Wireless communication networks are constantly evolving to meet the increasing demands and expectations of users and applications. The fifth generation (5G) of wireless networks is currently being deployed, offering faster speeds, lower latency, higher capacity, and more reliability than the previous generations. However, 5G is not the end of the road. Beyond 5G (B5G) networks are expected to be developed over the next decade, aiming to provide even more advanced and diverse services and capabilities, such as ultra-reliable low-latency communication (URLLC), massive machine-type communication (mMTC), enhanced mobile broadband (eMBB), and ubiquitous intelligent connectivity.
To achieve these ambitious goals, B5G networks will need to overcome several technical and operational challenges, such as spectrum scarcity, network complexity, resource management, security, and interoperability. This is where artificial intelligence (AI) comes in. AI is the branch of computer science that aims to create machines or software that can perform tasks that normally require human intelligence, such as reasoning, learning, decision making, perception, communication, creativity, etc. AI has the potential to transform and improve wireless communication networks in various ways, such as:
- Designing and optimizing wireless systems and protocols: AI can help design and optimize wireless systems and protocols that can adapt to the dynamic and heterogeneous environments and requirements of B5G networks. AI can also help improve the performance and efficiency of wireless systems and protocols by learning from data and feedback, and by applying intelligent algorithms and techniques. For example, AI can help design an AI-native air interface that can support continual improvements through self-learning, where both sides of the air interface — the network and device — can adapt to their surroundings dynamically and optimize operations based on what they experience
- Enhancing network management and operation: AI can help enhance network management and operation by enabling autonomous and intelligent functions and processes that can reduce human intervention and errors. AI can also help improve network reliability and resilience by enabling self-healing, self-optimizing, and self-adapting capabilities. For example, AI can help implement network slicing, which is a technique that allows multiple virtual networks to be created on top of a shared physical infrastructure, each with different characteristics and service levels
- Empowering new applications and services: AI can help empower new applications and services that can leverage the advanced features and capabilities of B5G networks. AI can also help create new value and opportunities for users and stakeholders by enabling personalized, contextualized, and interactive experiences. For example, AI can help enable immersive media applications, such as virtual reality (VR) or augmented reality (AR), that can provide realistic and engaging content delivery over B5G networks
- Ensuring security and privacy: AI can help ensure security and privacy by detecting and preventing cyberattacks that can compromise the integrity, availability, or confidentiality of wireless systems and data. AI can also help protect the security and privacy of users and stakeholders by enabling encryption, authentication, anonymization, or other techniques. For example, AI can help implement privacy-preserving AI techniques that can protect the privacy of data or users while enabling AI applications or services over B5G networks
However, AI is not a panacea for wireless communication networks. AI also poses some challenges and risks for wireless communication networks, such as:
- Misusing or abusing AI for malicious purposes: AI can be used by malicious actors to launch more sophisticated and effective cyberattacks that can evade detection or attribution. AI can also be used to create or enhance cyber weapons or tools that can exploit vulnerabilities or cause harm. For example, AI-generated jamming signals are signals that can interfere with wireless communications by using AI to learn and mimic the characteristics of legitimate signals.
- Ensuring the reliability and ethics of AI systems: AI systems themselves can be vulnerable to cyberattacks that can compromise their integrity, availability, or confidentiality. AI systems can also be biased, unfair, or unethical in their design, development, or deployment. For example, adversarial attacks are techniques that can fool or manipulate AI systems by adding subtle perturbations or noise to their inputs.
- Balancing the trade-offs between performance and other values: AI systems can have positive or negative impacts on other values or interests that are important for users and stakeholders, such as energy efficiency, spectrum utilization, innovation, etc. For example, AI-based beamforming is a technique that can improve the signal quality and coverage of wireless communications by using AI to steer the direction of radio waves.
Therefore, it is essential and urgent to ensure the safety and alignment of AI systems with human values and goals. To achieve this, we need to collaborate and coordinate among researchers, policymakers, stakeholders, and the general public on how to develop and deploy AI systems for wireless communication networks in a responsible, trustworthy, and inclusive manner.
In conclusion, we have discussed how AI will improve wireless communication networks in various ways, but also how it will pose some challenges and risks for wireless communication networks. We have also highlighted the importance and urgency of ensuring the safety and alignment of AI systems with human values and goals. We hope this article has provided you with some insights and inspiration on beyond 5G networks: what will AI do in the next generation of wireless connectivity.
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