The slice-wise multiplication of two tensors is required in a variety of tensor decompositions (including PARAFAC2 and PARATUCK2) and is encountered in many applications, including the analysis of multidimensional biomedical data (EEG, MEG, etc.) or multi-carrier MIMO systems. In this paper, we propose a new tensor representation that is not based on a slice-wise (matrix) description, but can be represented by a double contraction of two tensors. Such a double contraction of two tensors can be efficiently calculated via generalized unfoldings. It leads to new tensor models of the investigated system that do not depend on the chosen unfolding and reveal the tensor structure of the data model (such that all possible unfoldings can be seen at the same time). As an example, we apply this new concept to the design of new receivers for multi-carrier MIMO systems in wireless communications. In particular, we consider MIMO OFDM systems with and without Khatri-Rao coding. The proposed receivers exploit the channel correlation between adjacent subcarriers, require the same amount of training symbols as traditional OFDM techniques, but have an improved performance in terms of the symbol error rate. Furthermore, we show that the spectral efficiency of the Khatri-Rao coded MIMO-OFDM can be increased by introducing "random coding" such that the "coding matrix" also contains useful information symbols. Considering this transmission technique, we derive a tensor model and two types of receivers for randomly coded MIMO-OFDM systems using the double contraction of two tensors.
Blind signal identification has important applications in both civilian and military communications. Previous investigations on blind identification of space-frequency block codes (SFBCs) only considered identifying Alamouti and spatial multiplexing transmission schemes. In this paper, we propose a novel algorithm to identify SFBCs by analyzing discriminating features for different SFBCs, calculated by separating the signal subspace and noise subspace of the received signals at different adjacent OFDM subcarriers. Relying on random matrix theory, this algorithm utilizes a serial hypothesis tes…
In this paper, a low complexity time domain semi-blind algorithm is proposed to estimate and track the time varying MIMO OFDM channels. First, the proposed least mean squares (LMS) based algorithm is developed for the training mode and then is extended for the blind mode of the operation by combining with the decision direction (DD) or adaptive Bussgang algorithm (ABA) techniques. In the blind mode, because of decision errors, a smaller step size is considered for the LMS algorithm and the channel estimation is run a few times to improve its precision. In each round of the estimation in the bl…
National Institute of Standards and Technology (NIST)（美国国家标准与技术研究院）；École de Technologie Supérieure (ÉTS)（高等技术学院）
We provide a new generation solution to the fundamental old problem of a doubly selective fading channel estimation for orthogonal frequency division multiplexing (OFDM) systems. For systems based on OFDM, we propose a deep learning (DL)-based blind doubly selective channel estimator. This estimator does require no pilot symbols, unlike the corresponding state-of-the-art estimators, even during the estimation of a deep fading doubly selective channel. We also provide the first of its kind theory on the testing mean squared error (MSE) performance of our investigated blind OFDM channel estimato…
Previous approaches for blind identification of space-frequency block codes (SFBC) do not perform well for short observation periods due to their inefficient utilization of frequency-domain redundancy. This paper proposes a hypothesis test (HT)-based algorithm and a support vector machine (SVM)-based algorithm for SFBC signals identification over frequency-selective fading channels to exploit two-dimensional space-frequency domain redundancy. Based on the central limit theorem, space-domain redundancy is exploited to construct the cross-correlation function of the estimator and frequency-domai…
Phase-Aware Code-Aided EM Algorithm for Blind Channel Estimation in PSK-Modulated OFDM
Information and Communication Theory Lab, Eindhoven University of Technology, The Netherlands（信息与通信理论实验室，埃因霍温理工大学，荷兰）；NXP Semiconductors, Eindhoven, The Netherlands（NXP半导体，埃因霍温，荷兰）
Blind enumeration of the number of transmit antennas and blind identification of multiple-input multiple-output (MIMO) schemes are two pivotal steps in MIMO signal identification for both military and commercial applications. Conventional approaches treat them as two independent problems, namely the source number enumeration and the presence detection of space-time redundancy, respectively. In this paper, we develop a joint blind identification algorithm to determine the number of transmit antennas and MIMO scheme simultaneously. By restructuring the received signals, we derive three subspace-…
We propose in this work a radar detection system for orthogonal-frequency division multiplexing (OFDM) transmission. We assume that the transmitting antenna Tx is colocated with a monostatic radar. The latter knows the transmitted signal and listens to echoes coming from the reflection of fixed or moving targets. We estimate the targets parameters (range and velocity) using a 2D-Periodogram. Moreover, we improve the estimation performance in low signal to noise ratio (SNR) conditions using the discrete Fourier transform channel estimation (DFT-CE) and we show that Zadoff-Chu precoding (ZCP) ad…
Institut National de la Recherche Scientifique (INRS-EMT)（加拿大魁北克大学国家科学研究院（电磁与电信研究所））
In this paper, we propose a multi-layer artificial neural network (ANN) that is trained with the Levenberg-Marquardt algorithm for use in signal detection over multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems, particularly those with low-resolution analog-to-digital converters (LR-ADCs). We consider a blind detection scheme where data symbol estimation is carried out without knowing the channel state information at the receiver (CSIR)---in contrast to classical algorithms. The main power of the proposed ANN-based detector (ANND) lies in its versatil…
Large scale multiple-input multiple-output (MIMO) or Massive MIMO is one of the pivotal technologies for future wireless networks. However, the performance of massive MIMO systems heavily relies on accurate channel estimation. While the acquisition of channel state information (CSI) in such systems requires an increasingly large amount of training overhead as the number of users grows. To tackle this issue, in this paper, we propose a deep learning assisted blind channel estimation technique for orthogonal frequency division multiplexing (OFDM) based massive MIMO systems. We prove that by expl…
Shanghai Jiao Tong University（上海交通大学）；University of Tennessee（田纳西大学）；Huazhong University of Science and Technology（华中科技大学）；Tennessee Technological University（田纳西理工大学）
This paper develops a new deep neural network optimized equalization framework for massive multiple input multiple output orthogonal frequency division multiplexing (MIMOOFDM) systems that employ low-resolution analog-to-digital converters (ADCs) at the base station (BS). The use of lowresolution ADCs could largely reduce hardware complexity and circuit power consumption, however, it makes the channel station information almost blind to the BS, hence causing difficulty in solving the equalization problem. In this paper, we consider a supervised learning architecture, where the goal is to learn…
Filter bank-based multicarrier (FBMC) systems have attracted increasing attention recently in view of their many advantages over the classical cyclic prefix (CP)-based orthogonal frequency division multiplexing (CP-OFDM) modulation. However, their more advanced structure (resulting in, for example, self interference) complicates signal processing tasks at the receiver, including synchronization, channel estimation and equalization. In a multiple-input multiple-output (MIMO) configuration, the multi-antenna interference has also to be taken into account. (Semi-) blind receivers, of increasing i…
