Lukas Bruderer

VLSI implementation of a low-complexity LLL lattice reduction algorithm for MIMO detection

Lattice-reduction (LR)-aided successive interference cancellation (SIC) is able to achieve close-to optimum error-rate performance for data detection in multiple-input multiple-output (MIMO) wireless communication systems. In this work, we propose a hardware-efficient VLSI architecture of the Lenstra-Lenstra-Lovász (LLL) LR algorithm for SIC-based data detection. For this purpose, we introduce various algorithmic modifications that enable an efficient hardware implementation. Comparisons with existing FPGA implementations show that our design outperforms state-of-the-art LR implementations in terms of hardware-efficiency and throughput. We finally provide reference ASIC implementation results for 130nm CMOS technology.

Area- and throughput-optimized VLSI architecture of sphere decoding

Sphere decoding (SD) is a promising means for implementing high-performance data detection in multiple-input multiple-output (MIMO) wireless communication systems. In this paper, we focus on the register transfer level implementation of SD with minimum area-delay product for application in wideband MIMO communication systems, such as IEEE 802.11n, where multiple SD cores need to be instantiated. The basic architectural considerations and the proposed optimizations are explained based on hard-output SD, but are also applicable to soft-output SD. Corresponding VLSI implementation results (for both hard-output and soft-output SD) show an improvement in the area-delay product by almost 50% compared to that of other SD implementations reported in the literature.

A Lattice Reduction-Aided MIMO Channel Equalizer in 90 nm CMOS Achieving 720 Mb/s

In this paper, a VLSI implementation of a complete MIMO channel equalization ASIC based on lattice reduction-aided linear detection is presented. The architecture performs preprocessing steps at channel rate and low-complexity linear data detection at symbol rate. Preprocessing is based on Seysens algorithm for lattice reduction. We present algorithmic improvements of the lattice reduction preprocessing in terms of area and throughput of the VLSI implementation with minor impact on the error-rate. Due to the low-complexity implementation of the lattice reduction-aided data detection stage, our architecture is able to achieve very low power in typical packet-based MIMO wireless data transmission scenarios. The final 90 nm CMOS ASIC achieves an energy efficiency for the detection of 24 pJ/bit at a throughput of 720 Mbps with near-optimal error-rate performance.

On sparsity by NUV-EM, Gaussian message passing, and Kalman smoothing

Normal priors with unknown variance (NUV) have long been known to promote sparsity and to blend well with parameter learning by expectation maximization (EM). In this paper, we advocate this approach for linear state space models for applications such as the estimation of impulsive signals, the detection of localized events, smoothing with occasional jumps in the state space, and the detection and removal of outliers. The actual computations boil down to multivariate-Gaussian message passing algorithms that are closely related to Kalman smoothing. We give improved tables of Gaussian-message computations from which such algorithms are easily synthesized, and we point out two preferred such algorithms.

Deconvolution of weakly-sparse signals and dynamical-system identification by Gaussian message passing

We use ideas from sparse Bayesian learning for estimating the (weakly) sparse input signal of a linear state space model. Variational representations of the sparsifying prior lead to algorithms that essentially amount to Gaussian message passing. The approach is extended to the case where the state space model is not known and must be estimated. Experimental results with a real-world application substantiate the applicability of the proposed method.

Local statistical models from deterministic state space models, likelihood filtering, and local typicality.

Surprisingly many signal processing problems can be approached by locally fitting autonomous deterministic linear state space models to the data. In this paper, we introduce local statistical models for such cases and discuss the computation both of the corresponding estimates and of local likelihoods for different models.

Low-complexity Seysen's algorithm based lattice reduction-aided MIMO detection for hardware implementations

Lattice reduction-aided linear detectors for MIMO systems are a promising receiver structure for low-complexity implementations. In this paper we present a lattice reduction-aided MIMO data detection architecture based on Seysens algorithm, where the algorithm is carried out exclusively on the Gram matrix of the channel and its inverse. Furthermore, we describe and evaluate several modification of Seysens algorithm tailored to reduce the total complexity with respect to hardware implementation. By means of complexity-performance trade-offs we demonstrate the potential benefit of the various algorithmic modifications. First, novel schemes to identify the update steps in each iteration of Seysens algorithm are presented. Second, we show that further complexity reduction of Seysens algorithm can be obtained by severely constraining the update coefficients. Eventually, methods are devised that terminate Seysens algorithm prematurely and thus result in a reduction of the average complexity.

Estimation of sensor input signals that are neither bandlimited nor sparse

The paper addresses the estimation of the continuous-time input signal to a linear sensor that is given in state-space form. In previous work, Bolliger et al. proposed to model the input signal as (continuous-time) white Gaussian noise and derived a corresponding estimator that is based on Kalman filtering. The present paper elaborates on this new estimator. In particular, it establishes the continuity (or the piecewise continuity) of the estimate, presents a new interpolation formula between samples, complements the Kalman-filter perspective by a Wiener-filter perspective, and demonstrates practicality by numerical experiments.

Outlier-insensitive Kalman smoothing and marginal message passing

We propose a new approach to outlier-insensitive Kalman smoothing based on normal priors with unknown variance (NUV). In contrast to prior work, the actual computations amount essentially to iterations of a standard Kalman smoother (with few extra computations). The proposed approach is easily extended to nonlinear estimation problems by combining the outlier detection with an extended Kalman smoother. For the Kalman smoothing, we consider both a Modified Bryson-Frasier smoother and the recently proposed Backward Information Filter Forward Marginal smoother, neither of which requires matrix inversions.

Gesture recognition from magnetic field measurements using a bank of linear state space models and local likelihood filtering

Detecting and inferring the trajectory of a moving magnet from magnetic field measurements is a challenge due to a wide range of time scales and amplitudes of the recorded signals and limited computational power of devices embedding a magnetometer. In this paper, we model the magnetic field measurements using a bank of autonomous linear state space models and provide an efficient algorithm based on local likelihood filtering for reliably detecting and inferring the gesture causing the magnetic field variations.