Читать книгу The Smart Cyber Ecosystem for Sustainable Development - Группа авторов - Страница 63

Power control is a well-known approach used to mitigate interference in wireless networks. In SDN-based management and control of WLANs, the centralized CP can be used to implement the mechanisms for power control to minimize interference through coverage optimization of WLANs cells.

Wireless interference classification is a process of identifying the type of wireless emitters exist in the local RF environment [55]. This is important for enabling coexistence of wireless technologies that operate in the same frequency band. ML-based solutions are being developed to achieve this goal.

In [56], the authors propose a RL mechanism for interference mitigation in small cell networks. The algorithm represents the state of each AP as a binary variable that indicates whether the QoS requirement is violated. The action is a selection of power values from a set of power values. The reward is defined as the achieved rate. The algorithm iterates until a predefined level of QoS is met.

The work in [57] develops a solution that uses ML-SVM for interference classification in wireless sensor networks from IEEE 802.11 signals and microwave ovens. Another deep learning approach for classification of WiFi, Zigbee, and Bluetooth was proposed in [58]. The authors defined fifteen classification tasks assuming a flat fading channel with additive white Gaussian noise. The research work of [59] compares different types of ML models for classifying signals, including deep feed-forward networks, deep convolutional networks, SVM and a multi-stage training algorithm.

In [60], the authors propose a ML-based framework for mitigating the effect of jammers in WLANs, called “DeepWifi”. The system consists of an RF front end processing unit which applies a deep learning-based auto-encoder to extract spectrum-representative features. The system leverages the advances in ML algorithms to enhance the performance and security in WLANs. A deep neural network is then trained to classify signals as idle, WiFi, or jammer. In standard WiFi, the user backs off backs off regardless of the type of interference. However, DeepWiFi which is able to classify signals backs off when the interference is from another WLAN user, allowing user to operate in degraded mode and still receive non-zero throughput.