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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


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


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!


  • I am looking for students

    • Self-motivation

    • Welcome background in algorithms, optimization, probability, etc.

Thank You!