Fall 2014 – Halim YanikomerogluPage 1 of 28 SYSC 4600 Digital Communications Fundamental Dynamics Fundamental Dynamics of Digital Communications Halim

Fall 2014 – Halim Yanikomeroglu

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SYSC 4600 Digital Communications

Fundamental Dynamics

Fundamental Dynamics of Digital CommunicationsFundamental Dynamics of Digital Communications

Halim Yanıkömeroğlu

Department of Systems & Computer Engineering

Carleton University

Ottawa, Canada


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Outline

dB Notation

The Big Picture: OSI Model

Major impairments in communication systems

Noise (AWGN)

SNR

Main goals of digital communications

MAC, RRM, RAN


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What is wrong with the below figure?


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The detail is lost for the small values of the vertical axis!


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Want to show large and small values on the same scale?


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Want to show large and small values on the same scale? Use logarithmic scale (not linear scale)

Logarithmic versus Linear Scale


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dB Notation

logc(a x b) = logc(a) + logc(b) logc(a ÷ b) = logc(a) – logc(b)

Decibel notation: Field quantities: 20 log10 (.)

Power quantities: 10 log10 (.)

In this course: 10 log10 (.) x + (increased by 1,000,000 times increased by 60 dB)

÷ - (decreased by 50 times decreased by 17 dB)

A [U] = (10 log10 A) [dBU]

A [unitless] = (10 log10 A) [dB]

Linear dB

5000 37

400 26

10 10

8 9

5 7

2 3

1 0

0.5 -3

0.125 -9

0.01 -20

0.0005 -33


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dB Notation

logc(a x b) = logc(a) + logc(b) logc(a ÷ b) = logc(a) – logc(b)

Decibel notation: Field quantities: 20 log10 (.)

Power quantities: 10 log10 (.)

In this course: 10 log10 (.) x + (increased by 1,000,000 times increased by 60 dB)

÷ - (decreased by 50 times decreased by 17 dB)

A [U] = (10 log10 A) [dBU]

A [unitless] = (10 log10 A) [dB]

P [W] = (10 log10P[W]) [dBW] Ex: 2 [W] = 3 [dBW]

P [mW] = (10 log10P[mW]) [dBm] Ex: 2 [mW] = 3 [dBm] P [dBW] = (P+30) [dBm] Ex: 5 [dBW] = 35 [dBm]

10 log10SNR = (10 log10(Psignal [mW] / Pnoise [mW])) [dB]

10 log10SNR = (10 log10Psignal) [dBm] – (10 log10Pnoise) [dBm]

X [dBm] – Y [dBm] = Z [dB]; X [dBm] + Y [dB] = Z [dBm]

Linear dB

5000 37

400 26

10 10

8 9

5 7

2 3

1 0

0.5 -3

0.125 -9

0.01 -20

0.0005 -33


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The Open Systems Interconnection (OSI) model is a prescription of characterizing and standardizing the functions of a communications system in terms of abstraction layers. [Wiki]

http://www.hill2dot0.com/wiki/index.php?title=OSI_reference_model

For example, a layer that provides error-free communications across a network provides the path needed by applications above it, while it calls the next lower layer to send and receive packets that make up the contents of that path. Two instances at one layer are connected by a horizontal connection on that layer. [Wiki]


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The physical layer defines the means of transmitting raw bits rather than logical data packets over a physical link connecting network nodes. The bit stream may be grouped into code words or symbols and converted to a physical signal that is transmitted over a hardware transmission medium.

http://baluinfo.com/networking/basic-networking-part-2/


The physical layer provides an electrical, mechanical, and procedural interface to the transmission medium. The shapes and properties of the electrical connectors, the frequencies to broadcast on, the modulation scheme to use and similar low-level parameters, are specified here. [Wiki]


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Imprecise Terminology

Often used synonymously in industry:

Digital Communications (SYSC 4600)

Transmission Technologies

Physical Layer

But they have slightly different meanings


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Digital Communications Block Diagram

Digital Communications, Sklar


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Major Impairments in Communication Systems: A Simple Picture

ChannelTransmitter Receiver

noise

interference


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Noise: always present

noise

interference



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Channel Ideal channel (AWGN channel)

does not distort (change the shape of) the transmitted signalintroduces attenuation and delay

noise

interference



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Non-idealities in channelDistortion channel: distorts; may introduce self-interferenceFading channel: ideal channel with a time-varying impulse response

noise

interference



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Non-idealities in channelDistortion channel: distorts; may introduce self-interferenceFading channel: ideal channel with a time-varying impulse response

Interference (interference channel)Major source of interference: other-user interference (co-channel interference)Occurs mainly in wireless channelsCan be handled via signal processing, beamforming, RRM, …

noise

interference



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Additive White Gaussian Noise (AWGN)

AWGN is a channel model in which the only impairment to communication is noise

AWGN: A linear addition of white noise with a constant spectral density and a Gaussian distribution of amplitude. [Wiki]

The model does not account for channel impairments. However, it produces simple and tractable mathematical models which are useful for gaining insight into the underlying behavior of a system before these other phenomena are considered. [Wiki]

Gaussian noise: Noise amplitude is a Gaussian distributed random variable (central limit theorem).

White noise: An idealized noise process with a power spectral density independent of frequency.


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Additive White Gaussian Noise (AWGN)

Pnoise= k T B F = N0 B F

k: Boltzmann’s constant = 1.38 x 10-23 J/KT: Temperature in degrees Kelvin (generally taken as 290oK)

N0: Noise power spectral density (constant) B: Bandwidth (signal bandwidth) F: Noise figure

N0 = k T = -174 dBm/Hz

Ex: 200 KHz channel (LTE resource block)

F = 7 dB Pnoise = -114 dBm

Broadband signal Pnoise increases

f

SN(f)

N0/2

White noise power spectral density

Infinite total power (?)


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SNR, SINR

Signal-to-Noise Ratio: Defined at the receiver front endSNR = (signal power) ∕ (noise power)

SNR = Psignal ∕ Pnoise

SNR = (bit energy) ∕ (noise power spectral density)

SNR = Eb ∕ N0

Signal-to-Interference-plus-Noise Ratio:

SINR = Psignal ∕ (Pinterference+ Pnoise)

Classical view: Threat interference as noise business as usual (use the theory developed for AWGN channel)

Modern view: Can we exploit the structure in the interference signal?


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Wireless Channel: Fading Signal

AWGN channel: Ps: fixed SNR: fixedFading channel: Ps: variable SNR: variable

SNR


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Main Goal of Digital Communications


Main Goal: For a given fixed SNR or an SNR distribution what operations should take place at transmitter and receiver to improve the performance?

Performance: Some meaningful metric

User metrics: (ultimately) eye, ear, feeling, smell, …

MOS (mean opinion scores) frame error rate (FER) packet error rate (PER) symbol error rate (SER) bit error rate (BER)

maximize SNRresort to better transmission and/or reception techniques

noise

SNR


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SNR=10 dB



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ChannelTX RX

• How do you send information (reliably) through a channel?

• For a given channel (medium), design TX and RX for best performance

• Best? Maximize/minimize SER, BER, SNR, mutual information, …

• Network metrics may be different than link metrics:

number of users, outage, sum (aggregate) rate, revenue, …

noise

+



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For a given fixed SNR (or an SNR distribution) what operations should take place at transmitter and receiver to improve the performance?

Pulse shapingModulation, demodulationChannel coding, decodingDiversityEqualization…

noise

SNR



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Channel Capacity

Channel capacity, Shannon capacity, information-theoretic capacity

C = log2(1+SNR), bits per second per Hertz

Non-constructive existence theorem

Developments

Shannon’s original formulation: 1948Block codes, convolutional codes, …Turbo codes (1993)Low-density parity check (LDPC) codes (1963, 1996) Polar codes (2008)


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Bandwidth vs Rate

T: Pulse duration, R: Rate R = 1/TW: Bandwidth

Inverse relation between T and WDirect relation between R and W

Narrow pulses (high rates) Large bandwidth


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MAC, RRM, RAN

Want SNR ↑ ? PS ↑ and/or Pn ↓ (limited control on Pn)

Want SINR ↑ ? PS ↑ and/or PI ↓ and/or Pn ↓(limited control on Pn)

How can we increase PS ?

How can we decrease PI ?

Answer: Medium Access Control (MAC) [layer 2]Radio Resource Management (RRM) [layer 2]Radio Access Network (RAN)

How do we compute PS ? Propagation modeling

Documents

Fall 2014 – Halim YanikomerogluPage 1 of 28 SYSC 4600 Digital Communications Fundamental Dynamics Fundamental Dynamics of Digital Communications Halim