1. 1 On the Trade-Off between Energy and Multicast Efficiency in 802.16e-Like Mobile Networks Reuven Cohen, Liran Katzir, and Romeo Rizzi Department of Computer Science, Israel IEEE TRANSACTIONS ON MOBILE COMPUTING (IEEE TMC 2008)

2. 2 Outline  Introduction  Algorithms  Simulations  Conclusions

3. 3 Introduction  To reduce the power consumption of the mobile hosts is an important goal of the IEEE 802.16e standard  Sleep mode is negotiated between the host and the base station through the exchange MOB_SLP-REQ (from hosts) MOB_SLP-RSP (to hosts) Real-time applications require the host to return to the active mode after a short interval Non-real-time applications allow the host to stay in sleep mode much longer

4. 4 Introduction  Mobile network bandwidth is likely to be employed by push-based multicast services for the following reasons: The downlink channel is a broadcast physical (PHY) channel to which all mobile hosts can listen at the same time. The data needed by individual users will probably be location dependent.

5. 5 Introduction_ The concept of multicast superframes

6. 6 Introduction_ A multicast superframe in a TDD system BS h1h1 h2h2 Multicast Item 1 h2h2 2 times BS h1h1 Multicast Item 1 h2h2 1 times Better Solution item 1 h2h2 h1h1

7. 7 Introduction_ motivation item 1 item 2 item 3 item 4 h1h1 h2h2 item 1 item 2 item 3 item 4 h1h1 h2h2 active sleep Energy Waste (sleep and wake up or active all time) item 2 Bandwidth Waste The optimization problem is to determine what data should be transmitted in the multicast region of every frame when every host should become active

8. 8 Introduction_ goal item host Set 1 Set 2 sportnews Logical broadcast channel #1 Logical broadcast channel #2

9. 9 Introduction_ The concept of logical broadcast channels C = 2 and active ½ time

10. 10 Introduction_ A multicast superframe in a TDD system C = N and active 1/N time

11. 11 Algorithms  SMBC-S  SMBC-D SMBC-D by using a unit L 1 norm SMBC-D by using a general norm SMBC-D with variable size item  SMBC-AMC AMC-static AMC-dynamic

12. 12 Algorithms_ SMBC-S H( c ) is the set of hosts associated with logical broadcast channel c Merit attribute m(h,i) indicates the profit host h gains from receiving itemi The weighted profit of each item i in channel c Channel #1 Channel #2 Channel #3 How to assign ?

13. 13 Algorithms_ SMBC-S Channel #1 Channel #2 Channel #3 item 1 item n item 1 item n item 1 item n item 2 π( item 1,channel 1 ) = 3 / 1 = 3 π( item 2,channel 2 ) = 1 / 1 = 1...... (1) for every data item i,computer π(i,c) (2)Orfer the π(i,c) (3) Select the highest value

14. 14 Algorithms_ SMBC-D by using a unit L 1 norm How to assign ? item 1 Set S 1 item 2 item 3 V(S 1 )[i]= [ 3 0 1 ] 1 1 1 item 3 item 2 Set S 2 V(S 2 )[i]= [ 0 3 0 ] 1 1 1 The inner product of S 1 and S 2 = 0

15. 15 Algorithms_ SMBC-D by using a unit L 1 norm item 1 item 2 item 3 Problem 1: item 4 item 5 item 2 item 3 item 4 item 5 item 1 item 2 item 3 item 4 item 1 item 2 item 3 item 4 item 1 item 2 item 3 item 4 item 5 item 1 item 2 item 3 item 4 item 5 item 1 item 2 item 3 item 4 item 5 The total demand will exceed the bandwidth of a single channel Problem 2: item 1 channel Total demand Bandwidth waste channel

16. 16 Algorithms_ SMBC-D by using a unit L 1 norm item 1 item 2 item 3 item 4 item 5 h1h1 h2h2 Let c=2 h3h3 h4h4 h5h5 Repeat 5-2 times to cluster C set item 1 item 2 item 3 item 4 item 5 V(S 1 )[i]= [ 1 1 1 1 1 ] item 3 item 4 V(S 2 )[i]= [ 0 0 1 1 0 ] S 1 ={h 1 }S 2 ={h 2 } The L 1 norm (1) (3) Dividing its L1 norm (2) compute the binary demand vector v(S) V(S 1 )[i]= [ 1/5 1/5 1/5 1/5 1/5 ] V(S 2 )[i]= [ 0 0 1/2 1/2 0 ] (4) Computer inner product v(S 1,S 2 ) = v(S 1 ) T. v(S 2 ) v(S 1,S 2 ) = v(S 1 ) T. v(S 2 ) = 1/10 + 1/10 = 2/10 (5) Run algorithm SMBC-static

17. 17 Algorithms_ SMBC-D by using general norm item 1 item 2 item 3 item 4 item 5 h1h1 h2h2 Let c=2 h3h3 h4h4 h5h5 Repeat 5-2 times to cluster C set item 1 item 2 item 3 item 4 item 5 item 4 S 1 ={h 1 }S 2 ={h 2 }(1) (3) Dividing its general norm (2) compute the demand vector v(S) (4) Computer inner product v(S 1,S 2 ) = v(S 1 ) T. v(S 2 ) (5) Run algorithm SMBC-static

18. 18 Algorithms_ SMBC-D with variable size item item 1 item 2 item 3 item 4 item 5 h1h1 h2h2 Let c=2 h3h3 h4h4 h5h5 item 1 item 2 item 3 item 4 item 5 item 4 S 1 ={h 1 }S 2 ={h 2 }(1) (2) compute the demand vector v(S) (4) Computer inner product v(S 1,S 2 ) = v(S 1 ) T. v(S 2 ) The L 1 norm (3) Dividing its L1 norm V(S 1 )[i]= [ 1 2 3 4 5 ] V(S 2 )[i]= [ 0 0 3 4 0 ] V(S 1 )[i]= [ 1/15 1/15 1/15 1/15 1/15 ] V(S 2 )[i]= [ 0 0 1/7 1/7 0 ] item 3 (5) Run algorithm SMBC-static Repeat 5-2 times to cluster C set

19. 19 Algorithms_ SMBC-AMC Channel #1 16 QAM Channel #2 QPSK How to assign ? Assume half of hosts receive data using QPSK half of hosts receive data using 16 QAM The number of slots in a 16-QAM broadcast logical channel is two times the number of slots in a QPSK channel

20. 20 Algorithms_ AMC-static item 1 item n item 1 item n item 1 item n item 2 π( item 1,channel 1 ) = 3 / 1 = 3 π( item 2,channel 2 ) = 1 / 1 = 1...... (1) for every data item i,computer π(i,c) (2)Orfer the π(i,c) (3) Select the highest value Channel #1 16 QAM Channel #2 QPSK Channel #3 16 QAM

21. 21 Algorithms_ AMC-dynamic with p=0.5 item 1 item 2 item 3 item 4 item 5 h1h1 h2h2 Let c=2 h3h3 h4h4 h5h5 item 1 item 2 item 3 item 4 item 5 item 4 S 1 ={h 1 }S 2 ={h 2 }(1) (3) Dividing its general norm (2) compute the demand vector v(S) (4) Computer inner product v(S 1,S 2 ) = v(S 1 ) T. v(S 2 ) 16 QAMQPSK QAM (5) Run algorithm SMBC-static Repeat 5-2 times to cluster C set

22. 22 Simulation_ parameters  H = 64 hosts  I = 2 16 potential fixed-size data items  The distribution of requests is uniformm m( h, i ) =1 with probability 0.022  For the dynamic model,Algorithm is used with a norm of p=0.5  The number of slots in a 16- QAM is two times the number of slots in a QPSK channel

23. 23 Simulations 2 10 QPSK slots in a superframe 2 16 QPSK slots in a superframe

24. 24 Simulations_ Zipf distribution The distribution of visitors follows a universal power law The number of items is 2 8 The number of items is 2 12

25. 25 Simulations_ dynamic algorithm versus the number of channels for different norms Uniform-distribution fix-size items Zipf-distribution variable-size items

26. 26 Simulations

27. 27 Conclusions  The trade-off between energy efficiency and throughput is addressed for multicast services in mobile networks  Three different models for the association between hosts and channels is presented  AMC model performs significantly better than the other two models

28. 28 Thank You~