1. Joint Illumination-Communication Optimization in Visible Light Communication Zhongqiang Yao, Hui Tian and Bo Fan State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications (BUPT), China Email: yaozhongqiang2019@163.com Presenter: Zhongqiang Yao

2. Outlines 2 4 Proposed Algorithm 2 System Model 5 Simulation 3 Problem Formulation

3. Outlines 3

4. Introduction 4

5. 5 Illuminance VLC VLC : Visible Light Communication Communication

6. Introduction  Motivation 6 Jointly improving the illumination uniformity and communication signal quality is a challenging problem in VLC systems. Existing works target at achieving uniform SNR or maximizing average SNR. Conventional evolutionary algorithm (EA) is generally adopted as the optimization tool in references. The goal is rendering uniform illumination and guaranteeing the minimum SNR threshold.

7. Outlines 7 2 System Model

8. 8  Indoor VLC system

9. System Model  Received Signal Power  Receiver SNR 9 (1) (2)

10. System Model  Illuminance Feature where is the horizontal illuminance of the light undergoing exactly order reflections. 10 (3)

11. Outlines 11 2 System Model 3 Problem Formulation

12. 12 (4)

13. Problem Formulation  Evenness Measure Function 13 (5)

14. Problem Formulation  Mathematical formulation UIR: The uniformity illuminance ratio (UIR) is defined as the ratio of the minimum to the average illuminance. 14 Subject to: (6)

15. Outlines 15 4 Proposed Algorithm 2 System Model 3 Problem Formulation

16. Proposed Algorithm  Harmony Annealing algorithm (HA) HA is constructed on the benchmark of improved harmony search (IHS) algorithm and simulated annealing (SA) algorithm. 16 Weak Stability is poor Advantage Searching efficiency is high IHS Weak Searching efficiency is poor Advantage A powerful global optimization SA

17. Proposed Algorithm  Algorithm Flowchart 17 HM: harmony memory, the library of solution vectors NI: the stopping criterion

18. Proposed Algorithm  IHS 18 HMCR: help find global solution vectors PAR: the fine tuning probability

19. Proposed Algorithm  Algorithm Flowchart 19

20. Outlines 20 4 Proposed Algorithm 2 System Model 5 Simulation 3 Problem Formulation

21. Simulation  Simulation Parameter 21

22. Simulation  Convergence curves of the HA, EA, IHS and SA 22 SA doesn’t arrive in a stable state IHS converges after about 3800 iterations EA’s convergence speed is 5.3% slower than IHS. HA is 50% faster than EA.

23. Simulation  Optimization Result 23  Initial illuminance distribution and power distribution

24. Simulation  Optimized using HA  Optimized using EA 24 58.53% from the peak value UIR is 0.82 56.53% from the peak value UIR is 0.77

25. Conclusion  The optimization scheme works well.  The proposed HA algorithm outperforms EA algorithm.  The universality of HA algorithm is need further verification. 25

26. 26