Trace-based Evaluation of Rate Adaptation Schemes in Vehicular Environments Kevin C. Lee WiVeC 2010, 5/17/10

Rate Adaptation Overview The 802.11 a/b/g/n standards allow the use of multiple transmission rates 802.11b, 4 rate options (1,2,5.5,11Mbps) 802.11a, 8 rate options (6,9,12,18,24,36,48,54 Mbps) 802.11g, 12 rate options (11a set + 11b set) The method to select the transmission rate in real time is called “Rate Adaptation”

Why Rate Adaptation? Receiver 54Mbps Signal is good 12Mbps Signal is weaker Sender Ideally, the transmission rate should be adjusted according to the channel condition When the signal on the channel is good, one can pump out data at a higher rate; when the signal is poor, one can lower the rate so as to maintain a better throughput than the throughput @ a higher rate

Motivation MANY rate adaptation algorithms yet no fair comparison Unrealistic propagation (unrealistic) Dynamic changing conditions (non-repeatable) Long system setup and device driver implementation (impractical) The motivation behind this work is that there are MANY rate adaption algorithms yet there is no fair comparison of them due to unrealistic propagation models in the simulation, dynamic changing conditions in the environment that makes the field experiment hardly repeatable, and long system setup and device driver implementation that make evaluations extremely impractical

Framework & Goal Application Layer … MAC Layer Physical Layer Repeatable evaluation of rate adaptation schemes Implementation of different application Application Layer … Implementation of various rate adaptations schemes Rapid deployment independent of hardware spec MAC Layer Because of this, we propose our simulation framework that focuses on the application, mac, and physical layer of the network stack for repeatable evaluation of rate adaptation schemes with different application traffic, rapid deployment of these rate adaption schemes, independent of underlying hardware specifications on the mac layer, and realistic, field-collected SNR traces to reflect the environment in the physical layer Realistic SNR to reflect the environment Use field-collected SNR to replace synthetic value Physical Layer

Physical Layer Collect SNR traces from moving cars Server broadcasts @ 6Mbps 2 Clients receive and record SNR Increase range and power of signal with an external 7dBM antenna Replace SNR logic with SNR from the field Derive BER and then bit error probability We have a server that broadcasts small packets @ 6Mbps and 2 clients that would receive these packets and record SNR To increase the range and power of signal, we use an external 7dBM antenna. The collected SNR is subtracted of 7dBM at the receivers. BER is then computed based on the SNR. The corresponding error probability is then derived to determine whether to let the packets through.

Trace Collection Traces from 3 different areas: City, Residential, and Highway We collected traces from 3 different areas, city, residential, and highway. The table shows the distinguishing characteristics of these 3 environments in terms of speed, traffic density, and building types. Most highways are raised and built with tall walls for the purpose of blocking out noise.

Trace Collection Map

Static Traffic Route Car A centered at the mid point, stationary Car B and C move back and forth toward and away from A There is also static traffic route

Rate Adaptation Schemes Implementation RRAA-DYN adjusts rates before the current estimation windowm This slide shows the different adaption schemes that are implemented in our framework. They are classified by 4 categories. In transmitter-based schemes, a sender makes rate decisions without any feedback from the receiver. However, in receiver-based schemes, a sender bases its rate selection on the receiver’s feedback. ARF, AMRR, and SampleRate are transmitter-based schemes. The SNR-based schemes use signal strength for rate selection; whereas, the packet-based schemes use packet receipt. Frame-based schemes use frame transmission failure and success counts to adjust the rate, but window-based schemes rely on past history to predict the channel condition in the future. The subtle difference between the two is the unit and dynamism. Training-based schemes require training for precise rate selection in the current environment. The longer the training is, the more history there is in the table, the more accurate is the scheme. [Note that ARF, AMRR, and SampleRate are classified as transmitter-based schemes even though ack packets are received as feedback to determine successive packet successes and failures because the ack packets are not used in any other way (such as determining receiver’s channel quality) than signaling the receipt of the packets.] SampleRate uses the window (10 seconds) to determine the next rate to sample. RRAA uses the window to provide an opportunity to change to a different rate. RAM uses the sliding window to predict the SNR for the next frame. RRAA has a table of parameters after training in a certain environment. RAM also has a table of expected throughputs given the rate and signal strength. Unlike training-based schemes, ARF, AMRR, and SampleRate select a rate based on some fixed parameter (e.g., 2 failures for rate decrease, 10 successes for rate increase for AMRR) regardless of the dynamics of the past history. This may affect the performance of these schemes greatly as most realistic scenarios exhibit randomly distributed loss behaviors.

Static Traffic Route Result SNR from 440s to 540s 40 seconds to complete one loop Signal strength GPS record indicates @ 485s and 515s, the vehicles are closest to C. This indicates the signal strength is directly proportional to the distance between them. 20 seconds to be @ where A is directly proportional to the distance between them

Instantaneous Throughput for All Algorithms Packet-based rate adaption schemes react similarly to the SNR-based scheme (RAM) Sample rate plateau from 460-470s and 500-530s It shows SampleRate’s slow response to channel condition in adjusting the rate Increase: every 10th packet to sample (can’t sample if last 4 failed) Decrease: 4 successive failures

Throughput in Different Transmission Rates Throughput increases with transmission rate ARF, RRAA-DYN, and RAM top 3 AMRR and Sample bottom 2

Rate Distribution for All Schmes 6Mbps occupies the largest fraction for top 3 schemes but there are other rates => short-term lossy channel Sample & AMRR can’t adapt to short-term fluctuation AMRR has parameters of success rate 90% above which the rate is raised and failure rate 33% above which the rate is decreased. Because of short-term fluctuations, success rate never exceeds 90% and failure rate never exceeds 30%. The rate is left unchanged. In addition, most failed packets' retransmission count is 1. This sets the selected rate to the original rate; effectively not changing the rate at all. \emph{A proposed solution for AMRR to adapt quickly to short-term channel fluctuations is to lower failure rate parameter and set rate ($r_1$) for $c_1$ retransmission count to one rate lower than the original rate.}

Success of ARF Comes from the fact that rate increases conservatively and decreases drastically Not too good if the channel condition does not change frequently Conclusion: Packet-based scheme does a subpart job because of fixed parameters of packet statistics; adaptive parameters to improve Use adaptive parameters based on channel condition can improve packet-based schemes

Impact of Environments Throughput degradation from residential, highway, city; speed & traffic density play a factor RRAA-DYN beats RRAA & RRAA-BASIC => changing trans- mission wind. helps improve responsive- ness Despite cars are maintaining relatively constant speed, speed plays an important factor in the throughput of these rate adaption schemes. Since another distinguishing factor is traffic density, the traffic density also plays an important role. The lower the traffic density, the better the throughput

Impact of Propagation Model Rayleigh has higher throughput b/c it considers fading where there is no dominant propagation along a line of sight between transmitter and receiver A more accurate prop. model to use b/c lead car and trailing car are often separated by cars in between

Conclusion An integrated framework that utilizes empirical data collected from the testbed to objectively compare different rate adaption schemes Repeatable Rapid Realistic The experiments are repeatable, the developments for these rate adaption schemes are rapid, and since we use SNR from the field, the simulation reflects better of the environment in which the schemes run on.