Delay versus Energy Consumption of the IEEE 802.16e Sleep-Mode
Mechanism
Fen Hou and Pin-Han Ho Department of Electrical and Computer
Engineering, University of Waterloo, Waterloo, Ontario
Wireless Communications and Mobile Computing, vol. 10, no. 7,
July, 2010 An Efficient Scheduling Scheme with Diverse Traffic
Demands in IEEE 802.16 Networks1AgendaIntroductionSystem
ModelWeighted Proportional Fair (WPF) SchedulingPerformance
AnalysisSimulationsConclusions2Four QoS Types of Service
FlowsCommon Solutions for Four QoS TypesMotivations
ContributionsIntroduction3Four QoS Types of Service FlowsQoS
TypesDescriptionsExamplesConstraintsUnsolicited Grant Service
(UGS)Real-time constant-bit-rate (CBR) applicationsVoIP without
silence suppressionDelay, Delay jitter Real-time PollingService
(rtPS)Real-time variable-bit-rate (VBR) applicationsIPTV, video
conferencesDelay, Min. throughput, max. sustained
throughputNon-real-time Polling Service (nrtPS)Delay-tolerant
variable-bit-rate (VBR)applicationsFTP, Internet web servicesMin.
throughput Best Effort (BE)Delay-tolerant variable-bit-rate
(VBR)applicationsE-mailNoneIEEE 802.16 has attracted extensive
attentions from both industry and academia Provide QoS satisfaction
for different applications4Common Solutions for Four QoS Types
Packet scheduling plays a key role in fulfilling service
differentiation and QoS provisioningContains the packet
transmission order decisions and resource allocation mechanism
Common solutions for four QoS types UGS: periodically grant a
fixed amount of resources rtPS: Largest weighted delay first
[7]nrtPS, BE: Proportional fairness scheduling [12]provide a good
balance between the system throughput and
fairness5MotivationsConventional scheduling schemes based on
proportional fairness are focused on equivalently allocating the
available resource among the users which are rather efficient when
the traffic demand of each user is homogeneous
Traffic demands and channel conditions should be taken into
considerationsExample: BE traffic load/demand for an SS of office
building could be much higher than that for an SS of resident house
during the day time6ContributionsWeighted Fairness Scheduling
scheme is proposedAn analytical model is developed to quantify the
relation The weight of each SSChannel conditionsPerformance
metricsSystem spectral efficiencyResource utilization,Throughput
Fairness7Channel ModelAdaptive Modulation and Coding (AMC)System
Model8Channel ModelThe strength of received signals are decided by
two effectsLarge-scale path-loss attenuationDetermined by the
geographical environment and distance between the receiver and the
transmitterSmall-scale fadingCaused by multiple versions of a
transmitted signal with different delay and occurs spontaneously in
the time span with a random duration and depthCommon used channel
model for NLOS: Rayleigh flat fading channel
The channel condition of each SS varies on the frame
basis9Adaptive Modulation and Coding (AMC)An advanced technique at
the physical layer to achieve a high throughput by adaptively
adjusting the sending rate based on the channel conditions
Based on the perceived SNR of an SS, the BS selects a proper
modulation level and coding scheme for each SS.10Formal Definition
of WPFExamples of WPFImplementations of WPFWeighted Proportional
Fair (WPF) Scheduling11Formal Definition of WPFThe WPF scheduling
scheme selects SSs for service based on the weighted relative
channel conditions of SSs
Formal definition of WPF
The weighted relative channel condition of SSiThe weight of
SSiAverage channel condition for SSiInstance channel condition for
SSi12ExampleSuppose that six SSs in the IEEE 802.16 networksSS
IDswiInstance channel condition for SSiAverage channel condition
for SSiThe weighted relative channel condition of
SSi1512dB3dB5*(12/4)=1521024dB6dB10*(24/6)=4031554dB9dB15*(54/9)=904206dB12dB20*(6/12)=105255dB15dB24*(5/15)=8.x63036dB18dB30*(36/18)=60Selection
order: SS3 SS6 SS2 SS1 SS4 SS5 13Implementations of WPFThe weight
value of SSi (wi) is equal to its traffic demands Di The framework
of the scheduling module at the BS
14Service Probability of SSiSpectral
EfficiencyThroughputPerformance Analysis15Service Probability of
SSiThe service probability of SSi is defined as the probability
that SSi is selected for service in an arbitrary frame when the
system is stable
Given SSi is selected for service when its weighted x
16Spectral EfficiencyThe theoretical upper bound of spectral
efficiency can be obtained based on Shannons channel capacityGiven
SSi is selected for service when its weighted x
17Spectral EfficiencySpectral efficiency is defined as the
amount of information bits transmitted over a unit bandwidth
The probability that SSi is selected for service in an arbitrary
frameThe theoretical upper bound of spectral efficiency based on
Shannons channel capacity
Total spectral efficiency achieved by all SSs is the sum of
spectral efficiency of each SS18ThroughputSince BE service is the
lowest priority among the multiple service types, it only takes the
leftover resourceLet be the time ratio available for the downlink
transmission of BE flows, and denote the throughput of SSi for BE
service
19Main Simulation ParametersService Probability for Each
SSThroughputSystem EfficeincySimulations20Main Simulation
Parameters
Simulator: MatlabOne BS and 12 SSsRayleigh flat fading
channels21Service Probability for Each SS
22Throughput
23 System efficiency
24Conclusion WPF scheduling scheme for BE service in IEEE 802.16
networks is proposed
An analytical model has been developed for investigating the
performance of the proposed scheme in terms of Service probability
of each SSSpectral efficiencyAchieved throughput
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