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Power Control and Rate Adaptation in WCDMA. By Olufunmilola Awoniyi. Contents. Overview of WCDMA Paper summary - Goal System Model and Assumptions Approach Simulation Results Comments. WCDMA.

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Presentation Transcript
  • Overview of WCDMA
  • Paper summary - Goal
  • System Model and Assumptions
  • Approach
  • Simulation Results
  • Comments
  • Third generation wireless systems designed to fulfill the “communication to anybody, anywhere, anytime” vision.
  • Support voice, streaming video, high speed data.
  • Spread spectrum systems with spread bandwidth of >=5MHz
  • Support multirate services by using spreading codes
  • Different versions of WCDMA – check for names of standards

- Europe - UMTS (asynch).

- Japan - Core-A (asynch)

- Korea - TTA (I & II) (TTA I – synch, TTA II – asynch)

- US - CDMA2000 (synch)

- ITU - IMT-2000

*ARIB – Association of Radio Industries and Businesses

*ETSI – European Telecommunications Standardization Institute

*IMT- 2000 – International Mobile Telecommunications 2000

*ITU - International Telecommunication union

*TIA – Telecommunication Industry Association

*TTA – Telecommunication Technology Association

*UMTS - Universal Mobile Telecommunications System

paper summary
Paper Summary
  • “Power and rate allocation in multirate wideband CDMA system” by J.W Mark and S. Zhut ( University of Waterloo)
  • Goal – Develop a power distribution law the IMT-2000 WCDMA system so that the QOS requirements are met and transmit power is minimized.
  • Conclusion –

- Power adaptation is a function of spread bandwidth, data rates and QOS requirements.

- The closer the demand for resource is to the available resource, the higher the required transmit power.

system model
System Model
  • Uplink transmissions in a single cell – bottle-neck for capacity
  • M users in the cell
  • Number of channels for user j is Kj where Kj L
  • Channel – AWGN, denoted by nj for the jth user
  • Total Interference (Itj) = Thermal noise + MAI – Gaussian
  • QOS elements have factored in fading and shadowing effects – specified in terms of SIR (BER), j,, such that

with data rates Rbj, where

  • Total transmit power required (to transmit over Kj channels) for user j is Sj
  • Each user have a traffic demand, j, and a normalized traffic demand, j.

* MAI – Multiple access interference

system model equations

Rbj1, j1

Rbj2, j2




RbjKj, jKj


OVSF code 2

OVSF code Kj

OVSF code 1

System Model - Equations
  •  can be written in SIR terms as,
  • such that the required transmit power is
  • Therefore, Sj can be define as
  • with a normalized traffic demand defined as
  • Total interference is

* W – Spread bandwidth

approach 1
Approach (1)
  • If S = [S1, S2,…,SM ]’, with some manipulation,

such that

  • Perron-Frobenius Theorem – p has positive eigenvalue,  equal to the spectral radius and if  < 1, the solution is non- negative.
  • Example - M = 2

- By solving the characteristic polynomial, det[p- IM] = 0

- 1= 2 = , n1 = n2 = n (uniform traffic demands and noise)

  • Observations -

- For any power distribution, traffic demand is upper bounded by spread bandwidth.

- The higher the noise or the closer the traffic demands are to W, the higher the required transmit power.

approach 2
Approach (2)
  • Limiting case – Ignore n for each user and minimize transmit power

- By solving for a non-trivial solution, for uniform traffic demands,


– (necessary condition for convergence - 1)


  • Observation

- All users transmit the same power and raise the transmit power until interference can be ignored

approach 3
Approach (3)
  • General case - If Sj is such that



– (necessary condition for convergence - 2)

admission policy
Admission policy
  • The conditions sufficient for convergence will used to accept or reject a request for connection in the admission controller.

1) For all s (for users already connected and those requesting), calculate E() and Var() such that

2) Admission policy –

- Admit -

- Reject -

- Admit light traffic demand -


simulation results
Simulation Results
  • The higher the variation in the normalized traffic demand, the looser the bound and the higher the capacity.
  • Uniform traffic achieves the minimum capacity.
  • At M , the variation in traffic becomes less significant and the distribution of the traffic demand looks uniform.
  • Admission of a new call can lead to other users having to change their transmit power to achieve their desired SIR values.
  • Worst case scenario - When most users increase their transmit power to meet QOS constraints, the system blows up.

- Total traffic demand < 0.8W.

- Better to have power constraints (average or total power).

  • Multicell system - “Link Quality in SIR Based Power Control for UMTS CDMA system” by Oppermann et al.
  • Fading / ISI channel - “Adaptive Multicode CDMA for the uplink Throughput Maximization” by S.A Jafar and A. Goldsmith