Future Research Directions In 6G

The standard of cellular networks is gradually moving towards 6G. The pioneering and unique features that it introduces, such as connecting even in space and underwater, are going to make governments, organizations, and researchers focus a lot of time, money, and efforts towards it. Artificial Intelligence will provide the backbone in the intelligent management of the network for 6G and blockchain will support the distributed secure system of 6G. But, it is noted that the research in 6G architecture and technology has to be initiated in such a way that there will be proper scopes for researchers to improve the same. For this, we here in this survey describe the major requirement of 6G with an overview of the overall architecture along with the technology associated with 6G, the critical challenges, future research direction, and how to find your way towards achieving a leading end:

1. Applications of Machine Learning Algorithms: Since machine learning is an important domain of 6G, we need to study various machine learning algorithms. This will help in analyzing their effects on various performance factors of 6G. The normal process of machine learning will not give desirable results for frequently added data in an application. We have to keep better federated learning on the top priority list in machine learning applications, although they also suffer from fairness issues. Transfer learning can be an efficient-approach to make 6G systems compatible with wireless environments.

2. Scalable and Reliable Blockchain-Enabled 6G: For secure storage in various smart services in 6G such as immutable ledgers and transactions in a distributed manner, we generally use blockchain in 6G. Blockchain is used in various smart services in healthcare, smart supply chain management, etc. Here, 6G technology will have extreme low latency, low energy consumption, and all this while maintaining the security perspective. We also want to increase scalability and reliability in 6G systems. Blockchain can be an efficient tool to deal with all the mentioned features while ensuring that privacy concerns are preserved.

3. Meta-learning-enabled 6G: We intend to investigate specific machine learning techniques or algorithms for the application of 6G. In this direction, meta-learning provides us with models that learn metadata for machine learning-based experimentation. We merge general machine learning with meta-learning; this can give way to more intelligent technology in future 6G applications and wireless mobile technology development.

4. ​​Cloud-based architecture and technology: Fundamentally, 6G is a data-flow-based edge-centric technology. The chaining of network functions and services will be dynamic based on the efficient balance between the consumed and available cloud network resources. This system will be fundamentally based on the combination of different approaches to machine learning in the cloud environment. We will also look into the data with respect to privacy and security being used by this technology.

5. Terahertz frequencies for data rate growth: We will also wish to grow the data rate; high data rate is one of the highest requirements in 6G. The frequency band above 52.6 GHz is taken, which will have ultra-high data rate at least 100 Gbps or even more. The use of THz band will increase the spectrum efficiency since free space loss, molecular absorption and many others are reduced. We have to manage the field of energy efficiency in such a way that it can be made possible because when the number of antennas are integrated, the SNR can be increased very fast. Applying multiple-input and multiple-output (MIMO) configuration, we can allow cost-efficient distribution with high data rate over a wide area of ​​the 6G network. We also need to take care of electromagnetic fields and bio-aware beam streaming fields.

6. AI-based Edge Computing: Mobile computing, which is still in use in 5G, proves to be insufficient to process this huge number of data processing. So, mobile edge computing is a great impetus as it can partition the network into a cloud computing architecture. We should reduce the latency in the 6G network through which we can easily access the radio network. We want to use more AI in the end devices or in the process of 6G technology. So, here we are using machine learning methodology to help target offline computing capabilities in such a way that one can do the resource allocation and offloading of resources either to the clouds or the edges in an intelligent way. Now, we introduce central controllers that partition the provided application/dataset into mini batches that help users to allocate resources across multiple processing devise provided within the clouds or edges. A technical goal of ‘cloudification’ or ‘edge clouding’ of 6G connections for real-time communication can be reused along with the impact of AI and data. We will finally reach a new economy of scale along with many other scopes. Developing an online learning-based relationship between the various sub-components of the 6G framework will be helpful in this regard. In agriculture and industries, 6G will be able to intelligently generate output; it will then implement secure, very fast relationships between the various modules.

7. An intelligent system (LIS): (LIS) can be applied in localization-based 6G design on LIS-assisted MMWAVE system, with positioning as an application. Here we try to determine an optimal location and lace with that location through reflection and metasurface. It is challenging to be identified with it in almost all forms; it can be referred to as some inverse type of channel modeling. As we know the content efficient deployment of LIS needs further study.

Conclusion

In this article, we discuss the architecture and technology of 6G, where new features are introduced, focusing on the increase of speed compared to previous cellular standards. By using 6G, we are trying to reach everywhere, such as underwater and space, etc., where normally we cannot even imagine to reach. It is considered an intelligent network management with distributed nature of handling privacy and security based on machine learning and blockchain. This chapter discusses the challenges and future research directions in 6G networks, in order to identify the scopes of improvement in 6G systems with which successful deployment of 6G is possible as per the objective of the invention of 6G.

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