From Marconi to Wireless Internet: The Wireless (R)evolution Prof. Vijay K. Bhargava Professor and Head, Dept. of Electrical and Computer Engineering The University of British Columbia Vancouver, Canada Email: [email protected]Website: http://www.ece.ubc.ca
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From Marconi to Wireless Internet: The Wireless (R)evolution Prof. Vijay K. Bhargava Professor and Head, Dept. of Electrical and Computer Engineering The.
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From Marconi to Wireless Internet: The Wireless (R)evolution
Turbo Decoding approaches Shannon’s capacity limit by less than 0.5dB
Theory of CDMA and Multiuser Transmission
Space-Time Coding for Mobiles
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Cryptography
Secret Key System
Public Key System
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Digital Signature
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Source Coding for Voice and Video
Variants of Predictive Coding
For voice CELP, VCELP, RPE-LTP and other Variants
H.261 and H.263 Standards support low bit rate (30-64 kbps) Video for mobile communication
MPEG-4 (with Reed-Solomon Codes)
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Turbo Codes
Originally Turbo code encoder was built using a parallel concatenation of two (or more) relatively simple recursive systematic convolutional (RSC) codes with large interleaving
Although the component codes are weak, the output turbo codeword is very powerful due to the “Interleaver gain” which produces a random-like codeword
dk
RSC1
RSC2
Interleaver
dk
d1,k
d2,k
Turbo Code Encoder
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After miterations
Decoder1
Decoder2
rk
r1,k
r2,k
Turbo code Decoder
d
Turbo Codes (Iterative Decoding)
Soft Input/Soft Output MAP Decoder
Use “extrinsic information” produced from past decoder as á priori information
Gradually improvement of knowledge on transmitted information through iterations
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Turbo Codes
Turbo codes will be used in high data rate services in the next generation CDMA systems above 32 kbps
There are already existing standards for ½ and 1/3 coding rate turbo code for 3GPP systems
The same iterative decoding principle can be applied in various different areas
Turbo decoding with estimation of parameters of an unknown time-varying channel
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Multiuser Detection (MUD)
Signals from all users are considered useful instead of only interference to each other
MUD provides important performance gains over the conventional single-user receiver
Performance of single-user conventional receivers are limited in fading channel due to the near-far effect which necessitates use of strict power control
In certain cases multiuser receivers can even benefit from the diversity in powers of the received users and have better performances than in the case of equal received powers of users
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Multiuser Detection (MUD)
•In the single user approach, each detector focuses on extracting the data of a single user•Other users are considered as interference•Simple
h(t)
1st Rake Receiver
Kth Rake Receiver
Rx MultiuserDetector
b1
bK
oscillator
• •
•In the multi-user detection approach, the common detector uses available information from all users to detect each user•Complex
oscillator
Rxh(t)
1st Rake Receiver
Kth Rake Receiver
Detector
Detector
b1
bK
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Multiuser Detection (MUD)
Benefits of using multiuser detection
More efficient spectrum utilization (in some situations we can expect ten fold increase in spectrum efficiency)
Reduced precision requirement for power control
More efficient power utilization
Main difficulties in implementing multiuser detection
Existence of the other-cell multiple-access interference (MAI)
Difficulty in implementing multiuser detection on the downlink (cost, size, weight are of much larger concern for mobile terminals)
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Multiuser Detection (MUD)
Some of the MUD algorithms areMaximum Likelihood (Optimal) Decoding (complexity increases exponentially with the number of users)Linear detectors (similar to the linear equalization techniques)
decor relating detectorMMSE detector
• MMSE detector can be implemented as an adaptive filter to reduce complexity
• Blind adaptive implementation of the MMSE detectorDecision Feedback Detectors where past decisions are used to improve the current ones
Conventional Decision Feedback or Successive Interference CancellationParallel Interference CancellationOptimum Decision Feedback Receiver (has the spectral efficiency equal to the optimum detector)
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Multiuser Detection (MUD)
Implementation examples of multistage parallel interference cancellation multiuser detection:
DSP implementation [Buehrer, Woerner, 1999]VLSI implementation [Aazhang et al., 2000]
The parallel interference cancellation is prefered due to its performance complexity tradeoffHowever, MUD research is still in a phase that would not justify to make it a mandatory feature for 3G WCDMA standardsMost probably, its practical implementation and standardization is going to be defered for 4G systems
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Space-Time Codes
Capacity of a multi-antenna systems far exceeds that of single-antenna system
Multiple transmit/receive antennas provide diversity in the space domain (space diversity)
Channel coding designed for wireless communication systems with multiple transmit/receive antennas provide both space and time diversity Space-time codes