ad Hoc Networks (VANETs) call for wireless communication systems with high reliability and low transmission latency. Recently, a very promising communication system standardized as IEEE 802.11 bd was proposed and studied to address the reliability deficiency of IEEE 802.11 P based Dedicated Short Range Communications (DSRC) system in the adverse vehicular situations. Seamless evolution of radio access technology from IEEE 802.11p to IEEE 802.11bd have undergone performance improvement evaluations in the physical layer. However, the impact of the enhancements on the network layer and safety services in V ANETs remain unknown. In this paper, we conduct analysis of IEEE 802.11bd beyond the physical layer. First, the performance gains of IEEE 802.11bd comparing to IEEE 802.11p in the physical layer are summarized. Then, packet loss rate (PLR) curves as a function of signal-to-noise ratio (SNR) in the physical layer are quantified and connected to the quality of service (QoS) metrics in the MAC layer, while considering the effect of channel access and interference among network nodes. Furthermore, application-level analysis is conducted to identify if the proposed IEEE 802.11bd can meet the QoS requirements of the selected safety applications where IEEE 802.11p previously failed. Finally, constructive conclusions are presented on IEEE 802.11bd's suitability for V ANET critical safety services and future developments.

The safety-critical applications of vehicular ad hoc networks (VANETs) require high reliability and low transmission latency. IEEE 802.11p and IEEE 802.11bd are two standards proposed for such vehicular communication systems. In this paper, we propose an effective SINR-based model to conduct the QoS analysis of IEEE 802.11p/bd driven VANETs for safety applications. First, a semi-Markov process (SMP) model with a D/G/1/1 queue is tailored and modified for characterizing channel access behavior of IEEE 802.11 broadcast networks with deterministic message generation rate. Then, a novel approach is developed to derive the distribution of signal-to-interference-to-noise ratio (SINR) on each receiver in the VANET, given general channel fading/shadowing models as well as general node geometry distributions. Consequently, various QoS metrics in Physical/MAC/Application layer of VANETs are defined and evaluated. Compared with the existed analytic SINR-based models for broadcast VANETs, the proposed models are more general, more accurate, and are faster to solve. Finally, a new mechanism is shown to map the performance in physical layer into the QoS quantification in higher layer so that the QoS prediction of IEEE 80211bd driven VANETs is enabled. Furthermore, comparison between 802.11p and 802.11bd is conducted regarding how the stringent QoS requirements of selected safety applications in both human driving VANETs and autonomous driving VANETs are met by the two communication systems. Constructive conclusions are given on the suitability of IEEE 802.11bd for VANET critical safety services and the directions of future development.

Vehicular ad-hoc networks (VANETs) has gained a significant attention during the last decades both from industrial and academia communities. Novel VANET enabled active safety automotive applications are heavily dependent on reliability and security of underlying intervehicle communication protocols. The error-prone nature of the wireless channel and its openness to external invasions as well as dynamic VANET environment, impose the need for intensive studies before applications like platooning or collision avoidance can become a part of our daily life. The aim of this special issue is to encompass research advances in all areas of reliability and security in VANETs.

In this paper, we propose a reliable dual-radio based DSRC channel assignment scheme and a new application- level based receiver-oriented repetition (AROR) scheme for one-hop event-driven emergency message broadcast in vehicular ad hoc networks (VANETs). The new scheme takes advantage of DSRC dual-radio devices to provide preemptive access for one-hop event-driven safety services without conflicts with basic safety message (BSM) based services. Furthermore, an application-level concept of region of interest (ROI) is introduced in selecting nodes for rebroadcast and repetitions of one-hop emergency warning message. In this way, both the reliability and transmission delay can be significantly improved. Additionally, we investigate and compare the reliability and performance of the proposed scheme and other broadcast schemes for DSRC VANET safety services on highway analytically and by simulations. The new analytic model accounts for the impact of the Nakagami fading channel with distance dependent path loss on the reliability and performance. The numerical results verify effectiveness of the new proposed schemes and correctness of the analytic model.

In this paper, we derive application-level reliability metrics of various safety applications with worst-case settings and vehicular environments based on an accurate analytical model in one dimension vehicular communication networks. The reliability related QoS metrics in both the MAC-level and the Application-level including packet reception ratio, T-window reliability, and awareness probability are derived. Inspired by the correlation between road traffic and vehicle speed revealed by Greenshields model, we observe that the current reliability related QoS requirements for safety applications are set without considering change of road traffic. Based on the observation, a new reliability requirement setting is proposed to tune tolerance time for various safety applications with different road traffic. The QoS requirements for the three typical safety applications that are believed to have the most stringent QoS requirements are specified and discussed. Taking velocity, density and time into account, application-level reliability metrics are improved for high vehicle density. From the results obtained under worst case with different vehicle density, we can observe the feasibility of our proposed reliability requirement setting.

ABSTRACT GeoNetworking is a protocol specially designed to provide geographical addressing and forwarding for Cooperative Intelligent Transport Systems (ITS). Unlike advanced messages defined in GeoNetworking, beaconing messages are fundamental to periodically advertise essential information, e.g., a vehicle ITS station's geographical position and speed, to neighbor ITS stations. The beaconing messages should be delivered in a secure manner. Otherwise, geographical addressing and forwarding of GeoNetworking cannot be supported as unauthorized actions from attackers break down cooperative communication between ITS stations. However, security mechanisms for beaconing messages come with overhead that affect the overall performance. This being so, in this paper, we analyze the overhead of secure beaconing messages in terms of packet delivery ratio, delay, and normalized throughput. From the conducted numerical analysis, we demonstrate that 1) three parameters such as the number of ITS stations in a given radio range, the backoff window size, and the message size mainly affect the performance; 2) the delay of secure beaconing message is still acceptable with proper channel bit rates; and 3) the normalized throughput of secure beaconing message is better than that of the basic beaconing message. Copyright © 2011 John Wiley & Sons, Ltd.

IEEE 1609.4 protocol defines a channel switching mechanism to enable a single radio operating efficiently on multiple channels to support both safety and non-safety services. Basic safety message (BSM) is transmitted only through the control channel at regular intervals. In this paper, we propose an analytic model based on interacting semi-Markov process (SMP) to evaluate both Medium Access Control (MAC) and application level performance and reliability of safety message broadcasting incorporating the impact of multi-channel operations. Message service time distribution is derived using Laplace-Stieltjes transform based on the proposed model. Due to the cyclic interactions between the SMP models for vehicles contending the same channel, fixed-point iteration is used to obtain converged solutions. Subsequently, MAC and application-level performance and reliability metrics are derived based on the converged solutions. Channel fading with path loss is taken into consideration in this derivation. The analytic models are validated through extensive simulations in NS2 to verify the effectiveness and accuracy of our proposed fixed-point iteration based model decomposition. The effects of channel switching mechanism and channel fading are also evaluated by comparing with other analytic models.

ABSTRACT Reliability and timeliness are two critical requirements of vehicle safety‐related communication services in VANETs. In this paper, we develop an analytical model to analyze the packet reception rate and end‐to‐end delay of emergency message delivery operation in a VANET environment when multihop broadcast communications are used. The model is applied to derive closed‐form expressions of the end‐to‐end delay of two popular multihop message propagation methods, that is, the farthest‐distance method and the counter‐based method. Extensive simulations are conducted to validate the correctness of the theoretic results and compare the performance of the two message propagation methods. Observations are provided for the design of efficient and robust emergency message propagation methods for vehicular wireless communication networks. Copyright © 2012 John Wiley & Sons, Ltd.

This paper develops a new analytic model for the performance and reliability analysis of safety-related message broadcast in vehicular ad hoc networks (VANETs) at rural intersections. First, a semi-Markov process (SMP) model for characterizing channel behavior of IEEE 802.11 based VANET is deployed to interact with the M/G/1 queue through fixed-point iteration. Then, the mean transmission delay between two nodes is derived. Furthermore, given two communicating nodes placed at the same intersection, the node reception probability that a node successfully receives the broadcast message from a sending node is computed. Consequently, the packet reception ratios are derived through integration of the node reception probabilities over the intended range of the sending node. The analytical model takes intersection geometry, IEEE 802.11 backoff counter process, hidden terminal problem, and non-saturated message arrival into account. From the obtained numerical results under various network parameters, the new model is validated and new observations are obtained.

In this paper, we propose an accurate and efficient analytic model to evaluate the impact of channel fading on multi-hop safety message disseminations in vehicular ad hoc networks (VANET). A multi-hop relay strategy is also proposed by setting distance-based timers (the longer the distance, the shorter the timer) so that the farther receiver has the higher priority for it to rebroadcast the message. Important performance and reliability metrics are derived and analyzed for a thorough understanding of the safety message transmission behavior. Extensive simulations are conducted in Matlab to verify the correctness of our proposed model under realistic network parameter settings.

To support both safety and non-safety applications in vehicular communications, IEEE 1609.4 protocol defines a channel switching mechanism to enable a single radio to operate efficiently on multiple channels. Basic safety message is transmitted only through control channel at regular intervals. In this paper, an analytic model based on semi-Markov process is proposed to evaluate MAC level performance and reliability of safety message dissemination incorporating the impact of the multi- channel operations. Safety message service time distribution is derived using Laplace-Stieltjes transform based on the proposed model. Subsequently, fixed-point iteration is used to obtain converged solutions. MAC level performance metrics are then derived. Channel fading with path loss is taken into account in this derivation. The analytic models are validated through simulations in NS2 to verify the effectiveness and accuracy of the proposed model.

In this paper, an analytic model is proposed for the performance evaluation of vehicular safety related services in the dedicated short range communications (DSRC) system on highways. The generation and service of safety messages in each vehicle is modeled by a generalized M/G/1 queue. The overall model is a set of interacting M/G/1 queues, one queue for each vehicle. The interaction is that the server is shared as it is the contention medium. To make the model scalable, we use semi-Markov process (SMP) model to capture the shared server's behavior from one tagged vehicle's perspective, where the medium contention and back off behavior for this vehicle and influences from other vehicles are considered. Furthermore, this SMP interacts with the tagged vehicle's own M/G/1 queue through fixed-point iteration. The proof for the existence, uniqueness and convergence of the fixed point is provided. Based on the fixed-point solution, performance indices including mean transmission delay, packet delivery ratio (PDR), and packet reception ratio (PRR) are derived. Analytic-numeric results are verified through extensive simulations under various network parameters. Compared with the existing models, the proposed SMP model facilitates the impact analysis of hidden terminal problem on the PDR and PRR computation in a more precise manner.

In this paper, an analytic model is built to derive a closed form broadcast packet reception rates in IEEE 802.11 based two-dimensional (2-D) mobile ad-hoc networks (MANETs), which is the extension of 1-D MANET analytical model in. The analytic model takes two straight parallel line geometry, IEEE 802.11 backoff counter process, hidden terminal problem, message arrival interval, and fading transmission channel into account. The proposed analytic model can be applied to vehicular ad-hoc networks (VANETs) built along a highway. From the obtained numerical results under various network parameters, the new model is validated and new observations are obtained.

IEEE- and ASTM-adopted Dedicated Short Range Communications (DSRC) vehicle safety-related communication services, which require reliable and fast message delivery, usually demand broadcast communications in vehicular ad hoc networks (VANETs). In this paper, we propose and justify a distributive robust scheme for DSRC one-hop safety-critical services. The new scheme enhances broadcast reliability using dynamic receiver-oriented packet repetitions and mini-slot within DIFS in IEEE 802.11 for one-hop emergency warning message dissemination. In addition, we investigate the reliability and performance of the proposed broadcast scheme for DSRC VANET safety-related services on highway analytically and by simulations. The analytic model accounts for the impact of the beacon message broadcast and the fading channel conditions on the reliability and performance.

In this paper, a new analytic model is built to derive a closed-form expression of broadcast packet reception rates (PRRs) in IEEE 802.11 based one-dimensional (1-D) mobile ad-hoc networks (MANETs), which is the extension of 1-D MANET analytical model in. Comparing with the existing models, the proposed model accounts for the impact of more factors and network parameters on PRRs such as non-saturated packet arrival in each mobile node, varied transmission, carrier sensing, and interference ranges, distances of receivers to the sender, and collisions caused by concurrent transmissions. As an example, the proposed model is applied to evaluate a dedicated short range communication (DSRC) safety-related service on a highway. Based on the simulation results, some important observations to the design and analysis of such wireless communication networks are provided.

Abstract Vehicular ad hoc network (VANET) or inter‐vehicle communication (IVC) provides many brand new and powerful capabilities for transportation system applications. Safety massaging, as one of the capabilities, supports many potential safety applications such as emergency electronic brake lights, lane change warning, pre‐crash warning. These applications require a low latency and high reliability communication service. In this paper, we first analyze these critical requirements. The under‐development 802.11p for VANET is then discussed with respect to those requirements. An extensive research on the enhancements for 802.11 media access control (MAC) is conducted in order to show their suitability for safety applications. As a conclusion, a realistically feasible MAC enhancement scheme is still needed to achieve the high performance requirements and the compatibility. Copyright © 2008 John Wiley & Sons, Ltd.

In this paper, we investigate saturation performance of broadcast service in IEEE 802.11 based one-dimensional mobile ad hoc networks (MANETs) under multi-hop environment. Having analyzed the features of the multi-hop broadcast MANETs, for the first time we derive a closed form expression of broadcast packet reception rates, which is one of the important reliability metrics for broadcast communications. The analytic model takes IEEE 802.11 backoff counter process and hidden terminal problem into account. The proposed analytic model is validated by extensive simulations. From the obtained numerical results under various network parameters, new observations and potential enhancement suggestions are given.

We consider the multi-valued discrete real training set that can not be separated by one multi-valued multi-threshold neuron. Such training set is defined as linearly nonseparable set in this paper. Our objective is to use multi-valued multi-threshold neural networks to learn nonseparable training sets. First we give the method that how to determine a training set is separable or nonseparable (i.e., the necessary and sufficient condition for linearly nonseparable is given). Then we analyze the structures within linearly nonseparable sets: not all the vectors in a linearly nonseparable set are responsible for nonseparability. So the vectors in such set can be partitioned to separable vectors and nonseparable vectors. Finally, we discuss the learning problems for a linearly nonseparable set. Such set can be learned by a three-layer feedforward neural network with one hidden layer. An example throughout the paper further clarifies the results of this paper.

Inter-vehicle safety applications on highways rely considerably on the real-time and reliable communication in the vehicular ad hoc network. For engineers and researchers, it is greatly necessary and important to evaluate the provisions of the communication systems before they start designing them for such applications. This paper presents a quantitative approach to evaluate the quality of service that the inter-vehicle communication (using IEEE- and ASTM-adopted dedicated short range communication (DSRC)) can provide to the inter-vehicle safety application in the context of highway scenarios. Based on the proposed model, we analyze performance indices such as delay and packet delivery ratio (PDR). Outputs of the model are validated by computer simulations. On one hand, our quantitative model provides not only a convenient framework to conduct fast evaluation for the communication of the inter-vehicle safety applications but also a means to analyze the suitability of 802.11a- based DSRC for highway safety applications. On the other hand, it reveals the requirements for further improvement of the performance.

Broadcast communication is important in inter- vehicle communication. Vehicles broadcast safety related messages to pre-warn the vicinity vehicles. The new IEEE 802.11p is being developed based on IEEE 802.11a to address the need for vehicle-to-vehicle and vehicle-to-roadside-unit communication. In this paper, we study the saturation performance of IEEE 802.11 broadcast by developing an analytic model as well as by conducting simulation. Having investigated the literature and studied the features of the broadcast, we point out that the analytic models for IEEE 802.11 unicast cannot be simply applied for broadcast. We construct our model to characterize the operation of the backoff counter in broadcast. Based on solutions to the model, closed form expressions of performance indices including throughput, packet delay, and packet deliver ratio are derived. Numerical results reveal several characteristics of the broadcast service. Our research work provides a new means to approach analysis and enhancement of IEEE 802.11 broadcast performance and reliability in wireless ad hoc networks.

In this paper, we propose an analytic model for performance evaluation of IEEE 802.11 ad hoc broadcast networks without the assumption of saturation condition that is not true in safety related vehicle-to-vehicle (v2v) communication. We use a discrete time M/G/l queue to model occasional occurrences of safety related message in each vehicle. By means of probability generating function (PGF) and a recursive algorithm, we obtain solution to the analytic model. Based on the solution, we derive several important performance indices for IEEE 802.11 broadcast service, such as packet delay, throughput, packet delivery ratio, and service time distribution. The proposed analytic model is validated by extensive simulations. Numerical results also reveal characteristics of the broadcast service in a typical v2v wireless network.

In this paper, equilibrium point analysis (EPA) and Monte Carlo modeling are conducted to evaluate performance of sliding frame R-ALOHA protocol for real-time ad hoc wireless networks in a fading environment with error channels, which include throughput and the average message delay. Terminals with Poisson message arrivals and Poisson message length are analyzed. Transmission errors and capture effects on the R-ALOHA in the presence of multipath and shadowing are also taken into account by the EPA model. The results reveal performance characteristics of the R-ALOHA system and also show effectiveness and correctness of the developed models.

In this paper, performance of sliding frame R-ALOHA protocol for real-time mobile ad hoc wireless networks is investigated analytically and by simulation. A discrete Markov chain and Monte Carlo modeling are constructed to evaluate dynamic behavior of the protocol in transient state, which includes distribution and mean of the system stabilization time (SST), and the average number of successful terminals. Capture effects on the R-ALOHA in the presence of multipath and shadowing are also addressed by the Markov chain analysis. The results reveal dynamic characteristics of the R-ALOHA system, and also show effectiveness and correctness of the models we developed.