A dynamic scheduling mechanism in cellular networks Qiuyang Tang Supervisor : Prof. Riku Jäntti Instructor : Zhonghong Ou Aalto University Department of Communications and Networking

Outline Introduction Background System model and assumptions Scheduling method Numerical study Conclusions

Introduction Increasingly more energy-hungry applications have been used by mobile users. Energy consumption of mobile handsets had increasingly become a concern for various parties. Signal strength plays an important role in energy consumption of mobile devices in cellular network. In this thesis, a dynamic scheduling mechanism is presented to optimize energy efficiency of mobile devices in HSDPA and LTE networks.

Background Figure 1: UMTS system architecture(Harri&Antti 2010)

Background Figure 2: Basic LTE system architecture(Harri&Antti 2011)

System model and assumptions HSDPA simulation topology and link characteristics

System model and assumptions LTE simulation topology

System model and assumptions The propagation model includes distance loss and fast fading models. The distance loss model is described by Okumura-Hata model for medium cities: where The fast fading model is based on Jakes model for Rayleigh fading.

System model and assumptions The energy consumption model contains transmission energy model and tail energy model. Downlink transmission energy model: Uplink transmission energy model: Tail energy model: tail-time = 6 seconds, tail-power=620 mW

Scheduling method The basic idea of the scheduling mechanism is that the transmission is suspended if the received signal strength is below the predefined signal strength threshold.

Scheduling method- signal smoothing In order to remove the signal strength variations due to fast fading, the LOWESS signal smoothing algorithm is applied along with the scheduler.

Numerical study-HSDPA Impact of signal strength Simulation tool: NS-2. Simulation time: 10 seconds Cell radius: 1000 m

Numerical study-HSDPA Simulation tool: NS-2. Simulation time: 600 seconds X 3 iterations UE mobility: Random waypoint model with speed from 5 m/s to 30 m/s Number of UEs: [20, 200] Received power threshold: -89 dBm

Numerical study-HSDPA Opportunistic transmission: data transmission is triggered only when the signal strength is larger than the threshold value. Continuous transmission: data transmission is triggered independent of the signal strength Average UE throughput

Numerical study-HSDPA Instant throughput CDF Number of UEs: 200

Numerical study-HSDPA Energy consumption for downloading a 100 MB file Number of UEs: 200 Average energy saving: 62.35%

Numerical study-LTE Impact of signal strength Simulation tool: NS-2. Simulation time: 10 seconds Cell radius: 1000 m

Numerical study-LTE Simulation tool: NS-3. Simulation time: 600 seconds X 3 iterations UE mobility: Random waypoint model with speed from 5 m/s to 30 m/s Received power threshold: -89 dBm Number of cells: 1 Number of UEs: 50 Downlink instant throughput CDF Uplink instant throughput CDF

Numerical study-LTE Average energy saving

Numerical study-LTE LOWESS signal smoothing algorithm

Numerical study -LTE Energy consumption comparison using signal smoothing Average downlink energy consumption Average uplink energy consumption

Conclusion Signal strength has significant impact on throughput and energy consumption. The dynamic scheduling mechanism works better when the number of users is relatively high. Average energy saving for HSDPA is 62.35%. Average energy saving for LTE downlink is 25% and for uplink is 27%. Signal smoothing algorithm reduces 15%-25% energy consumption.

Thank you!