A Survey on Architectural Approaches for 6G Networks: Implementation Challenges, Current Trends, and Future Directions

Abstract

As the discussions on sixth-generation (6G) wireless networks progress at a rapid pace, various approaches have emerged over the last few years regarding new architectural concepts that can support the 6G vision. Therefore, the goal of this work is to highlight the most important technological efforts in relation to the definition of a 6G architectural concept. To this end, the primary challenges are first described, which can be viewed as the driving forces for the 6G architectural standardization. Afterwards, novel technological approaches are discussed to support the 6G concept, such as the introduction of artificial intelligence and machine learning for resource optimization and threat mitigation, cell-free deployments, and novel physical layer techniques to leverage high data rates. In the same context, open-access protocols for flexible resource integration, security, and privacy protection in the 6G era, as well as the digital twin concept, are discussed as well. Finally, recent research efforts are analyzed, with an emphasis on the combination of the aforementioned aspects towards a unified 6G architectural approach. To this end, limitations and open issues are highlighted as well.

1. Introduction

The introduction of fifth-generation (5G) networks has made the deployment of new services and applications feasible. This is achieved via the support of highly demanding features such as ultra-reliable low-latency communications (URLLC), enhanced mobile broadband (eMBB), and massive machine-type communications (mMTC) [1,2,3]. In this context, various novel technologies are integrated both in the physical and in the network layer, including millimeter wave (mmWave) transmission, massive multiple input multiple output (mMIMO) deployments, and non-orthogonal multiple access (NOMA), as well as network function virtualization (NFV) and software-defined networking (SDN). mmWave transmission leverages improved data rates over higher-frequency bands [4], mMIMO systems increase spatial isolation among mobile stations (MSs) and provide flexible interference management [5], and NOMA improves overall spectral efficiency [6]. Finally, NFV and SDN contribute to the decoupling of application deployment from hardware-specific devices [7,8]. In parallel, novel architectural approaches have been introduced as well, such as the service-based architecture (SBA), which leverages application deployment from various resources [9], and ultra-dense networking [10]. Hence, a variety of new applications can now be supported, including augmented and virtual reality (AR/VR), remote health care, and advanced fleet management in industrial environments [11,12].

However, the need for advanced services and applications has led to ongoing discussions for sixth-generation (6G) networks [13,14,15]. This transition is also dictated by additional advancements in other adjacent fields, such as the Internet of Things (IoT) and the support of advanced applications based on wireless sensor nodes [16]. Hence, there is the need to define a new network paradigm able to interconnect a vast number of heterogeneous resources and protocols and at the same time to effectively manage the huge volume of data generated by IoT devices. Although there is not a concrete standardization yet, the 6G concept will be based both on a number of key-enabling technologies and on architectural concepts. These include distributed mMIMO systems and intelligent reflecting surfaces (IRSs) that can further leverage high transmission rates and improve resilience over physical-layer attacks [17]; software and hardware decoupling during application deployment and execution, along with open access protocols [18]; intent-based networking (IBN) [19]; and the evolution of the SBA in 6G networks [20]. The ultimate goal of 6G infrastructures will be to provide ubiquitous coverage in a wide geographic range, and at the same time support multiple demanding applications in terms of both throughput and latency. Hence, there is a need for a holistic transformation of the underlying architectural design to support more advanced and decentralized applications, such as holoportation and autonomous driving [21].

To this end, various approaches have been studied over the last few years, such as cell-free (CF) systems [22], open radio access networks (O-RANs) [23], the advancement of SBA to an organic concept [24], and decentralized and distributed machine learning (ML) deployments [25,26]. At the same time, 6G networks should be reconfigurable, able to recover from unwanted situations, and self-optimized. Therefore, various challenges should now be dealt with, such as (a) the ability to collect a vast amount of data from heterogeneous networks and environments in a secure and privacy-preserving way, (b) the ability to effectively train ML models for a variety of use cases and scenarios, (c) dynamic network reconfiguration, and (d) deployment of services and applications according to user needs by properly optimizing related resources. Finally, since 6G networks will rely on the integration of heterogeneous resources, an increased number of potential attacks might now take place [27]. Hence, advanced security protocols and threat mitigation techniques are also required.

The goal of this work is to summarize all recent developments regarding the design of an architectural framework for 6G networks. In this context, the most important challenges towards the 6G vision are described, as well as the basic concepts of the most important functionalities among different layers. In the same perspective, several recent works are presented as well, while a discussion takes place that summarizes all key outcomes of this work.

The rest of this work is organized as follows: In Section 1.1 of the introductory part, related survey papers are discussed, along with their key contributions. The contributions of our work are mainly highlighted in Section 1.2. In Section 2, the most important key 6G-enabling technologies are presented. These include decentralized and distributed ML for resource optimization and threat mitigation, cell-free implementations, advanced physical-layer protocols, interconnection of a vast number of heterogeneous devices with open access protocols, and blockchain technology and digital twins (DTs).

In Section 3, various recent architectural approaches are highlighted and discussed with respect to the aforementioned key pillars. An overall discussion takes place in Section 4, while concluding remarks are highlighted in Section 5. For illustration purposes, the paper structure is also depicted in Figure 1.

1.1. Related Surveys

In this subsection, selected recent survey papers are analyzed in the context of 6G networks. To this end, the work in [28] focuses on cloud RAN (CRAN), which is considered one of the major concepts in 5G/6G wireless networks since it decouples RAN software execution from specific hardware deployments. In this context, CRAN approaches can leverage efficient and dynamic network configurations, improve latency and resource allocation, and support the connectivity of a mass number of devices, since scaling can be performed on demand. When it comes to the 6G concept, resource optimization can be a highly demanding task due to the number of devices involved and the amount of data exchanged. Therefore, the article also highlights the importance of AI/ML algorithms towards effective resource management.

The work in [29] analyzes all key aspects of machine-to-machine (M2M) communications in 6G networks. To this end, various issues are discussed, such as secure communications and power management of the devices involved, scalability, and interoperability of the connected devices. The work in [30] is focused on terahertz (THz) communications and sensing for 6G networks. Integrated sensing and communication (ISAC), also referred to as joint communication and sensing (JCAS), is a technology candidate with promising potential. ISAC integrates sensing and spatial location of passive (not connected) objects into the mobile communication network, expanding the network’s functionality beyond just communication. Hence, advanced applications can be supported, such as robotics fleet management, AR/VR, and autonomous driving.

In [31], all cutting-edge technologies in the framework of 6G networks are presented, such as THz communications, ultra-massive MIMO, artificial intelligence (AI), ML, quantum communication, and reconfigurable intelligent surfaces (RISs). Emphasis is given to AI/ML technologies and their integration in 6G networks. In the same context, potential use cases are presented as well. Finally, the work in [32] summarizes all key aspects of AI/ML deployments in future 6G networks. The key outcomes of each work are also highlighted in

Table 1. Indicative related survey papers on 6G networks.

Related Work	Year	Topic	Key Contributions
[28]	2024	CRAN	Resource management in CRAN Machine learning in 6G
[29]	2024	M2M communications in the 6G era	Security and privacy concerns Integration with edge computing
[30]	2024	Joint sensing and communication in the 6G era	Transceiver requirements Antenna design and beamforming for high path loss compensation
[31]	2024	Key enabling technologies for 6G networks Potential of AI/ML	The role of AI in 6G networks Critical challenges in the deployment of 6G
[32]	2024	AI-enabled 6G networks	Architecture of endogenous networks Applications of AI in intelligence-endogenous 6G networks
Our work	-	Current trends in the architectural design of 6G networks	State-of-the-art approaches to the architectural design of 6G networks A high-level approach to 6G network architecture

.

1.2. Contributions

Our work mainly focuses on the architectural design aspects of future 6G networks via the integration of several key enabling technologies. Emphasis is given to the interconnection of these technologies in the considered architectural layers, as well as in certain limitations and drawbacks. Hence, unlike other works in the literature, our goal is to report all recent developments towards a unified 6G framework. In the same context, a high-level architectural approach is presented as well. In particular, the main novel points of our work are listed below:

• Integration of various technologies and key factors towards 6G architectural design. In the same context, potential limitations for each case are highlighted.
• Discussion of the current trends of 6G architectural design based on the presented works. To this end, the most important driving factors are also highlighted, such as AI/ML.
• Presentation of a high-level approach of the 6G concept that integrates secure data collection, flexible network deployment based on O-RAN specifications, privacy preserving decentralized machine learning, and digital twins and secure service provisioning. To this end, the synergy among well-established 5G network functions (NFs) is highlighted, such as the network data analytics function (NWDAF).
• Identification of limitations that should also be considered in the design and deployment of 6G networks.

2. 6G Key Enabling Technologies

In this section, the most important 6G-enabling technologies are presented and discussed. These include AI/ML, cell-free architectural approaches, advanced physical-layer technologies, the O-RAN concept, blockchain technology, and DTs.

2.1. AI/ML

AI/ML approaches are already in use in a wide range of potential applications in 5G networks [33]. To this end, data collected directly from various network entities are used to train appropriate ML models in three ways: (a) supervised learning, where model training takes place with the help of a labeled data set, (b) unsupervised learning, where the goal is to extract data patterns, and (c) reinforcement learning (RL). The latter case is based on the existence of a mobile agent that interacts with the environment under consideration and assigns certain rewards or penalties for specific actions. In most use cases, however, the set of potential actions–results can be quite large, and look-up tables can be hardware-consuming. In this case, training with the help of neural networks (NNs) is favored, a concept that is also known as deep reinforcement learning (DRL).

In the context of 6G networks, AI/ML can contribute to various operations, such as security and privacy protection via the extraction of abnormal patterns [27], as well as in network reconfiguration and optimization when needed [34]. However, the development of 6G networks brings forward several research gaps that need to be addressed to fully leverage AI-enabled technologies [35]. Since 6G networks are based on the integration of various heterogeneous networks and devices, radio resource management (RRM) can be a highly complex task due to the multiple associated parameters. Hence, a large amount of real network data is required for fast decision-making. In the same context, privacy concerns when data exchange takes place in ML model training is another issue of debate. Finally, high resilience, scalability, and flexibility in learning frameworks are essential for sustaining an infinite number of interacting entities and providing high-quality services in actual dynamic 6G networks.

As previously mentioned, since 6G networks are expected to integrate and process a large amount of data from heterogeneous resources, data collection and processing in a single node might significantly increase computational burden and training times. Moreover, such an approach would be prohibitive, since it results in a single point of failure. Therefore, over the last few years, the concept of federated learning (FL) [36] has emerged as an alternate approach that can leverage execution times and relax computational burden. To this end, several participating nodes locally train an ML model based on the data collected from their surrounding environment. Afterwards, ML model parameters, such as weights in the case of NN training, are sent to a master ML node for aggregation and updating. The master node, after processing and aggregating all parameters, sends the updated weights to the participating nodes. Therefore, on one hand, the computational burden is divided among the participating nodes, and on the other hand, no sensitive data are transmitted. Hence, privacy-sensitive applications such as e-health can now be deployed more easily. An overview of FL is depicted in Figure 2. To this end, a three-layered approach is considered that includes training nodes, multi-access edge computing (MEC) servers, and cloud servers. Therefore, model parameters are locally generated and then updated at the MEC level. Afterwards, master model aggregation in the cloud domain takes place as well. This approach can be highly beneficial when fast training times are required, since MEC servers are located close to the participating training nodes. Consequently, model updates based on MEC processing generally undergo a short round-trip time.

However, there are certain challenges related to the proper deployment of FL in next-generation networks. First, although, as previously mentioned, transmission of sensitive information does not take place, it is still possible to reveal such information through the parameters that are exchanged during the learning process. Moreover, since training nodes can be highly diverse in nature, data heterogeneity might severely affect the final aggregation of the master model [37]. Additionally, as the number of clients participating in FL grows, the communications overhead between nodes starts to significantly exceed the computational overhead required to train a model. Finally, since training nodes might be geographically dispersed over large areas, response times can be highly unequal due to harsh propagation conditions. Therefore, improving efficiency in communications becomes another major challenge.

2.2. Cell-Free Architectural Approaches

In conventional wireless networks, the traditional concept of base station (BS) MS has been adopted in the vast majority of network deployments. Ιn this case, the cellular topology is divided into a finite number of BSs, where each BS can cover a certain geographical area according to its technical specifications. However, the interconnection of IoT devices on one hand, and the need for ubiquitous connectivity with ultra-high data rates and low latency on the other, necessitate more flexible approaches regarding connectivity and resource allocation. Therefore, over the last decade, the concept of CF networks has emerged. To this end, various access points (APs) are deployed in dense geographical areas, and an MS can be served by multiple APs. Several APs are connected to a common central processing unit (CPU), which manages connectivity issues. Hence, the handover and associated signaling burden can then be reduced. However, fronthaul capacity can be severely affected in cases where each AP simultaneously serves all MSs under consideration. In practical CF deployments, there can be multiple CPUs, where each one serves a particular subset of APs to equally divide the computational burden. Moreover, MSs are not simultaneously served by all APs but rather only from the ones with good signal reception, which also minimizes interference and hardware complexity [22].

In general, the concept of CF systems is also combined with mMIMO technology. To this end, distributed MIMO (dMIMO) antennas are located per AP, which can provide connectivity in harsh propagation conditions. CF-mMIMO can be considered as a potential physical-layer technology for future wireless networks since it can benefit from all the advantages of distributed antenna systems (DASs) and network MIMOs, such as macro-diversity gain, high channel capacity, link reliability, and, in general, more degrees of freedom in the wireless link [38]. An overview of CF technology is also depicted in Figure 3. As can be observed, an arbitrary MS is served by two APs for improved signal reception. This set of serving APs changes dynamically, as the MS changes position in the wireless orientation. This feature can be particularly beneficial for highly demanding applications in terms of coverage, transmission rates, and latency, such as autonomous driving. To this end, dMIMO configurations are deployed per AP that facilitate high signal diversity.

2.3. Advanced Physical-Layer Technologies

A key concept in 6G networks will be advanced physical-layer technologies that can support ultra-high data rates with minimal latency. The concept of mMIMO deployments has already been introduced in 5G networks. Moving forward, in 6G networks, distributed mMIMO configurations are expected to play a key role in the provision of high data rate services, as also mentioned in the previous section. In the same context, NOMA and relay nodes (RNs) that can amplify the signal towards desired users are also of significant interest [39]. In this case, MSs can also play the role of an RN that can relay selected information to other MSs in its area of coverage based on channel conditions. Hence, latency times can be improved. Finaly, THz communications are also a promising technology for upcoming 6G networks. The THz band has large unused frequency areas and high spatial resolution enabled by the short signal wavelength and large bandwidth [40]. It works by leveraging the high-frequency spectrum to achieve ultra-high-speed wireless communication, surpassing the capabilities of current microwave and mmWave technologies. Consequently, highly demanding application scenarios in terms of transmission rates can be supported.

However, there are certain challenges and limitations regarding the usage of the THz frequency band. It is apparent that as we move deeper into the THz spectrum, corresponding free space losses increase accordingly. Hence, appropriate strategies are required that can enhance the transmitted signal over these areas. In the same context, a holistic transformation approach is required regarding the design and implementation of transceivers that can operate in the THz band. Till now, there are no channel models that have been proposed for this spectrum area to examine all potentials. Finally, it should be noted that highly directional signals are easily blocked and hard for mobility applications. Thus, new error control mechanisms should be proposed, and new networking strategies should be developed to improve the coverage and support the seamless connection [41].

2.4. O-RAN

The concept of O-RAN allows service providers to use resources or non-proprietary subcomponents from a variety of vendors. Therefore, disaggregated services can be supported by various heterogeneous infrastructures [42]. O-RAN enables programmable, intelligent, disaggregated, virtualized, and interoperable functions. AI/ML technologies can also be deployed with the O-RAN radio intelligence controller (RIC) architecture. According to the O-RAN Alliance specifications describing the AI/ML workflow and requirements [43,44], two main entities, namely the non-real-time RAN intelligent controller (non-RT RIC) and the near-real-time RAN intelligent controller (near-RT RIC), will play a critical role in the AI/ML assistance and control loops, determining the optimization rationale of the O-RAN deployments according to the decision time scale each of them handles (e.g., the near-RT RIC operates in order of ms, while the non-RT RIC decides above 500 ms).

A schematic overview of O-RAN in the context of 6G networks is depicted in Figure 4. The figure illustrates the architecture of an O-RAN framework, emphasizing the interaction between different functional components across various cloud domains. The service management and orchestration (SMO) layer oversees the network and interfaces with the non-RT RIC via the O1 interface. The non-RT RIC hosts rApps, which are AI/ML-driven applications that operate on a non-real-time scale (greater than one second). These applications focus on policy-driven, long-term network optimizations such as traffic forecasting, anomaly detection, energy-efficiency enhancements, and capacity planning. By leveraging extensive historical data, rApps contribute to proactive and large-scale adjustments that improve overall network performance.

Below, the near-RT RIC operates within the edge cloud and manages network optimizations via the E2 interface. It hosts xApps, which execute real-time optimizations (10 ms to 1 s latency) for network elements. These xApps are responsible for functions such as dynamic resource allocation, interference management, power control, and handover optimization. By continuously analyzing network conditions and making rapid adjustments, xApps enhance user experience, minimize latency, and improve spectral efficiency. Their placement within the near-RT RIC ensures that they can make decisions quickly without the delays associated with higher-layer optimizations.

Further down in the architecture, the centralized unit (CU) is split into the control plane (CU-CP) and user plane (CU-UP), which communicate over the E1 interface. The CU components connect with the distributed unit (DU) through the F1-c (control) and F1-u (user data) interfaces. The DU, in turn, interacts with the radio units (RUs) over an open fronthaul (FH) interface, enabling flexible and vendor-neutral RAN deployments. Within the DU, dApps operate as distributed applications tailored to localized and fine-grained optimizations. Unlike xApps, which function at the near-RT RIC level, dApps are deployed directly on the DU, allowing them to make ultra-low-latency optimizations at the radio access level. These applications can perform localized power management, adaptive modulation control, and beamforming adjustments, ensuring efficient and real-time operation at the edge of the network.

The regional cloud, edge cloud, and cell area layers visually segment the architecture, showing where different components reside. The yellow arrows indicate the interaction of AI-driven applications (rApps, xApps, and dApps) within the O-RAN architecture, contributing to intelligent RAN optimization and management. By distributing intelligence across different levels of the network, O-RAN can facilitate a flexible, efficient, and vendor-neutral approach to modern wireless communication.

On the other side, O-RAN introduces several potential security risks due to its open and disaggregated architecture. Vulnerabilities in open interfaces, such as the A1, E2, and O1 interfaces, can expose the network to threats like man-in-the-middle attacks, data breaches, or unauthorized access. The increased number of third-party xApps and rApps also raises concerns about supply chain security and the trustworthiness of software components. Additionally, the real-time exchange of control information between distributed units may be susceptible to disruption if not protected properly.

2.5. Blockchain Technology

Blockchain technology is well known from previous generation networks. In this context, each sensitive message is divided into a finite number of blocks, which are then connected via cryptographic hashes. Hence, message interception can be extremely difficult due to the multitude of blocks, since the identity of the next block as well as the previous block in the chain is encrypted. Previous records of blockchain as well as transactions cannot be deleted or altered. A key novelty of blockchain is that the participating nodes do not have to trust each other, since they all have the capability to store local records of the blocks and transactions. Moreover, decentralization is leveraged as well, since there is no need to involve third parties and authorities. All nodes that participate in the transaction are treated as equal [45].

Within the context of 6G networks, not all lightweight IoT devices will have the capability to execute advanced security protocols or to create and decode blockchains. To this end, novel architectural approaches have been proposed over the last few years in an effort to equally divide network load among the participating entities. In [46], for example, the concept of IoT–edge–cloud (IEC) systems is introduced, where IoT devices with limited processing capacity can offload certain computationally demanding tasks either to the closest MEC servers or to the cloud. Hence, blockchain creation as well as decoding can be offloaded to the MEC server closest to the IoT device. In non-latency-critical applications, cloud processing may be performed as well. In all cases of task offloading, various factors are considered, such as the complexity of the task, the offloading time, the computational burden either in the IoT devices or in the servers, the energy consumption, etc.

Blockchain can also be combined with AI/ML. As mentioned in Section 2.1, AI/ML deployments over 6G networks should follow a decentralized/distributed approach. Hence, with the help of blockchain, secure data exchange among the participating nodes in ML model training can take place (blockchain in AI) [47]. This strategy can be applied, for example, to FL scenarios to enhance secure data exchange and avoid data replication from model parameters. Blockchain can securely manage and authenticate device identities, which is crucial for the large-scale deployment and interoperability of devices in 6G networks. In 6G, devices or services can be authenticated through the blockchain, with each device’s status updates or service requests recorded and verified on the blockchain, thus achieving true end-to-end security [48]. To this end, a challenging research field that has attracted scientific interest over the last few years is the concept of zero trust. In this context, this security model requires strict identity verification for every person and device trying to access resources on a private network, regardless of whether they were granted similar access in a previous state. In [49], for example, a novel blockchain-based zero-trust supply chain security framework integrated with DRL is proposed and evaluated. To this end, the blockchain architecture ensures secure, transparent, and traceable record-keeping and automated execution of supply chain transactions. The integration of the zero-trust security model enables continuous verification and validation of all supply chain entities and transactions, thus mitigating the risk of security breaches and unauthorized access. In the same framework, the usage of smart contracts minimizes human errors that can jeopardize the system’s integrity.

2.6. Digital Twins

The concept of DTs enables the evaluation and optimization of various physical entities via digital replicas [50,51]. Hence, the application of DTs in 6G wireless networks can be a quite challenging issue due to the integration of various cutting-edge technologies. Sixth-generation networks will be dynamic, with network operators being able to modify key network parameters and architecture in the time scale of minutes and hours, rather than months or years. In this case, DT is a virtual sandbox that can identify various network reconfigurations and leverage optimum decision-making. In real-world scenarios, for example, network failures or security attacks might not take place quite often; hence, this would significantly increase the training time of DRL agents. With DTs, however, various such occasions can be generated by deliberately injecting misfunctionalities in the DT representation.

A schematic overview of the DT is depicted in Figure 5. To this end, for specific real-world physical objects, all related information is collected and digitally stored. This information is related both to the objects’ technical specifications and to the way they interact with the surrounding environment. Therefore, a virtual representation can then be formulated that can accurately model the object under consideration. Data manipulation in the DT representation obviously leads to alternations in its operational behavior. All these input–output sets are used to train appropriate AI/ML models and thus provide the physical asset with a multitude of potential responses for various system configurations.

In practical DT applications, however, in the context of 6G networks, mass data generated by IoT devices might pose significant challenges to communication networks [52]. Given the computing capacity required to process all real-world information for a particular object with low delay, the data gathered by IoT devices must be offloaded to more powerful servers. Hence, if this traffic is not managed correctly, it can severely degrade network performance. Moreover, the number of IoT devices interconnected in a specific DT application may be expected to grow in the future, increasing the saturation of the networks. To mitigate this effect, computations must occur closer to the data source, reducing latency and improving response times. Hence, the concept of the IEC architectural approach that was discussed in the previous subsection of blockchain can be highly applicable in DTs as well, since edge computing ensures the proper processing of IoT data.

A schematic summary of the 6G key enabling technologies is depicted in Figure 6. In Figure 7, the most important key 6G requirements are presented. These can be divided into three major categories: (a) flexible architecture deployments that include CF approaches and the extensions of the SBA in 5G, (b) AI/ML integration both for threat mitigation and for resource optimization, and (c) advanced physical-layer techniques that can leverage ultra-high data rates with minimal latency.

A high-level concept of 6G network architecture is depicted in Figure 8. In general, four layers can be identified, including (a) the heterogeneous resource layer, which includes the integration of all physical layer components. These include all physical resources, such as mobile phones, drones, industrial IoT equipment, industrial robots, and indoor or smart grid equipment. This layer is also responsible for proper resource allocation and power management among the participating components via AI/ML. The upper layer is the network layer, which is responsible for all network operations. These include the management of various network topologies, such as CF networks, the integration of ML approaches towards network management and reconfiguration, and open-air interfaces. Finally, subnetworks are also a key driving force in current 6G trends. To this end, smaller and more flexible groups are formulated with specific 6G components [53]. Subnetworks, also referred to as in-X networks, can be particularly important in cases where the connection with the main 6G core network (CN) is lost. In this case, a device near other network entities can take the role of network controller. In the same context, subnetworks can be also beneficial in search-and-rescue operations, where fast deployment times and minimum latency are of utmost importance.

Moving forward, in the service layer, all current 6G trends will be integrated. These include 6G security, which is a key aspect of 6G networks; network slicing, which allows for the simultaneous execution of various services; and the deployment of microservices. The latter feature is extremely important, as it allows for the decomposition of various applications into smaller tasks, which are known as microservices [54]. Hence, dynamic resource allocation during application execution can be achieved.

Finally, the application layer includes the support of all novel applications in the 6G context, such as autonomous vehicles, smart cities, and AR/VR. The coexistence of these applications in certain domains can be supported by the slicing concept, as previously mentioned. As a final remark, it should be noted that although the AI/ML concept is depicted only in the network layer, its applications span across multiple domains in the other three layers.

3. Architectural Approaches

In this section, the most important architectural approaches in the context of 6G networks are described. To this end, in [55], the authors introduce the concept of organic 6G network architecture, the main core of which relies on the execution of all architectural protocols as web-based services. This approach tries to extend the SBA that was introduced in the 5G era as an attempt to decouple network deployment from hardware-specific and constrained devices. The organic concept moves a step forward by allowing each functionality to be executed as a microservice. Therefore, the concept of infrastructure-free implementation can be supported. In parallel, each service requests a specific amount of network infrastructure; hence, network size depends on the actual requirements of the application. In [56], a novel architectural approach is presented that can support threat prediction and mitigation, as well as privacy protection, in 6G networks. To this end, one of the key components of the presented approach is the distributed trustable engine (DTE), which collects real-world network data and trains ML models in FL mode. This architectural approach supports DTs as well, since the DTE can also work with the emulated context. This training has a dual goal: (a) to identify abnormal data patterns and (b) to identify potential security attacks. Afterwards, the intent-based interface (IBI) creates appropriate intents that are translated to specific network reconfigurations. Each such intent can be either predictive (i.e., the appropriate actions to avoid an attack) or mitigative (i.e., the set of appropriate actions for the network to recover from an attack). Hence, this approach can be highly applicable in zero-day attacks: if an actual attack takes place, then the IBI can create the appropriate intents for network restoration. In the same context, if a similar attack is identified in another network topology, then mitigation actions can be deployed in the considered topology either with emulated content from the DT to the DTE, or via transfer learning, which integrates additive knowledge from the specific attack [57].

In [58], various security aspects are analyzed in the context of 6G networks. This analysis includes both the physical and the network layer. In the physical layer, for example, potential attacks involve eavesdropping, jamming, pilot contamination attacks, and spoofing. In the same context, specific countermeasures are also proposed for each type of attack. The use of IRSs, for example, which reflect the signal in unpredicted ways, can be beneficial for attack mitigation, since the attacker will be unable to know the exact direction of the incoming signal. Moving forward, the use of network slices can partially mitigate network attacks, since inter-slice information is not transmitted. Hence, potential attacks can be limited to only a single slice.

In [59], the network-slicing concept is introduced in 6G networks. To this end, a detailed approach is provided that formulates the optimization problem by taking into account various performance metrics, such as resource allocation, service provisioning, and performance optimization. In the same context, the process of slice allocation first considers all resource requests from the various 6G layers. Afterwards, the optimization problem leads to optimum slice allocation. In [60], the concept of CF networks is introduced. As previously mentioned, various APs are placed within the wireless topology and MSs are connected to each AP based on their signal strength and channel quality. In this work, the coexistence of CF approaches with current network architectures is examined, in particular, the criteria for either centralized or distributed signal processing.

In [61], various key enabling technologies in the context of 6G networks are introduced and analyzed. These technologies include AI/ML, blockchain technology for security provision, big data analytics, advanced encryption techniques, and cloud–edge IoT technologies. In [62], the concept of O-RAN is analyzed, as also explained in the previous section. To this end, the basic principles of O-RAN are discussed, which include (i) a disaggregated architecture with modular and standardized interfaces; (ii) cloudification, programmability, and orchestration; and (iii) AI-enabled data-centric closed-loop control and automation.

In [63], a native intelligent and security architecture for 6G networks is proposed. In this context, the concept of a super network and multi-bodies is adopted. A super network includes all the main 6G functionalities. Multi-bodies are separate 6G networks with a subset of the central system’s functionalities that operate autonomously. The involved NFs communicate with the NFs of the master network to achieve service collaboration and resource optimization. In the same context, in [64], a novel architectural approach is presented that is based on four (4) layers: the resource layer, which is responsible for all data gathering from 6G devices and power allocation. Next, we have the routing and connection layer, which connects all 6G entities. The service-based network function layer is an extension of the SBA approach of 5G networks and can support distributed deployment of services and applications. Finally, the exposure layer can invoke the APIs of other layers to obtain their capabilities.

In [65], various use cases are described in the context of 6G networks. These include network-enabled robotic and autonomous systems, multi-sensory extended reality, distributed sensing and communications, and personalized user experience. In [66], the paper investigates the modeling, evaluation, and optimization of DT-based 6G network architecture. To this end, the 6G network architecture is mathematically modelled by intra-domain and inter-domain hypergraphs to characterize the complex changing rules of various elements in the network architecture and their relationships with the supply of services. The inter-domain architecture entropy is introduced to quantify the statistical characteristics of the degree of overlap between network services.

In [67], the 6G recursive user plane architecture (6G-RUPA) is described, which is designed to be scalable, flexible, and energy-efficient. A key novelty of 6G networks, as previously mentioned, will be their ability to form a dynamic federation of 6G network operators. Therefore, this federation will allow connected devices to roam freely across different domains with session continuity, without the need to relay traffic through external data networks such as the internet. To this end, this study highlights the appropriate changes that must take place in the user data plane to effectively support device-to-device (D2D) communications without requiring an intermediate data network. The 6G-RUPA approach offers multiple novelties, such as a flexible number of layers, a multilayer quality-of-service (QoS) framework, support for multiple control planes, and AI. In addition to simplifying the user plane network architecture, 6G-RUPA introduces significant improvements to existing verticals through enhanced features like network slicing, edge computing, and support for emerging technologies such as quantum computing and tactile internet.

In [68], various technologies are presented with respect to the 6G network, such as DT, AI, distributed ledger technology (DLT), and post-quantum cryptography (PQC). In the same context, in [69], new technological enablers are described, along with current requirements, e.g., new mobility components, CRAN solutions, programmability, and new architecture components for AI. In [70], a self-evolving architectural approach is presented based on self-healing. As also mentioned in the previous section, the extension of the SBA may rely on a similar three-level hierarchical structure, where microservices compose NFs, and the NFs compose the CN. Hence, to adapt to the changing scenarios, varying microservices can compose different network functions and then CNs. Therefore, the term “self-evolution” refers to the capability of 6G networks to autonomously adjust and optimize their structure in response to environmental changes during the operation process. In this context, the work in [71] adopts the idea of a self-evolving agent that constantly monitors the network via DRL and evaluates each action based on rewards and penalties. Hence, a closed-loop control is formulated that adjusts the network accordingly.

In [72], a new architectural approach for 6G networks is presented that is based on the decomposition of the 6G ecosystem into various building blocks. In this framework, four main blocks are identified: (a) the platform block, which is responsible for data collection and resource management in the physical layer; (b) the functional block, which refers to RAN–core convergence, CF approaches, and the application of AI/ML approaches; (c) the specialized block, which contains various novel services in the 6G context, such as flexible offloading, slicing, and deployment of subnetworks; and (d) the orchestration block, which contains open services and closed-loop control.

In [73], a novel architectural approach is presented based on the European project 6G-ANNA. The main 6G innovations, such as the main research directions of 6G-ANNA, are described, including 6G RAN, network of networks, automation and simplification, DTs and extended reality, security, privacy, and sustainability. In [74], a free-space optical (FSO) communication network in 160 Gbps is proposed and evaluated that can be used in 6G networks and related applications, such as the transmission of information between drones and buildings, vehicle to vehicle, in hospitals, and in hard-to-reach areas. In [75], the work mainly deals with the slicing concept and how it can be applied to 6G networks. Various challenges are identified, such as the need for ML training over diverse datasets, dynamic spectrum sharing, and authentication mechanisms for tenants that share the same resources. The proposed architecture embraces openness and employs ML algorithms, such as RL, deep learning (DL), FL, etc., to eliminate vendor lock-in and empower the 6G slicing framework with advanced intelligence and automation capabilities.

In [76], the key technologies for smart sustainable cities (SSCs) are discussed, such as AI/ML, non-terrestrial networks, and ISAC. In [77], in the same context of smart cities, a potential architectural approach is presented that includes four (4) layers, in particular, the sensing layer, the transmission layer, the data layer, and the application layer. To this end, various use cases are described that benefit from the 6G concept in smart cities, such as industrial automation and smart manufacturing, vehicle-to-everything (V2X) technology, smart health cases, and the smart grid concept, which is based on decentralized energy production from renewable energy sources. In this context, an IT infrastructure is used to interconnect the various production units of the smart grid, while data collection from various entities should also be leveraged for efficient load forecasting. In [78], the concept of the nested bee hive is presented, which is a flexible multi-layered approach designed to meet the needs of futuristic smart cities. To this end, a nested approach is considered that contains both macroscopical cells within the smart city and 6G cells that are connected with fiber links. Within a 6G cell, mMIMO configurations are deployed per AP. In general, this approach consists of four (4) logical layers, in particular, the sensing layer, which consists of multiple devices and sensors within a 6G cell; the access point layer, which manages advanced physical-layer techniques (i.e., mMIMO, NOMA) for the connection of devices; and the distribution layer, which encapsulates connection protocols and slicing capabilities. Finally, the cloud layer demonstrates the distributed cloud concept of bee hive architecture, which is similar to the fog architecture of computing; each town cell device communicates with the local town cell cloud in the form of slices, then the town cloud communicates with the city gateway cloud (CGC).

In [79], the paper discusses the different ongoing research activities on 6G architecture and outlines the potential evolution of the different parts of the network. In this context, the necessity of a large-scale intent manager is highlighted, which can provide, among others, dynamic NF onboarding as well as optimization of network resources. Moreover, a RAN low-layer split is also highlighted in 6G, which in turn would enable technologies such as CF-mMIMO that can potentially increase the user spectral efficiency compared to other cellular RAN architectures. In [80], the PREDICT-6G framework is presented and discussed, which includes, among others, AI-driven network management, multi-domain service composition, and model-driven open interfaces.

Finally, in [81], various 6G use cases are presented and discussed, such as high-performance precision agriculture, intelligent transport systems, and intelligent automation systems. In the same context, the evolution and potential of vehicular ad hoc networks (VANETs) in 6G networks are discussed well, as well as the incorporation of air and space networks to ensure seamless vehicle communication across global locations. To this end, the authors highlight the importance of MEC solutions and AI/ML approaches that can leverage big data collection and analytics with minimal response times.

4. Discussion—Open Issues

In

Table 2. Categorization of indicative presented studies.

Related Work	Main Concept	Key 6G-Enabling Technologies
		AI/ML	Digital Twins	Open Interfaces	Network Slicing	Cell-Free Approaches
[55]	Organic 6G networks			√
[56]	Threat prediction and mitigation	√	√
[59]	Network slicing				√
[60]	Cell-free networks					√
[61]	6G vision	√
[62]	Open RAN	√		√
[63]	Multi-layered architecture	√	√		√
[64]	6G multi-layer vision	√	√
[66]	6G architectural design based on DTs		√
[67]	Flexible layered architecture	√			√
[68]	Basic 6G trends	√	√
[70]	Self-evolving 6G networks	√
[71]	Deep reinforcement learning in 6G networks	√
[72]	Building blocks for 6G	√			√
[73]	6G project ANNA	√	√
[75]	Slicing concept in 6G	√		√	√
[76]	6G for smart cities	√
[77]	6G for smart cities	√
[78]	6G for smart cities	√
[79]	Current 6G trends	√				√
[80]	PREDICT-6G	√		√
[81]	Advanced 6G use cases	√			√

, the indicative presented studies are categorized according to various 6G key-enabling technologies, in particular, AI/ML, DTs, open-air interfaces, network slicing, and CF approaches. From the discussion in the previous section, all current state-of-the-art approaches have adopted AI/ML solutions that are implemented in almost all considered architectural layers. AI/ML can contribute to various issues in the 6G context, such as resource optimization, threat prediction, and mitigation. With respect to resource optimization and in conjunction with other cutting-edge technologies, such as NFV and the organic 6G concept, the appropriate number of resources can be committed during service execution, which in turn leads to optimal resource management. These resources are not only related to hardware constrained nodes but also include optimum slice selection and configuration.

Since 6G networks can change dynamically based on traffic conditions and overall user demands, in many of the presented works the concept of DRL was adopted. To this end, as previously mentioned, a mobile agent interacts with the surrounding environment and sends positive or negative feedback on certain decisions. In the same framework, since various potential misconfigurations can occur in 6G networks, the concept of DTs in parallel with ML has also been adopted. In this case, digital representations of the real-world environment assist in the examination of various scenarios that can be helpful for ML model training. To this end, transfer learning can also leverage fast decision-making, since knowledge of similar tasks is shared among the 6G network.

One other issue is the ability of the network to collect a vast amount of data from heterogeneous resources. In this case, two major assumptions have mainly been followed in the literature. In the first case, the use of open-access protocols is favored. In the second case, aggregated data from heterogenous resources are sent to the data management layer for proper preprocessing prior to manipulation from the business or functional layer. In both cases, data collection from diverse devices and entities may lead to additional security threats, since not all parts have the capability to execute advanced security protocols. In this case, the use of blockchain technology is preferred, with efficient task offloading among the IoT devices and edge/cloud servers. In this context, IoT data are not directly inferred to the 6G network but are encrypted with blockchain from edge/cloud servers.

Data collection can be also leveraged with the appropriate extension of well-known 5G NFs, such as the NWDAF [82], which has already been defined in the 5G architecture from release 15 of 3GPP. NWDAF can perform data collection from various NFs. When combined with advanced AI/ML techniques, full-scale network optimization can be supported based on traffic demands and service requirements [83]. NWDAF can be deployed with other key enabling technologies, such as O-RAN, and support large-scale data collection and optimization in a secure and trusted environment.

Based on the previous discussion, a conceptual 6G framework is depicted in Figure 9 (the application layer has been omitted for illustration purposes). To this end, various CF deployments are considered in the 6G area of coverage. Each CPU is connected to a separate NWDAF instance that collects all data related to the specific deployment. The FL-agent provides full network optimization in all CF segments. Aggregated data are also used in the service layer, where the DT agent executes multiple simulation scenarios to deal with various network instances and configurations, as well as in the microservice aggregator, which optimizes resource usage. Finally, the security agent can perform various tasks related to threat prediction and mitigation based on the outcomes of the FL agent, such as malicious node exclusion and intent-based creation for network recovery.

However, also from the analysis of the previous sections, certain open issues and limitations can be identified. These are summarized below:

• Deployment capabilities and costs throughout large geographical areas. To this end, the support of ultra-high data rates with minimal latency necessitates dense deployments that may increase the cost of 6G infrastructures.
• In the same context, the 6G approach should be also adopted by lightweight devices that will have the capability to run light versions of the 6G architecture.
• Although ML models are a key innovation approach in 5G/6G networks, improved model performance is often accompanied by increased model complexity. In this context, a key innovation over the last few years is the concept of explainable artificial intelligence (XAI), a field that is concerned with the development of new methods that explain and interpret ML models [84,85]. The main focus is on the reasoning behind the decisions or predictions made by the AI algorithms to make them more understandable and transparent. Therefore, XAI assists in making ML models lead to decisions that are not based on irrelevant or otherwise unfair criteria.
• Integration of various cutting-edge technologies. As discussed thoroughly in this article, a key concept in 6G networks will be the integration of various technologies, both in the physical and in the network layer. Hence, a challenging issue would be to limit overall complexity and signaling burden. For example, since 6G networks involve the collection of a vast amount of data, appropriate processing algorithms are required that can effectively manage this volume. In the same context, more complex transceiver designs are required to support transmission in much larger frequencies compared to 5G, as is the concept of ultra-dense massive MIMO systems. Moreover, the adoption of certain technologies that facilitate the transition to the 6G era might come in contrast to other important features, such as security by design. For example, as discussed in this work, O-RAN introduces several potential security risks due to its open and disaggregated architecture.
• Coexistence with previous generations of networks. As also anticipated in the 5G era, the full transition to a new generation of networks will gradually take place. Until then, coexistence with well-established protocols is of utmost importance. In this case, one solution that has been proposed is the one in [78], where nested networks are formulated. In this context, small 6G cells can be deployed in areas with increased traffic distribution and can communicate with large 5G cells. However, there are not many works in the literature that, on the one hand, deal with the coexistence of 5G/6G and associated issues (e.g., handover and mobility management, resource allocation, etc.), and, on the other hand, with interference mitigation mechanisms. To this end, the work in [86] presents an interference analysis for the coexistence of terrestrial networks with satellite services. In this work, extensive simulations are carried out regarding cellular coexistence with low-earth-orbit (LEO) satellites in the 47.2–50.2 GHz band.
• Novel channel estimation techniques for THz communications. An efficient channel estimation is of vital importance to help THz communication systems achieve their full potential. Conventional channel estimation techniques that are used in currently established networks, such as least-squares algorithms in the uplink received signals, are practically inefficient for THz systems because of their large computation overhead.
• Energy harvesting strategies for low-power consumption networks. Since 6G networks deal with the integration of various low -power devices, there is a need to identify suitable energy-efficient mechanisms for such devices. To this end, energy harvesting is defined as the process of extracting energy from external network sources (e.g., solar energy) that is later either used to power network devices or is preserved for wireless autonomous devices, like wearable biomedical monitoring sensors.
• In the same context, ML models can also leverage energy efficiency in network design. To this end, a model can be trained to constantly monitor the network traffic and computational burden of each node in the network. Hence, this makes it possible to implement energy-saving techniques like turning down specific base stations or lowering their power consumption during these periods.
• Integration of non-terrestrial networks (NTNs) over 6G interfaces [87]. In this context, NTNs could supplement terrestrial 6G infrastructure by extending coverage to remote and under-served areas, where deploying traditional terrestrial networks is challenging or economically impractical. In the same framework, the potential of stringent IoT services worldwide is leveraged. In this context, a promising secure transmission technology rooted in information theory is wireless covert communication, which can conceal the presence of transmission so as to eliminate possible attack threats [88].

5. Conclusions

Ιn this work, all recent developments towards the definition of an architectural approach for 6G networks were described. In particular, 6G networks will enhance current 5G functionalities, such as service-based architecture and network function virtualization, and at the same time will introduce novel features such as THz communications, the organic concept for flexible application deployment and reconfiguration, and distributed and decentralized machine learning approaches. A key concept in 6G networks will be their ability to process a large amount of data from heterogeneous resources. Therefore, open-access interfaces are expected to leverage data collection and processing along with IoT–edge–cloud frameworks.

Sixth-generation networks can alternately be viewed as a collection of networks, or a network of networks, where various novel services can be integrated via flexible network slicing. Efficient mechanisms for dynamic network deployment can leverage optimum hardware utilization and green computing. Τo this end, the combination of the digital twin concept along with advanced machine learning approaches (e.g., deep reinforcement learning or explainable AI) can provide a multitude of potential responses for various network configurations and a deeper understanding of important 6G functionalities.