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We investigate remote estimation over a Gilbert-Elliot channel with feedback. The channel is modelled as an ON/OFF channel, where the state of the channel evolves as a Markov chain. The channel state is observed by the receiver and fed back to the transmitter with one unit delay. In addition, the transmitter gets ACK/NACK feedback for successful/unsuccessful transmission. Using ideas from team theory,...
We study the relationship between information- and estimation-theoretic quantities in time-evolving systems. We focus on the Fokker-Planck channel defined by a general stochastic differential equation, and show that the time derivatives of entropy, KL divergence, and mutual information are characterized by estimation-theoretic quantities involving an appropriate generalization of the Fisher information...
Doubly selective channels or time-varying multipath channels occur when communication systems are expected to work in a highly mobile environment. The estimation and the subsequent equalization of such channels is a non-trivial task. Thus, a channel estimation scheme that is robust, precise and works with a complexity that is applicable for consumer applications is vital to overcome the effects of...
In this paper, we investigate the prediction of mobile MIMO channels with varying multipath parameters. Based on the PAST algorithm, we propose a multidimensional adaptive ESPRIT approach for jointly tracking the evolution of the Doppler frequencies and spatial directions of arrival and departure of the propagation paths. Future states of the channel are predicted using the last estimate of the propagation...
The method of image defogging mainly includes two aspects: image enhancement and image restoration. This article mainly focus on the image restoration. First of all, it studies the He's defogging algorithm based on dark channel prior and make some improvement based on this theory. Aiming at solving the defects of inaccurate estimation of atmospheric light and long time running of He's algorithm, the...
This paper proposes a novel method combining dark channel prior (DCP) and bright channel prior (BCP) for single image dehazing. The proposed method achieves airlight approximations by implementing numerical proximity of atmospheric light, which use the average value of the DCP and BCP. Meanwhile, in order to reduce computational time, a fast guided filtration method is used for the transmission map...
In this paper we study single image haze removal techniques on outdoor images for visibility enhancement in foggy weather conditions. Haze removal techniques based on dark channel prior model have used different filters for estimating the transmission. We have studied effect of using different filters along with the fundamental mean and gaussian filters in the visibility enhancement in foggy conditions...
Channel state information (CSI) acquisition is a crucial issue in downlink FDD-based massive multi-input multioutput (MIMO) networks, where the channel reciprocity is not applicable. Thus, users are expected to feedback the bestmatch quantized channels to serving transmitters. Hence, an extensively large size of the feedback overhead is needed, which is linearly scaled at each user with the number...
This paper proposes a practical channel estimation scheme for downlink 60GHz indoor systems with the massive uniform rectangular array (URA) at base station (BS). Through array signal processing theory, the parameter of each channel path can be decomposed into the angular information and the channel gain information that can be estimated separately. We first prove that the two dimensional Discrete...
Pilot contamination is a throughput limiting factor in cellular massive MIMO systems. Previous work has shown that the impact of pilot-contamination can be reduced by exploiting structural information in form of channel covariance matrices. Additionally, significant gains can be obtained through coordinated user assignment. In this paper, we extend these approaches to a realistic scenario with imperfect...
In the last few years, there has been a growing interest in developing sensing systems using RF signals of opportunity, especially exploiting radar-based techniques. Long Term Evolution (LTE) signals are excellent candidates as signals of opportunity thanks to their wide availability and penetration in indoor environments. This is the first work investigating the possibility to use LTE signals for...
Simultaneous collection of electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) is a promising neuroimaging technique, which can provide high resolution in both spatial and temporal domain. Because EEG recorded in MRI scanners is heavily contaminated with gradient artefact (GA), removal of GA from EEG is a crucial step in EEG-fMRI data analysis. To date, the most efficient methods...
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 considers the channel estimation (CE) and multi-user detection (MUD) problems in cloud radio access network (C-RAN). Assuming that active users are sparse in the network, we solve CE and MUD problems with compressed sensing (CS) technology to greatly reduce the long identification pilot overhead. A mixed ℓ2.1-regularization functional for extended sparse group-sparsity recovery is proposed...
In LTE-Advanced (LTE-A) demodulation reference symbols are employed for pilot aided channel estimation to perform coherent detection. Since these reference symbols are allocated on the same time-frequency positions for all users in Multi-User MIMO (MU-MIMO) operation or for all spatial layers in Single-User MIMO (SU-MIMO), they are designed to be code-domain orthogonal in order to be separable at...
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 this paper, we propose a new channel tracking method for massive multiple-input multiple-output (MIMO) systems under both the time-varying and spatial-varying circumstance. With spatial-temporal basis expansion model (ST-BEM), the channel information is decomposed into the spatial information and gain information, where the former is determined by the central angle as well as the angular spread...
In this paper we analyze the performance of a full-duplex relay system in the presence of residual self-interference (SI) and frequency-selective fading. In particular, the residual SI channel estimation performance is evaluated both analytically and via simulation. The bit error rate (BER) performance at the destination is also characterized via simulation. Two schemes are considered for the equalization...
This paper presents a modified MMSE channel estimation for turbo equalization in uplink massive multiple-input multiple-output (MIMO) systems with pilot contamination. Unlike other work on channel estimation in massive MIMO systems where perfect knowledge of inter-cell large scale channel coefficients is assumed, a modified method for data-aided MMSE estimation is proposed without requiring the knowledge...
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...
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