Wenyang Guan

Adaptive congestion control of DSRC vehicle networks for collaborative road safety applications

Congestion control is critical for the provisioning of quality of services (QoS) over dedicated short range communications (DSRC) vehicle networks for road safety applications. In this paper we propose a congestion control method for DSRC vehicle networks at road intersection, with the aims of providing high availability and low latency channels for high priority emergency safety applications while maximizing channel utilization for low priority routine safety applications. In this method a offline simulation based approach is used to find out the best possible configurations of message rate and MAC layer backoff exponent (BE) for a given number of vehicles equipped with DSRC radios. The identified best configurations are then used online by an roadside access point (AP) for system operation. Simulation results demonstrated that this adaptive method significantly outperforms the fixed control method under varying number of vehicles. The impact of estimation error on the number of vehicles in the network on system level performance is also investigated.

Adaptive Rate Control of Dedicated Short Range Communications Based Vehicle Networks for Road Safety Applications

Dedicated Short Range Communication (DSRC) is a promising technique for vehicle ad-hoc network (VANET) and collaborative road safety applications. As road safety applications require strict quality of services (QoS) from the VANET, it is crucial for DSRC to provide timely and reliable communications to make safety applications successful. In this paper we propose two adaptive message rate control algorithms for low priority safety messages, in order to provide highly available channel for high priority emergency messages while improve channel utilization. In the algorithms each vehicle monitors channel loads and independently controls message rate by a modified additive increase and multiplicative decrease (AIMD) method. Simulation results demonstrated the effectiveness of the proposed rate control algorithms in adapting to dynamic traffic load.

Collaborative Relay Beamforming Strategies for Multiple Destinations with Guaranteed QoS in Wireless Machine-to-Machine Networks

The Machine-to-Machine (M2M) communications allow information exchange between machine devices, which can be carried out without any human interaction. However, there are a large number of small and low-power machine devices in the wireless M2M networks. To guarantee the quality of service (QoS) requirements of the destination devices, we study the amplify-and-forward (AF) relay beamforming, where multiple relay M2M devices can transmit signals from the source M2M device to multiple destination M2M devices. In this paper, we propose two iterative strategies to jointly optimize the source antenna selection and the collaborative relay beamforming weights with the aid of perfect channel state information (CSI). The aim of the proposed strategies is to maximize the worst-case received signal-to-interference-and-noise ratio (SINR) under two different types of relay power constraints, which are the total relay power constraint and individual relay power constraints, respectively. Using the semidefinite relaxation (SDR) technique, the optimization problem of collaborative relay beamforming can be formulated as a semidefinite programming (SDP) problem, which can be optimally solved. Simulation results validate our theoretical analysis and demonstrate that after several iterations, the performance of the proposed iterative strategies can obtain near-optimal performance.

Theoretic Analysis of IEEE 802.11 Rate Adaptation Algorithm SampleRate

In this paper we investigate rate adaptation algorithm SampleRate, which spends a fixed time on bit-rates other than the currently measured best bit-rate. A simple but effective analytic model is proposed to study the steady-state behavior of the algorithm. Impacts of link condition, channel congestion and multi-rate retry on the algorithm performance are modeled. Simulations validate the model. It is also observed there is still a large performance gap between SampleRate and optimal scheme in case of high frame collision probability.

Adaptive Message Rate Control of Infrastructured DSRC Vehicle Networks for Coexisting Road Safety and Non-Safety Applications

Intelligent transport system (ITS) has large potentials on road safety applications as well as nonsafety applications. One of the big challenges for ITS is on the reliable and cost-effective vehicle communications due to the large quantity of vehicles, high mobility, and bursty traffic from the safety and non-safety applications. In this paper, we investigate the use of dedicated short-range communications (DSRC) for coexisting safety and non-safety applications over infrastructured vehicle networks. The main objective of this work is to improve the scalability of communications for vehicles networks, ensure QoS for safety applications, and leave as much as possible bandwidth for non-safety applications. A two-level adaptive control scheme is proposed to find appropriate message rate and control channel interval for safety applications. Simulation results demonstrated that this adaptive method outperforms the fixed control method under varying number of vehicles.

Channel congestion performance analysis for DSRC vehicle ad hoc network

In this paper, the analytic model for single class broadcast based frame transmission by IEEE 802.11 MAC is presented. A Markov chain is used to derive a transmission probability that a station may transmit a frame in a random time slot. The collision probability is calculated based on the transmission probability. After that the per-node throughput is calculated based on the stationary transmission probabilities. With the proposed analytic model, we studied the network QoS performances in terms of message success probability, network throughput and channel access delay for both emerging and routine messages. The proposed analytic model was verified by simulations, which shows very high accuracy.

The Performance of Multimessage Algebraic Gossip in a Random Geometric Graph

Gossip algorithm has been widely regarded as a simple and efficient method to improve quality of service (QoS) in large-scale network which requires rapid information dissemination. In this paper, information dissemination based on algebraic gossip in a random geometric graph (RGG) is considered. The n nodes only have knowledge about their own contents. In every time slot, each node communicates with a neighbor partner chosen randomly. The goal is to disseminate all of the messages rapidly among the nodes. We show that the gain of the convergence time is O n 1 / 2 log ε - 1 / log 1 / 2 n with network coding. Simulation results show that these bounds are valid for the random geometric graph and demonstrate that network coding significantly improves the bounds with the number of users increasing.

QoS Guarantee with Adaptive Transmit Power and Message Rate Control for DSRC Vehicle Network Based Road Safety Applications

Quality of services (QoS) support is critical for dedicated short range communications (DSRC) vehicle networks based collaborative road safety applications. In this paper we propose an adaptive power and message rate control method for DSRC vehicle networks at road intersections. The design objective is to provide high availability and low latency channels for high priority emergency safety applications while maximizing channel utilization for low priority routine safety applications. In this method an offline simulation based approach is used to find out the best possible configurations of transmit power and message rate for given numbers of vehicles in the network. The identified best configurations are then used online by roadside access points (AP) according to estimated number of vehicles. Simulation results show that this adaptive method significantly outperforms a fixed control method.

Multiple Beam Selection for Combing M2M Communication Networks and Cellular Networks with Limited Feedback

We study the scenario in which a large number of machine-type communication devices (MTCDs) communicate with each other by utilizing the help of the base station (BS) through some MTCD gateways. We consider an overlay mode of an orthogonal frequency division multiple access (OFDMA) based cellular system using orthogonal beamforming to provide broadband wireless access for the MTCD gateways. In order to avoid the interference with mobile users, the beamforming vectors to the MTCD gateways have to be orthogonal to the channel vectors of mobile users, which become a beamforming constraint for the MTCD gateways. However, with limited feedback of channel state information (CSI) at a BS, the orthogonal beamforming constraints may not be achieved. In such a practical case, conventional feedback schemes are feasible but not efficient due to the orthogonality constraints. In this paper, we propose a novel multiple beam selection (MBS) approach with limited feedback for MTCD gateways by taking into account the previous orthogonality constraints. Simulation results show that the performance improvement of the proposed approach over the conventional ones is generally about 10% when a BS is equipped with an array of 6 elements.

Channel Estimation for Relay-Based M2M Two-Way Communications Using Expectation-Maximization

The growing popularity of machine-to-machine (M2M) communications in wireless networks is driving the need to update the corresponding receiver technology based on the characteristics of M2M. In this paper, an expectation-maximization-based maximum likelihood cascaded channel estimation method is developed for relay-based M2M two-way communications. As the closed-form solution of maximum likelihood channel estimation does not exist, and the superimposed signal structure at the receiver is conducive to the expectation-maximization application, the expectation-maximization algorithm is utilized to provide the maximum likelihood solution in the presence of unobserved data through stable iterations. Even in the absence of the training sequence, the cascaded channel estimates are obtained through the expectation-maximization iterations. The Bayesian Cramér-Rao lower bounds are derived under random parameters for the channel estimation, and the simulation demonstrates the validity of the proposed studies.