Ultra-Dense Networks Taxonomy

Version	Summary	Created by	Modification	Content Size	Created at	Operation
1		Zlatka Valkova-Jarvis	--	2708	2023-01-30 09:41:54	\|
2	Reference format revised.	Lindsay Dong	+ 4 word(s)	2712	2023-01-31 10:04:21	\|

This entry is adapted from the peer-reviewed paper 10.3390/sym15010002

Ultra-Dense Network (UDN) is a network with a spatial density of access points (APs) (or base stations) identical to or larger than the number of active end devices—EDs (user equipments (UEs) or physical devices (PDs)). UDNs can be seen as a network paradigm which can be implemented in the context of various kinds of wireless networks, such as sensor/IoT (Internet of Things) networks, mobile networks, aerial networks, and even satellite networks.

ultra-dense networks UDN UUDN

1. Network Connectivity

1.1. Network-Centric Connectivity

Heterogeneous Ultra-dense networks (HUDNs) are the most important example of the realization of a network-centric connectivity concept (Figure 1). HUDNs are made up of a variety of access technologies, each with its own set of capabilities and limitations. This allows for efficient spectrum reuse over the region of interest, which is one of the major approaches to boosting the capacity of next-generation wireless networks ^[1]^[2]^[3]. Generally, there are three types of cells in an HUDN: (a) fully functional, high-power macrocells (legacy cells); (b) fully functional small cells (picocells and femtocells) which can perform the functions of macrocells, in a smaller coverage area and with low power; and (c) macro extension APs, for example remote radio heads (RRHs) and relays.

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Figure 1. Network-centric connectivity architecture.

Macrocells are composed of exterior eNodeBs (eNBs) that are strategically positioned by the operator to provide open public access and cover a large area, usually several kilometres. On the other hand, picocells are low-power fully functioning eNBs, usually distributed by a provider following a particular plan, both outdoors and indoors. Picocells share the same backhaul and access features as macrocells, allowing for low latency and high bandwidth. Indoors, femtocells are commonly used (in homes, offices, and meeting rooms, etc.). They are ad hoc low-power access points with a typical transmit power of 100 mW or less. A femtocell can function in one of three modes: open, closed, or hybrid, depending on the access it has. In the same way as with macro extension access points, relays are access points installed by the operator and used to cover dead zones and low coverage regions in macrocells. The data of the users are sent back and forth between the macro cell and the relays and in this case the latter are extensions of a macro eNB rather than fully functional APs. Finally, the RRHs are light-weight RF units installed outside macrocells to allow the core eNBs to cover a larger area. There is no baseband unit (BBU) on the RRH. High-speed fibre or millimetre waves are used to link RRHs to the Macro eNB (MeNB) or BBU pool. All signal processing is handled by central eNBs or BBU pools. As a result, instead of distributed densification, RRHs are used to provide centralized densification. RRHs can be simple and inexpensive to manage.

Unmanned aerial vehicles (UAVs) are another component that may be included in HUDNs. UAVs are predicted to be a major element of the next generation wireless networks, allowing for wireless broadcast and high-speed transfers. In comparison to fixed infrastructure communications, UAVs offer flexibility of deployment, robust line-of-sight (LoS) connection links, and extra levels of design freedom, due to their controlled mobility. UAVs may be used as airborne communications platforms (i.e., as flying base stations/mobile relays) by equipping them with communication transceivers, in order to supply, or improve, communication services to ground-based targets in areas of high traffic demand or overload situations, a practice known as UAV-assisted communications ^[4]^[5].

Another essential component that may be included in HUDNs are satellite networks, particularly LEO (Low Earth Orbit) satellites. These are regarded as APs in HUDNs and have altitudes ranging from 500 km to 2000 km, together with orbital plane inclination angles ranging from 0 to 180 degrees (prograde and retrograde orbits). The ultra-dense LEO Satellite access networks (UD-LEO-SatNets) are typically characterized by the capability to deliver services in isolated and remote, or underserved, locations (e.g., suburban and rural areas). In addition, they can improve the performance of limited terrestrial networks in a cost-effective manner. Finally, UD-LEO-SatNets ensure service availability and reliability in any location, especially when it concerns critical communication systems, as well as railway, shipping, or aircraft requirements ^[6]^[7].

LED arrays can be used for APs for indoor users, providing another way to connect to the HUDN ^[8], as Visible Light Communication (VLC) technology provides an effective connection solution for UDNs due to its LoS propagation properties. With VLC, communication and illumination are available at the same time by regulating the intensity of light emitting diodes (LED) ^[9]. Furthermore, visible light cannot pass through barriers, resulting in a secure communication environment ^[10].

In heterogeneous ultra-dense networks with network-centric connectivity, interference and traffic imbalances impede the improvement of system performance. Network collaboration has developed into a promising paradigm, incorporating complex strategies capable of greatly improving performance. In this regard, Ref. ^[11] characterizes the cooperative behaviour of small cells by establishing a coalition game-theory framework and then investigating the advantages of cooperation and the gains due to diversity. Ref. ^[12] investigated 5G HUDNs and provided a system architecture comprising core networks and virtualized integrated ground–air–space radio access networks. Reference ^[13] is a significant work that focuses on interference management for UDNs using dynamic resource allocation. The downlink precoding investigated in ^[14] overcomes the issue of co- and cross-layer interference of macro users (MUEs) and FUEs and offers a theoretical framework for the deployment of UDNs and heterogeneous networks. As a result of the random deployment of small cells, the mobility of user equipment, and the preference of small cells during selection/reselection, the load across the small cells is unevenly distributed. To solve this issue, the scholars of ^[15] developed a Radio over fibre-based load balancing method within UDN hotspots. Finally, in ^[16], a joint strategy of SBSs sleep and spectrum allocation is presented, to address the problem of massive power consumption and spectrum resource tension in heterogeneous UDNs.

To summarize, network-centric HUDNs bring the following new issues for 5G and beyond due to heterogeneity and densification:

Interference between multiple network tiers is exacerbated by network heterogeneity and densification. To counter this, intelligent methods and algorithms for interference cancellation, mitigation, and management must be designed and implemented.
Network heterogeneity makes network management and planning more challenging, especially with the emergence of airborne BSs like UAVs and satellites.
The management of connectivity is also complicated by network heterogeneity. Smart connectivity should be considered, so as to intelligently release redundant connections and ensure reliable wireless communications and service continuity.
The preference of macrocells during selection/reselection, the mobility of users, and the random deployment of small cells, all lead to unevenly distributed traffic load across the small cells.
The complexity and overhead of network coordination inevitably increase when network densification takes place. In view of this, achieving optimum network coordination and intelligent cooperative BS clustering is of great importance.
High-frequency bands, such as millimetre-wave and THz bands, will be used to meet the massive spectrum requirements for 5G, and particularly 6G. Hence, the creation of innovative network coordination for multi-RAT multiband HUDNs is of great importance.
Clearly, continuous network densification and heterogeneity bring issues for mobility management, particularly when considering network-centric UDNs. Although fixed indoor users handle a significant amount of data, cellular networks’ ability for mobility and always-on connection are perhaps their most essential characteristics when compared to Wi-Fi. Thus, the implementation of user-centric ultra-dense networks (UUDN), enabling the smooth transition from a network-centric to a totally user-centric connectivity concept without the need for handover, thanks to its cell-free nature, is of great importance in terms of mobility.

1.2. User-Centric Connectivity

The distribution of APs in a UDN is particularly complicated because of the dense deployment, together with a possible significant overlap in coverage. As a result, the traditional network-centric (cell-based) connectivity approach will result in difficulties with resource management, severe inter-cell interference, and significant signalling overhead. Furthermore, due to the inconsistent design of the cells, some users will be located in an overlapping region, causing significant interference, while others may be located at the cell border or in an area without service. Such scenarios will have a significant impact on the overall quality of service.

A paradigm-shifting user-centric UDN is proposed in ^[17] to better leverage the potential capability of UDNs. UUDNs use the de-cellular technique to move the design concept from a cell-centric to a user-centric paradigm which defines an evolution in the UDN concept ^[18]^[19]. Each base station in a UUDN becomes an access point, and the network creates an AP group (APG) for each user to ensure reliable access and data transfer (Figure 2). For this reason, the user-centric connectivity concept can be seen as a cell-less structure where the traditional cell unit is replaced by access point groups. In addition, each of the APs which were considered for HUDN deployment could be used in the context of UUDN.

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Figure 2. User-centric connectivity architecture.

Several important features of UUDNs can be formulated. Primarily, the UUDN network is capable of recognizing the specific capabilities, requirements and radio environment of the end devices automatically. Moreover, APGs are formed and dynamically updated according to the end devices’ requirements and geographical position. In this context, all APs are able to share data and work together in order to enhance energy/spectrum efficiency (EE/SE) and user experience. Finally, network authentication procedures are conducted to ensure high security and privacy levels ^[17].

Clustering and grouping

In essence, UUDN is a cell-free wireless network with an AP density that is equivalent to the user density. To ensure stable connectivity for mobile users and an extremely high level of area throughput, dynamic AP grouping is regarded as a fundamental requirement for these networks. Every user in a UUDN is assigned to a unique dynamic APG and, in theory, is served by a number of APs allocated entirely to that user. As users move, their APG dynamically change to facilitate their movement while preserving connectivity. The APG members’ creation and rearrangement will adjust to their needs and their desired services. To this end, the network must first intelligently detect users’ wireless communications environments before smoothly organizing the requisite APG and resources to enable extreme, user-centric, spatial resource reuse.

The topic of intelligent AP clustering and grouping in UUDNs has been gaining attention over the last few years. Ref. ^[20] is a tutorial on user-centric clustering in general, focussing on compelling solutions. A user mobility management strategy for UDNs with UCWA based on dynamic AP grouping is presented in ^[21]^[22]. A user-centric dynamic AP grouping (DAPGing) approach in which each UE can design its own APG based on local measurements alone and regardless of other UE’s decisions is discussed in ^[23]. Ref. ^[24] proposes a maximum data transmission rate oriented Dynamic AP Grouping (MDTR-DAPGing) system. Meanwhile, Refs. ^[25]^[26] study a technique in which collaborating APs offer access services for users in a NOMA-based UUDN, with the goal of improving system EE performance. Finally, a unique modularity-based user-centric (MUC) clustering approach for UDNs that relies on a co-designed resource allocation strategy to maximize RB utilization is presented in ^[27].

Security and privacy

In UUDNs the APs will be located extremely close to the users, depending on the network architecture, and the users will also be able to install the APs. As a result, ensuring a secure wireless transmission environment is challenging. An attacker could acquire, or duplicate, the APs’ digital certificate and the sensitive data stored in them. Following this, the counterfeit APs could illicitly access the UUDN, potentially compromising its security. In addition, the APG’s dynamic transformation will pose other security risks to the UUDN. The APs attached to the network will communicate with each other, rendering self-healing, self-optimization, and self-configuration difficult.

There are several publications looking at security and privacy optimization in UUDNs. Ref. ^[28] examines secure UDNs in relation to their user-centric clustering from a secrecy and energy efficiency standpoint. Ref. ^[29] summarizes the security aspects of UUDNs’ architecture, while also discussing the problems and needs associated with the UUDN’s security concerns. Finally, a novel lightweight batch authentication and key agreement (LBAKA) strategy for user-centric UDN scenarios, which also utilizes mutual authentication and one-to-one key agreement to check the communication’s trustworthiness on both sides is proposed in ^[30].

2. Network Access

2.1. Massive MIMO-Based Access

Massive MIMO-capable BSs will contain many hundreds, or even thousands, of antennae. This may be regarded as one means of spatially densifying the network, as projected by 5G use cases. Massive MIMO (alternatively called large-scale antennae systems, very-large MIMO, or HyperMIMO) denotes systems which employ substantial numbers of service antennae across active terminals, and function in the time-division duplex mode using more antennae focussing energy into ever-smaller sectors of space ^[31]. A larger number of users is able to be supplied by any given resource unit of a particular BS, resulting in significant benefits. Massive MIMO, in comparison to standard MIMO, can deliver ultra-high reliability, enhanced throughput and radiated EE, resilience to deliberate interference, together with decreased latency, by utilising less sophisticated signal processing algorithms with low-cost and low-power components ^[32].

2.2. High Frequency-Based Access

Since many of the present-day wireless communications systems rely on spectrum scarcity in the 300 MHz to 3 GHz frequency range, mmWave communication is seen as the better option. The 6 to 100 GHz frequency band may give orders of magnitude more spectrum than existing cellular spectrum allocations, allowing for the use of beamforming and spatial multiplexing with a large number of antennas ^[33]. Thanks to directional antennas, the networks should have more bandwidth, greater isolation, and better coexistence ^[34].

2.3. Flexible RANs

Cloud Radio Access Network (CRAN) comprises an architecture which incorporates cloud technology with cellular systems’ radio access networks. Baseband processing operations in CRAN are performed in a centralised BBU pool or central cloud ^[35]. As a result, the base stations are simplified to basic Radio Remote Heads (RRHs). Fronthaul lines connect the multiple RRHs to the central cloud. The transport network provides communication between the central cloud and the network’s core. The RRH and central cloud connection is specified as optical fibre fronthaul in CRAN’s initial proposal.

CRAN offers a number of benefits, ranging from simpler BSs to cloud-based processing. In the same way as today’s IT cloud computing, cloud operation leads to more resource effective exploitation of available resources. CRAN also allows for the separation of processing and transmission, permitting the use of data plane cooperation methods such as CoMP ^[35]. A novel attractive network design, termed ultra-dense CRAN, is developed via the dense deployment of the available RRHs in CRANs. Because of the centralization of resource allocation and collaborative signal processing across the RRHs, ultra-dense CRANs may considerably enhance not only spectrum efficiency (SE) but also energy efficiency in comparison to standard cellular networks.

The benefits of fog computing are proposed to be included in ultra-dense Fog RANs (FRAN) to reduce the constraints of CRAN, which include capacity restricted fronthaul, latency, and high load at the central cloud. In ultra-dense FRAN, a massive number of edge devices, such as RRHs and UEs may be employed for local signal processing, cooperative radio resource management, and content storage, as well as to the central cloud of CRAN.

2.4. Machine-Type Communication-Based Access

Massive device connectivity has become one of the hurdles for the IoT to overcome so as to facilitate the growing use of billions of smart gadgets. Meanwhile, a broad variety of high efficiency communication infrastructures, including human-to-human (H2H), human-to-machine (H2M), machine-to-human (M2H), and machine-to-machine (M2M) communications, should be included to deliver ubiquitous IoT services ^[36]. Simultaneously, future use cases will include smart buildings, smart agriculture, industrial automation, and auto-drive robot interaction, with the network expanding to a large IoT ecosystem. With the help of several new technologies and techniques, such as artificial intelligence (AI), cloud computing, and information sensing ^[37], it is expected that massive IoT will become a worldwide network of interconnected systems, enabling a wide range of data collection, exchange, and decisions, while also making measurement and management more efficient ^[37].

With the rise of smartphones and tablets, proximity-based communication that can handle the flow of data has gained a lot of traction. To allow proximity-based communication, D2D-enabled users may communicate data directly without passing via BSs or the main network. A new network structure known as an ultra-dense D2D network has come about as a result of the large number of D2D-enabled users, with consumers benefitting from improved SE and EE, and reductions in communication time, network load, and power consumption ^[38].

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Viktor Stoynov

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Zlatka Valkova-Jarvis

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