Browsing by Subject "Limited feedback"

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    Channel reconstruction based multiuser precoding with limited feedback
    (IEEE, 2021-09-06) Özateş, Mert; Kazemi, Mohammad; Göken, Çağrı; Duman, Tolga M.
    We consider the downlink of a multiuser multiple-input multiple-output (MU-MIMO) system, where each user feeds back a partial channel state information (CSI), namely, the quantized version of the dominant eigenvector of its channel covariance matrix, to the base station (BS) for precoding. Specifically, we propose a downlink multiuser precoding scheme by first reconstructing the equivalent channel matrix of each user via a limited feedback, and then by employing a precoder to suppress the multiuser interference at the receivers. For the single stream case, a signal-to-leakage-and-noise ratio (SLNR) based precoding is employed, while for the full stream case with limited feedback, we employ a lattice reduction aided block diagonalization type precoding with suitable modifications at the receiver side. Extensive numerical examples which are provided using the 5G new radio (5G-NR) channel models demonstrate that the proposed schemes outperform the existing eigenvector based algorithms, and they are more robust against the downlink channel estimation errors.
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    A low-complexity transmission and scheduling scheme for wimax systems with base station cooperation
    (SpringerOpen, 2010) Aktas, D.; Tokel, T. B.
    This paper considers base station cooperation as an interference management technique for the downlink of a WiMAX network (IEEE 802.16 standard) with frequency reuse factor of 1. A low-complexity cooperative transmission and scheduling scheme is proposed that requires limited feedback from the users and limited information exchange between the base stations. The proposed scheme requires minor modifications to the legacy IEEE 802.16e systems. The performance of the proposed scheme is compared with noncooperative schemes with similar complexity through computer simulations. Results demonstrate that base station cooperation provides an attractive solution for mitigating the cochannel interference and increases the system spectral efficiency compared to traditional cellular architectures based on frequency reuse.
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    Online anomaly detection in case of limited feedback with accurate distribution learning
    (IEEE, 2017) Marivani, Iman; Kari, Dariush; Kurt, Ali Emirhan; Manış, Eren
    We propose a high-performance algorithm for sequential anomaly detection. The proposed algorithm sequentially runs over data streams, accurately estimates the nominal distribution using exponential family and then declares an anomaly when the assigned likelihood of the current observation is less than a threshold. We use the estimated nominal distribution to assign a likelihood to the current observation and employ limited feedback from the end user to adjust the threshold. The high performance of our algorithm is due to accurate estimation of the nominal distribution, where we achieve this by preventing anomalous data to corrupt the update process. Our method is generic in the sense that it can operate successfully over a wide range of data distributions. We demonstrate the performance of our algorithm with respect to the state-of-the-art over time varying distributions.