# Adaptive Timers and Buffer Optimization for Layer-2 Protocols in 5G Non-
Terrestrial Networks

Chandan Kumar Sheemar  Sumit Kumar  Jorge Querol  and Symeon Chatzinotas
Interdisciplinary Centre for Security  Reliability and Trust (SnT)  University
of Luxembourg  Luxembourg
emails: {chandankumar.sheemar sumit.kumar jorge.querol
<EMAIL_ADDRESS>
###### Abstract

Interest in the integration of Terrestrial Networks (TN) and Non-Terrestrial
Networks (NTN); primarily satellites; has been rekindled due to the potential
of NTN to provide ubiquitous coverage. Especially with the peculiar and
flexible physical layer properties of 5G-NR, now direct access to 5G services
through satellites could become possible. However, the large Round-Trip Delays
(RTD) in NTNs require a re-evaluation of the design of RLC and PDCP layers
timers ( and associated buffers), in particular for the regenerative payload
satellites which have limited computational resources, and hence need to be
optimally utilized. Our aim in this work is to initiate a new line of research
for emerging NTNs with limited resources from a higher-layer perspective. To
this end, we propose a novel and efficient method for optimally designing the
RLC and PDCP layers’ buffers and timers without the need for intensive
computations. This approach is relevant for low-cost satellites, which have
limited computational and energy resources. The simulation results show that
the proposed methods can significantly improve the performance in terms of
resource utilization and delays.

###### Index Terms:

Non-Terrestrial Networks, 5G-NR, RLC, PDCP, Buffer Optimization

2G

    Second Generation
3G

    3${}^{\text{rd}}$ Generation
3GPP

    3${}^{\text{rd}}$ Generation Partnership Project
4G

    4${}^{\text{th}}$ Generation
5G

    5${}^{\text{th}}$ Generation
AA

    Antenna Array
AC

    Admission Control
ACL

    adjacent channel leakage
AD

    Attack-Decay
ADC

    analog-to-digital converter
ADSL

    Asymmetric Digital Subscriber Line
AHW

    Alternate Hop-and-Wait
AMC

    Adaptive Modulation and Coding
AP

    Access Point
APA

    Adaptive Power Allocation
AR

    autoregressive
ARMA

    Autoregressive Moving Average
ATES

    Adaptive Throughput-based Efficiency-Satisfaction Trade-Off
AWGN

    additive white Gaussian noise
A/D

    analog and digital
BB

    branch and bound
BD

    block diagonalization
BER

    bit error rate
BF

    Best Fit
BLER

    BLock Error Rate
BPC

    Binary power control
BPSK

    binary phase-shift keying
BPA

    Best pilot-to-data power ratio (PDPR) Algorithm
BRA

    Balanced Random Allocation
BS

    base station
CAP

    Combinatorial Allocation Problem
CAPEX

    Capital Expenditure
CBF

    Coordinated Beamforming
CBR

    Constant Bit Rate
CBS

    Class Based Scheduling
CC

    Congestion Control
CDF

    Cumulative Distribution Function
CDMA

    Code-Division Multiple Access
CL

    Closed Loop
CLI

    cross-link interference
CLPC

    Closed Loop Power Control
CNR

    Channel-to-Noise Ratio
CPA

    Cellular Protection Algorithm
CPICH

    Common Pilot Channel
CoMP

    Coordinated Multi-Point
CQI

    Channel Quality Indicator
CRM

    Constrained Rate Maximization
CRN

    Cognitive Radio Network
CS

    Coordinated Scheduling
CSI

    channel state information
CSIR

    channel state information at the receiver
CSIT

    channel state information at the transmitter
CUE

    cellular user equipment
DAC

    digital-to-analog converter
D2D

    device-to-device
DCA

    Dynamic Channel Allocation
DE

    Differential Evolution
DFT

    Discrete Fourier Transform
DIST

    Distance
DL

    downlink
DMA

    Double Moving Average
DMRS

    Demodulation Reference Signal
D2DM

    D2D Mode
DMS

    D2D Mode Selection
DOCSIS

    Data Over Cable Service Interface Specification
DPC

    Dirty Paper Coding
DRA

    Dynamic Resource Assignment
DSA

    Dynamic Spectrum Access
DSM

    Delay-based Satisfaction Maximization
EBD

    electrical balance duplexer
ECC

    Electronic Communications Committee
EFLC

    Error Feedback Based Load Control
EI

    Efficiency Indicator
eNB

    Evolved Node B
EPA

    Equal Power Allocation
EPC

    Evolved Packet Core
EPS

    Evolved Packet System
E-UTRAN

    Evolved Universal Terrestrial Radio Access Network
ES

    Exhaustive Search
FD

    full-duplex
FDD

    frequency division duplexing
FDM

    Frequency Division Multiplexing
FER

    Frame Erasure Rate
FF

    Fast Fading
FSB

    Fixed Switched Beamforming
FST

    Fixed SNR Target
FTP

    File Transfer Protocol
GA

    Genetic Algorithm
GBR

    Guaranteed Bit Rate
GLR

    Gain to Leakage Ratio
GOS

    Generated Orthogonal Sequence
GPL

    GNU General Public License
GRP

    Grouping
HARQ

    Hybrid Automatic Repeat Request
HD

    half-duplex
HMS

    Harmonic Mode Selection
HOL

    Head Of Line
HSDPA

    High-Speed Downlink Packet Access
HSPA

    High Speed Packet Access
HTTP

    HyperText Transfer Protocol
ICMP

    Internet Control Message Protocol
ICI

    Intercell Interference
ID

    Identification
IETF

    Internet Engineering Task Force
ILP

    Integer Linear Program
JRAPAP

    Joint RB Assignment and Power Allocation Problem
UID

    Unique Identification
IBFD

    in-band full-duplex
IID

    Independent and Identically Distributed
IIR

    Infinite Impulse Response
ILP

    Integer Linear Problem
IMT

    International Mobile Telecommunications
INV

    Inverted Norm-based Grouping
IoT

    Internet of Things
IP

    Internet Protocol
IPv6

    Internet Protocol Version 6
I/Q

    in-phase/quadrature
IRS

    Intelligent Reflective Surface
ISAC

    integrated sensing and communications
ISD

    Inter-Site Distance
ISI

    Inter Symbol Interference
ITU

    International Telecommunication Union
JCAS

    joint communications and sensing
JOAS

    Joint Opportunistic Assignment and Scheduling
JOS

    Joint Opportunistic Scheduling
JP

    Joint Processing
JS

    Jump-Stay
KF

    Kalman filter
KKT

    Karush-Kuhn-Tucker
L3

    Layer-3
LAC

    Link Admission Control
LA

    Link Adaptation
LC

    Load Control
LNA

    low-noise amplifier
LOS

    Line of Sight
LP

    Linear Programming
LS

    least squares
LTE

    Long Term Evolution
LTE-A

    LTE-Advanced
LTE-Advanced

    Long Term Evolution Advanced
M2M

    Machine-to-Machine
MAC

    Medium Access Control
MANET

    Mobile Ad hoc Network
MC

    Modular Clock
MCS

    Modulation and Coding Scheme
MDB

    Measured Delay Based
MDI

    Minimum D2D Interference
MF

    Matched Filter
MG

    Maximum Gain
MH

    Multi-Hop
MIMO

    multiple-input multiple-output
MINLP

    Mixed Integer Nonlinear Programming
MIP

    Mixed Integer Programming
MISO

    Multiple Input Single Output
ML

    maximum likelihood
MLWDF

    Modified Largest Weighted Delay First
mmWave

    millimeter wave
MME

    Mobility Management Entity
MMSE

    minimum mean squared error
MOS

    Mean Opinion Score
MPF

    Multicarrier Proportional Fair
MRA

    Maximum Rate Allocation
MR

    Maximum Rate
MRC

    maximum ratio combining
MRT

    Maximum Ratio Transmission
MRUS

    Maximum Rate with User Satisfaction
MS

    mobile station
MSE

    mean squared error
MSI

    Multi-Stream Interference
MTC

    Machine-Type Communication
MTSI

    Multimedia Telephony Services over IMS
MTSM

    Modified Throughput-based Satisfaction Maximization
MU-MIMO

    multiuser multiple input multiple output
MU

    multi-user
NAS

    Non-Access Stratum
NB

    Node B
NE

    Nash equilibrium
NCL

    Neighbor Cell List
NLP

    Nonlinear Programming
NLOS

    Non-Line of Sight
NMSE

    Normalized Mean Square Error
NORM

    Normalized Projection-based Grouping
NP

    Non-Polynomial Time
NR

    New Radio
NRT

    Non-Real Time
NSPS

    National Security and Public Safety Services
O2I

    Outdoor to Indoor
OFDMA

    orthogonal frequency division multiple access
OFDM

    orthogonal frequency division multiplexing
OFPC

    Open Loop with Fractional Path Loss Compensation
O2I

    Outdoor-to-Indoor
OL

    Open Loop
OLPC

    Open-Loop Power Control
OL-PC

    Open-Loop Power Control
OPEX

    Operational Expenditure
ORB

    Orthogonal Random Beamforming
JO-PF

    Joint Opportunistic Proportional Fair
OSI

    Open Systems Interconnection
PAIR

    D2D Pair Gain-based Grouping
PAPR

    Peak-to-Average Power Ratio
PBCH

    physical broadcast channel
P2P

    Peer-to-Peer
PC

    Power Control
PCI

    Physical Cell ID
PDF

    Probability Density Function
PDPR

    pilot-to-data power ratio
PER

    Packet Error Rate
PF

    Proportional Fair
P-GW

    Packet Data Network Gateway
PL

    Pathloss
PMN

    Perceptive Mobile Network
PPR

    pilot power ratio
PRB

    physical resource block
PROJ

    Projection-based Grouping
ProSe

    Proximity Services
PS

    Packet Scheduling
PSAM

    pilot symbol assisted modulation
PSO

    Particle Swarm Optimization
PZF

    Projected Zero-Forcing
QAM

    Quadrature Amplitude Modulation
QoS

    Quality of Service
QPSK

    Quadri-Phase Shift Keying
RAISES

    Reallocation-based Assignment for Improved Spectral Efficiency and Satisfaction
RAN

    Radio Access Network
RA

    Resource Allocation
RAT

    Radio Access Technology
RATE

    Rate-based
RB

    resource block
RBG

    Resource Block Group
REF

    Reference Grouping
RF

    radio frequency
RIS

    reconfigurable intelligent surface
RLC

    Radio Link Control
RM

    Rate Maximization
RNC

    Radio Network Controller
RND

    Random Grouping
RRA

    Radio Resource Allocation
RRM

    Radio Resource Management
RSCP

    Received Signal Code Power
RSRP

    Reference Signal Receive Power
RSRQ

    Reference Signal Receive Quality
RR

    Round Robin
RRC

    Radio Resource Control
RSSI

    Received Signal Strength Indicator
RT

    Real Time
RU

    Resource Unit
RUNE

    RUdimentary Network Emulator
RV

    Random Variable
RX

    receiver
SAC

    Session Admission Control
SBFD

    sub-band full-duplex
SCM

    Spatial Channel Model
SC-FDMA

    Single Carrier - Frequency Division Multiple Access
SD

    Soft Dropping
S-D

    Source-Destination
SDPC

    Soft Dropping Power Control
SDMA

    Space-Division Multiple Access
SER

    Symbol Error Rate
SES

    Simple Exponential Smoothing
S-GW

    Serving Gateway
SI

    self-interference
SINR

    signal-to-interference-plus-noise ratio
SIC

    self-interference cancellation
SIP

    Session Initiation Protocol
SISO

    single-input single-output
SIMO

    Single Input Multiple Output
SIR

    signal-to-interference ratio
SLNR

    Signal-to-Leakage-plus-Noise Ratio
SMA

    Simple Moving Average
SNR

    signal-to-noise ratio
SORA

    Satisfaction Oriented Resource Allocation
SORA-NRT

    Satisfaction-Oriented Resource Allocation for Non-Real Time Services
SORA-RT

    Satisfaction-Oriented Resource Allocation for Real Time Services
SPF

    Single-Carrier Proportional Fair
SRA

    Sequential Removal Algorithm
SRS

    Sounding Reference Signal
STAR

    simultaneous transmit-and-receive
SU-MIMO

    single-user multiple input multiple output
SU

    Single-User
SVD

    Singular Value Decomposition
TCP

    Transmission Control Protocol
TDD

    time division duplexing
TDMA

    Time Division Multiple Access
TETRA

    Terrestrial Trunked Radio
TP

    Transmit Power
TPC

    Transmit Power Control
TTI

    Transmission Time Interval
TTR

    Time-To-Rendezvous
TSM

    Throughput-based Satisfaction Maximization
TU

    Typical Urban
TX

    transmit
RX

    receive
UE

    User Equipment
UEPS

    Urgency and Efficiency-based Packet Scheduling
UL

    uplink
UMTS

    Universal Mobile Telecommunications System
URI

    Uniform Resource Identifier
URM

    Unconstrained Rate Maximization
UT

    user terminal
VR

    Virtual Resource
VoIP

    Voice over IP
WAN

    Wireless Access Network
WCDMA

    Wideband Code Division Multiple Access
WF

    Water-filling
WiMAX

    Worldwide Interoperability for Microwave Access
WINNER

    Wireless World Initiative New Radio
WLAN

    Wireless Local Area Network
WMPF

    Weighted Multicarrier Proportional Fair
WPF

    Weighted Proportional Fair
WSN

    Wireless Sensor Network
WWW

    World Wide Web
XIXO

    (Single or Multiple) Input (Single or Multiple) Output
XDD

    cross-division duplex
ZF

    zero-forcing
ZMCSCG

    Zero Mean Circularly Symmetric Complex Gaussian

##  I Introduction

Non-Terrestrial Networks (NTNs) refer to communication networks that operate
in space, beyond the boundaries of Earth’s surface [1]. These networks utilize
satellites, space-based platforms, or other orbiting infrastructure to
establish connectivity and enable communication services. NTNs play a crucial
role in bridging the digital divide, providing global coverage, and extending
connectivity to remote and un-derserved areas where terrestrial infrastructure
is limited or absent. They are instrumental in supporting various
applications, including telecommunications, internet access, broadcasting, and
disaster response [2], by leveraging the advantages of satellite-based
communication systems in terms of wide coverage, mobility, and scalability
[3].

However, compared to conventional terrestrial networks (TNs) [4, 5, 6, 7, 8],
NTNs suffer additional challenges due to extremely large round-trip delays
(RTDs) for geosynchronous Earth orbits (GEO), medium Earth orbit (MEO), and
low Earth orbit (LEO) satellites[9]. However, the benefits of NTNs have
motivated 3GPP, which has been involved in the standardization of TN
communications only [10, 11, 12], to take a big leap toward studying the
issues and providing solutions to integrate NTN in the 5G ecosystem. From 3GPP
Release 17 onward, satellites have become an integral part of 5G deployment
[13, 14], and this integration is coined as 5G-NTN.

In 3GPP Release 17, a particular emphasis was given to the transparent
payload, with the objective of providing direct access to the 5G services to
user equipments (UEs) on Earth. Transparent payload-based satellites do not
require expensive hardware. Their main goal is to amplify and forward the
signals coming from a ground gNB to a ground UE and vice versa, acting like a
high-altitude relay, with the potential to establish communication between two
entities located even very far on Earth. This is a simple setup that enables
the reuse of the existing fleet of satellites while both the gNB and the UE
operate on the ground.

Figure 1: Fifth generation non-terrestrial network with transparent payload.
Figure 2: Fifth generation non-terrestrial network with regenerative payload.

On the other hand, a regenerative payload satellite offers more advanced
capabilities by performing complex signal processing tasks based on specific
service requirements. This type of satellites combines all the functionalities
of a gNB and provides substantial advantages. However, implementing
regenerative payload systems incurs significant additional costs compared to
transparent satellite systems. But their deployment is still very desirable,
as the regenerative payload satellites can enable a range of new services and
applications while experiencing only half of the Round-Trip Delay (RTD)
because the signal processing takes place on the satellite itself. Moreover,
such payloads can be promising for enabling the Inter-Satellite-Links (ISL),
thus enhancing the flexibility and agility of satellite networks by enabling
dynamic reconfiguration and efficient resource utilization. However, the
implementation of regenerative payload-based 5G-NTNs is still in the early
stages of research, and while they bring new opportunities, they also pose
unique challenges that demand extensive efforts from academia and industry.
Realizing such networks will require new approaches and innovative thinking in
terms of system design. Fig. 1-2 highlights an example of the deployed
transparent and regenerative 5G-NTN payloads, respectively.

##  II 5G-NTNs from the Protocol Stack Perspective and Challenges at the
Higher Layers

The NTNs radio interface consists of four primary user-plane protocol layers
for secure and reliable data transfer between the UE and gNB. Such layers,
which are part of the protocol stack of both the UE and gNB, are the
following: 1) Physical (PHY) layer, 2) Medium Access Control (MAC) layer, 3)
Radio Link Control (RLC) layer, and 4) Packet Data Convergence Protocol (PDCP)
layer, as shown in Fig. 3. The most widely investigated layers are the PHY and
MAC, for which significant contributions are available for the 5G-NTNs in [15,
16] and [17, 18, 19], respectively. However, challenges at the RLC and PDCP
layers remain unexplored. The main difference between TN [20, 21, 21] and NTN
is that the latter suffers from large RTDs. This leads to novel challenges to
be tackled at the timing and buffer optimization levels, due to the limited
memory capacity of the satellites and also to restrict the hardware cost,
especially for small and low-cost regenerative architectures. We shed light on
the newly emerging challenges at the higher layers in the following.

Figure 3: Fifth generation new radio user plane protocol stack.

The PDCP layer, which lies on top of the RLC layer, is responsible for
maintaining the integrity and confidentiality of user data. It performs header
compression, duplication removal, and in-sequence delivery of user data. The
PDCP layer relies on the _discard timer_ and the _transmit buffer_ at the
transmitter and _reordering timer_ at the receiver to function. The discard
timer is used to track the sojourn time of each service data unit (SDU) at the
PDCP layer, which might need to be discarded from the transmission buffer when
their sojourn timer exceeds the discard timer value or has been successfully
received. The reordering timer is another very important parameter used at the
receiver PDCP entity to assure that the packets are delivered to the upper
layer in the correct order. Namely, when packets arrive our-of-order, the
optimal value of the reordering timer should be chosen closer to the expected
delay for the missing packets. The PDCP layer relies on the transmission
buffer to store a copy of the data to be transmitted over the wireless link.
The transmission buffer is used to ensure that the user data is transmitted in
the correct sequence and that any lost packets are retransmitted, for which
its copy is saved in the transmit buffer. Already larger RTDs significantly
increase the number of packets to be stored in the buffer. Sub-optimally
chosen discard timer values can further impact the sojourn time of the packets
in the buffer, thus leading to high memory requirements to save unnecessarily
an extremely large number of packets in the transmit buffer. A typical example
includes the case in which the packets are correctly received on the receiver
side and their acknowledgement (ACK) is sent to the transmitter. However, the
ACK is lost and the transmitter instead of triggering the retransmission waits
for the discard timer to expire, while other packets are being sent for each
of which a copy must be saved in the buffer. Note that this can considerably
enhance the delay in the system and therefore degrade the performance in terms
of throughput/rate. Therefore, optimally tuned discard timer values will not
only remarkably decrease the amount of memory required at the regenerative
payloads but are also essential to minimize unnecessary delays in the system.
This could further open the possibility to adopt NTN solutions also for the
services demanding low latency.

The RLC layer is responsible for managing the reliable transmission of data
and operates in three different modes. Namely, 1) RLC transparent mode (TM),
2) Unacknowledged Mode (UM), and 3) RLC Acknowledged Mode (AM). In RLC TM, the
RLC layer simply passes the data received from the upper layer to the lower
layer without adding any reliability mechanisms. This mode is suitable for
applications that do not require high reliability, such as streaming video or
audio. In RLC UM, the RLC layer sends the data without waiting for an ACK from
the receiving end. The UM mode is suitable for applications that can tolerate
some degree of packet loss, such as file transfers or web browsing. In RLC AM,
the RLC layer uses various reliability mechanisms to ensure that all packets
are successfully delivered to the receiving end. This mechanism includes
segmentation, retransmission, and status reporting. The AM mode is suitable
for applications that require high reliability. For the NTNs, the biggest
challenge lies in the RLC AM as it performs retransmissions. Namely, in RLC
AM, the receiver uses the _reassembly timer_ to ensure that all packets are
received in the correct order and reassembled into the original data. When a
packet is received out of order, the RLC layer stores it in a buffer until all
the missing packets have been received. The reassembly timer is started when
the first out-of-order packet is received, and it determines the maximum time
that the RLC layer will wait for the missing packets to arrive. Due to large
RTDs, the values of the reassembly timer should be tightly tuned, such that
the packets do not unnecessarily wait before being forwarded to the higher
layers. Otherwise, the throughput/rate at the higher layers can degrade.

The aforementioned challenges at the RLC and PDCP are a function of the delay.
Namely, on average, the time required for the packets to be correctly received
at these layers strictly depends on the position of the satellite with respect
to Earth, which dictates the RTDs. Therefore, when its position changes
significantly, such parameters need to be re-adapted.

Recall that the optimization of the PDCP and RLC layers in the NTNs is still
an open research problem and has not yet been tackled. The solution proposed
by the 3GPP so far [13, 14] advises setting the value of the PDCP discard
timer, the PDCP reassembly timer, the PDCP transmit buffer, and the RLC
reassembly timer by considering the worst-case scenarios consisting of the
maximum number of retransmissions allowed by the hybrid automatic repeat
request (HARQ) and ARQ processes in the lower layers to achieve reliable
communications. Therefore, such solutions represent an upper bound on the
values of such parameters and require allocating the maximum buffer size for
the PDCP layer and result in unnecessary delays for packets that require
retransmission but their ACK is low. However, in practice, the effective
number of retransmissions, depending on the position of the satellites,
channel conditions and signal-to-noise ratio (SNR) at the PHY layer, could
result to be considerably less. To this end, we present an adaptive approach
to optimize the parameters of such layers by providing the tightest upper
bound for the timers by estimating the effective number of retransmissions
occurring in the systems. During packet loss, no ACKs are received, and the
transmitter must wait until the discard timer expires. Since we propose the
tightest upper bound for the discard timer value, this results in a minimum
waiting time for retransmitting the lost packets. Moreover, we remark that our
proposed adaptive solutions for the timers result in a minimum amount of
memory required to have no packet loss, which is very desirable for low-cost
next-generation NTN solutions.

##  III Problem Formulation and Adaptive Solutions

###  III-A PDCP Layer Optimization

####  III-A1 _PDCP Discard Timer_

Let $t_{d}$ denote the discard timer for the PDCP layer, which is typically
configured for each data radio bearer (DRB). After receiving the service data
unit (SDU) from the radio resource layer (RRC), the PDCP entity starts a timer
to track its sojourn time after placing the received SDU in the transmission
buffer. Either when the associated timer to each SDU exceeds the value of the
discard timer $t_{d}$ or the correct reception of the PDCP SDU is confirmed,
the PDCP entity should discard the PDCP SDU.

To optimize $t_{d}$, we assume that its value is periodically adapted over a
period of timer $T_{d}$ by assessing the system for a very limited duration
$T_{o}$ in which $O$ packets are correctly received, including all the
retransmissions. We propose to exploit the time stamps (TS) for each SDU
received from the upper layer, which will allow the correct adaptation of
$t_{d}$ for the NTNs. Namely, we start by setting the value of $t_{d}$ very
large, for example as recommended by 3GPP. Typical values include
$1000-1500\leavevmode\nobreak\ $ ms [13, 14]. Let
$\mathcal{T}_{d}=\\{t_{0},t_{1},....,t_{O}\\}$ denote the set containing the
indices of the TSs for the first $O$ SDUs that have been correctly received.
Note that for this period packets are discarded from the transmission buffer
only when the peer PDCP entity correctly receives them. However, it is
possible that in timer $T_{d}$ some packets are correctly received at the
receiver but their ACKs are lost. In such a case, the TSs of such packets can
provide erroneous estimates of the effective number of retransmissions and
therefore we consider discarding such samples from the set $\mathcal{T}_{d}$.
Let $r_{d}$ denote the round-trip delay for the 5G-LEO satellite seen between
the PHY layers. From the PDCP layer perspective, the discard timer value can
be written as
$t_{d}=N\;(r_{d}+4(t_{pro}^{PDCP}+t_{pro}^{RLC}\;+\;t_{pro}^{L}))$, where
$N\in\mathbb{N^{+}}$. Namely, it should be a positive integer multiple of the
RTD times the number of retransmissions $N$ that effectively took place.
Additionally, we have the constants $t_{pro}^{PDCP}$ and $t_{pro}^{RLC}$
denoting the processing time delays at the PDCP layer and RLC layer,
respectively, and $t_{pro}^{L}$ denote the total processing delay at the lower
layers including MAC and PHY layer, due to for example modulation,
demodulation, encoding, decoding, medium access control, flow control,
compression/decompression, ciphering/deciphering of data, packet reordering,
header removal etc. Finally, the scalar $4$ accounts for the path from the
transmitter to the receiver up to the PDCP layer and from the receive PDCP
entity to the transmit PDCP entity and therefore the delay
$(t_{pro}^{PDCP}+t_{pro}^{RLC}\;+\;t_{pro}^{L})$ is experienced $4$ times.
Remark that such constants are inherent to the regenerative payload and can be
easily known at the systems level. Depending on the channel conditions and/or
SNR, the average effective number of retransmissions required at the lower
layers seen from the PDCP layer perspective may vary and thus needs to be
accurately estimated.

Consider the observation packets with their time stamps in $\mathcal{T}_{d}$.
We aim at estimating the maximum number of retransmissions from the
observation stamps so that the discard timer value can be set as tight as
possible based on the effective number of retransmissions occurring in the NTN
system. From the observation data in $\mathcal{T}_{d}$, the maximum number of
retransmissions can be estimated by solving the following optimization
problem.

$\max_{t}\;\;\;\;\frac{t-4(t_{pro}^{PDCP}+t_{pro}^{RLC}\;+\;t_{pro}^{L})}{r_{d}}$
(1a) $\text{s.t.}\quad\;\;t_{d}\in\mathcal{T}_{d}$ (1b)

Such a problem can be easily solved by comparing the values of the times that
the $O$ packets have stayed in the buffer available in $\mathcal{T}_{d}$. Let
$t_{d}^{*}$ denote the optimal solution for (1), which can be easily obtained.
An estimator for the effective number of maximum retransmissions $N$ can be
built as

$N=\lceil{\frac{t_{d}^{*}-4(t_{pro}^{PDCP}+t_{pro}^{RLC}\;+\;t_{pro}^{L})}{r_{d}}}\rceil$
(2)

where $\lceil\cdot\rceil$ denotes the least greatest integer.

It should be noted that the proposed solution remains valid until time
$T_{D}$, during which the value $r_{d}$ can be assumed to be constant.
However, as the satellite position changes, the propagation delay also
changes. Hence, it becomes necessary to adjust the discard timer value again
by observing the $O$ TSs in the new time interval. This observation helps in
estimating the maximum number of retransmissions in the system. The overall
procedure to build an adaptive estimator for the effective maximum number of
retransmissions occurring in the system is reported in Fig. 4. This can value
can be further used to set the optimal value of the discard timer.

Figure 4: Flowchart of the adaptive estimator of the discard timer value.

####  III-A2 PDCP Transmit Buffer

The buffer size at the PDCP layers is crucial in the upcoming generation of
NTNs, particularly in compact and affordable NTNs. This is primarily because
substantial propagation delays can result in a rapid escalation of the number
of packets that need to be stored in the buffer while awaiting their ACKs.
Consequently, this phenomenon imposes stringent demands on a large memory
capacity, which can significantly increase the costs associated with NTNs.

The optimization of the transmit buffer can be based on the throughput of the
system from the PDCP layer perspective at the transmit side. Furthermore, on
the time required to receive the ACKs of the correct reception of the packets
such that some portion of the memory can be emptied. Note that this includes
the RTD and the number of retransmissions $N$ at the lower layer. Namely, let
$R_{p}$ denote the effective rate at the PDCP layer that the transmit buffer
must support to have no packet loss, denoted as the number of packets
transmitted from the PDCP layer in one millisecond. The effective number of
retransmission occurring in the system has been estimated when optimally
tuning the discard timer value in (2). To find the optimal size of the buffer,
we must calculate the total number of packets which will be stored as a copy
in the transmit buffer when the transmit effective rate results to be $R_{p}$.
It can be shown that the number of packets effectively transmitted over time
while waiting for the packets’ ACKs is given as

$\mbox{Number of
Packets}=N\;(r_{d}+4(t_{pro}^{PDCP}+t_{pro}^{RLC}\;+\;t_{pro}^{L}))\;R_{p}.$
(3)

Assuming that one memory cell of the buffer stores one packet, then (3) also
represents the minimum buffer size, denoted as $B^{*}$ in terms of memory
cells required to support the throughput $R_{p}$ and have no packet loss in
the 5G-NTN system. In case the buffer size is chosen to be small than $B^{*}$
is will result in packet loss at the transmitter itself, which will notify the
higher layers to reduce the packet rate. Note that the ideal buffer size
relies on the RTD value $r_{d}$. Consequently, as the regenerative payload
changes its position, the RTD value and the optimal buffer size also change.
Therefore, the proposed solution enables dynamic allocation of the minimum
memory for 5G-NTNs to support the desired throughput/effective rate $R_{p}$.
When the RTD is large, a significant portion of memory is allocated to store
packet copies. However, when the satellite orbits closer to Earth, the RTD
decreases, resulting in the release of a substantial portion of memory that
can be utilized for other purposes.

####  III-A3 _PDCP Reordering Timer_

Let $t_{r}$ denote the reordering timer, which is another key component of the
PDCP layer at the PDCP receiver entity and is used to ensure that packets are
delivered to the upper layers of the protocol stack in the correct order. When
a packet is transmitted over the air interface, it may experience delays or be
received out of order due to various factors such as congestion or
interference. When a packet is received by the PDCP layer, it is assigned a
sequence number (SN) based on its order of transmission. The PDCP layer checks
the SN of each received packet against the expected SN, and if the received
packet’s SN is lower than the expected SN, it is considered an out-of-order
packet and stored in the reordering buffer. The reordering timer starts when
the first out-of-order packet is received and is set to a value based on the
expected delay for the missing packet to arrive. We propose to solve this
challenge by exploiting the information obtained from the initial $O$ samples
for which we first set the values to the maximum according to the 3GPP
solution. As for different received packets, a different value of delay can be
observed, and we aim at obtaining information about the maximum number of
retransmissions seen at the receiver, given the observation data. Note that on
the receiver side, only half of the RTD is experienced compared to the
transmit PDCP entity which must wait for the ACK. Therefore, let $r_{d}/2$
denote the propagation delay for the PDCP receiver. The expected delay to
receive the packets at the PDCP receiver entity, including retransmissions and
processing delay at the lower layers, can be written as

$t_{r}=M(r_{d}/2+2(t_{pro}^{PDCP}+t_{pro}^{RLC}\;+\;t_{pro}^{L})),$ (4)

The scalar $r_{d}/2$ takes into account the exact propagation delay from the
transmitter to the receiver at the PHY layer and $M$ denote the effective
number of retransmissions. We note that the number of effective
retransmissions observed at the receiver may result to be different than $N$.
This is mainly due to the fact that a packet may be received correctly at the
PDCP layer, but its ACK is lost. In such a case, the transmitter will
retransmit the packets for which no ACK is received, although they will be
discarded on the receiver side. Therefore, $M$ should be estimated separately
by the entity located on the ground. To build an estimator for the maximum
number of retransmissions occurring in the system, we need to estimate and
solve the following optimization problem based on the observations

$\max_{t}\;\;\;\;\frac{t-2(t_{pro}^{PDCP}+t_{pro}^{RLC}\;+\;t_{pro}^{L})}{r_{d}/2}$
(5a) $\text{s.t.}\quad\;\;t\in\mathcal{T}_{r}$ (5b)

where $\mathcal{T}_{r}$ is a set which contains the $O$ observation TSs at the
receiver, for which the packets are correctly received, including
retransmission. This problem can be easily solved by comparing the ratio
$\eqref{problem_1}$, with $t$ evaluated over the observation data in
$\mathcal{T}_{r}$. Let $t_{r}^{*}$ denote the optimal solution of the problem
(5), then the maximum expected number of retransmissions in the system is
given as

$M^{*}=\lceil{\frac{t_{r}^{*}-2(t_{pro}^{PDCP}+t_{pro}^{RLC}\;+\;t_{pro}^{L})}{r_{d}/2}}\rceil$
(6)

###  III-B RLC Layer Optimization

The RLC layer with acknowledged mode (RLC AM) is responsible for providing
reliable data transfer between the NTN wireless interface and the core
network. It ensures the delivery of user data by performing functions such as
segmentation and reassembling of data, error correction, and flow control. The
RLC layer operates on top of the physical layer and provides a service to the
upper layer protocols. The main challenge for the RLC layer due to large RTDs
lies on the receiver side. Namely, the RLC layer uses the reassembly timer
$t_{re}$ as a key parameter to ensure that all RLC protocol data units (PDUs)
belonging to a specific RLC SDU are received in a timely manner. When an RLC
PDU is received by the RLC layer, it is stored in a reassembly buffer. The
reassembly buffer holds all RLC PDUs that belong to a specific RLC SDU until
all the PDUs have been received. If an RLC PDU is lost or corrupted during
transmission, the RLC layer requests a retransmission of the missing or
corrupted PDU. The reassembly timer is started when the first RLC PDU
belonging to an RLC SDU is received. The timer is set to a value based on the
expected delay for all the PDUs belonging to the same RLC SDU to arrive. If
all PDUs are not received within the timer duration, the RLC layer will
discard the incomplete RLC SDU.

Since we previously estimated the effective maximum time to receive the packet
which depends on the scalar $M$, at the low layer the required time is the
same except for the processing time at the PDCP layer, which lies at the top
of the RLC layer. Therefore, the timer $t_{re}$ can be optimally tuned as

$t_{re}=M^{*}(r_{d}/2+2(t_{pro}^{RLC}+t_{pro}^{L})),$ (7)

Note that buffer optimization is not required at the UE located on the ground
as the UEs have enough memory to save a large amount of data. However, if
required, a similar problem to (3) can be solved to obtain the optimal buffer
size.

##  IV Simulation Results

In this section, we present simulation results to evaluate the performance of
the proposed adaptive timer and buffer approach. We consider the case of one
5G LEO regenerative satellite serving one UE on Earth. We assume a round trip
delay of $20$ ms for a satellite at $1200$ km altitude and $10^{\circ}$
elevation angle. We assume that the effective number of retransmissions
observed at the transmitter and receiver are the same $N=M$ and assume that it
is uniformly distributed in the interval $[\mu-3,\mu+4]$, where $\mu$ denotes
the average number of retransmissions. Therefore, higher values of $\mu$
emphasize a larger number of retransmissions. The total processing delay is
assumed to be
$t_{pro}^{PDCP}+t_{pro}^{RLC}\;+\;t_{pro}^{L}=0.5\leavevmode\nobreak\ $ms.

For comparison, we consider the case where the discard timer value is set
based on the maximum number of HARQ processes being $32$, computed according
to the formula provided above. We assume that the effective rate supported by
the PDCP layer is $R_{p}=10\leavevmode\nobreak\ pck/ms$. To yield the
estimators for the effective maximum number of transmissions, we assume that a
total of $1000$ packets are sent and from which the first $20$ packets are
derived to estimate the effective number of retransmissions $N$ and $M$, and
then the values of the discard timer $t_{d}$, reordering timer $t_{r}$ and
reassembly timer $t_{re}$ are optimized. Since a similar approach is being
adopted to tune the reordering timer and the reassembly timer, we consider
plotting the results only for $t_{d}$.

Figure 5: PDCP transmission rata as a function of the buffer size.

Fig. 5 highlights the throughput at the PDCP layer as a function of the buffer
size that varies in the interval $[0.2B^{*},1.4B*]$, where $B^{*}$ denotes the
optimal buffer size computed according to (3). We can see that when the buffer
size is chosen to be small, $B<B*$ this results in significant packet loss at
the PDCP transmitter, which cannot send packets to the lower layers. That is,
the effective number of transmitted packets is equal to the available memory
cells in which a copy of the transmitted packets can be saved. The effective
rate improves as the size of the buffer increases. We can see that the buffer
size computed according to (3) results in the minimum buffer size such that
the effective rate matches the rate requirement that the PDCP layer is
expected to support.

Figure 6 shows the average additional delay experienced for a packet which
requires to be transmitted but its ACK is lost as a function of the average
number of retransmission $\mu$, when the worst case for the number of
retransmissions is considered, in contrast to the proposed adapted solution of
this work. We can see that each packet which requires retransmission but whose
ACK is lost will be retransmitted very late, especially when the effective
number of retransmissions occurring in the system is small. On the other hand,
when our proposed solution is adopted, the retransmission is triggered
immediately as the $t_{d}^{*}$ expires based on the effective maximum number
of retransmission, thus resulting in negligible additional delay.

Figure 6: Average additional delay experienced by the packet which requires
retransmission but its ACK is lost.

##  V Conclusions

The 5G-NTNs experience significant RTDs, which requires the development of
innovative mechanisms for designing the higher layers of the protocol stack.
This is particularly crucial for low-cost satellites with limited resources.
In this study, a novel adaptive approach is introduced to optimize the discard
timer, transmit buffer, reordering timer for the PDCP layer, and the
reassembly timer for the RLC layer. The proposed approach accurately estimates
the effective number of retransmissions in the 5G-NTN system and adjusts the
timer values accordingly. In addition, it determines the optimal buffer size
required to support efficient throughput. Through the results of the
simulation, it is demonstrated that the proposed approach achieves significant
performance improvements in terms of memory utilization efficiency. Moreover,
it enables the early triggering of packet retransmissions that would otherwise
unnecessarily wait for transmission due to ACK loss.

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  *[PDPR]: pilot-to-data power ratio