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

Device-to-Device Communication in 5G: Towards Efficient Scheduling

5G wireless networks are expected to carry large traffic volumes due to the growth of mobile devices and the increasing demand for high data rates from applications. Device to device communication is one of the suggested technologies to support this increasing load and enhance the capacity of networks. However, the implementation of D2D communication reveals many barriers that include communication scheduling, for which the architecture remains complex and obscure. In this paper, an overview of the available literature on the implementation of networks supporting D2D communication is presented, emphasizing the complexity of the offered solutions. This paper also offers a study of the impact of different device distribution models on the throughput of the devices. The paper introduces the challenges and makes the case for the need to find a more efficient D2D scheduler providing less complexity