Architectures for Baseband Processing in Future Wireless Base-Station Receivers
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Transcript of Architectures for Baseband Processing in Future Wireless Base-Station Receivers
Architectures for Baseband Processing in Future Wireless
Base-Station Receivers
Sridhar Rajagopal
ECE Department
Rice University
March 22,2000
This work is supported by Nokia, Texas Instruments, Texas Advanced Technology Program and NSF
CAIN Project 2
Third Generation Wireless
First Generation
Voice Eg: AMPS
Second/Current Generation
Voice + Low-rate Data (9.6Kbps)Eg : IS-95(N-CDMA)
Third Generation +Voice + High-rate Data (2 Mbps) + Multimedia
W-CDMA
CAIN Project 3
Main Parts of Base-Station Receiver
Channel Estimation– Noise, MAI
– Attenuation
– Fading
Detection– Detect user’s information
– Multiple Users
Decoding– Coding/Decoding improve
error rate Performance
– Coding done at handset
Direct PathReflected Paths
Noise +MAI
User 1
User 2
Base Station
Wireless Communication Uplink
CAIN Project 4
Base-Station Receiver
User InterfaceTranslation
SynchronizationTransport Network
OSILayers
3-7
Data Link Layer(Converts Frames
to Bits)
OSILayer
2
Physical Layer(hardware;
raw bit stream)
OSILayer
1
Channel
Estimator
Multiuser
Detector
Demux Decoder
Data
Pilot
Estimated Amplitudes & Delays
Antenna
Physical Layer
CAIN Project 5
Need for Better Architectures
Current DSPs need orders of magnitude improvement to meet real-time requirements.
Reason– Sophisticated Algorithms, Computationally Intensive
Operations
– Floating Point Accuracy
Solution– Try sub-optimal/iterative schemes
– Fixed Point Implementation
– Use structure in the algorithms Parallelism / Pipelining Task Partitioning
– Bit Level Arithmetic
9 10 11 12 13 14 150
0.5
1
1.5
2x 105
Number of Users
Da
ta R
ate
s
Data Rates for a typical DSP Implementation
Data Rate Requirement = 128 Kbps
CAIN Project 6
Channel Estimation - An example
Channel Estimation† includes
– Matrix Correlations, Matrix Inversions, Multiplications
– Floating Point Accuracy
– Need to wait till all bits are received.
Modified Channel Estimation Algorithm
– Matrix Inversion eliminated by Iterative Scheme
Based on Gradient / Method of Steepest Descent
– Negligible effect on Bit error Performance
– Fixed Point accuracy, Computation spread over incoming bits
– Features to support Tracking over Fading Channels easily added.
† Maximum Likelihood Based Channel Estimation [C.Sengupta et al. : PIMRC’1998, WCNC’1999]
CAIN Project 7
Simulations - AWGN Channel
Detection Window =
12
SINR = 0
Paths =3
Preamble L =150
Spreading N = 31
Users K = 15
10000 bits/userMF – Matched Filter
ML- Maximum
Likelihood
ACT – using inversion4 5 6 7 8 9 10 11 1210
-3
10-2
10-1 Comparison of Bit Error Rates (BER)
Signal to Noise Ratio (SNR)
BER
MF ActMFML ActML
O(K2N)
O(K2NL)
CAIN Project 8
DSP Implementation
Advantages
– Programmability
– Ease of implementation
– High Performance
– Low Cost
Disadvantages
– Improvements necessary to meet real-time requirements!
– Sequential Processing
Parallelism not fully exploited
– Cannot process or store data at granularity of bits.
CAIN Project 9
VLSI Implementation
Task Partition Algorithm into Parallel Tasks Take Advantage of Bit Level Operations Find Area-Time Efficient Architecture Meets Real-Time Requirements!
Task A
Task C
Task B
Time
CAIN Project 10
Conclusions
Better Performance achieved by– Modifications in the Algorithm
– Application Specific Architectures
Algorithmic Modifications – reduce the complexity of the algorithms
– develop sub-optimal or iterative schemes.
Custom hardware solutions – bit level operations and parallel structure.
Together, algorithm simplifications and custom VLSI implementation can be used to meet the performance requirements of the Base-Station Receiver.
CAIN Project 11
Future Work
Analysis for Detection and Decoding
Mobile Handsets
– Mobile handsets have similar algorithms
– Need to account for POWER too.
General Purpose Enhancements [But, VLSI first ]
– Explore Instruction Set Extensions / Architectures for DSPs
– Exploit Matrix Oriented Structures
– Bit Level Support
– Complex Arithmetic
CAIN Project 12
Fading Channel with Tracking
4 5 6 7 8 9 10 11 1210
-3
10-2
10-1
100
SNR
BE
R
MF - Static MF - TrackingML - Static ML - Tracking
Doppler Frequency = 10 Hz, 1000 Bits,15 users, 3 Paths
CAIN Project 13
Talk Outline
Introduction
Need for better Architectures
Channel Estimation - An example
Simulation Results
Implementation Issues
– General Purpose/Application Specific
Conclusions
Future Work