Implementing an adaptive sending rate by exploiting SNR in relation to throughput over Wireless Mesh Networks

Wireless Mesh Networks (WMNs) have emerged as a key technology for the next generation of wireless networking. Instead ofbeing another type of ad-hoc networking, WMNs diversify the capabilities of ad-hoc networks. There are many kinds of protocols that work over WMNs, such as IEEE 802.11a/b/g, 802.15 and 802.16. To bring about a high throughput under varying conditions, these protocols have to adapt their transmission rate. While transmission rate is a significant part, only a few algorithms such as Auto Rate Fallback (ARF) or Receiver Based Auto Rate (RBAR) have been published. In this paper we will show MAC, packet loss and physical layer conditions play important role for having good channel condition. Also we perform rate adaption along with multiple packet transmission for better throughput. By allowing for dynamically monitored, multiple packet transmission and adaptation to changes in channel quality by adjusting the packet transmission rates according to certain optimization criteria improvements in performance can be obtained. The proposed method is the detection of channel congestion by measuring the fluctuation of signal to the standard deviation of and the detection of packet loss before channel performance diminishes. We will show that the use of such techniques in WMN can significantly improve performance. The effectiveness of the proposed method is presented in an experimental wireless network testbed via packet-level simulation. Our simulation results show that regardless of the channel condition we were to improve the performance in the throughput.

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.

Performance analysis of ONOE protocol - an IEEE 802.11 link adaptation algorithm

Link adaptation (LA) plays an important role in adapting an IEEE 802.11 network to wireless link conditions and maximizing its capacity. However, there is a lack of theoretic analysis of IEEE 802.11 LA algorithms. In this article, we propose a Markov chain model for an 802.11 LA algorithm (ONOE algorithm), aiming to identify the problems and finding the space of improvement for LA algorithms. We systematically model the impacts of frame corruption and collision on IEEE 802.11 network performance. The proposed analytic model was verified by computer simulations. With the analytic model, it can be observed that ONOE algorithm performance is highly dependent on the initial bit rate and parameter configurations. The algorithm may perform badly even under light channel congestion, and thus, ONOE algorithm parameters should be configured carefully to ensure a satisfactory system performance. Copyright © 2011 John Wiley & Sons, Ltd.

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.