1. Self-Organized Resource Allocation in LTE Systems with Weighted Proportional Fairness I-Hong Hou and Chung Shue Chen

2. Motivation 4G LTE networks are being deployed With the exponentially increasing number of devices and traffic, centralized control and resource management becomes too costly A protocol for self-organizing LTE systems is needed

3. Challenges LTE employs OFDMA Link gains can vary from subcarriers to subcarriers due to frequency-selective fading Need to consider interference between links A protocol needs to achieve both high performance and fairness

4. Our Contributions Propose a model that considers all the challenges in self-organizing LTE networks Identify three important components Propose solutions for these components that aim to achieve weighted proportional fairness

5. Outline System Model and Problem Formulation An algorithm for Packet Scheduling A Heuristic for Power Control A Selfish Strategy for Client Association Simulation Results Conclusion

6. System Model A system with a number of base stations and mobile clients that operate in a number of resource blocks A typical LTE system consists of about 1000 resource blocks Each client is associated with one base station

7. Channel Model G i,m,z := the channel gain between client i and base station m on resource block z G i,m,z varies with z, so frequency-selective fading is considered

8. Channel Model Suppose base station m allocates P m,z power on resource block z Received power at i is G i,m,z P m,z The power can be either signal or interference SINR of i on z can be hence computed as Signal Interference

9. Channel Model H i,m,z := data rate of i when m serves it on z H i,m,z depends on SINR Base station m can serve i on any number of resource blocks ø i,m,z := proportion of time that m serves i on z Throughput of i :

10. Problem Formulation Goal: Achieve weighted proportional fairness Max ( w i := weight of client) Choose suitable ø i,m,z (Scheduling) Choose P m,z (Power Control) Each client is associated with one base station (Client Association)

11. An Online Algorithm for Scheduling Let r i [ t ] be the actual throughput of i up to time t Algorithm: at each time t, each base station m schedules i that maximizes w i H i,m,z /r i [ t ] on resource block z Base stations only need to know information on its clients The algorithm is fully distributed and can be easily implemented

12. Optimality of Scheduling Algorithm Theorem: Fix Power Control and Client Association, The scheduling algorithm optimally solves Scheduling Problem Can be extended to consider fast-fading channels

13. Challenges for Power Control Find P m,z that maximizes Challenges: The problem is non-convex Need to consider the channel gains between all base stations and all clients Need to consider the influence on Scheduling Problem

14. Relax Conditions Assume: The channel gains between a base station m and all its clients are the same, G m The channel gains between a base station m and all clients of base station o are the same G m,o We can directly obtain the solutions of Scheduling Problem

15. A Heuristic for Power Control Propose a gradient-based heuristic The heuristic converges to a local optimal solution The heuristic only requires base stations to know local information that is readily available in LTE standards Can be easily implemented

16. Client Association Problem Assume that each client aims to choose the base station that offers most throughput Consistent with client’s own interest In a dense network, a client’s decision has little effects to the overall performance of other clients

17. Estimating Throughput To know the throughput that a base station offers, client needs to know: H i,m,z : throughput on each resource block, can be obtained by measurements ø i,m,z : amount of time client is scheduled Develop an efficient algorithm that estimates ø i,m,z Solves Client Association Problem

18. Simulation Topology 500 mX25X16 X9

19. Simulation Settings Channel gains depend on: Distance Log-normal shadowing on each frequency Rayleigh fast fading

20. Compared Policies Default –Round-robin for Scheduling –Use the same power on all resource blocks –Associate with the closest base station Fast Feedback: has instant knowledge of channels Slow Feedback: only has knowledge on time-average channel qualities

21. Simulation Results

23. Conclusion We investigate the problem of self- organizing LTE networks We identify that there are three important components: Scheduling, Power Control, Client Association We provide solutions for these problems Simulations show that our protocol provides significant improvement over current Default policy