Oliver Holland from King's College London talks about energy efficiency challenges of data volume increases and the use of sleep modes facilitated by opportunistic cognitive radio networking as a solution

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
IEEE VTS-UKRI Dublin Meeting
26 July 2012

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
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26 July 2012

3. Implications for energy consumption
• How do we maintain this same expectation?
illustration courtesy
of IEEE Spectrum
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26 July 2012

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
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SINR
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26 July 2012

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
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ICT-EARTH D2.3
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26 July 2012

6. Implications for energy consumption
• Increase density of frequency reuse
– Far smaller cells—embodied energy
smaller
cells
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26 July 2012

7. Implications for energy consumption
• Embodied energy
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26 July 2012

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 8
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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
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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
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11. Opportunistic Networking Using
Cognitive Radio to Save Energy
• Transmission of delay-tolerant traffic at a more appropriate
time based on mobility
?
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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.
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13. Opportunistic Networking Using
Cognitive Radio to Save Energy
• Network elements shutdown when p2p connectivity is
sufficient
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14. Awareness of Opportunistic
Networking Using IEEE 1900.6
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ILet’s check with things!
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But I now more! ‘R’, a
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But there’s hostedThatthere DA = Data Archive
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transmitting 1900.6‘E’ and ‘F’ all
autocorrelation function I know
device at RATs
communication capabilities
prospective link
be able device, which I
location ‘C’ looks is a RAT ‘P’, e.g.,
system. Bet This is I lot
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somewhere near use this associate in
link capabilities whichone knowledge with
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ableinformation duration between
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due to the time match of
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devicesfindnetworksconnect capable
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Let’s could
peaks. Ior ‘C’ alsolinks?
location form
autonomouslyradio” such Inetworks thatof
traffic capabilities andas am with
mobility, etc
RATs ‘E’ and ‘F’ CE/DA
Over S-S Interface (e.g., collaborative sensing scenario)
I am ‘A’ type of sensor with ‘B’ serial number
Request
My location is ‘C’
Device 1 I have detected RATs ‘D’, ‘E’ and ‘F’ at ‘G’, ‘H’, and ‘I’ frequency Device 2
(S and CE embedded) I have found ‘J’ signal autocorrelation function at ‘K’ frequency (S embedded)
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(Perhaps future addition) I have ‘L’, ‘M’, ‘N’ radio configuration capability
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26 July 2012

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)
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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)
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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
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Wi-Fi Offloading”
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26 July 2012

18. Example: Offload to Wi-Fi enabling
Cellular Power Saving Modes
• Mix of FTP, HTTP and video streaming traffic, 15%, 45% and 40% respectively
…
…
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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)
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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 20
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26 July 2012

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 21
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26 July 2012

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 22
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26 July 2012

23. Acknowledgement
• This work has been supported by the ICT-
ACROPOLIS Network of Excellence, www.ict-
acropolis.eu, FP7 project number 257626
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26 July 2012

24. Thank you!
oliver.holland@kcl.ac.uk
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26 July 2012