In this paper, we describe the design, implementation and evaluation of a new framework for the trace-based evaluation of 802.11n networks, which we call T-SIMn. In order to accurately estimate the fate of frames that could have been sent at any rate, traces collected for use in trace-based simulators, like T-SIMn, require a sufficiently large number of samples to be collected using each different rate in a relatively short period of time. In this paper, we devise two novel techniques for collecting and processing traces for 802.11n networks that incorporate Frame Aggregation (FA). The first technique, called direct measurement, samples all rates while aggregating the maximum number of possible frames for each sample. This approach is attractive because frame error rates (FERs) may vary with the position of the frame within the aggregated frame and this techniques directly captures the fate of each subframe. However, the length of the aggregated frames limits this approach to smaller numbers of rates, making it unusable for devices with 3 antennas (e.g., 96 rates). As a result, we also devise second technique that collects traces with frame aggregation turned off, permitting a larger number of rates to be sampled within the same period of time. This approach, called inferred measurement, infers the FER of each subframe using models derived from calibration traces combined with a new measure of changing channel conditions we call the channel dynamic indicator (CDI). Using the direct measurement methodology, we evaluate the T-SIMn framework by collecting traces using an iPhone, which is representative of a wide variety of one antenna devices. We show that our framework can be used to accurately simulate several scenarios and demonstrate the fidelity of SIMn by uncovering problems with our initial evaluation methodology. We then demonstrate that our inferred measurement technique permits us to collect traces that sample all 96 rates in a 3x3:3 802.11n MIMO systems. These traces are then used to accurately simulate transmissions in environments with highly variable channel conditions that include mobility and multiple sources of interference.We expect that the T-SIMn framework will be suitable for easily and fairly comparing algorithms that must be optimized for different and varying 802.11n channel conditions, which are challenging to evaluate experimentally. These include rate adaptation, frame aggregation and channel bandwidth adaptation algorithms.
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