Technical Seminar Report

VISVESVARAYA TECHNOLOGICAL UNIVERSITY
Jnana Sangama, Belagavi - 590 018
Technical Seminar Report

on
Power Saving Techniques for 5G and Beyond
Submitted in partial fulfillment for the award of degree of
Bachelor of Engineering
in
Electronics and Communication Engineering
Submitted by
Srinath Reddy B S
1RN18EC029
Internal External
Reviewer Reviewer
Mrs.Roopa K R Dr.Ohileshwari M S
Assistant Professor Assistant Professor
ECE Dept. ECE Dept.
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING

(Accredited by NBA for the Academic Years 2018-19, 2019-20, 2020-2021 and
2021-2022)
RNS INSTITUTE OF TECHNOLOGY

(AICTE Approved, VTU Affiliated and NAAC ‘A’ accredited)
(UG Programs - CSE, ECE, ISE, EIE and EEE have been Accredited by NBA
for the Academic Years 2018-19, 2019-20, 2020-2021 and 2021-2022)
Channasandra,Dr.Vishnuvardhan Road,Bengaluru-560098
2021-22
VISVESVARAYA TECHNOLOGICAL UNIVERSITY
Jnana Sangama, Belagavi - 590 018
Internship /Technical Seminar Report

on
Power Saving Techniques for 5G and Beyond
Submitted in partial fulfillment for the award of degree of
Bachelor of Engineering
in
Electronics and Communication Engineering
Submitted by
Srinath Reddy B S
1RN18EC029
Internal External
Reviewer Reviewer
Mrs.Roopa K R Dr.Ohileshwari M S
Assistant Professor Assistant Professor
ECE Dept. ECE Dept.
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING

(Accredited by NBA for the Academic Years 2018-19, 2019-20, 2020-2021 and
2021-2022)

(AICTE Approved, VTU Affiliated and NAAC ‘A’ Accredited)
for the Academic Years 2018-19, 2019-20,2020-2021 and 2021-2022)
2021-22
DEPARTMENT OF ELECTRONICS AND COMMUNICATION

ENGINEERING
(Accredited by NBA for the Academic Years 2018-19,2019-20,2020-2021 and
2021-22)
CERTIFICATE
This is to certify that the Technical Seminar entitled “Power Saving Techniques
for 5G and Beyond” has been successfully carried by Srinath Reddy B S bear-
ing the usn 1RN18EC029, bonafide student of RNS Institute of Technology
in partial fulfillment for the award of Bachelor of Engineering in Electronics and
Communication Engineering from Visvesvaraya Technological University,
Belagavi, during the year 2021-2022. It is certified that all corrections / suggestions
indicated for internal assessment have been incorporated in the report.
................................ ................................ ................................

Mrs. Roopa K R Dr. Vipula Singh Dr. M K Venkatesha
Assistant professor Head of Department Principal
DEPARTMENT OF ELECTRONICS AND COMMUNICATION

ENGINEERING
(Accredited by NBA for the acadamic Years 2018-19,2019-20,2020-2021 and
2021-22)
DECLARATION
I, Srinath Reddy B S bearing the USN: 1RN18EC029, pursuing Bachelor
of Engineering in Electronics & Communication, RNS Institute of Technology, Ban-
galore. I hereby declare that the Technical Seminar paper titled, “Power Saving
Techniques for 5G and Beyond” has been independently carried out under the
supervision and guidance of Mrs.Roopa K R, Assistant Professor. Submitted as
a partial fulfilment for the award of Bachelor of Engineering degree in Electronics
and Communication Engineering from Visvesvaraya Technological Univer-
sity, Belagavi during the academic year 2021-22.
Srinath Reddy B S
1RN18EC029
Acknowledgement
The joy and satisfaction that accompany the successful completion of
any task would be incomplete without thanking those who made it possi-
ble. I consider myself proud to be a part of RNS Institute of Technology,
the institution which molded me in all my endeavours.
I express my gratitude to our Chairman late Dr.R N Shetty and

MD Satish R Shetty, for providing state of art facilities.
I would like to express my sincere thanks to Dr.M K Venkatesha,

Principal and Dr.Vipula Singh, Professor and Head, Department of
ECE, for their valuable guidance and encouragement throughout our pro-
gram.
I extend my sincere thanks and heartfelt gratitude to my internal re-

viewer Mrs.Roopa K R, Assistant Professor, Department of ECE, for
their valuable guidance, suggestions and cheerful encouragement.
Finally, I take this opportunity to extend my earnest gratitude and

respect to my parents, teaching and non-teaching staff of the department,
the library staff and all my friends who have directly or indirectly sup-
ported me during the period of my Seminar.
Srinath Reddy B S
i
Table of Contents
Table of Contents ii
List of Figures iii
Acronyms iv
1 Introduction 1
2 Literature Survey 5
3 Methodology 11
3.1 Standard Framework for Energy Efficient 5G System . . . . . . . . . . 11
4 Simulation and Evaluation 20

4.1 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
5 Conclusion and Future scope 26

5.1 Future Trends for Beyond 5G . . . . . . . . . . . . . . . . . . . . . . . 26
References 32
ii
List of Figures
1.1 5G Applications in Cloud and IOT . . . . . . . . . . . . . . . . . . . . . . 1

1.2 5G Applications in all Sectors . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.3 5G Use Cases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
3.1 Load-dependent base station power model. . . . . . . . . . . . . . . . . . . 13

3.2 Power model parameters for pico small cell. . . . . . . . . . . . . . . . . . 13
3.3 BWP adaptation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
3.4 NR RRC state machine. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
3.5 DRX mechanism. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.6 PDCCH monitoring occasions . . . . . . . . . . . . . . . . . . . . . . . . . 18
3.7 Same-slot scheduling and cross-slot scheduling . . . . . . . . . . . . . . . . 18
4.1 Energy saving percentage gain with different sleep modes. . . . . . . . . . 20

4.2 Power consumption distribution of different power states for FTP and VoIP
traffic model. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
4.3 Simulation results of DRX mechanism without/with wake-up indication
for traffic model (DRX cycle OnDuration timer Inactivity timer). . . . . . 21
4.4 Simulation results of DRX mechanism without/with wake-up indication
for traffic model (DRX cycle OnDuration timer Inactivity timer). . . . . . 22
4.5 Simulation results of two component carriers without/with SCell dormancy
behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4.6 Simulation results of same-slot scheduling and cross-slot scheduling. . . . . 23
4.7 Simulation results of without/with MIMO layer adaptation. . . . . . . . . 24
4.8 Power saving gain of 2-step RACH over 4-step RACH. . . . . . . . . . . . 24
4.9 UE power consumption distribution in RRC idle state. . . . . . . . . . . . 25
5.1 UE behavior in RRC idle state. . . . . . . . . . . . . . . . . . . . . . . . . 28

5.2 UE behavior for exceptional cases of WUS detection. . . . . . . . . . . . . 29
5.3 PDCCH skipping. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
5.4 Multi-panel transmission. . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
iii
Acronyms
3GPP : Third Generation Partnership Project
3G : Third Generation
4G : Fourth Generation
5G : Fifth Generation
5GC : 5G Core network
BCH : Broadcast Channel
BS : Base Station
BWP : Bandwidth Part
CoMP : Coordinated multi-point
CRAN : Centralized Radio Access Network
C-RAN : Cloud Radio Access Network
CRS : Cell-Specific Reference Symbol
CSI : Channel State Information
CSI-RS : Channel State Information-Reference Signals
D2D : Device to Device
DCI : Downlink Control Information
DL : Downlink
DRAN : Distributed Radio Access Network
DTX : Discontinuous Transmission
DRX : Discontinuous Reception
DU : Distributed Unit
eMBB : enhanced Mobile Broadband
ETSI : European Telecommunications Standards Institute
iv
FDD : Frequency Division Duplexing
FeMBMS : Further evolved Multimedia Broadcast Multicast Service
gNB : next generation NodeB
LTE : Long Term Evolution
LTE-A : Long Term Evolution Advanced
MIMO : Multiple-Input Multiple-Output
mMIMO : masssive Multiple-Input Multiple-Output
mMTC : massive Machine Type Communication
NG : Next Generation
NG-RAN : Next Generation Radio Access Network
NR : New Radio
OFDM : Orthogonal Frequency Division Multiplexing
RAN : Radio Access Network
RAT : Radio Access Technology
RE : Resource Efficiency
RF : Radio Frequency
RLC : Radio Link Control
RLF : Radio Link Failure
SRS : Sounding Reference Signals
SCell : Secondary Cell
UE : User Equipment
URLLC : Ultra-Reliable Low-Latency Communication
VRAN : Virtualized Radio Access Network
VNF : Virtual Network Function
v
Power Saving Techniques for 5G and Beyond 2021-22
Chapter 1
Introduction
The Third Generation Partnership Project (3GPP) completed the 5G standardization

process of Release 15 New Radio (NR) access technology which is the first version of
the 5th Generation wireless standard [1]. Meanwhile, the stan dardization process
of next 5G release i.e. Release 16 is still on-going and will be expected to finish in
June 2020. The plan for the third 5G release i.e. Release 17 has been discussed
and topics to be worked on have been identified in the plan [2]. Compared to 4G
Long Term Evolution (LTE), 5G NR stan dard aims to support more diversified use
cases. Major 5G use cases can be generalized to three usage scenarios, namely en-
hanced mobile broadband (eMBB), massive machine type communications (mMTC)
and ultra-reliable and low latency communications (URLLC) [3]. For different use
cases, there are corresponding key performance indicators. For example, user experi-
enced data rate is important for eMBB. Latency and reliability are the key parameters
for URLLC traffic. While for mMTC, it focuses more on connection density.
One important aspect for all traffic types is energy efficiency which is a key per-
formance indicator (KPI) on both UE side and network side as shown in Figure 1.1.
Figure 1.1: 5G Applications in Cloud and IOT
Dept Of ECE, RNSIT, Bengaluru 1

Figure 1.2: 5G Applications in all Sectors
The impact of energy saving techniques on network energy efficiency, which is

defined by bits per Joule, is analyzed in [4], [5]. Solutions including multiple input
multiple out (MIMO), heterogeneous networks (HetNets), non-orthogonal aggregation
(NOA), and exploiting unused and unlicensed spectrum, etc., are provided to improve
system throughput. RF energy reduction is proposed to achieve a trade-off between
energy efficiency and throughput [5]. In some scenarios, e.g. massive MIMO, power
consumption is still expected to be one of the bottlenecks on both UE and network
sides in early 5G deployment Hence, standardization support of enhancements on
power saving techniques are essential to realize these techniques in practical network
deployment of 5G and beyond as shown in Figure 1.2.
Network energy efficiency for different deployment scenarios and traffic character-
istics has been taken into account in NR design [3], [6]. In NR Release 15, an energy
efficient 5G standardized framework has been built with high flexibility and scalabil-
ity. Several features make the system more flexible to adapt to different traffic loads
for better energy efficiency. In 4G LTE, always-on CRS (cell specific reference signal)
for channel estimation impose restrictions on energy efficient network implementation
and generates unnecessary interference. Some kind of small cell muting schemes were
proposed to reduce interference and power consumption in LTE [7] but it is hard to
address this issue in LTE con sidering the backward compatibility constraint.

Figure 1.3: 5G Use Cases
In 5G NR standard [8], redesign of reference signal is possible. The flexibility

and configurability of reference signal design in 5G minimizes always-on signals. In
addition, multiple options of rate-matching resources, e.g. resource block (RB) level
or resource-element (RE) level, dynamic or semi-static, enable flexible muting of re-
sources and ensure forward compatibility for energy efficient network implementation.
With the flexibility provided by the standardized frame work, the trade-off be-
tween power saving and performance including throughput and latency can be more
easily con trolled by network implementation. Regarding power saving on UE side,
some power saving schemes like discontinuous reception (DRX) [9] mechanism are
inherited from 4G LTE. However, it is not sufficient to only reuse the schemes from
4G LTE due to more diversified use cases and scenarios in 5G NR.
Cellular networks have changed the world we are living in, and the fifth gen-
eration (5G) of radio technology is expected to further revolutionise our everyday
lives, by enabling a high degree of automation, through its larger capacity, massive
connectivity, and ultra-reliable low latency communications. In addition, the third
generation partnership project (3GPP) new radio (NR) specification also provides
tools to significantly decrease the energy consumption and the green house emissions
of next generations networks, thus contributing towards information and communi-
cation technology (ICT) sustainability targets. In this survey paper, we thoroughly
review the state-of-the-art on current energy efficiency research as shown in Figure 1.3.

We first categorise and carefully analyse the different power consumption models
and energy efficiency metrics, which have helped to make progress on the understand-
ing of green networks. Then, as a main contribution, we survey in detail —from a
theoretical and a practical viewpoint— the main energy efficiency enabling features
that 3GPP NR provides, together with their main benefits and challenges. Special
attention is paid to four key technology features, i.e., massive multiple-input multiple-
output (MIMO), lean carrier design, and advanced idle modes, together with the role
of artificial intelligence capabilities. We dive into their implementation and opera-
tional details, and thoroughly discuss their optimal operation points and theoretical-
trade-offs from an energy consumption perspective. This will help the reader to grasp
the fundamentals of —and the status on— green networking.
Hence, the standardized framework in NR provides more flexibility to allow the

network to implement different kinds of adaptation based on the power saving need
from UE side and network side. These different kinds of adapta tion includes adapta-
tion to bandwidth, MIMO layers, control channel monitoring, etc. Brief overview on
the standard evo lution of different techniques, including UE power saving, towards
5G-advanced is provided in [10] but it lacks the details for providing a more complete
picture on standard ization support of power saving techniques.
This paper provides an overview of 5G evolution path on recently standardized

power saving techniques in more details. In addition to flexible reference signal design,
some essential standardized components for energy efficient 5G network supported in
Release 15 NR standard are described in Section II, including bandwidth adaptation,
Radio Resource Control (RRC) inactive state, DRX mechanism, control chan nel de-
sign and cross-slot scheduling. In Section III, details of enhancements on power saving
introduced in Release 16 are provided. Simulation results showing power saving gain
of these enhancements are provided. Future trends of power saving enhancements
considering beyond 5G are discussed in Section IV.

Chapter 2
Literature Survey
Advances in telecommunication systems around the world have always been push-
ing the wireless infrastructure to be more resilient and scalable. Ever growing faster
data rates and a demand for the highest quality of service has been a strong con-
straint when energy conservation needs to be considered. Data rates as high as that
of 1 Gbps have been foreseen with the advent of 5G. In addition, with an explosive
number of heterogeneous devices coming online, including sensors for home security,
tablets, and wearable health monitors, the computational power of base stations must
increase. An estimated 50increase in the computing power of baseband units has been
predicted to handle this traffic burst [1]. Thus, the focus on energy-efficiency needs
to include optimization of computational complexity in addition to optimization of
transmission power.
An estimated 75of the Information and Communications Technology (ICT) indus-

try is supposed to be wireless by 2020 and today 5of the world’s carbon footprint is
coming from this industry alone. A consensus between academia and industry dic-
tates that the foreseen 1000 capacity gain must be achieved with either the present
energy consumption or lower [2]. Thanks to energy-efficiency efforts world-wide, en-
ergy consumption in the 5G realm, in terms of bits/joule, has been considered as an
important design parameter. In 4th generation (4G), the concept of small cells has
been introduced to increase the coverage and capacity. Therefore, [3] conducted an
analysis on energy consumption per unit area for a heterogeneous deployment of cells
for fourth generation networks.
With 5G, small cells are inevitable in deployments due to their advantage of im-
proved traffic handling within a smaller area as well as the shorter cell ranges that
result from the use of higher frequencies. Yet, the increasing number of base sta-
tions translate into more energy consumption, although the increase in consumption
will not be linear. Small cells, or in other words densification, calls for sophisticated
management of resources. Most recently, intelligent resource allocation and control
techniques utilizing machine learning algorithms have been suggested to help next
generation radios in their autonomous reconfiguration for improving the data rates,
energy efficiency and interference mitigation.

Overall, the emerging sophistication in both User Equipment (UE) and network side
has increased the energy consumption and thus objective functions have been devised
to maximize the energy efficiency, harvested energy and energy aware transmission
[4]. Many of the existing energy efficiency improvement techniques include the use of
green energy sources for base stations, modifying the coverage area of a base station
depending upon the load level, putting lightly loaded base stations to sleep and load
balancing by handing over the UEs to the macro base station. A survey on these
technologies for the 5G Radio Access Network (RAN) can be found in [5].
This survey has been aimed to contribute towards a greener and a sustainable
telecommunication’s ecosystem by reviewing and bringing together some of the latest
ideas and techniques of energy conservation at base station and network level. A
high level diagram shows the areas addressed in Figure 1. A few of the prominent
examples include the introduction of a newer Radio Resource Control (RRC) state
for context signalling and cutting down on the redundant state changes [6]. Utiliza-
tion of advanced clustering and caching techniques on the RAN side have been highly
appreciated for their benefits of improving the latency of getting the data requested
by a group of users and possibly eliminating the factor of clogging the network by a
huge number of requests for the same content [7,8].
A case study of commercial resource sharing among different operators bears fruit-
ful results in terms of reduced deployment costs and good data rates with minimum
interference among them [9]. The upcoming sections introduce the basics of energy
efficiency, provide justification for the need of gauging the energy consumption and
then present the most recent research works carried out for the optimization at differ-
ent levels of the architecture. This survey bears its uniqueness in its holistic approach
to energy-efficiency by covering radio, core and computing side of 5G. This paper is
also different than the surveys in the literature [1–4], as it focuses on works published
in the last few years where the majority of the studies focus on concepts specific to
the new 5G standard.
A formal relationship between energy efficiency and Signal to Interference Noise

Ratio (SINR) has been presented in [2] using the bit/joule notion. Meanwhile, Ref-
erence [4] lays the foundation for energy efficiency in different parts of the network
including base stations and the core network. In the literature, energy saving and use
of green energy resources have been the two mainstream approaches to offer energy
efficiency. Among the energy saving techniques, cell-switch off techniques have been
widely exploited.

For instance, in the EU FP7 ABSOLUTE project, an energy aware middleware has
been proposed that would use the capacity-based thresholds for activation of the
base stations [10]. In several other studies, data offloading has been considered as
an energy-efficient approach. Furthermore, authors in [11] have put together several
techniques for not only reducing the energy consumption from the traditional energy
sources but also for surveying newer Energy Efficiency (EE) schemes in the End-
to-End (E2E) system. One of the remarkable mentions by the authors includes the
implementation of 3rd Generation Partnership Project (3GPP) compliant EE man-
ager that would be responsible for monitoring energy demands in an E2E session and
for implementation of the policies needed for catering to the ongoing energy demand.
In addition to energy saving approaches, recently simultaneous wireless energy

transfer has been studied. Furthermore, local caching techniques have been proved
to be beneficial for relieving the load on the backhaul network by storing the con-
tent locally and limiting the re-transmissions, hence reducing energy consumption.
Similarly, a cloud based RAN has been envisioned as a possible solution for the com-
putational redistribution in [2,4,12]. Many of the tasks previously performed by a
base station (BS) would be taken away to a data center and only decision making for
Radio Frequency (RF) chains as well as baseband to RF conversion would be given to
base stations. Traffic pattern and demands would then be catered for well before time
and redundant BS would be put to sleep mode according to [13]. Furthermore, full
duplex Device-to-Device (D2D) communication with uplink channel reuse has been
considered to improve SINR and transmission power constraints. A gain of 36energy
efficiency has been demonstrated using the full duplex scheme with enhanced self-
interference mitigation mechanism instead of half duplex [14].
As machine learning is penetrating more and more into the operation of wire-
less networks, Reference [15] suggests that machine learning algorithms would greatly
help to predict the hot spots so that other resources could be switched off when not
needed. The concept of energy efficiency being treated as a key performance indicator
in the upcoming 5G standard considers it to be a global ambition, but it cannot be
declared as a specific actionable item on either the operator or vendor side. Divide
and conquer approach has been applied to the entire network and improvements have
been targeted at either component level, equipment level or at network level employ-
ing newer algorithms at both BS and UE side. This discussion advocates the fact
that operators would have the leverage of tuning their network for a balance between
quality of service and energy consumption.

Knowing the accurate energy consumption of a base station constitutes an impor-
tant part of the understanding of the energy budget of a wireless network. For this
purpose, authors in [1] have specifically discussed energy conservation at equipment
level by presenting the breakdown of a base station. A typical BS has been presented
by dividing it into five parts, namely antenna interface, power amplifier, RF chains,
Baseband unit, mains power supply and the DC-DC supply. These modules have been
shown in Figure 2. An important claim has been made stating that up to 57power
consumption at a base station is experienced at the transmission end, i.e., the power
amplifier and antenna interface. Yet, with small cells, the power consumption per base
station has been reduced due to shorter distances between the base stations and the
users [1,19]. In [19], analytical modelling of the energy efficiency for a heterogeneous
network comprising upon macro, pico and femto base stations has been discussed.
To a certain extent emphasis has been put on the baseband unit which is specif-
ically in charge of the computing operations and must be sophisticated enough to
handle huge bursts of traffic. A baseband unit has been described to be composed
of four different logical systems including a baseband system used for evaluating Fast
Fourier Transforms (FFT) and wireless channel coding, the control system for resource
allocation, the transfer system used for management operations among neighbouring
base stations and finally the system for powering up the entire base station site in-
cluding cooling and monitoring systems.
Furthermore, the use of mmWave and massive MIMO would need an even greater
push on the computation side of the base station since more and more users are now
being accommodated. The study in [16] discusses the achievable sum rates and energy
efficiency of a downlink single cell M-MIMO systems under various precoding schemes
whereas several design constraints and future opportunities concerning existing and
upcoming MIMO technologies have been discussed in [17].The computation power of
base station would increase when number of antennas and the bandwidth increases.
In the case of using 128 antennas the computation power would go as high as 3000W
for a macrocell and 800 W for a small cell according to [1].
Authors in [18] have discussed the utility of taking most of the baseband pro-
cessing functionality away from the base station towards a central, more powerful
and organized unit for supporting higher data rates and traffic density. Users have
envisioned experiencing more flexibility using this central RAN since they would be
able to get signaling from one BS and get data transfer through another best possible
neighboring BS.

Visible gains in latency and fronthaul bandwidth have thus been observed by having
stronger backhaul links but this research avenue still needs to be formally exploited
for devising globally energy efficient mechanisms. The choice of the best suited BS
would allow the network to have a lower transmission power thus increasing the en-
ergy efficiency. An analysis of throughput as a performance metric has been provided
for a two-tier heterogeneous network comprising upon macro and femto cells in [20].
The claimed improvement in throughput originates from a distributed mesh of small
cells so that the minimal transmission distance between the end user and the serving
base station would be cashed out in terms of reduced antenna’s transmission power.
Considering these findings on BS energy consumption, cell switch-off techniques

have been explored in the literature. An incentive based sleeping mechanism for
densely deployed femto cells has been considered in [21] and energy consumption re-
duction up to 40the backhaul links alive. The key enabler here would be to have
prompt toggling between active and sleep modes for maintaining the quality of ser-
vice. According to [21], a “sniffer” component installed at these small cells that would
be responsible for detecting activity in the network by checking the power in uplink
connections, a value surpassing the threshold, would indicate a connection with the
macrocell. Mobility Management Entity (MME) has also been suggested to poten-
tially take a lead by sending wake up signals to the respective femtocells and keeping
others asleep. In contrast to the usual techniques of handing their users over to the
neighbouring base stations and turning that cell off, it would be beneficial to give
incentives to users for connecting to a neighbouring cell if they get to have better
data rates.
Authors in [22] have conducted a thorough study for classification of the switch-
ing techniques as well as calculation of the outage probability of UEs, under realistic
constraints. Their claim states that the energy consumption of the base station is
not directly proportional to its load so an improved switching algorithm was needed
that would allow the UEs to maintain the SINR thresholds. They have thus brought
forward a sector based switching technique for the first time. Furthermore, their
claim favors an offline switching technique instead of a more dynamic online scheme
because of practical constraints such as random UE distribution and realistic inter-
ference modelling. Authors in [23] discuss influence of the transmit power scaling
and on/off switching on instantaneous macro base stations power consumption. The
proposed power consumption models have been claimed to be used as generic models
for the relationship between transmitted and consumed power for macro base stations
of different technologies and generations.

In addition to these techniques, recently, machine learning techniques have been used
to implement cell switch off. The research in [47] discusses a use case of shared UE
side distributed antenna system for indoor usage where a combination of distributed
antenna and MIMO technology is used for getting enhancements in the coverage area
and utilization of unlicensed frequencies for accommodating more users. The use of
both licensed as well as unlicensed bands simultaneously needs a redesign of the cur-
rent resource allocation algorithms [47]. In this work, resource allocation has been
considered to be a non-convex optimization for increasing the end to end energy effi-
ciency.
The suggested topology demands installation of a shared UE side multiple antenna

hardware between a single antenna base station (outdoor) and arbitrary number of
single antenna UEs (indoor) which are called shared user equipment (UE)-side dis-
tributed antenna system (SUDACs). These SUDACs would be able to communicate
the channel information with their neighbouring SUDAC units installed. In contrast
with the relaying in the LTE-A system, SUDACs could be installed at different loca-
tions by the users and still be able to operate in both licensed and unlicensed bands
simultaneously. The problem statement boils down to defining the energy efficiency
in terms of the bits exchanged between base station and the UEs via SUDACs per
joule of energy. It has been shown in [47] that application of this model exploits
the frequency and spatial multiplexing of UEs and increases the system efficiency as
compared to the case when SUDACs is not involved.

Chapter 3
Methodology
3.1 Standard Framework for Energy Efficient 5G

System
The energy efficiency can be expressed as the following formula without considering
different deployment scenarios [3], [11]
EE DX iiViEi (1)
where for each traffic load level i Vi denotes the traffic load per second processed by
a wireless device, Ei refers to the power consumed by the wireless device to process the
traffic load, i is the weight of traffic load level i. The definition of energy efficiency can
be applied to both base station and user equipment (UE). According to the formula,
the energy efficiency can be enhanced by boosting up the traffic load using techniques,
such as utilizing wider spectral resources, more antennas, higher modulation order,
provided that power consumption to process the increased traffic load can be kept low.
Therefore, energy saving is a key factor for sustainable energy-efficient 5G net-

work deployment. This paper focuses on improving energy efficiency by reducing
network and UE power consumption. In addition to UE implementation, UE power
consumption relies on the configurations by network, which needs to be guaranteed
by specification. Tech niques that are aimed to reduce UE power consumption are
mainly analyzed in this paper.
In this section, some essential components for building a standardized framework of

energy efficient 5G system are provided. These components include flexible reference
signal design, efficient sleep modes, bandwidth adaptation, RRC inactive state, DRX
mechanism, control channel design and cross-slot scheduling. Some of the description
is more from UE power saving perspective. Some techniques can be used for power
saving from network perspective in combi nation with flexible and scalable framework
as described in Section I.

3.1.1 Flexible Reference Signal Design for Efficient Sleep
Mode
As mentioned in Section I, always-on reference signals on LTE has limited the energy
efficiency of 4G network. In contrast, flexible reference signal design in 5G enables
more efficient sleep mode at the base station. Without always-on reference signals,
the base station can just wake up periodically to send synchronization signals, broad-
cast necessary system information and detect random access channel (RACH). Here
we briefly discuss on two mandatory downlink signals and the possible periodicities
in 5G NR [8].
-Synchronization Signal Block (SSB): Multi-beam based SSB is defined in

5G NR to include Primary SS (PSS), Secondary SS (SSS) and Physical Broadcast
Channel (PBCH) transmitted by the base station periodically. Periodicity of SSB is
assumed to be not longer than 20ms for standalone NR cases, i.e. it can be 5ms,
10ms or 20ms. While for non-standalone case with LTE-NR Dual Connectivity, the
periodicity can be 5, 10, 20, 40, 80, 160ms.
-Channel State Information Reference Signal (CSI-RS): CSI-RS can be

used for channel tracking, CSI acquisi tion, mobility or beam management. Periodic
CSI-RS for channel tracking is mandatory for UEs under con nected state to perform
fine time and frequency channel tracking. It is transmitted in bursts of two or four
sym bols which are spanned across one or two consecutive slots. Periodicity of this
channel tracking RS can be flexibly configured as 10, 20, 40, 80ms. One energy effi-
cient implementation is to configure long periodic ity such 80ms for periodic CSI-RS
for tracking. Once traffic comes, aperiodic tracking RS can be triggered before data
so that the UE can perform fine channel tracking before data reception and demodu-
lation.
The flexible periodicity of reference signal design in NR is a significant improve-

ment on network energy efficiency compared to LTE in which CRS has to be trans-
mitted in every 1ms. With this flexibility, 5G base station can support different levels
of sleep mode depending on the traffic load. In [12], four levels of sleep modes in the
power model are defined to represent different depths of sleep modes measured by
transition latency including deactivation and reactivation latency on the sleep mode.
The four levels respectively correspond to OFDM symbol level (i.e. 71.4us), sub-frame
level (i.e. 1ms), radio frame level (i.e. 10ms) and the deep sleep stand-by level in the
order of 1s. In LTE, OFDM symbol level is the only level it can use due to always-on
reference signals.
Figure 3.1: Load-dependent base station power model.
In NR, four levels of sleep modes can be achieved by different periodicity of ref-
erence signals and power saving schemes like Secondary Cell (SCell) dormancy for
carrier aggregation. Energy efficiency can be evaluated using the data from the recent
power model in [12] together with the simplified esti mate of a power model for base
station proposed in [13], [14] as shown in Figure 3.1 and the formula (2) below. The
base station power consumption Pin can be obtained from the following function:
where NCC is the number of component carriers (CC), Nsec is the number of
sectors per site, 1p is the slope of the load dependent power consumption, D [0; 1] is
the ratio of the number of transmitted resource elements (REs) to the total number
of REs.
Figure 3.2: Power model parameters for pico small cell.
Using this model and the updated parameters for 5G base station in Figure 3.2,
energy saving performance is evaluated using system level simulation on small cell
deployment with different densities. Different cell densities are achieved by deploying
different number of small pico cells (N cell) per macro area. To compare power
consumption used with different densities and sleep modes, the same traffic load using

FTP model 1 [15] with file sizeD0.5MByte and packet arrival rate D 15 per second
per macro area is used for all simulations. More detailed simulation assumptions can
be found in [8] as similar simulation setup is used here. In the simulation, sleep modes
are applied to small cells only. With this simulation setup, energy saving of level 2
(L2) and level 3 (L3) sleep modes are obtained as shown in Figure 3.2, compared
to the baseline 4G LTE deployment which level 1 (L1) sleep mode is used due to
always-on reference signals.
3.1.2 Bandwidth Adaption

The key performance requirements of 5G technologies by IMT-2020 include downlink
peak data rate is 20Gbit/s and uplink peak data rate is 10Gbit/s. To achieve the
require ments, a wide bandwidth is supported by the new radio access technique de-
veloped by 3GPP. According to 3GPP specifica tion, UE channel bandwidth is up
to 100MHz for sub-6GHz bands [16] and 400MHz for above 6GHz bands [17], which
is much wider than the bandwidth of 20MHz in LTE. It should be noted that UE
capability of supported bandwidth varies and is often limited to the bandwidth less
than the maximum bandwidth supported in the specification especially in the early
stage of 5G deployment. Besides, high UE power con sumption can be a major issue
if UE is required to perform transmission or reception in a wide bandwidth all the
time regardless of how much the actual traffic load is.
Figure 3.3: BWP adaptation.
To reduce UE power consumption and guarantee the data transmission rate, the
concept of bandwidth part (BWP) was adopted by 3GPP. A BWP is comprised of a
number of continuous physical resource blocks (PRB) with specific numerology. For
each serving cell, there are at most four BWPs can be configured for downlink (DL)
or uplink (UL). Only one ULBWPand one DLBWPare active at a given time instant.
Furthermore, UE is not required to transmit or receive data outside an active BWP.
The BWP can be activated or de-activated by a timer, physical layer Downlink Con-
trol Information (DCI) signaling or higher layer RRC signaling. When a large data
packet needs to be transmitted, UE can be indicated to activate a BWP with a wide
bandwidth. Other wise, UE can be informed to switch to a BWP with a narrow band-
width to save power [18]. BWP switch delay is defined with regard to the activation
signaling. An example is shown in Figure 3.3.
Overall, standardized BWP switching framework in NR is beneficial for building

an energy efficient network. In addi tion to bandwidth adaptation, this can also serve
as a general framework which can be used for adaptation of other power saving pa-
rameters introduced in the 5G evolution.
3.1.3 RRC Inactive State

In LTE, RRC connected state and RRC idle state are defined so that UE can operate
under low power mode in idle state when there is no data transfer. When there is data
activity, UE transitions back to RRC connected state by going through a cumbersome
procedure described in [19] which requires extensive signaling between UE and the
network. Inactivity timer is set to trigger UE to idle state if there is no data activ-
ity in a certain duration. The timer setting can be considered as a tradeoff between
power consumption and data transmis sion efficiency including signaling overhead and
latency. The timer setting with short duration favors more on idle state but it costs
higher signaling overhead and latency if there is frequent data transmission. However,
the power saving gain from idle state is reduced if the timer expires only after a long
duration. The major issue of this state transition is high signaling and latency cost.
Figure 3.4: NR RRC state machine.
To address this issue, a new state called RRC inactive state [1], [19] has been
introduced in NR, in addition to RRC connected and RRC idle states as shown in
Figure 3.4. The motivation of this new RRC state is to allow faster and more efficient
resumption to RRC connected state so that data transmission can be done with less
signaling overhead, lower latency and lower power consumption. Information such as
UE identity, security information and mobility control information is saved in both
UE and network sides when the UE transitions from RRC connected state to inactive
state by going through the RRC suspend procedure. The stored information is neces-
sary when UE wants to resume the con nection from RRC inactive to RRC connected
state for data transmission. With this RRC inactive state, the state transition to
RRC connected state becomes more efficient.
Overall, it is beneficial to UE power consumption espe cially if we consider heavy

traffic load or long data packets. For small data packets, the cost, including overhead,
latency and power consumption, coming from random access pro cedure and RRC
connection establishment is still too high relative to small data payload [20]. There-
fore, introduction of inactive state alone is not sufficient if small data transmission
cannot be done under inactive state. This requires enhance ments in further steps of
5G evolution.
3.1.4 DRX Mechanism

The data packet arrival is observed to be intermittent. It is power consuming if UE
is always required to monitor Physi cal Downlink Control Channel (PDCCH) for the
DL assign ment or UL grant. To prolong UE battery lifetime, DRX mechanism was
introduced in the early stage of LTE and inherited by NR [9].
Figure 3.5: DRX mechanism.
If a UE under RRC connected state is configured with DRX, it monitors PDCCH

periodically during a configured time duration which is called DRX OnDuration pe-
riod as shown in Figure 3.5. If there is no data scheduled, the UE can turn off its RF
chain and enter into power saving state. If UE detects a DCI indicating a new DL or
UL transmission, DRX Inactivity Timer would be started. Until the DRX Inactivity
Timer expires, the UE needs to keep monitoring PDCCH for the potential subsequent
data scheduling. For a UE under RRC idle/inactive state, the DRX mecha nism is
related to paging detection. Specifically, the UE needs to detect paging occasion per
DRX cycle for paging message and system information update.
The DRX mechanism is a tradeoff between UE power efficiency and data trans-
mission latency. The tradeoff depends on the parameters related to inactivity timer,
DRX OnDuration and DRX cycle. Overall, DRX mechanism is benefi cial for lower-
ing UE power consumption by allowing UEs to enter power saving mode periodically.
There are rooms for further improvement especially considering bursty data. In ad-
dition, further enhancements of DRX mechanism for 5G multi-beam millimeter wave
communication can be con sidered, e.g. enhanced beam based DRX measurements
[21], directional DRX [22], etc.
3.1.5 Control Channel Design

PDCCH Search Space set refers to a set of downlink resources where PDCCH can
potentially be carried [23], [24]. To mon itor PDCCH, UE performs blind decoding
in configured search space set. PDCCH monitoring is a primary contributor to UE
power consumption [25]. Another UE power saving feature in Release 15 is that the
PDCCH monitoring occa sion(s) of a search space set is determined by the parame ters
such as periodicity(ks), offset(os), duration (Ts) and the monitoring pattern within a
slot. Furthermore, the starting symbol for PDCCH monitoring within a slot is repre-
sented by a bitmap and the corresponding duration depends on control resource set
(CORESET) duration. Specifically, the PDCCH monitoring occasions exist in a slot
if the following formula is satisfied and then UE detects PDCCH candidates for con
secutive Ts slots.
where nf is the frame number, Nframe; slot is the number of slots per frame, n s;f
is the slot number within a frame for subcarrier spacing (SCS) .
A PDCCH monitoring pattern is illustrated in Figure 3.6. It is assumed that ks

D 4 slot, os D 1 slot, Ts D 2 slots, bitmap D 10001001000000, CORESET duration
is 2 OFDM symbols and SCS D 15kHz. Each “1” bit in the bitmap represents a

Figure 3.6: PDCCH monitoring occasions
two-symbol CORESET. Hence, symbols 0, 1, 4, 5, 7, 8 are for PDCCH monitoring in

the highlighted slots.
The search space set is configured per BWP, UE can adapt to different PDCCH
monitoring periodicity through BWP switch. This provides flexibility in some extent
but further enhancements to reduce PDCCH monitoring are beneficial especially for
the UEs which are not capable of dynamic BWP switching.
3.1.6 Cross-Slot Scheduling

For the slots that are configured with PDCCH monitoring occasions, UE needs to
detect PDCCH for data scheduling. It often takes several symbols for UE to finish
PDCCH decoding. If Physical Downlink Shared Channel (PDSCH) carrying downlink
data scheduled by PDCCH is allocated in the same slot, i.e., same-slot scheduling,
UE needs to keep the RF chain on to buffer any potential DL transmission when it is
decoding PDCCH.
Figure 3.7: Same-slot scheduling and cross-slot scheduling

As shown in Figure 3.7, the gap between PDCCH and the scheduled PDSCH is
denoted as K0. For the same-slot scheduling in Figure 7(a), UE has to buffer the
potential PDSCH for each slot but the DL data is only transmitted in slot1 Power
consumption can be reduced if UE does not buffer PDSCH in slot 0,slot 2 and slot 3.
For this purpose, cross-slot scheduling with K0 ¿ 0 is introduced [26], [27]. As shown in
Figure 7(b), the PDSCH is assumed to be allocated 1 slot (K0 D 1) after the PDCCH,
UE can turn off part of the RF processors and enter into micro sleep after PDCCH is
received when there is no PDSCH scheduled in slot 0,slot 1 and slot 3. Compared with
same-slot scheduling, the technique of cross-slot scheduling can save a considerable
power consumption when the data arrival is sparse. Similar to DL, the time gap (K2)
between PDCCH and the corresponding uplink channel, i.e. Physical Uplink Shared
Channel (PUSCH) is introduced to reduce UE’s UL processing timeline.

Chapter 4
Simulation and Evaluation
4.1 Results and Discussion

It can be observed from the results that more energy can be saved for denser small
cell deployment. The energy saving can be more than 70for the scenarios where 20 or
40 small cells are deployed per macro area, compared to 4G LTE deployment. With
denser small cell deployment, resource utilization is lower which favors energy saving
due to more frequent sleep. In 4G LTE, the performance of energy saving is limited by
level 1 sleep mode and the extra overhead and interference due to always-on reference
signals. Therefore, flexible reference signals in 5G has enabled more energy efficient
dense network deployment.
Figure 4.1: Energy saving percentage gain with different sleep modes.
To identify the contributors to UE power consumption, the power consumption

distribution of different power states is simulated for File Transfer Protocol (FTP) [6]
and Voice over Internet Protocol (VoIP) traffic model [30]. The DRX configurations
of (DRX cycle OnDuration timer Inactiv- ity timer) D (160ms, 8ms, 100ms) and (4ms,
4ms, 10ms) in Figure 4.1 are used for FTP and VoIP, respectively.

Figure 4.2: Power consumption distribution of different power states for FTP and
VoIP traffic model.
According to the simulation results in Figure 4.2, it can be observed that the
PDCCH-only state dominates UE power consumption. Therefore, it is crucial to re-
duce the power consumed in PDCCH-only state.
Figure 4.3: Simulation results of DRX mechanism without/with wake-up indication

for traffic model (DRX cycle OnDuration timer Inactivity timer).
To evaluate the power saving gain from wake-up indication, the DRX mechanisms
without/with wake-up indication are simulated. The results of average power per slot
and average latency per packet are shown in Figure 4.3. The notation of FTP(160, 8,
40) denotes traffic model (DRX cycle in ms OnDuration timer in ms Inactivity timer
in ms). The same notation is also applied in the subsequent figures in this paper.
Figure 4.4: Simulation results of DRX mechanism without/with wake-up indication

for traffic model (DRX cycle OnDuration timer Inactivity timer).
It is observed that the mean power of DRX mechanism with wake-up indication can
reduce almost 9-33power consumption compared with the DRX operation in Release
15 Hence, WUS can provide promising gain on power saving on top of the DRX mech-
anism. In addition, trade-off between power saving and latency is studied. It can be
observed that latency increase ranges from 9to 34with this power saving technique. It
is expected that packet throughput would also be impacted due to additional latency.
Figure 4.5: Simulation results of two component carriers without/with SCell dor-
mancy behavior.
To evaluate the power saving gain from SCell dormancy behavior, it is assumed
that two BWPs are configured for both PCell and SCell, where the bandwidths of
these two BWPs are 20MHz and 100MHz, respectively. In the simulation, UE is
indicated to switch to the BWP of 100MHz for PCell and SCell when data packet
arrives. After the BWP inactivity timer expires, UE falls back to the BWP of 20MHz.
In the cases when SCell dormancy is supported, the BWP of 20MHz is dormant
BWP. The results of average power per slot are shown in Figure 4.4. It is observed that
the power saving gain of SCell dormancy behavior under FTP traffic is 18.727.4As
the principles of BWP switching in the two cases are the same, no additional latency
is observed when SCell dormancy is supported.
The results of average power per slot of same-slot scheduling and cross-slot
scheduling are shown in Figure 4.5. For cross-slot scheduling, the additional latency is
mainly determined by the value of K0min. To reduce latency, K0min is assumed to
be 1 slot in the simulation.The relative power of PDCCH-only for cross-slot schedul-
ing is 70. It is observed that the power saving gain from cross-slot scheduling is 20, 27.
Figure 4.6: Simulation results of same-slot scheduling and cross-slot scheduling.
The results of average power per slot and average latency per packet are shown in
Figure 4.6. It is observed that the power saving gain from MIMO layer adaptation is
about 2.3, 25.2and the average latency increases by almost 1.7, 14.7.
The power saving techniques involved in higher layer procedure are also consid-
ered in Release 16. The power consumption is closely related to UE implementation.
It is challenging for network to customize configurations that suit the needs of power
saving for all the UEs. To acquire the preferred configuration at UE side, more UE
assistance information is introduced, such as a request of transition from RRC con-

Figure 4.7: Simulation results of without/with MIMO layer adaptation.
nected mode to RRC idle/inactive mode, minimum scheduling offset values, etc.
In RRC idle/inactive mode, the power consumed by Radio Resource Management

(RRM) measurement is significant. To reduce power consumption, neighbor cell RRM
measurement can be relaxed based on the evaluation of serving cell for UE with low
mobility or at the cell center.
Figure 4.8: Power saving gain of 2-step RACH over 4-step RACH.
In the simulation results provided by Figure 4.7, the average power per slot is 2.28
and the micro sleep contributes 35power consumption when the number of detected
SSB is 3. When the detected SSB before PO is reduced to 1, the average power per

slot is 1.5 and the power saving gain is 34.2. To reduce the unnecessary paging re-
ception, solutions such as sub-group paging, group wake up signal (WUS), extended
DRX, etc., can be considered.
Figure 4.9: UE power consumption distribution in RRC idle state.
Meanwhile, UEs under RRC idle/inactive state should perform serving cell mea-
surement and evaluate the cell selection criterion at least once every N DRX cycle,
wherein N is determined by DRX cycle. Also, for these UEs, RRM measurement
is based on SSB. The periodicity of SSB burst is 20ms by default. If the SSB is not
aligned with the paging occasion as shown in Figure 4.8, UE needs to wake up multiple
times to detect the paging occasion and perform RRM measurement. During the time
gap between SSB and paging occasion, UE cannot enter into deep sleep, which in-
creases the power consumption. To minimize the gap between the RRM measurement
and the paging occasion, additional RS can be considered in Release 17 as shown in
Figure 4.9. To reduce the impact on network power efficiency and resource overhead,
reference signals, such as CSI-RS, configured to UEs under RRC connected state can
be also signaled to UEs under RRC idle/inactive state in broadcast messages. Further,
RRM relaxation can be considered for stationary devices.

Chapter 5
Conclusion and Future scope
In this paper, we provide an overview on the standardized framework supporting

operations of energy efficient networks in 5G NR. The 5G evolution path on power
saving techniques is discussed considering from the first version of 5G standards to
future beyond 5G standard development towards 6G. Flexible and scalable system
design in 5G NR enables different means of adaptation to various traffic loads and
traffic types in the networks. For example, the NR standard supports flexible reference
signal design, band width adaptation, DRX mechanism, flexible control channel design
and more efficient RRC state transition in Release 15. In Release 16, enhancements
on power saving and 2-step RACH have been introduced. Moreover, future trends
for beyond 5G evolution on standardization support of enhanced power saving are
discussed, taking into account various types of UEs including NR Lite UEs which are
potentially intro duced in Release 17 with lower complexity targeting at use cases of
industrial wireless sensors, smart wearables and video surveillance.
5.1 Future Trends for Beyond 5G

As the submission of 3GPP 5G solutions for IMT-2020 to ITU has been finalized
in June 2019 [37], the standardization process for beyond 5G has been initialized
and planned. After two 5G releases, plans for Release 17 enhancements have been
mostly finalized [2]. At least the following three topics are related to power saving. -
Support of NR devices with reduced capability, also known as NR Lite UEs [38] - Small
data transmission in RRC inactive state [39] - UE Power saving enhancements [40]
Regarding NR Lite UEs, it targets at reducing device complexity for UEs applicable
to various use cases includ ing industrial wireless sensors, smart wearables and video
surveillance. For these new Release 17 UE types, different power saving techniques
can be considered compared to typ ical 5G UEs which have design targets to meet
the eMBB and URLLC requirements for in Release 15 and Release 16.
5.1.1 Small Data Transmission in RRC Inactive

Support of Small data transmission in RRC inactive state is one of the planned topics
in Release 17 [39]. It targets at traffic with infrequent small data transmission. The

exem plary use cases with small data packet include meters or sensors-type NR Lite
UEs with periodic measurement report ing, traffic from wearables with periodic posi-
tioning infor mation, traffic generated from instant messaging services or heart-beat
messages, etc. Under these traffic types, UE is often maintained by network in RRC
inactive state. Without support of data transmission in RRC inactive state, UE has
to resume the connection with RRC connected state. Connection setup and subse-
quent release to INACTIVE state happens for each data transmission regardless of
how small and infrequent the data packets are. This results in unnecessary power con
sumption and signaling overhead.
As analyzed in Table 2-4 in Section III Part F, support of small data in RRC
inactive state together with 2-step RACH can achieve significant power saving gain
compared to the cases which data has to be transmitted under RRC con nected state.
In addition to RACH-based scheme, RACH-less scheme will also be supported. With-
out RACH, small data transmission can be done directly on preconfigured PUSCH
resource without preamble. This is expected to provide further power saving gain but
this only works under the assumption that transmit timing is known. This applies to
the scenarios where UEs are stationary so that synchronization timing acquired in the
past can be re-used. For example, industrial sensors are often fixed in the locations
with the same indoor environment.
5.1.2 UE Power Saving in RRC Idle/Inactive

Given that the major focus in Release 16 is power saving under RRC connected mode,
enhancement on the power efficiency of RRC idle/inactive state becomes more urgent
considering new use cases for NR Lite UEs. In addition to support of small data
transmission in RRC inactive state, power saving technique can be applied to paging
and RRM measurement.
The RRC idle/inactive state UE is required to monitor one paging occasion per
DRX cycle to detect the scheduling of paging and system information update. The
paging occasion location is determined by the UE identification. The false alarm
paging rate contributes to the power consumption of RRC idle/inactive state UE,
especially in the case of low paging rate.
To ensure the decoding performance of PDCCH and paging message when channel
condition is not good enough, UE needs to detect multiple SSBs before PO to adjust
automatic control gain (AGC), acquire synchronization in time and frequency domain

Figure 5.1: UE behavior in RRC idle state.
and RRM measurements, etc. As shown in Figure 5.1, it is assumed that UE detects
three SSB before the PO. In this case, UE cannot enter into deep sleep, the power
consumed by the multiplewake-up times and micro sleep state is signicant.
If the number of detected SSB before PO and the paging reception can be reduced,
as shown in Figure 5.1, it enables UE to enter into deep sleep and the power con-
sumption can be significantly decreased.
5.1.3 UE Power Saving in RRC Connected

In Release 16, wake-up indication can be used to indicate UE to not start the DRX
OnDuration Timer when there is no data arrival. However, if UE wakes up for data
transmis sion, the DRX Inactivity Timer would be started and UE has to monitor
PDCCH before the timer expiration. There fore, the following power saving tech-
niques to further reduce PDCCH monitoring within DRX Active Time can be consid
ered in Release 17 [41].
PDCCH Monitoring Adaptation: PDCCH monitoring adaptation allows UE

to switch PDCCH monitoring behav ior with a sparser PDCCH monitoring occasions
within one BWP when data arrives sparsely.
For example, in Figure 5.2, UE can be triggered by DCI to switch PDCCH moni-
toring occasions from (Ts D 1 slot, ks D 1 slot) to (Ts D 1 slot, ks D 2 slots), where
Ts is PDCCH monitoring duration and ks is the monitoring periodicity.

Figure 5.2: UE behavior for exceptional cases of WUS detection.
Figure 5.3: PDCCH skipping.
PDCCH Skipping: As shown in Figure 5.3, with PDCCH skipping technique,

base station can send a DCI to indicate UE to perform PDCCH skipping if there
is no data to be transmitted to UE. After UE receives the indication, UE can stop
monitoring PDCCH to save power.
5.1.4 Power Saving for Massive MIMO

As mentioned in Section I, massive MIMO can provide throughput boost and better
coverage in 5G network but the bottleneck still exists in the aspect of power consump-
tion and hardware cost. In this part of section, two future trends on massive MIMO
deployment are discussed to address these issues.

Multiple Antenna Panels: Support of Multi-antenna-panel simultaneous trans-
mission is a trend for millimeter wave communications as shown in Figure 5.4 [42]. In
NR beam management and CSI acquisition, multi-panel UE may need to use multiple
antenna panels to measure CSI or beam infor mation. The utilization of multiple
panels can enable beam sweeping across multiple UE and/or base station panels.
In power saving perspective, it is more exible to turn on/off antenna panel ac-
cording to the trafc and channel conditions. Multi-panel measurement is not always
needed as multi-panel transmission is not always necessary according to the channel
variation. Always requiring UE or base station to use multiple antenna panels for
beam measurement would cost high power consumption as panel switching. Hence, it
is not energy efcient to keep all the panels on for beam measurement.
For the above power consumption aspects, it would be helpful to introduce a mech-
anism to let UE turn off some antenna ports/panels and keep the panel status aligned
with base station. For example, base station can inform UE to activate or de-activate
some non-useful ports/panels through dynamic signaling. Further, UE may need to
report some information so that base station can make a proper decision consider ing
performance, power consumption and latency. Overall, a standardized panel specic
power saving mechanism would be helpful for base station to understand the oper-
ating state of UE panels. One example is to apply directional or panel specic DRX
mechanism [22].
Intelligent Reecting Surfaces: Intelligent reecting sur faces (IRS) is a new

promising hardware solution to enhance future wireless communication systems, with
considera tion of lowering power consumption and enhancing ef ciency of massive
MIMO networks at the same time. IRS consists of many congurable electromagnetic
units (EUs). With the recent developments in meta-surfaces [43], these EUs are low-
cost and energy-efcient.
These units can be used to radiate or reflect electromagnetic waves, via control
ling the electromagnetic properties (e.g. phase, amplitude) in real time [44], [45],
adaptive smart beam can be formed at the expected direction(s), achieving coherent
superposition at the location of desired receiver, while interference can be kept minimal
for the receivers in other locations. With the devel opment of electromagnetic material
technology, new types of EUs enabling strong ability and better features have become
more practical. Strong ability here means more accurate control of various types of
electromagnetic properties, e.g. phase, amplitude, frequency, orbital (OAM) and spin

Figure 5.4: Multi-panel transmission.
(SAM) angular momentum. Better features include low cost and complexity, thin and
light form factors, low power consumption. As long as there are massive number of
electromagnetic units and wide distribution, holographic effect can be realized.

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Technical Seminar Report - Srinath Reddy B S

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VISVESVARAYA TECHNOLOGICAL UNIVERSITY

Jnana Sangama, Belagavi - 590 018

DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING

RNS INSTITUTE OF TECHNOLOGY

Internship /Technical Seminar Report

DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING

RNS INSTITUTE OF TECHNOLOGY

DEPARTMENT OF ELECTRONICS AND COMMUNICATION

................................ ................................ ................................

DEPARTMENT OF ELECTRONICS AND COMMUNICATION

I express my gratitude to our Chairman late Dr.R N Shetty and

I would like to express my sincere thanks to Dr.M K Venkatesha,

I extend my sincere thanks and heartfelt gratitude to my internal re-

Finally, I take this opportunity to extend my earnest gratitude and

List of Figures iii

4 Simulation and Evaluation 20

5 Conclusion and Future scope 26

1.1 5G Applications in Cloud and IOT . . . . . . . . . . . . . . . . . . . . . . 1

3.1 Load-dependent base station power model. . . . . . . . . . . . . . . . . . . 13

4.1 Energy saving percentage gain with different sleep modes. . . . . . . . . . 20

5.1 UE behavior in RRC idle state. . . . . . . . . . . . . . . . . . . . . . . . . 28

3GPP : Third Generation Partnership Project

5GC : 5G Core network

BCH : Broadcast Channel

BWP : Bandwidth Part

CoMP : Coordinated multi-point

CRAN : Centralized Radio Access Network

C-RAN : Cloud Radio Access Network

CRS : Cell-Specific Reference Symbol

CSI : Channel State Information

CSI-RS : Channel State Information-Reference Signals

D2D : Device to Device

DCI : Downlink Control Information

DRAN : Distributed Radio Access Network

DTX : Discontinuous Transmission

DRX : Discontinuous Reception

eMBB : enhanced Mobile Broadband

ETSI : European Telecommunications Standards Institute

FeMBMS : Further evolved Multimedia Broadcast Multicast Service

gNB : next generation NodeB

LTE : Long Term Evolution

LTE-A : Long Term Evolution Advanced

MIMO : Multiple-Input Multiple-Output

mMIMO : masssive Multiple-Input Multiple-Output

mMTC : massive Machine Type Communication

NG-RAN : Next Generation Radio Access Network

OFDM : Orthogonal Frequency Division Multiplexing

RAN : Radio Access Network

RAT : Radio Access Technology

RLC : Radio Link Control

RLF : Radio Link Failure

SRS : Sounding Reference Signals

SCell : Secondary Cell

URLLC : Ultra-Reliable Low-Latency Communication

VRAN : Virtualized Radio Access Network

VNF : Virtual Network Function

The Third Generation Partnership Project (3GPP) completed the 5G standardization