SelfOrganized Resource Allocation in LTE Systems with Weighted

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

Motivation • 4 G 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

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

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

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

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

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

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

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:

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)

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 wi. Hi, 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

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

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

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

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

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

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

Simulation Topology X 25 X 16 X 9 500 m

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

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

Simulation Results Total Throughput (Mbps) 300 250 200 150 100 50 0 Default Slow Feedback Fast Feedback

Simulation Results 1 0. 9 0. 8 0. 7 CDF 0. 6 0. 5 Default 0. 4 Slow Feedback 0. 3 0. 2 Fast Feedback 0. 1 0 0 2 4 6 Throughput (Mbps) 8 10

Conclusion • We investigate the problem of selforganizing 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