EE 6332, Spring, 2014 Wireless Communication

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EE 6332, Spring, 2014 Wireless Communication. Zhu Han Department of Electrical and Computer Engineering Class 11 Feb. 19 th , 2014. Outline. Capacity in AWGN (Chapter 4.1) Entropy Source: independent Gaussian distribution Channel capacity: R

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EE 6332, Spring, 2017

Wireless Communication

Zhu Han

Department of Electrical and Computer Engineering

Class 11

Feb. 22nd, 2017

                                                           

                                                           

OutlineOutline Capacity in AWGN (Chapter 4.1)

– Entropy– Source: independent Gaussian distribution– Channel capacity: R<=C=Wlog(1+SNR)

Capacity of flat fading channel (Chapter 4.2) Capacity of frequency-selective fading channel (Chapter 4.3)

                                                           

Discrete time modelDiscrete time model A simple discrete time model

where h is a complex Gaussian distributed fading coefficient Information about channel

1. Channel distribution information (CDI) at transmitter and receiver

2. Channel state information at receiver (and CDI)3. Channel state information at transmitter and receiver (and CDI)

                                                           

Case 1 Channel Distribution Information (CDI)Case 1 Channel Distribution Information (CDI) Achievable rate

– Finding the maximizer is non trivial– For Rayleigh independent channel coefficients

Maximizing input is discrete with finite number of mass points Mass at zero

– Achievable rate computed numerically– Maximizing input distribution computed numerically– Not much to discuss—little analytical results

                                                           

Channel State Information (CSI)Channel State Information (CSI) State of the channel S (a function of h )

– Known to the receiver as V– Known to the transmitter as U

Channel state as a part of channel output

since fading (or more precisely CSI at receiver) is independent of the channel input

                                                           

Ergodic CapacityErgodic Capacity The achievable rate when CSI at receiver but no CSI at

transmitter

The model

Perfect channel state information at receiver

                                                           

ErgodicErgodic The achievable rate is not a variable in time

– If channel gain changes instantaneously the rate does not change

The rate is achieved over a long long codebook across different realizations of the channel– Long long decoding delay

Fading does not improve Ergodic capacity

The key to the proof is Jensen’s inequality

                                                           

ExampleExample A flat fading (frequency nonselective) with independent

identically distributed channel gain as

CSIR no CSIT

                                                           

CSI at Transmitter and ReceiverCSI at Transmitter and Receiver

The mutual information

Capacity when there is CSI at transmitter and receiver The original definition is not applicable Define fading channel capacity

                                                           

CSITR Ergodic CapacityCSITR Ergodic Capacity A result for multi-state channel due to Wolfowitz

capacity for each state Applied to CSITR

Channel state information at transmitter and receiver

Power adjusted with constraint

                                                           

Achievable Rate with CSITRAchievable Rate with CSITR Constraint optimization

Solving via differentiation

The solution is power control Temporal water filling Variable rate and variable power

– Different size code books– Multiplexing encoders and decoders

                                                           

Capacity with CSITRCapacity with CSITR The maximized rate

The threshold not a function of average power limit

                                                           

CSITR ExampleCSITR Example A flat fading (frequency nonselective) with independent

identically distributed channel gain as

                                                           

ExampleExample The three possible signal to noise ratios

Calculate the threshold

If the weakest channel is not used a consistent threshold emerges

Ergodic capacity

                                                           

Probability of OutageProbability of Outage Achieving ergodic channel capacity

– Codewords much be longer than coherence time

Slow fading channels have long coherence times Ergodic capacity more relevant in fast fading cases A burst with signal to noise ratio Probability of outage

Capacity with outage– Information sent over a burst– Limited decoding delay– Nonzero probability of decoding error

                                                           

OutageOutage The minimum required channel gain depends on the target rate. When instantaneous mutual information is less than target rate

depends on the channel realization Probability of outage (CSIR)

Fading channel (CSIR)

                                                           

Outage with CSITROutage with CSITR Use CSITR to meet a target rate

– Channel inversion– Minimize outage

Truncated channel inversion Probability of outage with CSITR

Fading channel with CSITR

                                                           

Outage CapacityOutage Capacity Target probability of outage Fixed power The outage capacity

Frame Error Rate– An appropriate performance metric– In many examples, probability of outage is a lower bound to

Frame Error Rate

                                                           

Frequency Selective (Chapter 4.3) Frequency Selective (Chapter 4.3) Input output relationship

Consider a time invariant channel CSI is available at transmitter and receiver Block frequency selective fading

An equivalent parallel channel model

                                                           

Power ControlPower Control The power distribution threshold

Spectral water filling Variable rate and variable power across channels

– Different size code books– Multiplexing encoders and decoders

Achievable Rate

                                                           

Frequency Selective FadingFrequency Selective Fading Continuous transfer function

Power distribution across spectrum

                                                           

Techniques to Approach CapacityTechniques to Approach Capacity Coding Accurate model

– Statistical– Deterministic

Feedback– Power control– Rate control

Multipath maximal ratio combing

HW3: 4.3, 4.5, 4.6, 4.8, 4.11, due 3/6/17