Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

RESOURCE ALLOCATION IN 802.11AX NETWORKS

Methods of selection of Voice over Internet Protocol (VoIP), video and other users to meet quality of service (QoS) goals and optimize overall performance in 802.11ax networks are provided. These methods allow policy based decisions such as controlling the number of video, VoIP or other users or sub-channel sizes for video (or other) users or deciding data rate (or associated modulation and coding scheme) for each user in each scheduling interval (SI), and allow dynamic decisions for the value of the SI

An Approach to Finding Parking Space Using the CSI-based WiFi Technology

With ever-increasing number of vehicles and shortages of parking spaces, parking has always been a very important issue in transportation. It is necessary to use advanced intelligent technologies to help drivers find parking spaces, quickly. In this thesis, an approach to finding empty spaces in parking lots using the CSI-based WiFi technology is presented. First, the channel state information (CSI) of received WiFi signals is analyzed. The features of CSI data that are strongly correlated with the number of empty slots in parking lots are identified and extracted. A machine learning technique to perform multi-class classification that categorizes the input data into classes representing the number of empty slots is employed. A prototype system of the proposed approach is developed. Experiments are performed and it is shown that the system is feasible. Compared with traditional approaches based on magnetic sensors deployed on individual parking slots, the proposed approach is non-intrusive as it does not require to install specialized devices in a parking lot, and is cost-effective since it utilizes either existing WiFi infrastructure or only a pair of WiFi devices. As a result, the average classification accuracy of system is 80.8%, and the accuracy is improved to 93.8% with a tolerance of one empty slot

Network Traffic Aware Smartphone Energy Savings

In today\u27s world of ubiquitous Smartphone use, extending the battery life has become an important issue. A significant contributor to battery drain is wireless networking. Common usage patterns expect Smartphones to maintain a constant Internet connection which exacerbates the problem.;Our research entitled A Network Traffic Approach to Smartphone Energy Savings focuses on extending Smartphone battery life by investigating how network traffic impacts power management of wireless devices. We explore 1) Real-time VoIP application energy savings by exploiting silence periods in conversation. WiFi is opportunistically placed into low power mode during Silence periods. 2.) The priority of Smartphone Application network traffic is used to modifiy WiFi radio power management using machine learning assisted prioritization. High priority network traffic is optimized for performance, consuming more energy while low priority network traffic is optimized for energy conservation. 3.) A hybrid multiple PHY, MAC layer approach to saving energy is also utilized. The Bluetooth assisted WiFi approach saves energy by combining high power, high throughput WiFi with low power, lower throughput Bluetooth. The switch between Bluetooth and WiFi is done opportunistically based upon the current data rate and health of the Bluetooth connection.;Our results show that application specific methods for wireless energy savings are very effective. We have demonstrated energy savings exceeding 50% in generic cases. With real-time VoIP applications we have shown upwards of 40% energy savings while maintaining good call quality. The hybrid multiple PHY approach saves more than 25% energy over existing solutions while attaining the capability of quickly adapting to changes in network traffic