## 2 Vision and Key Features for Future 6G Networks
### Artificial Intelligence  
Machine learning (ML), along with AI and deep neural networks (DNNs), are revolutionizing technology that drives new research opportunities in various areas, including 6G communications and IoTs [10]. The link and system-level solutions for 6G communication are being built using AI and ML. AI-empowered 6G is expected to extend several features including self-configuration, aggregation, opportunistic set-up, and context awareness [15]. The potential of radio signaling and maximum cognitive to intelligent radio transmission can be realized fully through AI-empowered 6G with an aid from ML algorithms [11; 49]. Additionally, building the hardware foundations for wireless technologies is expected through intelligent and reconfigurable materials [50]; they are predicted as the MIMO 2.0 and explored in detail in [51; 52]. The summaries of ML methods for device-to-device (D2D) communication, massive MIMP optimization, and the design of heterogeneous networks are provided in [53; 54]. Furthermore, [55] proposed a new network architecture for mobile communication for evaluating the analytics related  
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to big data, and such analysis can facilitate optimizations on the physical layer. The authors in [56] suggested the incorporation of an index modulation, which can serve as an aid to enhance 6G network efficiency. Notably, high intelligence and ML techniques may not only increase performance but may also change the configuration and design of 6G networks. The advantages of intelligent 6G functions in wireless and non-wireless communication services are detailed in the following three classifications:  
* _Operational intelligence (OI)_: This technology allocates resources (i.e., bands and power) efficiently to achieve satisfactory network-operations instead of involving traditional methods, using multi objective optimizations that can work in the highly complex and dynamic nature of 6G owing to its heterogeneity, density, and scalability. Such optimizations that measure prioritizing multi-objective performance are typically NP hard problems and are difficult to measure in real time [57]. Today, advances in AI technologies and ML techniques including deep reinforcement learning (DRL) can aid the decision maker in iteratively refining and eventually optimizing their decisions using the feedback generated through a loop established by DRL, which can be termed AI's major application in 6G [11]. By aiding optimization, such learning algorithms can be utilized for efficient resource allocation [58]. Quite recently, [PERSON] et al. [59] addressed many emerging issues including data offloading, caching, and adaptive modulation;
* _Environmental intelligence (EI)_: The pervasive and distributed intelligence may become a reality in holistic technology, including wireless communication environments with the advancements in smart radio spaces and materials [50]. The services based on intelligence could realize many application scenarios including data centers, IoT devices such as unmanned aerial and road vehicles and auto-robots [60] with self-organizing and self-healing properties, while increasing the reliability in D2D communication for a 6G network using intelligent frameworks [17]. In [50; 61], some of the latest developments described involve adaptive manners on the bases of sensing for the radio wave customized transformations using reconfigurable intelligent surfaces. These developments set the foundation for specialized hardware that is usable for EI.