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

• 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,zPm,z

• The power can be either signal or interference

• SINR of i on z can be hence computed as

SignalInterference

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 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

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

500 mX25 X16

X16 X9

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

Simulation Results

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