Biconvex Relaxation for Semidefinite Programming in Computer Vision

IEEE J. Select. Areas Commun.

Jacobsson

Durisi

et al. 2020

Self Cite

We propose finite-alphabet equalization, a new paradigm that restricts the entries of the spatial equalization matrix to low-resolution numbers, enabling high-throughput, lowpower, and low-cost hardware equalizers. To minimize the performance loss of this paradigm, we introduce FAME, short for finitealphabet minimum mean-square error (MMSE) equalization, which is able to significantly outperform a naïve quantization of the linear MMSE matrix. We develop efficient algorithms to approximately solve the NP-hard FAME problem and showcase that near-optimal performance can be achieved with equalization coefficients quantized to only 1-3 bits for massive multiuser multipleinput multiple-output (MU-MIMO) millimeter-wave (mmWave) systems. We provide very-large scale integration (VLSI) results that demonstrate a reduction in equalization power and area by at least a factor of 3.9× and 5.8×, respectively.

Section: B Fame With Forward-backward Splitting (Fbs)mentioning

confidence: 99%

Finite-Alphabet MMSE Equalization for All-Digital Massive MU-MIMO mmWave Communication

IEEE J. Select. Areas Commun.

Jacobsson

Durisi

et al. 2020

Self Cite

“…where I B B (ã) is the indicator function, which is zero ifã ∈ B B and infinity otherwise. We use the indicator function to incorporate the convex constraintã ∈ B B in (17) into the function g(ã). These choices for f (ã) and g(ã) result in:…”

Section: B Fawp Via Forward-backward Splitting (Fbs)mentioning

confidence: 99%

Finite-Alphabet Wiener Filter Precoding for mmWave Massive MU-MIMO Systems

2019 53rd Asilomar Conference on Signals, Systems, and Computers

Jacobsson

Durisi

et al. 2019

Self Cite

Power consumption of multiuser (MU) precoding is a major concern in all-digital massive MU multiple-input multipleoutput (MIMO) base-stations with hundreds of antenna elements operating at millimeter-wave (mmWave) frequencies. We propose to replace part of the linear Wiener filter (WF) precoding matrix by a finite-alphabet WF precoding (FAWP) matrix, which enables the use of low-precision hardware that consumes low power and area. To minimize the performance loss of our approach, we present methods that efficiently compute FAWP matrices that best mimic the WF precoder. Our results show that FAWP matrices approach infinite-precision error-rate and error-vector magnitude performance with only 3-bit precoding weights, even when operating in realistic mmWave channels. Hence, FAWP is a promising approach to substantially reduce power consumption and silicon area in all-digital mmWave massive MU-MIMO systems.

“…We now develop a novel algorithm to approximately solve the ML-JED problem in (3) using biconvex relaxation (BCR) [18], a recent framework to solve large semidefinite programs in computer vision. The resulting algorithm is referred to as PRojection Onto conveX hull (PrOX), requires low computational complexity, and achieves near-ML-JED performance.…”

Section: Prox: Projection Onto Convex Hullmentioning

confidence: 99%

VLSI Designs for Joint Channel Estimation and Data Detection in Large SIMO Wireless Systems

IEEE Trans. Circuits Syst. I

Goldstein

Studer

2018

Self Cite

Abstract-Channel estimation errors have a critical impact on the reliability of wireless communication systems. While virtually all existing wireless receivers separate channel estimation from data detection, it is well known that joint channel estimation and data detection (JED) significantly outperforms conventional methods at the cost of high computational complexity. In this paper, we propose a novel JED algorithm and corresponding VLSI designs for large single-input multiple-output (SIMO) wireless systems that use constant-modulus constellations. The proposed algorithm is referred to as PRojection Onto conveX hull (PrOX) and relies on biconvex relaxation (BCR), which enables us to efficiently compute an approximate solution of the maximumlikelihood JED problem. Since BCR solves a biconvex problem via alternating optimization, we provide a theoretical convergence analysis for PrOX. We design a scalable, high-throughput VLSI architecture that uses a linear array of processing elements to minimize hardware complexity. We develop corresponding field-programmable gate array (FPGA) and application-specific integrated circuit (ASIC) designs, and we demonstrate that PrOX significantly outperforms the only other existing JED design in terms of throughput, hardware-efficiency, and energy-efficiency.Index Terms-FPGA and ASIC designs, joint channel estimation and data detection (JED), large single-input multiple-output (SIMO) wireless systems, biconvex relaxation (BCR).