Machine Learning Empowers Next Generation WLANs: A Survey of WiFi and IEEE 802.11 Optimization (Nov. 2023) [2024 ed.]

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Date of publication 2023.

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Machine Learning Empowers Next Generation WLANs: A Survey of WiFi and IEEE 802.11 Optimization (Nov. 2023) DR. KHALID ZEROUAL1 1

Mohammed V university in Rabat Morocco

ABSTRACT Wireless Local Area Networks (WLANs), standardized as IEEE 802.11 and popularly known as Wi-Fi, are the dominant means of providing wireless Internet access. In 2022, Wi-Fi is estimated to account for 51to the development of Wi-Fi 7 represents a continuous pursuit of improved performance and features. Wi-Fi’s popularity stems from well-defined use cases, flexible deployment options in unlicensed bands, and the widespread availability of affordable and interoperable devices. However, the introduction of new technical innovations and features in the 802.11 protocol family has introduced complexity. Each new mechanism designed to enhance network performance comes with a plethora of configuration parameters. Additionally, Wi-Fi is no longer limited to broadband Internet access; it’s also used for ultra-low latency machine-to-machine communication, necessitating proper configuration to meet these diverse needs. The challenge lies in the joint optimization of multiple parameters, with highly non-linear dependencies that impact network performance. For instance, sensitivity and transmission power settings in overlapping Wi-Fi networks exhibit non-linear relationships. The complexity further escalates in the presence of coexisting network technologies. Future WiFi generations aim to support ultra-low latency and ultra-high reliability traffic, requiring timely adjustments to transmission settings. Traditional algorithms struggle to find adequate configurations in the vast search space efficiently. Additionally, new WLAN mechanisms introduce additional overhead through the need for extra measurements to inform their control algorithms. Testing and selecting the best modulation and coding schemes, which was feasible in earlier Wi-Fi versions, has become impractical with the multitude of choices available today This article explores the evolving landscape of Wi-Fi and the challenges of configuring these nextgeneration WLANs, highlighting the growing importance of machine learning in addressing these complexities. INDEX TERMS Wi-Fi, WLAN, IEEE 802.11, machine learning, deep learning.

I. INTRODUCTION

Wireless Local Area Networks (WLANs), often recognized by their IEEE 802.11 standard name, commonly referred to as Wi-Fi, have evolved into a fundamental cornerstone for internet access. Their ubiquity can be attributed to the flexibility of deployment and configuration, coupled with the availability of cost-effective, interoperable devices. As of 2022, Wi-Fi commands a staggering 51internet traffic, underlining its pivotal role in the modern digital landscape [1]. The relentless pursuit of technological progress has brought about the transition from Wi-Fi 6 to the ongoing development of Wi-Fi 7. This next generation of Wi-Fi promises significant advancements, including higher data rates, enhanced multiuser and multi-access point support, and notably, improved configurational flexibility. These innovations, while promising, introduce a level of complexity previously unseen. Each IEEE JOURNAL OF THE INDUSTRIAL ELECTRONICS SOCIETY. 2023

new feature or mechanism aimed at enhancing network performance comes with a multitude of configuration parameters. The complexity is exacerbated when these parameters exhibit intricate dependencies, often resulting in non-linear impacts on network performance. This complexity is further amplified in densely populated network deployments and in environments where multiple network technologies coexist. In such intricate scenarios, classical optimization approaches often fall short, unable to effectively grapple with the complexity inherent to next-generation WLANs. Enter Machine Learning (ML), a 2burgeoning field of artificial intelligence that thrives on handling complexity and non-linearity. ML offers a promising avenue for addressing the intricate challenges posed by modern Wi-Fi networks. A burgeoning body of research has emerged, detailing the application of ML to enhance Wi-Fi performance. Solutions stemming from this 1

research are gradually making their way into practical deployments. This survey embarks on a structured exploration of the diverse domains within Wi-Fi where ML applications are having a transformative impact. Drawing insights from an analysis of more than 250 research papers in this field, we aim to provide readers with a comprehensive overview of the predominant trends. In the pages that follow, we will not only unveil the landscape of ML’s influence on Wi-Fi but also pinpoint specific challenges that remain unaddressed. Furthermore, we propose general directions for future research, underlining the ongoing evolution of this dynamic field and its potential to reshape the future of wireless communication. II. UNDERSTANDING IEEE 802.11 AND COMMON WI-FI CHALLENGE A. INTRODUCTION TO IEEE 802.11 AND ITS SIGNIFICANCE

Wireless Local Area Networks (WLANs) have revolutionized the way we connect to the internet and local network resources. At the heart of this wireless connectivity lies the IEEE 802.11 standard, widely known as Wi-Fi. In this chapter, we will delve into the fundamentals of IEEE 802.11, its various standards, and the profound significance it holds in the realm of wireless communication. 1) What is IEEE 802.11?

The IEEE 802.11 standard family, commonly referred to as Wi-Fi, is a set of specifications that define how wireless communication should function in the 2.4 GHz and 5 GHz frequency bands. Wi-Fi enables devices to connect to a network without the need for physical cables, offering users the freedom to move and connect in a seamless and flexible manner 2) Wi-Fi Standards

The IEEE 802.11 standard has evolved over time, with various amendments and updates. Each standard brings new features and improvements. Some of the key standards include: 802.11b: The first widely adopted standard, offering data rates of up to 11 Mbps in the 2.4 GHz band. 802.11g: Introduced higher data rates in the 2.4 GHz band, up to 54 Mbps. 802.11n: Provided significant performance enhancements with data rates up to 600 Mbps in both 2.4 GHz and 5 GHz bands. 802.11ac: Focused on the 5 GHz band, reaching data rates of up to 3.5 Gbps. 802.11ax: Introduced the concept of High-Efficiency Wireless, improving overall network performance and efficiency. 802.11ay: Focused on providing high throughput in the 60 GHz frequency band. 3) Significance of Wi-Fi

Wi-Fi has become an integral part of our daily lives and business operations. Its significance can be attributed to: Flexibility: Wi-Fi networks are easily deployable and can be customized to meet specific requirements. Ubiquity: WiFi is nearly everywhere, from homes to public spaces and workplaces. Affordability: Wi-Fi technology is cost-effective 2

and accessible. Interoperability: Wi-Fi devices from different manufacturers can communicate seamlessly. B. COMMON ISSUES AND CHALLENGES IN WI-FI NETWORKS

While Wi-Fi is incredibly popular, it is not without its challenges. In this section, we will explore the common issues faced in Wi-Fi networks, including congestion, interference, and signal quality 1) Congestion

Wi-Fi networks can become congested when there are too many devices competing for limited bandwidth. This can lead to slow data transfer rates and a degraded user experience. We will discuss strategies to manage and mitigate congestion. 2) Interference

Interference from other electronic devices, neighboring WiFi networks, and physical obstacles can disrupt Wi-Fi signals. Understanding interference sources and ways to minimize their impact is crucial for maintaining a reliable network. 3) Signal Quality

Signal quality is essential for stable and high-speed connections. We will explore factors that affect signal quality, such as distance from the access point, obstructions, and noise, and discuss techniques for improving it. III. THE INTERSECTION OF WIFI AND MACHINE LEARNING A. HARNESSING MACHINE LEARNING FOR WIFI ENHANCEMENT

In this chapter, we delve into the exciting world where Wi-Fi and Machine Learning (ML) converge, offering innovative solutions to the common challenges faced in wireless networks 1) Machine Learning Fundamentals

Before exploring the application of ML in Wi-Fi networks, we’ll establish fundamental concepts of machine learning, including: Supervised Learning Unsupervised Learning Reinforcement Learning Neural Networks 2) Why Machine Learning in WiFi?

The marriage of Wi-Fi and machine learning is not only promising but necessary. Here, we will discuss why ML is uniquely positioned to address the challenges inherent in Wi-Fi networks: Adaptive Optimization: ML can adapt to changing network conditions and dynamically optimize configurations. Non-Linear Dependencies: ML algorithms can handle the non-linear relationships between configuration parameters, a key challenge in Wi-Fi network optimization. Real-Time Decision Making: ML enables real-time decisionmaking, crucial for handling rapidly changing network environments. Anomaly Detection: ML can identify and respond to unusual network behavior IEEE JOURNAL OF THE INDUSTRIAL ELECTRONICS SOCIETY. 2023

B. POTENTIAL BENEFITS OF ML IN WIFI NETWORK OPTIMIZATION

Machine learning offers a wealth of benefits when applied to Wi-Fi networks. In this section, we explore these advantages: 1) Improved Network Performance

ML algorithms can continuously analyze network conditions and adjust parameters to maintain optimal performance. We discuss how this dynamic optimization can lead to higher data rates, reduced latency, and enhanced user experiences. 2) Enhanced system

ML can bolster by identifying and responding to anomalies in network traffic, helping detect and mitigate potential threats more effectively 3) Adaptive Configuration

ML allows for the adaptive configuration of Wi-Fi networks. We’ll explore how this adaptability caters to different usage scenarios, from broadband internet access to ultra-low latency machine-to-machine communication. 4) Reduced Human Intervention

Automation through ML minimizes the need for manual intervention in network management, reducing the risk of human errors and ensuring consistent performance. 5) Scalability and Future Proofing

ML can help networks adapt to new standards and evolving technology, ensuring they remain relevant and efficient in the face of change. As we navigate this chapter, we will gain insights into how ML revolutionizes Wi-Fi networks, optimizing performance, and preparing networks for the challenges of the future. IV. MACHINE LEARNING APPLICATIONS FOR WI-FI PERFORMANCE ENHANCEMENT A. INTRODUCTION TO MACHINE LEARNING USE CASES IN WI-FI

In this chapter, we dive deeper into practical applications where machine learning is transforming the Wi-Fi landscape. From adaptive channel selection to load balancing and anomaly detection, we explore how ML is revolutionizing Wi-Fi performance optimization. B. ADAPTIVE CHANNEL SELECTION

One of the key challenges in Wi-Fi networks is selecting the most suitable channel for communication. Adaptive channel selection is the process of dynamically choosing the optimal channel based on real-time conditions. In this section, we will discuss how machine learning algorithms are leveraged to: Monitor channel congestion and interference. Predict and respond to changing network conditions. Automatically adjust channel assignments for optimal performance. IEEE JOURNAL OF THE INDUSTRIAL ELECTRONICS SOCIETY. 2023

C. LOAD BALANCING

Load balancing is essential for distributing network traffic efficiently among multiple access points to prevent congestion and ensure a consistent user experience. Machine learning plays a crucial role in load balancing by: Analyzing network traffic patterns. Predicting network load in different areas. Dynamically redistributing clients among access points. Maintaining a balanced network performance D. ANOMALY DETECTION

Identifying and addressing unusual network behavior, such as network errors, is vital for maintaining sytems and stable WiFi network. Machine learning can be applied for: Real-time monitoring of network traffic and device behavior. Taking immediate actions to mitigate threats or network issues. E. QUALITY OF SERVICE (QOS) OPTIMIZATION

Quality of Service (QoS) is critical for ensuring that specific applications or devices receive the necessary bandwidth and priority in the network. Machine learning is used to: Identify and prioritize critical applications or devices. Allocate resources efficiently to maintain optimal QoS. Adjust network parameters to meet the requirements of different applications or services. F. PREDICTIVE MAINTENANCE

Predictive maintenance is another powerful application of machine learning in Wi-Fi networks. By continuously monitoring network performance, ML can predict potential issues, allowing for proactive maintenance to prevent network disruptions. G. CASE STUDIES AND REAL-WORLD EXAMPLES

Throughout this chapter, we will present real-world case studies and examples of organizations or networks that have successfully implemented machine learning for Wi-Fi performance enhancement. These case studies will provide practical insights into the benefits and challenges of applying ML in Wi-Fi networks. V. MACHINE LEARNING ALGORITHMS FOR WI-FI OPTIMIZATION A. INTRODUCTION TO MACHINE LEARNING IN WI-FI OPTIMIZATION

Machine learning offers a diverse range of algorithms that can be harnessed to optimize WiFi networks. In this chapter, we will explore several key machine learning algorithms and their application in Wi-Fi optimization. We will also delve into the strengths and weaknesses of each algorithm in this context. B. NEURAL NETWORKS 1) Description

Neural networks, particularly deep learning models, have gained prominence in WiFi optimization. They can be used 3

for tasks such as channel selection, load balancing, and anomaly detection.

E. CLUSTERING AND ANOMALY DETECTION 1) Description

2) Strengths

Clustering algorithms and anomaly detection methods are vital for identifying network issues and optimizing resource allocation.

Complex Pattern Recognition: Neural networks excel at recognizing complex patterns in data, making them suitable for tasks involving diverse and non-linear relationships. Adaptability: They can adapt to changing network conditions, learning and adjusting to new patterns 3) Weaknesses

Data Intensive: Deep neural networks often require a substantial amount of labeled data for training, which may be challenging to obtain in some Wi-Fi environments. Complexity: Deep learning models can be computationally intensive and may require powerful hardware. C. DECISION TREES 1) Description

Decision trees are a popular choice for tasks like load balancing and adaptive channel selection in WiFi networks. 2) Strengths

Interpretability: Decision trees offer transparency, making it easier to understand the decision-making process. Efficiency: They are computationally efficient and can handle large datasets with relative ease. 3) Weaknesses

Overfitting: Decision trees are prone to overfitting, which can lead to suboptimal performance if not properly managed. Limited Complexity: They may struggle with capturing highly complex relationships in the data. 4.4 Reinforcement Learning D. REINFORCEMENT LEARNING 1) Description

Reinforcement learning is well-suited for dynamic optimization tasks in Wi-Fi networks, such as resource allocation and QoS management. 2) Strengths

Dynamic Decision-Making: Reinforcement learning agents can make real-time decisions based on feedback, adapting to changing network conditions. Versatility: It can handle a wide range of tasks and is particularly effective in scenarios with sequential decision-making. 3) Weaknesses

Exploration vs. Exploitation Trade-off: Striking the right balance between exploration and exploitation can be challenging in WiFi optimization, where network stability is crucial. Complex Training: Training reinforcement learning agents can be computationally expensive, and it may be challenging to simulate all possible network scenarios. 4

2) Strengths

Identifying Patterns: These algorithms are adept at identifying unusual patterns or behaviors in network traffic. Resource Allocation: They help in allocating resources more efficiently based on usage patterns. 3) Weaknesses

Initial Setup: Effective use of clustering and anomaly detection may require a comprehensive understanding of the network and fine-tuning for optimal results. False Positives: Anomaly detection can F. MODEL SELECTION AND HYBRID APPROACHES

The selection of the most suitable machine learning algorithm often depends on the specific Wi-Fi optimization task and the available data. In practice, hybrid approaches that combine multiple algorithms are increasingly common to leverage the strengths of different techniques. VI. CHALLENGES AND CONSIDERATIONS IN IMPLEMENTING MACHINE LEARNING IN WIFI NETWORKS A. INTRODUCTION TO CHALLENGES AND CONSIDERATIONS

As the application of machine learning in WiFi networks becomes more prevalent, it’s crucial to be aware of the challenges and considerations that come with this transformative technology. In this chapter, we explore the hurdles and limitations, with a particular focus on data privacy . B. CHALLENGES IN IMPLEMENTING MACHINE LEARNING 1) Data Availability and Quality

Machine learning models rely heavily on data. One significant challenge is obtaining sufficient, high-quality training data for WiFi optimization. Factors such as data labeling, data diversity, and data collection can pose significant obstacles. 2) Model Complexity

Machine learning models can be highly complex, requiring substantial computational resources for training . Implementing these models in resource constrained WiFi devices or networks can be a challenge. 3) Scalability

Adapting machine learning solutions to networks with varying sizes and architectures can be challenging. Ensuring that models are scalable and able to handle the growth of network devices is crucial. IEEE JOURNAL OF THE INDUSTRIAL ELECTRONICS SOCIETY. 2023

C. DATA PRIVACY CONCERNS 1) sensitive Information

Wi-Fi networks often carry sensitive data, including user information and device communications. Machine learning algorithms need access to this data for optimization, raising concerns about data privacy 2) Data Encryption

Balancing data privacy and machine learning requires addressing encryption methods.

future to anticipate how this intersection is likely to evolve. In this chapter, we explore the prospects and emerging trends that will shape the landscape of Wi-Fi and machine learning. B. WI-FI 7 AND BEYOND

Wi-Fi is not standing still. The development of Wi-Fi 7 and subsequent generations promises to bring even higher data rates, better reliability, and improved efficiency. Machine learning is expected to play a pivotal role in optimizing these new standards.

3) Data Anonymization

C. AUTONOMOUS NETWORKS

Anonymization techniques can be applied to mask or transform data to preserve privacy while still enabling machine learning tasks. These techniques must be carefully implemented to prevent re-identification.

The concept of autonomous networks, where Wi-Fi networks self-optimize and adapt to changing conditions, is on the horizon. Machine learning algorithms will drive this autonomy, dynamically adjusting network parameters to deliver optimal performance.

D. ATTACKS CONCERNS 1) Adversarial Attacks

Machine learning models are vulnerable to adversarial attacks, where malicious actors attempt to manipulate the model’s behavior. In WiFi networks, these attacks can disrupt network performance. Machine learning models themselves can be exploited. Model vulnerabilities is a critical concern, particularly in applications like anomaly detection and intrusion prevention. 2) Accountability and Transparency

Understanding and explaining machine learning decisions can be difficult. Ensuring transparency in machine learning models in WiFi networks is crucial for auditing and accountability

D. EDGE COMPUTING AND AI

The convergence of edge computing and artificial intelligence is set to transform Wi-Fi networks. Machine learning models deployed at the network’s edge can provide real-time insights and decisionmaking, reducing latency and enhancing user experiences. E. 6G AND BEYOND

As 6G wireless technology advances, it’s expected to usher in a new era of ultra-high-speed, lowlatency communication. Machine learning will enable networks to meet the demands of these advanced applications, such as augmented reality, virtual reality, and the Internet of Things (IoT). F. ADVANCEMENTS

E. REGULATORY COMPLIANCE

Compliance with data regulations, such as GDR (General Data Regulation), and others, is essential. Wi-Fi network operators must navigate the regulatory landscape while implementing machine learning solutions.

Machine learning will continue to evolve in the context of attacks . Behavioral analysis, anomaly detection, and threat mitigation powered by ML will become even more critical as cyber threats become more sophisticated G. RESEARCH AND DEVELOPMENT

F. ETHICAL CONSIDERATIONS

Ethical concerns arise when deploying machine learning in Wi-Fi networks, especially regarding user consent, fairness, and the potential for bias in algorithmic decisions. G. MITIGATING CHALLENGES AND MEASURES

his chapter will also discuss strategies and best practices for addressing these challenges and considerations, including data handling, model robustness, and regulatory compliance measures. VII. FUTURE PROSPECTS AND EMERGING TRENDS IN WI-FI AND MACHINE LEARNING A. INTRODUCTION TO FUTURE PROSPECTS

As Wi-Fi networks continue to evolve and machine learning finds increasing applications, it’s essential to peer into the IEEE JOURNAL OF THE INDUSTRIAL ELECTRONICS SOCIETY. 2023

The field of Wi-Fi and machine learning is rich with ongoing research. Academic and industrial researchers are exploring new techniques, models, and applications. Emerging trends may include more advanced reinforcement learning methods, unsupervised learning for anomaly detection, and federated learning for privacy-preserving optimizations. H. ADOPTION IN VARIOUS SECTORS

Machine learning’s impact on Wi-Fi is likely to extend to various sectors. The adoption of Wi-Fi and machine learning is expected to lead to innovative applications and improved services. I. SUSTAINABILITY AND ENERGY EFFICIENCY

Machine learning can contribute to making Wi-Fi networks more energy-efficient and environmentally sustainable. By 5

optimizing network resource usage, reducing energy consumption, and minimizing waste, machine learning can help align Wi-Fi networks with global sustainability goals. J. HUMAN-MACHINE INTERACTION

The evolution of Wi-Fi and machine learning may also enhance human-machine interaction. Advanced applications like gesture recognition, emotion analysis, and context-aware computing could become more commonplace. K. CONCLUSION

The future prospects of WiFi and machine learning are indeed promising. As technology advances, the intersection of these fields will bring about new innovations, improved network performance, and a host of exciting applications across various industries. CONCLUSION: THE TRANSFORMATIVE POWER OF MACHINE LEARNING IN IEEE 802.11 PERFORMANCE

the ever evolvng landscape of computer science and networking, the intersection of WiFi and machine learning stands as a beacon of innovation and transformation. IEEE 802.11, commonly known as WiFi, has been a cornerstone of wireless communication for decades, providing Internet access and connectivity across the globe. As we navigate an increasingly data-driven and interconnected world, the optimization and enhancement of WiFi networks have become paramount. Machine learning, with its ability to adapt, learn, and make real-time decisions, has emerged as a changer in the pursuit of Wi-Fi network excellence. It is not merely a promising avenue; it is a transformative force, a technological synergy poised to reshape the future of IEEE 802.11 and beyond. Unleashing the Potential of IEEE 802.11: The IEEE 802.11 standard has undergone remarkable evolution, from its early days of enabling basic wireless connectivity to the impending Wi-Fi 7, which promises higher data rates, better multiuser support, and superior configuration flexibility. However, these advancements also introduce complexity, with numerous configuration parameters and intricate dependencies. The traditional methods of optimization often fall short, unable to handle the non-linear relationships and rapid adaptability demanded by modern Wi-Fi networks. Machine learning steps in as a dynamic, adaptable solution capable of handling the intricacies of nextgeneration Wi-Fi networks. Its ability to optimize, predict, and adapt in real-time is a perfect fit for the adaptive demands of modern network environments. The Significance of Machine Learning in Computer Science and Networking: Machine learning’s significance extends beyond Wi-Fi networks. In the broader realm of computer science and networking, its transformative power is felt in several ways: Adaptive Networks: Machine learning is catalyzing the shift toward autonomous networks, where Wi-Fi adapts to changing conditions, optimizes performance, and minimizes human intervention. Enhancement: Machine learning reinforces with advanced threat detection, anomaly identification, and proactive measures against cyberattacks. Efficiency 6

and Scalability: It facilitates more efficient resource allocation, improved scalability, and adaptive configurations in rapidly growing and diverse network environments. HumanMachine Interaction: It opens new horizons for humanmachine interaction through contextaware computing, gesture recognition, and emotion analysis. Global Sustainability: By optimizing resource usage and reducing energy consumption, machine learning aligns Wi-Fi networks with global sustainability goals. The Future is Here: The marriage of WiFi and machine learning is not a future dream; it’s a present reality. Ongoing research, emerging trends, and real-world applications are laying the groundwork for the future of wireless communication. From Wi-Fi 7 to autonomous networks, advancements to ethical considerations, the prospects are as vast as the digital landscape itself. As we embark on this transformative journey, the fusion of IEEE 802.11 and machine learning is poised to unlock a world of possibilities. It’s not merely about enhancing network performance; it’s about unleashing the full potential of interconnectedness, transforming the way we live, work, and communicate. In the realms of computer science and networking, where the digital meets the physical, machine learning is the guiding force, and IEEE 802.11 is the canvas upon which this masterpiece of connectivity is painted. It’s a future we can already glimpse, and it’s a future that’s here to stay. REFERENCES

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