This document discusses device-to-device (D2D) communication in heterogeneous networks (HetNets). It contains 3 key contributions: 1. Analyzing whether D2D communication can improve throughput in HetNets when small cells reuse the same spectrum as macro cells. A sequential max search algorithm is proposed for resource allocation. 2. Proposing ways to enhance energy efficiency in D2D-powered HetNets through dynamic mode selection, resource allocation, and power control. Both dedicated and reuse modes of D2D communication are considered. 3. Developing a stochastic analytical model to quantify the impact of LTE scheduler type on D2D communication in HetNets.

1. The Coexistence of Device -to- Device (D2D) Communication
under Heterogeneous networks ( HetNets)
Ph.D. dissertation by : Amal Algedir

2. Contents
1. Motivation
2. Introduction
 5G Vision
 D2D Communication
3. Contributions
 Can D2D improve throughput in HetNets environment as well when small cells re-use same spectrum as macro cells?
 How can we enhance Energy efficiency as well in D2D communication powered HetNets?
 Can we quantify the impact of LTE scheduler type in D2D communication powered HetNets using stochastic analytical model
4. Conclusions and Future work
2

3. Introduction
Motivation
 Massive growth on network traffic
 Mobile data traffic will increase sevenfold between 2017 and 2022.
 46% CAGR expected
 Massive growth in connected devices
 Global mobile devices will grow from 8.6 billion in 2017 to
12.3 billion by 2022
 Scarcity of radio-frequency spectrum
 Increase of the energy consumption
 Increase the global footprint of CO2 of Mobile Communications
3
Source:ciscoVNI2017–2022

4. 5G future network vision
5G
Requirements
1000X
More traffic
10-100X
More devices
<1
Millisecond
latency
10 years
battery life for
IOT
1000 x
Bandwidth per
unit area
90 % reduction
in energy
usage
UP TO 10G bps
Data rate
Availability
99.999%
 5G is expected to support a massive requirement
where networks can serve communication needs for
billions of connected devices, with the right trade-
offs between speed, latency and cost.
4

5. 5G
Device-to device
Communication
Utra-
densification
Massive MIMO
Radio Access
techniques
Millimeter wave
(mmWave)
& terahertz band
Internet of
things
(IOT)
5G Technologies
Direct communication between users in close proximity
Deploy more small base station under
macro base station (capacity ,coverage )
The concept of group antennas at the
transmitter , receiver
( throughput, spectrum efficiency)
The use of under-utilized
spectrum ( bandwidth
shortage)
The concept of connecting
any device to the Internet
(and/or to each other)
Introduction 5
Evolution of existing technology + New radio-access technology

6. Introduction
D2D communication Technology
D2D
Cellular
D2D
Overlay
Inband
D2D
Cellular
Underlay
Cellular Spectrum Cellular Spectrum
Cellular
Cellular Spectrum
D2D Comm.
ISM Spectrum
Outband
Tim
e
6
 The connection between user equipment necessitates the
use of BS. D2D communication refers to a radio
technology that allows devices to directly exchange data
without use of a BS
 Inband
D2D communication uses cellular network licensed
spectrum.
 Underlay
 Overlay
 Outband
D2D communication exploits the unlicensed industrial,
scientific, and medical (ISM) band spectrum.

7. Introduction
Why D2D communication ?
 Device-centric architectures
 Shifting from an architecture-based (e.g. involving base stations) to a device-centric
approach (e.g. ability to establish and exchange information between nodes).
 Proximity Gain
 Low- end-to- end latency
 Low power consumption
 High data rate.
 Reuse gain
 Reuse of cellular resources – improve spectral efficiency
 Improve energy efficiency
7

8. Introduction
Why D2D communication?
 Support wide range of applications
 Public safety, Commercial / social services, Network offloading, etc.
Traffic Safety
Public Safety
Relaying
SHARE
SHARE
SHARE
Content Sharing
Social and commercial services Game application
Special
offer
8

9. Introduction
D2D Challenges
 Peer discovery and synchronization.
 Open discovery ( UE battery drain, increase energy consumption, security threat )
 Network assistance discovery (large signal overhead, limitation of scalability)
 Mode selection
 What timescale should mode selection be performed ( Static Vs dynamic)
 Which Measurements (e.g., Signal-to-Noise ratio (SNR), pathloss, distance) should be used to decide the mode of
the users
 Interference management.
 Interference management is the most critical issue in underlaying D2D communication ( power control , resources
allocation )
9

10. C1: Interference management
Can D2D improve throughput in HetNets environment as well when small cells reuse same spectrum as macro cells?
GSB
,s
m
s
Macro Bs
Small BS
Macro
small/user
D2D user
Macro BS Interference
Communication Link
Small BS Interference
Device-to- Device Interference
 Consider downlink reuse.
 Frequency reuse of one.
 Cellular allocation is not considered in this work.
 Cellular users associated with base stations that based on
maximum reference received power.
 In each tier, a cellular user occupied only one RB.
 only one D2D pair can share RB with preassigned cellular user
 Base stations and D2Dtx transmission powers are assumed
fixed.
Assumption System model
10Contribution 1

11. Contribution 1
C1: D2D Resources Allocation under HetNets
𝜸 𝒎
𝒌
=
𝑃 𝑀𝐵 ∗ 𝐺 𝑀𝐵,𝑚
𝑘
𝑁0 + 𝑗=1
𝑑
𝑥𝑗
𝑘
ℎ𝑗,𝑚
𝑘
𝑝𝑗 + 𝑗=1
𝑁
𝑌𝑆𝐵 𝑗
𝑘
ℎ 𝑆𝐵𝑗,𝑚 𝑃𝑆𝐵𝑗
𝜸 𝒔
𝒌
=
𝑃𝑆𝐵 ∗ 𝐺𝑆𝐵,𝑠
𝑘
𝑁0 + 𝑗=1
𝑑
𝑥𝑗
𝑘
ℎ𝑗,𝑚
𝑘
𝑝𝑗 + 𝑌 𝑀
𝑘
ℎ 𝑀𝐵,𝑠
𝑘
ℎ 𝑀𝐵,𝑠 𝑃 𝑀𝐵
𝜸𝒊
𝒌
=
𝑥𝑖
𝑘
𝑝𝑖 ∗ 𝐺𝑖
𝑘
𝑁0 + 𝑌 𝑀
𝑘
ℎ 𝑀𝐵,𝑖 𝑃 𝑀𝐵 + 𝑗=1
𝑁
𝑌𝑆𝐵 𝑗
𝑘
ℎ 𝑆𝐵,𝑖 𝑃𝑆𝐵𝑗
 Signal to Interference-plus-Noise Ratio (SINR)
𝑇 = 𝑤 𝐵 log2(1 + 𝛾(𝑥𝑖
𝑘
))
𝒙𝒋
𝒌
= 1 𝑘 𝑡ℎ
𝑅𝐵 𝑎𝑠𝑠𝑔𝑖𝑛 𝑡𝑜𝒋
0 𝑜𝑤
11
G
SB,
s
m
s

12. Contribution 1
C1: Throughput Maximization Problem Formulation
 Objective: maximize overall throughput by minimizing interference from D2D communicationto cellular users
 Constraints: minimum rate requirement of users all users
 Optimization variables: D2D resource allocation (𝑋 𝐷
𝐾
)
max
𝑋 𝐷
𝐾
𝑘=1
𝑘
𝑇 𝑈 𝑀(𝑋 𝐷
𝐾
) + 𝑇 𝑈 𝑆(𝑋 𝐷
𝐾
) + 𝑇 𝑈 𝐷 (𝑋 𝐷
𝐾
)
𝑤 𝐵log2(1 +
𝑥𝑖
𝑘
𝑝𝑖 ∗ 𝐺𝑖
𝑘
𝑁0 + 𝑌 𝑀
𝑘
ℎ 𝑀𝐵,𝑖 𝑃 𝑀𝐵 + 𝑗=1
𝑁
𝑌𝑆𝐵 𝑗
𝑘
ℎ 𝑆𝐵,𝑖 𝑃𝑆𝐵𝑗
) ≥ 𝑅𝑖
𝑚𝑖𝑛
𝑤 𝐵log2(1 +
𝑃 𝑀𝐵 ∗ 𝐺 𝑀𝐵,𝑚
𝑘
𝑁0 + 𝑗=1
𝑑
𝑥𝑗
𝑘
ℎ𝑗,𝑚
𝑘
𝑝𝑗 + 𝑗=1
𝑁
𝑌𝑆𝐵 𝑗
𝑘
ℎ 𝑆𝐵𝑗,𝑚 𝑃𝑆𝐵𝑗
) ≥ 𝑅 𝑚
𝑚𝑖𝑛
𝑤 𝐵log2(1 +
𝑃𝑆𝐵 ∗ 𝐺𝑆𝐵,𝑠
𝑘
𝑁0 + 𝑗=1
𝑑
𝑥𝑗
𝑘
ℎ𝑗,𝑚
𝑘
𝑝𝑗 + 𝑌 𝑀
𝑘
ℎ 𝑀𝐵,𝑠
𝑘
ℎ 𝑀𝐵,𝑠 𝑃 𝑀𝐵
) ≥ 𝑅 𝑠
𝑚𝑖𝑛
𝑘=1
1
𝑥𝑖
𝑘
= 1 ∀ 𝑘 ∈ 𝐾
𝑑=1
1
𝑥𝑖
𝑘
= 1 ∀ 𝑖 ∈ 𝐷
12
Subject. To
MINP problem
difficult to be solved
in schedule time

13. Contribution 1
C1: Sequential Max Search (SMS) Algorithm
1) Set Maximum Interference Threshold
2) Identify Optimal Resource Blocks Candidate
13

14. Contribution 1
C1: SMS Algorithm (cont)
ψRBs(i): a set contains RBs that can be share without violating constraints C4 and C5
3) Allocate Resources Blocks
 Compute the throughput at optimal resource blocks.
 Sequential search is performed to match a D2D pair to an RB once at the time given the priority to D2D pair
that achieved maximum gain in each RB.
14

15. Contribution 1
C1: Simulation Setting
15
 SINR distribution
 SINR of D2D pairs separation distance less than 40 m was
better than SINR of SB users.
 The interference from D2D user does not signicantly aect the
SINR of users under MB. Since the power of the UE is smaller
compared to the power of MB.

16. Contribution 1
C1: Simulation Results
 Throughput verse number D2D number.  Throughput verse D2D separation distance.
16
 D2D communication showed an improvement of HetNets
throughput.
 Throughput obtained using SMS allocation was very close to
throughput obtained using brute-force.
 SMS results always outperforms random or Hungarian allocation.
 As the separation distance increases, the throughput
gain reduces consequently.
 Brute force and SMS allocations follow the same trend,
and they were achieving a gain in HetNets throughput up t
80m.

17. Contribution 2
C2: Energy- Efficient D2D Communication
𝐸𝐸 =
𝑇ℎ𝑟𝑜𝑢𝑔ℎ𝑝𝑢𝑡
𝑝𝑜𝑤𝑒𝑟 𝑐𝑜𝑛𝑠𝑢𝑚𝑝𝑡𝑖𝑜𝑛
What is Energy efficiency(EE)?
 EE is the ratio of the throughput to power consumption
(bits-per-joule)
max
max
{𝑧 𝑑𝑚,𝑍 𝑅𝑠,𝑌 𝐷
𝐾,𝑃 𝐷,𝑃 𝑀𝐵,𝑃 𝑆𝐵} 𝑖=𝑖
𝑑
𝐸𝐸𝑖
Maximize the sum of EE for D2D users through dynamic mode selection, resource allocation (reuse mode),
Power control.
17
How can we enhance Energy efficiency as well in D2D communication powered HetNets ?

18. C2: System Model
 HetNets supporting D2D communication in dedicated
and reuse modes.
 Frequency reuse of one.
 a set of small BSs distributed within the MB coverage
area.
 Cellular allocation is not considered in this work.
 In each tier, a cellular user occupied only one RB.
 only one D2D pair can share RB with preassigned
cellular user
Assumption
System model
Contribution 2 18

19. Contribution 2
C2:D2D Communication Mode
 Dedicated Mode (DM).
 orthogonal resources assign to D2D users so no co-channel
interference occur.
 Ruse Mode (RS).
 D2D users share the CUEs channel, co- channel interference for users
in each tier
𝜸 𝒎
𝒌 =
𝑃 𝑀𝐵 ∗ 𝐺 𝑀𝐵,𝑚
𝑘
𝑁0 + 𝑗=1
𝑑
𝑦𝑗
𝑘
ℎ𝑗,𝑚
𝑘
𝑝𝑗 + 𝑗=1
𝑁
𝑌𝑆𝐵 𝑗
𝑘
ℎ 𝑆𝐵𝑗,𝑚 𝑃𝑆𝐵𝑗
𝜸 𝒔
𝒌 =
𝑃𝑆𝐵 ∗ 𝐺𝑆𝐵,𝑠
𝑘
𝑁0 + 𝑗=1
𝑑
𝑦𝑗
𝑘
ℎ𝑗,𝑚
𝑘
𝑝𝑗 + 𝑌 𝑀
𝑘
ℎ 𝑀𝐵,𝑠
𝑘
ℎ 𝑀𝐵,𝑠 𝑃 𝑀𝐵
𝜸𝒊
𝒌
=
𝑦𝑖
𝑘
𝑝𝑖 ∗ 𝐺𝑖
𝑘
𝑁0 + 𝑌 𝑀
𝑘
ℎ 𝑀𝐵,𝑖 𝑃 𝑀𝐵 + 𝑗=1
𝑁
𝑌𝑆𝐵 𝑗
𝑘
ℎ 𝑆𝐵,𝑖 𝑃𝑆𝐵𝑗
𝛾𝑖
𝑘
=
𝑝𝑖 𝐺𝑖
𝑘
𝑁0
19

20. Contribution 2
C2:D2D User Selection
 Transmitter to receiver (RSRPDr) is greater than the
minimum association RSRP
RSRPDr ≥ βmin
 RSRPDr is higher than minimum RSRPUL and RSRPDL.
RSRPDr ≥ min{RSRPDL,RSRPUL}.
 Otherwise, users are associated with either the MB or an
SBj and marked as CUEs(e.g., based on maximum RSRP).
20

21. Contribution 2
C2 : Energy Efficiency Problem Formulation
Ω = max
{𝑍 𝑑𝑚,𝑍 𝑅𝑠,𝑌 𝐷
𝐾,𝑃 𝐷,𝑃 𝑀𝐵,𝑃 𝑆𝐵}
𝑖=1
𝑑
𝑍𝑖
𝑑𝑚
𝑤 𝐵 𝑙𝑜𝑔2(1 +
𝑝𝑖 𝐺𝑖
𝑘
𝑁0
)
𝑝𝑖 + 𝑝0
+ 𝑍𝑖
𝑅𝑠
𝑤 𝐵 𝑙𝑜𝑔2(1 +
𝑦𝑖
𝑘
𝑝𝑖 ∗ 𝐺𝑖
𝑘
𝑁0 + 𝑌 𝑀
𝑘
ℎ 𝑀𝐵,𝑖 𝑃 𝑀𝐵 + 𝑗=1
𝑁
𝑌𝑆𝐵 𝑗
𝑘
ℎ 𝑆𝐵,𝑖 𝑃𝑆𝐵𝑗
)
𝑝𝑖 + 𝑝0
Ω = max
{𝑧 𝑑𝑚,𝑍 𝑅𝑠,𝑌 𝐷
𝐾,𝑃 𝐷,𝑃 𝑀𝐵,𝑃 𝑆𝐵}
𝑖=1
𝑑
𝑍𝑖
𝑑𝑚
𝜂𝑖
𝑑𝑚
+ 𝑍𝑖
𝑅𝑠
𝜂𝑖
𝑅𝑠
Sum of fraction optimization functions & mixed of binary and
continuous variables (NP –hard Problem )
21

22. Contribution 2
C2: Optimization constraints
Mode Selection constraints
D2D Resource allocation constraints (RS mode)
Power constraints
QoS constraints
0 ≤ 𝑝𝑖 ≤ 𝑝𝑖
𝑚𝑎𝑥
∀𝑖 ∈ 𝐷
𝑝 𝑀𝐵
𝑚𝑖𝑛
≤ 𝑃 𝑀𝐵 ≤ 𝑝 𝑀𝐵
𝑚𝑎𝑥
𝑃𝑆𝐵𝑗
𝑚𝑖𝑛
≤ 𝑃𝑆𝐵𝑗 ≤ 𝑃𝑆𝐵𝑗
𝑚𝑎𝑥
∀𝑗 ∈ j
𝑘=1
1
𝑦𝑖
𝑘
= 1 ∀ 𝑘 ∈ 𝐾
𝑑=1
1
𝑦𝑖
𝑘
= 1 ∀ 𝑖 ∈ 𝐷
log2(1 + 𝛾 𝑚) ≥ 𝑅 𝑚
𝑚𝑖𝑛
∀ 𝑚 ∈ 𝑀
log2(1 + 𝛾𝑠) ≥ 𝑅 𝑠
𝑚𝑖𝑛 ∀ 𝑠 ∈ 𝑆
log2(1 + 𝛾𝑖) ≥ 𝑅𝑖
𝑚𝑖𝑛
∀ 𝑖 ∈ 𝐷
𝑍𝑖
𝑑𝑚
+ 𝑍𝑖
𝑅𝑠
≤ 1 ∀ 𝑖 ∈ 𝐷
𝑍𝑖
𝑑𝑚
, 𝑍𝑖
𝑅𝑠
, 𝑦𝑖
𝑘
∈ {0,1}
22
Subject. To

23. Contribution 2
 Low load Network
Number of available
resources RBfree is
greater than the
number of D2D users.
 Medium Load Network
Number of available
resources RBfree is less
than D2D users.
 Full Load Network
all channels are
occupied by CUEs
and RBfree equals
zero.
Proposed solution for EE maximization based on network load
23

24. EE Maximization in Low Load Network
Ω = max
{𝑍 𝑑𝑚,𝑌 𝐷
𝐾,𝑃 𝐷}
𝑖=1
𝑑
𝑍𝑖
𝑑𝑚
𝑤 𝐵 𝑙𝑜𝑔2(1 +
𝑝𝑖 𝐺𝑖
𝑘
𝑁0
)
𝑝𝑖 + 𝑝0
 EE maximization is performed by minimizing D2D user transmission power while maintaining minimum rate
requirements.
𝐶2: 0 ≤ 𝑝𝑖 ≤ 𝑝𝑖
𝑚𝑎𝑥
∀𝑖 ∈ 𝐷
 RBs are sufficient for D2D users to operate in DM, Set 𝑍𝑖
𝑑𝑚
=1 ( mode selection )
 Numerator non negative & concave function in pi
 Denominator is positive and an affine function.
 A Dinkelbach-like algorithm is applied to change (SORPs) to a parametric function
𝐶1: 𝑙𝑜𝑔2 1 +
𝑝𝑖 𝐺𝑖
𝑘
𝑁0
∀𝑖 ∈ 𝐷
Sum of ratio functions (SoRPs)
Subject. To
Contribution 2 24

25. EE Maximization in Low Load Network
𝜂 𝑑𝑚 𝜆𝑖 =
𝑖=0
𝑑
{𝑤 𝐵 𝑙𝑜𝑔2 1 +
𝑝𝑖 𝐺𝑖
𝑘
𝑁0
− 𝜆𝑖(𝑝𝑖 + 𝑝0)}
𝐶1: 𝑙𝑜𝑔2 1 +
𝑝𝑖 𝐺𝑖
𝑘
𝑁0
∀𝑖 ∈ 𝐷
𝐶2: 0 ≤ 𝑝𝑖 ≤ 𝑝𝑖
𝑚𝑎𝑥
∀𝑖 ∈ 𝐷
 An interior-point method to solve a sequence of convex problems (line 2) .
Subject. To
Contribution 2 25

26. Contribution 2
EE Maximization in High Load Network
Ω = max
{𝑍 𝑅𝑠,𝑌 𝐷
𝐾,𝑃 𝐷,𝑃 𝑀𝐵,𝑃 𝑆𝐵}
𝑖=1
𝑑
𝑍𝑖
𝑅𝑠
𝑤 𝐵 𝑙𝑜𝑔2(1 +
𝑦𝑖
𝑘
𝑝𝑖 ∗ 𝐺𝑖
𝑘
𝑁0 + 𝑌 𝑀
𝑘
ℎ 𝑀𝐵,𝑖 𝑃 𝑀𝐵 + 𝑗=1
𝑁
𝑌𝑆𝐵 𝑗
𝑘
ℎ 𝑆𝐵,𝑖 𝑃𝑆𝐵𝑗
)
𝑝𝑖 + 𝑝0
log2(1 +
𝑦𝑖
𝑘
𝑝𝑖 ∗ 𝐺𝑖
𝑘
𝑁0 + 𝑌 𝑀
𝑘
ℎ 𝑀𝐵,𝑖 𝑃 𝑀𝐵 + 𝑗=1
𝑁
𝑌𝑆𝐵 𝑗
𝑘
ℎ 𝑆𝐵,𝑖 𝑃𝑆𝐵𝑗
) ≥ 𝑅𝑖
𝑚𝑖𝑛
log2(1 +
𝑃 𝑀𝐵 ∗ 𝐺 𝑀𝐵,𝑚
𝑘
𝑁0 + 𝑗=1
𝑑
𝑦𝑗
𝑘
ℎ𝑗,𝑚
𝑘
𝑝𝑗 + 𝑗=1
𝑁
𝑌𝑆𝐵 𝑗
𝑘
ℎ 𝑆𝐵𝑗,𝑚 𝑃𝑆𝐵𝑗
) ≥ 𝑅 𝑚
𝑚𝑖𝑛
log2(1 +
𝑃𝑆𝐵 ∗ 𝐺𝑆𝐵,𝑠
𝑘
𝑁0 + 𝑗=1
𝑑
𝑦𝑗
𝑘
ℎ𝑗,𝑚
𝑘
𝑝𝑗 + 𝑌 𝑀
𝑘
ℎ 𝑀𝐵,𝑠
𝑘
ℎ 𝑀𝐵,𝑠 𝑃 𝑀𝐵
) ≥ 𝑅 𝑠
𝑚𝑖𝑛
0 ≤ 𝑝𝑖 ≤ 𝑝𝑖
𝑚𝑎𝑥
∀𝑖 ∈ 𝐷
𝑝 𝑀𝐵
𝑚𝑖𝑛
≤ 𝑃 𝑀𝐵 ≤ 𝑝 𝑀𝐵
𝑚𝑎𝑥
𝑃𝑆𝐵𝑗
𝑚𝑖𝑛
≤ 𝑃𝑆𝐵𝑗 ≤ 𝑃𝑆𝐵𝑗
𝑚𝑎𝑥
∀𝑗 ∈ j
𝑘=1
1
𝑦𝑖
𝑘
= 1 ∀ 𝑘 ∈ 𝐾
𝑑=1
1
𝑦𝑖
𝑘
= 1 ∀ 𝑖 ∈ 𝐷
Interference-limited Scenario
 All RBs occupied, Set 𝑍𝑖
𝑑𝑚
=0, optimization problem
Subject. To
26

27. EE Maximization in High Load Network
Ω = max
{𝑌 𝐷
𝐾,𝑃 𝐷,𝑃 𝑀𝐵,𝑃 𝑆𝐵}
𝑖=1
𝑑 𝑤 𝐵 𝑙𝑜𝑔2(1 +
𝑦𝑖
𝑘
𝑝𝑖 ∗ 𝐺𝑖
𝑘
𝑁0 + 𝑌 𝑀
𝑘
ℎ 𝑀𝐵,𝑖 𝑃 𝑀𝐵 + 𝑗=1
𝑁
𝑌𝑆𝐵 𝑗
𝑘
ℎ 𝑆𝐵,𝑖 𝑃𝑆𝐵𝑗
)
𝑝𝑖 + 𝑝0
Sum of fraction optimization functions & mixed of binary and continuous variables (NP –hard Problem )
Difficult to be solved in high dynamics environment like HetNets
Resource Allocation (SMS)
 All users in RS mode, mode selection indicators 𝑍𝑖
𝑅𝑠
= 1
Power Control
GA
27Contribution 2

28. Contribution 2
Genetic Algorithm (GA) Power Control
Ω = max
{𝑃 𝐷,𝑃 𝑀𝐵,𝑃 𝑆𝐵}
𝑖=1
𝑑 𝑤 𝐵 𝑙𝑜𝑔2(1 +
𝑦𝑖
𝑘
𝑝𝑖 ∗ 𝐺𝑖
𝑘
𝑁0 + 𝑌 𝑀
𝑘
ℎ 𝑀𝐵,𝑖 𝑃 𝑀𝐵 + 𝑗=1
𝑁
𝑌𝑆𝐵 𝑗
𝑘
ℎ 𝑆𝐵,𝑖 𝑃𝑆𝐵𝑗
)
𝑝𝑖 + 𝑝0
 Numerator function in number of varying variable (𝑝𝑖, 𝑃 𝑀𝐵, 𝑃𝑆𝐵𝑗).
 EE fraction function is neither concave nor convex.
 Saddle point results from summation term in equation (Ω)
Numerator function
in number of varying variable
28

29. Contribution 2
EE Maximization in Medium Load Network
Ω = max
{𝑍 𝑑𝑚,𝑍 𝑅𝑠,𝑌 𝐷
𝐾,𝑃 𝐷,𝑃 𝑀𝐵,𝑃 𝑆𝐵}
𝑖=1
𝑑
𝑍𝑖
𝑑𝑚
𝑤 𝐵 𝑙𝑜𝑔2(1 +
𝑝𝑖 𝐺𝑖
𝑘
𝑁0
)
𝑝𝑖 + 𝑝0
+ 𝑍𝑖
𝑅𝑠
𝑤 𝐵 𝑙𝑜𝑔2(1 +
𝑦𝑖
𝑘
𝑝𝑖 ∗ 𝐺𝑖
𝑘
𝑁0 + 𝑌 𝑀
𝑘
ℎ 𝑀𝐵,𝑖 𝑃 𝑀𝐵 + 𝑗=1
𝑁
𝑌𝑆𝐵 𝑗
𝑘
ℎ 𝑆𝐵,𝑖 𝑃𝑆𝐵𝑗
)
𝑝𝑖 + 𝑝0
 Under a medium load, the number of D2D users is greater than the number of free resources RBfree. Hence,
some D2D users work in DM, while others remain in RS mode
Mode Selection constraints
Power constraints
QoS constraints
0 ≤ 𝑝𝑖 ≤ 𝑝𝑖
𝑚𝑎𝑥
∀𝑖 ∈ 𝐷
𝑝 𝑀𝐵
𝑚𝑖𝑛
≤ 𝑃 𝑀𝐵 ≤ 𝑝 𝑀𝐵
𝑚𝑎𝑥
𝑃𝑆𝐵𝑗
𝑚𝑖𝑛
≤ 𝑃𝑆𝐵𝑗 ≤ 𝑃𝑆𝐵𝑗
𝑚𝑎𝑥
∀𝑗 ∈ j
𝑘=1
1
𝑦𝑖
𝑘
= 1 ∀ 𝑘 ∈ 𝐾
𝑑=1
1
𝑦𝑖
𝑘
= 1 ∀ 𝑖 ∈ 𝐷
log2(1 + 𝛾 𝑚) ≥ 𝑅 𝑚
𝑚𝑖𝑛
∀ 𝑚 ∈ 𝑀
log2(1 + 𝛾𝑠) ≥ 𝑅 𝑠
𝑚𝑖𝑛 ∀ 𝑠 ∈ 𝑆
log2(1 + 𝛾𝑖) ≥ 𝑅𝑖
𝑚𝑖𝑛
∀ 𝑖 ∈ 𝐷
𝑍𝑖
𝑑𝑚
+ 𝑍𝑖
𝑅𝑠
≤ 1 ∀ 𝑖 ∈ 𝐷𝑍𝑖
𝑑𝑚
, 𝑍𝑖
𝑅𝑠
, 𝑦𝑖
𝑘
∈ {0,1}
Resources allocation constraints
29

30. Contribution 2
Fuzzy C mean (FCM) Clustering Mode Selection Algorithm
FCM clustering
With post processing
RBfree, RSRPDr , 𝛾𝑖
𝑘
DUEDM, DUERS
U: coefficient membership
Construct Udm vector
Construct Urs vector
NDm > RBfree
Sort(Udm, descend) for DUERS
m=NDm-RBfreem=RBfree - NDm
Up date (DUEDM ,DUERS)
Update (DUEDM ,DUERS)
Start
𝑍𝑖
𝑑𝑚
=1 , 𝑖 ∈ DUEDM
𝑍𝑖
𝑅𝑠
=1 , 𝑖 ∈ DUE 𝑅𝑠
Move m pairs to RS mode Move m pairs to DM mode
Sort (URs, descend) for
DUEDM
YesNo
30

31. Contribution 2
Simulation Results: D2D User Selection
 D2D Separation distance Topology snapshot
 The guard distance surrounding BSs was not considered.
 Does not restrict separation distance to a specific distance.
 Up to 400m in DM mode
 160m maximum distance in RS mode
31

32. Contribution 2
Simulation Results: Low & High load network
 D2D users are not assigned to a permanent mode, as is the case
in static mode selection.
 In static mode selection, users are unable to switch from DM to
RS mode when orthogonal resources become unavailable.
 The proposed scheme forced D2D users to operate in
DM mode when free RBs were available.
 Achieved EE is nearly two times EE obtained when
using random and static mode selection.
32

33. Contribution 2
Simulation Results: Medium Load Network Result
 Clustering Analysis (FCM algorithm)
 Post-processing steps were implemented to correct cluster centroids, adjusting membership coefficients
 Users grouped in the blue cluster are with low RSRP and low SINR measurements and assigned DM mode
 Users grouped in the red cluster are high RSRP and high SINR and assigned RS mode
 The FCM algorithm groups users with small separation distance in the RS cluster regardless of their location with respect to
MB
33

34. Contribution 2
Simulation Results: FCM Mode Selection
 FCM based mode selection
 Switch two pairs From RS to DM mode base on
membership coefficient.
 Switch five pairs From DM to RS mode base on
membership coefficient.
34

35. Contribution 2
Simulation Results: Medium Load Network Result
 EE verse Network load  Number of blocked pairs
 The proposed scheme shows improvements over other selection modes for most network load conditions.
 It also maximizes the number of connected pairs.
 Static mode selection outperform the proposed scheme in a number of cases at the expense of increasing the number
of blocked D2D.
35

36. Simulation Results
 D2D power Consumption
 Power consumption gradually increased as more users
shifted from DM to RS mode.
 Rate of power consumption increased, as well, since
switched DM cluster users required more power due to
increase separation distance and interference.
 Some switching users were blocked, power consumption
decreased
Contribution 2 36

37. Contribution 2
Simulation Results
 Overall Energy Efficiency
 D2D improves HetNets EE.
 When network load is light, there is a significant
improvement in EE, since D2D users operate in DM mode.
 As network load increases, EE gain and losses are due to
D2D mode switching to RS required more power to maintain
QoS. As well as, co- channel interference between D2D and
cellular users.
37

38.  Analytical Model
1. Cellular and D2D users arrival is Poisson process with arrival rates
(𝜆𝑐) and (𝜆𝑑) respectively and departure rates of 𝜇 𝑐 and 𝜇 𝑑 .
2. User inter-arrival times are independent and follows exponential
distribution exp(𝜆𝑐), and exp(𝜆𝑑).
3. Scheduling times are independent exponential random variables
with mean (1/𝜇𝑐 ) 𝑎𝑛𝑑 (1/𝜇𝑑 ) respectively.
4. No two users could arrive or depart at exactly the same time. This
assumption is justified for independent Poisson processes.
5. The birth is state independent and death rates is state
dependent.
C3:Analytical Model for LTE scheduler with D2D communication for Throughput estimation

39. C3:Analytical Model for LTE Scheduler with D2D Communication for Throughput Estimation
 Total number of scheduled users at each TTI can be modeled by the
stochastic process .
𝑋 𝑡 = 𝑋 𝐷 𝑡 , 𝑋𝑐 𝑡 , 𝑡 ≥ 0
𝑋 𝐷 𝑡 : Number of D2D users. 𝑋𝑐 𝑡 : Number of cellular users
 The process 𝑋 𝑡 , 𝑡 ≥ 0 is a homogeneous 2D-CTMC of birth-
death type with finite state space S.
𝑆 = 𝑖, 𝑗 ; 0 ≤ 𝑖 ≤ 𝑘 , 0 ≤ 𝑗 ≤ 𝑘
2D-CTMC model is composed of (k + 1)2 states. CTMC generate matrix
𝑄 , and rate matrix 𝑅 can be found from Rate diagram State transition rate diagram of 2D-CTMC

40. Transient Analysis
 Kolmogorov differential equations is used to described the dynamic behavior of the 2D-CTMC.
𝑃` 𝑡 = 𝑃 𝑡 𝑄
 Uniformization method is implemented to compute transition probablilty matrix 𝑃 𝑡 .
𝑃 𝑡 =
𝑘=0
∞
𝑒−𝛽𝑡
𝛽𝑡 𝑘
𝑘!
𝑃 𝑘

41. LTE –scheduler Next State Estimation
 During TTI, LTE scheduler stays in one state.
 Assume that LTE scheduler 𝑠 0 = 𝜋(0,0).
 Compute transition matrix P(t) for a duration of one TTI (t=1msec).
 Define the state with maximum transition probability as the next state for next TTI.
𝑆 𝑡 + 1 = 𝑃 𝑡 𝑆 𝑡 0 ≤ 𝑡 ≤ 𝐿
 Compute Estimate the throughput for a given time (L TTI)
𝑇𝐿 =
𝑡=1
𝐿
𝑘=1
𝑘
𝑇𝑘
𝑡
𝐿

42. Steady state distribution Analysis
 Scheduler long term behavior can be explained by determining the steady state distribution of the 2D-CTMC model
𝜋 𝑖, 𝑗 ≔ 𝑃 𝑋 𝐷 = 𝑖, 𝑋𝑐 = 𝑗 𝜋 𝑖, 𝑗 = lim
𝑡→∞
Pr( 𝑋 𝑡 = (𝑖, 𝑗))

43. Numerical Results
𝜋(𝑖, 0)=
𝜌 𝑑
𝑖
𝑖!
𝜋(0,0) 𝜋 (0, 𝑗)=
𝜌 𝑐
𝑗
𝑗!
𝜋(0,0) 𝜋 (𝑖, 𝑗)=
𝜌 𝑑
𝑖
𝜌 𝑐
𝑗
𝑖!𝑗!
𝜋(0,0)
𝑖=1
𝑘
𝑗=1
𝑘
𝜋(𝑖, 𝑗) = 1 𝜌𝑐 =
𝜆 𝑐
𝜇𝑐
𝜌 𝑑 =
𝜆 𝑑
𝜇𝑑
Traffic intensity
𝜋(0,0) =
1
1 + 𝑖=1
𝑘 𝜌 𝑑
𝑖
𝑖!
+ 𝑗=1
𝐾 𝜌𝑐
𝑗
𝑗!
+ 𝑖=1
𝑘
𝑗=1
𝑘 𝜌 𝑑
𝑖
𝜌𝑐
𝑗
𝑖! 𝑗!
 The closed form solution of steady state probabilities obtained by solving the system of balance equations using
the recursive substitution method.

44. Performance Evaluation
 Overall long term throughput
 An expected number of D2D users in LTE scheduler
𝑇 =
𝑖=1
𝑘
𝑗=
𝑘
𝜋(𝑖, 𝑗 ) 𝑇 (𝑖, 𝑗)
𝑁 𝐷 =
𝑖=1
𝑘
𝑗=
𝑘
𝑖 𝜋(𝑖, 𝑗 )
𝑁 𝐷𝑀 =
𝑖=0,𝑖≤{𝑘−𝑗}
𝑘
𝑖 𝜋 𝑖, 𝑗 +
𝑖>{𝑘−𝑗}
𝑘
(𝑘 − 𝑗) 𝜋 𝑖, 𝑗
𝑁 𝑅𝑆 =
𝑖≥{𝑘−𝑗}
𝑘
(𝑖 − 𝑘 − 𝑗 ) 𝜋 𝑖, 𝑗
Total number of D2D
Number in DM Mode
Number in RS Mode

45. Numerical Results
Gray indicates the state in which only cellular users are
scheduled
Blue indicates scheduler state, where D2D users
are allocated free channels and operate in DM mode
Yellow indicates mixed states, where some of D2D users
allocated free RBs and others shared RBs with cellular users.
Green indicates full reuse state with RBs allocated to cellular
users.
45

46. Scheduler Next State Prediction and Throughput Calculation
 We considered three scenarios with following parameter settings
scenarios scenarios scenarios
𝜆𝑐 user/TTI 2 ( low traffic) 4 6 (high traffic)
𝜆𝑑 user/TTI 1:10 1:10 1:10
𝜇 𝑐 user/TTI 1 1 1
𝜇 𝑑 user/TTI 1 1 1
 Most RBs were not assigned to cellular users causing of increasing
probability of DM (blue) states. Scheduler sojourn is typically blue states.
 Significant throughput improvement was obtained when the cellular
arrival rate was low.

47. Scheduler Next State Prediction and Throughput Calculation
 An average number of cellular users in scheduler was equal to three users /TTI over time.
 When D2D arrival rate increased, the scheduler transitioned from DM states (blue) to mixed states (yellow) with
some D2D-allocated free RBs and others shared RBs

48. Scheduler Next State Prediction and Throughput Calculation
 The scheduler remained in RS states (green) most of the time, wherein D2D users shared RBs with cellular users.
 Although the number of scheduled D2D increased with rising D2D arrival rate, the throughput achieved per link
(cellular or D2D) decreased, primarily due to co-channel interference

49. Steady State Performance
 Expected number of D2D users in DM mode and RS
mode
 Number of scheduled D2D users increased as D2D user
arrival rate increased, albeit the change was limited by the
number of RBs in the system.
 The blue line shows average number of D2D users when
cellular arrival rate = 2 users/TTI. Expected number of D2D
users in DM mode was notably large when compared with
D2D users in RS mode as a result of free RBs availability.
 As cellular user arrival rate increased and more cellular
users were scheduled, average number of D2D users in DM
mode declined. Also, average number of D2D users in RS
mode increased.
49

50. Steady State Performance
 Long Term Network Throughput
 Results were matched when D2D user arrival rate was less
than three users/TTI.
 Both scheduling algorithm and D2D user mode impacted
network throughput.
 When most of D2D users scheduled on free RBs, RR algorithm
results were very close to Max-T result, since all users have
similar average SINR.
 However, when cellular user arrival increased, D2D users in
RS mode experienced low SINR. As such, the Max-T algorithm
outperformed RR.

51. Conclusions
Conclusions
This dissertation addresses some of D2D communication challenges introduced into a cellular network
 A low-complexity D2D resource allocation was presented to minimize interference from D2D communication to
cellular users and to maximize the overall throughput of network.
 A comprehensive framework for energy-efficient D2D communication was proposed, we demonstrated that the
optimization problem is NP-hard and extremely difficult to solve. To remedy this, an instantaneous network load was
utilized to simplify the optimization problem, and different optimization approaches were applied.
 An analytical model for LTE scheduler with D2D communication was also developed in this work. Steady state
probabilities for scheduler were derived.
51