Probability Of Successful Transmission Research Articles

Using ranging for collision-immune IEEE 802.11 rate selection with statistical learning

Appropriate data rate selection at the physical layer is crucial for Wi-Fi network performance: too high rates lead to loss of data frames, while too low rates cause increased latency and inefficient channel use. Most existing methods adopt a probing approach and empirically assess the transmission success probability for each available rate. However, a transmission failure can also be caused by frame collisions. Thus, each collision leads to an unnecessary decrease in the data rate. We avoid this issue by resorting to the fine timing measurement (FTM) procedure, part of IEEE 802.11, which allows stations to perform ranging, i.e., measure their spatial distance to the AP. Since distance is not affected by sporadic distortions such as internal and external channel interference, we use this knowledge for data rate selection. Specifically, we propose FTMRate, which applies statistical learning (a form of machine learning) to estimate the distance based on measurements, predicts channel quality from the distance, and selects data rates based on channel quality. We define three distinct estimation approaches: exponential smoothing, Kalman filter, and particle filter. Then, with a thorough performance evaluation using simulations and an experimental validation with real-world devices, we show that our approach has several positive features: it is resilient to collisions, provides near-instantaneous convergence, is compatible with commercial-off-the-shelf devices, and supports pedestrian mobility. Thanks to these features, FTMRate outperforms existing solutions in a variety of line-of-sight scenarios, providing close to optimal results. Additionally, we introduce Hybrid FTMRate, which can intelligently fall back to a probing-based approach to cover non-line-of-sight cases. Finally, we discuss the applicability of the method and its usefulness in various scenarios.

Analyzing the blockchain mining strategy in Industrial Internet of Things: A game theoretical approach

Deploying Proof-of-Work (PoW) consensus based blockchain systems in Industrial Internet of Things (IIoT) scenarios that rely on wireless channels for data transmission faces new challenges. An IIoT device working in industrial production may participate in the operation of blockchain as a miner. These devices not only need to consume their own computation capability to compete for the right to generate blocks, but also need to transmit newly generated blocks to other devices through wireless channel to continuously maintain the global ledger. Asynchronous mining and data transmission by IIoT devices in wireless environments may significantly interfere with the propagation of blocks in the system and result the reduction of mining payoff, thereby affecting the willingness of IIoT devices to participate in maintaining blockchain system. This paper considers the behavior of independent mining between multiple IIoT devices as a non-cooperative game, which we call the Wireless Mining Game (WMG). Markov chain is adopted to derive the block propagation model under the wireless channel, and then the payoff model of IIoT device is formulated. IIoT devices can make reasonable mining decisions by evaluating the computation capability of other IIoT devices and the probability of successful block transmission. We analyze the Nash equilibrium of the WMG and the corresponding mining behavior set between two-players scenario through theoretical modeling and experiments. We also analyzed the expected mining payoff of the IIoT devices in the multiple devices scenario.

Wireless Powered Cooperative NOMA With Alamouti Coding and Selection Relaying

Nonorthogonal multiple access (NOMA) can facilitate simultaneous data transmissions towards multiple users by using the superposition coding and successive interference cancelation techniques, which can greatly improve the spectrum efficiency. Cooperative relaying and space-time coding can promisingly improve the communication robustness of poor-quality links by achieving the space-diversity gain. Energy harvesting (EH) can prolong the lifetime of energy-limited terminals and make them work continuously. In order to enhance the spectrum efficiency as well as the communication quality, we enable a cluster of EH relays to assist the data transmissions from a base station (BS) to two far-users by using the Alamouti coding based cooperative NOMA strategy. The relays are capable of harvesting wireless energy from a power beacon as well as BS by using time-switching or power-splitting method. According to the energy status and the data decoding status, one relay is selected in a distributed manner according to either Max-min, Max-sum, or Random criterion. We analyze the transmission success probability and the system throughput performance. Extensive simulations are performed to compare the performance of different EH-based space-time coded cooperative NOMA with various relay selection schemes as well as the counterpart orthogonal transmission schemes.

Reinforcement Learning-Based Power Control for Reliable Mission-Critical Wireless Transmission

In this paper, we investigate sequential power allocation over fast varying channels for mission-critical applications, aiming to minimize the expected sum power while guaranteeing the transmission success probability. In particular, a reinforcement learning framework is constructed with appropriate reward design so that the optimal policy maximizes the Lagrangian of the primal problem, where the maximizer of the Lagrangian is shown to have several good properties. For the model-based case, a fast converging algorithm is proposed to find the optimal Lagrange multiplier and thus the corresponding optimal policy. For the model-free case, we develop a three-stage strategy, composed in order of online sampling, offline learning, and online operation, where a backward Q-learning with full exploitation of sampled channel realizations is designed to accelerate the learning process. According to our simulation, the proposed reinforcement learning framework can solve the primal optimization problem from the dual perspective. Moreover, the model-free strategy achieves a performance close to that of the optimal model-based algorithm.

PERFORMANCE ANALYSIS OF WIRELESS COMPUTER NETWORKS IN CONDITIONS OF HIGH INTERFERENCE INTENSITY

Context. The decrease in the probability of successful frame transmission in the infrastructure domain of IEEE 802.11 DCF wireless network is caused both by the influence of the collision intensity and by the impact of external interference in the radio path. Using the Markov chain approach as a baseline, we explicitly expressed the dependence of the network throughput on the number of operating stations, bit error rate (BER), and the frame fragmentation factor.
 Objective. The purpose of this article is to study the influence of interference intensity on the throughput of a wireless network domain in a wide range of the number of operating stations when transmitting frames of various lengths in the absence and with the use of the fragmentation mechanism.
 Method. The performed mathematical modelling showed, that in the range of increased and high noise intensity (BER = 10–5 – 10–4), a decrease in the length of the frame data field from the standard length of 12000 bits to 3000 bits is accompanied by a decrease in the throughput for all values of the number of competing stations. At the same time, it must be noted that as the amount of the frame data decreases, the throughput becomes less susceptible to an increase in the noise intensity. Qualitatively different results are obtained in the region of very high interference intensity (BER = 2·10–4). A significant increase in the probability of frame transmission in this region observed with a decrease in the standard length of the frame data field by 2–3 times, made it possible to increase the throughput compared to the original one. This effect is especially pronounced when the length is halved.
 Results. The study of the standard frame transmitting process, but with a fragmented data field, showed that if for BER = 5·10–5 and less with an increase in fragmentation factor, the throughput values decrease, in the entire range of the number of stations due to the predominant increase in overhead costs, then in the region of high (BER = 10–4) and very high noise intensity (BER = 2·10–4) we have the opposite effect. To the greatest extent, the throughput increases when the frame data is transmitted in two equal fragments. We have made a comparison of the network throughput determined by simply reducing the length of the frame data field and using fragmentation of a standard frame. The comparison showed that the use of the fragmentation mechanism is more beneficial both when throughput is stabilized under conditions of increased noise intensity and when the throughput is increased under conditions of high and very high noise intensity.
 Conclusions. In this article, a mathematical model has been modified for direct calculation of the wireless network throughput. Using this model, we studied the changes in throughput over a wide range of BER and a number of operation stations for various values of the transmitted frame fragmentation factor. The conditions for increasing the throughput are determined.

OTOP: Optimized Transmission Power Controlled OBSS PD-Based Spatial Reuse for High Throughput in IEEE 802.11be WLANs

As wireless local area network (WLAN) devices become more prevalent in various environments, the IEEE 802.11ax standard (Wi-Fi 6), released in 2021, aims to improve the spectral efficiency. The overlapping basic service set (OBSS) packet detection (PD) based spatial reuse (SR) scheme is a main technology for this purpose which can increase channel access opportunities by adjusting parameters. However, the standard only provides boundary values for parameter adjustment and does not provide details on how to optimize the performance, which can lead to a performance degradation. The IEEE 802.11be standard (Wi-Fi 7), established to satisfy extremely high throughput (EHT) demands of next-generation applications, includes the OBSS PD based SR scheme and the coordinated spatial reuse (CSR) scheme to maximize area throughput in dense networks. The CSR scheme optimizes the transmission performance through coordination between access points (APs), which results in additional signaling overhead and protocol complexity. In this paper, an enhanced SR scheme named optimized transmission power based OBSS PD (OTOP) SR is proposed to achieve high throughput in dense networks. The OTOP scheme enhances the parameter control operation of OBSS PD based SR to take advantage of its distributed nature which keeps the signaling overhead and protocol complexity low. The OTOP scheme derives the optimal transmission power that can maximize the transmission success probability of WLAN devices based on stochastic geometry analysis. The simulation results show that the proposed OTOP scheme performs better than the existing SRGM and OBSS PD based SR schemes in terms of throughput, frame error rate, and fairness.

A Deep Reinforcement Learning Scheme for Spectrum Sensing and Resource Allocation in ITS

In recent years, the Internet of Vehicles (IoV) has been found to be of huge potential value in the promotion of the development of intelligent transportation systems (ITSs) and smart cities. However, the traditional scheme in IoV has difficulty in dealing with an uncertain environment, while reinforcement learning has the advantage of being able to deal with an uncertain environment. Spectrum resource allocation in IoV faces the uncertain environment in most cases. Therefore, this paper investigates the spectrum resource allocation problem by deep reinforcement learning after using spectrum sensing technology in the ITS, including the vehicle-to-infrastructure (V2I) link and the vehicle-to-vehicle (V2V) link. The spectrum resource allocation is modeled as a reinforcement learning-based multi-agent problem which is solved by using the soft actor critic (SAC) algorithm. Considered an agent, each V2V link interacts with the vehicle environment and makes a joint action. After that, each agent receives different observations as well as the same reward, and updates networks through the experiences from the memory. Therefore, during a certain time, each V2V link can optimize its spectrum allocation scheme to maximize the V2I capacity as well as increase the V2V payload delivery transmission rate. However, the number of SAC networks increases linearly as the number of V2V links increases, which means that the networks may have a problem in terms of convergence when there are an excessive number of V2V links. Consequently, a new algorithm, namely parameter sharing soft actor critic (PSSAC), is proposed to reduce the complexity for which the model is easier to converge. The simulation results show that both SAC and PSSAC can improve the V2I capacity and increase the V2V payload transmission success probability within a certain time. Specifically, these novel schemes have a 10 percent performance improvement compared with the existing scheme in the vehicular environment. Additionally, PSSAC has a lower complexity.

MDS coding enabled proxy‐based Internet censorship circumvention system

AbstractIn this letter, a novel maximum distance separable coding enabled proxy‐based Internet censorship circumvention system is proposed to handle severe security threats. By introducing redundancy encoding and multi‐proxy transmission, its anti‐censorship capability is significantly enhanced in terms of transmission reliability and data security. A generalized characterization method is constructed for the proposed system utilizing a six‐parameter tuple. Defining successful transmission probability and secure transmission probability as the evaluation metrics of system performances, their mathematical formulations are further derived theoretically. Extensive simulation results are provided to demonstrate the effectiveness and advantages of the proposed system.

Multi-agent deep reinforcement learning based resource management in heterogeneous V2X networks

In Heterogeneous Vehicle-to-Everything Networks (HVNs), multiple users such as vehicles and handheld devices and infrastructure can communicate with each other to obtain more advanced services. However, the increasing number of entities accessing HVNs presents a huge technical challenge to allocate the limited wireless resources. Traditional model-driven resource allocation approaches are no longer applicable because of rich data and the interference problem of multiple communication modes reusing resources in HVNs. In this paper, we investigate a wireless resource allocation scheme including power control and spectrum allocation based on the resource block reuse strategy. To meet the high capacity of cellular users and the high reliability of Vehicle-to-Vehicle (V2V) user pairs, we propose a data-driven Multi-Agent Deep Reinforcement Learning (MADRL) resource allocation scheme for the HVN. Simulation results demonstrate that compared to existing algorithms, the proposed MADRL-based scheme achieves a high sum capacity and probability of successful V2V transmission, while providing close-to-limit performance.

Joint Optimization of Platoon Control and Resource Scheduling in Cooperative Vehicle-Infrastructure System

Vehicle platooning technology is essential in achieving group consensus, on-road safety, and fuel-saving. Meanwhile, Vehicle-to-Infrastructure (V2I) communication significantly facilitates the development of connected vehicles. However, the coupled effects of the longitudinal vehicle's mobility, platoon control and V2I communication may result in a low reliable communication network between the platoon vehicle and the roadside unit, there is a tradeoff between the platoon control and communication reliability. In this paper, we investigate a bi-objective joint optimization problem where the first objective is to maximize the success probability of data transmission (communication reliability) and the second objective function is to minimize the traffic oscillation flow. The vehicle's mobility state of the platoon vehicle affects the channel capacity and transmission performance. In this context, we deeply explore the relationship between control signals and resource scheduling and theoretically deduce a closed-form expression of the optimal communication reliability objective. Through this closed expression, we transform the bi-objective model into a single objective MPC model by using <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -constraint method. We design an efficient algorithm for solving the joint optimization model and prove the convergence. To verify the effectiveness of the proposed method, we finally evaluate the spacing error, speed error, and resource scheduling of platooning vehicles through simulation experiments in two experimental scenarios. The results show that the proposed control-communication co-design can improve the platoon control performance while satisfying the high reliability of V2I communications.

OPTIMIZING THE ACTION OF IEEE 802.11 COMPUTER NETWORKS AT SIGNIFICANT LEVELS OF BIT ERROR RATE

For a given level of noise, fragmentation of the frame’s data field increases the probability of transmitting each individual fragment. But this is accompanied by an increase in the rela-tive weight of overhead costs, i.e. the amount of service information that is necessary to sup-port the frame transfer process. We propose to determine the fragmentation optimum under condition of increased noise intensity by the maximum value of the product of fragmented frame successful transmission probability by the relative weight of overhead costs. The arti-cle presents detailed results of calculations of the optimality criterion dependence on the fragmentation coefficient at different levels of noise, which allow optimizing the process of fragmentation of the transmitted frames.

Implicit Overhearing Node-Based Multi-Hop Communication Scheme in IoT LoRa Networks.

Long range (LoRa) is a low-power wide-area technology because it is eminent for robust long-distance, low-bitrate, and low-power communications in the unlicensed sub-GHz spectrum used for the Internet of things (IoT) networks. Recently, several multi-hop LoRa networks have proposed schemes with explicit relay nodes to partially mitigate the path loss and longer transmission time bottlenecks of the conventional single-hop LoRa by focusing more on coverage expansion. However, they do not consider improving the packet delivery success ratio (PDSR) and the packet reduction ratio (PRR) by using the overhearing technique. Thus, this paper proposes an implicit overhearing node-based multi-hop communication (IOMC) scheme in IoT LoRa networks, which exploits implicit relay nodes for performing the overhearing to promote relay operation while satisfying the duty cycle regulation. In IOMC, implicit relay nodes are selected as overhearing nodes (OHs) among end devices with a low spreading factor (SF) in order to improve PDSR and PRR for distant end devices (EDs). A theoretical framework for designing and determining the OH nodes to execute the relay operations was developed with consideration of the LoRaWAN MAC protocol. Simulation results verify that IOMC significantly increases the probability of successful transmission, performs best in high node density, and is more resilient to poor RSSI than the existing schemes.

A dynamic even distribution resource scheduling mechanism combined with network coding for inter‐LEO satellite networks

SummaryResource scheduling mechanism of LEO satellite networks is the key to determining communication efficiency. Facing the LEO satellite networks with the dynamic topology changes, varying service requirements, and intermittent inter‐satellite links (ISLs), the state‐of‐the‐art cannot achieve high resource efficiency under both heavy and burst traffic loads, and the applicability of parameters design is insufficient under intermittent ISLs. Considering this, we propose a dynamic even distribution mechanism combined with network coding DENC. This novel mechanism obtains the service requirements and allocates resources dynamically through the even distribution algorithm to balance network maintenance overhead and resource waste and improves the success probability of transmission based on network coding to balance retransmission and redundancy. In this paper, we establish performance analysis models to optimize the parameters such as maintenance frequency and coding coefficient. Besides, we construct a system‐level simulation platform. Mathematical and simulation results indicate that the resource efficiency of EMNC can be improved by more than 48% compared with SAHN‐MAC, ICSMA, CSMA‐TDMA, and HTM when all nodes have service needs, and the ISL outage rate is 20%. As the outage probability of ISL increases and the proportion of nodes with service requirements decreases, the performance advantage of EMNC becomes more apparent.

Analysis and optimization on partition-based caching and delivery in satellite-terrestrial edge computing networks

As a viable component of 6G wireless communication architecture, satellite-terrestrial networks support efficient file delivery by leveraging the innate broadcast ability of satellite and the enhanced powerful file transmission approaches of multi-tier terrestrial networks. In the paper, we introduce edge computing technology into the satellite-terrestrial network and propose a partition-based cache and delivery strategy to make full use of the integrated resources and reducing the backhaul load. Focusing on the interference effect from varied nodes in different geographical distances, we derive the file successful transmission probability of the typical user and by utilizing the tool of stochastic geometry. Considering the constraint of nodes cache space and file sets parameters, we propose a near-optimal partition-based cache and delivery strategy by optimizing the asymptotic successful transmission probability of the typical user. The complex nonlinear programming problem is settled by jointly utilizing standard particle-based swarm optimization (PSO) method and greedy based multiple knapsack choice problem (MKCP) optimization method. Numerical results show that compared with the terrestrial only cache strategy, Ground Popular Strategy, Satellite Popular Strategy, and Independent and identically distributed popularity strategy, the performance of the proposed scheme improve by 30.5%, 9.3%, 12.5% and 13.7%.

Reinforcement Learning for Link Adaptation and Channel Selection in LEO Satellite Cognitive Communications

In this letter, we solve the link adaptation and channel selection problem in next generation satellite cognitive networks under dynamically varying channel availability and time-varying channel statistics. Primary user (PU) activity in Low Earth Orbit (LEO) satellite cognitive communications forces the set of available transmission channels for a secondary user (SU) to vary dynamically over time. We consider the scenario where the channel state varies in a piecewise-stationary mode, referred to as quasi-static (block-fading) channels. We formalize the problem as a reinforcement learning problem, and propose Discounted Structured and Sleeping Thompson Sampling (dSTS), which maximizes the SU’s throughput by selecting the optimum modulation and coding scheme (MCS) and the transmission channel under volatile and piecewise-stationary settings. When channel characteristics are unknown as well as piecewise-stationary, the proposed algorithm adapts the SU’s link-rate by exploiting the structure of the transmission success probability in transmission rates over the selected available channel. Furthermore, channel state information (CSI) is absent and feedback is limited to 1-bit (success/failure).

Dynamic Beacon Distribution Mechanism for Internet of Vehicles: An Analytical Study

In the last decade, with the arrival of the 5G communication technology and the increasing numbers of vehicles being connected to the internet, conventional vehicle ad-hoc networks (VANETs) are evolving towards the internet of vehicles (IoV), which makes the co-existence of IEEE 802.11p and 5G-based technologies very important for the design of a heterogeneous IoV system that takes advantage of both. The IEEE 802.11p standard is still the best candidate to support direct communications for safety critical services. In fact, both the ETSI ITS-G5 and the IEEE 1609 standard families adopt the IEEE 802.11p standard as a medium access control (MAC) mechanism, and they require vehicles to exchange periodic awareness messages to avoid dangerous situations. When the density of vehicles increases, the MAC layer will suffer from radio channel congestion problems, and this may affect the various VANET applications, especially safety applications. Therefore, the decentralized congestion control (DCC) mechanism has been specified by ETSI to mitigate the channel congestion; this was achieved by adapting the transmission parameters, such as the transmit power and data-rate. However, many research studies have demonstrated limitations and a low performance of DCC, especially when the channel load is extremely high. To deal with this, in this paper, we investigate a new promising technique, called the transmission timing control (TTC), to control the channel load for periodic cooperative awareness. It consists of spreading the transmissions over time in order to avoid contention on the transmission channel. The objective of the paper is to propose an analytical study to calculate the probability of successful transmission using TTC. The demonstrated results show the efficiency of our timing control-enabled scheme to deal with the channel load on top of different conditions.

Backhaul Capacity-Intent Interference Easing for Sum Rate Improving in 6G Cellular Internet of Things

A mmWave-enabled integrated access backhaul (IAB) network for the 6G cellular Internet of Things (IoT) is necessary to handle the escalating wideband communications needs. A decreased sum rate and low successful transmission probability were the results of the mmWave-enabled IAB network's significant interference problems between access links and differentiated small-cell base stations' backhaul capacities. This article suggests the Q-learn JUP algorithm, which stands for joint user equipment association and power allocation. In order to establish the UA and PA problem, we first investigate how the SBSs-UE association and transmit power are related. Second, using the interference and backhaul burden as the state, UA and PA as the action, and the overall sum rate increment as the reward function of Q-learning, we determine the best joint optimization framework through off-line training. In order to lessen interference and backhaul load, a new backhaul capacity and cautious matching approach utility function has also been designed. Simulation results demonstrate that, in contrast to existing algorithms, the proposed algorithm may vastly enhance the network sum rate and successful transmission probability.

Joint C-V2X Based Offloading and Resource Allocation in Multi-Tier Vehicular Edge Computing System

Emerging intelligent transportation services are latency-sensitive with heavy demand for computing resources, which can be supported by a multi-tier computing system composed of vehicular edge computing (VEC) servers along the roads and micro servers on vehicles. In this work, we investigate the dual Uu/PC5 interface offloading and resource allocation strategy in Cellular Vehicle-to-Everything (C-V2X) enabled multi-tier VEC system. The successful transmission probability is characterized to obtain the normalized transmission rate of PC5 interface. We aim to minimize the system latency of task processing while satisfying the resource requirements of Uu and PC5 interfaces. Due to the non-convex and variables coupling, we decompose the original problem into two subproblems, i.e., resource allocation and offloading strategy subproblems. Specifically, we derive the closed-form expressions of packet transmit frequency of PC5 interface, transmission power of Uu interface, and CPU computation frequency in the resource allocation subproblem. Moreover, for the offloading strategy subproblem, the offloading ratio matrix is obtained by proposing the PC5 interface based greedy offloading (PC5-GO) algorithm, which concludes offloading decision and ratio. Simulation results are provided that the proposed PC5-GO algorithm can significantly improve the system performance compared with other baseline schemes by 13.7% at least.

On the Deployment of Clustered Power Beacons in Random Wireless Powered Communication

Wireless powered communication (WPC) is able to provide wireless devices (WDs) practically infinite energy by using the radio frequency energy harvesting technique. The electricity of WDs for the wireless information transmission (WIT) is converted from the received electromagnetic signals of wireless energy transfer (WET). Power beacon (PB) is usually deployed as the dedicated energy source in WPC networks and the performance of WET is sensitive to the path-loss effect. Thus, both WET and WIT are significantly influenced by the locations of the PBs. To enable efficient transmission in WPC, this paper analytically investigates the impact of the PB deployment strategies on the WPC performance when WDs are clustered around the access points (APs). Specifically, we propose the PB deployment strategy based on the truncated Poisson cluster process (PCP) model. The distribution of PBs is correlated to the locations of WDs by choosing APs as their common parent points. Then, we derive the semi-closed form expressions for the energy outage probability and the transmission success probability. We further present the expressions of the meta distributions of WET and WIT, and investigate the proportion of the WDs in one cluster that achieves successful transmission while satisfying the reliability constraint. Moreover, the distance distribution between the WD and the intra-cluster PBs is analytically derived for the proposed correlated PCP model. By comparing the performance with other deployment strategies, it is shown that the preference of the PB deployment strategy depends on the PB density. The analytical and numerical results can provide insightful guidelines for designing practical WPC systems.

Analysis of Congestion Control Mechanisms for Cooperative Awareness in IoV Environments

Traditional vehicle ad hoc networks (VANETs) have evolved toward the Internet of Vehicles (IoV) during the past ten years with the introduction of 5G communication technology and the growing number of vehicles linked to the Internet. The coexistence of IEEE 802.11p and 5G becomes critical to build a heterogeneous IoV system that benefits from both technologies, being that the IEEE 802.11p standard remains the best option for direct communications and safety-critical applications. The IEEE 1609 standard family and the ETSI ITS-G5 standard family both use the IEEE 802.11p standard as a MAC mechanism. To avoid dangerous situations, vehicles require the periodic exchange of awareness messages. With the increase in vehicle density, the MAC layer will suffer from radio channel congestion problems, which in turn will have a negative impact on the safety application requirements. Therefore, the decentralized congestion control (DCC) mechanism has been specified by ETSI to mitigate channel congestion; this was achieved by adapting transmission parameters such as transmit power and data rate. However, several studies have demonstrated that DCC has drawbacks and suffers from poor performance when the channel load is very high. This paper investigates a new promising DCC technique called transmission timing control (TTC), to reduce the channel load for periodic cooperative awareness. It consists of spreading the transmissions over time to avoid contention on the transmission channel. The objective of the paper is to propose an analytical study to calculate the probability of successful transmission using TTC. Obtained results showed a convergence between the applied experiments and our mathematical model, achieving an error margin of only 5%, which confirms the validity of the equation proposed.