Advances in AI for 6G Signal Processing

Dear Colleagues,

Traditional mobile communications are well designed and have been extensively deployed by mobile operators. However, future 6G systems consider more complicated communication and network scenarios such as automated vehicles, factory automation, telemedicine, and so on; thus, the traditional approach may not be suitable for supporting multiple 6G requirements. Furthermore, Artificial Intelligence (AI) now seems to dominate many aspects of current technology. In 6G wireless systems, the data-driven approach of AI algorithms will be equipped with heuristic parameter settings and thresholds. A promising concept regarding the improvement of 6G system design and optimization is the adoption of AI algorithms. This is because AI algorithms already play a key role in research fields such as image recognition (deep learning).

The optimization parameters associated with wireless communication systems are complexity, cost, energy, latency, throughput, and so on. In this regard, AI and Machine Learning (ML) algorithms will be employed to solve multi-parameter optimization problems, enhance the performance of  6G and develop new services. It is well known that AI and ML algorithms are utilized for classification, clustering, regression, dimension reduction and decision making. It appears that many 6G signal processing elements have increased levels of similarity to these uses. For example, resource allocation and scheduling represent a classification and clustering problem. Moreover, channel estimation represents a regression problem, Viterbi decoding is based on dynamic programming, and network traffic management corresponds to a sequential decision-making problem. In this Special Issue, we discuss the domains of signal processing in which chain AI and ML algorithms can be implemented in 6G systems. The key technical aspects to be investigated regarding the application of AI algorithms to wireless communications are as follows:

• Collection and data processing using AI algorithms;
• Matching of signal processing elements for wireless communications with processes, functions and applications of AI algorithms;
• The key parameters for the measurement of the performance of wireless communications are throughput, energy efficiency, latency and others. AI algorithms have to improve these parameters;
• AI algorithms have limitations such as an extensive training data set and high computational power, which must be considered;
• AI algorithms and Shannon-based current wireless communications systems have to find a common theoretical background.

This Special Issue aims to present an overview of recent advances in AI processes and algorithms with regard to the following research areas (topics) for future 6G mobile communications systems:

• Physical-layer signal processing with the aid of AI (modulation, error correction coding, power level, MIMO techniques, channel estimation in multicarrier systems, etc.)
• Data-link-layer signal processing with the aid of AI (resource allocation and scheduling, handover, etc.)
• Network-layer signal processing with the aid of AI (cell planning, network traffic, etc.)
• AI-based cross-layer optimization techniques (Cybersecurity, real-time data transmission for vehicle to vehicle communication, Internet of Things, wireless sensor networks,)

Dr. Costas Chaikalis
Dr. Apostolis Xenakis
Dr. Dimitrios Kosmanos
Guest Editors

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• artificial intelligence
• 6G
• signal processing
• wireless communications