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

Unequal error protection for power line communications over impulsive noise channels

Power line communication (PLC) has recently attracted a lot of interest with many application areas including smart grids\u27 data communication, where data (from sensors or other measurement units) with different QoS may be transmitted. Power line communications suffer from the excessive power lines\u27 impulsive noise (which can be caused by shedding loads on and off). In this thesis, we present a study of power line communications with unequal error protection for two and four data priority levels hierarchical QAM modulation and space-time block coding. We consider the two commonly used power lines\u27 impulsive noise models with Bernoulli and Poisson arrivals. In our proposed approaches, we achieve UEP on both of bit and symbol levels. Approximate closed form expressions for the error rates are derived for each priority level for both single carrier and OFDM in SISO and MIMO systems. In addition, these simpli fied expressions are used to implement a bit loading algorithm to provide UEP for frequency-selective PLC channels. For the case of MIMO PLC channels, we describe three different MIMO schemes to allow more control over the UEP levels. The three schemes are namely: maximum ratio combiner (MRC) receive diversity, Alamouti space-time block code, and a new structure for a space-time code that allows for unequal error protection at the symbol level. Finally, we apply an Eigen beamforming technique, assuming channel knowledge at transmitter, which improves the BER as compared to the other MIMO PLC schemes

Quantifying Potential Energy Efficiency Gain in Green Cellular Wireless Networks

Conventional cellular wireless networks were designed with the purpose of providing high throughput for the user and high capacity for the service provider, without any provisions of energy efficiency. As a result, these networks have an enormous Carbon footprint. In this paper, we describe the sources of the inefficiencies in such networks. First we present results of the studies on how much Carbon footprint such networks generate. We also discuss how much more mobile traffic is expected to increase so that this Carbon footprint will even increase tremendously more. We then discuss specific sources of inefficiency and potential sources of improvement at the physical layer as well as at higher layers of the communication protocol hierarchy. In particular, considering that most of the energy inefficiency in cellular wireless networks is at the base stations, we discuss multi-tier networks and point to the potential of exploiting mobility patterns in order to use base station energy judiciously. We then investigate potential methods to reduce this inefficiency and quantify their individual contributions. By a consideration of the combination of all potential gains, we conclude that an improvement in energy consumption in cellular wireless networks by two orders of magnitude, or even more, is possible.Comment: arXiv admin note: text overlap with arXiv:1210.843