Adaptation in 4g and 5g

1. Link Adaptation and Adaptive
Modulation and Coding
Name- Dilshad Ahmad
Roll No-MT/EC/10007/19
Subject Code-EC560
ECE Dept. , BIT Mesra, Ranchi, 835215
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Contents:
 Motivation
 Introduction
 Working
 Coding Gain and BER
 Adapting Energy per bit
 Adapting Coding Technique
 Adapting Modulation Technique
 Adapting Energy per bit, Coding and Modulation Technique
 Enabling AMC in 4G and 5G Technology
 RL Learning in 5G
 Challenges
 Pros and Cons
 Conclusion
 References

3. Introduction
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 Link adaptation is technique to adapt the link efficiently in the actual channel conditions
by varying certain transmission parameters.
 Transmission power
 Code rate,
 constellation size and
 coding scheme can be dynamically adapted in response to the time-varying channel.
 So In link adaptation, whole study about to maintain a certain QoS(BER) by dynamically
changing certain parameters according to time varying channel…

4. Motivation
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 There is Continuously Variation in channel condition
 There is also limitation of Transmission power available (Energy per bit),Bandwidth
Limitation , Coding Limitations and many like things
 Wireless spectrum is a scarce resource, and how to use this resource efficiently has been
the main driving requirement for all past, current, and future standards
 So as continuously variation in behavior of channel there should not a fixed Techniques
 So We require Adaptive Techniques for Modulation, coding ,Power and Bandwidth

5. WORKING of ACM
 The receiver constantly measures the received signal-to-noise ratio (SNR) and block
error rate (BLER)
 It selects an appropriate modulation and coding scheme (MCS) from the available
AMC set to meet the BLER requirement, and reports that selection (known as channel
quality information (CQI)) to the transmitter through a feedback channel
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6. 5/30/2020
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Fig. Coding Gain
Fig. Modulation Gain
Coding and Modulation and BER

7. Adapting Energy per Bit
7
So Now We know
No. of error we
can correct
Modulation
Technique
NOW FIX BER
Tolerable
AWGN
Channel
Checking
No of
errors and
BER
IF BER
Exceed,
under the
range
Decrease
EbNo
Increase
EbNo
 Let Hamming Coding (7,4)
 10000 blocks ,In 1 block 4 msg bit
 Total And 40000 Bits sending
 So 10000 errors can be recovered in worst Situation
Initial EbNo
YES
NO
Choose A
coding
Technique
Fixed ModulationFixed Coding Adapting Power

8. Adapting Coding Techniques
8
So Now We
know No. of
error we can
correct
Modulation
Technique
NOW FIX BER
Tolerable
AWGN
Channel
Checking
No of
errors and
BER
IF BER
Exceed,
under the
range
Update
Coding
Technique
Fixed Power
Initial EbNo
Choose A
coding
Technique
 Let Initial Hamming Coding (7,4) for One Error Correcting
 As we have fixed EbNo so update Coding Technique like 2 error Correcting , or Cyclic code
,convolutional code ,Turbo code etc.

9. Adapting Modulation Techniques
9
So Now We
know No. of
error we can
correct
Modulation
Technique
NOW FIX BER
Tolerable
AWGN
Channel
Checking
No of
errors and
BER
IF BER
Exceed,
under the
range
Update
Modulation
Technique
Fixed Power
Initial EbNo
Choose A
coding
Technique
 Let Hamming Coding (7,4)
 1000 blocks ,In 1 block 4 msg bit
 Total And 40000 Msg Bits sending
Fixed Coding
Technique
Adapting Modn
Adapting Modulation
BPSK,QPSK,M-PSK,QAM

10. Adapting Energy per Bit, Coding and Modulation
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So Now We know
No. of error we
can correct
Modulation
Technique
NOW FIX BER
Tolerable
AWGN
Channel
Checking
No of
errors and
BER
IF BER
Exceed,
under the
range
Decrease
EbNo
Increase
EbNo
Initial EbNo
YES
NO
Choose A
coding
Technique
Adapting Power
Update
Modulation
Technique
Update
Coding
Technique
 Most Complex System
 All are Adapting at a time
 Energy per Bit
 Coding Technique
 Modulation Technique

11. Enabling AMC in 4G and 5G Technology
 4G long term evolution (LTE), the BLER target is fixed at 10% but for 5G it Much Enhanced
 4G (LTE) as an example OF where the BS uses downlink control information (DCI) embedded into
the physical downlink control channel (PDCCH)
 In 5G AMC potentially addressed by machine learning. While in 4G LTE, a look-up table provides
fixed AMC rules for all the users,
 Emerging systems need a more flexible approach that can automatically adjust physical layer
parameters (such as the modulation and coding scheme) according to the user channel state and
service type.
 Reinforcement learning (RL) refers to a category of ML techniques that has been applied to
problems such as
 Backhaul optimization
 Coverage
 Capacity optimization
 Resource optimization 5/30/2020
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12. RL Learning/Q-learnings in 5G
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Fig. 3: Basic diagram of a RL scheme
 RL is a ML technique that aims to find the best behavior in a
given situation in order to maximize a notion of accumulated
reward .
 RL agent learns from trial and error, i.e., from its
experience, by interacting with the environment.
• Agent, which is the
learner and the
decision maker
• At each time step t, the agent
receives the state st of the
environment and chooses an action
at.
• As consequence of its action, the
agent receives a Reward rt+1
• The goal of the RL agent is to find the best policy that represents the best
mapping of states to actions
Adaptive Modulation and Coding based on Reinforcement Learning for 5G Networks,25 Nov 2019https://www.researchgate.net/publication/337855423
Mateus P. Mota, Daniel C. Ara´ujo, Francisco Hugo Costa Neto, Andr´e L. F. de Almeida, F. Rodrigo P. Cavalcanti GTEL - Wireless Telecommunications Research Group
Federal University of Cear´ a Fortaleza, Brazil {mateus, araujo, hugo, andre, rodrigo}@gtel.ufc.br

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Adaptive Modulation and Coding based on Reinforcement Learning for 5G Networks,25 Nov 2019https://www.researchgate.net/publication/337855423
Mateus P. Mota, Daniel C. Ara´ujo, Francisco Hugo Costa Neto, Andr´e L. F. de Almeida, F. Rodrigo P. Cavalcanti GTEL - Wireless Telecommunications Research Group
Federal University of Cear´ a Fortaleza, Brazil {mateus, araujo, hugo, andre, rodrigo}@gtel.ufc.br
Proposed approach, the BS selects the MCS based on the state-action mapping obtained from
the Q-learning algorithm. More specifically, the BS chooses the MCS using the Q-table obtained
from the RL algorithm. The RL based solution enables the system to learn the particularities of
the environment and adapt to it.
The goal of this reward function is to allow the agent to choose the best MCS that satisfies the BLER
target. The second reward is defined in terms of the spectral efficiency (in bits/second/hertz):
µ - number of bits per modn symbol
ν - code rate
BLERT - target BLER of the system
Continued…

14. Implementation using MATLAB
% AS THIS SYSTEM IS DESIGNED FOR MAINTAING BER
=0.084 TO 0.125 AND ERRORS
% BELOW 5000 ACCEPTABLE BUT WE CAN CORRECT
10000 ERRORS IN WORST CASE WITH
% BER =0.25 IN WORST CASE
EbN0dB=input("PLZ ENTER Initial Eb/No (dB) = ");
t=1;
for i=1:1:50
R=4/7; %K=4 and n=3
EbN0=10^(EbN0dB/10);
sigma=sqrt(1/(2*R*EbN0)); % EbNo=1/2R(sigma)^2
k=4; % Message Bits
n=7; % Total Number of Bits 5/30/2020
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Nerrs=0; Nblocks=10000;
for i = 1:Nblocks
msg=randi([0,1],1,k);
%**************Encoding**********
cword=[msg
mod(msg(1)+msg(2)+msg(3),2)...
mod(msg(2)+msg(3)+msg(4),2)...
mod(msg(1)+msg(2)+msg(4),2)];
s=1-2*cword; % BPSK bit to symbol
conversion mapping
r= s+sigma*randn(1,n);

15. Continued…
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% ************Hard Decoding*****************
b=(r<0); % Thresholg at Zero best for bpsk demod
dist= mod(repmat(b,16,1)+cwords,2)*ones(7,1);
[mind1,pos]=min(dist);
msg_cap1=cwords(pos,1:4);
%**********Soft Decoding*************
corr=(1-2*cwords).*r;
[mind2,pos]=max(corr);
msg_cap2=cwords(pos,1:4);
Nerrs=Nerrs+sum(msg~=msg_cap1); % Total Errors
end
BER_sim(t)=Nerrs/k/Nblocks; %Bit Error Rate
Calculation
%**** QoS Varifying and Updating to Transmitter
******
if(Nerrs>4000)
disp('Increasing SNR ');
EbN0dB=EbN0dB+1;
else
EbN0dB=EbN0dB-1;
disp('Decreasing SNR ');
end
disp([EbN0dB R BER_sim(t) Nerrs k*Nblocks]);
y(t)=10*log(BER_sim(t)); %log BER
t=t+1;
End
x=1:1:50;
plot(x,BER_sim,'ro’);
title(' BER PLOT');
xlabel('Iterations Adapting EbNo');
ylabel('BER(dB)');
legend('Adaptive EbNo');
grid on;
Error
Verification

16. Final Plot of BER
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 Al last it locked to the
specified BER (QoS)
 If in between channel
varies and errors are
more again after some
iteration it locked with
specified QoS

17. Challenges
 Complexity is very High
 Continuously Update of Lookup Table as for Requirement
 A Dedicated Unit is required to handle all of that
 Implementation of Learning Algorithms
 Latency (for 5g its recovered as 1ms only)
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18. Conclusion
 Today’s Device is integrated with many features in one unit and at any time it
changes so this is Most important for todays to adapt the Modulation, Coding and
sometime power level for utilize resources Efficiently, and lots of research is going
on to do this efficiently.
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19. [1].Adaptive Modulation and Coding based on Reinforcement Learning for 5G Networks, Mateus P.
Mota, Daniel C. Ara´ujo, Francisco Hugo Costa Neto, Andr´e L. F. de Almeida, F. Rodrigo P. Cavalcanti
GTEL - Wireless Telecommunications Research Group Federal University of Cear´ a Fortaleza, Brazil
{mateus, araujo, hugo, andre, rodrigo}@gtel.ufc.br, November 2019
[2].Live modulation and coding (AMC) selection in LTE systems using reinforcement learning,” in 2014
IEEE 80th Vehicular Technology Conference (VTC2014-Fall), IEEE, 2014, pp. 1–6.
[3].M. Miozzo, L. Giupponi, M. Rossi, and P. Dini, “SwitchOn/Off Policies for Energy Harvesting Small
Cells through Distributed Q-Learning,” 2017 IEEE Wireless Communications and Networking
Conference Workshops (WCNCW), pp. 1–6, 2017.
[4].E. Dahlman, S. Parkvall, and J. Skold, 5g nr: The next generation wireless access technology.
Academic Press, Aug. 2018, vol. 1, ISBN: 978-01-2814-323-0.
[5].M. G. Sarret, D. Catania, F. Frederiksen, A. F. Cattoni, G. Berardinelli, and P. Mogensen, “Dynamic
Outer Loop Link Adaptation for the 5G Centimeter-Wave Concept,” in Proceedings of European
Wireless 2015; 21th European Wireless Conference, May 2015, pp. 1–6.
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References

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