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Resource Allocation in Wireless Communication Networks. Xin Liu Computer Science Dept. University of California, Davis. Wireless Communication Networks. Cellular networks WiFi, WiMAX Ad hoc networks Mesh/community networks Wireless sensor networks …. Resource Management.

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resource allocation in wireless communication networks

Resource Allocation in Wireless Communication Networks

Xin Liu

Computer Science Dept.

University of California, Davis

wireless communication networks
Wireless Communication Networks
  • Cellular networks
  • WiFi, WiMAX
  • Ad hoc networks
  • Mesh/community networks
  • Wireless sensor networks
resource management
Resource Management
  • Scarce radio resource
  • Timing-varying and location-dependent channel conditions
  • Limited battery power
  • Sharedmedium
  • Mobility
research topics
Research Topics
  • Opportunistic scheduling
  • Spectrum-agile communication
  • Wireless sensor networks
opportunistic scheduling
Opportunistic Scheduling
  • Objective
    • Efficient spectrum utilization
    • QoS provisioning
  • Motivation
    • Scarce radio resource
    • Timing-varying channel conditions
    • Multi-user diversity
channel conditions
Channel Conditions
  • Decides transmission performance
  • Determined by
    • Strength of desired signal
    • Noise level
      • Interference from other transmissions
      • Background noise
    • Time-varying and location-dependent.
time varying channel conditions
Time-varying Channel Conditions
  • Due to users’ mobility and variability in the propagation environment, both desired signal and interference are time-varying and location-dependent
  • A measure of channel quality:

SINR (Signal to Interference plus Noise Ratio)

performance vs channel condition
Performance vs. Channel Condition
  • Voice users: better voice quality at high SINR for a fixed transmission rate;
  • Data users: higher transmission rate at high SINR for a given bit error rate;
  • Adaptation techniques are specified in 3G standards.
    • TDMA: adaptive coding and modulation
    • CDMA: variable spreading and coding
multi user diversity
Multi-user Diversity

Scheduling question: given this channel condition, which user should transmit at a given time?

a greedy scheduling scheme
A Greedy Scheduling Scheme
  • Always choose the user with the best channel condition to transmit
  • Improve the spectrum efficiency
  • Unfairness among users

Starvation

opportunistic scheduling1
Opportunistic Scheduling
  • Basic idea: schedule users in a way that exploits variability in channel conditions
  • Opportunistic: choose a user to transmit when its channel condition is good.
  • Fairness/QoS requirements: opportunism cannot be too myopic.
  • Each scheduling decision depends on
    • channel conditions
    • fairness or QoS requirements
    • Select the “relatively-best” user
system model
System Model
  • Time-slotted systems
  • Each user has a certain requirement
  • TDMA or time-slotted CDMA systems (e.g., IS-856)
notion of utility
Notion of Utility
  • Uik: data rate of user iat time k
  • If time slot k is assigned to user i, useri will receive a throughput of Uik.
  • Measures the worth of the time slot to user i.
  • Generalize to the notion of utility:
    • throughput
    • throughput – cost of power consumption
  • {Uik, k=1,2,3…} is a stochastic process.
  • Utility values are comparable and additive.
a framework for scheduling
A Framework for Scheduling
  • Objective: Maximize the sum of all users’ throughput while satisfying the QoS requirements of users.
  • Scheduling decision depends on:
    • Channel conditions
    • QoS/fairness requirements
objective
Objective

Maximize average system throughput subject to the fairness constraints ri.

System utility:

  • is the indicator function
scheduling problem formulation
Scheduling Problem Formulation
  • Optimal scheduling problem

where  is the set of all policies.

  • No channel model assumed
  • No assumption on utility functions
  • General distributions of
  • Users’ utility values can be arbitrarily correlated across time and among users.
an optimal scheduling policy
An Optimal Scheduling Policy
  • Choose the ``relatively-best\'\' user to transmit
  • vi*: “off-sets” used to achieve the fairness requirement.
parameter estimation
Parameter Estimation
  • We estimate vi* based on measurements of the channel using stochastic approximation.
  • Consider the root-finding algorithm for each threshold vi*:
  • vik → vi* with appropriately chosen
  • However,
parameter estimation cont d
Parameter Estimation (Cont\'d)
  • vik → vi* w.p.1 under appropriate conditions (e.g., ak=1/k).
  • Simulation results show the estimation works well.
case 1 simulation of a wireless system
Case 1: Simulation of a Wireless System
  • Fair sharing: ri=1/N, N is number of active users
  • Non-opportunistic scheme: round-robin
  • Concentrate on the downlink. Reuse factor is 3.
  • Consider co-channel interference from first-ring neighbor cells;
  • Consider path loss (Lee\'s model) and log-normal shadowing;
  • Each user moves in the cell with a certain speed and its direction, which can change periodically;
  • 25 users/cell with exponentially distributed on-off periods.
utility values
Utility Values
  • Step function - user 1-2;
  • Linear function - user 3-4;
  • S-shape function -user 5-8;
conclusions on opportunistic scheduling
Conclusions on Opportunistic Scheduling
  • Traditional setting: performance of system depends on average channel conditions.
  • Opportunistic setting: performance of system depends on peak channel conditions.
  • Opportunistic gain increases with
    • channel variability (over time)
    • number of users
    • channel independence (across users).
  • Current and Future wireless systems:
    • exploit opportunistic methods (IS-856).
where do we stand
Where do We Stand?
  • History: a successful story, a $$$$$$ industry
  • Current
    • Rapid proliferation
    • Policy evolution
  • Future:
    • More spectrum
    • Advanced DSP and radio technologies
    • Cool applications

An Exciting Area, a Long Way to Go!

slide30

Recruitment

  • I am looking for students
    • Self-motivation
    • Welcome background in algorithms, optimization, probability, etc.

Thank You!

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