35 research outputs found

    Iterative Equalization and Interference Alignment for Multiuser MIMO HetNets with Imperfect CSI

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    In this paper we consider a scenario, where several small-cells work under the same coverage area and spectrum of a macrocell. The signals stemming from the small-cell (macrocell) users if not carefully dealt with will generate harmful interference into the macrocell (small-cell). To tackle this problem interference alignment and iterative equalization techniques are considered. By using IA all interference generated by the small-cell (macrocell) users is aligned along a low dimensional subspace, at the macrocell (small-cells). This reduces considerably the amount of resources allocated, to enable the coexistence of the two systems. However, perfect IA requires the availability of error-free channel state information (CSI) at the transmitters. Due to CSI errors one can have substantial performance degradation due to imperfect alignments. Since in this work the IA precoders are based on imperfect CSI, an efficient iterative space-frequency equalization is designed at the receiver side to cope with the residual aligned interference.The results demonstrate that iterative equalization is robust to imperfect CSI and removes efficiently the interference generated by the poorly aligned interference. Close to matched filter bound performance is achieved, with a very few number of iterations

    D13.2 Techniques and performance analysis on energy- and bandwidth-efficient communications and networking

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    Deliverable D13.2 del projecte europeu NEWCOM#The report presents the status of the research work of the various Joint Research Activities (JRA) in WP1.3 and the results that were developed up to the second year of the project. For each activity there is a description, an illustration of the adherence to and relevance with the identified fundamental open issues, a short presentation of the main results, and a roadmap for the future joint research. In the Annex, for each JRA, the main technical details on specific scientific activities are described in detail.Peer ReviewedPostprint (published version

    Nonorthogonal Multiple Access for 5G and Beyond

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    This work was supported in part by the U.K. Engineering and Physical Sciences Research Council (EPSRC) under Grant EP/N029720/1 and Grant EP/N029720/2. The work of L. Hanzo was supported by the ERC Advanced Fellow Grant Beam-me-up

    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

    D13.1 Fundamental issues on energy- and bandwidth-efficient communications and networking

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    Deliverable D13.1 del projecte europeu NEWCOM#The report presents the current status in the research area of energy- and bandwidth-efficient communications and networking and highlights the fundamental issues still open for further investigation. Furthermore, the report presents the Joint Research Activities (JRAs) which will be performed within WP1.3. For each activity there is the description, the identification of the adherence with the identified fundamental open issues, a presentation of the initial results, and a roadmap for the planned joint research work in each topic.Preprin

    Adjustable dynamic range for paper reduction schemes in large-scale MIMO-OFDM systems

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    In a multi-input-multi-output (MIMO) communication system there is a necessity to limit the power that the output antenna amplifiers can deliver. Their signal is a combination of many independent channels, so the demanded amplitude can peak to many times the average value. The orthogonal frequency division multiplexing (OFDM) system causes high peak signals to occur because many subcarrier components are added by an inverse discrete Fourier transformation process at the base station. This causes out-of-band spectral regrowth. If simple clipping of the input signal is used, there will be in-band distortions in the transmitted signals and the bit error rate will increase substantially. This work presents a novel technique that reduces the peak-to-average power ratio (PAPR). It is a combination of two main stages, a variable clipping level and an Adaptive Optimizer that takes advantage of the channel state information sent from all users in the cell. Simulation results show that the proposed method achieves a better overall system performance than that of conventional peak reduction systems in terms of the symbol error rate. As a result, the linear output of the power amplifiers can be minimized with a great saving in cost
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