3 research outputs found

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

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    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

    An interference self-cancellation technique for SC-FDMA systems

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    A new interference self-cancellation (ISC) method for Single Carrier-FDMA (SC-FDMA) systems is proposed to mitigate the inter-user interference caused by frequency offset or Doppler effect. By transmitting a compensation symbol at the first symbol location in each resource block, the energy leakage can be significantly suppressed. With little bandwidth and power sacrifice, the proposed method can greatly improve the system robustness against frequency offset. Simulation results show that the signal-to-interference ratio (SIR) can be improved by 7 dB on average for the entire system band, and up to 11.7 dB for an individual user. © 2010 IEEE
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