Jun Won Choi

As a paradigm to recover the sparse signal from a small set of linear measurements, compressed sensing (CS) has stimulated a great deal of interest in recent years. In order to apply the CS techniques to wireless communication systems, there are a number of things to know and also several issues to be considered. However, it is not easy to grasp simple and easy answers to the issues raised while carrying out research on CS. The main purpose of this paper is to provide essential knowledge and use

We propose an improved minimum mean square error (MMSE) vertical Bell Labs layered space-time (V-BLAST) detection technique, called a soft input, soft output, and soft feedback (SIOF) V-BLAST detector, for turbo multi-input multioutput (turbo-MIMO) systems. We derive a symbol estimator by minimizing the power of the interference plus noise, given a priori probabilities of undetected layer symbols and a posteriori probabilities for past detected layer symbols. For a low-complexity implementation,

Over the last decade, tremendous gains, leading to near-capacity achieving performance, have been shown for a variety of communication systems through the application of the turbo principle, i.e., the exchange of extrinsic information between constituent algorithms for tasks such as channel decoding, equalization, and multiple-input–multiple-output (MIMO) detection. In this paper, we study the practical application of such an iterative detection and decoding (IDD) framework to underwater acousti

In this paper, we propose a deep neural network (DNN)-based automatic modulation classification (AMC) for digital communications. While conventional AMC techniques perform well for additive white Gaussian noise (AWGN) channels, classification accuracy degrades for fading channels where the amplitude and phase of channel gain change in time. The key contributions of this paper are in two phases. First, we analyze the effectiveness of a variety of statistical features for AMC task in fading channe

In this paper, an iterative multi-channel equalization and decoding technique is introduced to improve system performance in underwater acoustic communications. The turbo principle is applied to the existing canonical receiver structure including fractionally spaced decision feedback equalization with phase synchronization. The performance of multi-channel equalization and adaptive weight update algorithms are aided by soft information delivered from the channel decoder. The complexity of the pr

In this paper, we consider a low-complexity detection technique referred to as a reduced dimension maximum-likelihood search (RD-MLS). RD-MLS is based on a partitioned search which approximates the maximum-likelihood (ML) estimate of symbols by searching a partitioned symbol vector space rather than that spanned by the whole symbol vector. The inevitable performance loss due to a reduction in the search space is compensated by 1) the use of a list tree search, which is an extension of a single b

This letter addresses a problem of downlink pilot allocation for massive multiple-input multiple-output (MIMO) systems. When a massive MIMO is employed in frequency division duplex (FDD) systems, significant amount of radio resources are dedicated to the transmission of downlink pilots. Such huge pilot overhead leads to a substantial loss in the maximum data throughput, which motivates us to reduce the number of pilots. In this letter, we propose a pilot reduction strategy based on compressed se

In this paper, we propose a new sparse signal recovery algorithm, referred to as sparse Kalman tree search (sKTS), that provides a robust reconstruction of the sparse vector when the sequence of correlated observation vectors are available. The proposed sKTS algorithm builds on expectation-maximization (EM) algorithm and consists of two main operations: 1) Kalman smoothing to obtain the a posteriori statistics of the source signal vectors and 2) greedy tree search to estimate the support of the

<para xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> In this paper, an energy-efficient estimation and detection problem is formulated for low-power digital filtering. Building on the soft digital signal processing technique proposed by Hegde and Shanbhag, which combines algorithmic noise tolerance and voltage scaling to reduce power, the proposed minimum power soft error cancellation (MP-SEC) technique detects, estimates, and corrects transient errors

In this paper, we introduce a new symbol detection technique for large-scale multi-input multi-output (MIMO) systems. Based on the observation that detection errors produced by conventional linear detectors tend to be sparse in practical communication regime, we employ compressed sensing techniques to correct the symbol errors from the output of the linear detectors. The proposed symbol detector, referred to as post detection sparse error recovery (PDSR) technique is derived in two steps (1) spa

Tree detection techniques are often used to reduce the complexity of a posteriori probability (APP) detection in multiantenna wireless communication systems. In this paper, we introduce an efficient soft-input soft-output tree detection algorithm that employs a new type of look-ahead path metric in the process of branch pruning (or sorting). While conventional path metrics depend only on symbols on a visited path, the new path metric accounts for unvisited parts of the tree in advance through an

As a paradigm to recover the sparse signal from a small set of linear measurements, compressed sensing (CS) has stimulated a great deal of interest in recent years. In order to apply the CS techniques to wireless communication systems, there are a number of things to know and also several issues to be considered. However, it is not easy to grasp simple and easy answers to the issues raised while carrying out research on CS. The main purpose of this paper is to provide essential knowledge and use

Recently, a variety of low complexity soft-input soft-output detection algorithms have been introduced for iterative detection and decoding (IDD) systems. However, it is still challenging to implement soft-input soft-output detector detector at feasible complexity for massive MIMO systems due to the heavy burden in computational complexity. In this paper, we present a novel soft-input soft-output detector for massive MIMO systems that offers substantial complexity reduction over the existing det