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Channel estimation allows millimeter wave (mmWave) MIMO communication systems to design pre-coders and combiners under different objective functions. Hybrid MIMO architectures provide a good trade-off power consumption-performance at mmWave frequencies, but most of the prior work on channel estimation for these structures assumes a narrowband channel model. In this paper, we propose a sparse approach...
Massive MIMO is a variant of multiuser MIMO, in which the number of antennas M at the base-station is very large and generally much larger than the number of spatially multiplexed data streams to the users. It turns out that by increasing the number of antennas M at the base-station and as a result increasing the spatial resolution of the array, although the received signal from each user tends to...
This paper develops a novel channel estimation approach for multi-user millimeter wave (mmWave) wireless systems with large antenna arrays. By exploiting the inherent mmWave channel sparsity, we propose a novel simultaneous-estimation with iterative fountain training (SWIFT) framework, in which the average number of channel measurements is adapted to various channel conditions. To this end, the base...
In this paper, we propose a new recursive sparse channel recovery algorithm which can track time-varying support of angular domain channel response vector in mobility scenario for millimeter wave-band communications. We model the angle of departure (AoD) and the angle of arrival (AoA) using discrete state Markov random process and derive joint estimation of the time-varying support and amplitude of...
In this paper, we shall develop a generic channel estimation framework based on the convex formulation for dense cloud radio access networks (Cloud-RAN). Due to the training resource constraint and the large number of transmit antennas, the pilot length is smaller than the antenna number, and thus channel estimation becomes an ill-posed inverse problem. By observing that the wireless channel possesses...
In a practical large scale antenna system (LSAS) over the millimeter Wave (mmWave) bands, the implementation of hybrid beamforming structure plays an important role as it provides high precoding gains to overcome the high path loss and achieve sufficient link margins. While employing such large antenna arrays, it becomes challenging to estimate the mmWave channel using the conventional algorithms...
In this paper, channel estimation in millimeter wave (mmWave) communication systems is considered. In contrast to prevailing mmWave channel estimation methods exploiting the sparsity nature of the channel, we move one step further by exploiting the joint AoD-AoA angular spread. By formulating the channel estimation as a block-sparse signal recovery with an underlying two-dimensional cluster feature,...
Due to radio-frequency (RF) circuit mismatch, the channel reciprocity of time-division duplex massive multiple-input multiple-output system is impaired. Under this condition, there exist several different approaches for base station (BS) to obtain the downlink (DL) channel estimate based on the minimum mean-square-error (MMSE) estimation method. We show that with the RF mismatch parameters BS will...
In this work, we propose a new channel training (CT) scheme to enhance physical layer security in a full-duplex wiretap channel, where the multi-antenna and full-duplex receiver simultaneously receives the information signal and transmits artificial noise (AN). In order to suppress the self-interference caused by AN, the receiver has to estimate the self-interference channel prior to the data communication...
In millimeter-wave (mmWave) multiple-input multiple-output (MIMO) systems, channel estimation in the presence of sparse multipath fading boils down to two-dimensional (2D) direction-of-arrival (DOA) estimation followed by path gain estimation. To achieve super-resolution angle estimation at affordable complexity, this paper develops an efficient channel estimation approach by applying a truncated...
In this paper, the problem of sequential beam construction and adaptive channel estimation based on reduced rank (RR) Kalman filtering for frequency-selective massive multiple-input multiple-output (MIMO) systems employing single-carrier (SC) in time division duplex (TDD) mode are considered. In two-stage beamforming, a new algorithm for statistical pre-beamformer design is proposed for spatially...
We propose a novel channel estimation technique for intensity modulation/direct detection (IM/DD) based orthogonal frequency division multiplexing visible light communication (OFDM-VLC) systems, utilizing sparse Bayesian dual-variate relevance vector machine (RVM) regression. By exploiting sparse Bayesian framework, dual-variate RVM regression can provide accurate estimation of the real and imaginary...
This work proposes a new limited feedback channel estimation framework. The proposed approach exploits a sparse representation of the double directional wireless channel model involving an over complete dictionary that accounts for the antenna directivity patterns at both base station (BS) and user equipment (UE). Under this sparse representation, a computationally efficient limited feedback algorithm...
This paper proposes a novel approach for optimal pilot sequence design for channel estimation in single carrier (SC) and multi carrier (MC) block transmission systems over frequency selective channels. The training design is based on minimizing the Bayesian Cramer-Rao bound (BCRB) for mean square error (MSE) of channel estimation which also employs the prior knowledge of the channel in conjunction...
Although the benefits of precoding and combining of data streams are widely recognized, the potential of precoding the pilot signals at the user equipment (UE) side and combining them at the base station (BS) side has not received adequate attention. This paper considers a multiuser multiple input multiple output (MU-MIMO) cellular system in which the BS acquires channel state information (CSI) by...
We propose two novel, low-complexity techniques for beam alignment in time division duplexing (TDD) multiple-input multiple-output (MIMO) systems. The techniques are inspired by the power method, an iterative algorithm to determine eigenvalues and eigenvectors through repeated matrix multiplications and improve upon this simple idea by providing a better performance in the low-SNR regime. The first...
In a time-division duplex (TDD) multiple antenna system, the channel state information (CSI) can be estimated using reverse training. A pilot contamination (spoofing) attack occurs when during the training phase, an adversary also sends identical training (pilot) signal as that of the legitimate receiver. This contaminates channel estimation and alters the legitimate beamformimg design, facilitating...
Acoustic beamforming has played a key role in the robust automatic speech recognition (ASR) applications. Accurate estimates of the speech and noise spatial covariance matrices (SCM) are crucial for successfully applying the minimum variance distortionless response (MVDR) beamforming. Reliable estimation of time-frequency (TF) masks can improve the estimation of the SCMs and significantly improve...
Probabilistic linear discriminant analysis (PLDA) is widely described as an effective model for text-independent speaker verification in the i-vector space. The PLDA scoring function is typically formulated as the likelihood ratio between the speaker-adapted and the universal PLDAs. In this case, the adaptation of PLDA was performed through the speaker factors. In this paper, we show that the channel...
In massive multiple-input multiple-output (MIMO) systems, superimposed (SP) and time-multiplexed (TM) pilots exhibit a complementary behavior, with the former and latter schemes offering a higher throughput in high and low inter-cell interference scenarios, respectively. Based on this observation, in this paper, we propose an algorithm for partitioning users into two disjoint sets comprising users...
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