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Journal ArticleDOI

Interference Coordination for 5G New Radio

01 Jun 2018-IEEE Wireless Communications (IEEE)-Vol. 25, Iss: 3, pp 131-137

TL;DR: Simulation results quantify the performance benefits of the different techniques under heterogeneous key performance indicators (KPIs) and discuss the standardization effort required for having each of these techniques included in the 5G NR specifications.
Abstract: The arrival of the 5G NR provides a unique opportunity for introducing new inter-cell interference coordination (ICIC) mechanisms. The objective is twofold: to better exploit the benefits of ICIC in coherence with the rest of radio resource management (RRM) principles in 5G, and to support new services and deployment scenarios. We propose several enhanced techniques. In the uplink, inter-cell coordination of the pilot sequence configuration mitigates the inter-cell interference problem of such pilots, which is especially severe for cell-edge users. In the downlink, coordinated small cell DTX aims at network interference control and energy consumption reduction, whereas intercell rank coordination can unleash the potential of advanced receivers with minimal control overhead. Besides, on-demand power boosting and coordinated muting can be tailored to meet URLLC requirements. The simulation results quantify the performance benefits of the different techniques under heterogeneous key performance indicators (KPIs). We also discuss the standardization effort required for having each of these techniques included in the 5G NR specifications.

Summary (2 min read)

OverAll Inter-cell Interference cOOrdInAtIOn desIgn PrIncIPles fOr 5g new rAdIO

  • Co-channel inter-cell interference is known to be one of the limiting factors of cellular systems, and it has triggered numerous academic research studies and industrial standardization and implementation efforts in LTE/LTE-A.
  • Fifth generation new radio (5G NR) [4, 5] is expected to experience a proliferation in the number of emerging use cases, categorized into three broad service groups [6] .
  • Inter-cell pilot sequence coordination techniques are proposed, which improve the link performance because of enhanced channel estimation and coherent demodulation [8] .
  • The details of the proposed mechanisms are presented in the next sections.
  • For the sake of conciseness, each downlink solution is tailored for a given service, although all UL and DL proposals are applicable to both eMBB and URLLC.

LTE-A can allow for pilot orthogonality among multiple cells:

  • A BS assigns, from its pool of available orthogonal sequences, pilot sequences for users in neighboring cells.

Low latency for very fast coordination

  • Xn activation of the muting works in a fast basis, and implies the cell sending the protected data to ask the aggressor cell to mute •.
  • The authors identify and address major interference challenges in the uplink and the downlink.
  • They are compatible and they all build toward 5G NR, being highly dynamic, flexible, and multi-service capable.
  • Also, on-demand power boosting and coordinated muting is tailored to meet URLLC requirements.
  • The performance gains show clear benefits of network coordination with limited complexity and standardization effort.

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Aalborg Universitet
Interference Coordination for 5G New Radio
Alvarez, Beatriz Soret; De Domenico, Antonio; Bazzi, Samer; Mahmood, Nurul Huda;
Pedersen, Klaus I.
Published in:
I E E E Wireless Communications Magazine
DOI (link to publication from Publisher):
10.1109/MWC.2017.1600441
Publication date:
2018
Document Version
Accepted author manuscript, peer reviewed version
Link to publication from Aalborg University
Citation for published version (APA):
Alvarez, B. S., De Domenico, A., Bazzi, S., Mahmood, N. H., & Pedersen, K. I. (2018). Interference Coordination
for 5G New Radio. I E E E Wireless Communications Magazine, 25(3), 131-137.
https://doi.org/10.1109/MWC.2017.1600441
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1536-1284/17/$25.00 © 2017 IEEE
IEEE Wireless Communications • Accepted for Publication
2
AbstrAct
The arrival of the 5G NR provides a unique
opportunity for introducing new inter-cell interfer-
ence coordination (ICIC) mechanisms. The objec-
tive is twofold: to better exploit the benefits of
ICIC in coherence with the rest of radio resource
management (RRM) principles in 5G, and to
support new services and deployment scenarios.
We propose several enhanced techniques. In the
uplink, inter-cell coordination of the pilot sequence
configuration mitigates the inter-cell interference
problem of such pilots, which is especially severe
for cell-edge users. In the downlink, coordinated
small cell DTX aims at network interference control
and energy consumption reduction, whereas inter-
cell rank coordination can unleash the potential of
advanced receivers with minimal control overhead.
Besides, on-demand power boosting and coor-
dinated muting can be tailored to meet URLLC
requirements. The simulation results quantify the
performance benefits of the different techniques
under heterogeneous key performance indicators
(KPIs). We also discuss the standardization effort
required for having each of these techniques
included in the 5G NR specifications.
OverAll Inter-cell Interference cOOrdInAtIOn
desIgn PrIncIPles fOr 5g new rAdIO
Co-channel inter-cell interference is known to be
one of the limiting factors of cellular systems, and it
has triggered numerous academic research studies
and industrial standardization and implementation
efforts in LTE/LTE-A. Standardized network-based
inter-cell interference coordination (ICIC) schemes
for LTE range from basic coordination to further
enhanced ICIC, and more elaborate coordinated
multi-point (CoMP) communication techniques
[1–3]. The solutions for LTE were mainly designed
to offer spectral efficiency benefits for data channel
transmissions by applying various forms of inter-
cell coordinated muting (or power adjustments)
or interference randomization, while offering only
limited benefits for control channel performance.
Fifth generation new radio (5G NR) [4, 5] is
expected to experience a proliferation in the num-
ber of emerging use cases, categorized into three
broad service groups [6]. Enhanced mobile broad-
band (eMBB), an evolution of today’s broadband
traffic, will still be a key driver, with a main key
performance indicator (KPI) in the form of a tar-
get peak data rate of 20 Gb/s. Also, 5G opens the
door to new use cases with heterogeneous require-
ments, like ultra-reliable low-latency communica-
tions (URLLC), where messages must be correctly
decoded with very high probability (10
–5
outage
probability) and in a very short time (1 ms); and
massive machine type communications (mMTC),
catering to a large number (1 million devices/km2)
of low-data rate, low-cost services. The first phase
of the standardization process will primarily focus
on the first two categories, namely eMBB and
URLLC [4].
The set of radio features to support eMBB
and URLLC is broad, and it can be categorized
as follows: spectrum enhancements, with the use
of licensed, lightly licensed, and unlicensed bands
spanning microwave and millimeter wave frequen-
cies; deployment enhancements, for instance in the
form of ultra-dense networks with self-backhauling;
and capacity enhancements, like non-orthogonal
access, device-to-device and massive MIMO [7].
One important design principle in NR is to have
a flexible and efficient use of radio resources and
available spectrum [6]. As per the architecture, the
latency requirements also pose new challenges for
the backhaul, both in classical distributed cases and
in emerging centralized RAN (C-RAN) [4], where
a shared pool of centralized baseband resources
serves a large number of remote radio heads. In
this context, the ICIC framework evolution must go
hand in hand with the new radio access.
In this article, we present a set of interference
management advances for 5G NR. To fulfill the
promise of a comprehensive and integrated net-
work, 5G should move from a network-oriented
to a service-oriented paradigm, where differenti-
ated services with diverse KPIs can coexist on the
same infrastructure. Moreover, ICIC design prin-
ciples must exploit the new degrees of freedom
that come with 5G NR, especially taking advan-
tage of flexible physical-layer and medium access
(MAC) design [5], as well as the richer architecture
options [4]. In this light, we describe solutions to
address major interference challenges. First, we
consider the uplink (UL) inter-cell pilot (also known
as the reference symbol) interference problem.
Inter-cell pilot sequence coordination techniques
are proposed, which improve the link performance
because of enhanced channel estimation and
coherent demodulation [8]. Another advantage
of such techniques is their ability to support more
users than the current LTE solutions. In the down-
link (DL), we propose a scheme for joint interfer-
ence control and energy efficiency in dense small
cell scenarios [9] by means of enhanced methods
for discontinuous transmissions at the cell level
(cell discontinuous transmission DTX) based on
fuzzy Q-learning [10]. We elaborate on the new
Beatriz Soret, Antonio De Domenico, Samer Bazzi, Nurul H. Mahmood, and Klaus I. Pedersen
I C  5G N R
ACCEPTED FROM OPEN CALL
Beatriz Soret is with GomSpace; Antonio De Domenico is with the CEA-LETI MINATEC; Samer Bazzi is with Huawei Technologies
Duesseldorf GmbH; Nurul Huda Mahmood is with Aalborg University; Klaus I. Pedersen is with Nokia Bell Labs.
Digital Object Identifier:
10.1109/MWC.2017.1600441
This article has been accepted for inclusion in a future issue of this magazine. Content is final as presented, with the exception of pagination.

IEEE Wireless Communications • Accepted for Publication
3
opportunities that come from assuming multi-user
multiple-input-multiple-output (MU-MIMO) and
advanced interference-aware receivers as the base-
line for 5G [11]. Building on the earlier work in
[12], novel solutions for coordination of the maxi-
mum transmission rank between neighboring cells
is also presented. Another proposal is to support
the challenging reliability and delay requirements
of URLLC through highly agile and fast coordina-
tion techniques, offering benefits for both control
and the data channel performance [13].
It is worth highlighting that the proposed
schemes are complementary, addressing differ-
ent interference challenges but sharing the 5G
NR philosophy of more dynamic coordination
for a multi-service air interface. The details of the
proposed mechanisms are presented in the next
sections. The delay over the backhaul in the sig-
naling exchange between base station (BS) nodes
(through the so called Xn interface [4]) is a limit-
ing factor in inter-cell coordination. In all cases, we
strive for a generic design that is applicable both in
distributed architectures with Xn interface as well
as in C-RAN scenarios with a centralized control-
ler. For the sake of conciseness, each downlink
solution is tailored for a given service, although all
UL and DL proposals are applicable to both eMBB
and URLLC.
UPlInk Inter-cell PIlOt cOOrdInAtIOn
In UL, inter-cell pilot interference arises when the
assigned uplink pilot sequences across multiple
cells, which are non-orthogonal, are scheduled on
the same time-frequency resources. The received
pilots from a target user suffer from pilot interfer-
ence coming from neighboring cells, resulting in
poor channel estimation. This problem is especial-
ly severe for cell-edge users, as the power of the
interfering pilots is comparable to that of desired
pilots. It leads to errors in uplink coherent demod-
ulation, and it is very detrimental in uplink multi-us-
er MIMO scenarios that heavily rely on accurate
channel knowledge to perform receive filtering.
Additionally, in a calibrated time-division-duplex
(TDD) system where channel reciprocity holds, the
BS can acquire the channel knowledge necessary
for downlink multi-user MIMO precoding via the
uplink pilots sent by the users. In this case, pilot
interference leads to erroneous channel knowl-
edge, which affects the precoding quality and the
downlink throughput.
In LTE-A, users across cells are assigned non-or-
thogonal yet distinguishable sequences. These
sequences are cyclic extended Zadoff-Chu (ZC)
sequences, which are spread over the subcarriers
of interest. Cyclic-extension is necessary to maxi-
mize the number of distinguishable sequences. The
available sequences in each cell are constructed by
phase rotation of a root sequence identified by a
root index, and are mutually orthogonal. The root
sequences (and the corresponding root indices)
across cells are different. Different root sequenc-
es or phase rotations thereof are not orthogonal,
though they are distinguishable via their root indi-
ces.
Few solutions exist to mitigate inter-cell pilot
interference via a distributed or centralized
sequence assignment over the cells. A related work
is [14], where the authors propose an assignment
of ZC sequences in an Orthogonal Frequency Divi-
sion Multiplexed (OFDM) system, such that the
worst-case channel estimation mean square error
(MSE) is minimized. However, a key assumption
of [14] is that user pilots occupy all available sub-
carriers, which is not the case in a practical system,
rendering the performed analysis inapplicable. Fur-
thermore, the BSs treat pilot interference as noise,
which is suboptimal at high uplink signal-to-noise
ratios (SNRs) occurring in, for example, small cell
scenarios. In such scenarios, a better approach
would be the suppression of pilot interference at
the BS to recover the desired pilots with as little
interference as possible.
LTE-A can allow for pilot orthogonality among
multiple cells: a BS assigns, from its pool of avail-
able orthogonal sequences, pilot sequences for
users in neighboring cells. Such a solution is not
scalable for many 5G applications, as the number
of users a BS can serve within its cell decreases.
One possibility to suppress the pilot interfer-
ence in 5G NR and leave the number of served
users within a cell unchanged can be realized by
exchanging ZC root indices among BSs through
the backhaul Xn interface. An alternative imple-
mentation is a centralized approach with a central
controller sending the indices of all concerned BSs
to each BS. Both implementations allow a given
BS to construct the sequences used in neighboring
cells and perform channel estimation, including not
only the channel of the desired user but also that
of users in neighboring cells [8]. The channel of the
former is then estimated with some residual inter-
ference (due to the non-orthogonality of sequenc-
es across the cells), while the estimated channels
of the latter can be dropped or used according
to the desired application (e.g., CoMP beamform-
ing or joint transmission rely on the knowledge of
channels of users in neighboring cells). The chan-
nel estimation is performed in the time domain and
exploits the fact that, in practical OFDM systems,
the number of taps is (much) smaller than the
number of subcarriers, which results in a reduced
number of variables in the time domain (i.e., taps)
that can be efficiently estimated. Going one step
further, [8] proposes to optimize the choice of the
used sequences such that the channel estimation
MSE is further reduced. The gains of optimized
sequence selection are mainly seen in the medium
to high SNR regime where the non-orthogonality
of used sequences becomes the limiting factor. Fig-
ure 1 shows the signaling steps necessary both for
a centralized and a decentralized implementation.
The first step consists of the signaling/exchange of
sequence indices, while the second one involves
informing the users within each cell of the chosen
sequence within the respective cell.
Summing up, this procedure generalizes the
idea of uplink CoMP data reception to pilot
sequence reception. It can be implemented for
ZC as well as other types of sequences (e.g.,
pseudo-noise sequences). It improves the channel
estimation quality for non-CoMP applications and
allows efficient CoMP operation without reducing
the number of users that can be simultaneously
assigned pilot sequences. As observed in [8], a
careful choice of sequences can allow the achiev-
able MSE to closely follow the interference-free
MSE. In contrast to LTE solutions, keeping the num-
ber of users that can be served unchanged is espe-
cially important for eMBB and URLLC 5G services.
LTE-A can allow for
pilot orthogonality
among multiple cells:
a BS assigns, from
its pool of available
orthogonal sequences,
pilot sequences for
users in neighboring
cells. Such a solution is
not scalable for many
5G applications, as the
number of users a BS
can serve within its
cell decreases.
This article has been accepted for inclusion in a future issue of this magazine. Content is final as presented, with the exception of pagination.

IEEE Wireless Communications • Accepted for Publication
4
dOwnlInk Inter-cell
Interference cOOrdInAtIOn
In the DL, the trend is toward more dynamic ICIC
solutions, as already agreed in 3GPP for 5G NR
[5], as well as addressing various network deploy-
ments (small cell and macro scenarios), key 5G
technologies (dense small cell networks and
MU-MIMO), and KPI requirements (spectral effi-
ciency, energy, and reliability).
dOwnlInk Inter-cell cOOrdInAted smAll cell dtX
Cell discontinuous transmission is an energy sav-
ing technology that adapts the cell activity to its
instantaneous load. Within each frame, the cell
DTX will instantaneously activate (deactivate) the
cell components and the associated functional-
ities when the user data is present (absent) in the
cell queue. Furthermore, it is possible to increase
the period in which a cell switches off or mutes by
maximizing the usage of the available frequency
resources at each active TTI, that is, trading off
latency for energy efficiency.
In dense small cell deployments, this approach
comes with the challenge of orchestrating the net-
work activity in order to limit simultaneous activa-
tion of nearby cells. First, the optimal selection of
the subset of small cells to activate at each frame
is a combinatorial problem, which is complex to
solve. Second, dormant cells cannot exchange
information and implement baseline ICIC solu-
tions or CoMP schemes. Finally, a reliable solution
needs to take into account the stochastic nature
of both the traffic and the radio channel. Existing
ICIC mechanisms are not designed to deal with
multi-objective optimization problems, e.g., jointly
reducing interference and energy consumption
while satisfying traffic latency constraints.
Reinforcement learning solutions provide an
efficient framework to learn an optimal activation
strategy by interacting with stochastic environ-
ments [9]. We design a fuzzy Q-learning based
cell DTX controller that uses its decisions in the
previous time slot to estimate the interference level
experienced by the active small cells. In addition,
the controller observes the queued data pending
for transmission per cell, the expected capacity,
and the requirements of the active services to
decide whether to activate a small cell.
The sketch of the architecture and the detailed
signaling exchange required by the scheme are
shown in Figs. 2a and 2b, respectively. Notice that
the pictured solutions here utilize the enhanced
support for different architectures and functional
splits that comes with 5G NR [4]. In those exam-
ples, the aggregation node buffers the data relat-
ed to nearby small cells while the orchestration
functions are deployed at the network controller.
Additionally, the measurements related to the
radio access network capacity can be forwarded
to the controller node during the small cell acti-
vation. Radio resource management (RRM) and
lower-layer functions are implemented locally at
the transmission points; thus, the controller and the
small cells do not need to continuously exchange
messages through the backhaul. On the contrary, a
fully distributed architecture requires coordination
across nearby small cells, which in turn increases
the small cell (and the backhaul) energy consump-
tion. In the same way, implementing centralized
scheduling or coordinated beamforming schemes
at the network controller 1) increases network
complexity; 2) needs regular transmission of channel
quality indicators (CQIs) over the backhaul link; and
3) is affected by the backhaul latency and capacity
constraints. In any case, the proposed solution with
reinforcement learning manages the small cell activ-
ity to limit network energy consumption without
reducing the system quality of service (QoS).
dOwnlInk Inter-cell rAnk AdAPtAtIOn
In a MIMO setting, the downlink serving rank (or
number of transmission streams) plays a major role
in the interference suppression levels of interfer-
ence rejection combining (IRC) receivers. This is
because significant interference suppression is only
possible when the number of desired data streams
and dominant interference streams are collectively
fewer than the receiver dimension, i.e., the number
of receive antennas. Traditionally, rank selection
at each user is essentially performed in a selfish
manner independently per link, without taking into
account the interference caused by such selec-
tions. For 5G NR, an inter-cell rank coordination
mechanism can improve the network and the cell-
edge user throughput by coordinating the gener-
ated inter-cell interference from the aggressor cell.
Consider a MU-MIMO TDD system. The
FIGURE 1. Signaling steps in a centralized or decentralized implementation of pilot sequence allocation.
i(s
1
),
i(s
2
)
Cell 1 uses
sequence s
1
Network controller selects
sequences s
1
and s
2
Cell 2 uses
sequence s
2
i(s
1
),
i(s
2
)
i(s
l
): index of sequence s
l
, l=1,2
Signaling step 1
i(s
1
)
i(s
2
)
Signaling step 2
Signaling step 1
Signaling step 2
i(s
1
)
Cell 1 selects
sequence s
1
Cell 2 selects
sequence s
2
i(s
2
)
i(s
l
): index of sequence s
l
, l=1,2
i(s
1
)
i(s
2
)
This article has been accepted for inclusion in a future issue of this magazine. Content is final as presented, with the exception of pagination.

IEEE Wireless Communications • Accepted for Publication
5
available resources are divided into time-frequen-
cy slots, with the smallest unit being a physical
resource block (PRB), corresponding to the dura-
tion of a single time transmission interval (TTI)
over a single frequency channel. The transmission
toward a desired UE from its serving BS generates
interference toward out-of-cell interfered receivers.
A cell-edge user scheduled on a given set of PRBs
in a neighboring cell is most likely to be affected
by the transmission on the same PRBs, and hence
requires interference coordination. Studies have
shown that coordinating the transmission rank can
help improve the performance of interference sup-
pressing receivers, such as the IRC [11].
The proposed inter-cell rank coordination aims
at limiting the maximum rank of an aggressor,
thus providing a guarantee on the experienced
interference. To further limit the complexity of
the scheme, only the strongest interferer, known
as the dominant interferer (DI), of the victim UE
is considered in the coordination. The victim
UE reports the DI physical cell id together with
a measure of the dominant to interference ratio
(DIR), defined as the ratio between the DI power
to the rest of interference and noise power in the
network. As happens with the interference, the
DIR can change very fast in fractional load sce-
narios [15], and therefore the LTE-A measures of
received signal power are not sufficient.
The proposed coordination mechanism involves
the following steps, as illustrated in Fig. 3:
1) The UEs report the DIR and the CQI to the
serving BS. The serving BS determines whether the
DIR is above a certain pre-specified threshold, and
rank coordination is only invoked for those UEs with
a strong DI. The UEs selected for rank coordination
are then grouped according to the DI, to avoid con-
flicting coordination requests from the same BS.
2) The serving BS decides what will be the
maximum transmission rank for each of the UEs
in each group, along with the interference rank it
would like to have. The signal to interference and
noise (SINR) ratio is used for the decision. The
ranks are chosen based on the estimated post IRC
SINR.
1
The proposed rank coordination mecha-
nism is not bound to any specific rank adaptation
algorithm, though interference-aware rank adap-
tation algorithms such as those presented in [12]
are best suited for such applications.
3) The serving BS sends the desired rank mes-
sage to the respective interfering BS. The desired
rank message is indicated as the maximum allow-
able transmission rank for a given set of PRBs.
These messages can be per single PRB, or several
PRBs can be grouped into a single desired rank
and priority level. The granularity provides a trade-
off among performance, overhead and complexity.
4) The serving BS updates its transmission
parameters according to the feedback mes-
sage from the interfering BS. Such updates can
include re-scheduling the users, re-adjusting
the transmission parameters, or re-adapting the
transmission rank with respect to the feedback
message. The 5G TTI is expected to be shorter
than the current 1 ms of LTE [5]. The rank coor-
dination could occur over a longer time basis
(in the range of 5-10 ms), therefore suitable for
heavy payload traffic spanning over multiple
TTIs. For random intermittent traffic with small
payload, the interference rank can be pre-co-
ordinated to cater to such bursty but critical
payloads.
The algorithm in Fig. 3 is applicable to both dis-
tributed and C-RAN architectures, taking advan-
tage of the flexible architecture options that come
with 5G NR [4]. Naturally, when having a central-
ized unit, the scheme simplifies since there is no
FIGURE 2. Downlink inter-cell coordinated small cell DTX: a) sketch of the architecture; b) signaling exchange.
Aggregation
node
User plane buffers
Network
controller
1
2
3
Power saving
mode
Backhaul
Low buffer
(a)
(b)
High buffer
Interference level
1
2
3
Low
interference
High
interference
SC set
Aggregation
node
Backhaul
node
Network
controller
1. RAN information
(Capacity estimate,
dominant interferer)
2. Buffer information
(cell load, packet TTL)
4. Fuzzy controller
5. BH node mode control
8. Data Forwarding
6. SC mode control
B) if SC switched on
A) if SC switched off
7. Buffering
3. Interference estimate
FIGURE 3. Flowchart of Message Flow with multiple UEs.
Dominant interferer (DIR, AP
ID) info + CQI measurements
DIR >
DIR ?
t
Group UEs
according to
dominant
interfering AP
No rank
coordination
Joint rank
coordination
for each
group
Coordination
message
Accept?
Reason, and additional
response (e.g., alternative rank)
UE domain
Serving eNB
domain
DL transmission
Update parameters
(if required)
Interfering
eNB domain
NACK
+ info
ACK
1
The post IRC SINR is the
criterion for eMBB services.
For URLLC, the probability of
satisfying a target SINR can
be used instead.
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01 Oct 2018-
TL;DR: The downlink performance of the system for zero-forcing precoding and propose centralized and decentralized power control schemes are analyzed and results reveal that sectorization, and multi-point coordinated sectorization lead to 2.36× and 8.56× improvements in the 95%-likely per-user throughput, respectively.
Abstract: Non-cooperative cellular Massive MIMO, combined with max-min power control, is known to give substantial improvements in per-user throughput compared with conventional 4G technology. We investigate further refinements to Massive MIMO, first, in the form of three-fold sectorization, which can be viewed as an effective reduction in cell size, and second, three-fold sectorization combined with coordinated multi-point operation, in which the three sectors cooperate in the joint service of their users. We analyze the downlink performance of the system for zero-forcing precoding and propose centralized and decentralized power control schemes. The simulation results reveal that sectorization, and multi-point coordinated sectorization lead to 2.36× and 8.56× improvements in the 95%-likely per-user throughput, respectively.

12 citations


Cites methods from "Interference Coordination for 5G Ne..."

  • ...Furthermore, coordinated multi-point (CoMP) is another technique envisioned to play an important role in 5G systems [9]....

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Journal ArticleDOI
TL;DR: This article discusses how 5 G New Radio (NR) favors the integration of UAVs into cellular networks, as its flexible air interface and beamforming-suited frequencies facilitate the deployment of interference-management solutions.
Abstract: Due to safety concerns, a reliable radio communication link is a key component in the future application of unmanned aerial vehicles (UAVs), as it will enable beyond visual line-of-sight (BVLOS) operations. In terms of cost and deployment time, radio communication for aerial vehicles will greatly benefit from the ready-to-market infrastructure and ubiquitous coverage of cellular networks. However, these are optimized for terrestrial users, and the different propagation environment experienced by aerial vehicles poses some interference challenges. In this article, field measurements and system-level simulations are used to assess interferencemitigation solutions that can improve aerial-link reliability. We then discuss how 5 G New Radio (NR) favors the integration of UAVs into cellular networks, as its flexible air interface and beamforming-suited frequencies facilitate the deployment of interference-management solutions.

11 citations


Cites background from "Interference Coordination for 5G Ne..."

  • ...On-Demand Power Boost 5G NR will likely introduce more advanced techniques for interference coordination than LTE [14]....

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Journal ArticleDOI
Yun Kuei Hua1, Wenson Chang1Institutions (1)
TL;DR: A cocktail approach is proposed to delicately integrate the two conventional countermeasures, i.e., the fractional pilot reuse and time-shifted pilots (TSP) schemes, for the pilot contamination problem in the full-duplex massive multiple-input multiple-output system.
Abstract: In this paper, a cocktail approach is proposed to delicately integrate the two conventional countermeasures, i.e., the fractional pilot reuse and time-shifted pilots (TSP) schemes, for the pilot contamination problem in the full-duplex massive multiple-input multiple-output system. Specifically, the TSP scheme is applied to duplicate for the inner and edge regions of a cell such that the number of accommodated users can be doubled. To achieve this, the base station should operate in the full-duplex mode; also, the pilot symbols rather than the pilot sequences are used to attain the user diversity gain. The simulation results verify the accuracy of the numerical analysis and the remarkably boosted sum rate for both the downlink and uplink cases.

7 citations


Cites background from "Interference Coordination for 5G Ne..."

  • ..., (1) power control mechanism for inner and edge regions [10]; (2) adaptive user selection scheme [4]; (3) the wellknown inter-cell interference coordination for the edge regions [11]; and (4) multiple regions within each cell for the TSP-based scheme....

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References
More filters

Journal ArticleDOI
Naga Bhushan1, Junyi Li1, Durga Prasad Malladi1, Rob Gilmore1  +5 moreInstitutions (1)
TL;DR: This article explores network densification as the key mechanism for wireless evolution over the next decade if it is complemented by backhaul densification, and advanced receivers capable of interference cancellation.
Abstract: This article explores network densification as the key mechanism for wireless evolution over the next decade. Network densification includes densification over space (e.g, dense deployment of small cells) and frequency (utilizing larger portions of radio spectrum in diverse bands). Large-scale cost-effective spatial densification is facilitated by self-organizing networks and intercell interference management. Full benefits of network densification can be realized only if it is complemented by backhaul densification, and advanced receivers capable of interference cancellation.

1,270 citations


"Interference Coordination for 5G Ne..." refers background or methods in this paper

  • ...In the downlink (DL), we propose a scheme for joint interference control and energy efficiency in dense small cell scenarios [9] by means of enhanced methods for discontinuous transmissions at the cell level (cell discontinuous transmission DTX) based on fuzzy Q-learning [10]....

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  • ...Reinforcement learning solutions provide an efficient framework to learn an optimal activation strategy by interacting with stochastic environments [9]....

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Journal ArticleDOI
G. Boudreau1, J. Panicker1, Ning Guo1, Rui Chang1  +2 moreInstitutions (1)
TL;DR: Viable approaches include the use of power control, opportunistic spectrum access, intra and inter-base station interference cancellation, adaptive fractional frequency reuse, spatial antenna techniques such as MIMO and SDMA, and adaptive beamforming, as well as recent innovations in decoding algorithms.
Abstract: This article provides an overview of contemporary and forward looking inter-cell interference coordination techniques for 4G OFDM systems with a specific emphasis on implementations for LTE. Viable approaches include the use of power control, opportunistic spectrum access, intra and inter-base station interference cancellation, adaptive fractional frequency reuse, spatial antenna techniques such as MIMO and SDMA, and adaptive beamforming, as well as recent innovations in decoding algorithms. The applicability, complexity, and performance gains possible with each of these techniques based on simulations and empirical measurements will be highlighted for specific cellular topologies relevant to LTE macro, pico, and femto deployments for both standalone and overlay networks.

742 citations


Journal ArticleDOI
TL;DR: This article provides the vision on advanced interference management for 5G cellular systems: network-side interference management needs to be complemented by UE- side interference management to realize true factor-one resource reuse.
Abstract: As 4G cellular systems densify their cell deployment, co-channel interference becomes a major source of obstacles to cell throughput improvement. In addition, cell edge users suffer more from co-channel interference, which may govern end users? experiences. Although some network-side solutions for co-channel interference management have been introduced in current 4G standards, it turns out that most of those solutions yield only meager gains in realistic cellular environments. In this article, we pay attention to recent advances in the network information theory and discuss the benefits of UE-side approaches. Based on this understanding, we provide our vision on advanced interference management for 5G cellular systems: network-side interference management needs to be complemented by UE-side interference management to realize true factor-one resource reuse. We also discuss practical challenges to deploy advanced interference management and their implications on 5G system design. Prospective gains of advanced interference management are demonstrated, and it is shown that the benefits of advanced receivers can be well exploited if 5G cellular networks employ elaborated joint scheduling.

177 citations


"Interference Coordination for 5G Ne..." refers background in this paper

  • ...We use 8x8 MIMO, IRC receiver and a DIR threshold value of 5 dB....

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  • ...The ranks are chosen based on the estimated post IRC SINR.1 The proposed rank coordination mechanism is not bound to any specific rank adaptation algorithm, though interference-aware rank adaptation algorithms such as those presented in [12] are best suited for such applications....

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  • ...IEEE Wireless Communications • June 2018 132 opportunities that come from assuming multi-user multiple-input-multiple-output (MU-MIMO) and advanced interference-aware receivers as the baseline for 5G [11]....

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  • ...Naturally, when having a central- 1 The post IRC SINR is the criterion for eMBB services....

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  • ...Studies have shown that coordinating the transmission rank can help improve the performance of interference suppressing receivers, such as the IRC [11]....

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Journal ArticleDOI
06 Oct 2016-Iet Communications
TL;DR: This paper presents an overview of the basic concepts of massive multiple-input multiple-output, with a focus on the challenges and opportunities, based on contemporary research.
Abstract: Massive multiple-input multiple-output technology has been considered a breakthrough in wireless communication systems It consists of equipping a base station with a large number of antennas to serve many active users in the same time-frequency block Among its underlying advantages is the possibility to focus transmitted signal energy into very short-range areas, which will provide huge improvements in terms of system capacity However, while this new concept renders many interesting benefits, it brings up new challenges that have called the attention of both industry and academia: channel state information acquisition, channel feedback, instantaneous reciprocity, statistical reciprocity, architectures, and hardware impairments, just to mention a few This paper presents an overview of the basic concepts of massive multiple-input multiple-output, with a focus on the challenges and opportunities, based on contemporary research

121 citations


"Interference Coordination for 5G Ne..." refers background in this paper

  • ...We use 8x8 MIMO, IRC receiver and a DIR threshold value of 5 dB....

    [...]

  • ...[7] D. C. Arajo et al., “Massive MIMO: Survey and Future Research Topics,” IET Commun., vol. 10, no. 15, Oct. 2016, pp. 1938–46....

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  • ...The scenario comprises 19 tri-sectorized macro cells in a hexagonal grid, with MxM MIMO and 10 MHz bandwidth....

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  • ...IEEE Wireless Communications • June 2018134 Consider a MU-MIMO TDD system....

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  • ...Finally, for on-demand power boost and cell muting, the analyzed KPI is the delay reduction for a 10–4 reliability target, and we evaluate the case with only power boost and with power boost and cell muting for a 2×2 MIMO....

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Journal ArticleDOI
TL;DR: Two algorithms to apply time domain and frequency domain small cell interference coordination in a DenseNet are proposed, which try to anticipate the future in a proactive way and simply react to an identified interference problem.
Abstract: The promise of ubiquitous and super-fast connectivity for the upcoming years will be in large part fulfilled by the addition of base stations and spectral aggregation. The resulting very dense networks (DenseNets) will face a number of technical challenges. Among others, the interference emerges as an old acquaintance with new significance. As a matter of fact, the interference conditions and the role of aggressor and victim depend to a large extent on the density and the scenario. To illustrate this, downlink interference statistics for different 3GPP simulation scenarios and a more irregular and dense deployment in Tokyo are compared. Evolution to DenseNets offers new opportunities for further development of downlink interference cooperation techniques. Various mechanisms in LTE and LTE-Advanced are revisited. Some techniques try to anticipate the future in a proactive way, whereas others simply react to an identified interference problem. As an example, we propose two algorithms to apply time domain and frequency domain small cell interference coordination in a DenseNet.

109 citations


Performance
Metrics
No. of citations received by the Paper in previous years
YearCitations
202111
20206
20195
20187
20173