Patrick Maechler

VLSI Design of Approximate Message Passing for Signal Restoration and Compressive Sensing

Sparse signal recovery finds use in a variety of practical applications, such as signal and image restoration and the recovery of signals acquired by compressive sensing. In this paper, we present two generic very-large-scale integration (VLSI) architectures that implement the approximate message passing (AMP) algorithm for sparse signal recovery. The first architecture, referred to as AMP-M, employs parallel multiply-accumulate units and is suitable for recovery problems based on unstructured (e.g., random) matrices. The second architecture, referred to as AMP-T, takes advantage of fast linear transforms, which arise in many real-world applications. To demonstrate the effectiveness of both architectures, we present corresponding VLSI and field-programmable gate array implementation results for an audio restoration application. We show that AMP-T is superior to AMP-M with respect to silicon area, throughput, and power consumption, whereas AMP-M offers more flexibility.

High-speed compressed sensing reconstruction on FPGA using OMP and AMP

Compressed sensing allows to reconstruct sparse signals sampled at sub-Nyquist rates. However, reconstruction of the original signal requires high computational effort, even for problems of moderate size. Especially for applications with real-time requirements, software realizations are not fast enough. We therefore present generic high-speed FPGA implementations of two fast reconstruction algorithms: orthogonal matching pursuit (OMP) and approximate message passing (AMP). Our implementations also support less sparse signals, which makes them suitable for, e.g., image reconstruction. The two implementations are optimized for highly parallel processing on FPGAs and have similar hardware structures, which allows comparisons in terms of resource usage and performance.

Matching pursuit: Evaluation and implementatio for LTE channel estimation

The emerging research field of compressed sensing (CS) promises better signal reconstruction out of fewer measurements if a sparse representation of the signal exists. Since wireless broadband channels often exhibit a sparse impulse response, CS reconstruction algorithms were proposed for channel estimation. In this paper, a hardware architecture for channel estimation using the matching pursuit algorithm is presented. The reference design targets the 3GPP LTE standard with a channel bandwidth of up to 20 MHz. Achievable performance gains over least squares channel estimation are illustrated by means of simulations. The costs in terms of chip area and reconstruction time for 180 nm CMOS technology are presented together with an analysis of the tradeoff between hardware complexity and reconstruction performance.

Implementation of greedy algorithms for LTE sparse channel estimation

Broadband wireless systems often operate under channel conditions that are characterized by a sparse channel impulse response. When the amount of training is given by the standard, compressed sensing channel estimation can exploit this sparsity to improve the quality of the channel estimate. In this paper, we analyze and compare the hardware complexity and denoising performance of three greedy algorithms for the 3GPP LTE system. The complexity/performance trade-off is analyzed using parameterized designs with varying configurations. One configuration of each algorithm is fabricated in a 180nm process and measured.

Robust asynchronous indoor localization using LED lighting

We propose a low-cost system for indoor self-localization of mobile devices using modulated LED ceiling lamps that are fully autonomous and broadcast their identifiers without any synchronization. The proposed self-localization method is designed to handle this lack of synchronization as well as the possibility of blocked line-of-sight connections or severe attenuation in real-world environments. This robustness is achieved by applying a suitable Bayesian signal model and by taking into account the inherent sparsity in detecting the concurrently visible lamps. The proposed estimator of the location approximates optimal Bayesian estimation while maintaining low complexity. Simulation results confirm a significant gain in performance compared to a classical matched-filter approach.

Hardware platform and implementation of a real-time multi-user MIMO-OFDM testbed

This paper describes a modular hardware platform of a multi-user (MU) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) testbed. The hardware platform is based on multiple field programmable gate arrays (FPGAs), provides four integrated radio-frequency (RF) chains, and has capabilities for extension boards. The performance and modularity of the testbed enables real-time MU-MIMO-OFDM experiments as well as offline processing experiments. To this end, the MIMO physical (PHY) layer of Haene et al., IEEE J-SAC, 2008, has been adapted to the new hardware platform and extended with bi-directional communication facilities and a basic media access control (MAC) layer equipped with Ethernet connectivity.

Hardware-efficient random sampling of fourier-sparse signals

Spectrum sensing, i.e. the identification of occupied frequencies within a large bandwidth, requires complex sampling hardware. Measurements suggest that only a small fraction of the available spectrum is actually used at any time and place, which allows a sparse characterization of the frequency domain signal. Compressed sensing (CS) can exploit this sparsity and simplify measurements. We investigate the performance of a very simple hardware architecture based on the slope analog-to-digital converter (ADC), which allows to sample signals at unevenly spaced points in time. CS algorithms are used to identify the occupied frequencies, which can be continuously distributed across a large bandwidth.

Live demonstration: Real-time audio restoration using sparse signal recovery

We demonstrate the restoration of audio signals corrupted by clicks and pops using techniques from sparse signal recovery and compressive sensing. The demonstration features real-time signal restoration using the approximate message passing algorithm on an FPGA prototyping board. To highlight the restoration performance of our implementation, we remove clicks and pops from old phonograph recordings in real time.

Live demonstration: Hardware platform and implementation of a real-time multi-user MIMO-OFDM testbed

The goal of the demonstration is to show the visitor how MIMO-OFDM communication works and what gains in terms of throughput, link reliability, etc. can be achieved, visualized by different experiments.