Browsing by Author "Studer, C."

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    Approximate Matrix Inversion for High-Throughput Data Detection in Large-Scale MIMO Uplink
    (IEEE, 2013-05) Wu, M.; Yin, B.; Vosoughi, A.; Studer, C.; Cavallaro, Joseph R.; Dick, C.
    The high processing complexity of data detection in the large-scale multiple-input multiple-output (MIMO) uplink necessitates high-throughput VLSI implementations. In this paper, we propose—to the best of our knowledge—first matrix inversion implementation suitable for data detection in systems having hundreds of antennas at the base station (BS). The underlying idea is to carry out an approximate matrix inversion using a small number of Neumann-series terms, which allows one to achieve near-optimal performance at low complexity. We propose a novel VLSI architecture to efficiently compute the approximate inverse using a systolic array and show reference FPGA implementation results for various system configurations. For a system where 128 BS antennas receive data from 8 single-antenna users, a single instance of our design processes 1.9Mmatrices/s on a Xilinx Virtex-7 FPGA, while using only 3.9% of the available slices and 3.6% of the available DSP48 units.
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    Signal Representation with Minimum L_Infinity Norm
    (2012-10) Studer, C.; Yin, W.; Baraniuk, R.G.
    Maximum (or L_infinity) norm minimization subject to an underdetermined system of linear equations finds use in a large number of practical applications, such as vector quantization, peak-to-average power ratio (PAPR) (or "crest factor") reduction in wireless communication systems, approximate neighbor search, robotics, and control. In this paper, we analyze the fundamental properties of signal representations with minimum L_infinity-norm. In particular, we develop bounds on the maximum magnitude of such representations using the uncertainty principle (UP) introduced by Lyubarskii and Vershynin, 2010, and we characterize the limits of l_infinity-norm-based PAPR reduction. Our results show that matrices satisfying the UP, such as randomly subsampled Fourier or i.i.d. Gaussian matrices, enable the effcient computation of so-called democratic representations, which have both provably small l_infinity-norm and low PAPR.