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Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence

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Fundamental tradeoffs in green cellular networkswith coexistence of machine-oriented and human-oriented trafficsare investigated. First, we present a queuing system to modelthe uplink transmission of a green base station which servestwo types of distinct traffics with strict requirements on delayand battery lifetime. Then, the energy-lifetime and energydelaytradeoffs are introduced, and closed-form expressions forenergy consumption of the base station, average experienceddelay in data transmission, and expected battery lifetime ofmachine devices are derived. Furthermore, we extend the derivedresults to the multi-cell scenario, and investigate the impacts ofsystem and traffic parameters on the energy-lifetime and energydelaytradeoffs using analytical and numerical results. Numericalresults show the impact of energy saving for the access network onthe introduced tradeoffs, and figure out the ways in which energycould be saved by compromising on the level of performance.

Published in: Engineering
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Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence

  1. 1. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence A Battery Lifetime-Aware Cellular Network Design Framework Amin Azari, Guowang Miao CoS Department, ICT School KTH Royal Institute of Technology December 5, 2016 Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 1 / 34
  2. 2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Outline 1 Introduction Background and Motivation Paper Focus and High-Level Research Questions State of the Art 2 Detailed Research Questions and Contributions Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings 3 Summary Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 2 / 34
  3. 3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Background and Motivation Paper Focus and High-Level Research Questions State of the Art Outline 1 Introduction Background and Motivation Paper Focus and High-Level Research Questions State of the Art 2 Detailed Research Questions and Contributions Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings 3 Summary Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 3 / 34
  4. 4. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Background and Motivation Paper Focus and High-Level Research Questions State of the Art Telecommunications Yesterday, Today, Tomorrow Internet of Things: Everything that benefits from being connected will be connected. Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 4 / 34
  5. 5. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Background and Motivation Paper Focus and High-Level Research Questions State of the Art IoT over Cellular Networks Regarding unique characteristics of cellular networks like ubiquitous coverage, cellular-based M2M will be a key enabler of IoT. In 1G to 4G: high-capacity high-throughput low-latency infrastructure, forgotten about large-scale small-data communications, forgotten about mission-critical communications. Need for evolutionary and revolutionary changes. Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 5 / 34
  6. 6. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Background and Motivation Paper Focus and High-Level Research Questions State of the Art Massive M2M Communications Main challenges in enabling Massive M2M : Scalability: up to one million simultaneous connections per square kilometera. Energy efficiency: over 10 years battery lifetime 10 times more bit-per-joule energy efficiencyb . Battery lifetime → Maintenance cost a Samsung. 5G vision. Tech. rep. 2015. b Nokia. Looking ahead to 5G. . Tech. rep. 2014. Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 6 / 34
  7. 7. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Background and Motivation Paper Focus and High-Level Research Questions State of the Art Outline 1 Introduction Background and Motivation Paper Focus and High-Level Research Questions State of the Art 2 Detailed Research Questions and Contributions Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings 3 Summary Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 7 / 34
  8. 8. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Background and Motivation Paper Focus and High-Level Research Questions State of the Art Paper Focus Paper Focus To incorporate battery lifetime-awareness into the design of 5G cellular networks High-Level Research Questions Identify deployment and operational solutions enabling serving a massive number of energy-limited devices: with minimum increase in CAPEX and OPEX, without degrading human-type users perceived QoS. Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 8 / 34
  9. 9. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Background and Motivation Paper Focus and High-Level Research Questions State of the Art Outline 1 Introduction Background and Motivation Paper Focus and High-Level Research Questions State of the Art 2 Detailed Research Questions and Contributions Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings 3 Summary Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 9 / 34
  10. 10. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Background and Motivation Paper Focus and High-Level Research Questions State of the Art State of the Art on BS Sleeping (1/2) BS sleeping and its impacts on downlink communications have been investigated. Optimal density of macro and micro BSs have been founda. BS sleeping with constraint on transmit power of users has been investigatedb. a Hina Tabassum et al. “Downlink performance of cellular systems with base station sleeping, user association, and scheduling”. In: IEEE TWC (2014), Jyri H¨am¨al¨ainen et al. “A Novel Multiobjective Cell Switch-Off Method with Low Complexity for Realistic Cellular Deployments”. In: arXiv (2015), Sheng Soh et al. “Energy efficient heterogeneous cellular networks”. In: IEEE JSAC (2013), Dongxu Cao et al. “Optimal combination of base station densities for energy-efficient two-tier heterogeneous cellular networks”. In: IEEE TWC (2013). b Jinlin Peng et al. “Stochastic analysis of optimal base station energy saving in cellular networks with sleep mode”. In: IEEE Commun. Lett. (2014). Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 10 / 34
  11. 11. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Background and Motivation Paper Focus and High-Level Research Questions State of the Art State of the Art (1/2) Summary of literature study To the best of our knowledge, accurate energy consumption, individual and network battery lifetime modeling for MTC, battery lifetime-aware deployment and operation design approaches for cellular networks, and study of tradeoffs between optimizing cellular network for: improving battery lifetime of MTC, decreasing energy/cost of access network, improving QoS of non-MTC are absent in literature. Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 11 / 34
  12. 12. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings Outline 1 Introduction Background and Motivation Paper Focus and High-Level Research Questions State of the Art 2 Detailed Research Questions and Contributions Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings 3 Summary Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 12 / 34
  13. 13. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings RQs and Contributions (1/3) Battery lifetime Assessment The initial problem faced in lifetime-aware cellular network design: → lack of a methodology to model the network battery lifetime. RQ1: How to derive a low-complexity model of individual and network battery lifetimes? Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 13 / 34
  14. 14. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings RQs and Contributions (1/3) Energy consumption → a semi-regenerative process Reg. point → end of each successful data transmission epoch. UE BS Cell Info PRACH: Random Access Request (RN, BSR, Cause, PDCCH CC) PDCCH: Uplink Assignment (RACH reference, PUSCH allocation, BS VR = 0, C- RNTI assignment) PUSCH: Data transfer (TLLI/S-TMSI, MS VS = 0, last = true, data) PDCCH: Uplink Ack (TLLI/S-TMSI, C-RNTI confirmation, BS VR=1) !"# + $!% &',( &',$ )*- (Turn radio on) (Sleep) DutyCycle ReportingPeriod (Wake up) Data gathering (Wake up) )!!. time Power Sleep Data gathering/pr ocessing Listening to eNodeB Scheduled transmission Connection establishme nt Reporting period Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 14 / 34
  15. 15. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings RQs and Contributions (1/3) Battery lifetime Assessment ∗ Expected lifetime of node i = Energy storage at time t Energy consumption per reporting period × Reporting period = Ei (t) Ei perperiod Ti , Eperpacket = Estatic + Edynamic, Edynamic = Di Ri (Pc + αPt), Estatic = K(tDRX PDRX + tsyncPsync + tactPact) + tsyncPsync, K = Number of active intervals per reporting period. * Network lifetime: Average of individual lifetimes. Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 15 / 34
  16. 16. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings Outline 1 Introduction Background and Motivation Paper Focus and High-Level Research Questions State of the Art 2 Detailed Research Questions and Contributions Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings 3 Summary Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 16 / 34
  17. 17. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings RQs and Contributions (2/3) Performance Tradeoff Analysis in Single Cell Scenario Consider a massive M2M/H2H deployment in a single-cell scenario. We are interested in coupling between optimizing BS operation for: improving battery lifetime of MTC devices, decreasing energy/cost of the access network, improving QoS of non-MTC traffic. Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 17 / 34
  18. 18. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings RQs and Contributions (2/3) Performance Tradeoff Analysis in Single Cell Scenario Consider a massive M2M/H2H deployment in a single-cell scenario. We are interested in coupling between optimizing BS operation for: improving battery lifetime of MTC devices, decreasing energy/cost of the access network, improving QoS of non-MTC traffic. RQ2: What are the tradeoffs between green and lifetime-aware cellular network design in the operation phase? What is the optimal BS sleeping strategy w.r.t. batetry lifetime of devices? Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 17 / 34
  19. 19. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings RQs and Contributions (2/3) Performance Tradeoff Analysis in Single Cell Scenario What is BS Sleeping? Imapct on uplink communications is absent in literature. Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 18 / 34
  20. 20. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings RQs and Contributions (2/3) Performance Tradeoff Analysis in Single Cell Scenario How do we model the problem (1/3): Consider uplink communication of a green BS in a single cell Massive number of deployed sensors (P2), with bounded transmit power, and need for long battery lifetime. A number of human users (P1), with non-preemptive priority over P2, require low delay. Consider ACB for MTC: Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 19 / 34
  21. 21. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings RQs and Contributions (2/3) Performance Tradeoff Analysis in Single Cell Scenario How do we model the problem (1/3): Consider uplink communication of a green BS in a single cell Massive number of deployed sensors (P2), with bounded transmit power, and need for long battery lifetime. A number of human users (P1), with non-preemptive priority over P2, require low delay. Consider ACB for MTC: For P1 devices, when the BS is busy, they are queued to be served, based on processor sharing, with non-preemtive priority. For P2 devices, when the BS is asleep or busy, P2 devices retry after a random backoff time which is exponentially distributed with rate α. When the BS is asleep, keep listening to find the BS available and send their data. Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 19 / 34
  22. 22. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings RQs and Contributions (2/3) Performance Tradeoff Analysis in Single Cell Scenario How do we model the problem (2/3): Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 20 / 34
  23. 23. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings RQs and Contributions (2/3) Performance Tradeoff Analysis in Single Cell Scenario How do we model the problem (3/3): We use M/M/1 queuing model with processor sharing service discipline Sleeping time: General distribution Listening time: Exponential distribution Uplink service requirement: Exponential distribution Power control: Channel inversion, fixed SINR requirement for H2H and M2M Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 21 / 34
  24. 24. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings RQs and Contributions (2/3) Performance Tradeoff Analysis in Single Cell Scenario Results (1/2): Derive closed-form expressions for energy consumption of the BS, experienced delay by users and machines, and expected battery lifetime of machine devices. Introduce the fundamental tradeoffs, and explore the impact of system and traffic parameters on the introduced tradeoffs. Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 22 / 34
  25. 25. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings RQs and Contributions (2/3) Performance Tradeoff Analysis in Single Cell Scenario Results (2/2): Example of derived expressions: Eb cons = ρPs + 1 − ρ 1 + µ¯v (Pl + µ¯vPsl + 2µEsw ) DP1 = ¯u1 + µP3(1)ˆv/2 + λ2 ¯u2 2 1 − ¯u1λ1 LP2 = E0T Pcατ / λ2 ∑ m E(N (m) 2 ) + [ [Pc + η ¯Pt2 ] + Est ] Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 23 / 34
  26. 26. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings Outline 1 Introduction Background and Motivation Paper Focus and High-Level Research Questions State of the Art 2 Detailed Research Questions and Contributions Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings 3 Summary Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 24 / 34
  27. 27. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings RQs and Contributions (3/3) Performance Tradeoff Analysis in Multi Cell Scenario RQ3: What are the tradeoffs between green and lifetime-aware cellular network design in the deployment phase? What is the optimal density of BSs w.r.t. batetry lifetime of devices? Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 25 / 34
  28. 28. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings RQs and Contributions (3/3) Performance Tradeoff Analysis in Multi Cell Scenario BS Sleeping in a Multi-cell Scenario: Imapct on uplink communications is absent in literature1. 1 Hina Tabassum et al. “Downlink performance of cellular systems with base station sleeping, user association, and scheduling”. In: IEEE TWC (2014). Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 26 / 34
  29. 29. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings RQs and Contributions (3/3) Performance Tradeoff Analysis in Multi Cell Scenario Results: using a similar methodology as for RQ2, the following results are derived: Given a density of BSs, we model the operation of BSs in serving mixed M2M and H2H traffic. Derive closed-form expressions for energy consumption of the BSs, experienced delay by users and machines, and expected battery lifetime of machine devices. Introduce the fundamental tradeoffs, and explore the impact of system and traffic parameters on the introduced tradeoffs. Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 27 / 34
  30. 30. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings Outline 1 Introduction Background and Motivation Paper Focus and High-Level Research Questions State of the Art 2 Detailed Research Questions and Contributions Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings 3 Summary Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 28 / 34
  31. 31. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings Simulation Results and Findings Analytical and Simulation Results Enery consumption for BS/Delay for HoC and MTC 1 10 100 1000 60 70 80 90 100 110 Energy(Joule) Econs b , simulation Econs b , analytic D2 , simulation D2 , analytic D1 , simulation D1 , analytic 0 14 28 42 56 70 Delay(sec) Mean listening time (sec) Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 29 / 34
  32. 32. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings Simulation Results and Findings Analytical and Simulation Results Enery consumption for BS/EE for MTC 100 101 102 103 Mean listening time (sec) 60 70 80 90 100 110 Energy(Joule) 0.5 1.2 1.9 2.6 3.3 4 EnergyEfficiency(bpj) ×107 Econs b for the BS Energy efiiciency for P2 devices Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 30 / 34
  33. 33. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings Simulation Results and Findings Analytical and Simulation Results Enery consumption for BS/Battery Lifetime for MTC 0 2 4 6 8 10 12 14 16 Time (× T) ×105 0 0.2 0.4 0.6 0.8 1 EmpricalCDFoflifetimes Mean lis. time=714 sec Mean lis. time=100 sec Mean lis. time=10 sec Mean lis. time=2 sec Mean lis. time==1 sec Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 31 / 34
  34. 34. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Battery lifetime Assessment Performance Tradeoff Analysis in Single Cell Scenario Performance Tradeoff Analysis in Multi Cell Scenario Simulation Results and Findings Simulation Results and Findings Analytical and Simulation Results Findings: Significant impact of the BSs’ energy saving strategies BS sleeping BS deployment density on the UEs’ battery lifetimes has been presented. Promote revisiting traditional energy saving strategies to cope with the ever increasing number of connected machine-type devices in cellular networks. Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 32 / 34
  35. 35. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Summary Providing scalable yet energy-efficient small data communications is a key requirement for realization of IoT. To realize long lasting MTC services over cellular networks, different aspects of cellular networks must be optimized. Performance tradeoffs have been explored to control the impact of MTC on existing services as well as resource allocation for MTC on MTC battery lifetime. Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 33 / 34
  36. 36. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Introduction Detailed Research Questions and Contributions Summary Summary Providing scalable yet energy-efficient small data communications is a key requirement for realization of IoT. To realize long lasting MTC services over cellular networks, different aspects of cellular networks must be optimized. Performance tradeoffs have been explored to control the impact of MTC on existing services as well as resource allocation for MTC on MTC battery lifetime. More on battery lifetime-aware network design: Licentiate Thesis: Amin Azari, Energy Efficient Machine-Type Communications over Cellular Networks, KTH University, 2016, Available Online. Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 33 / 34
  37. 37. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix Thanks and Question Questions Thanks for your attention. Questions? Amin Azari, Guowang Miao Battery Lifetime-Aware Base Station Sleeping Control with M2M/H2H Coexistence 34 / 34

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