Abstract-Current 802.11 networks do not typically achieve the maximum potential throughput despite link adaptation and crosslayer optimization techniques designed to alleviate many causes of packet loss. A primary contributing factor is the difficulty in distinguishing between various causes of packet loss, including collisions caused by high network use, co-channel interference from neighboring networks, and errors due to poor channel conditions. In this paper, we propose a novel method for estimating various collision type probabilities locally at a given node of an 802.11 network. Our approach is based on combining locally observable quantities with information observed and broadcast by the access point (AP) in order to obtain partial spatial information about the network traffic. We provide a systematic assessment and definition of the different types of collision, and show how to approximate each of them using only local and AP information. Additionally, we show how to approximate the sensitivity of these probabilities to key related configuration parameters including carrier sense threshold and packet length. We verify our methods through NS-2 simulations, and characterize estimation accuracy of each of the considered collision types.
Abstract-In a wireless local area network (LAN), packets can be lost due to a multitude of reasons. It is possible to reduce the probability of occurrence of some of these loss mechanisms by reducing packet length at the medium access control (MAC) layer. However, there is an inherent tradeoff in that shorter packets decrease efficiency with respect to overhead. In current packet length adaptation literature, simplified or incomplete packet loss models are used, neglecting channel fading or collisions due to hidden nodes. In this paper, we apply a more complete packet loss model and propose a local packet length adaptation algorithm whereby each node dynamically adjusts its packet length based on estimates of the probabilities of each significant type of packet loss. In our technique, the access point periodically broadcasts channel occupancy information which each node uses in conjunction with its own local observations in order to estimate current network conditions. These are used to estimate the derivative of throughput with respect to packet length at each node under the current network conditions and to adapt the packet lengths accordingly. We demonstrate throughput gains of up to 20% via NS-2 simulations.
Abstract-As a Carrier Sense Multiple Access (CSMA) network, the performance of IEEE 802.11 networks highly depends on the accuracy of the carrier sensing procedure. However, conventional carrier sensing approaches suffer from the well known hidden and exposed node problems, adversely affecting aggregate throughput of the IEEE 802.11 networks. In this paper, we propose a novel scheme through which each station can adaptively select its Carrier Sense Threshold (CST) in order to mitigate the hidden/exposed node problems. The basic idea behind our approach is for the Access Point (AP) to periodically transmit a Busy/Idle (BI) signal to all the stations. Individual stations then use the BI signal from the AP together with their own local BI signal in order to adjust their CST. We use NS-2 simulations to show that our approach can enhance the aggregate throughput by as much as 50%.
Abstract-The 802.11 standard includes several modulation rates, each of which is optimal for a different channel condition. However, there are no simple and reliable methods for nodes to determine their current channel conditions. Existing link adaptation techniques use packet losses as an indication of poor channel conditions; however, when there is a significant probability of collision, this assumption fails, leading to degraded throughput. In this paper, we show that an estimate of the probability of collision can be used to improve link adaptation in 802.11 networks with hidden terminals, and significantly increase throughput by up to a factor of five. We demonstrate this through NS-2 simulations of a few link adaptation techniques including a new algorithm, called SNRg.
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