On Challenges of Sixth-Generation (6G) Wireless Networks: A Comprehensive Survey of Requirements, Applications, and Security Issues

This survey tracks the path towards 6G comprehensively by considering a broad range of aspects mentioned in Figure 2 and the main contributions are as follows:

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  Comprehensive comparative analysis. Most of the previous surveys [10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21] used only “Yes” or “No” for comparing different aspects of the 6G ecosystem which hardly provides any details. Unlike the previous surveys, this work provides (Section  2) a fine-grained comparison like Low (L), Medium (M), High (H), and No (N) for different factors. In essence, we correlate the various technologies and architectures (such as Blockchain, Industry 5.0, augmented reality (AR), mixed reality (MR), virtual reality (VR), mmwave, Unmanned Aerial Vehicle (UAV), software-defined networks (SDN), and many more) with 6G networks by addressing their integration issues such as required data rate in terabyte per second (Tbps), minimum specified delay, mobility, energy efficiency, and spectrum issues.

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  Outline the requirements and vision for 6G. By 2030, a new generation of mobile communication networks is expected, driven by sociological and technological developments discussed in this article. The discourse elucidates the emergence of various novel applications and details how the capabilities of forthcoming 6G networks will facilitate their realization. Taking into account the challenges of building 6G and the lessons learned during the development of 5G (Section  3), we offer a roadmap for future 6G research directions. Our paper outlines the anticipated requirements for 6G (Section 3) and conducts a comprehensive discussion on the enabling technologies that will meet these requirements for upcoming applications (Section  4).

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  Use cases categorization. Our survey offers an extensive classification of diverse 6G-related usecases (Section 5). Specifically, these scenarios are grouped into categories such as VR, Healthcare, Industry 5.0, as well as enabling technologies, both disruptive and innovative, and AI, encompassing machine learning and big data.

Organization. The remainder of our paper is structured as follows: Section 2 discusses the related work and Section  3 provides the background for communication technologies. Section  4 describes the 6G key enabling technologies while in Section 4.7, we provide a discussion on AI-enabled technologies and applications for future 6G. Section 5 presents the potential use cases of 6G technology. Section 6 discusses the challenges in the 6G network and the future research directions, and Section 7 concludes our work.

Since the beginning of the first analog communication system in the early eighties, a new mobile communication generation has been launched every ten years. In the following sections, we go through these generations and present their strengths and limitations.

First Generation. First Generation (1G) provided the service of voice calling with large network coverage. A partial fax service was also introduced in 1G networks. 1G networks faced issues like incompatibility of different market standards. For instance, at that time the USA was using an advanced mobile phone system; whereas several European countries adopted the Nordic mobile radio system (NMR). Both standards were incompatible with each other. Digital speech codecs were not introduced in the 1G era [42].

Second Generation. The global system for mobile communications (GSM) is considered the key standard of second-generation (2G). It began in Europe with a French name Groupe Speciale Mobile, and within years, GSM had around 90 percent of the market share. In GSM, newer voice codecs improved the voice quality. One of the key features of the GSM was the short messaging service (SMS). In addition, GSM also launched digital data services [43]. Initially, a data rate of 9.6 kb/s was achieved; later, the data rate was further improved to 200 kb/s with the launch of general packet radio service (GPRS) and enhanced data rates for GSM evolution (EDGE). 2G had various challenges that included low data rate, high cost, delay, etc.

Third Generation. Several European regulators agreed to establish a new approach to granting licenses by the middle and end of the 1990s. It was expensive to buy a spectrum from auctions in open markets. Even after the license was granted, no standard came out. However, after several years of license granting, the universal mobile telecommunication system (UMTS)-based cell phones came onto the market. UMTS used code division multiple access (CDMA). Initially, UMTS provided data rates up to 364 kb/s which was far less than the expected rate; however, in the later modifications, higher data rates were achieved. UMTS could not provide the expected performance due to bandwidth issues. Therefore, operators decided to move towards 4G long-term evolution (LTE) [44].

Fourth Generation. While working with UMTS, operators learned lessons and they were not fully confident to invest in the fourth generation (4G). Also, various technologies were introduced regarding spectrum efficiency which provided the flexibility for operators to select the appropriate option according to their requirements. Most of the LTE standards kept upgrading 3G UMTS to develop 4G communications. The LTE aimed at simplifying the existing UMTS architecture from circuit and packet switching to IP-based architecture. LTE provides up to 300 Mb/s and uploads data rates of up to 75 Mb/s. LTE offers low latency (up to 5 ms) with mobility support. It uses orthogonal frequency division multiple access (FDMA). Although, 4G provides solutions to several problems faced by 3G like low data rates and higher latency values; 4G faces other issues such as performance management for the rapid growth of the number of devices that require ubiquitous connectivity and higher data rates [45].

Fifth Generation. 5G is the new generation of cellular networks. According to the 3GPP project, if a device is using 5G new radio (5G NR) software, it is called a 5G device. A 5G network is supposed to provide data rates of up to 20 Gb/s. The 5G technology promises to offer a latency between 1 to 5 ms. The speed of 5G in the sub-6GHz band will be greater than the 4G with a similar number of antennas and spectrum. Overall, 5G uses a high-frequency band (above 30 GHz). In recent years, mmWave appeared as one of the potential candidates for 5G by providing data rates in gigabits/sec. It uses a spectrum between 30 and 300 GHz and corresponds to wavelengths between 10mm to 1mm. The characteristics of mmW include high bandwidth, short-wavelength/high frequency, and high attenuation [46]. High-power levels are required to overcome the huge path loss, which is expected in mmW communications. The coverage is up to 20 meters at 60 GHz. IEEE 802.15.3c and IEEE 802.11ad standards provide support for mmW. Moreover, 5G will deliver the services like device-to-device (D2D) connectivity and massive machine-to-machine communications, etc. 5G bands have several challenges including poor foliage penetration, atmospheric and free space path loss, and high deployment cost.

Table 3 shows a performance comparison for different generations of communication systems.

There are several performance trade-offs in reliability, delay, throughput, and power consumption for 5G networks and these trade-offs give an impression that 5G could not fulfill the market challenges beyond 2030. The characteristics and features of 6G networks have the potential to overcome these challenges. The main features of 6G networks include high data rates, energy efficiency, reliability, and intelligent decision-making per device as well as a whole network at the global scale [47]. The 6G networks are supposed to provide services including enhanced mobile broadband (eMBB), ultra-reliable low latency communications (URLLC), massive machine-type communication (mMTC), and AI and machine learning integration. URLLC, promises to provide a delay of less than 1 ms [48]. Furthermore, the 6G system makes use of energy harvesting techniques so that ultra-lifetime could be provided to the devices.

For the satellite-integrated network, 6G is expected to provide improved QoS for satellite communication. 6G aims to make a single wireless operating network by integrating terrestrial and satellite networks. 6G will provide massive connectivity to wireless devices in the form of IoE; however, such huge connectivity creates challenges like quick processing and transmission delays. To cater to this, 6G will make use of extensive AI techniques at each phase of the communication including signaling, forwarding, and decision-making [47].

Ubiquitous connectivity is considered one of the key requirements for 6G networks. In a broader aspect, drones and satellites in orbit will help to create super-3D connectivity for 6G to provide ubiquitous communication [47]. 5G successfully uses the small cell communication networks to improve QoS including throughput, energy, signal quality, and spectral efficiency; however, it lacks the integration of disciplines like AI, ML, and automation [49, 50, 51].

6G networks will incorporate the approach of small cell networks in a larger aspect. Due to massive device connectivity, the future 6G networks will be ultra-dense with heterogeneous connectivity characteristics.

In recent years, millimeter wave (mmW) appeared as one of the potential candidates for 5G by providing data rates up to gigabits/sec and it could be also useful for the initial implementation of 6G networks. It uses a spectrum between 30 and 300 GHz and corresponds to wavelengths between 10 mm to 1mm. The characteristics of mmW include high bandwidth, short-wavelength/high frequency, and high attenuation [52, 53]. Huge path loss is expected in mmW communications, to recover from these path losses, high power levels are required. Due to high attenuation from solid materials (bricks and buildings), mmW requires a line of sight (LoS) for efficient and reliable communication. The interference levels in mmW communication are much lower than the 2.4 GHz and 5 GHz. Multiple-antennas solutions in mmW allow the transmission to use narrow beams which help to reduce the attenuation and the interference [54, 55, 56]. There have been several standardization activities for mmW MAC in the 60 GHz band. Most of these standardization efforts are for personal and local area networks under IEEE 802.15.3c and IEEE 802.11ad. IEEE 802.15.3c standard also known as piconet specifies the mmW by supporting a high data rate of over 2 Gbps in the 60 GHz band. Among a cluster of IEEE 802.15.3c-based devices, one will be selected as the piconet coordinator (PNC) which manages the synchronization among devices by broadcasting beacon messages. The device content for the time slots using carrier sense multiple access/collision avoidance (CSMA/CA) and sending data using time division multiple access (TDMA) [57, 58]. The IEEE 802.11ad introduced several modifications at MAC and the physical layer of the existing IEEE 802.11 standard to enable mmW support. It claims to provide a 6.75 Gbps data rate. The coordinator uses a superframe structure to manage the channel access of the connected stations which is composed of beacons, contention access period (CAP), and contention-free period (CFP) [59, 60, 61, 62, 63].

High-rate wireless personal area networks (WPANs) are a good fit for the IEEE 802.15.3 MAC protocol since it provides high data rates, low latency, and energy efficiency. In particular, its characteristics and capabilities support high-frequency transmission, provide quality of service, and enable sophisticated applications—all of which align with the objectives of 5G and 6G networks. IEEE 802.15.3 can greatly improve the overall performance and capabilities of future wireless networks when it is integrated into the larger 5G/6G ecosystem. It is important to understand the performance computation process for various technologies under the 5G and 6G network could be different and dependent on a specific technology. As an example, a discussion is provided regarding the performance evaluation of a 5th generation’s medium access control (MAC) mechanism of the IEEE 802.15.3C standard in terms of end-to-end delay (ED) and maximum throughput (MT). The purpose is to understand how much time it takes for a channel time allocation (CTA) request in the worst-case scenario. The ED can be calculated as given in Equation 1 [64, 61, 65]:

Where $T_{\mathrm{frame}}$ represents frame transmission time for a CTA request frame. Further, $T_{\mathrm{frame}}$ can be computed as given in Equation 2:

Where $T_{\mathrm{Preamble}}$ is the duration of PLCP preamble, $T_{\mathrm{Header}}$ is the duration of PLCP header and $T_{\mathrm{Payload}}$ is the duration of the payload. These durations are given in the IEEE 802.15.3C standard.

$T_{\mathrm{ACK}}$ represents the time duration of the ACK, in this case, ACK duration is computed as given in Equation 3:

Where $T_{\mathrm{ImmACK}}$ is the time duration of the immediate ACK and can be computed as given in Equation 4:

The ACK of the ImmACK has only a MAC header and not a payload as each packet is expected to be acknowledged immediately. $T_{\mathrm{CH}}$ represents the time to access the channel, which is computed as given in Equation 5:

Where RC is the retry counter in the backoff process and its value will be 3 in the worst case as default, BIFS is the backoff IFS and it is calculated by Equation 6:

The values of pSifsTime and pCcaDetectTime are given in the Table I mentioned in the IEEE 802.15.3C standard. BC is the backoff counter calculated as given in Equation 7:

BC is computed using a random function that finds a random integer value between zero and BW (backoff window). The value of BW is given in Table 4.

The maximum throughput (MT) is defined as the ratio of transmitted information in bits to the transmission duration. Throughput is defined as the ratio of payload size ($X$) to the total time required to transmit the payload, in the case when there is no priority set the maximum network throughput can be computed as given in Equation 8:

In this regard, multi-tier network architecture will help to manage these ultra-dense networks in 6G. To manage the huge traffic in 6G networks, high-capacity backhaul networks are required [67]. This can be possible by using free-space optical (FSO) systems and a fast optical fiber network. Overall, 6G wireless networks will be able to:

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  Provide super-high-definition (SHD) and extremely high-definition (EHD) video transmission which requires ultra-high throughput.

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  Support extremely low latency up to 10 microseconds.

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  Manage the Internet of Nano-Things and Internet of Bodies using smart implantable and wearable devices under very low power consumption.

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  Enable efficient underwater and space transmissions to drastically increase the limits of human activity, for example, deep-sea exploration and space traveling.

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  Support various advanced service practices like hyper-high-speed railway (HSR).

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  Improve 5G applications, like the huge Internet of Things (IoT) and entirely autonomous vehicles.

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  Support enhanced mobile broadband communications, ultra-huge machine-type communications, enhanced ultra-reliable and low-latency communications, and long-distance and high-mobility communications.

The following are some potential regulatory requirements that may be relevant for 6G networks: Spectrum Allocation. Regulatory bodies need to allocate sufficient spectrum resources for 6G networks to operate. This may involve identifying new frequency bands or optimizing the use of existing bands to accommodate the higher data rates and increased capacity demands of 6G.

Licensing and Spectrum Management. Governments may establish licensing frameworks and spectrum management policies to ensure fair access to spectrum resources. This may include licensing conditions, auction processes, and spectrum-sharing mechanisms to promote competition and efficient spectrum utilization.

Security and Privacy. Regulatory requirements related to security and privacy are likely to be strengthened for 6G networks. This may involve mandating robust encryption standards, authentication mechanisms, and privacy safeguards to protect user data and ensure network integrity.

Quality of Service (QoS). Regulatory bodies may define minimum QoS requirements to ensure reliable and consistent service delivery in 6G networks. This may include criteria for latency, throughput, reliability, and other performance metrics.

Interoperability and Standards. Regulatory frameworks may encourage the adoption of common technical standards and interoperability among 6G networks and devices. This promotes seamless connectivity, and interoperability between different vendors, and avoids fragmentation in the network ecosystem.

Environmental and Health Considerations As with previous generations of wireless networks, 6G may be subject to regulations addressing potential environmental and health concerns. This may involve assessing and mitigating the impact of radiofrequency emissions and ensuring compliance with safety guidelines.

Data storage issues, resource restrictions on edge devices, heterogeneous device management, and performance QoS issues are potential obstacles for 6G networks. Here are a few instances:

Data management. Managing the enormous volumes of data that will be produced by the growing number of connected devices will be one of the issues faced by 6G networks. The Internet of Things (IoT) will bring forth an influx of data that needs to be processed, stored, and analyzed. This will need for brand-new data management and storage strategies that can scale to meet the needs of 6G networks. For instance, edge computing and distributed storage systems can ease the strain of centralized data centers by moving computation closer to the source of data generation.

Resource restrictions on edge devices. Managing resource limitations on edge devices will be a difficulty for 6G networks. Smartphones, wearables, and IoT devices will have to function with constrained processing speed, memory, and battery life. To do this, new methods of resource optimization, such as edge caching, network slicing, and adaptive resource allocation, will be needed. To save battery life and limit the need for network connectivity, edge caching, for instance, can be used to save frequently accessed data locally on the device.

Management of heterogeneous devices. 6G networks will also need to handle a variety of heterogeneous devices with various capabilities and needs. To achieve interoperability and support device diversity, new methods of device management will be necessary. For managing devices with various operating systems and hardware architectures, for instance, device virtualization and containerization can offer a uniform platform.

Performance and QoS. To support applications with strict performance requirements, such as AR, VR, and autonomous vehicles, 6G networks will need to offer high levels of QoS. This calls for innovative methods for network optimization and scheduling that can give traffic the attention it deserves based on its QoS specifications. Network slicing, for instance, can be used to designate specific network slices for applications with various QoS requirements.

AI has performed well in many domains including computer vision and natural language processing. AI processes are computationally rigorous and implemented at data centers with custom-designed servers. With the advancement of mobile communication and Internet of Things devices, numerous applications will be operating in the future and are expected to be implemented at the edge of wireless networks. The design of the 6G wireless network needs to consider the hardware, QoS, and software requirements for IoT-based applications. Wireless networks in the 6G-AI context face various challenges including privacy, data storage, resource limitations on edge devices, managing heterogeneous devices, and performance QoS [94]. AI techniques such as federated learning, deep learning, and neural learning have the potential to meet the challenges of 6G wireless networks [95].

Security and privacy are paramount concerns in the development of 6G networks, particularly given the complexity and novelty of the technologies involved. In this subsection, we aim to delve deeper into these critical issues, which are crucial for ensuring the robustness and reliability of 6G networks across various applications, including healthcare sensing devices and industrial automation.

The security and privacy challenges encompass several key aspects such as access control, detection of malicious activities, authentication, and encryption, all of which are fundamental to safeguarding sensitive data and ensuring the integrity of communications. These issues are discussed comprehensively about the enabling technologies discussed earlier in this paper.

Table 5 provides an overview mapping these security and privacy concerns with specific enabling technologies such as Terahertz, AI, and blockchain. For instance, in Terahertz communications, authentication mechanisms based on electromagnetic signatures have been proposed to mitigate security risks [86]. Similarly, in the context of AI applications within 6G networks, access control mechanisms, malicious activity detection algorithms, and robust authentication protocols have been developed to enhance security [87, 88, 89, 90, 84].

Furthermore, blockchain technology offers promising solutions for ensuring secure transactions and communications in 6G networks through innovations in authentication and decentralized access control [91, 92, 93].

Despite these advancements, it is acknowledged that the discussion provided here requires further depth and critical analysis. Future research and development efforts should focus on addressing the inherent challenges and complexities of implementing robust security and privacy measures in 6G networks. This includes not only technical considerations but also regulatory and ethical implications to ensure comprehensive protection against emerging threats. While significant strides have been made in addressing security and privacy concerns in the context of 6G, ongoing efforts are essential to fully harness the potential of these technologies while mitigating associated risks effectively.

Along with AI techniques, high-level distributed machine learning techniques are required to make a successful execution of wireless networks in a 6G environment. The machine learning techniques elevate the complexities of the cloud wireless architecture, decision-making at the network edge, and end devices.

Communication-Efficient Distributed Training. The expanding processing and storage capacity of advanced communication makes devices capable of on-device training, learning, and processing of received data. Yet, transmitting over the unpredictable wireless channel turns into a substantial performance bottleneck for mobile distributed networks. To handle the security and privacy issues, federated learning [96] keeps the training data at each device. Federated learning is an emerging distributed ML/AI approach with privacy preservation, is specifically useful for various wireless applications, and is used as one of the key solutions to obtain ubiquitous AI in 6G.

Communication-Efficient Distributed Inference. In 6G networks, it is expected that AI services will be implemented at data centers and end IoT devices, e.g., unmanned vehicles and drones. A lot of research efforts have been made to design inference processes with very low latency and cost. To handle issues like massive computation, processing, power, and privacy in 6G networks, mobile edge computing is considered a potential candidate [95]. For instance, in the case of a deep neural network, at end devices, the features can be extracted for decision-making, and later this information can be communicated to the edge/cloud computed devices. However, the heterogeneous nature of devices in 6G networks makes computing and inter-processing mechanisms more challenging for neural networks [94].

Communication technologies like multiple-input-multiple-output (MIMO) and mmWave are considered key enablers of 5G networks and provide the required QoS. To meet the requirements of 6G networks, 6G will exploit the unused terahertz band and visible light communications (VLC).

Terahertz Communications. The radio frequency (RF) band is nearly exhausted and not enough to fulfill the 6G requirements. Spectral efficiency can be enhanced by expanding the bandwidth, which can be achieved by applying THz bands and massive MIMO technologies. The THz band will play an important role in 6G communication [74]. The THz band is intended to be the next frontier of high-data-rate communications. THz communications use frequency bands between 100 GHz and 10 THz with wavelengths in the 0.03 mm – 3 mm range. ITU-R recommends 275 GHz - 3 THz bands for the communication of cellular networks [75]. In comparison with mmWave, THz faces challenges due to ultra-high frequencies. The main challenges that are hurdles for terahertz’s commercial adoption are propagation loss, molecular absorption at a higher frequency, the high penetration loss for solid objects, and, the antenna design complexity. In mmWave, the propagation loss can be controlled using the directional antenna arrays mechanism. However, some of the frequencies in the terahertz spectrum are more sensitive to atmospheric molecular absorption. This issue can be avoided by only using selected frequency bands in the Terahertz spectrum [68].

Optical Wireless Technology. Optical wireless technology (OWC) is considered a key enabler for 6G communications; however, it is already being used partially in 4G/5G networks. Main OWC technologies include light fidelity, optical camera communication, VLC, and FSO communication [97, 98, 99, 100]. Networks with OWC technologies have the potential to provide ultra-throughput and very low latency. VLC is proposed to support RF communications using light-emitting diode (LED) luminaries. These devices can switch among various light intensities to modulate the signal. The research activities on VLC are more progressive than the terahertz communications. Although, the IEEE 802.15.7 standard is proposed for VLC, yet not been adopted by 3GPP. VLC has a short range, requires an illumination source for communication, and is also sensitive to the noise created by any light source including the sun [69]. Due to these reasons, it is mostly recommended for indoor environments.

Full-Duplex Communication. Recently, cellular networks have adopted full-duplex solutions which will make the base station’s transceiver capable of transmitting a signal while receiving within a limited range, such design is achievable using innovative design of self-interference suppression circuits [70]. The main innovation is being achieved by improving the design of the antenna and circuit which reduces the cross-talk between receiver and transmitter for a wireless device [101].

In the future, technological advancement will be capable of simultaneous transmission and reception. Such advancements enhance the multiplexing capabilities of the existing cellular systems in terms of throughput. A 6G network could be very useful for such a full-duplex network in terms of performance requirements.

Novel Channel Estimation Techniques. For cellular networks, channel estimation for beam tracking is considered an important element for ultra-high frequencies. Therefore, advanced channel estimation techniques will be highly beneficial in cellular networks. If we talk about mmWave, design challenges exist for channel estimation techniques due to complex antenna architectures. A recent research effort has been made to provide channel estimation for mmWave, known as out-of-band estimation, which provides an improvement in the reactiveness of beam management schemes [71].

Sensing and Network-Based Localization. Techniques based on RF signals have been broadly used to empower concurrent operation of localization and mapping techniques [102]. However, it is still not adopted for the case of cellular networks. In this regard, 6G will provide an integrated interface to support localization and mapping mechanisms which will offer multiple advantages including enhancement in control operations, less interference, and novel services for applications like eHealth.

Advanced Multiple-Input Multiple-Output. Multiple antenna technologies have achieved considerable response from both academia and industry as they are capable of providing high geographical coverage and spatial multiplexing which helps to boost spectral efficiency [103, 104] and could be very useful to achieve the performance goals of 6G networks. Therefore MIMO is a potential area that can integrate itself with 6G networks which will increase the capability of 6G network devices in terms of transmission.

The 6G network offers various innovations to the existing communication architecture, such as virtualization. Significant developments in wireless communication technology are being driven by the future implementation of 6G networks and the transition to 5G. Among these developments, the terahertz (THz) and millimeter-wave (mmWave) frequencies are proving to be crucial facilitators due to their capacity to accommodate extremely high data rates and extensive communication. Two key elements of this architecture are the provision of numerous network services and coordinated multiuser beamforming training. This document describes the system architecture for these capabilities, with an emphasis on the integration and optimization of THz/mmWave technologies within the framework of 5G and 6G networks. The 6G network architecture includes multiple components, such as Base Stations (BS) and Access Points (AP), User Equipment (UE), Beamforming Modules, Network Core, Beam Training Protocols, and Multiple Network Services Framework [101].

In the following, we discuss some of the promising architectural domains and examine all the main components.

Cell-Less Architecture and Tight Integration of Multiple Frequencies The 6G network will create a strong relationship between cells and the user equipment (UEs) and will create a cell-less architecture. This can be accomplished using dual-band techniques with multiple radios. Such architectures provide high mobility support with fewer overheads. It will increase the user capacity by seamlessly transitioning to multiple heterogeneous links (e.g., to identify the best channel) without any prior configurations [20].

Ultra-Massive MIMO (Um-MIMO). A major increase in antenna count is incorporated into Um-MIMO, an advancement of MIMO technology that improves spectrum efficiency, capacity, and reliability. Because it allows for the integration of a large number of devices and the transmission of high data rates, this technology is essential for 6G networks. Recent developments concentrate on enhancing beamforming methods and lowering interference, which makes Um-MIMO a crucial enabler for high throughput and low latency applications like virtual reality (VR) and augmented reality (AR).

Radio Resource Management (RRM). To satisfy the varied needs of different applications and services, RRM in 6G networks involves dynamic allocation and optimization of radio resources. RRM incorporates artificial intelligence and machine learning techniques to improve decision-making processes that will lead to better quality of service (QoS) and more effective spectrum utilization. These developments are crucial for controlling the higher demand and complexity in 6G networks, especially in situations where there is a high level of mobility and a dense population of devices.

Non-Orthogonal Multiple Access (NOMA). NOMA dramatically increases spectrum efficiency by leveraging power domain separation to enable several users to share the same frequency resources. By allowing more users to share the same bandwidth, NOMA is anticipated to provide huge connections and high data rate transmission in 6G networks. This is very helpful for Internet of Things applications and other situations where a lot of devices need to talk to each other at once. To improve NOMA’s performance in 6G, research is still focused on power allocation and interference management optimization [33].

Coordinated Multi-Point (CoMP). CoMP allows numerous base stations to coordinate their receptions and transmissions, improving network performance. At the cell borders, this synchronization enhances signal quality and lessens inter-cell interference. CoMP plays a key role in enabling ultra-reliable and low-latency communications (URLLC) in 6G networks, which are necessary for applications like remote surgery and autonomous driving. Research endeavors to enhance coordination mechanisms and create more advanced algorithms to optimize the advantages of CoMP in intricate network settings [21].

Rate-Splitting Multiple Access (RSMA). By splitting the data stream into common and private sections, RSMA, a cutting-edge multiple-access technique, enables more effective and flexible resource allocation. This technique can improve communications reliability, reduce latency, and increase spectrum efficiency, making it viable for 6G networks. When it comes to managing the varied and dynamic traffic patterns in 6G, RSMA is anticipated to be crucial to meeting the network’s performance objectives [33].

3D Network Architecture. 6G networks will support three-dimensional (3D) visualization by using heterogeneous architecture. Communication technologies like drones and satellites will use these architectures to improve related QoS. Furthermore, these heterogeneous communication architectures could be easily deployed in rural and disaster areas [105].

Virtualization of Networking Equipment. In 6G networks, network virtualization will be one of the key concepts. The virtualization will be implemented at different layers i.e., the network layer, MAC layer, and physical layer. It will reduce the management burden from hardware devices cost-effectively. It will create a centralized virtual controller which will be responsible for the policy and data forwarding [106].

Advanced Access-Backhaul Network (FSO) Integration. In 6G networks, technologies are supposed to provide ultra-high data rates which also require sufficient expansion of backhaul capacity. Furthermore, future VLC and terahertz implementations will use a massive number of devices, which need backhaul connectivity with the core network and neighboring nodes [72].

In some cases, such as, at remote geographical locations (e.g., sea, space, and underwater), it is not feasible to deploy optical fiber as a backhaul network. For such scenarios, the FSO backhaul network is a potential communication system for 6G networks [76, 77, 107]. FSO performs like an optical fiber network and provides similar communication services. FSO can provide a large communication range, even greater than 10,000 km. Figure 3 shows an example of an integrated backhaul link.

Energy Harvesting Strategies for Low-Power Consumption Network. Energy/power harvesting is defined as the process by which energy is extracted from external resources including solar, thermal, wind, and kinetic energy, etc., and then preserved for wireless autonomous devices, like wearable biomedical monitoring sensors. The energy harvesting process is capable of producing a small amount of energy which is an ongoing challenge for the industry. One of the oldest applications of energy derived from ambient electromagnetic radiation (EMR) is known as crystal radio [109].

5G networks do consider energy harvesting techniques; however, not much work is done on that due to a lack of interest from manufacturers. As 6G networks deal with a massive number of interconnected smart devices (e.g., IoT devices ) that require energy resources to keep the device alive. There is a need to identify suitable energy-efficient mechanisms for such devices [73].

Blockchain. Blockchain manages huge datasets in an efficient way [110, 80, 81]. Blockchain works using the approach of distributed ledger technology. A distributed ledger’s database operates in a distributed manner by using various nodes. Every node replicates a copy of the ledger. The nodes can work independently without involving the centralized node. In the blockchain, the data is managed in the form of blocks that are connected securely. The blockchain can manage huge data involved in IoTs-based systems [82]. Hence, 6G capabilities will support blockchain-based applications in terms of their performance goal.

Integration of Wireless Information and Energy Transfer (WIET). Recently, producing energy from the open environment is one of the potential areas that can help to increase the lifetime of battery-operated wireless sensor devices. WIET is recognized as one of the innovative technologies for 6G networks. WIET also utilizes the wireless spectrum for communication. WEIT can be very useful for battery-less devices which are supposed to operate in 6G networks. The base stations in 6G will be used for transferring power as Wireless Information and Energy Transfer (WIET) uses the same fields and waves used in communication systems. WIET is an innovative technology that will allow the development of battery-less smart devices, charging wireless networks, and saving the battery life of other devices [111, 83].

Open Radio Access Network (O-RAN). It is an architecture that has emerged as a promising candidate for next-generation 6G networks [112]. O-RAN seeks to promote openness, intelligence, and flexibility in the radio access network (RAN) by leveraging standardized interfaces and modular components [113, 114]. This approach facilitates the integration of multi-vendor equipment and advanced technologies, driving innovation and reducing deployment costs. O-RAN is built on several foundational principles that involve the adoption of standardized open interfaces that allow the interoperability of equipment from different vendors. This openness helps break the traditional dependency on single-vendor solutions, fostering a more competitive and innovative ecosystem. By encouraging a multi-vendor ecosystem, O-RAN reduces costs and enhances innovation [113]. The modular design and open interfaces of O-RAN enable more flexible and efficient network deployments and upgrades. Furthermore, the integration of AI and ML supports advanced network management capabilities, improving overall performance and user experience. Achieving seamless interoperability across different vendors’ equipment requires rigorous standardization and testing. Additionally, the openness and modularity of O-RAN introduce new security challenges, discussed in Section 4.2, that must be addressed to ensure robust protection against cyber threats [115, 116].

The 6G network complexity in terms of architecture and data requires AI and machine learning techniques to play their role in the successful execution of 6G services. Although 6G does not specify specific techniques, more likely AI based on a data-driven model could be used for the successful deployment of 6G networks [73]. Currently, 5G networks only integrate partial AI and ML techniques while it is expected that in 6G networks AI and ML will be fully integrated with 6G networks like the concept of Industry 5.0.

Unsupervised and Reinforcement Learning. Unsupervised and reinforcement learning techniques could be used as a potential learning technique in 6G networks. In 6G networks, a massive amount of data will be generated and it will not be an easy task to assign labels as required in supervised learning; whereas, unsupervised learning does not require such labeling and is capable of representing a learning model for such data. Furthermore, unsupervised learning in combination with reinforcement learning could produce efficient autonomous systems [47].

AI technologies have the potential to be extremely important in addressing several issues related to the installation and functioning of 6G networks. The rapid advancement of wireless networks will make 6G significantly distinct from the earlier generations, as it will be exemplified by a high level of heterogeneity in several aspects including network infrastructures, network access mechanisms, dual-band devices, processing resources, etc. Furthermore, the size of the massive data generated in wireless networks is rising substantially. In this section, we promote AI algorithms (detailed in Table 9) as an essential tool to expedite intelligent decision-making and learning for 6G wireless networks [96]. We discuss and review different works which addressed these issues [107, 103, 104, 110, 95, 94, 96, 97, 98, 99, 100, 76, 117, 118].

Big Data Analytics for 6G. For 6G wireless networks, four main categories of data analytics can be potentially applied. These categories include descriptive, diagnostic, predictive, and prescriptive analytics. Descriptive analytics uses historical data sets to obtain perceptions performance indicators including traffic profiles, channel states, user viewpoints, etc. Descriptive analysis helps to extend the familiarity of the network operators [87, 88, 89]. Diagnostic analytics is used for the identification of network failures with the potential reason for failure or fault so that future systems can be improved. Predictive analytics closely observes the data pattern in historical data and predicts future behavior and predictions. Predictive analytics are tied with machine learning techniques to get optimal results. Prescriptive analytics helps to improve various domains in learning including resource allocation, network virtualization, cache management, edge, fog computing, etc [94].

AI-Enabled Closed-Loop Optimization. Existing optimization mechanisms for wireless networks are considered to be inappropriate for 6G wireless networks due to various characteristics of 6G including dynamicity, complexity, the massive density of devices, and heterogeneity. Furthermore, existing mathematical optimization techniques have shortcomings for such massive 6G networks. For example, the objective functions in the algebraic form help optimizers and make a simple solution; however, 6G wireless networks have different scenarios, and these solutions are not suitable. One of the potential AI mechanisms is an automated and closed loop that can be used for 6G wireless networks. In this regard, current AI mechanisms, like reinforcement and deep reinforcement learning (DRL) can be used, which can establish a feedback loop between the decision process and the wireless network system. By using this approach, the decision-making process will make the best decision based on the current situation [95].

Efficiency and Spectrum Management. Managing the increasingly crowded spectrum effectively, especially with the introduction of new frequency bands such as mmWave is a huge challenge for 6G networks. In this context, Real-time usage patterns can be analyzed and spectral resource distribution can be optimized by AI-driven dynamic spectrum management. By predicting spectrum demand and dynamically allocating frequencies to various users and applications, machine learning algorithms can improve spectral efficiency.

Network Optimization and Self-Organization. Another difficult task is handling the complexity of heterogeneous 6G networks, which will have a variety of frequency bands, several access protocols, and a large number of linked devices. Self-organizing networks (SONs) with AI capabilities can automate tasks including network optimization, configuration, and repair. To guarantee smooth connectivity and performance, machine learning models can optimize factors like power levels, antenna tilts, and handover thresholds by analyzing network data and predicting and mitigating possible problems.

Latency Reduction and Reliability. 6G faces challenges in achieving ultra-low latency and excellent dependability, which are essential for applications like industrial automation, remote surgery, and autonomous driving. AI can forecast traffic trends and instantly modify network resources to reduce latency. When AI and edge computing are used together, latency can be greatly decreased by processing data closer to the end consumers. AI can also improve mistake detection and correction systems, guaranteeing improved data transmission dependability.

Energy Efficiency. It is a difficult issue to reduce the energy consumption of 6G networks, which will consist of a large number of base stations and connected devices. By dynamically modifying the power consumption of network nodes in response to actual demand, artificial intelligence (AI) can optimize energy usage. Machine learning algorithms can recognize trends in network utilization and forecast times of low traffic, which makes it possible to implement energy-saving techniques like turning down specific base stations or lowering their power consumption during these periods.

Self-healing in Wireless Networks. Advanced AI technologies could be useful to optimize the physical layer’s performance of wireless networks. The existing wireless systems have various physical layer issues including noise, channel and hardware impairments, quadrature imbalance, interference, and fading. AI technologies can provide an optimal way to communicate among different hardware. In the future, AI is supposed to provide self-healing and self-optimizing mechanisms for sensor-based 6G networks [119].

Table 6 shows the relation between various network layers and potential AI algorithms for those layers. AI algorithms (detailed in Table 9) can play a critical role in improving the performance of operational activities of the network model layers. AI algorithms will somehow automate the functionality of these layers. For the physical layer, K-Means, DNNs, CNNs, and CCNSs algorithms are suggested. These algorithms can easily be mapped with physical layers modulating techniques like OFDM and MIMO. To optimize the frame performance in terms of scheduling and error detection, at the data link layer, Q-learning, supervised learning, and reinforcement learning are suggested. At the network layer, to increase the performance for routing and mobility, reinforcement learning, supervised and unsupervised learning, K-Mean, and Q-Leaning are advised.

Table 7 provides a comparison of 4G, 5G, and 6G technologies in terms of usage scenarios, applications, network characteristics, and technologies. In 4G networks, a simple implementation of MBB was available which evolved into eMBB and provides better mobile broadband. 5G networks also support eMBB, mMTC, and URLLC and it is expected that these technologies with further enhanced like umMTC and ERLLC will support enhanced low latency and massive machine-to-machine communication. The use of ERLLC, umMTC, and FeMBB 6G will support innovative applications like Holographic verticals, Internet of bio-Nano-Things, and deep-sea sightseeing, etc. A huge shift in technologies can also be seen among 4G, 5G, and 6G networks. 4G mainly used OFDM and MIMO; 5G uses mm-wave and massive MIMO, and 6G will use THz band and SM-MIMO, etc. to achieve its goal in terms of delay and throughput.

It is expected that in 6G networks, a fully ML and AI-enabled network will be in operation. That is why it is important to provide a detailed analysis of upcoming network architectures with their requirement in terms of AI. Table 8 shows the link between various existing and future network architectures and related AI algorithms. SDN, NFV, and Cloud/fog/edge computing are considered potential network architectures that will use a fully AI-based environment to support 6G networks. Currently, these architectures partially support AI and ML aspects.

VR can be further divided into the following two categories:

Extended Reality (XR). XR is a broader term that includes technologies to enhance our senses [159]. It mainly includes augmented reality (AR), mixed reality (MR), and virtual reality (VR). AR, MR, and VR over wireless links have been emerging as potential future applications targeting various areas such as education, training, entertainment, tourism, sport, and gaming.

AR takes a real-world environment and adds computer-based input to the environment. Hence, AR is a collaborative activity where real-world environment experience is obtained. The environment is built of enhanced objects and these objects are available in a real-world environment. AR technology makes use of various sensors to experience modalities like visual, haptic, olfactory, motion, and somatosensory. AR consists of three basic features: 3D visualization of objects, real-time collaboration, and a mixture of virtual and real-world environments [160].

Figure 4 shows the AR-based user interface for the mobile applications of IKEA, where customers can try various options according to their real-world environment. For example, in a Dulux-based AR application, a customer can apply different colors in his real home environment, and in an IKEA application, a customer can try various pieces of furniture in a real home environment. Furthermore, these applications also handle the measurement and size issues where you need to deploy the furniture.

VR provides a purely virtual environment without considering the user’s surroundings. VR-based devices such as HTC Vive and Google Cardboard provide users with a completely imaginary environment such as a squawking penguin colony with dragons. MR combines the characteristics of VR and AR. MR technology has started to take off with Microsoft’s HoloLens, one of the most famous mixed reality apparatuses [161].

These applications process exceptional QoS challenges including quality of experience, latency, and capacity/user. To achieve this, technologies like fog and cloud computing will help to act as computing and intelligence layers for AR/VR applications. 4G has been proven as a key enabling technology for data-hungry applications like video-over-wireless. Later, the high use of these multimedia applications was managed by 5G using the mmW spectrum. The mmW spectrum attracted more data-intensive applications like AR, VR, and MR. It is expected that in the coming years, the 5G spectrum will be depleted due to the increasing use of these data-intensive applications as they demand system capacity up to 1 Tbps [162]. One of the reasons for high system capacity is that the data used in AR/VR/MR-based applications cannot be compressed as it is presenting real-time interaction. Furthermore, managing real-time user interaction with AR/VR/MR-based data-intensive applications requires a delay in microseconds.

Holographic Telepresence. Holographic telepresence is a growing technology for fully interactive 3D video conferencing. Holographic telepresence systems are capable of presenting real-time, full-motion-based 3D images of people and objects in a room. It also accommodates real-time audio and associates it with a particular 3D image of a person. Images of people and surrounding objects at a remote location are initially captured, then compressed and transmitted over a broadband network. At the destination, the data is decompressed and similarly projected through laser beams as a conventional hologram is produced. Holographic telepresence is capable of revolutionizing various types of communication. For example, telepresence in teleoperation can allow medical specialists to engage in real-time complex operations from thousands of miles away. Furthermore, this technology can also reduce the travel requirement for personal and business meetings. Distance learning using telepresence can shape the future of universities and colleges. Other prominent examples include programmed movies, entertaining games, 3D navigation applications, etc. The applications mentioned above like a 3D holographic display require a set of high-level QoS requirements. According to a study, [118], a raw uncompressed hologram having colors, and parallax, with 30 fps requires a data rate of up to 4.32 Tbps with a strictly bounded delay of a few milliseconds. 3D holographics require such a high data rate due to the requirement of thousands of synchronized angles. 6G network is capable of providing such high throughput and less delay required by these applications. Furthermore, an acceptable visual quality at the remote site is a challenging task.

The primary healthcare challenges confronting the global population encompass a growing elderly demographic, escalating healthcare expenditures, and a substantial mortality rate stemming from chronic ailments [65, 163]. Notably, in nations such as the United States, Germany, the United Kingdom, France, Switzerland, Japan, and Australia, life expectancy has risen to 79.3 years, 81 years, 81.2 years, 82.4 years, 83.4 years, 83.7 years, and 82.8 years, respectively. This upward trajectory in life expectancy is expected to exert significant pressure on existing healthcare systems and concomitantly elevate healthcare costs.

Furthermore, a substantial portion of the population dies from fatal and chronic diseases including hypertension, cardiovascular disease, diabetes, and asthma. Empirical investigations have unveiled that the impact of these maladies can be mitigated when detected in their nascent stages. To realize this objective, forthcoming healthcare systems must transform, leveraging digital and communication technologies to facilitate proactive wellness through early detection and continuous monitoring services. It is estimated that the demand for remote healthcare monitoring services will burgeon to 761 million by the year 2025 [164, 165]. This underscores the imperative for advancing healthcare infrastructure and technology to address the burgeoning healthcare needs of an aging and chronically ill population while containing costs.

Smart healthcare services will encompass a broad spectrum of functionalities, ranging from continuous monitoring to advanced telemedicine solutions, such as real-time remote surgical procedures. While the current 5G spectrum has initiated its role in enhancing remote healthcare capabilities, certain demanding scenarios, such as remote surgeries, comprehensive health monitoring within elderly care facilities equipped with an array of extensive biomedical sensors, and the provision of remote healthcare services to numerous soldiers on battlefields, necessitate ubiquitous health monitoring characterized by ultra-high data rates measured in gigabits per second (Gbps) and terabits per second (Tbps), as well as ultra-low latency to support real-time solutions. In this context, 6G networks stand poised to revolutionize remote healthcare by surmounting the limitations of time and space[4].

The technological advancements inherent to 6G networks will usher in a new era for eHealth applications, harnessing groundbreaking technologies such as mobile edge computing, artificial intelligence (AI), fog computing, and cloud computing.

Figure 5 shows a typical remote patient monitoring system for hospitals where patients, nurses, and medical devices are equipped with various healthcare sensors, and the monitoring data is sent to the Internet and cloud computing architectures for further processing. The doctors can see the different results online and can monitor and diagnose the patients in real-time from any remote location.

Brain–Computer Interaction. A brain-computer interface (BCI) is a communication pathway between a sensor-equipped and an external device. The main objectives of BCIs include repairing human cognitive or sensory-motor functions [66, 151]. The sensors collect the brain signals and send them to a digital device for analysis.

Research on BCIs started at the University of California, Los Angeles (UCLA) in the early 1970s, under research funding from the National Science Foundation. Recent studies in the area of Human-Computer Interaction (HCI) show that machine learning algorithms can play a key role in successfully extracting features from the frontal lobe and then classifying mental states (Normal, Neutral, focused) and emotional states (Good/bad mood) using EEG generated brain-data [66, 151]. Such communication requires ultra-reliable communication with very low delay, values in microseconds. The characteristics of 6G architecture will support the deployment of BCI systems.

Human Senses-Based Applications. Humans have five basic senses: hearing, vision, taste, smell, and touch. In the future, applications will be available that will be capable of transferring human senses data to a remote location for processing. Such applications use sensor-based technologies which make use of neurological methods. It requires ultra-low delay and high throughput. The 6G networks can provide such services [1, 10].

Industry 5.0 or the Fourth Industrial Revolution, is the recent revolution of traditional manufacturing using smart technology. Technically, it mainly emphasizes the usage of large-scale machine-to-machine communication (M2M) with the help of Internet of Things (IoT) architectures and deployments to obtain automation [153]. Industry 5.0 will make the machine’s components capable of performing self-diagnostic and predicting any possible future issues using AI algorithms so that any future failure can be avoided. Such decisions constitute the first steps of the Industry 5.0 era, where machines will make autonomous decisions and these decisions will have significant effects on people’s lives. High achievements obtained in many areas with AI algorithms are the basis of these developments in the industry.

Furthermore, machine components should be capable of communicating with the other components and other machines. Predictive maintenance, IoTs, smart sensors, and 3D printers are the main key enablers for Industry 5.0. 6G technology will further promote the Industry 5.0 concept which was initiated with 5G. Industry 5.0 introduced the idea of the digital transformation of manufacturing through cyber-physical systems (CPS), which opens the gates between physical factories and virtual computation. Such a combination will promote new emerging technologies like M2M communications in a cost-effective way [153]. Efficient automation systems in terms of asynchronous communication are considered key enablers of Industry 5.0, in this regard, 6G is proposing the use of terahertz domains to provide the required QoS for smart integrated systems. Figure 6 shows the evolution of Industry 5.0.

Indoor Coverage. The 5G infrastructure usually operates under the milimeterWave spectrum (60 GHz) for indoor scenarios; however, it faces issues like penetration in solid materials. 5G also proposed the use of distributed antenna systems (DASs) for indoor environments which poses issues like scalability and deployment. 6G focuses on cost-efficient solutions for indoor communication scenarios using ultra-high capacity wireless relays with the visible light spectrum [147].

Smart City. A smart city can be defined as an urban area equipped with various types of sensors that collect data and then perform analysis of the collected data to manage and improve the infrastructure, services, and quality of life. The collected data may belong to various resources including citizens, installed devices, and buildings. The embedded data analytical capabilities help to manage traffic systems, power plants, schools, libraries, hospitals, water supply networks, waste handling, crime detection, etc [167].

5G technologies act as a key enabler for a smart city; however, 5G only made the city partially smart which means that inside a city, only the fragments (traffic management, water management, electricity, etc.) are individually smart but not holistically as a smart city. 6G technologies have the potential to speed up the adoption of smart cities by offering required QoS using innovative and disruptive communication technologies [102].

6G will provide support for user-centric M2M transmissions to further improve the deployment of smart cities. Furthermore, to provide a long battery life for the devices in smart cities, energy harvesting approaches will be used, though, 5G initially promised to adopt energy harvesting solutions but still, it is not being adopted.

Internet of Vehicular Services. The creation of a highly networked and intelligent transportation system is a compelling use case for the Internet of Vehicles (IoV) utilizing 6G networks. This system uses distributed intelligence and advanced communication technologies to improve traffic efficiency, road safety, and the entire driving experience. Unmanned vehicular mobility refers to vehicular mobility without a driver/pilot on board. These vehicles can be controlled remotely and they are capable of operating and navigating autonomously by sensing their environment. There are different types of unmanned vehicles including Unmanned ground vehicles (UGV) like cars and combat vehicles, Unmanned aerial vehicles (UAV) such as drones and autonomous spaceport drone ships, and unmanned underwater vehicles (UUV) and driver-less trains.

The locomotive industry is precipitously advancing towards fully autonomous transport systems which will ease the management burden on the organization [149]. Connected and autonomous vehicles (CAVs) are one of the potential use cases for future unmanned mobility. However, its deployment is still challenging in terms of time-bounded services and high mobility (up to 1000 Km/h). Further, it is estimated that vehicles will be fitted out with a high number of sensors (greater than 200 per vehicle by 2020) which can be only served with terahertz spectrum [150]. Figure 7 shows a self-driving car example in Australia with all embedded features and requirements. Moreover, flying vehicles like drones indicate a massive potential for several use cases: industrial construction, the agriculture sector, and entertainment. 6G can provide services for such massive data-hungry use cases.

Internet of Everything. The term IoE evolved a few years ago, and there is a clear difference between the definition of IoE and IoT. IoE is defined as intelligent connectivity among things, data, processes, and people. The data is converted into useful information and delivered to the right person/object at the right time. Billions of objects update their status by using various sensors. The IoT can be defined as the network of physical objects that are accessed on the Internet. IoE incorporates a massive network of things where each thing-network is an autonomous system and capable of coordination with other networks [46]. IoT and IoE have the same purpose in terms of connectivity; however, IoE’s terminology is used for a broader aspect with massive data. IoE also introduces the intelligence layer for decision-making. The IoE is expected to provide massive connectivity in various domains including healthcare, agriculture, logistics, manufacturing, and education. The 6G networks will support such massive data connectivity using its flexible heterogeneous architecture [152].

Underwater Networks. Underwater networks are seeing new opportunities as a result of the advancement of 6G technology. A noteworthy application of 6G networks to improve the effectiveness and dependability of data transfer in marine environments is the integration of underwater communication devices. Underwater exploration, environmental monitoring, and maritime security are just a few of the operations that this integration can greatly enhance. To tackle issues like scarce wireless resources and high connection demands in underwater locations, for instance, 6G can be combined with multiple-input multiple-output (MIMO) systems and non-orthogonal multiple access (NOMA) technology [33]. Drones for underwater communication is another exciting application. In order to link underwater sensor networks with terrestrial base stations and enable real-time data transmission and monitoring, these drones can serve as relay nodes. Applications needing low latency and high reliability, such environmental monitoring and search and rescue, may find this approach especially helpful.

Terrestrial Network Technologies.

6G technology promises ultra-low latency, huge connection, and hitherto unheard-of data rates, which might completely transform terrestrial networks. A significant use for 6G is in the field of smart cities, where it can facilitate the widespread installation of IoT devices and provide cutting-edge features like real-time surveillance, smart grid management, and driverless cars. Reconfigurable intelligent surfaces (RIS) and unmanned aerial vehicles (UAVs) are two examples of technologies that can be integrated with 6G to significantly improve network coverage and capacity, guaranteeing smooth connectivity in metropolitan areas. Disaster management and emergency response represent yet another important use. In places where the current infrastructure has been destroyed or is nonexistent, 6G-enabled UAVs can swiftly set up communication networks. The coordination and effectiveness of relief efforts can be enhanced by the ad hoc networks that these UAVs can create, which will allow rescue teams and control centres to communicate in real time [102].

Section V provides a categorization of potential use cases for 6G networks, the categorization includes VR, health, and Industry 5.0. VR is further subdivided into two categories including XR and teleportation. Both XR and teleportation are discussed in detail with examples. The second important category belongs to healthcare and a couple of potential use cases are discussed under this category including BCI and human sense-based applications. A discussion about the implementation of BCI for the 6G network is provided. Industry 5.0 is discussed as the last category of the potential use cases that maps AI and ML with industrial applications. Industry 5.0 is being considered a revolution for the automated industry and it is vital to link it with upcoming 6G networks.