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.
[51][28] examines secure UDNs in relation to their user-centric clustering from a secrecy and energy efficiency standpoint. Ref.
[52][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
[53][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
[54][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
[55][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
[56][33]. Thanks to directional antennas, the networks should have more bandwidth, greater isolation, and better coexistence
[57][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
[59][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
[59][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
[20][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
[21][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
[21][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
[22][38].