Radio Resource Management in Wireless Mobile Networks

Emre A. Yavuz, Ph.D. candidateSupervised by : Dr. Victor C.M. Leung

Communications Lab., Elec. & Comp. Eng.

University of British Columbia, UBC emrey@ece.ubc.ca

RNC

BS

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Motivation

To build mathematical models for Radio Resource Management and to simulate the resource allocation process in order to look for optimum algorithms and develop better admission and congestion control

procedures. Extra capacity that is provided to the network will

result in higher end-user average bit rates, low delays and BER and lower blocking/dropping ratios.

Radio Resource Management will be the major differentiator between the overall QoS provisioning offered by different operators’ networks.

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Agenda

What makes Wireless Mobile Networks different ? The Radio Resource Management Methods Resource Usage in CDMA Networks Admission Control & Its Logical Dependencies Congestion Control & Actions to be Taken Traffic Types, Adaptation of Quality Indicators &

Cost Resource Management based on Utility Function

Approach Pricing Frameworks Future Research

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What Makes Wireless Mobile Networks Different ?

The probabilistic behaviour of the wireless channel (shadow fading, rayleigh fading, path loss) as opposed to almost deterministic behavior of the wired lines.

Soft Capacity (changes with other & own cell interference)

User - mobility (handovers, shadowing effects etc.) Call Dropping Probability constraint besides Call

Blocking Probability constraint. Cell Coverage guarantee for each service.

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The Radio Resource Management Methods

Admission Control Handles new incoming traffic (new connections,

handover decisions and bearer modifications)

Congestion Control Manages the system when the load exceeds

threshold Traffic Scheduling (mostly for non-real time traffic)

Handles packet data users to initiate the packet transmissions and guarantee QoS through bit rate, BER and delay adjustments

Power Control - to maintain radio link quality

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Resource Usage in CDMA

Uplink (Interference Limited)

Downlink (Power Limited)

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Admission Control & Its Logical Dependencies

Estimate the load and fills the system up to the limit without having impact on coverage and quality of existing connections.

Separate admission for UL and DL. Uses load info from Congestion Control, Traffic

Scheduling, Power Control and Handover Control.

Derives the transmission bit rate, target BER, processing gain, initial link quality parameters.

Initiates the forced call release and interfrequency or intersystem handover.

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Congestion Control & Actions to be Taken

Optimize the capacity of a cell and prevent overloading by measuring application parameters from planning and UL & DL interference.

Congestion Control takes care of the network to prevent overloading and to preserve the stability.

Consider load control actions on the network traffic. Lower bit rates of the rate-adaptive traffic. Lower SIR target based on the type of application. Interact with TS and throttle back packet data traffic. Force interfrequency or intersystem handover. Drop calls in a controlled manner.

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Traffic Types, Adaptation of Quality Indicators & Load

Quality of Service (QoS) Classes (Conversational, Streaming, Interactive and Background)

Main concern: Real-time and Non-real-time Traffic. (More compatible with scheduling when compared toreal-time traffic)

Regrouping the traffic Adaptive rate and adaptive signal quality requirements. Adaptive rate and fixed signal requirements. Fixed rate and adaptive signal requirements. Fixed rate and fixed signal requirements.

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Resource Management based on Utility Function Approach

Objective: To maximize aggregate utility subject to Available transmission power and spreading codes (at

the base station) Allowed interference (at the mobile stations)

A solution based on a pricing framework, where prices per unit power, code or load are announced by the network (RNC) in order the users to maximize their net utilities while the system tries to maximize the total sum of utilities

and the network tries to maximize its revenue.

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Three Cases of Optimization

From User point of view

From System point of view

From Network point of view

i

iiii SIRrUkMAX ** where ki is a coefficient chosen for QoS or price-based class

SIRrSIRrU iiii *** MAX where λ is the price that is announced by RNC

MAX ρ and MIN blocked/dropped calls and delays

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How should a Utility Function for a SIR Adaptive Application be ?

U(SIRi)=1/(1+exp(-(a*(SIRi -b))))

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Cost Calculation based on Pricing Frameworks

Cost can be based on a shadow price and a constraint related load parameters like rate, BER, power etc. Based on the rate and BER of the applicationCost = Shadow price * rate * SIR Based on the application powerCost = Shadow price * power

Shadow price will be announced by RNC for each period and be adjusted dynamically based on the network load.

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How does the Derivative of the Utility Function Look Like ?

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How does the Net Utility Function Look Like ?

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How does the Radio Network Control make the Upgrade/Downgrade Decision ?

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And What About Power ?

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Future Research

What comes next ? To look into issues like;

Behavior of the uplink/downlink load from the channel efficiency point of view

Changes in the quality of connection with the changing mobile users’ distance from the Base station (near-far fairness).

Behavior of the rate during the whole life of a connection.

Behavior of the signal quality (BER) during the whole life of a connection.

Call Dropping and Blocking Probabilities. Possible Applications of Adaptive Learning

Techniques.

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THANKS !