Energy Efficiency Challenges of Data Volume Increases and Sleep Modes Enabled by Cognitive Radio Networking

1. Energy Efficiency Challenges of Data Volume Increases, and the use of Sleep Modes facilitated by Opportunistic Cognitive Radio Networking as a Solution Oliver Holland King’s College London, UK

2. Overview • Energy consumption Implications of data volume increases • Opportunistic networking using cognitive radio to facilitate sleep modes for radio network equipment • Scenarios • Example mechanism facilitating awareness • Some example results • Conclusion and future considerations

3. Implications for energy consumption • How do we maintain this same expectation? illustration courtesy of IEEE Spectrum

4. Implications for energy consumption • Three ways to increase capacity (with fixed spectrum) • Achieve better link performance (closer to Shannon limit) • Increase Tx power • Increase density of frequency reuse Capacity SINR

5. Implications for energy consumption • Increase density of frequency reuse • Far smaller cells • Lower power per cell consumption and better able to take advantage of environment (e.g., propagation), BUT • Latent energy consumption an issue; still very low Tx-to-input power efficiency ICT-EARTH D2.3

6. Implications for energy consumption • Increase density of frequency reuse • Far smaller cells—embodied energy smaller cells

7. Implications for energy consumption • Embodied energy

8. Opportunistic Networking Using Cognitive Radio to Save Energy  • So what can we do? • Opportunistic cognitive radio connectivity/networking • To minimise number of network elements that are active at any one point in time through facilitating sleep modes • To minimise the number that are deployed in first place • Achieved by awareness through cognitive radio of what is deployed and available (connectivity options) • Awareness/prediction through cognitive radio of what has happened and will happen in the future (user mobility affecting availability of connectivity options, traffic variations, traffic requirements, etc.) • Planning for connectivity options based on all this awareness

9. Opportunistic Networking Using Cognitive Radio to Save Energy ? • Opportunistic peer-to-peer to reduce necessary transmission power and number of transmissions, given awareness of the end-node being in the vicinity and with a good channel

10. Opportunistic Networking Using Cognitive Radio to Save Energy ? • Opportunistic usage of a more power efficient or better channel connectivity means given awareness of the connectivity means existing

11. Opportunistic Networking Using Cognitive Radio to Save Energy • Transmission of delay-tolerant traffic at a more appropriate time based on mobility ?

12. Opportunistic Networking Using Cognitive Radio to Save Energy • “Store-carry-forward” for delay-tolerant traffic; facilitating the powering down of network elements (e.g., reducing necessary cell density) by transmitting at a more appropriate time.

13. Opportunistic Networking Using Cognitive Radio to Save Energy • Network elements shutdown when p2p connectivity is sufficient

14. Awareness of Opportunistic Networking Using IEEE 1900.6 Now I know lots of things! I can connect with ‘Q’ network at location ‘R’, ‘S’ network at location ‘T’, ‘U’ device at location ‘V’. I know all the RATs and link capabilities which I can associate with at given locations, and can match that to my expected future traffic capabilities and mobility, etc I can even have a fair idea of cognitive radio ad-hoc networking possibilities (e.g., routes and prospective link capabilities over multiple hops) and use this knowledge in collaboration with other devices to autonomously form such networks Great! If his serial is ‘B’ then he is hosted by ‘O’ type of device, which I can connect to! This is one connection option at location ‘C’ I wonder which devices are in the area that I might be able to communicate with through the opportunistic formation of “cognitive radio” links? Let’s check with IEEE 1900.6, the communication subsystem of which I am connected to… S = Sensor CE = Cognitive Engine DA = Data Archive But wait! There is also a DA in this 1900.6 system. Bet there is a lot of information there! Let’s find out But there’s more! That autocorrelation function ‘J’ found at location ‘C’ looks like RAT ‘P’, e.g., due to the time duration between its peaks. I could also connect with that Also, I now know that there are devices transmitting RATs ‘E’ and ‘F’ somewhere near location ‘C’, and I am able to communicate with those devices or networks as I am capable of RATs ‘E’ and ‘F’ CE/DA Over CE-CE/DA Interface Request Information from a zillion other sensors hosted on devices/networks Over S-S Interface (e.g., collaborative sensing scenario) I am ‘A’ type of sensor with ‘B’ serial number My location is ‘C’ I have detected RATs ‘D’, ‘E’ and ‘F’ at ‘G’, ‘H’, and ‘I’ frequency I have found ‘J’ signal autocorrelation function at ‘K’ frequency (Perhaps future addition) I have ‘L’, ‘M’, ‘N’ radio configuration capability Request Device 1 (S and CE embedded) Device 2 (S embedded)

15. Example: Offload to Wi-Fi enabling Cellular Power Saving Modes • Opportunistic usage of Wi-Fi access points (including in TV white space!) to enable power saving modes for cellular network equipment (powering down cells where possible and sectorization switching—20% Wi-Fi access point deployment)

16. Example: Offload to Wi-Fi enabling Cellular Power Saving Modes • Opportunistic usage of Wi-Fi access points (including in TV white space!) to enable power saving modes for cellular network equipment (powering down cells where possible and sectorization switching—5% Wi-Fi access point deployment)

17. Example: Offload to Wi-Fi enabling Cellular Power Saving Modes • Results on previous slides obtained through simulations using following coverage analyses as basis: S. Kawade and M. Nekovee, “Broadband wireless delivery using an inside-out TV white space network architecture,” IEEE Globecom 2011 • Further detail can be obtained in A. Aijaz, O. Holland, P. Pangalos, H. Aghvami, H. Bogucka, “Energy Savings for Mobile Communication Networks through Dynamic Spectrum and Traffic Load Management,” to appear in Green Communications: Theoretical Fundamentals, Algorithms and Applications, CRC Press, 2012 • Further related work has been presented in ICC 2012: A. Aijaz, O. Holland, P. Pangalos, and H. Aghvami, “Energy Savings for Cellular Access Network through Wi-Fi Offloading”

18. Example: Offload to Wi-Fi enabling Cellular Power Saving Modes … • Mix of FTP, HTTP and video streaming traffic, 15%, 45% and 40% respectively …

19. Example: Offload to Wi-Fi enabling Cellular Power Saving Modes • Opportunistic reallocation between frequency bands/networks to enable power saving modes (base station powering down and sectorization switching) • Can also extend to network-side reconfiguration decisions (power consumption model similar to macro case on slide 5)

20. Example: Offload to Wi-Fi enabling Cellular Power Saving Modes • Using cognition on the network side (fuzzy cognitive maps) to learn about traffic variations on make decisions on power saving modes • Cumulative energy consumption and blocking rate

21. Conclusion • Big energy consumption issues caused by data volume increases • Capacity provision ultimately will require greater frequency reuse and smaller cells (under assumption of the same spectrum) • Presents energy issues, both operational and embodied • Presented opportunistic cognitive radio networking as a means to save energy by facilitating power saving modes • Discussed various scenarios in which such solutions might apply • Shown performance examples indicating very significant savings • Future prospects • “Green communications” research has to consider from-the-socket power rather than just minimising transmission power (is beginning to happen to some extent) as well as embodied energy (hardly considered thus far) • Solution such as presented here help address/consider both such issues

22. References [1] O. Holland, T. Dodgson, A. H. Aghvami., and H. Bogucka, “Intra-Operator Dynamic Spectrum Management for Energy Efficiency,” IEEE Communications Magazine, to appear [2] O. Holland, O. Cabral, F. Velez, A. Aijaz, P. Pangalos and A. H. Aghvami, “Opportunistic Load and Spectrum Management for Mobile Communications Energy Efficiency,” IEEE PIMRC 2011, Toronto, Canada, Sept. 2011 [3] O. Holland, C. Facchini, A. H. Aghvami, O. Cabral, and F. Velez, “Opportunistic Spectrum and Load Management for Green Radio,” chapter appearing in: E. Hossein, V. Bhargava, G. Fettweis, 2011, Green Radio Communication Networks, Cambridge University Press, 2011 [4] O. Holland, Vasilis Friderikos, A. H. Aghvami, “Green Spectrum Management for Mobile Operators,” IEEE Globecom, Miami, FL, USA, December 2010 [5] O. Holland et al., “Intra-Operator Spectrum Sharing Concepts for Energy Efficiency and Throughput Enhancement,” CogART 2010, Rome, Italy, November 2010 (invited paper) [6] A. Aijaz, O. Holland, P. Pangalos, A.H. Aghvami, “Energy Savings for Cellular Access Network through Wi-Fi Offloading,” IEEE ICC 2012, Ottawa, ON, Canada, June 2012 [7] A. Aijaz, O. Holland, P. Pangalos, H. Aghvami, H. Bogucka, “Energy Savings for Mobile Communication Networks through Dynamic Spectrum and Traffic Load Management,” appearing in Green Communications: Theoretical Fundamentals, Algorithms, and Applications, Auerbach Publications, CRC Press, Taylor & Francis Group [8] C. Facchini, O. Holland, F. Granelli, N. Fonseca, A. H. Aghvami, “Dynamic Green Self-Configuration of 3G Base Stations using Fuzzy Cognitive Maps,” submitted to Elsevier Computer Networks

23. Acknowledgement • This work has been supported by the ICT-ACROPOLIS Network of Excellence, www.ict-acropolis.eu, FP7 project number 257626

24. Thank you! oliver.holland@kcl.ac.uk