ECE 256: Wireless Networking and Mobile Computing Diagnosing Wireless Packet Losses in 802.11: Separating Collision from Weak Signal Presented By: Jacob H. Cox Jr For ECE 256: Wireless Networking and Mobile Computing February 10, 2009

Acknowledgments Authors ~ Shravan Rayanchu, Arunesh Mishra, Dheeraj Agrawal, Sharad Saha, Suman Banerjee Kuo-Chung Wang (Slide Presentation) http://lion.cs.uiuc.edu/group_seminar_past/fall06/group_seminar_slides/kim-rateadaptation06.ppt+RRAA

Presentation Outline Packet Loss Problem Current Rate Adaption Schemes COLLIE Overview COLLIE Metrics COLLIE Analysis Conclusion

Motivation Packet Loss 802.11 Solution Inadequate Question: 2 Causes: Weak Signal and Collision 802.11 Solution Inadequate defaults to BEB for a substantial number of packet losses Question: Does the type of packet loss matter? What if we could determine its cause?

Problem Defined Collision or Weak Signal, why does knowing matter? Beamforming? Depending on the reason for packet loss, the data-rate/power adaptation may decide to backoff, adjust its rate, or make some other modification.

Fixing packet loss Appropriate actions For collision BEB CW Max Binary-Exponential Backoff (BEB) Retries REF: http://pages.cs.wisc.edu/~shravan/coll-infocom.pdf

Rate Adaptation 802.11 a/b/g standards allow for the use of multiple transmission rates 802.11a, 8 rate options (6,9,12,18,24,36,48,54 Mbps) 802.11b, 4 rate options (1,2,5.5,11Mbps) 802.11g, 12 rate options (11a set + 11b set) Some papers report that rate adaptation is important yet unspecified in 802.11 standards I assume that unspecified means that RA still has some flexibility with how it can be implemented. Reference: Robust Rate Adaptation in 802.11 Networks Presentation by Kuo-Chung Wang

Rate Adaptation Example Rate adaptation affects throughput performance and should be adjusted by channel condition 54Mbps Signal is good 12Mbps Receiver Signal becomes weaker Sender Rate Too High Rate Too Low Increases Loss Ratio Capacity Under-Utilized Decreased Throughput Most designs follow a few conceptually intuitive and seemingly effective guidelines Decrease rate upon severe loss Use deterministic success/loss patterns Use probe packets Use PHY-layer metrics Use long-term statistics Reference: Robust Rate Adaptation in 802.11 Networks Presentation by Kuo-Chung Wang

Related Work Rate Adaptation Algorithms ARF ~ Auto-rate Fallback –Differentiate between loss behaviors –Adapt to realistic scenarios –Handle hidden stations ARF ~ Auto-rate Fallback CARA ~ Collision-Aware Rate Adaptation MRD ~ Multi-Radio Diversity RBAR ~ Receiver Based Auto Rate RRAA ~ Robust Rate Adaptation Algorithm Most designs follow a few conceptually intuitive and seemingly effective guidelines Decrease rate upon severe loss Use deterministic success/loss patterns Use probe packets Use PHY-layer metrics Use long-term statistics

RAA Problem Sender 12Mbps Signal is still good 54Mbps Signal is good Sender 12 Mbps Sender 12Mbps Signal is still good Receiver In the presence of hidden stations, a receiver may experience significant packet losses. This subsequently triggers rate adaptation at the sender to decrease its rate according to the stated guideline. However, the sender should not decrease its transmission rate because reducing the rate prolongs the transmission time for each packet, which worsens channel collisions and further reduces the rate. The fundamental problem is that rate adaptation may ex- perience much richer set of packet loss scenarios in practice, which are well beyond the simplistic one of only fading/path loss envisioned by the original designs. The guideline of de- creasing rate upon severe packet loss does not apply in other lossy scenarios. The rate adaptation solution has to differ- entiate various losses and react accordingly. Sender 54Mbps Signal is good With hidden terminals, reducing the rate prolongs transmission time for each packet and results in more collisions Sender 54Mbps Signal is good

Introduction to COLLIE 802.11, CARA, and RRAA use multiple attempts to deduce cause of packet loss COLLIE uses a direct approach Error packet kickback Client analysis

COLLIE Collision Inferencing Engine Utilizes receiver feedback Analyzes: Bit and symbol level error patterns Received signal strength Design: Signal analysis algorithms Link layer protocol which adjusts link layer parameters Claim to obtain “significant throughput and capacity improvements for high mobility usage scenarios.”

Link Adaptation Mechanism Enhancements Auto Rate Fallback (ARF) Used in conjunction w/COLLIE for this paper Rate adaption mechanism enhanced with inferencing component Using COLLIE, observed throughput gains of 20-60% Gains based on channel conditions and level of contention

? COLLIE Continued X Collision Inference Algorithm Client AP Data Feedback Received Signal Strength Adjust Data Rate/Power Or Contention Window Collision Inference Algorithm Assumes Feedback is successfully received and that receiver is able to successfully abstract the Sender’s MAC Address. Symbol error patterns Bit error distribution and patterns Note: assumes Feedback is successfully received and sender’s MAC address is decoded correctly by the AP

Metrics for Analysis Received Signal Strength (RSS) = S + I S ~ Signal Strength I ~ Interference Bit Error Rate (BER) = total % incorrect bits Symbol level errors: errors within transmission frame Multiple tools used to analyze symbol-level errors Symbol-level Network Coding forWireless Mesh Networks Sachin Katti, Dina Katabi, Hari Balakrishnan, and Muriel Medard Massachusetts Institute of Technology A symbol is a small sequence of bits (typically a few bytes) that the code treats as a single value. http://pages.cs.wisc.edu/~shravan/coll-infocom.pdf

Symbol-level Errors Symbol Error Rate (SER)- % symbols received in error Errors Per Symbol (EPS)- average # errors within each symbol Symbol Error Score (S-score): , where Bi is a burst of n bits

S-Score Collision 0011 0011 0011  0111 1011 0010 S-Score = Channel Fluctuation We find that, for example, 98% of the packets in error due to weak signal have an S-Score of 500 or less, while 26% packets in error due to collision have an S-Score of 500 or less. Thus, by using a cutoff of 500, we would be able to detect 74% of collision cases. 0011 0011 0011  0011 1101 0011 S-Score = http://pages.cs.wisc.edu/~shravan/coll-infocom.pdf

Experimental Design Three possibilities at R: Packet received without error Packet received in error No packet received

Experimental Design Two transmitters, T1 and T2 Two receivers, R1 and R2 Receiver R hears all signals

Analysis of Results Metric Collision Weak Signal RSS Higher (90% > -73dBm) Lower (98% < -73dBm) BER Higher (24% =< 12% BER) Lower (98% =< 12% BER) SER Unremarkable EPS Higher (45% =< 28% EPS) Lower (98% =< 28% EPS) S_Score Higher (28% =< 500) Lower (98% =< 500) Plots are a cut and paste from the paper. By using a cutoff of 500, Paper claims that it achieves the ability to detect 74% of collisions, with 2% being false positives.

Analysis of Results While it is clear that using RSS in this case clearly distinguishes between the cases of collision and weak signal, using BER does not provide the same level of accuracy. In particular, we see that it becomes difficult to distinguish between cases (i) and (ii) using BER because a smaller colliding packet (200-byte in this case) would cause fewer bits in error. On the other hand, as shown in Figure 8, the joint distribution of SER and EPS is useful in distinguishing these cases. The intuition follows from the observation that error packets in collision suffer higher symbol-error rates and correspondingly higher errors per symbol as a function of the symbol-error rates. From the scatter plot shown in Figure 8, we can observe that for higher values of SER, the values of EPS get streamlined into a high yet narrow range allowing for a more accurate prediction of collision versus signal as to the cause of a packet loss.

Begs the Question Is it worth it? Successful almost 60%, false positive rate of 2.4% Check out this accuracy? Check out this accuracy?

Design Components Client Module Optimation Logic place on Client Module and requires on minimal support from APs Client Module

Multi-AP COLLIE Error packet sent to a central COLLIE server Most important where the capture effect is dominant

Multi-AP Results Static situation averaged 30% gains in throughput For multiple collision sources and high mobility, throughput gains reached 15-60%

Collision Analysis

Some Problems Capture Effect Packet size Packet Kickback

Conclusions COLLIE implementation achieves increased throughput (20-60%) while optimizing channel use 40% reduction in retransmission costs Implementation can be done over standard 802.11, resulting in much lower startup costs than other protocols

Questions?