∎

11institutetext: Vanlin Sathya
Department of CSE, The University of Chicago, Illinois, USA.
11email<EMAIL_ADDRESS>
Srikant Manas Kala
Mobile Computing Laboratory, Osaka University, Japan.
11email<EMAIL_ADDRESS>
Kalpana Naidu
Department of ECE, NIT Warangal, India
11email<EMAIL_ADDRESS>
# Heterogenous Networks: From small cells to 5G NR-U

Vanlin Sathya  Srikant Manas Kala  and Kalpana Naidu

(Received: date / Accepted: date)

###### Abstract

With the exponential increase in mobile users, the mobile data demand has
grown tremendously. To meet these demands, cellular operators are constantly
innovating to enhance the capacity of cellular systems. Consequently,
operators have been reusing the licensed spectrum “spatially," by deploying
4G/LTE small cells (_e.g.,_ Femto Cells) in the past. However, despite the use
of small cells, licensed spectrum will be unable to meet the consistently
rising data traffic because of data-intensive applications such as augmented
reality/virtual reality (AR/VR) and on-the-go high-definition video streaming.
Applications such AR/VR and online gaming not only place extreme data demands
on the network, but are also latency-critical. To meet the QoS guarantees,
cellular operators have begun leveraging the unlicensed spectrum by coexisting
with Wi-Fi in the 5 GHz band. The standardizing body 3GPP, has prescribed
cellular standards for fair unlicensed coexistence with Wi-Fi, namely LTE
Licensed Assisted Access (LAA), New Radio in unlicensed (NR-U), and NR in
Millimeter. The rapid roll-out of LAA deployments in developed nations like
the US, offers an opportunity to study and analyze the performance of
unlicensed coexistence networks through real-world ground truth. Thus, this
paper presents a high-level overview of past, present, and future of the
research in small cell and unlicensed coexistence communication technologies.
It outlines the vision for future research work in the recently allocated
unlicensed spectrum: The 6 GHz band, where the latest Wi-Fi standard, IEEE
802.11ax, will coexist with the latest cellular technology, 5G New Radio (NR)
in unlicensed.

###### Keywords:

: Femtocells, Small cells, LAA, NR-U, Wi-Fi

††journal: my journal

##  1 Introduction

Mobile networks or cellular networks have evolved rapidly over the course of
the last four decades. The first generation of mobile networks (1G) were
introduced towards the end of 1970s. They relied almost entirely on analog
communication techniques and were primarily designed to offer voice services
to the end-users. A few of the noteworthy first generation systems include the
Extended Total Access Communication Systems (ETACS), the Narrowband Total
Access Communication Systems (NTACS), and the Advanced Mobile Phone Service
(AMPS). 1G cellular networks were followed by the second generation of mobile
networks (2G) that became publicly and commercially available in the 1990s.
The hallmark of 2G communication was a giant leap towards the digital mode of
communication, a clear break from its analog predecessor. The switch to
digital communication significantly enhanced the capabilities of the cellular
systems as it offered greater network capacity, enhanced quality of voice
services through intelligent speech codecs, reduced drain on the battery, and
greater security. However, the landmark technological advancement was the
commencement of digital data services including the Short Message Service
(SMS) and Internet access over wireless cellular networks. Global System for
Mobile Communications (GSM), IS-95 Code Division Multiple Access (CDMA), and
IS- 136 Time Division Multiple Access (TDMA) represent the most significant 2G
systems.

A few years later, General Packet Radio Service (GPRS) was introduced which
employs packet switching _i.e.,_ data is transmitted in the form of packets.
In sharp contrast, in 1G and 2G systems circuit switching was used for data
transmission. A major advantage that packet switching has over circuit
switching, is that the resources are reserved for an entire session and only
released when the session ends (for both voice and data sessions). Packet
switching became more relevant as the _world wide web_ or the Internet evolved
and the Internet Protocol (IP) was widely adopted. These developments paved
the way for an enhanced GPRS standard known as the Enhanced Data for Global
Evolution (EDGE), which is an important feature of the 2.75G cellular
technology stack.

Despite, offering voice/data services and internet access of a higher quality
than the earlier standards, EDGE failed to catch up with the surge in mobile
data demand as the penetration of mobile phones increased and there was a
tremendous increase in the number of end-users.

To meet the end-user demands, 3G cellular standards were adopted in late 1990s
with the promise of higher data rates and and enhanced Quality of Service
(QoS) for several data-intensive web applications _viz.,_ browsing, multimedia
streaming, and online gaming. CDMA was one of the primary 3G technologies,
W-CDMA and CDMA2000 being the most important CDMA versions.

However, the CDMA technology was not without flaws, the most important being
its inherent inability to scale adequately at higher bandwidths _i.e.,_ at 5
MHz and beyond. Reason being, that at higher bandwidths, the data transmission
rate is usually higher, leading to a shorter transmission step, which in turn
exacerbates the adverse impact of multipath fading on the received signal
quality.

The demand for higher data rates continued to rise, which necessitated the
research community in academia and industry to design and develop new
technologies that operate well at higher bandwidths. One of the first key
developments was the deployment of Orthogonal Frequency Division Multiplexing
(OFDM) technology. OFDM is designed to split the data signal into multiple
data streams, where each individual data-stream is allocated to a narrowband
channel. Doing so ensured an increased transmission step, mitigating the
adverse impact of multipath fading on the received signal. This enabled
cellular providers to scale their systems for higher bandwidths by
provisioning adequate number of narrowband carriers,

Multiple Input Multiple Output (MIMO) technology further enhanced the network
capacity overcoming the challenges of adverse wireless environments _viz.,_
co-channel interference and multipath fading. In MIMO, both sender and
receiver are equipped with multiple antennas, which can be used to
transmit/receive data over multiple streams using special MIMO techniques.
Together, MIMO and OFDM ushered in the era of high-capacity 4G cellular
networks.

Table 1: Comparison of LTE and LTE-Advanced Networks sathya2016improving ; kumar2019enhancing  Parameter |  LTE |  LTE-Advanced
---|---|---
Downlink peak data rate |  300 Mbps |  1 Gbps
Uplink peak data rate |  75 Mbps |  500 Mbps
Downlink bandwidth |  20 MHz |  100 MHz
Uplink bandwidth |  20 MHz |  40 MHz
Bandwidth scalability |  1.4, 3, 5, 10, 15 & 20 MHz |  20, 40, 60, 80 & 100 MHz

Leveraging the technological foundations of the GSM technologies, standards
and specifications for 3G mobile systems were designed by the Third Generation
Partnership Project (3GPP), structured as ’releases’. The vision and purpose
of 3GPP is to continually develop and maintain cellular technologies and their
related standards such as GSM and GPRS/EDGE, UMTS and HSPA, LTE and LTE-A. 4G
or Long Term Evolution (LTE) standards were introduced by 3GPP in Release 8,
with numerous technological advantages that offer enhanced network coverage
and throughput, enhanced Quality of Experience (QoE), and seamless backward
compatibility with legacy cellular technologies such as 2G and 3G. Further,
LTE-Advanced (LTE-A) meets the specifications of LTE prescribed by
International Mobile Telecommunications-Advanced (IMT-Advanced) and represents
the improvements made in LTE on and after Release 10. A comparison of LTE and
LTE-A networks is presented in Table 1 and a summary of the technological
advancements in LTE-A is presented below:

  1. 1.

Carrier Aggregation: Introduced in Release 10, the technique seeks to
aggregate multiple carriers to enhance system bandwidth alkhansa2014lte .
While, the maximum network bandwidth is 100 MHz, carrier aggregation makes it
possible to achieve peak data rates of upto 1 Gbps in downlink and upto 500
Mbps in uplink. Carrier aggregation also offers several other benefits such as
carrier load balancing, interference management and mitigation, QoS
differentiation, and optimal heterogeneous network deployment sathya2019auto ;
adam2019detection ; sathya2020machine ; garg2019sla ; kala2019odin ;
sathya2020wi . Three carrier aggregation scenarios are possible depending upon
the bandwidth available. They are: (a) contiguous aggregation in a single
radio band, (b) non-contiguous aggregation in a single radio band, and (c)
non-contiguous aggregation in multiple radio bands.

  2. 2.

Co-ordinated Multi-point Transmission and Reception (COMP): Proposed in
Release 11, COMP makes it possible for users in the overlapping zone of
multiple BSs sathya2015femto  to be jointly scheduled. In COMP, multiple
neighbouring BSs allocate the same radio resources to the UE leading to an
enhanced throughput and improved QoS for the end-users at the edge of the
cell. Further, dynamic coordination of transmission and reception over several
BSs within the network is also made possible by COMP. Both, homogeneous
networks comprising of BSs of identical types and power class, and
heterogeneous networks comprising of BSs of different type and/or power
classes, are supported by COMP.

  3. 3.

Device to Device (D2D) Communication: Generally, when two UEs communicate in
the legacy cellular networks, the base station is in charge of the data plane
and the control plane. ramamurthy2016improving ; kumar2017enhancing ;
akilesh2016novel ; ramamurthy2019dynamic . However, in D2D communication, the
communicating end-user devices data exchange directly without involving the
BSs, while the control-plane signalling is overseen br the BSs. Thus, the BSs
regulate the use of shared system resources to ensure optimal performance in
both, regular and D2D sessions kala2018exploring ; kala2019designing . D2D is
standardized for proximity-centric communication by 3GPP in Release 12 and a
typical D2D communication scenario is presented in Fig. 1. The major
challenges in D2D communication include, but are not limited to, interference
mitigation and management, optimal resource allocation, session management,
mobility and multi-hop network management, power control, communication
security, location estimation, _etc_.

Figure 1: Typical example of D2D communication

  4. 4.

Dual Connectivity: The concept of dual connectivity was introduced by 3 GPP in
release 12 agiwal2021survey ; polese2017improved , which allowed a UE to
connect to both, a Macro evolved NodeB (eNB) and a small cell eNB. Further,
both data and control plane communication is facilitated through both Macro
and small cell eNBs. This is particularly useful for UEs located in the cell
edge or in the overlapping coverage region of both type of eNBs, as it
significantly enhances the throughput.

  5. 5.

LTE-WiFi Interworking A1 ; BI ; A4 ; A3 ; 8 ; PL : This feature was introduced
by 3GPP in Release 9. Wi-Fi, operates in the unlicensed spectrum and offers
the cellular operators and opportunity to enhance network capacity by
offloading mobile data from cellular networks onto Wi-Fi.
baswade2016unlicensed ; iqbal2017impact ; mehrnoush2018analytical ;
sathya2018association ; sathya2018analysis ; sathya2018energy . This is
particularly true for indoor environments where cellular penetration maybe low
and mobility is not a primary concern comsnets . Interworking of Wi-Fi and LTE
ensures maximal spectral utilization, thereby improving QoS of data flows and
in turn, overall network capacity. Release 13 further standardized LTE-WiFi
aggregation (LWA)to facilitate optimal interworking of LTE-WiFi at the UE/eNB
protocol stack level.

  6. 6.

Machine-to-Machine (M2M) Communication: M2M communication is characterized by
low power, low cost, resource constrained, and low bandwidth communication of
a large number of Internet of Things (IoT) devices communicate with a remote
server, primarily on the uplink giluka2014class . M2M communication differs
from traditional UE-UE or H2H (Human-to-Human) communication with respect to
nature, scope, and quantum of data traffic, types of communicating devices,
the number of devices participating, and quite importantly, delay tolerance
info ; W2 ; W3 ; E1 ; LWS11 . Consequently, the challenges in M2M are also
quite different from the H2H communication paradigm. The primary challenges
include excessive signaling overhead generated by a large number of IoT
devices, providing support to devices with longer sleep cycles, ensuring low
power communication, and facilitating reliable latency-critical small data
uplink transmission. Since the traditional cellular networks were desgined
with the vision and scope of facilitating H2H communication, 3GPP introduced
solutions to the expected challenges in M2M communication in Release 11, where
solutions such as RAN overload control (due to excess signaling dama2016novel
) are proposed. Further, advanced technical solutions to the problems of
power-constrained small data transmission for low-cost IoT devices have been
specified in Release 12 and Release 13.

Motivation for 5G and Beyond: The growing penetration of high-end consumer
devices (smartphones, tablets, etc.) running bandwidth-hungry applications
(e.g., mobile multimedia streaming) has led to a commensurate surge in demand
for mobile data (pegged to soar up to 77 exabytes by 2022). An anticipated
second wave will result from the emerging Augmented/Virtual Reality (AR/VR)
industry and, more broadly, the Internet-of-Things that will connect an
unprecedented number of intelligent devices to next-generation (5G and beyond)
mobile networks. These must, therefore, greatly expand their aggregate network
capacity to meet this challenge. It is achieved by combining approaches,
including multi-input, multi-output (MIMO) techniques, network densification
(_i.e.,_ deploying small cells), and more efficient traffic management and
radio resource allocation. The other alternate approach the operators are
looking towards the Millimeter technology wei2014key , where there is more
spectrum beyond 28 GHz frequency. In US, the operators like Verizon and AT&T
they deployed more small cell base stations in the downtown, stadium, open-
park to boost the overall spectrum efficiency. But in real-time the challenges
of NR-Milimeter in terms of blockage, multi-path, penetration, beam
management, etc.

On the other side cheap, fast, and portable computing devices with ubiquitous
wireless connectivity giluka2014class ; dama2016novel ; dama2016feasible  can
revolutionize the personal computing kala2019designing  landscape by creating
an opportunity to design an unprecedented array of new services and
applications. Keeping the same philosophy in mind, we have focused mainly on
maximizing the experienced data rate of an end-user by offering increased
bandwidth through the coexistence of licensed wireless services (e.g., Long
Term Evolution (LTE)) an unlicensed band E1 ; sagari12 ; LWS22 ; LWS11 . To
achieve the goal of throughput maximization Cisco , we focused on minimizing
the interference and handling frequent handovers. It is done by optimally
placing the small cells ramamurthy2019dynamic ; kala2018exploring  (_i.e.,_ a
miniature base station, specifically designed to extend the data capacity,
speed, and efficiency of a cellular network lokhandwalaeai ) and controlling
their emitting power in a dense small cell deployment scenario.

Figure 2: Wi-Fi spectrum allocation in the 5 GHz Bands

5G LTE, which operates in the licensed band and 802.11 wireless LAN (Wi-Fi)
kala2019socio ; kala2018icalm , which operates in an unlicensed band, has some
fundamental structural differences. In Wi-Fi 5 GHz, there are more spectrum
available for the operators to boost the performance of the end customers as
shown in Fig. 2. For example, LTE control is where a base station (BS)
exclusively allocates the radio resources to the users, interference due to
concurrent transmission by the users handled. On the other hand, Wi-Fi ieee
follows a distributed approach boe ; bianchi  where each user independently
contests to occupy the channel, thereby concurrent transmission results in
interference. The main motive of LTE/Wi-Fi coexistence in unlicensed bands for
LTE users in case of very few or no Wi-Fi users. So, the research on the fair
coexistence of LTE/Wi-Fi mainly focuses on the intelligent use of the
unlicensed band by the LTE users to keep the Wi-Fi users unaffected so that
the aim of formation of the unlicensed band remains unaltered.

The standard development community has accepted two mechanisms for using the
unlicensed band by LTE. These mechanisms are licensed assisted access (LAA)
and LTE Unlicensed (LTE-U). LAA follows the same approach of sensing the
unlicensed channel, called Listen Before Talk (LBT), as Wi-Fi. LTE-U estimates
its duty cycle to access the unlicensed channel based on the various
parameters such as interference, type of traffic, and load on the track. In
our recent research, we have focused on both LAA and LTE-U mechanisms. In both
mechanisms, we keenly observe the various aspects of real-time, which can
adversely affect the Wi-Fi users and were ignored by the existing literature.
Some of the observations are:

  * •

Static channel allocation to LTE-U node in an unlicensed band

  * •

Difficulty in association to the Wi-Fi access point (AP) by the Wi-Fi users
due to high duty cycle (_i.e.,_ repeating ON and OFF intervals in the medium)
in LTE-U

  * •

A considerable reduction in the duration of duty cycle in LTE-U if several
surrounded APs considered in its estimation.

We used a Machine Learning approach to propose solutions based on these
observations. As part of my future research plan, We want to explore the
research challenges in the fair coexistence of LTE and Wi-Fi on the 6 GHz band
used as an unlicensed band. Apart from that, We plan to provide Machine
Learning (ML) based solutions to some of the existing problems on LTE/Wi-Fi
coexistence on 5 GHz. These problems are an efficient use of high bandwidth by
Wi-Fi users and optimal channel selection by both LAA BS and a Wi-Fi AP in a
multi LTE operators-multi AP scenario. ML algorithms are used to closely
observe the system’s behavior on different conditions to make intelligent
decisions.

The paper is organized as follows. Section 2 provides a brief overview of 4G
and 5G Heterogeneous Networks. Section 3 describes the associated challenges
and solutions for the past and present small cell deployments. Section 4
focuses on the future and recent NR-U small cell in 6 GHz. Finally,
conclusions and future research directions are presented in Section 5.

##  2 4G & 5G Heterogeneous Networks (HetNets) krishna2014dynamic ;
madhuri2014dynamic :

Current cellular networks comprise of end-users that generate primarily uplink
traffic (_e.g.,_ M2M communication /IoT devices), primarily downlink (_e.g.,_
streaming and web browsing), and both uplink and downlink traffic (_e.g.,_
gaming and social networks). Further, to offer better coverage, higher data
rates, and enhanced QoS to the end-user, cellular operators have deployed low
power cellular stations called small cells such as, micro cell, pico cell,
Remote Radio Head (RRH), relay, and Femto cell. Together, the diversity in
types and nature of mobile traffic generated by end-users and the variation in
transmit powers and size of small cells, current cellular networks have lost
the homogeneity of traditional networks, and are heterogeneous in nature. A
typical heterogeneous network (HetNet) comprises of a Macro cell whose
coverage and capacity is augmented by a variety of small cells to meet the QoS
guarantees. Figure 3 shows a typical example of LTE heterogeneous network. The
characteristics of a variety of small cells are listed in Table 2 and a
precise description of several types of small cells is presented below.

Table 2: Characteristics of heterogeneous cells in 4G and 5G Technology |  Placement |  Transmit Power |  Backhaul Characteristic |  Number of Users
---|---|---|---|---
Macro BS |  Outdoor |  46 dBm |  Dedicated wireline |  1000-2000
Pico or Micro cell |  Outdoor |  30 dBm |  Dedicated wireline |  100-200
RRH |  Outdoor or Indoor |  30-35 dBm |  Dedicated wireline |  100-200
Relay |  Outdoor or Indoor |  30-35 dBm |  Wireless out-of-band or in-band |  60-100
Femotcell |  Indoor |  20-23 dBm |  Residential or enterprise broadband |  10-30
LAA WL ; 7  |  Outdoor or Indoor |  20-23 dBm |  Residential or enterprise broadband |  10-30
NR-U |  Outdoor or Indoor |  20-23 dBm |  Residential or enterprise broadband |  10-30
Figure 3: 4G and 5G Heterogeneous Network

  * •

Pico or Micro BS: A Pico BS transmits at 30 dBm, which is lower than the
transmission power of a Macro BS. It is primarily deployed outdoors
environment to offer coverage to an area of radius up to 300 m. It is
connected to the Macro cell, through a dedicated $X3$ backhaul connection for
efficient co-ordination as illustrated in Figure 3.

  * •

Relay: The Relay BS serves as a repeater by boosting and re-transmitting the
signal from the Macro BS, to the users connected to the Relay. Generally,
cellular operators prefer to deploy Relay BSs to extend the Macro coverage and
improve signal strength, especially in inaccessible terrain such as rural and
hilly regions.

  * •

RRH: RRH differs from a typical BS, in that it is a radio transceiver
component. It’s role is limited to receiving and transmitting the In-phase
Quadrature (IQ) samples. The rest of processing that usually happens at the BS
is performed by a Baseband Unit (BBU) at a centralized cloud in a data center.
Centralization of the core BS processing, offers the advantages of lower cost,
therby lowering the CAPEX and OPEX of cellular operators.

  * •

LAA: Unlike traditional Femtocells, LAA operates on the 5 GHz unlicensed
spectrum, which does implement an LBT protocol similar to that used by Wi-Fi,
with different values for parameters such as sensing threshold and
transmission intervals.

  * •

NR-U: Unlike LAA cells, NR-U operates on the 6 GHz unlicensed spectrum, which
implements an LBT protocol to fair share the spectrum protect the incumbent
users.

The advantage of HetNets is as follows.

  1. 1.

Cell range expansion (CRE): The possibility of increasing or decreasing the
transmit power of small cell, and therefore its coverage region, depending on
the traffic load significantly enhances overall network throughput
martolia2017enhancing .

  2. 2.

Integrating Macro and small cells: HetNets pave the way for a smooth
integration of Macro and small cells that, offering dual connectivity to each
device (_i.e.,_ a Macro and a small cell), improving the end-user QoS
experience.

  3. 3.

Self organizing network (SON): Given the large number of Macro and small cells
deployed in a HetNet, to ensure seamless configuration and negligible down-
times, SON capabilities are provided to every small cell within the HetNet.
This not only reduces manual intervention required in installation and
configuration process, but also optimizes the network performance
automatically by reducing collisions at the UE during cell-selection. The SON
paradigm of HetNets simplifies the OAM (operation, administration, management)
aspects of complex and dense network.

###  2.1 LTE Femtocell Networks

The existing Macro Base Stations (MBS) cannot satisfy mobile users because of
most users’ huge data demand and indoor locality. Reports by Cisco tell that
70% of the traffic is generated in indoor environments such as homes,
enterprise buildings, and hotspots. Hence, mobile operators must improve
coverage and capacity‘sathya2013dynamic  of indoor environments. But the basic
problem with the existing MBS (or small outdoor cells with shorter coverage)
is that they can only boost data rates of Outdoor User pieces of equipment
($OUEs$). But, they cannot do the same for Indoor User pieces of equipment
($IUEs$) because it is difficult for electromagnetic signals to penetrate
through walls and floors. Owing to numerous obstacles in the communication
path between MBS and $IUEs$ inside the building, radio signals attenuate
faster with an increase in the distance. Thus, $IUEs$ receive low signal
strength (_i.e.,_ Signal-to-Noise Ratio, SNR) compared to outdoor users.
Hence, mobile operators must improve coverage and capacity in indoor
environments.

As a solution, Femtocells are being deployed by both operators and end
customers. Femtocell is a low-cost, low-power consuming cellular base station
that operates only in a licensed spectrum and designed for outdoor and indoor
communication. The range of Femtocell is 100-150 meters for enterprise
environments consuming 100 mW power. A home-based Femto (HeNB) can serve 4-5
users, whereas an office-based Femto can serve a maximum of 64 users. Each
Femto requires a backhaul connection to the evolved packet core (EPC).
Advantages of using Femtos are described as follows:

Operator Advantages :

  1. 1.

The operator can increase the network capacity.

  2. 2.

The operator can reduce Operational expenditure (OPEX) and Capitational
expenditure (CAPEX).

  3. 3.

The operator can reduce Backhaul cost.

  4. 4.

The operator can reduce Traffic overload on MBSs.

User Advantages :

  1. 1.

Improved Quality of Experience (QoE).

  2. 2.

Improved energy efficiency/battery life.

###  2.2 Architecture of Indoor LTE Femto cells

In the LTE HetNet system’s architecture, Femtos are deployed inside the
building and connected to a Femto Gateway (F-GW) over the S1 interface. F-GW
is mainly used to reduce the load on MME. It acts as a virtual core network to
Femtos. The F-GW gets assigned with an eNB ID, and thus F-GW is considered
another eNB by the MME. The X2 interface X1 ; X2 ; X3 ; X4 ; X5  is introduced
between Femtos of enterprise Femtocell networks to avoid inter-cell
interference and directly route the data and signaling messages among Femtos,
thereby reducing the load on LTE core network and offering better coordination
among Femtos.

###  2.3 Access Modes in Femto

Since Femtos vanlin2013dynamic  are deployed for offering high data rates to
indoor (paid) users in enterprise and residential buildings, each Femto is
configured with a list of subscribers called Subscriber Group (SG) such that
only the users in the SG can access the Femto. The users not belonging to this
list are called Non-SG (NSG), and they may not be served by the Femto even
when they are close to the Femto. Following access modes are defined for
Femtos:

  * •

Open access: The open-access mode allows all users (_i.e.,_ SG & NSG) to
access the Femto without any restriction.

  * •

Closed access : The fast-access mode permits only authorized users (_i.e.,_
SG) to access the Femto.

  * •

Hybrid access: The hybrid access ghosh2017novel  is the combination of both
open and closed access. It allows all users (_i.e., SG & NSG_) by providing
preferential access for SG users over NSG users.

###  2.4 LTE Licensed Assisted Access

3GPP specifies LTE-LAA in Release 13 adopted the LBT approach for coexistence
with Wi-Fi and supported only DL transmissions in the unlicensed band: a
secondary cell (sCell) aggregated with a licensed primary cell (pCell).
Enhanced LAA (eLAA), as specified in Release 14, supports UL operation in the
unlicensed band. However, the legacy LTE UL schedule continued to be used in
eLAA, thus increasing the processing delay in scheduling grants due to LBT
procedures. Hence, in April 2017, 3GPP started the "further eLAA" (FeLAA)
working group (in Release 15) to improve LAA DL and UL performance through
enhanced support for autonomous UL transmissions. In the proposed FeLAA, a UL
transmission ought to receive a grant from the eNB before the transmission,
which solves the constraint imposed by the legacy eLAA. Most of these features
have not been tested in the field before deployment. As LTE-LAA deployments
are being rolled out in major cities in the US, they offer an opportunity for
real-world testing.

###  2.5 5G Small Cell in Unlicensed Deployment

The coexistence of small-cell LTE-U A1 ; BI ; A4 ; A3 ; 8 ; PL ; cts ; blank
and Wi-Fi networks in unlicensed bands at 5 GHz is a topic of active interest
baswade2016unlicensed ; iqbal2017impact , primarily driven by industry groups
affiliated with the two (cellular and Wi-Fi) segments. In contrast, there is a
body of analytical work info ; W2 ; W3 ; 3  exploring the coexistence of LTE-U
and Wi-Fi, our focus in this project has been on real-time measurements
xu2020understanding ; narayanan2020lumos5g  and real-time deployment aspects
of such coexisting networks. Coexistence is a topic that has seen little
traction in the existing literature. As per the scope of this project, we
actively design, analyze, and implement wireless network algorithms in
simulation (using ns-3), in a real-time National Instrument (NI) unlicensed
coexistence test-bed mehrnoush2018analytical ; mehrnoush2018fairness  and also
conduct measurements and analysis on recently deployed LAA in the Chicago
area.

In our previous work mehrnoush2018analytical ; mehrnoush2018fairness , we
investigated various aspects of coexistence between the two principle variants
of LTE in the unlicensed bands, LTE-U and LAA, and Wi-Fi wilhelmi2019potential
. For LTE-U, we analyzed the effect of the LTE-U duty cycle N1 ; E2 ; E3 ;
3GPP  on the performance of the association of Wi-Fi. We demonstrated
sathya2019auto ; adam2019detection ; sathya2020machine ; dziedzic2020machine
that using a high duty cycle adversely impacted Wi-Fi’s ability to access the
channel due to the Wi-Fi beacon transmission process’s disruption. Therefore,
we recommended using a lower duty cycle even if there was no Wi-Fi present in
the channel to enable fair access to a new Wi-Fi access point that may wish to
use the channel. We also developed machine-learning-based algorithms to
determine the number of Wi-Fi APs on the air to enable an appropriate duty
cycle setting. However, since LTE-U is not considered for wide deployment by
industry, we switched our attention to evaluating LAA coexistence.

We have made tremendous progress toward understanding Wi-Fi and LAA’s
coexistence behavior in our previous work’s unlicensed bands. Our theoretical
analysis, corroborated by detailed system-level simulations using ns-3 and use
of software-defined-radios, demonstrates that coexistence is improved
substantially when the two systems treat each other symmetrically. That is
when Wi-Fi and LAA use the same detection threshold to defer to each other. In
our recent work sathya2020measurement , we added a new dimension by performing
detailed measurements of deployed LAA networks by the three major carriers,
Verizon, AT&T, and T-Mobile, in Chicago. We conducted these measurements using
off-the-shelf and custom-designed apps to extract detailed network information
via APIs on Android smartphones. We believe this to be the first such exercise
in academia and the measurements revealed several interesting new directions,
which we will continue to research. Two such topics are: (i) Though most LAA
deployments are outdoors and Wi-Fi’s are indoors, the client devices that
connect to these networks can be used outdoor/indoor; this results in hidden-
node scenarios worse by the fact that two systems do not decode each other’s
signals (ii) Most academic analyses have focused on coexistence in a single 20
MHz channel, Whereas our measurements reveal that LAA usually aggregates three
unlicensed channels, therefore increasing Wi-Fi’s impact. These results have
been presented to the industry as well and been incorporated into
recommendations by Cisco.

Figure 4: Co-tier and Cross-tier Interference in HetNet Small Cell Network

##  3 Small Cell deployments: History, Associated Challenges, and Existing
Solutions

This section discusses the various challenges and problems faced while
deploying small cells in LTE HetNets. It also discuses the existing solutions
to address them which primarily focus on enhancing the overall system
performance. The major challenges are outlined below:

  1. 1.

Placement of Small Cells in 4G LTE
Femto cells have been deployed at a large scale in enterprise/office settings
and the main constraints that operators face are a lack of space and low power
consumption tahalani2014optimal ; sathya2015joint ; tahalani2013optimal . Due
to spatial constraints, operators often resort to a sub-optimal arbitrary
deployment of Femto cells. This causes coverage gaps and increases the number
of Femtos required, leading to increased CAPEX and OPEX. Moreover, arbitrary
placement involves human resource expenses incurred in field testing. A
feasible solution to these problems is to ensure an optimal placement of Femto
cells lokhandwala2014phantom ; optimal . An optimal placement guarantees a
robust SINR and high network capacity while ensuring that there are no
coverage holes within the building tahalani2014optimal ; sathya2014placement ;
sathya2020small . However, there are additional challenges that need to be
addressed. Solutions proposing optimal deployment of single Femto cell are
often not scalable to offer coverage to entire enterprise/office buildings.
Further, proposed placement strategies often fail to consider architectural
parameters of the built environment such as wall-thickness and dynamic power
transmission. Femtos may also inadvertently create coverage holes for indoor
users when the operate in the closed access mode and there are non-subscriber
UEs in close proximity. Consequently, some solutions have been proposed to
minimize coverage gaps through efficient Femto placement and power control
strategies lokhandwala2015phantom ; sathya2013efficient ; ramamurthy2015energy
.

In a HetNet, like most wireless networks, the dominant factor governing the
system throughput is the interference. Two types of interference affect a
HetNet system _viz.,_ interference between two or more Femtos, and
interference between a Macro and a Femto. A detailed description of each is
provided below:

    1. (a)

Co-tier Interference: Reusing spectrum for maximal utilization invariably
leads to interference from the neighboring small cells. This phenomenon is
called co-tier interference sathya2016improving ; sathya2016handover ;
sathya2020raptap . For example, UE2 is getting served by the Femto BS (F2),
but it is receiving interference from the neighboring Femto BSs (F1) as shown
in Fig. 4. The conventional approach to mitigating co-tier interference in a
cellular network is through a mechanism known as Inter-Cell Interference
Coordination (ICIC). The ICIC approach allows all BSs to communicate
cooperatively using the X2 interface. While it succeeds in ensuring efficient
allocation of RBs to users at the cell-edge, it leads to a significant
increase in the signaling overhead.

    2. (b)

Cross-tier Interference: This is the interference caused by transmission
conflicts between a Macro BS and a small cell sathya2015femto ;
sathya2016handover ; sathya2016maximizing . For example, UE1 is getting served
by the Macro BS, but it is receiving interference from small cells (_i.e.,_
Femtocell F1 as shown in Fig. 4). The enhanced ICIC (eICIC) approach is the
conventional solution to the problem of cross-tier interference
giluka2016handovers ; giluka2016leveraging ; sathya2020qos . It mitigates the
transmission conflicts between an MBS and a Femto BS by muting a few sub-
frames (Almost Blank Sub-frame) in MBS when the Femto BS is transmitting.
Doing so, alleviates the interference and enhances HetNet system capacity.

The spatial reuse of the spectrum enhances efficiency and capacity of a HetNet
system, but the co-tier and cross-tier interference cused by obstacles inside
buildings are unintended consequences that adversely affect the signal
strength and throughput of indoor devices. In the outdoor setting, in a
building under the coverage of LTE HetNet Macro BSs, signal leaks occur around
its edges/corners, creating a High Interference Zone (HIZone) in its vicinity.
This leads to cross-tier interference and causes similar performance
bottlenecks for $OUEs$. However, the challenge of cross-tier interference for
HIZone UEs ($HIZUEs$) has not been adequately addressed dynamically _i.e.,_ by
considering the UE occupancy level within the HIZone. However, studies
indicate that an active power control strategy at the Femtos is very likely to
reduce the cross-tier interference in the HIZone.

  2. 2.

Scheduling or Radio Resource Allocation

Femtos deployed in enterprise and residential buildings guarantee high
bandwidth services to a specific list of paid indoor users/subscribers called
the Subscribers Group (SG) ghosh2017novel . Each Femto allows access to users
only in the SG and the indoor users not in the list are known as Non-
subscriber Group (NSG), which are served by the MBSSs, regardless to their
proximity to a Femto. This subscriber-only access is known as closed access.
In sharp contrast, Femtos configured to operate in the open access mode do not
discriminate between SG and NSG users, and allow access to all indoor UEs
depending on their proximity to the Femto. The pitfall is that, the open
access mechanism may be unable to meet the QoS guarantees for all indoor
LTE/5G users during the peak-traffic hours. Consequently, hybrid access has
been conceptualized as a trade-off between the two mechanisms. Hybrid Access
Femtocells (HAFs) prioritize the QoS requirements of SG users over NSG users,
by offering preferential access to radio resources to the former. This
mechanism also simultaneously improves the overall capacity of the LTE HetNet
by serving the NSG users in proximity of a HAF. Consequently, several
rewarding strategies have been put forward to make hybrid access mode
attractive to the operators. However, the challenges of optimal placement of
HAFs and efficient splitting of radio resources between SG and NSG indoor
users need better solutions New222 ; New333 ; New444 ; New555 ; New666 .

  3. 3.

Improving Data Rates in LTE Small Cells and D2D Communication

Deployment of indoor Femtos significantly enhances network performance and
capacity, but it also triggers a new set of problems. The resulting HetNet has
to overcome the challenges of co-tier and cross tier-interference, frequent
handovers, and signal leakage around the building’s corners
sathya2016improving ; comsnets . The cross-tier interference in the HIZone
close to the building adversely impacts the performance of outdoor UEs served
by the one of the MBSs in the HetNet. Further, arbitrary or sub-optimal
placement of Femtos often leads to high co-channel cross-tier interference
among Femtos rangisetti2020qos ; sathyamodified . It is also responsible for
the existence of coverage holes in the indoor spaces. Moreover, lack of
adequate power control mechanisms in Femtos leads to a surge in power
consumption and also exacerbates inter-cell interference in large HetNets
deployments. These problems are usually addressed through well-designed LTE
small cell architecture, optimal Femto deployment, and smart power control to
maintain a SINR threshold in Indoor environment. The D2D paradigm can also be
leveraged by utilizing the idle IUEs connected to a Femto as relays to forward
downlink data plane traffic to other UEs connected to MBS.

  4. 4.

Asymmetric Vs. Symmetric ED threshold on LAA and Wi-Fi

With the exponential rise in the number of mobile end-users, the mobile data
demand on cellular and Wi-Fi infrastructure has also grown exponentially.
Thus, it has become imperative that the licensed (cellular) and unlicensed
(Wi-Fi) spectrum are utilized efficiently. Given the scarcity of spectrum for
radio communication, cellular industry has actively pursued technological
advancements that enable cellular networks to operate in the unlicensed
spectrum as well. This, however, would require a fair coexistence of cellular
technologies and Wi-Fi in the unlicensed spectrum. The IEEE 802.11 standard
has prescribed an energy detection (ED) threshold of -62 dBm for Wi-Fi,
whereas the LTE-LAA standard specifies that LTE-LAA nodes should detect Wi-Fi
at -72 dBm. The impact of this asymmetry in the ED threshold has serious
ramifications on fairness of LTE-WiFi coexistence iqbal2017impact . It has
been demonstrated that lowering the Wi-Fi ED threshold from the prescribed -62
dBm improves performance of both Wi-Fi and LTE-LAA subsystems. Therefore,
instead of treating LTE-LAA/LTE-U as noise, if Wi-Fi considers the coexisting
LTE nodes as if they were overlapping Wi-Fi nodes, the performance of
coexistence network improves significantly iqbal2017impact .

  5. 5.

Facilitating LAA/Wi-Fi Coexistence Using Machine Learning Approach

Several aspects of LTE-WiFi coexistence networks have been studied and
analyzed in the current body of academic and industry research literature.
However, analysis of real-world coexistence deployment data to study the
performance and behavior of these deployments is acutely lacking. The problem
of “fair coexistence" of LTE-LAA and Wi-Fi, cell-selection in an LAA HetNet as
compared to LTE HetNets, and performance prediction of LTE and LAA components
in coexistence deployments are open probelms. Fortunately, with the fast roll-
out of LAA deployments globally, LAA data can be collected and analyzed to
analyze coexistence performance in the real-world. Some recent studies have
adopted this approach and tried to address these problems. In
sathya2020measurement , several issues of LAA-WiFi coexistence in real-world
deployments of multiple cellular operators in downtown Chicago, have been
explored. Aspects analyzed included the type of mobile traffic (e.g., data,
video, and live streaming), extent of small cell coverage (Femto/LAA)
coverage, and presence of secondary unlicensed carriers, among others. The
study pointed out that a static channel allocation strategy of an LAA BS,
leads to a particular channel being occupied for longer duration, adversely
impacting the Wi-Fi APs as they face resource crunch due to the dynamic
channel allocation strategy. An ML inspired channel assignment by analyzing
the collected LAA data was proposed for the LAA BS and was shown to have a
minimal adverse impact on Wi-Fi users. Likewise, deployment data of three LAA
operators was analyzed to ascertain the impact of cell-selection mechanisms on
LAA performance icdcn . Further, a data-driven network hybrid optimization
model for LAA networks was proposed in mobiquitous .

  6. 6.

Association Issues in LTE-U/Wi-Fi Coexistence

There are several challenges in ensuring association fairness in LTE-WiFi
unlicensed coexistence in the same channel in the 5 GHz band. Beacon
transmission is the initial step in the association process in Wi-Fi, so the
association fairness can be defined in terms of how fair LTE-U in letting Wi-
Fi transmit beacons on a shared channel that LTE-U has occupied through
multiple duty cycles sathya2018association ; sathya2018analysis . As per the
LTE-U specification, when the LTE-U BS ascertains that the channel is vacant,
it can transmit for a duration up to 20ms and then turn OFF for only 1 ms,
thereby resulting in a duty cycle of 95%. However, a sizeable LTE-U duty cycle
is likely to have an immense adverse impact on the association fairness,
especially during Wi-Fi beacon transmission and reception. Studies have shown
using NI USRP based experiments that a huge proportion of Wi-Fi beacons are
neither transmitted in a timely manner nor are they received at the LTE-U BS
sathya2018association ; sathya2018analysis . An innovative Carrier Sense
Adaptive Transmission (CSAT) approach was proposed in sathya2020wi ;
manas2018socio ; kala2020cirno  that aims to solve the challenges in Wi-Fi
client association in a dense coexistence deployments while ensuring a fair
spectrum access and sharing.

  7. 7.

Optimal Scaling of LTE-U Duty cycle in LTE-U/Wi-Fi Coexistence

Machine learning (ML) techniques offer efficient solutions to complex
engineering problems and tasks such as video and image recognition, genome
analysis, recommender systems, logistics, and automation of industrial
operations. Given the potential of ML tools in solving non-linear problems, it
is desirable to apply to the challenges encountered in spectrum sharing
between unlicensed LTE (LAA/LTE-U) and Wi-Fi sathya2019auto ;
adam2019detection ; sathya2020machine ; dziedzic2020machine ; icdcn ;
mobiquitous ; comsnets . For example, LTE-U Forum has standardized the duty-
cycle approach for fair coexistence of LTE-U and Wi-Fi. However, the problem
of identifying the precise number of APs by the LTE-U BS is not adequately
addressed. If LTE-U BS can learn the exact numbers of Wi-Fi APs beforehand, it
is possible to scale the LTE-U duty cycle optimally. However, this is a non-
trivial problems as in order to detect the number of Wi-Fi APs operating on
the same channel in real-time, Wi-Fi packets need to be decoded which will
require a Wi-Fi receiver at the LTE-U BS. Solutions to this problem has been
put forward using innovative ML-based techniques which analyze power/energy
levels observed in the channel during the LTE-U OFF duration. ML powered
techniques offer higher accuracy when compared to the existing solutions based
on auto-correlation (AC) and energy detection (ED) sathya2018energy ;
kumar2019enhancing .

##  4 Future NR-U Small Cell in 6 GHz

The current research (on Spectrum Sharing on 5 GHz) has opened many exciting
possibilities to solve the research challenges for LTE/Wi-Fi coexistence in 6
GHz used as an unlicensed band garg2019sla ; kala2019odin . In the following,
we outline some of my future directions on the spectrum, sharing small cells.

Figure 5: Bandwidth Allocation on 6 GHz Spectrum FCC1

###  4.1 Fair Coexistence of NR-U and Wi-Fi in 6 GHz Spectrum
patriciello2020nr :

Since the licensed spectrum is a limited and expensive resource, its optimal
utilization may require spectrum sharing between multiple network
operators/providers of different types. Increasingly licensed-unlicensed
sharing is being contemplated to enhance network spectral efficiency beyond
the more traditional unlicensed-unlicensed sharing. As the most common
unlicensed incumbent, Wi-Fi is now broadly deployed in the unlicensed $5$ GHz
band in North America, where approximately $500$ MHz of bandwidth is
available. However, these $5$ GHz unlicensed bands are also seeing the
increasing deployment of cellular services such as LTE-LAA. Recently, the
Federal Communications Commission (FCC) sought to open up 1.2 GHz of
additional spectrum for unlicensed operation in the 6 GHz patriciello2020nr ;
coex  band through a Notice of Proposed Rule Making (NPRM) FCC1 ; WA ;
sathya2020standardization  as shown in Fig. 5. Thus, this spectrum allocation
for the unlicensed operation will only accelerate the need for other
coexistence solutions among heterogeneous systems. Hence it is clear that
regulatory authorities worldwide are paying close attention to the 6 GHz band
as the next spectrum band that will continue to enhance unlicensed services
across the world. However, it is also clear that this band, like the 5 GHz
band, will see both Wi-Fi and cellular systems being deployed, and hence the
coexistence issues played out in the 5 GHz band will repeat in this new
frequency as well. In recognition of this, the two principal stakeholder
standardization entities, IEEE and 3GPP, held a coexistence workshop in July
2019 coex  to discuss methods to address this before the next generation
standards being specified. This section discusses the recent activities on
FCC’s 6 GHz NPRM and IEEE & 3GPP efforts towards coexistence in the 6 GHz
band.

###  4.2 6 GHz Coexistence: Deployment Scenarios, and Channel Access

Although several industry entities were not in favor of a re-evaluation, IEEE
recommended that coexistence evaluations for NR-U should include 802.11ac (in
5 GHz), 802.11ax (in 6 GHz), and 802.11ad (in 60 GHz). Fig 6 shows the
possible interference scenario in the 6GHz spectrum band, where the
traditional licensed 6GHz link communication happens between transmitter and
reception. But the different future use case applications (in 6GHz link) such
as indoor AP with low power transmission (LPI), Google glass with very low
power (VLP), outdoor AP with AFC database, and NR-U AP with database can
potentially create the interference. For the sub-6 GHz bands, coexistence
evaluations will be technology-neutral (e.g., channel access mechanism) and
performed in random carrier frequencies in the 5 GHz band. These evaluations
also necessitate devising suitable 11ac/ax coexistence topologies with a
significant number of links below -72 dBm.

Figure 6: Interference Scenario on 6GHz SpectrumFCC1

###  4.3 Future NR-U: Deployment Scenarios

The NR-U work item recently approved by 3GPP supports the existing unlicensed
5 GHz band and the new unlicensed "greenfield" 6 GHz band. Industry players
such as Qualcomm expect that other unlicensed and shared spectrum bands,
including mmWave, will be added to this list in future releases. Researchers
will study the following deployment scenarios to investigate the
functionalities needed beyond the operation specifications in an unlicensed
spectrum. The 3GPP forum propose different varieties of NR-U deployments as
shown in Fig. 7.

  * •

Carrier aggregation between licensed band NR (PCell) and NR-U (SCell): (a)
NR-U SCell with both DL and UL. (b) NR-U SCell with DL-only.

  * •

Dual connectivity between licensed band LTE (PCell) and NR-U (PSCell)

  * •

Stand-alone NR-U

  * •

An NR cell with DL in the unlicensed band and UL in licensed band

  * •

Dual connectivity between licensed band NR (PCell) and NR-U (PSCell)

The Legacy cellular operators oppose the NR-U stand-alone scenario and want
3GPP to drop it. They fear stiff competition from new players who can use NR-U
stand-alone for limited cellular operation. NR-U is likely to be a more potent
competitor to 802.11 than LAA as it will have a more flexible and efficient
PHY/MAC marked by a shorter symbol duration, shorter HARQ Round Trip Time
(RTT), etc. Further, NR-U can be deployed in every configuration where 802.11
is currently operational if both stand-alone and dual connection is approved.
Also, unlike 802.11, NR-U will be capable of deploying the same PHY/MAC with
flexible configurations across all current and future unlicensed bands.

Figure 7: NR-U Deployment Scenarios

###  4.4 ML Based approaches to Solve Issues in Current and Future Spectrum
Sharing:

We have listed some of the interesting problems on LAA/Wi-Fi coexistence
solved through an ML-based approach (as shown in Fig. 8):

  * •

Narrowband vs. Wideband LBT in 6 GHz: The LBT mechanism is used by a device to
avoid collisions by ensuring that no other transmissions are concurrently
active in the channel. LTE-LAA follows CAT 4 LBT for most of its
transmissions, while CAT 2 LBT is used for about 5% of DL transmissions. NR-U
is likely to adopt a mechanism similar to the LAA LBT. NR-U Release 16, like
its predecessor, the NR Release 15, supports component carriers up to the
maximum limit of 100 MHz bandwidth. Besides, it supports the aggregation of
several inter and intraband component carriers. Multi-carrier LBT channel
access as defined in 5 GHz is assumed _i.e.,_ the Type A LBT in 3GPP TS37.213,
where each channel performs its independent LBT procedure. Consequently, there
is bound to be high complexity when the operation bandwidth is wide. The
alternative Type B LBT in 3GPP TS37.213 can reduce this complexity by
performing a single LBT on multiple channels. The wideband LBT could simplify
the implementation of wideband operation when it identifies that the channel
is free of narrowband interference _i.e.,_ limiting the narrowband signal (20
MHz) certain sub-bands or by long/short term measurements and LBT bandwidth
adaption. Hence, wideband LBT is beneficial for systems operating with wide
bandwidth as it simplifies LBT implementation.

  * •

Intelligent Selection of Unlicensed Channel by LAA BS: While analyzing the
collected data in real-time on LAA/Wi-Fi coexistence sathya2020measurement ,
we found that in an incredibly dense deployment scenario, multiple LAA
operators contest for the unlicensed channel. However, numerous unlicensed
channels are available, but choosing a particular channel and estimating the
duration to occupy the track, with the vision of not affecting the Wi-Fi users
and multiple LAA operators, is challenging. To solve this problem, we propose
to use a Q-learning based ML solution so that a channel and its occupancy time
decided intelligently based on the parameters such as interference from other
operators, load on the channels, channel activities of Wi-Fi users, etc.

  * •

Intelligent Channel Selection by Wi-Fi AP in LTE/Wi-Fi Coexistence: In a
multi-AP setting, an AP selects the channel for the operation of the expected
capacity of the existing links. The traditional way is to take the SINR-based
capacity estimate into account. However, this capacity model may sometimes
fail to represent the complex interactions between PHY and MAC layers due to
the presence of LAA, as it makes the scenario heterogeneous. As a result,
decisions regarding channel selection may be delayed or inaccurate. To solve
this problem, we propose to use supervised learning as a tool to model the
complex interactions between PHY and MAC layers based on factors such as power
and PHY rate of a neighboring Wi-Fi link.

Figure 8: Machine learning based approaches in spectrum sharing

  * •

Efficient Radio Resource Allocation Using Reinforcement Learning: In the
fifth-generation (5G) of mobile broadband band systems, Radio Resources
Management (RRM) has reached unprecedented levels of complexity. To cope with
the ever more sophisticated RRM functionalities and to make prompt decisions
required in 5G, efficient radio resource scheduling will play a critical role
in the RAN system. Depending on tasks’ purposes, the scheduling process is
divided into three steps: prioritization, resource number determination, and
resource allocation. However, to support a diverse range of applications such
as ultra-reliable low latency applications, IoT applications, V2X
applications, AR/VR applications, massive multimedia applications, etc., the
scheduling process’s steps become more complex. As a solution, we propose to
use deep reinforcement learning tools to get feedback about the traffic in
real-time so that efficient scheduling decisions and optimal use of radio
resources are made.

  * •

Indoor and Outdoor classification for spectrum sharing: The specifications for
6 GHz FCC1  provide two different power regimes for unlicensed UEs. Devices
within the built environment or IUEs are expected to stick to lower transmit
powers, but do no have to seek permission to use a channel by first accessing
an Automatic Frequency Control (AFC) database. On the other hand, outdoor
devicesor OUEs, are allowed to transmit at higher power levels, but are
required to seek permission through AFC access prior to transmitting on a
channel, so as to ensure that the OUE is not excluded from the particular
channel. However, determination of a UEs location, _i.e.,_ whether it is
indoors or outdoors is a non-trivial problem. Therefore, specifications
prescribed a few more restrictions. UEs which are able to connect to an indoor
AP but could be situated outdoors are subjected to the constraint of a 6 dB
lower transmit power threshold. Further, indoor APs can not be battery
powered, must be equipped with detachable antennas, or should have a
weatherized exterior. However, these constraints result in a sub-optimal
spectrum efficiency. For example, an IUE always has to transmit at a lower
power. Further, UEs are not permitted to communicate directly with each other
without first connecting through a Wi-Fi AP. More importantly, not permitting
APs to be battery powered erodes network resilience and robustness. Thus, it
is of extreme importance that the environment of a wireless device is reliably
detected so as to ensure optimal spectrum utilization, enhanced resilience,
and improved system capacity. It is noteworthy that the ambient RF
environment, differs markedly in an indoor setting as compared to outdoors, in
terms of the signal strength. This is true for all commercially used
communications bands, such as 2.4 GHz Wi-Fi, 5 GHz Wi-Fi, 6 GHz Wi-Fi (in the
future), several low cellular ($<$ 1 GHz), mid cellular (1 GHz - 6 GHz) and
high cellular ($>$ 24 GHz) bands. As highlighted earlier, measuring the signal
strengths in these different environments and analyzing the data using ML
algorithms, intelligent models and methods for the prediction of device-
environment can be designed. Doing so will enhance spectrum usage and offer
security and resilience, not only in the 6 GHz, but also in future bands such
as the 12 GHz satellite band.

##  5 Conclusions

A few important conclusions and inferences can be drawn from the discussion
presented in this work. Clearly, the best way to improve spectrum efficiency,
and in turn network performance, is by intelligently reusing the scarce radio
bands to the maximal extent possible. Deploying small cells is one way to
achieve the spatial reuse of spectrum. Thus, Pico cells and Femto cells are
deployed in indoor and outdoor settings and are designed to optimize
transmission power and perform interference management through techniques such
as Full reuse, Soft reuse, Hard reuse, and Strict reuse. Small cell
deployments can also leverage the state-of-the-art antenna technology at their
disposal such as directional and omni-directional antennas. With the the help
of these technologies, small cell deployments in LTE/5G are vital to
delivering high capacity and fault-tolerant networks to the end-user. Further,
this work outlined several open challenges and problems in the current (5GHz)
and the future (6GHz, NR-U) unlicensed spectrum sharing. Rapid proliferation
and adoption of latest Wi-Fi standard, _i.e.,_ 802.11ax, and the latest
cellular technology, _i.e.,_ 5G NR-U, is expected. Further, unlike LAA, 5G
NR-U will be capable of transmitting data in both uplink and downlink in the
unlicensed band. Likewise, unlike 802.11ac in 5 GHz, 802.11ax in 6 GHz will
employ Orthogonal Frequency Division Multiple Access (OFDMA). Together,
802.11ax and 5G NR-U coexistence will spawn new challenges and problems in
unlicensed coexistence. Thus, all future research in unlicensed coexistence is
likely to be focused on fair and efficient LTE-WiFi coexistence deployments in
the 6 GHz unlicensed band.

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