325 research outputs found

    Non-Coherent Open-Loop MIMO Communications Over Temporally-Correlated Channels

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    [EN] This paper investigates the use of non-coherent communication techniques for open-loop transmission over temporally-correlated Rayleigh-fading MIMO channels. These techniques perform data detection without knowing the instantaneous channel coefficients. Three non-coherent Multiple Input Multiple Output (MIMO) schemes, namely, differential unitary space-time modulation, differential space-time block code, and Grassmannian signaling, are compared with several state-of-the-art training-based coherent schemes. This paper shows that the non-coherent schemes are meaningful alternatives to training-based communication, specially as the number of transmit antennas increases. In particular, for more than two transmit antennas, non-coherent communication provides a clear advantage in medium to high mobility scenarios.This work was supported in part by the Ministerio de Economia y Competitividad, Spain, under Grant TEC2014-60258-C2-1-R, in part by the European Regional Development Fund, in part by the European Union through the H2020 Project METIS-II under Grant 671680, in part by Huawei Canada Company, Ltd., and in part by the Ontario Ministry of Economic Development and Innovation's through the Ontario Research Fund-Research Excellence Program.Cabrejas Peñuelas, J.; Roger Varea, S.; Calabuig Soler, D.; Fouad, YMM.; Gohary, RH.; Monserrat Del Río, JF.; Yanikomeroglu, H. (2016). Non-Coherent Open-Loop MIMO Communications Over Temporally-Correlated Channels. IEEE Access. 4:6161-6170. https://doi.org/10.1109/ACCESS.2016.2580680S61616170

    Advanced wireless communications using large numbers of transmit antennas and receive nodes

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    The concept of deploying a large number of antennas at the base station, often called massive multiple-input multiple-output (MIMO), has drawn considerable interest because of its potential ability to revolutionize current wireless communication systems. Most literature on massive MIMO systems assumes time division duplexing (TDD), although frequency division duplexing (FDD) dominates current cellular systems. Due to the large number of transmit antennas at the base station, currently standardized approaches would require a large percentage of the precious downlink and uplink resources in FDD massive MIMO be used for training signal transmissions and channel state information (CSI) feedback. First, we propose practical open-loop and closed-loop training frameworks to reduce the overhead of the downlink training phase. We then discuss efficient CSI quantization techniques using a trellis search. The proposed CSI quantization techniques can be implemented with a complexity that only grows linearly with the number of transmit antennas while the performance is close to the optimal case. We also analyze distributed reception using a large number of geographically separated nodes, a scenario that may become popular with the emergence of the Internet of Things. For distributed reception, we first propose coded distributed diversity to minimize the symbol error probability at the fusion center when the transmitter is equipped with a single antenna. Then we develop efficient receivers at the fusion center using minimal processing overhead at the receive nodes when the transmitter with multiple transmit antennas sends multiple symbols simultaneously using spatial multiplexing

    Transmit Signal Design for MIMO Radar and Massive MIMO Channel Estimation

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    The widespread availability of antenna arrays and the capability to independently control signal emissions from each antenna make transmit signal design increasingly important for radar and wireless communication systems. In the rst part of this work, we develop the framework for a MIMO radar transmit scheme which trades o waveform diversity for beampattern directivity. Time-division beamforming consists of a linear precoder that provides direct control of the transmit beampattern and is able to form multiple transmit beams in a single pulse. The MIMO receive ambiguity function, which incorporates the receiver structure, reveals a space and delay-Doppler separability that emphasizes the importance of the transmit-receive beampattern and single-input single-output (SISO) ambiguity function. The second part of this work focuses on channel estimation for massive MIMO systems. As the size of arrays increase, conventional channel estimation techniques no longer remain practical. In current systems, training sequences probe wireless channels in orthogonal directions to obtain channel state information for block fading channels. The training overhead becomes signicant as the number of transmit antennas increases, thereby creating a need for alternative channel estimation techniques. In this work, we relax the orthogonal restriction on the sounding vectors and introduce a feedback channel to enable closed-loop sounding vector design. A probability of misalignment framework is introduced, which provides a measure to sequentially design sounding vectors

    On the Integration of Grassmannian Constellations into LTE Networks: a Link-level Performance Study

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    This paper presents Grassmannian signaling as a transmission scheme that can be integrated in Long Term Evolution (LTE) to support higher user speeds and to increase the throughput achievable in the high Signal to Noise Ratio (SNR) regime. This signaling is compared, under realistic channel assumptions, with the diversity transmission modes standardized in LTE, in particular, Space-Frequency Block Coding and Frequency-Switched Transmit Diversity for two and four transmit antennas, respectively. In high-speed scenarios, and even with high antenna correlation, Grassmannian signaling outperforms the LTE diversity transmission modes starting from four transmit antennas. Furthermore, in the high SNR regime, Grassmannian signaling can increase the link data rate up to 10% and 15% for two and four antennas, respectively
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