Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
110 tokens/sec
GPT-4o
56 tokens/sec
Gemini 2.5 Pro Pro
44 tokens/sec
o3 Pro
6 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Densify & Conquer: Densified, smaller base-stations can conquer the increasing carbon footprint problem in nextG wireless (2403.13611v1)

Published 20 Mar 2024 in cs.NI and eess.SP

Abstract: Connectivity on-the-go has been one of the most impressive technological achievements in the 2010s decade. However, multiple studies show that this has come at an expense of increased carbon footprint, that also rivals the entire aviation sector's carbon footprint. The two major contributors of this increased footprint are (a) smartphone batteries which affect the embodied footprint and (b) base-stations that occupy ever-increasing energy footprint to provide the last mile wireless connectivity to smartphones. The root-cause of both these turn out to be the same, which is communicating over the last-mile lossy wireless medium. We show in this paper, titled DensQuer, how base-station densification, which is to replace a single larger base-station with multiple smaller ones, reduces the effect of the last-mile wireless, and in effect conquers both these adverse sources of increased carbon footprint. Backed by a open-source ray-tracing computation framework (Sionna), we show how a strategic densification strategy can minimize the number of required smaller base-stations to practically achievable numbers, which lead to about 3x power-savings in the base-station network. Also, DensQuer is able to also reduce the required deployment height of base-stations to as low as 15m, that makes the smaller cells easily deployable on trees/street poles instead of requiring a dedicated tower. Further, by utilizing newly introduced hardware power rails in Google Pixel 7a and above phones, we also show that this strategic densified network leads to reduction in mobile transmit power by 10-15 dB, leading to about 3x reduction in total cellular power consumption, and about 50% increase in smartphone battery life when it communicates data via the cellular network.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (64)
  1. Energy-efficient 5g for a greener future. Nature Electronics, 3(4):182–184, 2020.
  2. Understanding operational 5g: A first measurement study on its coverage, performance and energy consumption. In Proceedings of the Annual conference of the ACM Special Interest Group on Data Communication on the applications, technologies, architectures, and protocols for computer communication, pages 479–494, 2020.
  3. A technical look at 5g energy consumption and performance. https://www.ericsson.com/en/blog/2019/9/energy-consumption-5g-nr.
  4. Cradle to the grave: Sustainability and the life of a base station. https://www.azocleantech.com/article.aspx?ArticleID=1108.
  5. Nicole Robertson. How to cut carbon emissions in telecoms networks. https://www.rcrwireless.com/20220404/opinion/readerforum/how-to-cut-carbon-emissions-in-telecoms-networks-reader-forum.
  6. How to estimate carbon emissions in mobile networks: a streamlined approach. https://www.ericsson.com/en/blog/2021/5/how-to-estimate-carbon-emissions-from-mobile-networks.
  7. The wireless communications industry and its carbon footprint. https://www.azocleantech.com/article.aspx?ArticleID=1131.
  8. The case for committing to greener telecom networks (mckinsey report). https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/the-case-for-committing-to-greener-telecom-networks.
  9. Search for sustainable goods grows by 71% as ‘eco-wakening’ grips the globe. https://www.worldwildlife.org/press-releases/search-for-sustainable-goods-grows-by-71-as-eco-wakening-grips-the-globe.
  10. Designing wireless broadband access for energy efficiency: Are small cells the only answer? In 2015 IEEE International Conference on Communication Workshop (ICCW), pages 136–141. IEEE, 2015.
  11. Multiple smaller base stations are greener than a single powerful one: Densification of wireless cellular networks. In 1st Workshop on Sustainable Computer Systems Design and Implementation (HotCarbon), 2022.
  12. How much energy is needed to run a wireless network? IEEE wireless communications, 18(5):40–49, 2011.
  13. Dense subset sum may be the hardest. arXiv preprint arXiv:1508.06019, 2015.
  14. Deploying dense networks for maximal energy efficiency: Small cells meet massive mimo. IEEE Journal on Selected Areas in Communications, 34(4):832–847, 2016.
  15. Sionna rt: Differentiable ray tracing for radio propagation modeling. arXiv preprint arXiv:2303.11103, 2023.
  16. Energy efficiency aspects of base station deployment strategies for cellular networks. In 2009 IEEE 70th vehicular technology conference fall, pages 1–5. IEEE, 2009.
  17. Performance analysis of ultra-dense networks with regularly deployed base stations. IEEE Transactions on Wireless Communications, 19(5):3530–3545, 2020.
  18. Modelling multi-operator base station deployment patterns in cellular networks. IEEE Transactions on Mobile Computing, 15(12):3087–3099, 2015.
  19. User performance in a 5g multi-connectivity ultra-dense network city scenario. In 2020 IEEE 45th Conference on Local Computer Networks (LCN), pages 195–203. IEEE, 2020.
  20. Improving energy efficiency of femtocell base stations via user activity detection. In 2010 IEEE wireless communication and networking conference, pages 1–5. IEEE, 2010.
  21. Green cellular networks: A survey, some research issues and challenges. IEEE Communications surveys & tutorials, 13(4):524–540, 2011.
  22. A close look at 5g in the wild: Unrealized potentials and implications. In IEEE International Conference on Computer Communications (INFOCOM’23), 2023.
  23. A comprehensive analysis of the coverage and performance of 4g and 5g deployments. Computer Networks, 237:110060, 2023.
  24. A variegated look at 5g in the wild: performance, power, and qoe implications. In Proceedings of the 2021 ACM SIGCOMM 2021 Conference, pages 610–625, 2021.
  25. Baicells indoor cell has rx sensitivity. https://na.baicells.com/product/Details?id=5b8fd6dc-d2d4-491a-ae90-13c7b81d4a62#file.
  26. Baicells indoor cell has rx sensitivity. https://na.baicells.com/product/Details?id=17aaad9c-190a-4acc-bb91-1d5695e01167#parameter.
  27. Propagation path loss models for 5g urban micro-and macro-cellular scenarios. In 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring), pages 1–6. IEEE, 2016.
  28. Performance impact of los and nlos transmissions in dense cellular networks. IEEE Transactions on Wireless Communications, 15(3):2365–2380, 2015.
  29. Open street map software. https://www.openstreetmap.org.
  30. Blender software. https://www.blender.org/.
  31. Noga Alon. Subset sums. Journal of Number Theory, 27(2):196–205, 1987.
  32. The subset sum game. European Journal of Operational Research, 233(3):539–549, 2014.
  33. Faster space-efficient algorithms for subset sum, k-sum, and related problems. SIAM Journal on Computing, 47(5):1755–1777, 2018.
  34. Artificial intelligence a modern approach. London, 2010.
  35. A smart hill-climbing algorithm for application server configuration. In Proceedings of the 13th international conference on World Wide Web, pages 287–296, 2004.
  36. Analyzing the performance of generalized hill climbing algorithms. Journal of Heuristics, 10:387–405, 2004.
  37. Comparison of propagation models accuracy for wimax on 3.5 ghz. In 2007 14th IEEE international conference on electronics, circuits and systems, pages 111–114. IEEE, 2007.
  38. lte discovery android app. https://play.google.com/store/apps/details?id=net.simplyadvanced.ltediscovery&hl=en˙US&gl=US&pli=1.
  39. Energy consumption in wired and wireless access networks. IEEE Communications Magazine, 49(6):70–77, 2011.
  40. Pixel7a phone. https://store.google.com/us/product/pixel˙7a?pli=1&hl=en-US.
  41. signal hound bb60d. https://signalhound.com/products/bb60d-6-ghz-real-time-spectrum-analyzer/.
  42. power profiler tool android. https://developer.android.com/studio/profile/power-profiler.
  43. termux software. https://termux.dev/en/.
  44. perfetto python package. https://perfetto.dev/.
  45. Greenmo: Enabling virtualized, sustainable massive mimo with a single rf chain. In Proceedings of the 29th Annual International Conference on Mobile Computing and Networking, pages 1–17, 2023.
  46. Keysight wavejudge. https://www.keysight.com/us/en/products/wireless-analyzers/wavejudge-wireless-analyzer-solutions.html.
  47. Telecomtm: A fine-grained and ubiquitous traffic monitoring system using pre-existing telecommunication fiber-optic cables as sensors. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 7(2), jun 2023.
  48. mmwave ieee 802.11 ay for 5g fixed wireless access. IEEE Wireless Communications, 27(2):88–95, 2020.
  49. Mmwave physical layer network modeling and planning for fixed wireless access applications. Sensors, 23(4):2280, 2023.
  50. Geo2sigmap: High-fidelity rf signal mapping using geographic databases. arXiv preprint arXiv:2312.14303, 2023.
  51. Calibrating wireless ray tracing for digital twinning using local phase error estimates. arXiv preprint arXiv:2312.12625, 2023.
  52. 5g ultra-dense cellular networks. IEEE Wireless Communications, 23(1):72–79, 2016.
  53. Cellular mobile network densification utilizing micro base stations. In 2010 IEEE International Conference on Communications, pages 1–6. IEEE, 2010.
  54. Performance impact of los and nlos transmissions in dense cellular networks under rician fading. arXiv preprint arXiv:1610.09256, 2016.
  55. Enabling small cell deployment with hetnet. IEEE Wireless Communications, 19(2):4–5, 2012.
  56. Green small-cell networks. IEEE Vehicular Technology Magazine, 6(1):37–43, 2011.
  57. Optimizing cell size in pico-cell networks. In 2009 7th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks, pages 1–9. IEEE, 2009.
  58. Asymptotic behavior of ultra-dense cellular networks and its economic impact. In 2014 IEEE Global Communications Conference, pages 4941–4946. IEEE, 2014.
  59. Towards energy efficiency in ultra dense networks. In 2016 IEEE 35th International Performance Computing and Communications Conference (IPCCC), pages 1–8. IEEE, 2016.
  60. Maximum transmit power for ue in an lte small cell uplink. Electronics, 8(7):796, 2019.
  61. Base station on-off switching in 5g wireless networks: Approaches and challenges. IEEE Wireless Communications, 24(4):46–54, 2017.
  62. Energy-efficient base-stations sleep-mode techniques in green cellular networks: A survey. IEEE communications surveys & tutorials, 17(2):803–826, 2015.
  63. An overview of massive mimo: Benefits and challenges. IEEE journal of selected topics in signal processing, 8(5):742–758, 2014.
  64. Energy efficiency in massive mimo-based 5g networks: Opportunities and challenges. IEEE Wireless Communications, 24(3):86–94, 2017.
User Edit Pencil Streamline Icon: https://streamlinehq.com
Authors (4)
  1. Agrim Gupta (26 papers)
  2. Adel Heidari (2 papers)
  3. Jiaming Jin (2 papers)
  4. Dinesh Bharadia (33 papers)

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now