Channel State Information Estimation Research Articles

Secrecy enhancing for SSK‐based communications in the presence of imperfect CSI estimation

In this letter, a new security enhancement scheme for space shift keying (SSK) systems under imperfect channel state information (CSI) is proposed. In this scheme, the legitimate receiver is always accompanied by an eavesdropper, and the secrecy enhancing of the SSK system is realized by adding the designed artificial noise (AN). Since the AN is designed by the CSI of the legitimate channel, it can be canceled at the legitimate receiver and remained at the eavesdropper. In addition, the impacts of the CSI estimation error on the secrecy rate (SR) and bit error ratio (BER) of the SSK system have also been taken into account. The simulation results show that compared with the traditional SSK under imperfect CSI environment, the proposed scheme improves the SR and BER. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Optimization of Sparse Learning Problem of Signals on Hybrid mm-Wave MIMO Systems using Sparse Coding based Reconstruction Learning Mechanism

Researchers and industry experts are looking for the availability of large bandwidth spectrum due to high market demands and expectations for high data rates. And Millimeter Wave technology possess characteristics to fulfill these requirements. However, due to high power consumption and channel estimation requirements, massive MIMO is utilized in coordination with Millimeter Wave technology. Besides, the performance of mm-WAVE MIMO system is measured by the effective estimation of Channel State Information (CSI) which is a critical and challenging process. Therefore, a Sparse Coding based Reconstruction Learning (SCL) mechanism is presented to efficiently estimate Channel State Information (CSI) for Millimeter-WAVE massive Multiple Input Multiple Output (MIMO) system. For efficiency enhancement, joint sparse learning problem is formulated and a denoised joint sparsity learning matrix is obtained using proposed SCL mechanism. Here, optimization of joint sparse learning problem is summarized by reducing inconsistent and overfitting errors. The proposed SCL mechanism performs well under high as well as low SNR conditions. Moreover, joint sparse coding algorithm is utilized for efficient sparse signal restoration. The performance of proposed SCL mechanism is efficiently measured against several state-of-art-algorithms in terms of energy efficiency, NMSE, channel capacity etc.

Optical adaptive power transmission using APC-EDFA for turbulence-tolerant FSO communications

This paper proposes a novel optical adaptive power transmission using automatic power control (APC)-erbium-doped fiber amplifier (EDFA) for turbulence-tolerant free-space optical (FSO) communications. Based on the quasi-stationary characteristics of turbulence channel and average power dependent optical gain features of EDFA, the channel state information (CSI) of the received upstream on-off keying (OOK) signal is optically conveyed to the orthogonally polarized transmitted downstream OOK signal with channel inversion via EDFA in APC mode. The performance is analyzed under various dynamic gain frequencies of APC-EDFA and different power ratios between downstream and upstream signals. Simulation results revealed that the power of downstream signal was adaptively transmitted according to the received upstream signal under effective turbulence suppression, transmitted power efficiency, and required SNR reduction without the estimation of CSI.

A Novel Channel Model and Optimal Beam Tracking Schemes for Mobile Millimeter-Wave Massive MIMO Communications

A novel channel model has been proposed for mobile millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) communications to evaluate the effect of end-user mobility. In this model the variance of number of clusters and number of rays generated from each cluster is taken into account that is novel and different from widely used channel models. Two optimum codebook based beam-tracking schemes-multi-objective joint optimization codebook (MJOC) and linear hybrid combiner (LHC)- have been proposed for the novel channel model and their performance for spectral efficiency (SE) is presented. Performance for the two most commonly used channel state information (CSI) estimation approaches is investigated. Finally, the relationship between the beamforming training blocks and optimal beam tracking scheme is presented.

Using Channel State Information for Physical Tamper Attack Detection in OFDM Systems: A Deep Learning Approach

This letter proposes a deep learning approach to detect a change in the antenna orientation of transmitter or receiver as a physical tamper attack in OFDM systems using channel state information. We treat the physical tamper attack problem as a semi-supervised anomaly detection problem and utilize a deep convolutional autoencoder (DCAE) to tackle it. The past observations of the estimated channel state information (CSI) are used to train the DCAE. Then, a post-processing is deployed on the trained DCAE output to perform the physical tamper detection. Our experimental results show that the proposed approach, deployed in an office and a hall environment, is able to detect on average 99.6% of tamper events (TPR = 99.6%) while creating zero false alarms (FPR = 0%).

Acoustic-based Upper Facial Action Recognition for Smart Eyewear

Smart eyewear (e.g., AR glasses) is considered to be the next big breakthrough for wearable devices. The interaction of state-of-the-art smart eyewear mostly relies on the touchpad which is obtrusive and not user-friendly. In this work, we propose a novel acoustic-based upper facial action (UFA) recognition system that serves as a hands-free interaction mechanism for smart eyewear. The proposed system is a glass-mounted acoustic sensing system with several pairs of commercial speakers and microphones to sense UFAs. There are two main challenges in designing the system. The first challenge is that the system is in a severe multipath environment and the received signal could have large attenuation due to the frequency-selective fading which will degrade the system's performance. To overcome this challenge, we design an Orthogonal Frequency Division Multiplexing (OFDM)-based channel state information (CSI) estimation scheme that is able to measure the phase changes caused by a facial action while mitigating the frequency-selective fading. The second challenge is that because the skin deformation caused by a facial action is tiny, the received signal has very small variations. Thus, it is hard to derive useful information directly from the received signal. To resolve this challenge, we apply a time-frequency analysis to derive the time-frequency domain signal from the CSI. We show that the derived time-frequency domain signal contains distinct patterns for different UFAs. Furthermore, we design a Convolutional Neural Network (CNN) to extract high-level features from the time-frequency patterns and classify the features into six UFAs, namely, cheek-raiser, brow-raiser, brow-lower, wink, blink and neutral. We evaluate the performance of our system through experiments on data collected from 26 subjects. The experimental result shows that our system can recognize the six UFAs with an average F1-score of 0.92.

Reconfigurable Intelligent Surface Aided Mobile Edge Computing: From Optimization-Based to Location-Only Learning-Based Solutions

In this paper, we explore optimization-based and data-driven solutions in a reconfigurable intelligent surface (RIS)-aided multi-user mobile edge computing (MEC) system, where the user equipment (UEs) can partially offload their computation tasks to the access point (AP). We aim at maximizing the total completed task-input bits (TCTB) of all UEs with limited energy budgets during a given time slot, through jointly optimizing the RIS reflecting coefficients, the AP's receive beamforming vectors, and the UEs' energy partition strategies for local computing and offloading. A three-step block coordinate descending (BCD) algorithm is first proposed to effectively solve the non-convex TCTB maximization problem with guaranteed convergence. In order to reduce the computational complexity and facilitate lightweight online implementation of the optimization algorithm, we further construct two deep learning architectures. The first one takes channel state information (CSI) as input, while the second one exploits the UEs' locations only for online inference. The two data-driven approaches are trained using data samples generated by the BCD algorithm via supervised learning. Our simulation results reveal a close match between the performance of the optimization-based BCD algorithm and the low-complexity learning-based architectures, all with superior performance to existing schemes in both cases with perfect and imperfect input features. Importantly, the location-only deep learning method is shown to offer a particularly practical and robust solution alleviating the need for CSI estimation and feedback when line-of-sight (LoS) direct links exist between UEs and the AP.

Imperfect CSI Muting With Time-Domain Thresholding for Cooperative Intercell Interference Cancellation

This article improves spectral efficiency performance of the cooperative intercell interference (ICI) cancellation by channel state information (CSI) muting with time-domain thresholding. In the original scheme, each transmitter superposes ICI replica signal to the original transmission signal so as to cancel them at the receiver side. Suppose uplink CSI estimation, its imperfection becomes significant since the transmission power of the user terminal is generally lower than that of the base station; ICI cancellation capability is limited. In order to overcome this problem, incorrect CSI needs to be properly muted. However, the qualification of CSI around the noise level is a challenging issue. A key proposal is to focus on the time-domain impulse response wherein the channel power is concentrated to the specified part in the observation range. It can accurately differentiate useful CSI even in the noise-limited region; generation of erroneous ICI replica signals is prevented and remaining transmission power can be re-allocated to the desired signal. Therefore, the ICI cancellation capability can be fully provided to maximize overall spectral efficiency. Simulative evaluation validates its possibility and effectiveness; average spectral efficiency can be improved by up to 11.9% compared to the original approach using full CSI.

LPWAN in the TV White Spaces

Low-Power Wide-Area Network (LPWAN) is an enabling Internet-of-Things technology that supports long-range, low-power, and low-cost connectivity to numerous devices. To avoid the crowd in the limited ISM band (where most LPWANs operate) and cost of licensed band, the recently proposed Sensor Network over White Spaces (SNOW) is a promising LPWAN platform that operates over the TV white spaces. As it is a very recent technology and is still in its infancy, the current SNOW implementation uses the Universal Software Radio Peripheral devices as LPWAN nodes, which has high costs (≈$750 USD per device) and large form-factors, hindering its applicability in practical deployment. In this article, we implement SNOW using low-cost, low form-factor, low-power, and widely available commercial off-the-shelf (COTS) devices to enable its practical and large-scale deployment. Our choice of the COTS device (TI CC13x0: CC1310 or CC1350) consequently brings down the cost and form-factor of a SNOW node by 25× and 10×, respectively. Such implementation of SNOW on the CC13x0 devices, however, faces a number of challenges to enable link reliability and communication range. Our implementation addresses these challenges by handling peak-to-average power ratio problem, channel state information estimation, carrier frequency offset estimation, and near-far power problem. Our deployment in the city of Detroit, Michigan, demonstrates that CC13x0-based SNOW can achieve uplink and downlink throughputs of 11.2 and 4.8 kbps per node, respectively, over a distance of 1 km. Also, the overall throughput in the uplink increases linearly with the increase in the number of SNOW nodes.

SIABR: A Structured Intra-Attention Bidirectional Recurrent Deep Learning Method for Ultra-Accurate Terahertz Indoor Localization

High-accuracy localization technology has gained increasing attention in gesture and motion control and many diverse applications. Due to multi-path fading and blockage effects in indoor propagation, 0.1m-level precise localization is still challenging. Promising for 6G wireless communications, the Terahertz (THz) spectrum provides multi-GHz ultra-broad bandwidth. Applying the THz spectrum to indoor localization, the channel state information (CSI) of THz signals, including angle of arrival (AoA), received power, and delay, has unprecedented resolution that can be explored for positioning. In this paper, a Structured Intra-Attention Bidirectional Recurrent (SIABR) deep learning method is proposed to solve the CSI-based three-dimensional (3D) THz indoor localization problem with significantly improved accuracy. As a two-level structure, the features of individual multi-path rays are first analyzed in the recurrent neural network with the attention mechanism at the lower level. Furthermore, the upper-level residual network (ResNet) of the constructed SIABR network extracts hidden information to output the geometric coordinates. Simulation results demonstrate that the 3D localization accuracy in the metric of mean distance error is within 0.25m. The developed SIABR network has very fast convergence and is robust against THz indoor line-of-sight blockage, multi-path fading, channel sparsity and CSI estimation error.

Mixed RF/FSO Communications With Outdated-CSI-Based Relay Selection Under Double Generalized Gamma Turbulence, Generalized Pointing Errors, and Nakagami-m Fading

This paper investigates the performance of dual-hop amplify-and-forward (AF) mixed radio frequency (RF)/free-space optical (FSO) transmissions with partial relay selection (PRS) based on outdated channel state information (CSI) estimates. Turbulence-induced fading, path loss, and pointing errors, are all considered in the FSO channel modeling. Both the fading and the path loss are described through general models which encompass the commonly used models. Novel expressions for the cumulative distribution function, probability density function, moment generating function, and moments of the end-to-end signal-to-noise ratio (SNR) are obtained in closed-form. Thereafter, novel closed-form expressions for key performance metrics, namely, outage probability, average bit error probability and spectral efficiency, are derived. The analysis is unified, applying to both types of detection techniques, intensity modulation/direct direction and heterodyne detection. Asymptotic analysis is further conducted, which open the door to additional important results. Monte Carlo simulation results confirm the effectiveness of the proposed analysis, and attest that in AF RF/FSO systems with PRS based on outdated CSI estimates, where implementing higher number of relays is highly difficult and costly, the low SNR regime may be prohibitive because it hinders the diversity advantages promised by the use of more relays in PRS systems.

Power-Tolerant NOMA Using Data-Aware Adaptive Power Assignment for IoT Systems

Nonorthogonal multiple access (NOMA) is a promising candidate for future wireless networks due to its ability to improve the spectral efficiency and network connectivity. Nevertheless, the error rate performance of NOMA depends significantly on the power assignment for each user, which requires accurate knowledge of the channel state information (CSI) at the transmitter, which can be challenging for several applications, such as wireless sensor networks (WSNs) and Internet of Things (IoT). Therefore, this article proposes a power-tolerant NOMA by adaptively changing the signal power of each user to reduce the system sensitivity to inaccurate power assignment. The power adaptation in the power-adaptive NOMA (PANOMA) is performed based on the transmitted data, and it does not require accurate CSI. To quantify its potential, the bit error rate (BER) and the lower bound capacity performance, over Rayleigh fading channels, are derived in exact closed forms for two and three users scenarios. The results demonstrate that PANOMA provides a tangible BER performance improvement over conventional power-domain NOMA when both schemes use suboptimal power assignment, which is typically experienced in practical scenarios involving channel time variation and CSI estimation errors. Specifically, it will be shown that both schemes provide similar BERs using optimal assignment, but the PANOMA offers BER reduction by a factor of 10 for certain scenarios when suboptimal power values are assigned. The integrity of the analytical results is verified via matching extensive Monte Carlo simulation experiments.

On Delay-Limited Average Rate of HARQ-Based Predictor Antenna Systems

Predictor antenna (PA) system is referred to as a system with two sets of antennas on the roof of a vehicle, where the PAs positioned in the front of the vehicle are used to predict the channel state observed by the receive antennas (RAs) that are aligned behind the PAs. In this letter, we study the effect of spatial mismatch on the accuracy of channel state information estimation, and analyze the delay-constrained average rate of hybrid automatic repeat request (HARQ)-based PA systems. We consider variable-length incremental redundancy (INR) HARQ protocol, and derive a closed-form expression for the maximum average rate subject to a maximum delay constraint. Moreover, we study the effect of different parameters such as the vehicle speed, the antenna distance and the rate allocation on the system performance. The results indicate that, compared to the open-loop case, the delay-limited average rate is considerably improved with our proposed PA-HARQ scheme.

Dimensionality reduction of interference subspace for multiuser massive MIMO null-space expansion

This paper proposes a dimensionality reduction of interference subspace to improve the interference suppression capability of null-space expansion (NSE) for multiuser massive multiple-input-multiple-output (MIMO) systems. The fundamental concept of the NSE is to nullify the interference subspace generated by the combined multiple past channel state information (CSI) vectors. It can suppress inter-user interference (IUI) leaked due to outdated CSI in a mobility environment. It is enabled by plentiful degrees of freedom (DoF) brought by massive antenna elements the base station has, and hence NSE can outperform existing channel prediction based precoding approaches. However, additive noise-induced CSI estimation error limits its effectiveness; expanded dimension of null-space cannot be appropriately spanned to interference subspace. The key idea of our new proposal is to extract principal interference subspace channel. Null-space can be appropriately expanded with reduced its dimension, and remaining DoF can be assigned to beamforming. It results in improving overall signal-to-interference-plus-noise power ratio (SINR) performance which is verified via numerical simulation.

A Partial CSI Estimation Approach for Downlink FDD massive-MIMO System with Different Base Transceiver Station Topologies

Massive multiple-input multiple-output (massive-MIMO) is a promising technology for next generation wireless communications systems due to its capability to increase the data rate and meet the enormous ongoing data traffic explosion. However, in non-reciprocal channels, such as those encountered in frequency division duplex (FDD) systems, channel state information (CSI) estimation using downlink (DL) training sequence is to date very challenging issue, especially when the channel exhibits a shorter coherence time. In particular, the availability of sufficiently accurate CSI at the base transceiver station (BTS) allows an efficient precoding design in the DL transmission to be achieved, and thus, reliable communication systems can be obtained. In order to achieve the aforementioned objectives, this paper presents a feasible DL training sequence design based on a partial CSI estimation approach for an FDD massive-MIMO system with a shorter coherence time. To this end, a threshold-based approach is proposed for a suitable DL pilot selection by exploring the statistical information of the channel covariance matrix. The mean square error of the proposed design is derived, and the achievable sum rate and bit-error-rate for maximum ratio transmitter and regularized zero forcing precoding is investigated over different BTS topologies with uniform linear array and uniform rectangular array. The results show that a feasible performance in the DL FDD massive-MIMO systems can be achieved even when a large number of antenna elements are deployed by the BTS and a shorter coherence time is considered.

Analysis of LSE and MMSE Pilot Based Channel EstimationTechniques for MIMO-OFDM System

An Orthogonal Frequency Division Multiplexing(OFDM) has becoming a desirable modulation technique scheme for wireless systems. In recent years, with the advent of technology in Very Large-Scale Integration (VLSI), the number of higher data level is growing. Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO-OFDM) systems are used to meet these criteria due to their high performance, in terms of transmission range, data rate resistance to multi-path transmission. A MIMO OFDM framework is designed for systems such as IEEE 802.11n, 4 G and LTE in advanced communication systems. It provides secure transmission with a growing coverage area. The estimation of Channel State Information (CSI) is the biggest challenge in the implementation of MIMO-OFDM method. We evaluate and enforce the pilot-based channel analysis methods for MIMO-OFDM system such as Minimum Mean Square Errors (MMSE), Least Square (LS).

Energy-Efficient Resource Allocation for Ultra-Dense Licensed and Unlicensed Dual-Access Small Cell Networks

In this study, an energy-efficient self-organized framework for sub-channel allocation and power allocation is presented for ultra-dense small cell networks, which can operate in both licensed and unlicensed bands. In order to protect legacy WiFi devices (operating in unlicensed bands), we consider the Long-Term Evolution (LTE) operation in unlicensed bands based on Carrier Sense Adaptive Transmission (CSAT), in which 'ON' and 'OFF' duty cycle approach is utilized. On the other hand, there are severe interference management problems among small cells (operating in licensed and unlicensed bands) and between macro cells and small cells (operating in licensed bands) due to co-channel and ultra-dense deployment of small cells. This article proposes a self-organized optimization framework for the allocation of sub-channels and power levels by exploiting a non-cooperative game with the objective to maximize the energy efficiency of dual-access small cells without creating harmful impact on coexisting network entities including macro cell users, small cell users, and legacy WiFi devices. Simulation results show that the proposed scheme outperforms (6 and 11 percent) and (8 and 18 percent) the round-robin and the spectrum-efficient schemes, respectively, for two different small cell scenarios. In addition, it is shown that for less channel state information (CSI) estimation errors ς = 0.02, the maximum performance degradation of the proposed scheme is reasonably small (5.5 percent) as compared to the perfect CSI.

Zero-Forcing Precoding With Partial CSI in Multibeam High Throughput Satellite Systems

Precoding has emerged as a prominent technique to mitigate interbeam interference (IBI) in the forward link of a multibeam high throughput satellite system. In this paper, we consider zero-forcing (ZF) precoding to mitigate IBI. To reduce the channel state information (CSI) feedback from the user terminals, which consequently reduces the latency and signaling overheads, we design a ZF precoder with matrix and vector normalizations using partial CSI and analyze its ergodic SR performance. We consider a practical scenario where the links between the satellite and user terminals are affected by rain fading attenuation and derive an analytical expression, upper and lower bounds, and asymptotic (in terms of transmit power) approximations for the ergodic SR achieved by this ZF precoder. Many important insights are obtained from these results. Also, the robustness of this ZF precoder to CSI estimation errors is numerically analyzed.

Robust Transceiver Design for Multi-Hop AF MIMO Relay Multicasting From Multiple Sources

In this article, the transceiver design optimization problem is investigated for multi-hop multicasting amplify-and-forward (AF) multiple-input multiple-output (MIMO) relay systems, where multiple source nodes broadcast their message to multiple destination nodes via multiple serial relay nodes. Multiple antennas are installed at the sources, relays, and the destination nodes. In the transceiver design, we consider the mismatch between the true and the estimated channel state information (CSI), where the CSI mismatch follows the Gaussian-Kronecker model. A robust transceiver design algorithm is developed to jointly optimize the source, relay, and destination matrices to minimize the maximal weighted mean-squared error (WMSE) of the received message at all destination nodes. In particular, the WMSE is made statistically robust against the CSI mismatch by averaging through the distributions of the true CSI. Moreover, the WMSE decomposition is exploited to reduce the computational complexity of the transceiver optimization. Numerical simulations show a better performance of the proposed robust transceiver design against the channel mismatch.

Adaptive-Slope Squashing-Function-Based ANN for CSI Estimation and Symbol Detection in SFBC-OFDM System

This paper presents an adaptive-slope squashing-function (ASF)-based artificial neural network (ANN) for efficient estimation of smoothly time-varying multipath fading channels, in a $$4 \times 1$$ space-frequency-block-coded orthogonal-frequency-division-multiplexing (SFBC-OFDM) system using 64 subcarriers. The channel-state-information (CSI) estimated at first stage is further used for OFDM information symbol detection (through minimum mean square error criterion-based detection) at second stage. To combat the impact of smoothly time-varying environment, we emphasize on the utilization of ASF-ANN using backpropagation (BP) algorithm for the estimation of channel tap coefficients in frequency domain. The underlying ANN is modeled as feedforward multi-layered perceptron that updates the network weights. The major focus is on the gradient-descent algorithm-based adaptation of the slope of squashing-function (SF) along with other ANN parameters, which enhances the training efficiency of ASF-ANN in terms of the lower mean-squared channel estimation error in comparison with the traditional fixed-slope squashing-function (FSF) ANN technique. Simulation results corresponding to the underlying $$4 \times 1$$ SFBC-OFDM system are presented to depict that ASF-ANN-based approach outperforms the FSF-ANN technique by providing lower bit-error-rate (BER) due to the usage of well-estimated CSI. At 15 dB SNR and fade rate = 0.001, the average BER reduces to $$2.85 \times 10^{ - 4}$$ for the ASF-ANN based approach, due to improved CSI estimation, which accounts for approximately 5% improvement in the detection success rate as compared to the FSF-ANN-based approach.