## 3 Architectural Approaches
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.