# Successive Interference Cancellation: A Back of the Envelope Perspective Souvik Sen, Naveen Santhapuri, Romit Roy Choudhury, Srihari Nelakuditi.

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Successive Interference Cancellation: A Back of the Envelope Perspective Souvik Sen, Naveen Santhapuri, Romit Roy Choudhury, Srihari Nelakuditi

2 Simple Case of Wireless Transmission Decoding successful if: AP Signal Noise SNR = T1 > Threshold =

3 Interferer What if parallel transmissions? T1 AP T2 Decoding successful only if: Signal Interference + Noise SINR = > Threshold =

4 Collision Interferer T1 AP T2 Decoding fails when: Signal Interference + Noise SINR = < Threshold =

5 Successive Interference Cancellation Interferer T1 AP T2 1. Decode strongest signal first 1. Decode strongest signal first

6 Successive Interference Cancellation Interferer T1 AP T2 1. Decode strongest signal first 1. Decode strongest signal first 2. Model and subtract 2. Model and subtract

7 Successive Interference Cancellation Interferer T1 AP T2 3. Normal Decode 3. Normal Decode 1. Decode strongest signal first 1. Decode strongest signal first 2. Model and subtract 2. Model and subtract It is as if SIC can “uncollide” signals, resulting in two successful transmissions It is as if SIC can “uncollide” signals, resulting in two successful transmissions

8 Capacity with SIC SNR = R blue = S blue noise log 1 + SINR = R* green = S green S blue + noise log 1+ T1 T2 Interferer AP R SIC = S blue + S green noise log 1+ Green bit rate has to be far less Blue bit rate remains same Strong signal penalized, weak signal gets all the benefits

9 Channel Capacity w/o SIC SNR = R blue = S blue noise log 1 + T1 T2 Interferer AP SNR = S green R green = noise log 1 + R SIC = S blue + S green noise log 1+ R woSIC = max( R blue, R green ) Gain sic =

10 SIC Capacity Gain

11 SIC PHY Capacity Gain Max SIC capacity gain when equal signal strengths

We were tempted to schedule packet transmissions of similar signal strengths... As MAC protocol designers... Our interpretation was that... maximizing SIC capacity will maximize throughput

13 SIC: A Packet Perspective MAC Layer throughput can actually suffer T1 T2 Interferer AP HOLE Stronger green packet has to be at low rate Weaker blue packet can be at a high rate Packet Transmission Time Rate

14 Mathematically... T1 T2 Interferer AP Time SIC = L R blue L R* green max, = Transmission Time Time woSIC = L R blue L R green + = Transmission Time

15 Mathematically... T1 T2 Interferer AP Gain SIC = Time SIC = L R blue L R* green max, = Transmission Time Time woSIC = L R blue L R green + = Transmission Time

16 SIC Throughput Gain

17 SIC Throughput Gain Max throughput gain when signal strengths are 2:1

18 Capacity Vs. Throughput We expected:  Maximizing SIC capacity will immediately maximize throughput Reality:  Equal signal strengths maximize capacity  Disparate signal strengths (2:1) maximize throughput Capacity

by reducing size of the hole? Can’t we improve MAC layer throughput with SIC Certainly possible: 1. Power control 2. Scheduling 3. Multirate packetization 4. Packet packing

by reducing size of the hole? Can’t we improve MAC layer throughput with SIC Certainly possible: 1. Power control 2. Scheduling 3. Multirate packetization 4. Packet packing But at what cost?

We study SIC enabled throughput in two scenarios 1. Common receiver 2. Distinct receivers

We begin with 1. Common receiver

23 (1) Power Control Reduce power of blue Tx such that SINR* green = R green R blue = 2 * Reduce

24 (2) Client Pairing T2 T3 T4 T1 T1, T2 T3, T4

25 (2) Client Pairing T2 T3 T4 T1 T1, T3 T2, T4

26 (3) MultiRate Packetization Multirate Packetization  Send the strong packet at high rate after weak packet has finished R* green R blue R green R blue

27 (4) Packet Packing Packet Packing  Send multiple packets to fill up the hole  Hard because stronger signal modeling becomes difficult R* green R blue

28 Monte Carlo Simulations SIC Rate Power Control Packing

29 Considerable Improvement with Adaptation Monte Carlo Simulations SIC Rate Power Control Packing

Main Concern: Bit Rate of T1  R1 is optimal R2 has to decode T1’s signal at this bit rate Despite the presence of T2’s signal T1 R2 T2 R1

Gains available when several topological constraints hold: T1 R2 T2 R1 How often do these SIC permissible topologies occur?

33 Gain with SIC in less than 10% of the cases Monte Carlo Simulations (AP Transmit Range)

34 Not many topologies support SIC … thus limited scope for protocols Not many topologies support SIC … thus limited scope for protocols Does MAC Adaptation Help?

Implication on Network Architectures?

T1 R2 T2 R1 Enterprise WLANs: Clients likely to associate with stronger AP Such scenarios unlikely Enterprise WLANs: Clients likely to associate with stronger AP Such scenarios unlikely Residential WLANs: Neighbors AP may be stronger Some SIC scenarios possible Residential WLANs: Neighbors AP may be stronger Some SIC scenarios possible

37 Conclusion Successive Interference Cancellation  A PHY layer capability to “uncollide” transmissions Throughput gain not immediate from SIC  Permissible bit rates impact the length of packet transmission times  Creates under-utilization of the channel Protocol adaptations possible to cope with problem  Some gains available for common receiver scenarios  However, limited gains for networks with distinct receivers

38 Take Away Message: SIC aware protocol design fraught with pitfalls … Consider doing a back-of-the-envelope calculation before plunging into system design SIC aware protocol design fraught with pitfalls … Consider doing a back-of-the-envelope calculation before plunging into system design

Questions, comments? Thank you Duke SyNRG Research Group http://synrg.ee.duke.edu Thank You

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