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Self-Organized Resource Allocation in LTE Systems with Weighted Proportional Fairness

Self-Organized Resource Allocation in LTE Systems with Weighted Proportional Fairness. I-Hong Hou and Chung Shue Chen. Motivation. 4G LTE networks are being deployed With the exponentially increasing number of devices and traffic, centralized control and resource management becomes too costly

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Self-Organized Resource Allocation in LTE Systems with Weighted Proportional Fairness

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  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 • Gi,m,z := the channel gain between client i and base station m on resource block z • Gi,m,z varies with z, so frequency-selective fading is considered

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

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

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

  11. An Online Algorithm for Scheduling • Let ri[t] be the actual throughput of i up to time t • Algorithm: at each time t, each base station m schedules i that maximizes wiHi,m,z/ri[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 Pm,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, Gm • The channel gains between a base station m and all clients of base station o are the same Gm,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: • Hi,m,z : throughput on each resource block, can be obtained by measurements • øi,m,z : amount of time client is scheduled • Develop an efficient algorithm that estimates øi,m,z • Solves Client Association Problem

  18. Simulation Topology X25 X16 500 m X16 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

  22. 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

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