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Self-Organized Resource Allocation in LTE Systems with Weighted Proportional Fairness
I-Hong Hou and Chung Shue Chen
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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
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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
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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
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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
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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
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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
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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
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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:
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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)
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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
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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
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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
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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
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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
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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
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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
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Simulation Topology
500 mX25 X16
X16 X9
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Simulation Settings
• Channel gains depend on:
• Distance
• Log-normal shadowing on each frequency
• Rayleigh fast fading
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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
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Simulation Results
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Simulation Results
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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