Tae-Suk Kim Hyuk Lim Jennifer C. Hou ACM MobiCom 2006

1. Improving Spatial Reuse through Tuning Transmit Power, Carrier Sense Threshold, and Data Rate in Multi-hop Wireless Networks Tae-Suk Kim Hyuk Lim Jennifer C. Hou ACM MobiCom 2006

2. Contents • Background and Motivations • Network Capacity Analysis • Proposed Power and Rate Control (PRC) Algorithm • Simulation Study • Conclusions

3. Background and Motivations Network Capacity Analysis Proposed Power and Rate Control (PRC) Algorithm Simulation Study Conclusions

4. Background and Motivations • Multi-hop network capacity depends on • Achievable channel capacity at each individual wireless link • Level of spatial reuse • Tradeoff between the two factors • Increasing one factor inevitably causes decreasing the other • Control knobs for spatial reuse • Physical carrier sensing employed in IEEE 802.11 MAC • Carrier sense threshold • Determines the minimum distance, termed as the carrier sense range, between any pair of transmitters • Wireless medium is shared, and the shared range is determined by both the transmit power and the carrier sense threshold • One can control the level of spatial reuse by adjusting the transmit power or the carrier sense threshold

5. Background and Motivations (cont’d) • Two questions: • What is the relation between the transmit power and the carrier sense threshold? • Does increasing transmit power have the same effect as increasing the carrier sense threshold? • Our contributions • Analytic model that expresses the network capacity as a function of the transmit power and the carrier sense threshold • Spatial reuse depends only on the ratio of the transmit power and the carrier sense threshold • Several advantages of tuning the transmit power over tuning the carrier sense threshold • Analyze the number of power levels required to achieve the same control granularity as afforded by tuning the carrier sense threshold • Localized power and rate control (PRC) algorithm • Each transmitter dynamically determines its transmit power and data rate adapting to the interference level that it perceives.

6. Background and Motivations Network Capacity Analysis Proposed Power and Rate Control (PRC) Algorithm Simulation Study Conclusions

7. Interference Model • Assumptions • Nodes are randomly and uniformly distributed in an area U with reasonably high node density λ. • Distance between a transmitter and a receiver, R, is given • Path-loss radio propagation model: • Perfect MAC protocol • Interference level and SINR at a receiver • Consider the transmission between TX0 andRX0 that are R away from each other • Transmit power Ptx, Carrier sense threshold Tcs • Carrier sense range D: nodes concurrently transmitting with TX0 must be at least D away from TX0 and each other

8. Interference Model (cont’d) • The worst-case interference, I, as perceived at RX0 : • Corresponding SINR at RX0 :

9. Network Capacity as a Function of Transmit Power and Carrier Sense Threshold • Network Capacity: where , is the area of the system, and is the area consumed by each transmitter ( ). where is a constant. From the fact that , where is a constant.

10. Background and Motivations Network Capacity Analysis Proposed Power and Rate Control (PRC) Algorithm Simulation Study Conclusions

11. Determining Power Range • PRC algorithm: • A localized algorithm that enables each transmitter to adapt to the interference level that it perceives and determines its transmit power. • The transmit power is so determined that the transmitter can sustain the highest possible data rate, while keeping the adverse interference effect on the other neighboring concurrent transmissions minimal. • Transmit power range • Determine the minimum transmit power that ensures that a receiver can sustain the minimum data rate considering the worst-case where TX0 transmits with the minimum transmit power, while its six 1st tier interfering nodes transmit with the maximum power level. For this purpose, the SINR level at RX0 should satisfy

12. Determining Carrier Sense Threshold • Objective: if TX transmits with its minimum transmit power, then at RX, which is the maximum distance Rmax away from TX, the minimal data rate of r[1] can be sustained. • We determine Tcs at the transmitter to ensure the IRX requirement is satisfied at RX. However, it needs global knowledge of node distribution! → conservative but localized approach. • The most conservative scenario occurs when the distance between RX and the interferer closest to RX is minimized. This is when IRX is contributed by a single interfering node TXi using the minimum transmit power. • The minimum interference level perceived at TX:

13. Proposed Algorithm • Theoretical base • Find the maximal transmit power such that it does not deprive the other concurrent transmissions of sustaining their data rate.→ needs global knowledge!!! • Estimate the position of a hypothetical interfering node TXi based on the level of ITX under the conservative scenario. To ensure both TX and TXi can engage in transmission concurrently, • Decide the final transmit power • If the data rate r[i[ afforded by can be achieved with a smaller power level, there is no need to transmit with to mitigate the interference level of other transmissions.

14. Proposed Algorithm (cont’d) • Pseudo codes of PRC • Concurrent transmissions may commence and terminate dynamically, the interference level perceived at the receiver fluctuates with time. →Need to monitor the variation in the interference level. • Ns and Nf

15. Background and Motivations Network Capacity Analysis Analysis on Tuning Parameters Proposed Power and Rate Control (PRC) Algorithm Simulation Study Conclusions

16. Simulation Setup • Modified ns-2 Ver. 2.28 • The interference perceived at a receiver is the collective aggregate interference from all the concurrent transmissions • Each node uses physical carrier sense to determine if the medium is free • IEEE 802.11a radios supporting 8 discrete data rate (6 ~ 54 Mbps) • Random topology • 3, 10, 20, 30, and 50 transmitter-receiver pairs are randomly generated in a 300m X 300m area, and represent sparsely, moderately, and densely populated networks, respectively,. • Algorithms used for evaluations • Static • Dynamic Spatial Backoff (DSB) • Greedy Power Control (GPC) • Power and Rate Control (PRC)

17. Simulation Results • Sparse network: • Low effect of carrier sense range • PRC operates in an economical manner • Compared with Static: • Higher concurrent transmissions • Unnecessarily high transmit power can actually reduces the attainable level of spatial reuse

18. Simulation Results (cont’d) • Compared with DSB: • In spite of a smaller carrier sense range, using a lower transmit power leads to less interference, and enables more concurrent transmissions. • Comparison between DSB and Static • High carrier sense threshold, when combined with an inappropriately tuned transmit power, can actually impair the network throughput. • GPC: • Higher power level leads to the decrease in spatial reuse • Aggregate throughput approaches to that of static with the size of the network

19. Background and Motivations Related Works Network Capacity Analysis Analysis on Tuning Parameters Proposed Power and Rate Control (PRC) Algorithm Simulation Study Conclusions

20. Conclusions • Investigated the impact of spatial reuse on the network capacity • Derive the network capacity as a function of transmit power and carrier sense threshold • Proposed a localized power and rate control (PRC) algorithm • Each node can adjust transmit power and data rate dynamically based on its interference level. • A transmitter determines its transmit power so that it can sustain the highest possible data rate, while keeping the adverse interference effect on the other neighboring concurrent transmissions minimal • PRC achieves up to 22% improvement in the aggregate network throughput as compared to the DSB algorithm