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