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VLSI Design of Large-Scale Soft-Output MIMO Detection Using Conjugate Gradients
(2015)2015 IEEE International Symposium on Circuits and Systems (ISCAS)We propose an FPGA design for soft-output data detection in orthogonal frequency-division multiplexing (OFDM)-based large-scale (multi-user) MIMO systems. To reduce the high computational complexity of data detection, our design uses a modified version of the conjugate gradient least square (CGLS) algorithm. In contrast to existing linear detection algorithms for massive MIMO systems, our method avoids two of the most complex tasks, namely ...Conference Paper -
CS-MUVI: Video compressive sensing for spatial-multiplexing cameras
(2012)2012 IEEE International Conference on Computational Photography (ICCP)Compressive sensing (CS)-based spatial-multiplexing cameras (SMCs) sample a scene through a series of coded projections using a spatial light modulator and a few optical sensor elements. SMC architectures are particularly useful when imaging at wavelengths for which full-frame sensors are too cumbersome or expensive. While existing recovery algorithms for SMCs perform well for static images, they typically fail for time-varying scenes ...Conference Paper -
FPGA design of approximate semidefinite relaxation for data detection in large MIMO wireless systems
(2016)2016 IEEE International Symposium on Circuits and Systems (ISCAS)We propose a novel, near-optimal data detection algorithm and a corresponding FPGA design for large multiple-input multiple-output (MIMO) wireless systems. Our algorithm, referred to as TASER (short for triangular approximate semidefinite relaxation), relaxes the maximum-likelihood (ML) detection problem to a semidefinite program and solves a non-convex approximation using a preconditioned forward-backward splitting procedure. We show ...Conference Paper -
Implementation Trade-offs for Linear Detection in Large-Scale MIMO Systems
(2013)2013 IEEE International Conference on Acoustics, Speech and Signal ProcessingIn this paper, we analyze the VLSI implementation tradeoffs for linear data detection in the uplink of large-scale multiple-input multiple-output (MIMO) wireless systems. Specifically, we analyze the error incurred by using the sub-optimal, low-complexity matrix inverse proposed in Wu et al., 2013, ISCAS, and compare its performance and complexity to an exact matrix inversion algorithm. We propose a Cholesky-based reference architecture ...Conference Paper -
Training Quantized Nets: A Deeper Understanding
(2018)Advances in Neural Information Processing Systems 30Currently, deep neural networks are deployed on low-power portable devices by first training a full-precision model using powerful hardware, and then deriving a corresponding low-precision model for efficient inference on such systems. However, training models directly with coarsely quantized weights is a key step towards learning on embedded platforms that have limited computing resources, memory capacity, and power consumption. Numerous ...Conference Paper -
Unsupervised Charting of Wireless Channels
(2018)2018 IEEE Global Communications Conference (GLOBECOM)Future wireless communication systems will rely on large antenna arrays at the infrastructure base stations (BSs) to serve multiple users with high data rates in a single cell. We demonstrate that the availability of high-dimensional channel state information (CSI) acquired at such multi-antenna BSs enables one to learn a chart of the radio geometry, which captures the spatial geometry of the users so that points close in space are close ...Conference Paper -
Minimizing Pilot Overhead in Cell-Free Massive MIMO Systems via Joint Estimation and Detection
(2020)2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)We propose a joint channel estimation and data detection (JED) algorithm for cell-free massive multi-user (MU) multiple-input multiple-output (MIMO) systems. Our algorithm yields improved reliability and reduced latency while minimizing the pilot overhead of coherent uplink transmission. The proposed JED method builds upon a novel non-convex optimization problem that we solve approximately and efficiently using forward- backward splitting. ...Conference Paper -
Sparsity-Adaptive Beamspace Channel Estimation for 1-Bit mmWave Massive MIMO Systems
(2020)2020 IEEE 21st International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)Conference Paper -
BEACHES: Beamspace Channel Estimation for Multi-Antenna mmWave Systems and Beyond
(2019)2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)Conference Paper -
Sparse Beamspace Equalization for Massive MU-MIMO MMWave Systems
(2020)ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)Conference Paper