185, 79, 186, 72, 187, 184, 188]. For example, [183] introduced a digital
beamforming method that combined horizontal and vertical adjustments to
address the displacement of HAP cells projected on the ground due to
variations in stratospheric wind or turning flight. The work in [184]
investigated the interference mitigation problem in NSIN by optimizing
beamforming vectors of HAPs and formulated this optimization problem as
second-order cone programming, which was solved by an interior-point method.
Besides, the work in [185] studied the digital beamforming for HAP multiple-
input multiple-output (MIMO) systems and derived a beamformer matrix according
to the critical observation that signal power concentrated on a statistical
eigenmode instead of a statistical correlation matrix. However, the digital
beamforming architecture increases digital chip content and the corresponding
cost and power consumption.

Analog beamforming: The analog beamforming architecture incorporates a phase
shifter at each antenna element, allowing for flexible and adaptive beams that
can be directed in any direction. All phase shifters share a common RF chain
in this type of architecture; as a result, analog beamforming is power- and
cost-efficient. The authors in [189] investigated the problem of analog
beamforming to achieve low power consumption and exploited the benefits of
beamforming in HAP-based multi-user MIMO communication systems. They
formulated the analog beamforming problem as quadratically constrained
quadratic programming and proposed a semi-definite relaxation approach to
solve it. In [190], the authors investigated how to control the beam
directions of the antenna onboard a HAP with consideration of the position of
a ground gateway and attitude changes of a HAP. In [191], the authors proposed
to optimize beamwidth and beam directions to prevent existing TN from being
interfered with by a HAP. Besides, the authors in [192] studied the issue of
HAP interference alleviation in HAP massive MIMO communication systems through
user equipment beamforming. They proposed a semi-definite relaxation method to
obtain the user equipment beamforming vector by maximizing its signal-to-pilot
contamination ratio (SPR). However, the fine tuning of the beams in this type
of beamforming architecture is limited due to the low resolution of quantized
phase shifts.

Hybrid beamforming: This beamforming architecture utilizes the advantages of
both analog beamforming and digital beamforming architectures. It employs
precoding/beamforming at both RF and baseband, where the antenna elements are
still driven by analog phase shifters. Yet, the number of RF chains remains
smaller compared to the number of antennas. The reduction in the number of RF
chains and corresponding data converters results in less cost, computational
load, and power consumption. Overall, this type of beamforming architecture
strikes a trade-off between complexity and flexibility. As a result, it has
garnered increasing attention from the research community. For example, the
authors in [193] designed a hybrid beamforming scheme for HAP massive MIMO
systems. Particularly, they derived the relationship between RF beamforming
and statistical channel state information according to the duality theory and
utilized the zero-forcing method to conduct baseband beamforming. The authors
in [194] investigated the hybrid beamforming for HAP massive MIMO systems and
formulated the RF beamforming as an average signal-to-leakage-plus-noise ratio
maximization problem. A greedy algorithm was then designed to obtain the RF
beamforming matrix. Given the RF beamforming matrix, the baseband beamforming
matrix was obtained by a regularized zero-forcing method. The authors in [195]
proposed to solve the high power consumption, high cost, and high hardware
complexity issue of conducting hybrid beamforming for HAP massive MIMO
systems. To this end, they designed an adaptive cross-entropy-based
optimization method to update the RF beamforming matrix and employed a zero-
forcing method to obtain a digital beamforming matrix. Besides, the authors in
[196] developed a two-stage precoding scheme to alleviate the interference
between HAPs. Particularly, the proposed scheme separately controlled the
interference between HAPs and user-specific beams by factorizing the overall
precoding weight matrix into an outer precoder for interference reduction and
an inner precoder for user-specific beam generation.

####  V-A2 Passive beamforming

Beamforming architecture in which amplifiers or radios are used as external
elements of an antenna array is known as a passive beamforming array. A
typical passive beamforming pattern is RIS or IRS. In some scenarios, many
aerial platforms of NSIN are expected to act as aerial relays. To complete the
task of relaying signals, aerial platforms have two selections: 1) act as
radio relays that mount active beamforming payloads; 2) integrate with
RIS/IRS. When acting as radio relays, aerial platforms have to mount some
payloads, such as antennas, analog-to-digital converters (ADCs), digital-to-
analog converters (DACs), data converters, mixers, power amplifiers, and
filters, entailing high hardware complexity and large energy consumption. On
the contrary, RIS/IRS-mounted relays can be designed by coating the surfaces
of aerial platforms with a thin layer of meta-surfaces, entailing low hardware
complexity. Moreover, several studies have demonstrated that the capacity
attained through RIS/IRS-mounted relays is comparable to that achieved through
radio relays [197, 198]. Further, for NSIN, the energy consumption of using
RIS/IRS is much lower than that of using radio relays [199, 200]. For example,
using RIS/IRS reduces the communication energy consumption of NSIN to the
required power for the RIS/IRS controller. In a recent experiment, it was
found that the configuration of each reflector unit consumed 0.33 mW [201].
Studies have indicated that an RIS/IRS-assisted communication system could
achieve a remarkable $40$% increase in energy efficiency compared to a radio
relay-assisted system [197]. Therefore, RIS/IRS-mounted relays will be a
promising solution for NSIN to build multi-hop links in a cost-efficient way.

Actually, RIS/IRS are a planar array composed of a large number of passive
reflecting elements. By precisely manipulating the phases and directions of
incident signals in a controlled manner, the planar array enables passive
beamforming, which intelligently reflects signals towards targeted directions
using passive reflecting elements [12]. Mathematically, denote $x_{i}$ as the
incident wave of the $i$-th element of RIS/IRS. Then, the reflected signal can
be written as $y_{i}=I_{i}{\rm e}^{j\alpha_{i}}x_{i}$, $\forall i=1,\ldots,M$,
where $I_{i}\in\\{[0,1]\\}$ and $\alpha_{i}\in[0,2\pi)$ control the amplitude
and phase shift of the reflected signal, respectively, and $M$ denotes the
total number of array elements.

Recently, the investigation of integrating RIS/IRS on different aerial
platforms has attracted lots of attention in the research community. Table IX
compares the recent studies on aerial-RIS (ARIS). Summarily, the recent
researches can be classified into three groups: channel modeling of ARIS [202,
203, 204, 205, 206], deployment or trajectory planning of ARIS [207, 208, 209,
210, 211, 116, 212, 213, 214, 215, 216], and performance analysis of ARIS
[217, 218, 219, 220, 221, 222].

TABLE IX: Comparison of most recent studies on ARIS Refs. | Amplitude |  | Phase
---
shift
| Phase
---
error
| Channel
---
modeling
| ARIS
---
deploy-
ment
| ARIS
---
trajectory
| Aerial
---
platform
features
| Single
---
ARIS
| Multi-
---
ARIS
| Optimi-
---
zation
| Lea-
---
rn
| Perfor-
---
mance
analysis
[202, 203] |  | ✓ |  | ✓ |  |  |  | ✓ |  | ✓ |  |
[204] |  | ✓ |  | ✓ |  |  |  | ✓ |  |  | ✓ |
[205] | ✓ | ✓ |  | ✓ |  |  |  | ✓ |  |  |  |
[206] | ✓ | ✓ |  | ✓ |  |  | ✓ |  | ✓ |  |  |
[207] |  | ✓ |  |  | ✓ |  |  |  | ✓ |  | ✓ |
[208] |  | ✓ |  |  | ✓ |  |  | ✓ |  | ✓ |  |
[209] |  | ✓ |  |  | ✓ |  |  |  | ✓ | ✓ |  |
[210] | ✓ | ✓ |  |  |  | ✓ |  | ✓ |  | ✓ |  |
[116] |  | ✓ |  |  |  | ✓ |  | ✓ |  | ✓ | ✓ |
[211, 212] |  | ✓ |  |  |  | ✓ |  | ✓ |  | ✓ |  |
[213] |  | ✓ |  |  |  | ✓ |  | ✓ |  |  | ✓ |
[214] |  | ✓ |  |  |  | ✓ |  | ✓ |  | ✓ |  |
[215] |  | ✓ |  |  |  | ✓ |  |  | ✓ |  | ✓ |
[216] |  | ✓ |  |  |  | ✓ |  |  | ✓ | ✓ |  |
[217, 218] |  |  | ✓ |  |  |  |  | ✓ |  |  |  | ✓
[219, 222] |  |  |  |  |  |  |  | ✓ |  |  |  | ✓
[220] |  |  |  |  |  |  | ✓ | ✓ |  |  |  | ✓
[221] |  |  | ✓ |  |  |  | ✓ | ✓ |  |  |  | ✓

In terms of channel modeling of ARIS, for example, under the condition of
considering the correlations of fading channels in the spatial, time, and
frequency domains imposed by the AoA/AoD, the Doppler, and the orthogonal
frequency division multiplexing (OFDM) operations, the channel modeling of
high-dimensional and high-mobility ARIS was investigated in [202]. In [204],
the cascaded channel estimation in full-duplex ARIS communications under the
condition of varying channel characteristics was studied. For the channel
estimation, a graph attention network was utilized. Under Rician fading
conditions, a composite channel gain expression was proposed in [205].
Besides, considering the position perturbation of ARIS, a novel atomic norm
minimization method was developed to estimate the direction of arrival (DoA)
in [206].

In terms of the deployment or trajectory planning of ARIS, for instance, an
optimization framework was formulated and solved in [207], which considered
ARIS’s deployment, power allocation at a macro base station, phase shift of
ARIS, and blocklength of ultra-reliable low latency communications (URLLC).
The work presented in [208] investigated the optimization of ARIS’s horizontal
position and phase shift of ARIS to maximize the rate of a strong terrestrial
user while guaranteeing the target rate of a weak terrestrial user. In [210],
the problem of optimizing ARIS beamforming and trajectory to enable reliable
data transmissions from ARIS to an intended terrestrial user was explored.
Furthermore, in [211], the objective was to maximize the minimum throughput
among all mobile vehicles by jointly optimizing ARIS passive beamforming and
trajectory, with the aim of achieving communication fairness.

In terms of performance analysis of ARIS, for example, the outage probability
and ergodic spectral efficiency expressions of ARIS communications were
derived by the probability theory in [217]. The impact of imperfect phase
knowledge on the system capacity of ARIS communications was investigated in
[218], where the phase error was modeled as a von Mises random variable. On
the other hand, [219] derived tractable analytic expressions for the
achievable symbol error rate (SER), ergodic capacity, and outage probability
of ARIS communications. The tight upper and lower bounds of the average SNR
were also derived. Besides, the work in [220] theoretically analyzed the link
budget of ARIS communications for ARIS ‘specular’ and ‘scattering’ reflection
paradigms.

###  V-B MAC Layer

The primary goal of MAC protocols is to improve network capacity and reduce
packet transmission latency. MAC protocols greatly impact the performance of
wireless networks, including network throughput, latency, and reliability.
There are three categories of MAC protocols, which include fixed allocation
protocols, random contention protocols, and reservation protocols.

Fixed allocation protocols needing to reserve network resources are suitable
for wireless networks with stable topologies or associations, a delay-
sensitive constraint, and regular traffic flows. Random contention protocols
are suitable for wireless networks with random traffic flows, a delay-tolerant
constraint, and time-varying topologies or associations. Reservation protocols
are suitable for wireless networks with irregular and highly fluctuating
traffic flows and time-varying topologies or associations.

####  V-B1 Fixed allocation protocols

Channel resources in this kind of MAC protocol are allocated or reserved to
each node in wireless networks according to a certain allocation algorithm in
advance. Many types of multiple access techniques, including TDMA, code
division multiple access (CDMA), frequency division multiple access (FDMA),
spatial division multiple access (SDMA), and NOMA, can be explored in fixed
allocation protocols. It is appropriate to apply fixed allocation protocols to
the HAP sub-network in NSIN as a HAP is quasi-stationary and has a large
footprint. Numerous relevant studies have been presented in the literature.
For instance, the authors in [223] investigated the interference issue in a
HAP and fixed wireless access (FWA) coexistence system. Under the TDMA
assumption, they introduced a probabilistic model to analyze the aggregate
interference caused by FWA transmitters for users served by the HAP. The paper
[224] considered a HAP uplink communication scenario using a single-carrier
FDMA (SC-FDMA) scheme. The investigation on the performance of a pilot-based
uplink channel estimation method was conducted. The impact of some crucial
coefficients, such as a user’s elevation angle, modulation scheme, LTE channel
bandwidth, and Doppler shift, on a HAP uplink channel model was evaluated. The
paper [225] designed a joint power control and space diversity algorithm to
improve the performance of CDMA-enabled HAP communications at a low elevation
angle. The authors in [226] studied the wideband CDMA (WCDMA) uplink capacity
and the interference statistics of HAP macrocells using statistical geometry
and probability theories. Exploiting beamforming to design SDMA-enabled HAP
systems is currently under active research [227, 74, 75, 189, 228, 79, 185].
The work in [227] investigated the problem of augmenting ground communications
through deploying an SDMA-enabled HAP. The authors in [74] considered a
scenario of deploying a HAP to provide broadband infotainment services for
vehicular users in rural areas and proposed to utilize steerable beam
directional antennas to realize SDMA for vehicular users.

The investigation into the NOMA-enabled HAP networks has attracted much
attention from the research community. The papers [112, 229] proposed the
deployment of NSIN to provide reliable communication services to ground users,
with the adoption of the NOMA technique to enhance the quality of downlink
links for users located at the edge of NSIN. The paper [230] designed a novel
secure transmission framework to provide latency-sensitive medical-care
services for ground users by deploying a HAP and many low-altitude platforms.
The authors proposed to deliver medical-care information to multiple hotspots
via a NOMA transmission scheme and formulated an optimization problem for
minimizing transmission latency, subject to power and spectrum constraints. An
alternating optimization framework was designed to solve the optimization
problem. The authors of [76] investigated energy-efficient beamforming for
HAP-NOMA systems operating over Rician fading channels. Particularly, they
derived the expression of the NLoS channel of a HAP equipped with a uniform
planar array (UPA). Based on the obtained channel expression, a HAP-NOMA
scheme, including user grouping and power optimization, was designed. The
paper [231] considered clustered-NOMA NSIN and investigated the joint UAV
location optimization and resource allocation problem to maximize the overall
network uplink achievable rates. To solve this problem, UAV location
optimization and resource allocation were iteratively performed. A simulation
of HAP backbone networks utilizing the NOMA scheme was studied in [78]. The
paper [232] considered an integrated HAP space information network using a
NOMA scheme. The authors derived the closed-form expressions of the outage
probabilities of the integrated network in the presence of imperfect channel
state information (CSI) and successive interference cancellation (SIC). In
[233], the authors discussed the issue of exploiting NOMA-based HAP
communications and multiple antennas with user selection to meet the
connectivity, reliability, and high achievable data rate requirements of
users. In [234], the authors proposed to deploy a HAP communication system to
enable NOMA for ground multi-cell users. The work in [235] theoretically
analyzed the outage probability and ergodic capacity of a two-way satellite-
HAP-terrestrial network with NOMA. Besides, in [236], the authors investigated
the issue of deploying clustered-NOMA-enabled NSIN to cover remote areas where
the clustered-NOMA technology was leveraged to achieve channel multiplexing
and reduce the complexity of SIC.

####  V-B2 Random contention protocols

In this type of MAC protocol, each node in wireless networks accesses channels
in a competitive way. Once obtaining the opportunity to access a channel, a
node immediately accesses the channel or randomly accesses the channel with a
certain probability after detecting that the channel is idle. If a packet
collision occurs, the transmission fails, and the node chooses to back off and
wait for the next transmission. The greater the number of transmission
failures, the lower the random access probability. There are many famous
random contention protocols, such as additive links on-line hawaii area
(ALOHA), carrier sense multiple access (CSMA), and CSMA with collision
avoidance (CSMA/CA). The ALOHA protocol utilizes the simplest random
competition mechanism. Users access a channel immediately when they have data
to send. In order to reduce collisions, users should avoid accessing the
channel when the channel is not idle. For this purpose, the CSMA protocol has
been developed. In this type of protocol, a user first monitors the channel
before sending data, which thus reduces packet collisions to a certain extent.
However, packet collisions may still happen due to propagation delays. To
further reduce possible collisions, the CSMA/CA protocol is introduced. It
adopts collision avoidance methods to reduce possible collisions.
Specifically, it utilizes request-to-send (RTS) and allow-to-send control
messages to implement channel reservation and collision avoidance, which thus
improves the success probability of data transmission. For example, the
authors in [237] adopted the CSMA/CA and RTS/clear to send (CTS) handshake
mechanisms to solve the efficient access problem among a HAP and multiple UAVs
when sharing channel resources.

####  V-B3 Reservation protocols

In this type of protocol, according to the amount of network traffic in a
node, the node first competitively sends several short packets to reserve a
channel in advance. This type of protocol mandates the exchange of reservation
control information between nodes. As a result, a great deal of overhead will
be generated when the payload in the networks is limited or the number of
nodes varies. The impact of channel reservation information on channel
utilization is a major concern for this type of protocol. The polling MAC
protocol is a typical reservation protocol that maintains a central control
node (or master) to centrally control each node’s access to the channel
without contention.

Summarily, the random contention protocols and reservation protocols for
dynamic networks can be applied to HAUAV and LAUAV sub-networks in NSIN.The
HAUAV and LAUAV sub-networks are characterized by high mobility, which leads
to fluctuating link quality and time-varying topologies. Nevertheless, the
packet latency constraint is difficult to be satisfied in the dynamic HAUAV
and LAUAV sub-networks. During the past few years, the problem of designing
effective MAC protocols for UAV networks has been extensively studied in the
literature. Table X summarizes the surveys on UAV MAC protocols.

TABLE X: Relevant Surveys On UAV MAC Protocols. Refs. | Focus
---|---
[23] |  | Promising technological advancements, including directional antenna and full-duplex radio circuits
---
with multi-packet reception, which can be adopted by MAC protocols of FANET.
[238, 239] |  | Design of MAC protocols for UAV networks with
---
Omni-directional and directional antennas.
[240] |  | Classify the MAC protocols for UAV networks into two categories:
---
antenna category-based MAC protocols and access mechanism-based MAC protocols.
[241] |  | Discus the design considerations for MAC protocols of the Internet of Things based on UAV (UIoT),
---
and summarize the contention-based, contention-free, and AI-based MAC
protocols for UIoT.
[242] |  | Summarize the contention-based, contention-free, and hybrid MAC protocols
---
for UAV-aided wireless sensor networks and compare the MAC protocols
in terms of the features and focal ideas.
[24, 243] |  | Discuss the protocols that help save energy in the MAC layer of UAV networks.
---

###  V-C Network Layer

A routing protocol aimed at tackling the issue of obtaining the most connected
E2E path to reliably deliver traffic to one or more destination(s) with a
reduced E2E latency. Considering the high heterogeneity and complexity of
NSIN, it is highly challenging to design an efficient routing protocol to
enable packet transmission across different network segments. Most existing
routing protocols for UAV networks (partially) cannot be applied to NSIN. As
noted in numerous surveys of UAV routing protocols, these protocols can be
broadly categorized into topology-based routing protocols (such as flat,
cluster-based, and hybrid routing protocols) and geographical routing
protocols.

The topology-based routing protocol (exactly, flat routing protocol),
including some famous protocols such as proactive, reactive, and hybrid
routing protocols, cannot not be directly applied to route packets in NSIN.
This is because all of these routing protocols assume that the nodes in the
network have equal roles [244], which is not suitable for NSIN. The idea of
adopting a greedy scheme and recovery scheme in UAV geographical routing
protocols may be applicable for NSIN. However, the entire routing protocol
cannot be directly applied to NSIN, mainly due to the large footprint of HAP
as well as the long distance towards the destination. For instance, owing to
its large footprint, a destination may always be in the coverage range of a
HAP. Yet, owing to the high deployment altitude, the transmission distance of
a UAV-HAP link is greater than that of a UAV-UAV link. Besides, for the
geographic information-based routing protocols, they routed packets using the
location information of airborne platforms and adopted a greedy scheme to
forward packets. An important procedure in this type of routing protocol is
that an airborne platform will deliver packets to a neighboring platform
closer to the destination, and the routing process may fail if an invalid
route occurs.

Therefore, routing protocols for NSIN should be separately designed. Existing
routing protocols for NSIN can be categorized into two groups: HAP-assisted
routing protocols and unified routing protocols.

####  V-C1 HAP-assisted routing protocol

In this type of routing protocol, HAP networks are consistently utilized as a
control information delivery layer. Specifically, HAPs can broadcast control
information (e.g., automatic dependent surveillance-broadcast (ADS-B) and SDN
information) that will be useful for the efficient protocol design of UAV
networks. This type of protocol includes:

ADS-B system-aided Geographic Routing (A-GR) [245]: It is an ADS-B system-
aided geographic routing protocol. ADS-B is a cooperative surveillance system
for air traffic management (ATM). An ADS-B-enabled aircraft will broadcast a
state vector including identification, position, velocity, altitude, and other
flight-related information. Owing to their large footprints, HAPs are
leveraged to expand the coverage of the ADS-B system. HAPs will collect ADS-B
messages from different aircrafts and then broadcast this type of message.

A-GR differs from traditional routing protocols in that it builds a neighbor
table using ADS-B messages instead of sending hello beacons, which reduces the
beaconing overhead. Further, as the ADS-B system and the payload data
transmission system adopt different antennas and frequency bands, ADS-B
messages will interfere with payload packets. The update frequency of the
neighbor table is determined by the ADS-B message cycle. A-GR designs a
routing decision metric (i.e., instantaneous flight time, IFT) that considers
the distance towards the destination and the relative velocities of airborne
platforms

$IFT=\mathop{\min}\limits_{k\in{N_{i}}}\left\\{{\frac{{\Delta{d_{i,k}}(t,D)}}{{{v_{i}}(t)-{v_{k}}(t)}}}\right\\}$
(10)

where $N_{i}$ is the neighbor set of platform $i$, ${\Delta{d_{i,k}}(t,D)}$
denotes the geographic distance between $i$ and $j$ to destination $D$,
$v_{i}(t)$ and $v_{k}(t)$ are the velocities of platform $i$ and $k$ at moment
$t$, respectively.

During the packet forwarding phase, a typical platform searches its neighbor
table to determine the routing path for the packets. If multiple platforms are
within its transmission range, the one with the best IFT is selected as the
next hop. In the absence of platforms within the transmission range of the
platform, the packets are buffered for a certain period of time until suitable
platforms appear. If no suitable platform is found, the packets are dropped.

SDN-enabled approach [246, 247]: By providing an overview of NSIN and
decoupling the control plan and data plan of the network, SDN can be deployed
to design efficient routing protocols. For example, the authors in [246]
proposed to deploy HAP-SDN controllers in NSIN and designed a traffic-
differentiated routing (TDR) protocol according to information provided by
these controllers. Specifically, in this protocol, HAP-SDN controllers were
deployed to acquire a comprehensive abstract network view, facilitate unified
scheduling of resources, and guide efficient data processing and delivery.
Supported by controller-collected network state information (e.g., positions
and speeds of UAVs, the number of successfully/unsuccessfully delivered
packets, packet queuing delay, and transmission delay), an estimation of the
availability of links in the near future was performed. The forwarding
abilities of UAVs as well as packet delay on transmission links were
predicted. Meanwhile, to address specific delay and reliability requirements
of traffic flows, this protocol assigned different weights to flows based on
their sensitivity to delay and level of importance. An optimization problem
aiming to minimize the total cost of all traffic flows was then formulated and
solved to update traffic routing paths. In SDN-enabled NSIN, the fast movement
of UAVs may lead to long configuration update times in the data plane. In
[247], the authors proposed the deployment of a HAP-SDN controller to assist
in the design of the routing protocol for NSIN. Due to its wide coverage and
relative stability, the configuration updating time could be reduced by HAP-
SDN. The main functionalities of the controller include: 1) topology
discovery, which involves perceiving and updating network topology in real-
time as airborne platforms are added or removed from the data plane. 2)
routing decision, which involves selecting the optimal routing path based on
traffic distributions and QoS requirements within the integrated network.

####  V-C2 Unified routing protocols

Most existing routing protocols for heterogeneous networks are designed using
a hierarchical approach, where different strategies are employed for various
sub-networks. However, in the case of NSIN with limited network resources,
such as computation, storage, and bandwidth, using multiple strategies can
result in increased network complexity and resource waste. A unified routing
strategy is essential to selecting an E2E path in NSIN for achieving uniform
deployment and centralized management. During the past few years, some
researchers have paid much attention to the design of unified routing
protocols for non-terrestrial heterogeneous networks. This type of routing
protocol includes:

Hybrid time-space Graph supporting Hierarchical Routing (HGHR) [248]: The work
in [248] designed a unified routing protocol, HGHR, for space and UAV
networks. Yet, HGHR can be deployed in NSIN. Similar to space networks, the
topology of HAP networks is predictable.

HGHR is designed based on a hybrid time-space graph containing two subgraphs,
i.e., a semi-deterministic subgraph for UAV networks and a deterministic
subgraph for space networks. In the semi-deterministic subgraph, a UAV
represents a node, and the weight of an edge is measured by UAV contact time
and contact probability. The prediction of UAV contact time and contact
probability is accomplished by utilizing a discrete time-homogeneous semi-
Markov process with the state transition probability and sojourn time
probability of each UAV as inputs. A deterministic subgraph for space networks
can be constructed due to the predictable topology of space networks. By
combining two subgraphs, a hybrid graph for the integrated networks is
obtained. Under the hybrid graph, HGHR explores the store-carry-forward
mechanism in a delay-tolerant networks (DTN) architecture to implement packet
forwarding.

Greedy routing protocol [249]: The work in [249] proposed a greedy routing
protocol for packet forwarding in NSIN-ground integrated networks. During the
process of finding routes, this protocol allocated a higher priority to TN to
fully utilize its computation resources and reduce resource utilization costs.
When the E2E latency could not be guaranteed solely by TN, platforms in NSIN
were considered to reduce the number of transmission hops. The protocol
employed a greedy search approach to find the optimal route between a source
node and a destination node, taking into account factors such as available
bandwidth resources, transmission latency, and the level of function sharing
requirements. Specifically, for any physical links $(n,m)$ in the integrated
networks, their link weight takes the following form

$Weigh{t_{q}}(n,m)=\frac{{{D_{n,m}}I\left({B_{n,m}^{R}-{B_{q}}}\right)}}{{\exp\left({\rho\left({{W_{n,q}}+{W_{m,q}}}\right)}\right)}}$
(11)

where $D_{n,m}$ denotes the delay of the link, $I(\cdot)$ is an indicator
function, $B_{n,m}^{R}$ the remaining bandwidth resources of the link after
serving the last service request, $B_{q}$ the bandwidth requirement of service
request $q$, $\rho$ is the aggregation factor, which is a non-negative
parameter controlling the aggregation ratio, and $W_{n,q}$ the function
sharing factor.

Next, the route with the minimal total weights was selected. Finally, the
protocol selected the most suitable nodes along the chosen route based on the
available computation resources and the level of function sharing required.

Recurrent neural network (RNN)-based routing protocols [250]: The authors in
[250] investigated the challenging issue of finding effective paths to deliver
packets across a variety of network segments, including space networks, NSIN,
and TN, using RNNs. Particularly, the long short-term memory network (LSTM)
was explored to predict routes based on historical data. However, the training
of neural networks is time-consuming. To address this problem, the researchers
investigated a coded technique that could alleviate the computational burden.
By employing a coded computation model and selecting suitable machine learning
network models, the training process could be accelerated through coding and
computation offloading [251].

Reinforcement Learning-based Routing [252]: Considering that TN and NTN had
different characteristics concerning bandwidth resources, energy resources,
and transmission delay, the authors in [252] proposed an integrated space-air-
ground routing protocol based on a RL approach. Specifically, the researchers
formulated a path selection optimization problem aimed at minimizing the
transmission latency while considering the constraints on the remaining
network energy and bandwidth. A Q-learning method was explored to solve this
problem, and a penalty mechanism was activated when a platform had little
residual energy and/or its bandwidth utilization was too high.

Ant Colony Optimization (ACO)-based routing protocol [253]: The authors in
[253] designed an ACO-based routing protocol for space-air-ground networks.
The routing protocol designed a routing metric by exploring information,
including link quality, E2E latency, and queue length, and consisted of three
phases: route search, link selection, and route maintenance.

In the phase of route search, a source node will broadcast forward ants to
find new routes toward a destination if a route cannot be found in its routing
table. Once the forward ants reach the destination, the destination will
dispatch backward ants to inform the source node. During the process of route
searching, ants will find multiple routes. In the phase of link selection,
nodes on established routes will randomly select an effective link to deliver
packets based on a link selection probability, which is computed by

${p_{ij}}(t)=\frac{{{{\left[{{\tau_{ij}}(t)}\right]}^{\alpha}}{{\left[{SN{R_{ij}}(t){e^{\Delta
SN{R_{ij}}(t)}}}\right]}^{\beta}}}}{{\sum\limits_{s\in{N_{i}}}{{{\left[{{\tau_{ij}}(t)}\right]}^{\alpha}}{{\left[{SN{R_{is}}(t){e^{\Delta
SN{R_{is}}(t)}}}\right]}^{\beta}}}}}$ (12)

where ${\tau_{ij}}(t)=f({D_{j}},{T_{ij}})$ denotes the pheromone concentration
left by ants across the link $(i,j)$ at time $t$, $D_{j}$ the queue length of
$j$, $T_{ij}$ the transmission latency between nodes $i$ and $j$,
$SNR_{ij}(t)$ the SNR that may be experienced by $j$ at time $t$, $\Delta
SNR_{ij}(t)$ the SNR variation at time $t$, $N_{i}$ the neighboring node set
of $i$, $\alpha$ the pheromone influencing factor, and $\beta$ the heuristic
information influencing factor.

During the routing maintenance phase, the source node regularly sends forward
ants to the destination to assess the route quality. If a failure link is
detected during monitoring, the forward ants will promptly return, and the
source node will update the routing table.

###  V-D Transport Layer

The efficient design of transport layer protocols is a crucial and hot
research topic in the research area of NSIN. Reliable transmission, congestion
control, and flow control are three main responsibilities of the transport
layer protocol of NSIN. Particularly, a transport layer protocol in NSIN must
be designed to ensure reliable transmission. Certainly, as different types of
applications may have diverse requirements for transmission reliability and
latency, transport layer protocols should support diverse reliability levels
for diverse vertical applications. Congestion control is a key function of the
transport layer protocol. A congested NSIN will result in a decrease in the
packet delivery ratio and an increase in the E2E transmission latency.
Therefore, efficient congestion control mechanisms should be designed for NSIN
to avoid data congestion. Besides, flow control is a crucial function of the
transport layer protocol to tackle the possible overload issue at a receiver.

TCP is the most widely used transport layer protocol; however, the application
of conventional TCP to NSIN faces many challenges: 1) TCP’s flow control
relies on a framing mechanism, and the size of the congestion control window
fluctuates continuously, making it unable to adapt to the changing network
topology. Additionally, accurately estimating the round-trip time (RTT)
remains a challenging issue. 2) TCP will suffer from significant performance
degradation mainly due to the frequent handover in NSIN; 3) besides, complex
network structures, inconsistent communication protocols, and incompatible
network equipment greatly degrade the performance of conventional TCP when
applying in NSIN.

As a result, during the past few years, researchers had attempted to improve
TCP or design new transmission control protocols following different design
routes. Generally, the existing transmission control protocols in NSIN can be
classified into three groups: improved TCP-based, scalable traffic control-
based, and resource optimization-based protocols. Improved TCP-based protocol:
To tackle the issues of applying a TCP in NSIN, researchers have made some
improvements by considering the distinctive features of NSIN, which makes it
more appropriate for NSIN applications.

For example, the authors in [254] proposed a new TCP for satellite-HAP
networks to support high-speed data transmission. Unlike the conventional TCP
congestion control mechanism that utilized the standard additive increase
multiplicative decrease approach to adjust the size of the congestion window,
they classified the network congestion status into congestion status and non-
congestion status. Based on different network congestion statuses, the authors
designed a more aggressive congestion window size increment or decrease
mechanism. The work in [255] developed a fountain code-based multipath
transport protocol for NSIN. The protocol utilized the fountain code to
improve the probability of successfully decoding packets. Considering the
possibility of establishing multiple independent paths via diverse network
interfaces onboard NSIN platforms, the authors proposed to deploy an
independent TCP congestion control mechanism for each path. Through
transmitting valid packets and ACKs via different paths, the congestion issue
caused by the link capacity asymmetry was well addressed. The improvement of
TCP for a HAP network was also investigated in [256]. In particular, the
improved TCP involved three new mechanisms: 1) a relay node detecting the
network congestion would send congestion messages to the source to achieve
rapid congestion notification; 2) distinguished the reasons that resulted in
the packet loss to avoid decreasing TCP throughput by mistake; 3) designed
different congestion window adjustment schemes to adapt to the types of
traffic.

Scalable traffic control-based protocol: By deploying programmable network
devices or using the SDN technique, this type of control approach aims to
improve the flexibility of traffic transmission control in complex and
heterogeneous NSIN.

To adapt to the transmission requirements of large-scale space-air-ground
integrated networks (SAGINs), the authors in [257] proposed a dynamic handover
software-defined transmission control that supported heterogeneous users over
SAGINs. Particularly, they explored the SDN technique to increase the data
forwarding efficiency and implement the network platforms’ versatility by
separating the control and switch functionalities of SAGINs. They also
utilized a queued game theory to model the SDSAGINs’ transmission control
problem and determined the transmission strategy by maximizing the social
welfare of the queued game theory.

The work in [258] designed a traffic control-empowered satellite-HAP network
architecture. In this architecture, the deployment of programmable satellite-
HAP network nodes allowed for the utilization of telemetry functions and
dynamic probes to gather network status information, such as link utilization
and network congestion, and monitor network events, including congestion
information and handover errors. Upon the arrival of a probe at a destination
node or when the transmission hops of the probe reached a limit, feedback
signals carrying local congestion information or other network statuses were
generated and transmitted back to the source node. These feedback signals were
then utilized to adjust network policies, such as sending rates or sub-network
switching.

Resource optimization-based protocol: The primary concept behind this type of
control protocol is to address congestion alleviation by formulating and
resolving a network resource optimization problem.

The work in [259] studied the congestion control issue in a HAP-reserved
SAGIN. Initially, the authors formulated an optimization problem for
transmission control with the objective of maximizing the overall throughput
of ground users while taking into account congestion control constraints.
These constraints included the transmission probability of the ground-air-
space link for a user and the number of users selecting the ground-air-space
links. Subsequently, they presented a transmission control strategy to assist
ground users in determining the probability of switching between a ground-air-
space link and a ground-space link.

The work in [260] investigated the applicability of NOMA in supporting the
NSIN to alleviate transmission congestion. In this work, a HAP acting as a
centralized decision maker was utilized to perform semi-persistent scheduling
and non-orthogonally allocate time-frequency resources, and massive UAVs were
deployed to execute power control in a distributed manner. Through solving the
joint scheduling, frequency resource allocation, and power control problem,
the congestion issue of data traffic in NSIN could be alleviated.

###  V-E Summary and Discussion

Beamforming is an important and attractive PHY layer research topic in the
research field of NSIN, which can significantly improve the coverage
efficiency of NSIN. In this section, we distinguish two important beamforming
techniques, i.e., active beamforming and passive beamforming. Existing
beamforming techniques in recent ten years for NSIN have been comprehensively
surveyed. The active beamforming technique includes three types of beamforming
architectures. It is now widely accepted that the selection of beamforming
architecture should take into account various factors such as applications,
hardware and software complexities, technologies, and so on. Nevertheless,
things are changing rapidly in this area. With advances in data conversion
technologies and an increased level of on-chip integration, the era of all-
digital beamforming is on the horizon. RIS/IRS, a typical and emerging passive
beamforming technique, have attracted widespread interest in both academia and
industry. We survey three types of MAC protocols for NSIN, i.e., fixed
allocation protocol, random contention protocol, and reservation protocol. As
HAP networks have quasi-stationary network topologies, fixed allocation
protocols (e.g., TDMA, CDMA, FDMA, SDMA, and NOMA) for HAP networks have been
extensively studied. Considering the highly dynamic network topologies,
researchers have designed random contention and reservation protocols for UAV
networks. Routing protocol design is a popular topic in the research area of
UAV networks, with various schemes proposed to ensure efficient packet
delivery. Nonetheless, existing UAV routing protocols may not be fully
applicable to NSIN. We explain some reasons in this section and present some
routing protocols designed separately for NSIN. Besides, NSIN have some
specific characteristics, such as high mobility, frequently-changed topology,
mechanical and aerodynamic constraints, and a harsh communication environment.
These characteristics will result in the performance degradation of
conventional transport layer protocols (e.g., TCP) when appling in NSIN. We
have comprehensively surveyed the endeavors in the design of transport layer
protocols for NSIN over the past ten years. In summary, the design of NSIN
protocols should holistically consider the characteristics (i.e.,
heterogeneity, cooperation, and platform constraints) of NSIN to achieve an
integrated design.

##  VI Open issues and promising directions

In this section, we present open issues and promising directions of NSIN
deserved for future study and discuss the corresponding challenges.

###  VI-A Generalized Channel model

Generalization capability is an important performance evaluation metric for
channel models. It is expected that the standardized NSIN channel models can
produce a generalized channel model framework with varying parameter sets
tailored to different scenarios. However, due to the heterogeneity of
scenarios and different scales over the wavelengths, it is highly challenging
to develop a general channel model for NSIN. The general channel model is
likely to have a complex, non-linear relationship between the channel model
parameters and the channel characteristics. These parameters encompass antenna
patterns, large-scale model parameters such as various types of path loss and
shadowing, as well as small-scale model parameters including multipath
amplitudes and delays and Doppler shifts. The channel characteristics comprise
the time autocorrelation function, spatial cross-correlation function, root
mean square (RMS) delay spread, RMS angle spread, Doppler power spectral
density, and stationary interval. Then, how to design a general channel model
capturing the model parameters and characteristics requires careful
investigations.

####  VI-A1 Dynamic networking of large-scale NSIN

We know that NSIN exist a multi-layered network architecture consisting of HAP
networks and UAV networks. The definition of NSIN does not constrain the
number of platforms in NSIN. Unlike NSIN, there are a large number of aerial
platforms in large-scale NSIN. Similar to NSIN, large-scale NSIN are highly
dynamic and heterogeneous. For large-scale NSIN, one of the most important
research topics is its dynamic networking. However, this research topic is
challenging due to the unique network characteristics in terms of network
scale, network topology, and network heterogeneity. For example, the dynamic
networking of large-scale NSIN needs to achieve efficient interference
management. Yet, both the heterogeneity in terms of platform capability and
deployment altitude and the large number of network platforms bring in
complexly overlapped coverage; as a result, the interference experienced by
network nodes or terminals served by NSIN becomes incredibly complex. To
achieve this aim, one is encouraged to model the impact of the heterogeneity
of network platforms on coverage and utilize stochastic geometric theory to
analyze the statistical average interference. Network reconfiguration is
another interesting and challenging research direction within the field of
dynamic networking for large-scale NSIN. The network topology of large-scale
NSIN is highly dynamic, with many network nodes joining (e.g., new nodes or
recharged old nodes) and leaving (e.g., due to malfunction or energy
exhaustion) the network continuously. To maintain network connectivity, the
exchange of a tremendous amount of data among large-scale network nodes and
the process of searching for the optimal routine may be triggered
continuously. Further, the impact of increasing or decreasing the number of
different types of network nodes will be quite diverse. The complexity of
network reconfiguration will increase when deploying HAPs with different
functionalities and mobility features. In this context, the following issues
should be investigated to achieve effective network reconfiguration for large-
scale NSIN: How can we integrate restricted heterogeneous network resources to
achieve the cooperation of large-scale network nodes? How can we establish a
representation and virtualization method for heterogeneous resources in large-
scale NSIN to enable network reconfiguration?

####  VI-A2 Situation-aware (or Situation acquisition)

Given the deployment of HAPs at altitudes ranging from 17 km to 22 km, NSIN
need to produce situations for a expansive and complex airspace. Investigating
and prodocing situations in such an airspace pose significant challenges with
respect to data overload, network congestion, and scene complexity. This is
because a large amount of data (especially videos, images, and radar data) in
the airspace will be sensed, stored, and analyzed, which poses a huge pressure
on improving the utilization of the limited network resources. To tackle this
issue, a natural question arises: how do we acquire an accurate network
situation without directly delivering and sharing the massive amount of raw
data in NSIN? In addition, the scenes in such a vast airspace are susceptible
to sudden changes, which creates significant challenges for accurate sensing.
Moreover, the issue of the explosion of network data, network scale, and
scenarios in NSIN places immense strain on the computing power of UAVs and
HAPs, making it challenging for them to achieve efficient processing within
limited timeframes. In this case, prioritizing data, fusing it, generating
local situational information, and deducing the global situation will be
crucial issues for in-depth research in the future to effectively handle the
challenges posed by a vast and complex airspace.

####  VI-A3 Blockchain-enabled NSIN

With the increasing requirement for providing services via NSIN, the
feasibility of the traditional centralized application service architecture of
NSIN is threatened. In a traditional centralized NSIN architecture, a single
application service data center controlled by a subject or a third-party
certification agency manages the credibility, security, and integrity of all
network entities, network behaviors, and network data. As a result, once the
data center or certification agency was attacked, the NSIN would become
unbelievable. To address this key issue, blockchain-enabled NSIN should be
investigated.

As a distributed architecture, blockchain ensures the credibility of data and
prevents tampering. However, the process of achieving consensus requires
exchanging transaction data and authentication messages, which consumes a
significant amount of network bandwidth and computing resources. This type of
resource consumption becomes even greater when deploying NSIN with a larger
scale. Therefore, it is important to research and develop approaches to
orchestrate and manage resources effectively in order to provide sufficient
storage, bandwidth, and computing resources for deploying blockchain-enable
NSIN.

####  VI-A4 Exploration of all frequency spectra

To fully unlock the performance benefits of NSIN, it is essential to explore
all frequency spectra, including sub-6 GHz, mmWave, Tera hertz (THz), and
optical frequency bands. While sub-6 GHz bands have been the primary working
frequency in TN and are indispensable in NSIN, mmWave communications show
promise in providing ultra-high capacity. Several open issues remain to be
tackled before applying mmWave communications in NSIN. Firstly, the primary
challenge posed by current mmWave systems is their design for small cells with
radii of a few hundred meters or less. However, the ground coverage radius can
extend to tens of kilometers or more in NSIN. This calls for a revolutionary
redesign of mmWave systems to cater to the requirements of NSIN. Secondly, a
flexible channel sounder design becomes imperative in this scenario. Channel
sounding data is essential for understanding near-space mmWave channel
characteristics. However, conducting channel measurement campaigns for NSIN
proves to be costly and challenging. Furthermore, diverse NSIN scenarios need
to utilize different channel sounders [2]. Therefore, flexible channel
sounders are necessary to support sufficiently high measurement speeds and
multiple measurement scenarios [2]. In addition, THz communications are
envisioned to achieve high transmission rates ranging from hundreds of Gbps to
several Tbps. The practical implementation of THz communications in NSIN is
attractive and challenging. For instance, the inhomogeneous atmospheric
propagation medium in the near-space environment results in the undesirable
refraction effect, making it difficult to achieve THz beam alignment for
ultra-long distance THz propagation links. In addition, significant research
effort is needed to develop high-performance THz devices and packaging
technologies to make THz communications commercially viable for NSIN. The
optical communication system offers sufficient unlicensed bandwidth for
robust, fast, safe, and efficient transmission, with optical bandwidth being
three orders of magnitude larger than the spectrum resources available in the
RF bands [2]. Owing to the unique deployment environment, HAP networks
adopting optical communication techniques will be immune to atmospheric
turbulence and absorption effects. However, the limited transmit power under
sky radiance and background shot noise significantly affects the practical
application of optical communication techniques in NSIN. Advanced transceiver
design should be considered to tackle the above issue. Moreover, in order to
address the challenges of implementing optical communications in a near-space
environment, it is crucial to conduct comprehensive studies on issues like the
analysis of beam wandering and pointing errors and removing their adverse
effects.

####  VI-A5 AI/ML in NSIN

In NSIN, massive amounts of data and complex information in the large
temporal, spatial, and network domains will be produced constantly. Accurate
analysis and effective utilization of this data and information are essential
for the successful operation and control of NSIN. The data-driven
characteristic is one of the core advantages of AI and ML methods, and they
can achieve superior performance in the aspects of processing and analyzing
data. Thus, the development of AI- or ML-assisted solutions for NSIN is
indispensable to achieve flexible and scalable network management and improve
the overall network performance [2, 9]. Particularly, AI methods can
effectively address diverse types of problems, including sensing,
communication, computing, prediction, and reasoning, and can achieve solution
performance close to the optimum. Actually, the research direction of
developing AI methods to solve various types of problems has attracted
numerous attentions from both academia and industry in the past few years. AI
and ML techniques empower NSIN to possess self-awareness and the ability to
dynamically adapt to time-varying service requests through smart networking.
Besides, owing to their powerful capabilities, AI and ML methods are
applicable for the PHY layer and upper layers of NSIN, which will result in a
network with a very high level of intelligence. While many AI- or ML-based
solutions have been developed and offered useful insights for creating
intelligent NSIN, it will require painstaking efforts from both academia and
industry to meet the technical challenges towards achieving this goal.

####  VI-A6 Vertical integration network design

Space networks, NSIN, and TN have advantages and disadvantages in terms of
deployment and operation costs, persistence, responsiveness, vulnerability,
and footprint. Thus, the vertical integration of NSIN with space networks
(e.g., low-earth orbit satellite networks) and TN is widely recognized as a
crucial regime for providing superior services, such as ubiquitous coverage,
reliable, and low-latency connectivity on earth. To achieve the above
ambitious goal, the study on many important topics in this field is
imperative; for example, how to enable the real-time operation of integrated
networks? This may require an investigation of network edge intelligence and
computing. Edge intelligence and computing enable the possibility of locally
training sub-models and sharing the trained models rather than sharing the
massive amount of raw data in the integrated networks, which can significantly
improve the utilization of network resources and reduce E2E latency [7]. How
to manage the heterogeneous network efficiently? The heterogeneity of the
integrated networks lies in such aspects as protocols, devices, interfaces,
and functionality. Then, some new techniques are required to address the
challenging management problem of heterogeneous networks, including mobile
node management, cooperative transport control, hybrid control architecture
design, and hierarchical control architecture design [2]. Reliability, delay,
capacity, and coverage analysis in a multi-connectivity-enabled network
deserve to be tackled. The research on advanced and massive random access and
beamforming techniques in heterogeneous space-air networks for superior
service provision is also appealing. Besides, how can different network
segments cooperate to support seamless network access and enhance service
provision? Anyway, many crucial technological breakthroughs are needed before
the integrated network can contribute to future non-terrestrial wireless
communication standards.

##  VII Conclusion

This article highlighted the characteristics and potential of near-space
information networks (NSIN). With the potential to achieve sensing, seamless
communications, and ubiquitous computing in a wide area, NSIN had emerged as
an essential component of future wireless communication networks. However, the
specific characteristics of NSIN make the existing technologies designed for
terrestrial networks (TN), unmanned aerial vehicle (UAV) networks, and
satellite networks not completely suitable for NSIN. This article
comprehensively surveyed the latest advancements in the channel modeling,
networking, and transmission aspects of NSIN. A wide range of research topics
in these three aspects were discussed from a forward-looking, comparative, and
technological evolutionary perspective. The impact of unstable airborne
platforms on the phase delays of diverse types of onboard antenna arrays was
discussed. The recent advancements in high-altitude platform (HAP) channel
modeling were provided, and the differences between HAP channel modeling and
UAV channel modeling were revealed. Along with the integration of promising
passive beamforming, softwarized, and machine learning (ML) or artificial
intelligence (AI) techniques, a comprehensive review of the networking
strategies of NSIN in network deployment, handoff management, and network
management aspects was provided. Besides, the promising technologies and
communication protocols of the physical layer, medium access control (MAC)
layer, network layer, and transport layer of NSIN for achieving efficient
transmission over NSIN were overviewed. Overall, the goal of this article is
to characterize NSIN, present prospective use-cases of NSIN in the near future
and beyond, and disseminate the significant contributions in the crucial
research area of NSIN. By presenting a comprehensive overview, we expect that
more attention from the research community could be paid to this emerging and
promising research field.

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