Networking research benefits from controlled, repeatable experimentation using simulation and emulation systems. Making simulations realistic is a challenge for wireless systems and is especially difficult for vehicular networks. This paper presents a general framework for modeling and reproducing environment-specific channel properties. We show that one can estimate localized environment information, and adding such information to state-of-the art channel models significantly increases accuracy. We describe the proposed framework and validate it for fading and line-of-sight effects in vehicle-to-vehicle channels. While suitable models can provide a close approximation of channel conditions, accuracy is limited by the quality of the input information about the environment being modeled. We present a systematic approach to estimating location-specific scattering properties using aerial photography. Using signal-level channel emulation, we show that the improved fading models produce more accurate results at the packet/link level. The error rates of the improved model are 45 and 22 percent lower than using previous state of the art, for Doppler spectrum similarity and packet delivery ratio, respectively. The proposed models-and their implementation-have been designed to minimize run-time complexity while preserving accuracy. The implementation is efficient gh to allow concurrent real-time simulation of many of channels.

This paper presents a framework for signal-level emulation of propagation effects over generalized fading channels at the scale of entire networks. Network emulation enables research into network- scale systems - which would otherwise be limited to low-fidelity network simulators and one-off field experiments - to use real radio hardware and realistic channel models. Our hardware and software architecture goes beyond previous work in that it supports real-time emulation of a very general and parametric class of channels, which includes vehicular (broadband mobile-to-mobile) and indoor channels in addition to classical stationary-to- mobile and stationary-to-stationary channels.

Testing and evaluating protocols and applications for wireless networks and mobile users is challenging because the physical environment has a significant impact on the behavior and dynamics of the system. It is however important that these physical world effects are considered during system impleme

Increasingly, 802.11 devices are being used by mobile users. This results in very dynamic wireless channels that are difficult to use efficiently. Current rate selection algorithms are dominated by probe-based approaches that search for the best transmission rate using trial-and-error. In mobile environments, probe-based techniques often perform poorly because they inefficiently search for the moving target presented by the constantly changing channel. We have developed a channel-aware rate adaptation algorithm CHARM - that uses signal strength measurements collected by the wireless cards to help select the transmission rate. Moreover, unlike previous approaches CHARM leverages channel reciprocity to obtain channel information, so the information is available to the transmitter without incurring RTS/CTS overhead. This combination of techniques allows CHARM to respond quickly to dynamic channel changes. We implemented CHARM in the Madwifi driver for wireless cards using the Atheros chipset. Our evaluation both in the real world and on a controlled testbed shows that channel-aware rate selection can significantly outperform probe-based rate adaptation, especially over dynamic channels.

Current rate selection algorithms are dominated by probe-based approaches that search for the best transmission rate using trial-and-error. When operating over a dynamic channel, probe-based techniques can perform poorly since they inefficiently search for the moving target presented by the constantly changing channel. We have developed a channel-aware rate adaptation algorithm - CHARM - that responds quickly to dynamic channel changes, and significantly outperforms probe-based algorithms in many instances. Unlike previous approaches, CHARM leverages channel reciprocity to obtain channel information without incurring RTS/CTS overhead. Our work shows that channel-aware rate selection is viable, and can significantly outperform probe-based rate adaptation over both static and dynamic channels.