1. Radio Resource Management in Wireless Mobile Networks
RNC BS
Emre A. Yavuz, Ph.D. candidate
Supervised by : Dr. Victor C.M. Leung Communications Lab., Elec. & Comp. Eng.
University of British Columbia, UBC

2. 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.

3. 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

4. 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.

5. 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

6. Resource Usage in CDMA Uplink (Interference Limited)
RNC BS ri Pi
gi*Pi
Uplink (Interference Limited)
Downlink (Power Limited)

7. 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.

8. 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.

9. 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 to real-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.

10. 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.

11. Three Cases of Optimization
From User point of view
From System point of view
From Network point of view
where λ is the price that is announced by RNC
MAX
where ki is a coefficient chosen for QoS or price-based class

12. How should a Utility Function for a SIR Adaptive Application be ?
U(SIRi)=1/(1+exp(-(a*(SIRi -b))))

13. 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 application
Cost = Shadow price * rate * SIR
Based on the application power
Cost = Shadow price * power
Shadow price will be announced by RNC for each period and be adjusted dynamically based on the network load.

14. How does the Derivative of the Utility Function Look Like ?

15. How does the Net Utility Function Look Like ?

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

17. And What About Power ?

18. 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.

19. THANKS !