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Performance Enhancement of the EDCA Mechanism for DSRC Safety Communication

Performance Enhancement of the EDCA Mechanism for DSRC Safety Communication . Sarah Sharafkandi, Gaurav Bansal, John Kenney Toyota InfoTechnology Center, USA Presentation to IEEE 1609 WG June 18, 2012. Outline. Background Study of homogeneous case Study of heterogeneous case

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Performance Enhancement of the EDCA Mechanism for DSRC Safety Communication

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  1. Performance Enhancement of the EDCA Mechanism for DSRC Safety Communication Sarah Sharafkandi, Gaurav Bansal, John Kenney Toyota InfoTechnology Center, USA Presentation to IEEE 1609 WG June 18, 2012

  2. Outline • Background • Study of homogeneous case • Study of heterogeneous case • Isolation Technique • Virtual Division Technique

  3. Background • Focus of this study • Scope … • Goal …

  4. Focus: DSRC Safety • DSRC Safety Applications prevent collisions • In US, Ch. 172 used for safety • Dominated by period SAE Basic Safety Messages (BSMs) • May include other messages, e.g. SPaT, MAP, also likely to be periodic • Similar situation in Europe with Control Channel: • Cooperative Awareness Messages (CAMs) • DENM and other messages as well. • MAC protocol is 802.11p: CSMA/CA

  5. Scope: EDCA • Improve DSRC safety by reducing 802.11 MAC frame collisions for periodic traffic • Use standard Enhanced Distributed Channel Access (EDCA)

  6. Goals • Determine optimal strategy for EDCA on DSRC Safety Channel: • Parameter selection • Adaptive Inter-Frame Space Number: AIFSN • Contention Window minimum size: CWmin • Access Category use - Two cases: • Homogeneous: No classification of frames based on perceived differences in value of reception • Heterogeneous: Two or more classes of frames, with critical class comprising 10% or less of whole • Eventually standardize use of EDCA for DSRC Safety Channel (but not today)

  7. EDCA • Up to 4 queues, each associated with an Access Category (AC) • Head-of-line frame in a queue competes for channel access • Competes with frames in other STAs • Competes with other queues in same STA • Competition governed by: • Inter-Frame Space parameter (AIFSN) • Contention Window parameter (CWmin)

  8. EDCA: After busy period • When frame advances to head-of-line, choose countdown integer from [0, CWmin] • When channel becomes idle, decrement countdown 1 per slot time (13 μsec) • Channel considered idle at SIFS + (AIFSN * 13 μsec) after end of prior frame Lowest Priority = AC_Background AC0 (AIFSN =9, CW=15) Data AC1 (AIFSN =6, CW=7) Data AC_Best Effort AC2 (AIFSN =3, CW=3) AC3 (AIFSN =2, CW= 3) Data AC_Voice SIFS Prior Data Data Highest Priority = AC_Video Countdown while medium is idle Suspend countdown when medium becomes busy Defer access

  9. SIFS Interrupted Countdown AIFS Station A SIFS Data 4 3 2 1 0 AIFS AIFS Station B SIFS Busy medium Data 2 1 0 7 6 5 4 3 Countdown frozen at 3 during busy period

  10. Considerations • EDCA is designed for for “typical” LAN traffic. Well studied in that domain. • Video & Voice: delay sensitive, loss tolerant • Best Effort: delay tolerant, usually loss sensitive • Safety traffic is atypical • Dominantly periodic • Latency sensitive, but not like “real time” • Compare with usual video/voice assumptions • Channel access latencies on order of 10s msec OK • Loss sensitive, but not as strict as file transfer • Tolerable reception gaps on order 100s msec Specific Goal: minimize frame collisions while keeping queuing delay on order 10s msec

  11. Limitations • Assume initially all nodes are within reception range of each other

  12. Outline • Background • Study of homogeneous case • Study of heterogeneous case • Isolation Technique • Virtual Division Technique

  13. Simulation Setup • Simulation tools: • MATLAB, NS2 • Topology: • All the stations at the same location • Load: • 20 to 300 nodes each sending packets at the message rate of 10HZ

  14. Effect of AIFSN • All frames detect idle channel at same time (homogeneous case) • Increasing AIFSN just wastes time, reduces capacity

  15. Varying AIFSN • PER increases steadily with increasing AIFSN • Conclusion: Choose AIFSN = 2 (minimum allowed) for homogeneous case • No trade-offs involved

  16. Effect of CW • Low CW reduces average queue latency • Assume STA A and STA B enter countdown during same busy period • If they choose the same countdown value their frames will eventually collide • Probability of that is 1/(CW+1), so low CW increases chance of that collision • If 3 or more become active during a busy period, the probabilities are more complex • STA A’s frame can also collide with a STA that enters countdown during a different busy period • Analytical approach to computing collision probability is very difficult. Open research question.

  17. Effect of CWmin on PER Effect of CW goes away at high load • Clear PER advantage to higher CW for low-to-moderate load • Advantage diminishes with increasing CW (gap getting smaller) • Higher CW gives lower PER for low-to-moderate loading • Higher CW leads to higher latency • Therefore a trade-off exists

  18. Choosing CW • At very high load, the statistics of the nodes waiting in countdown are the same after every a busy period • Therefore, probability of colliding when node counts down to zero becomes independent of how long the node has been counting down, i.e. independent of CW (like slotted ALOHA)* • Every active queue counts down 1 per busy period • Max. queuing delay ≈ CW * busy period • For BSM-dominated channel, busy period ≈ 0.5 msec • For CW = 31, max. queuing delay on the order of 15 msec. Doubling to CW = 63 would double max delay with small additional PER benefit • Conclusion: For Homogeneous case, a single AC with AIFSN = 2 and CWmin = 31 is a good choice. * Congestion Control for Vehicular Safety: Synchronous and Asynchronous MAC Algorithms, Subramanian et al., 9th ACM VANET Workshop, June 2012

  19. Outline • Background • Study of homogeneous case • Study of heterogeneous case • Isolation Technique • Virtual Division Technique

  20. Classification • Now assume we classify the Safety Channel traffic into two or more ACs • Not concerned here with rules of classification. Just assume “high priority” AC has 10% - not a magic number, just convenient. • How to classify is hard problem, out of scope here: • Could be segregation of BSMs into small “critical” subset and large “regular” subset • Could involve a third class for SPaT, MAP, Security, Management, … • Take a look at performance using aggressive ACs, with 10% traffic in AC3 and 90% in AC2

  21. AC3 vs AC2 vs Homogeneous Heterogeneous Low Priority (LP) AC2 AIFSN = 3, CW = 3 Homogeneous AC3 AFISN = 2, CW = 3 Heterogeneous High Priority (HP) , AC3 AIFSN=2, CW=3 • Removing 90% of lower priority traffic from AC3 improves PER significantly • PER of that lower priority traffic is a bit higher than homogeneous case • Can we do better?

  22. Competition between AC3 and AC2 AC3 (High priority): AIFSN=2, CWmin=3 AC2 (Low priority): AIFSN=3, CWmin=3 AIFSN=3 AIFSN=2 SIFS Data Data CW=3 Slot reserved for high priority AC2 offers no competition in 3rd slot after SIFS

  23. Isolation Idea • High priority messages: • Further decrease the PER of HP class by completely isolating it from low priority messages • High priorities only collide within themselves • Lower number of high priority messages • Low priority messages: • Pros: Less intra-class collision • Cons: Less capacity

  24. Isolating Traffic Classes High priority AIFSN=2, CWmin=3 Low priority AIFSN=6, CWmin=3,7,… AIFSN=6 AIFSN=2 SIFS Data CW=3 Two classes won’t collide! AIFSN(LP) = AIFSN(HP) + CWmin(HP) +1 6 = 2 + 3 +1

  25. Isolating High Priority Traffic Blue and red are same as prior graph (2,3) and (3,3) LP with Isolation: PER improved for low/moderate load, somewhat higher for high load Note dramatic reduction in HP PER when isolation is applied

  26. Isolation for 3 ACs • Isolation can be applied to two ACs: • HP = (2,3) • LP = (6,x) x = 15 on prior slide • Isolation could be applied to 3 ACs: • HP = (2,3) • MP = (6,7) • LP = (14,x) • Isolation could be applied to 4 ACs, but AIFSN of 4th might lead to excessive latency

  27. Outline • Background • Study of homogeneous case • Study of heterogeneous case • Isolation Technique • Virtual Division Technique

  28. Observations from Isolation • Average PER of low and high priority traffic is a function of percentage of traffic at each priority class • In some cases, average PER can be even lower than the PER of homogeneous traffic with the “optimal” single-AC EDCA parameters • This relates to convexity of PER vs Load curve

  29. Convexity Example If 150-node traffic is divided into 3 ACs, the aggregate PER might improve

  30. Virtual Division • Idea: Within a set of traffic that is nominally of equal reception value, classify it into two or more ACs. We call this “virtual division,” since it is not based on a natural difference within the traffic. • How about fairness?: • Any given node assigns its traffic on a rotation basis to the set of virtual division ACs. Could be according to a deterministic schedule, or could be probabilistic. • We can subdivide to more than 2 categories

  31. Virtual Division: Homogeneous Virtual Classifier 3-way AC1 BSM sequence from a vehicle AC2 Alternating assignment among ACs with 1/3, 1/3, 1/3 distribution AC3

  32. Homogeneous: Virtual Division • Virtual Division offers general PER advantage with 1/3, 1/3, 1/3 split • More research needed to determine optimal split ratio

  33. Heterogeneous Traffic • What if we have “real” high priority class at 10% load? • Solution: • Keep EDCA parameters fixed for HP (CWmin=3, AIFSN=2) • Artificially Subdivide LP into two categories: • Class1: AIFSN=6, CW=7 • Class2: AIFSN=14, CW=15

  34. Virtual Division: Heterogeneous Classifier Virtual Classifier AC1 Alternating AC2 and AC1 BSM sequence from a vehicle AC2 Critical vs Regular Critical content BSMs AC3

  35. Heterogeneous: Virtual Division • Virtual Division offers general PER advantage with ½, ½ split among “regular” class • More research needed to determine optimal split ratio • PER of HP class unchanged

  36. IPG 95% quantile (Heterogeneous) • IPG = Inter-Packet reception Gap. Relates to application observability of remote vehicle • General IPG advantage with virtual division, especially at high load

  37. Conclusion IEEE 802.11p Default EDCA Parameter Set For Safety Channel: Use these defaults with virtual division • Determine if safety channel is homogeneous or heterogeneous, and proportions for latter • Determine optimal splits of virtual division

  38. A note about hidden nodes • In a hidden-node environment, advantages from EDCA shown above are muted • Reducing collisions among neighbor nodes leads to increased channel load for a given number of vehicles. Higher channel load increases probability of hidden node collisions • We have studied. Advantages are still present, but are not as dramatic.

  39. Interaction of EDCA and Congestion Control • Above work assumes constant rate traffic. • EDCA work and Congestion Control both trying to improve throughput – complementary • Some congestion control approaches adapt message rates – no longer constant • Further study warranted • Does improved EDCA suggest different target channel load? • Does adaptive message rate impact optimal splitting ratios?

  40. Thank you!

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