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

Factors Affecting QoS in Tanzania Cellular Networks

Quality of service in cellular communication system is a topic that recently has raised much interest for many researchers. This paper presents the findings obtained from the study on factors affecting QoS in Tanzania cellular networks. The study was carried out in Dodoma Municipal, Tanzania. The study employed cross sectional research design. Information was gathered from structured questionnaire of 240 subscribers during the study of quality of service for the four leading cellular networks in Tanzania. Both qualitative and quantitative data from field survey were collected and analyzed using Statistical Package for Social Sciences and Excel software. The study findings show that the major factors that degrade QoS in Tanzania cellular networks are inadequate network infrastructure, lack of fairness from service providers and little efforts taken by the government in enforcing the national agreed standards. Other factors are lack of reliable end to end systems, geographical terrain, low quality handsets, poor government monitoring on standards and lack of subscriber skills and training.Comment: 7 Page

Channel Dynamics and SNR Tracking in Millimeter Wave Cellular Systems

The millimeter wave (mmWave) frequencies are likely to play a significant role in fifth-generation (5G) cellular systems. A key challenge in developing systems in these bands is the potential for rapid channel dynamics: since mmWave signals are blocked by many materials, small changes in the position or orientation of the handset relative to objects in the environment can cause large swings in the channel quality. This paper addresses the issue of tracking the signal to noise ratio (SNR), which is an essential procedure for rate prediction, handover and radio link failure detection. A simple method for estimating the SNR from periodic synchronization signals is considered. The method is then evaluated using real experiments in common blockage scenarios combined with outdoor statistical models