machine learning assisted intelligent rx design

ACCESS IC LAB

Graduate Institute of Electronics Engineering, NTU

Machine Learning Assisted Intelligent RX Design

機器學習輔助之智慧接收機設計

台大電子所吳安宇教授

ACCESS IC LAB Graduate Institute of Electronics Engineering, NTU

P2

Evolution of Communication Systems

Analog voice

was amazing,

but limited

Mobile voice was amazing,

but consumers

wanted more Mobile

for data

increase

1G established

foundation of mobile

connectivity

introducing mobile

services

[1]

2G digital wireless

technologies

increased voice

capacity delivering

mobile to the

masses

3G optimized mobile

for data enabling

mobile broadband

services, and is

evolving for faster

and better

connectivity

4G LTE delivers

more data capacity

for faster and better

broadband

experiences, and is

also expending in to

new frontiers


P3

ITU-R Defines 5G Application Scenarios

Enhanced Mobile

Broadband

(eMBB)

Massive Machine Type

Communications

(mMTC)

Ultra-Reliability and

Low-Latency Communications (uRLLC)

Source: Recommendation ITU-R M.2083 on IMT-2020 Vision [2]


P4

5G Communication Systems

Flexible and diverse requirements in

5G communications

Requirements

High throughput/capacity

Low latency

High reliability

Massive connectivity

Huge gap between 4G and 5G

100x peak data rate [Gbps]

0.1x latency [ms]

10x simultaneous connection [M/km2]

Need a powerful technique to overcome the HUGE gap from 4G to 5G !

[2]


P5

Intelligent Communication Systems

Communications with the ability of learning = “Cognitive radio” (CR)

J. Mitola III - The “Godfather” of cognitive radio technologies

Dynamic spectrum access

RF sensing – sense the spectrum

Reconfigurable

Radio systems could achieve rapid adaptation, self-optimization, self-

organization, and autonomous operation

Cognitive

radio

Mobile frequencies crowded With CR you can use any channel

[3][4]


P6

Development of Machine Learning

1950’s

1960’s

1970’s

1980’s

1990’s

2000’s

2010’s

~

Now

Artificial Intelligence (AI)

Any technique which enables

computers to mimic human behavior

Machine Learning (ML)

Subset of AI techniques which

use statistical methods to enable

machines to improve with

experiences

Deep Learning (DL)

Subset of ML which uses neural

network models to understand

large amounts of data

Ex: computer vision, AI gaming,

image & speech recognition…


P7

Deep Learning (DL)

Heavily investing by Google, Microsoft, Amazon, and other tech. giants

Most seen in image/speech recognition, regression, dimension reduction

Deep learning in 5G has picked up momentum

Self-organizing resource allocation

Network management/routing

…, and so on.

[3]


P8

Convolutional Neural Network (CNN)

Recurrent Neural Network (RNN)

What is Deep Learning?

Training: define network structure as

neural network

Multiple layers cascading of

nonlinear processing units for

feature extraction and

transformation

Learn high levels of feature /

representations

Automatically features collection

For Sequential Problem

For Computer Vision Tasks

Capture different levels

of feature representation

Memorize and recall the memory

[5]


P9

Deep Learning + 5G Communications?

• Feature learning by DL itself

• Continuous improving performance

Deep Learning

• Massive connected devices

• Huge amount of communication data

5G Communications

Goal of DL for 5G: Meet requirements and make up the huge gap from 4G to 5G !

Replace the vast expert knowledge in the field of communications

Inadequate system models

Mathematically convenient models - not for real systems with imperfections

Limiting functional block-structure

Individual blocks optimization ≠ the best possible end-to-end performance

Handcrafted feature extraction

Machine can learn high-level feature autonomously which is more representative

Unlimited development potential

[6][7]


P10

Proposed Intelligent Transceiver Design

(A) Pre-distortion & Equalizer

(B) Automatic Modulation Classification

(C) Neural Network Demodulator & Decoder

(D) End-to-End Optimization


P11

Exploit deep learning to meet the requirements and make up the huge

gap from 4G to 5G

(A) Pre-distortion & Equalizer

DL-based nonlinear equalizer to eliminate ISI & PAPR low complexity

(B) Automatic Modulation Classification

Dynamically adjust the transmission data rate and avoid handshaking

spectral efficiency

(C) Neural Network Demodulator & Decoder

Avoid data dependency low latency

(D) End-to-End Optimization

Break functional blocks higher system reliability

Conclusion

Machine learning communication systems hold the potential to

improve the capacity, reliability, latency, and energy efficiency.


P12

Reference

[1] The Evolution of Mobile Technologies: 1G 2G 3G 4G LTE - Qualcomm

[2] Recommendation ITU-R M.2083 on IMT-2020 Vision

[3] Jiang, Chunxiao, et al. “Machine learning paradigms for next-generation wireless networks,” IEEE Wireless

Communications 24.2 (2017): 98-105.

[4] Mendis, Gihan J., Jin Wei, and Arjuna Madanayake, “Deep learning-based automated modulation classification for

cognitive radio,” 2016 IEEE International Conference on Communication Systems (ICCS).

[5] Fadlullah, Zubair, et al. “State-of-the-Art Deep Learning: Evolving Machine Intelligence Toward Tomorrow’s Intelligent

Network Traffic Control Systems,” IEEE Communications Surveys & Tutorials (2017).

[6] http://machinelearningmastery.com/improve-deep-learning-performance/

[7] O'Shea, Timothy J., and Jakob Hoydis. “An introduction to machine learning communications systems,” arXiv preprint

arXiv:1702.00832 (2017).

http://machinelearningmastery.com/improve-deep-learning-performance/

machine learning assisted intelligent rx design

Documents