Resource allocation in Green Wireless Communication
Dr. Muhammad Naeem
COMSATS University Islamabad, Wah Campus
&
WINCORE Lab, Ryerson University
Toronto, Canada
1
Outline
Why Green
Emerging Wireless Paradigms
Resource Allocation
Convex and Non-Convex Resource Allocation
Difference between traditional and green communication
modeling
Some formulations
2
Future wireless systems challenges
Wireless spectrum availability
High data rate
Coverage
Global warming
Limited resources
3
Green Communication
Information and communication technologies are responsible for 5% of global
greenhouse gas emissions.
Green communication deals
with the issue of designing
environmental friendly and
energy efficient
communication systems
4
Green Communication
In year 2002, ICTs caused 150 mega tons of CO2 emissions
In year 2020, ICTs will cause 350 mega tons of CO2 emissions
5
Global rise in energy demand – 37% annually
From 2013 to 2035
80 % met from Fossil fuels
Coal, Gas and Oil
Cellular Energy Consumption
Single BS
1.3
KWh
8,800
KWh/yr
12-15,000
Cell Sites
2G & 3G
3 Antenna
736,000
MWh/yr
Mobile broadband growth
• It is assumed that by
2025, 50 billion
devices will be
connected to the
global IP network
3/18/2019
7
Mobile broadband growth
• “Smart living” will
require constant and
ubiquitous mobile
connectivity to the
network to upload
activity data and IoT
control commands
3/18/2019
8
Mobile broadband growth
• 5G/B5G will provide seamless compatibility with
dense heterogeneous networks (HetNets) to satisfy
the high demand of real-time traffic
• 5G : high throughput, low-latency, high reliability,
increased scalability, and energy efficient mobile
communication technology
3/18/2019
9
Mobile Communication Growth & GHG Emissions
Global mobile communication growing rapidly
2020
7.6 billion
Increased energy consumption of networks
2020
98 TWh
Global warming from green house gas emissions
ICT
7.8 gtCO2
Most ICT Energy Consumption
Data Centre
Base Station
Green Base Station
3/18/2019
12
Resource Allocation
Power
Relay Selection
User scheduling
Routing
Delay
QoS
Fairness
Subcarrier Allocation
13
Resource Allocation
14
Resource Allocation
Power
Inputs :(any combination)
Relay Selection
Number of users
User scheduling
Number of primary/Secondary users
Routing
Number of relays/hops
Delay
Interference threshold on primary users
QoS
Cooperative protocol type
Fairness
Channel state/side information
Subcarrier Allocation
Geographic locations of Users
Network related custom inputs
15
Resource Allocation
Power
Relay Selection
Find: (any combination)
User Power
Source/Relay power
User scheduling
Assignment/Selection of relays
Routing
User Selection/Scheduling
Delay
Subcarrier/Band width Allocation
Routing
QoS
Relay deployment/placement
Fairness
Network related custom variables
Subcarrier Allocation
16
Resource Allocation
Power
Objectives:(any combination)
Minimize: Total power
Relay Selection
Minimize: Per node power/ Investment/Emissions
User scheduling
Minimize: Bit error rate
Routing
Minimize: Waste Material
Minimize: Outage probability
Delay
Minimize: Delay//Operating Cost/Fuel Cost
QoS
Maximize: Sum-Rate/Revenue
Fairness
Maximize: Weighted Sum-Rate
Maximize: Energy-Efficiency/ Life Span
Subcarrier Allocation
Maximize: Utility / Reliability/ Production
Max-Min: (Worst user’s) SNR/Capacity
Max/Min: Network related custom objectives
17
Resource Allocation
Constraints :(any combination)
Power
Source/Relay power constraint
Relay Selection
Interference constraint
User scheduling
Relay selection/Assignment constraint
QoS constraint
Routing
Bandwidth/Delay/Fairness constraint
Delay
Fairness constraint
QoS
Probabilistic interference and QoS constraints
Topology constraints
Fairness
Outage constraints
Subcarrier Allocation
Economic constraints
18
Green Multi-Objective Optimization Conflict Graph
QoS
Network/Battery
life
N/B
QoS
Coverage
Cov
Cost
D
PER
Cost
T
Throughput
PER
Conflicting
Design
dependent
Delay
Supporting
19
Green Multi-Objective Optimization Conflict Graph
Relation between conflicting objectives. Max-R: maximize revenue, Min-E: minimize emissions, Max-RL:
maximize reliability, Max-P: maximize production, Min-OC: minimize operating cost, Min-I: minimize investment,20
Min-FC: minimize fuel cost, Max-LS: maximize life span, Min-WM: minimize waste.
Difference Between Green and
Traditional Resource Allocation
: A CRN case
Channel between the mth PU and the kth SU
Channel between the BS and the kth SU
α
 do 
hk = Φ Ko  
 dk 
Protected distance based PU Model
K Secondary Users
M Primary Users
21
Difference Between Green and
Traditional Resource Allocation
1.
Cognitive radio technology itself has a built in green
communication capability.
2.
Cognitive radio sets an upper limit on the interference level on
the primary users that eventually limit the transmit power of
the secondary users.
Main Objectives:
1.
Maximize Total Throughput or Minimize Total Power
2.
Protect the Cognitive radio users
22
Traditional Resource Allocation
Maximize Total throughput of cognitive radio network
while meeting the interference and total power
constraints.
Max ( Total Spectral Efficiency )
23
Traditional Resource Allocation
Sum-Rate Maximization

pk hk 
log
1
+
∑

2
p
No
k =1


Subject to
K
max
C1 :
C2 :
C3 :
∑
∑
K
k =1
K
k =1
pk g m ,k ≤ I m , ∀m
pk ≤ P
pk ≥ 0, ∀k
Maximize Total throughput
of cognitive radio network
while meeting the
interference constraints due
to the primary users and
total power constraint
24
Traditional Resource Allocation
Sum-Power Minimization
Sum-Rate Maximization
K

pk hk 
log
1
+
∑

2
p
No
k =1


Subject to
K
min
max
C1 :
C2 :
C3 :
∑
∑
K
k =1
K
k =1
pk g m ,k ≤ I m , ∀m
pk ≤ P
pk ≥ 0, ∀k
Maximize Total throughput
of cognitive radio network
while meeting the
interference constraints due
to the primary users and
total power constraint
p
∑p
k
k =1
Subject to
Dual
C1 :
∑
K
k =1
pk g m ,k ≤ I m , ∀m

pk hk
C 2 : log 2 1 +
No

C 3 : pk ≥ 0, ∀k

 ≥ Rk , ∀k

Minimize Total Power of
cognitive radio network
while meeting the
interference constraints due
to the primary users and rate
constraint
25
Difference Between Green and
Traditional Resource Allocation
Sum-Rate Maximization
Traditional

ph 
max ∑ log 2 1 + k k 
p
No 
k =1

Subject to
K
C1 :
s
p
g
∑ k =1 k m,k ≤ I m , ∀m
Green
Spectral efficiency

pk hk 
log 2 1 +
∑

No
k =1


max
K
p
φ + ∑ k =1 pk
K
K
C2 :
∑
C3 :
pk ≥ 0, ∀k
K
k =1
pk ≤ P
Energy Efficiency
Total power
Subject to
C1 :
s
p
g
∑ k =1 k m,k ≤ I m , ∀m
K
C2 :
∑
C3 :
pk ≥ 0, ∀k
K
k =1
pk ≤ P
Maximize energy efficiency in bits per joule of cognitive radio network while
meeting the interference constraints due to the primary users
26
Difference Between Green and
Traditional Resource Allocation
Sum-Rate Maximization
Traditional

ph 
max ∑ log 2 1 + k k 
p
No 
k =1

Subject to
K
C1 :
s
p
g
∑ k =1 k m,k ≤ I m , ∀m
Green
Fractional
Spectral efficiency

pk hk 
log 2 1 +
∑

Programming
No
k =1


max
K
p
φ + ∑ k =1 pk
K
K
C2 :
∑
C3 :
pk ≥ 0, ∀k
K
k =1
pk ≤ P
Convex Optimization
Energy Efficiency
Total power
Subject to
C1 :
s
p
g
∑ k =1 k m,k ≤ I m , ∀m
K
C2 :
∑
C3 :
pk ≥ 0, ∀k
K
k =1
pk ≤ P
Maximize energy efficiency in bits per joule of cognitive radio network while
meeting the interference constraints due to the primary users
Non-Convex Optimization
27
Energy Efficiency Objective
The Optimization Objective is non-convex function of SUs’ Power
But
The objective function has special structure as shown in the figure
28
Energy Efficiency Objective
The Optimization Objective is non-convex function of SUs’ Power
But
The objective function has special structure as shown in the figure
0.05
0.138
0.045
1
0.04
0.035
0.6
0.172
0.03
p1 (watts)
Normalized EE
0.8
0.4
0.207
0.025
0.02
0.2
0.241
0.015
0
0.05
0.04
0.05
0.03
0.276
0.01
0.04
0.03
0.02
p1 (watts)
0.414
0.005
0.517
0.02
0.01
0.01
0
0
0
p2 (watts)
0.31
0.345
0.379
0
0.005
0.01
0.015
0.02
0.025
0.03
p (watts)
0.035
0.04
0.045
2
The objective function is a Quasi-Concave function
Local Maximum is the Global Maximum
29
0.05
Optimal and epsilon Optimal Solutions
Optimal
1.
Introduce a new variable and two constraints to transform the min term
inside the objective
2.
Apply Charnes-Cooper Transformation to get an equivalent convex
optimization problem
3.
Get optimal solution with any standard convex optimization tool
30
Optimal and epsilon Optimal Solutions
Optimal
Transformed
Original

pk hk 
log 2 1 +
∑

No
k =1


max
K
p
φ + ∑ k =1 pk

yk hk 
max t ∑ log 2 1 +

y ,t > 0
tNo
k =1


Subject to
Subject to
C1 :
K
K
C1 :
C2 :
C3 :
∑
∑
K
k =1
K
k =1
pk g
s
m,k
≤ I m , ∀m
pk ≤ P
CCT
∑
∑
K
k =1
C2 :
K
k =1
yk g m ,k −I m ≤ 0, ∀m
yk − tP ≤ 0
C 3 : tφ + ∑ k =1 yk = 1
K
pk ≥ 0, ∀k
Convex Optimization
Non-Convex Optimization
Parametric or charnes cooper transformation
31
ε–optimal power allocation
Iterative Power Allocation
1.
The EE fractional maximization problem is transformed into EE
parametric optimization problem.
2.
The optimal solution of the EE parametric optimization gives the optimal
solution of EE fractional maximization.
3.
For any nonnegative ε, the algorithm ensures the solution is always
within ε of optimal.
32
ε–optimal power allocation
33
Simulation Parameters
1.
Maximum BS coverage distance = 1000m
2.
Static and leakage power, Φ = 10-6 Watts
3.
Ko = 50
4.
PU protected distance = 10m
5.
Reference distance for the antenna far field, do = 10,
6.
Path loss exponent, α = 3
34
EE and SE comparison with K = 5 and M = 1
5
5
x 10
1.8
4.5
1.6
4
1.4
3.5
1.2
3
-6
10
-5
10
-4
10
Im (Watts)
-3
10
SE(bits/s/Hz)
EE(bits/Joule/Hz)
EE
SE
1
-2
10
35
Performance of IPA with number of iteration
Only few iterations
required by the
iterative algorithm
to converge
7
EE(bits/Joule/Hz)
10
More number of
user means more
EE
6
10
K = 5, Im = 1µW
K = 25, Im = 1µW
K = 45, Im = 1µW
K = 5, Im = 1mW
5
10
K = 25, Im = 1mW
K = 45, Im = 1mW
2
4
6
8
10
Number of Iterations
12
With increase in SUs,
IPA needs more
number of iteration for
its convergence
14
Due to cognitive radio’s built in green communication capability, the iterative
algorithm requires less number of iterations to converge at low interference
threshold
36
Effect of ε on the iterations of IPA
with Im=1µW
16
14
ε = 0.1
Increase in number of
ε = 0.000001
PUs decreases the
number iterations of
(K,M)=(45,1)
Number of iterations
12
(K,M)=(45,20)
(K,M)=(5,1)
10
IPA to converge to a
given epsilon
8
(K,M)=(5,20)
6
4
2
0
1
2
3
4
37
Effect of ε on the iterations of IPA with
Im=100µW
18
ε = 0.1
ε = 0.000001
16
(K,M)=(45,1)
Number of iterations
14 (K,M)=(5,1)
(K,M)=(45,20)
Increase in number of
PUs decreases the
number iterations of
IPA to converge to a
12
(K,M)=(5,20)
given epsilon
10
8
6
4
2
0
1
2
3
4
38
Effect of ε on the iterations of IPA with
Im=1W
ε = 0.1
18
ε = 0.000001
(K,M)=(45,1)
16 (K,M)=(5,1)
(K,M)=(45,20)
PUs decreases the
number iterations of
14
Number of iterations
Increase in number of
(K,M)=(5,20)
IPA to converge to a
12
given epsilon
10
8
6
4
2
0
1
2
3
4
39
Effect of ε on the iterations of IPA
Im=1uW
Im=100uW
18
16
ε = 0.1
ε = 0.000001
16
ε = 0.1
ε = 0.000001
14
(K,M)=(45,1)
(K,M)=(45,1)
12
(K,M)=(5,20)
10
8
6
(K,M)=(45,20)
(K,M)=(5,1)
10
8
(K,M)=(5,20)
6
4
4
2
2
0
12
(K,M)=(45,20)
Number of iterations
Number of iterations
14 (K,M)=(5,1)
1
2
3
4
0
1
2
3
4
40
Future Directions
Radio Resource Management for the following
Renewable Energy Assisted Base Station Placement
Green Aerial Base Stations
Energy Harvesting
Green Maintenance Scheduling in Smart Grid
Green Sensor Network With Wireless Power Transfer
Green Cyber Physical Systems
Energy Efficient D2D and M2M Networks
Energy Efficient Cloud Computing
41
Thank You
42