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Bounds On QoS - Constrained Energy Savings in Cellular Access Networks with Sleep Modes

Bounds On QoS - Constrained Energy Savings in Cellular Access Networks with Sleep Modes. - Sushant Bhardwaj. Part I (a)- Objectives .

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Bounds On QoS - Constrained Energy Savings in Cellular Access Networks with Sleep Modes

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  1. Bounds On QoS- Constrained Energy Savings in Cellular Access Networks with Sleep Modes -SushantBhardwaj

  2. Part I (a)- Objectives • The objective is to obtain a characterization of the energy savings that can be achieved by sleep mode schemes under fixed user performance constraints, and study the impact of base station topology, power consumption model, and user density on energy optimal configuration of access networks.

  3. Part I (b)- Results • Demonstrated that substantial energy savings is possible through schemes that adapt density of base stations to fluctuations in user density. • Showed that system level techniques are essential even if base stations become more energy proportional in the future.

  4. Part II – Outline of the paper • First ,the model for the distribution of users and of base stations is presented • Next, average per-bit delay and variance of the per bit delay are calculated. • After getting the results for the per bit delay, compute the energy optimal base station density for a given user density and from this estimate the achievable energy savings.

  5. Then, present the lower bounds on the base station densities required to satisfy the performance constraints. • Finally, presented the numerical and simulation results.

  6. Part III – Detailed explanation • MODEL AND ASSUMPTIONS: There are three layouts for the base station distribution in the service area: • Manhattan layout • Hexagonal layout • Poisson layout In Manhattan and Hexagonal layout the switching on and off of base station is not independent as is in the case of the Poisson layout.

  7. Performance metric used: Per-bit delay • Performance constraint used: If, Per-bit delay< Set threshold value, then, Users have satisfactory performance and corresponding BS distribution is possible. • The performance constraint used is the average per-bit delay and not its variance.

  8. MODELS: • Channel and Service Model: • Network serves only best effort traffic. • Capacity(bits/Hz) is divided among all the connected best effort users. • Energy Consumption model: • Power consumed by a BS=k1+k2.U • This is further subdivided into two types of energy models: • Energy consumption is accounted just by staying on i.e. k2=0. • Energy-proportional model where k2 not 0.

  9. MODELING USER PERCEIVED PERFORMANCE:

  10. Variance of the per-bit delay:

  11. OPTIMIZING BASE STATION ENERGY CONSUMPTION: Two cases: • For energy model with k2=0 • For energy model with k1<<k2

  12. NUMERICAL EVALUATION: • On-Off setting (for current BS): • k1=1500W, k2=0 i.e. energy consumption does not vary with utilization. • Energy propotional setting: • EP=k2/(k1+k2) • For, k1=100W and k2=1400W, EP=93.4% • For k1=500W and k2=1000W, EP=66.6%

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