• Journal of electromagnetic engineering and science
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• v.11 no.2
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• pp.128-132
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• 2011

In interference tolerance based spectrum sharing systems, primary receivers (PRs) are protected by a predefined peak or average interference power constraint. To implement such systems, cognitive radio (CR) transmitters are required to adjust their transmit power so that the interference power received at the PR receivers is kept below the threshold value. Hence, a CR-transmitter requires knowledge of its channel and the primary receiver in order to allocate the transmit power. In practice, it is impossible or very difficult for a CR transmitter to have perfect knowledge of this channel state information (CSI). In this paper, we investigate the impact of imperfect knowledge of this CSI on the performances of both a primary and cognitive radio network. For fixed transmit power, average interference power (AIP) constraint can be maintained through knowledge of the channel distribution information. To maintain the peak interference power (PIP) constraint, on the other hand, the CR-transmitter requires the instantaneous CSI of its channel with the primary receiver. First, we show that, compared to the PIP constraint with perfect CSI, the AIP constraint is advantageous for primary users but not for CR users. Then, we consider a PIP constraint with imperfect CSI at the CR-transmitter. We show that inaccuracy in CSI reduces the interference at the PR-receivers that is caused by the CR-transmitter. Consequently the proposed schemes improve the capacity of the primary links. Contrarily, the capacities of the CR links significantly degrade due to the inaccuracy in CSI.

• Journal of Communications and Networks
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• v.15 no.4
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• pp.352-361
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• 2013

Combining multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) with a massive number of transmit antennas (massive MIMO-OFDM) is an attractive way of increasing the spectrum efficiency or reducing the transmission energy per bit. The effectiveness of Massive MIMO-OFDM is strongly affected by the channel state information (CSI) estimation method used. The overheads of training frame transmission and CSI feedback decrease multiple access channel (MAC) efficiency and increase the CSI estimation cost at a user station (STA). This paper proposes a CSI estimation scheme that reduces the training frame length by using a novel pilot design and a novel unitary matrix feedback method. The proposed pilot design and unitary matrix feedback enable the access point (AP) to estimate the CSI of the signal space of all transmit antennas using a small number of training frames. Simulations in an IEEE 802.11n channel verify the attractive transmission performance of the proposed methods.

• ETRI Journal
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• v.45 no.6
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• pp.929-938
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• 2023

The detection of all the symbols transmitted simultaneously in multiuser systems using limited wireless resources is challenging. Traditional model-based methods show high performance with perfect channel state information (CSI); however, severe performance degradation will occur if perfect CSI cannot be acquired. In contrast, data-driven methods perform slightly worse than model-based methods in terms of symbol error ratio performance in perfect CSI states; however, they are also able to overcome extreme performance degradation in imperfect CSI states. This study proposes a novel deep learning-based method by improving a state-of-the-art data-driven technique called deep soft interference cancellation (DSIC). The enhanced DSIC (EDSIC) method detects multiuser symbols in a fully sequential manner and uses an efficient neural network structure to ensure high performance. Additionally, error-propagation mitigation techniques are used to ensure robustness against channel uncertainty. The EDSIC guarantees a performance that is very close to the optimal performance of the existing model-based methods in perfect CSI environments and the best performance in imperfect CSI environments.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.2
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• pp.872-877
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• 2011

In this paper, the new decoding algorithm of Block Turbo Codes using Channel State Information(CSI), which is estimated to compensate for the distorted signal caused by multi-path fading, is proposed in order to improve error correction capacity during decoding procedure in OFDM system. The performance of the new decoding algorithm is compared to that of the conventional decoding algorithm without using channel state information under the Rayleigh fading channel. Experimental results showed that in case of only one iteration coding gains of up to 5.0dB~9.0dB can be obtained by applying the channel state information to the conventional decoding algorithm according to the modulation methods. In addition to that, the new decoding algorithm using channel state information at only one iteration shows a performance improvement of 3.5dB to 5.0dB when compared to the conventional decoding algorithm after four iterations. This leads to reduce the considerable amount of computation.

• Journal of Electrical Engineering and Technology
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• v.12 no.3
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• pp.1286-1291
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• 2017

In this paper, we propose an indoor positioning system that makes use of the attenuation model for IEEE 802.11 Channel State Information (CSI) in order to determine its distance from an Access Point (AP) at a fixed position. With the use of CSI, we can mitigate the problems present in the use of Received Signal Strength Indicator (RSSI) data and increase the accuracy of the estimated mobile device's location. For the experiments we performed, we made use of the Intel 5300 Series Network Interface Card (NIC) in order to receive the channel frequency response. The Intel 5300 NIC differs from its counterparts in that it can obtain not only the RSSI but also the CSI between an access point and a mobile device. We can obtain the signal strengths and phases from subcarriers of a system which in turn means making use of this data in the estimation of a mobile device's position.

• Journal of IKEEE
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• v.26 no.3
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• pp.341-348
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• 2022

Wi-Fi Sensing, which uses Wi-Fi technology to sense the surrounding environments, has strong potentials in a variety of sensing applications. Recently several advanced deep learning-based solutions using CSI (Channel State Information) data have achieved high performance, but it is still difficult to use in practice without explicit data collection, which requires expensive adaptation efforts for model retraining. In this study, we propose a Channel State Information Automatic Labeling System (CALS) that automatically collects and labels training CSI data for deep learning-based Wi-Fi sensing systems. The proposed system allows the CSI data collection process to efficiently collect labeled CSI for labeling for supervised learning using computer vision technologies such as object detection algorithms. We built a prototype of CALS to demonstrate its efficiency and collected data to train deep learning models for detecting the presence of a person in an indoor environment, showing to achieve an accuracy of over 90% with the auto-labeled data sets generated by CALS.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.7A
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• pp.762-769
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• 2007

Adaptive MIMO-OFDM (Orthogonal Frequency Division Multiplexing) system adaptively changes modulation scheme depending on feedback channel state information (CSI). The CSI feedback channel which is the reverse link channel has multiple symbol delays including propagation delay, processing delay, frame delay, etc. The unreliable CSI due to feedback delay degrades adaptive modulation system performance. This paper compares the MSE and data capacity with respect to delay and channel signal to noise ratio for the two multi-step channel prediction schemes, CTSBP and BTSBP, such that robust adaptive SISO-OFDM/MIMO-OFDM is designed over severe mobile multipath channel conditions. This paper presents an interpolation method to reduce feedback overhead for adaptive MIMO-OFDM and shows MSE with respect to interpolation interval.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.253-271
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• 2017

In this paper, considering that perfect channel state information (CSI) is hard to achieve in practice, the downlink capacity of distributed antenna systems (DAS) with imperfect CSI and multiple receive antennas is investigated over composite Rayleigh fading channel. According to the performance analysis, using the numerical calculation, the probability density function (PDF) of the effective output SNR is derived. With this PDF, accurate closed-form expressions of ergodic capacity and outage probability of DAS with imperfect CSI are, respectively, obtained, and they include the ones under perfect CSI as special cases. Besides, the outage capacity of DAS in the presence of imperfect CSI is also derived, and a Newton's method based practical iterative algorithm is proposed to find the accurate outage capacity. By utilizing the Gaussian distribution approximation, another approximate closed-form expression of outage capacity is also derived, and it may simplify the calculation of accurate outage capacity. These theoretical expressions can provide good performance evaluation for downlink DAS for both perfect and imperfect CSI. Simulation results verify the effectiveness of the theoretical analysis, and the system capacity can be improved by increasing the receive antennas, and decreasing the estimation error or path loss. Moreover, the system can tolerate the estimation error variance up to about 0.01 with a slight degradation in the capacity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.11
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• pp.5305-5321
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• 2016

In wireless communication, the multiple-input multiple-output (MIMO) system is a well-known approach to improve the reliability as well as the data rate. In MIMO systems, channel state information (CSI) is typically required at the receiver to detect transmitted signals; however, in practical systems, the CSI is imperfect and contains errors, which affect the overall system performance. In this paper, we propose a novel maximum likelihood (ML) scheme for MIMO systems that is robust to the CSI errors. We apply an optimization method to estimate an instantaneous covariance matrix of the CSI errors in order to improve the detection performance. Furthermore, we propose the employment of the list sphere decoding (LSD) scheme to reduce the computational complexity, which is capable of efficiently finding a reduced set of the candidate symbol vectors for the computation of the covariance matrix of the CSI errors. An iterative detection scheme is also proposed to further improve the detection performance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.11
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• pp.2616-2635
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• 2013

In this paper, we propose an iterative transceiver design in a multi-relay multi-user multiple-input multiple-output (MIMO) system. The design criterion is to minimize sum mean squared error (SMSE) under relay sum power constraint (RSPC) where only local channel state information (CSI)s are available at relays. Local CSI at a relay is defined as the CSI of the channel between BS and the relay in the $1^{st}$ hop link, and the CSI of the channel between the relay and all users in the $2^{nd}$ hop link. Exploiting BS transmitter structure which is concatenated with block diagonalization (BD) precoder, each relay's precoder can be determined using local CSI at the relay. The proposed scheme is based on sequential iteration of two stages; stage 1 determines BS transmitter and relay precoders jointly with SMSE duality, and stage 2 determines user receivers. We verify that the proposed scheme outperforms simple amplify-and-forward (SAF), minimum mean squared error (MMSE) relay, and an existing good scheme of [13] in terms of both SMSE and sum-rate performances.