Michael N. Krishnan

Packet Length Adaptation in WLANs with Hidden Nodes and Time-Varying Channels

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.

Local Estimation of Probabilities of Direct and Staggered Collisions in 802.11 WLANs

Current 802.11 networks do not typically achieve the maximum potential throughput despite link adaptation and cross-layer 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.

Throughput Improvement in 802.11 WLANs Using Collision Probability Estimates in Link Adaptation

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 \tmphl{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.

Adaptive Carrier-Sensing for Throughput Improvement in IEEE 802.11 Networks

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%.

Simultaneous fingerprinting and mapping for multimodal image and WiFi indoor positioning

In this paper, we propose an end-to-end system which can be used to simultaneously generate (a) 3D models and associated 2D floor plans and (b) multiple sensor e.g. WiFi and imagery signature databases for the large scale indoor environments in a fast, automated, scalable way. We demonstrate ways of recovering the position of a user carrying a mobile device equipped with a camera and WiFi sensor in an indoor environment. The acquisition system consists of a man portable backpack of sensors carried by an operator inside buildings walking at normal speeds. The sensor suite consists of laser scanners, cameras and an IMU. Particle filtering algorithms are used to recover 2D and 3D path of the operator, a 3D point cloud, the 2D floor plan, and 3D models of the environment. The same walkthrough that produces 2D maps also generates multi-modal sensor databases, in our case WiFi and imagery. The resulting WiFi database is generated much more rapidly than existing systems due to continuous, rather than stop-and-go or crowd-sourced WiFi signature acquisition. We also use particle filtering algorithms in an Android application to combine inertial sensors on the mobile device, with 2D maps and WiFi and image sensor databases to localize the user. Experimental for the second floor of the electrical engineering building at UC Berkeley campus show that our system achieves an average localization error of under 2m.

Adaptive packetization for error-prone transmission over 802.11 WLANs with hidden terminals

Collision and fading are the two main sources of packet loss in wireless local area networks (WLANs) and as such, both are affected by the packetization at the medium access control (MAC) layer.While a larger packet is preferred to balance protocol header overhead, a shorter packet is less vulnerable to packet loss due to channel fading errors or staggered collisions in the presence of hidden terminals. Direct collisions due to backoff are not affected by packet size. Recently, Krishnan et. al. have developed a new technique for estimating probabilities of various components of packet loss, namely, direct and staggered collisions and fading. Motivated by this work, in this paper, we exploit ways in which packetization can be used to improve throughput performance of WLANs. We first show analytically that the effective throughput is a unimodal function of the packet size when considering both channel fading and staggered collisions. We then develop a measurement-based algorithm based on golden section search to arrive at an optimal packet size for MAC-layer transmissions. Our simulations demonstrate that packetization based on our search algorithm can greatly improve the effective throughput of sensing-limited nodes, and reduce video frame transfer delay in WLANs.

Local estimation of collision probabilities in 802.11 WLANs: An experimental study

Current 802.11 networks do not typically achieve the maximum potential throughput despite link adaptation and cross-layer 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 previous work, we used NS-2 simulations to show that estimating various components of loss probability such as direct collisions, staggered collisions, and physical layer errors, can be used to improve the throughput of 802.11 networks via link adaptation, carrier sense threshold adaptation, and MAC layer packet length adaptation. We have also proposed a method to estimate the various components of loss probability by comparing channel occupancy at a station with that of its access point. In this paper, we use Ath5k open source wireless card driver in an experimental testbed in order to experimentally verify the accuracy of our previously proposed approach to estimating collision probability. We show that our proposed methodology accurately estimates overall collision probability to within 5%. This experimental verification demonstrates the feasibility of our collision probability estimation approach and the resulting throughput gains in practice.

Contention window adaptation using the busy-idle signal in 802.11 WLANs

In a wireless local area network (LAN), packets can be lost for a variety of reasons, including collisions due to high traffic and channel errors due to poor channel conditions. In practice, however, nodes cannot easily differentiate between these types of loss. As a result, adaptations based on packet loss alone can result in significantly degraded performance. In 802.11 networks, wireless nodes avoid collisions via the Binary Exponential Backoff (BEB) protocol. This performs well for moderate numbers of nodes and low channel error rates, but is inefficient for large numbers of nodes, high channel error rates, or in the presence of hidden terminals. In this paper, we propose a contention window adaptation scheme in which nodes use information shared by the AP to optimize contention window sizes in a distributed fashion to improve network utility. We show via NS-2 simulations that our method can improve throughput by as much as 24% in the high node count scenario, 35% in the high channel error scenario, and 350% in the presence of hidden terminals.

A Method for Estimating Access Delay Distribution in IEEE 802.11 Networks

The volume of multimedia traffic over wireless networks has been steadily increasing over the past decade. Unlike web browsing applications, multimedia data needs to satisfy stringent delay requirements since late packets are as good as lost packets. In this paper, we present a framework for the nodes in 802.11 networks to estimate the distribution of uplink access delay in Distributed Coordination Function (DCF) MAC mechanism using locally available information. The access delay for a packet is defined as the time between the packet arriving at the head of line of MAC queue, and its ACK being received. In our proposed framework, each node periodically records channel occupancy information to estimate the distribution of access delay. We use NS-2 simulations to verify the accuracy of our proposed approach.

Access Point Selection for Multi-Rate IEEE 802.11 Wireless LANs

Access Point (AP) selection is important in WLANs as it affects the throughput of the joining station (STA). In this paper, we propose a class of AP selection algorithms to maximize the joining STAs expected throughput by considering interference at STAs, and transmit opportunities (TXOPs) at APs. Specifically, we collect a binary-valued local channel occupancy signal, called busy-idle (BI) signal, at each node and require the APs to periodically broadcast their BI signal and a quantity representing their TXOPs. This enables the joining STA to estimate throughput from candidate APs before selecting one. We use NS-2 simulations to demonstrate the effectiveness of our algorithms for saturated UDP and TCP downlink traffic, and compare them with received signal power (rxpwr) algorithm, load based algorithm, and Fukuda algorithm. For a random topology consisting of 24 APs and 60 STAs, our algorithms increase the joining STAs average throughput by as much as 42% and 24% compared to rxpwr for UDP and TCP respectively. In addition, the achieved average throughput is 90% and 93% of that obtained via the optimal selection. We also show that, in contrast to rxpwr, the throughput of proposed algorithms remains close to optimal with the increase in AP or STA density.