Channel State Estimation Research Articles

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%).

Improvement of Pilot Symbol Orthogonal Sequences in 2×2 to 4×4 MIMO Wireless Communication Systems with Channel State Estimation

AbstractMIMO wireless communication systems with channel state estimation, in which 2 to 4 transmit-receive antenna pairs are employed, are simulated. The channel estimation is fulfilled by the orthogonal pilot signal approach, where the Walsh Hadamard-ordered sequences are commonly used for piloting. The signal is modulated by applying the quaternary phase shift keying method. Maximum 250 000 packets are transmitted through flat-fading Rayleigh channels, to which white Gaussian noise is added. Based on simulating 10 subcases of the frame length and number of pilot symbols per frame, it is ascertained that pilot symbol orthogonal sequences in 2×2 to 4×4 MIMO systems can be improved by substituting Walsh functions with partially unsymmetrical binary functions constituting the eight known orthogonal bases. The benefit is that the bit-error rate is substantially decreased, especially for 2×2 MIMO systems. Considering three cases of the pilot signal de-orthogonalization caused by two indefinite and definite pilot sequence symbol errors, the relative decrement varies from 0.123 % to 14.7 %. However, the decrement becomes less significant as the number of transmit-receive antenna pairs is increased.

Deep-Learning-Based Blind Recognition of Channel Code Parameters Over Candidate Sets Under AWGN and Multi-Path Fading Conditions

We consider the problem of recovering channel code parameters over a candidate set by merely analyzing the received encoded signals. We propose a deep learning-based solution that I) is capable of identifying the channel code parameters for any coding scheme (such as LDPC, Convolutional, Turbo, and Polar codes), II) is robust against channel impairments like multi-path fading, III) does not require any previous knowledge or estimation of channel state or signal-to-noise ratio (SNR), and IV) outperforms related works in terms of probability of detecting the correct code parameters.

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.

A Novel Blind mmWave Channel Estimation Algorithm Based on ML-ELM

The millimeter wave (mmWave) with wide frequency spectrum can fulfill the demand of escalating communication system capacity. However, the large bandwidth of the mmWave channel leads to a large increase in the dimension of the received signal, which results to the increased computational complexity for channel estimation. In this letter, we propose a novel blind channel estimation algorithm based on Manifold Learning-Extreme Learning Machine (ML-ELM) in mmWave communication system. In the proposed ML-ELM algorithm, Manifold Learning (ML) is employed to reduce the feature dimension of received signal, and Extreme Learning Machine (ELM) with one-shot training is applied for the estimated Channel State Information (CSI). The ELM channel estimator is trained using the channel fading features extracted by ML algorithm. The CSI estimated by ML-ELM algorithm is more accurate, and the computational complexity satisfied the real-time requirements. Simulation results show that the proposed ML-ELM algorithm without any pilot aided achieves better MSE performance of CSI compared with the non-blind channel estimation algorithms in higher SNR scenarios, and better compared with pilot-based LS algorithm in lower SNR scenarios.

Development and analysis of downlink scheduling algorithm in LTE system with imperfect channel quality indicator

Long-term evolution (LTE) is the broadband technology, introduced by third generation partnership project (3GPP) to support variety of multimedia services. Scheduling in LTE plays an important role to meet the system performance targets. In this paper, a new scheduling method is proposed, which considers the achievable rate based on the estimated channel condition of user in the priority metric calculation. It is highly uncertain to have perfect channel quality indicator (CQI) report at scheduler end due to poor quality of the channel and the variation of current channel condition with that of received CQI report. So, the channel condition of the user is ascertained with imperfect CQI, where the Kalman filter is used to estimate the channel condition and effectively recover the correct CQI from imperfect CQI to improve the system performance. The proposed scheduling provides better performance in terms of throughput, delay and packet loss rate (PLR) when compared with the already proposed service based scheduler (SBS) method.

A Semi-Linear Approximation of the First-Order Marcum Q-Function With Application to Predictor Antenna Systems

First-order Marcum Q -function is observed in various problem formulations. However, it is not an easy-to-handle function. For this reason, in this article, we first present a semi-linear approximation of the Marcum Q -function. Our proposed approximation is useful because it simplifies, e.g., various integral calculations including Marcum Q -function as well as different operations such as parameter optimization. Then, as an example of interest, we apply our proposed approximation approach to the performance analysis of predictor antenna (PA) systems. Here, the PA system is referred to as a system with two sets of antennas on the roof of a vehicle. Then, the PA positioned in the front of the vehicle can be used to improve the channel state estimation for data transmission of the receive antenna that is aligned behind the PA. Considering spatial mismatch due to the mobility, we derive closed-form expressions for the instantaneous and average throughput as well as the throughput-optimized rate allocation. As we show, our proposed approximation scheme enables us to analyze PA systems with high accuracy. Moreover, our results show that rate adaptation can improve the performance of PA systems with different levels of spatial mismatch.

Channel Estimation and Hybrid Precoding for Millimeter Wave Communications: A Deep Learning-Based Approach

Hybrid analog and digital beamforming (HBF) has been regarded as a key technology for future millimeter wave (mmWave) communication systems due to its ability to obtain a good trade-off between achievable beamforming gain and hardware cost. In this paper, we investigate the channel estimation and hybrid precoding for mmWave MIMO systems with deep learning. We adopt the hierarchical codebook based algorithm for channel estimation as it requires limited number of pilot transmissions, and enhance its performance by proposing a new codebook design algorithm based on manifold optimization (MO). With the estimated channel state information (CSI) as the input, we develop a robust HBF network (HBF-Net) by applying convolutional layers and attention mechanism, which can be trained to generate a robust HBF matrix targeting at spectral efficiency maximization with imperfect CSI. To further improve the performance, we propose a joint channel estimation and HBF optimization network (CE-HBF-Net). Considering that the adaptively selected HBF vectors in the hierarchical codebook based channel estimation are different for different channel realizations, we skillfully propose an index assign-and-input method to efficiently feed such information to the CE-HBF-Net to reduce the network input dimensions and make the network trainable. Furthermore, we propose a signal self-attention mechanism to enable the CE-HBF-Net to intelligently assign larger weight coefficients to those signals that contribute more to channel estimation. Simulation results show that the well-designed HBF-Net and CE-HBF-Net outperform the conventional HBF algorithms with imperfect channel and exhibit robustness to mismatches between offline training and online deployment stages.

PERFORMANCE INVESTIGATION OF PHYSICAL LAYER AUTHENTICATION BASED ON STATISTICAL DECISION STRATEGIES

In this paper, physical layer authentication based on the estimated channel state information (CSI) over the subcarriers of wireless orthogonal frequency division multiplexing (OFDM) systems has been studied to provide reliable and augmented authentication solutions for wireless communication devices. The binary hypothesis test with channel-discriminative functions that are based on measuring the difference between channel features and CSI profile clustering is investigated. A comprehensive performance evaluation for the considered channel-discriminative functions in frequency-selective fading channels is presented. Simulation results demonstrated that the security performance depends on the channel frequency selectivity. The decision metrics that measure the difference between the channel features demonstrated improved performance with the increase of channel variation over the subcarriers. In contrast, the CSI profile clustering method exhibited significant degradation in the performance for moderate and severe frequency selectivity conditions. Therefore, for the clustering method the channel fingerprint should be extracted from the neighboring subcarriers, while in other methods the channel features should be extracted from disjoint pilots in order to optimize the security performance over frequency-selective channels.

Channel Estimation with an Interpolation Trained Deep Neural Network

This paper proposes a deep learning-based channel estimation method for orthogonal frequency-division multiplexing (OFDM) systems. The existing OFDM receiver has low estimation accuracy when estimating channel state information (CSI) with fewer pilots. To tackle the problem, in this paper, a deep learning model is first trained by the interpolated channel frequency responses (CFRs) and then used to denoise the CFR estimated by least square (LS) estimation. The proposed deep neural network (DNN) can also be trained in a short time because it only learns the CFR and the network structure is simple. According to the simulation results, the performance of the DNN estimator can be compared with the minimum mean-square error (MMSE) estimator. Furthermore, the DNN approach is more robust than conventional methods when fewer pilots are used. In summary, deep learning is a promising tool for channel estimation in wireless communications.

COMBINED INFLUENCE OF DOPPLER EFFECT AND PILOT DE-ORTHOGONALIZATION ON 2×2 TO 4×4 MIMO SYSTEMS AND IMPROVEMENT OF ORTHOGONAL SEQUENCES

The Doppler effect in 2×2, 3×3, and 4×4 MIMO wireless communication systems with channel state estimation is studied. The orthogonal pilot signal approach is used for the channel estimation, where the Hadamard sequences are used for piloting along with the eight Romanuke orthogonal sets similar to the Walsh set. The Doppler effect is additionally aggravated by the pilot signal de-orthogonalization, where two negative-to-positive symbol errors are assumed to have occurred while signal is transmitted. MIMO transmissions are simulated for 10 cases of the frame length and pilot symbols per frame by no Doppler shift to 1100 Hz Doppler shift with a step of 100 Hz. By assuming that the carrier frequency is 5,9 GHz, the step corresponds to a motion speed of about 18.3 km/hr. Based on the simulations, it is ascertained that the Doppler effect negatively influences transmissions of long data packets. It is impracticable to apply MIMO transmissions of long packets at speeds exceeding 100 km/hr. To maintain an appropriate MIMO link data rate, the packet length should be shortened as the motion speed increases. On the other hand, the MIMO performance is substantially improved by increasing the number of antennas, except for the case of transmitting long packets. Besides, under the de-orthogonalization caused by two negative-to-positive symbol errors, the MIMO Walsh pilot sequences are outperformed by MIMO Romanuke pilot sequences, so the latter are considered as an improvement of MIMO orthogonal sequences. However, the performance difference between the Romanuke and Walsh pilot sequences decays as a greater number of transmit-receive antenna pairs is used and the motion speed increases.

INFLUENCE OF DOPPLER EFFECTS ON 2×2 TO 4×4 MIMO SYSTEMS WITH DIRECT LINE-OF-SIGHT PATH BY RICIAN FADING DISTRIBUTION

Background. For multiplying the capacity of a radio link by using multiple antennas at the transmitter and receiver ends, wireless communication networks use the MIMO technique. The quality of MIMO wireless communication may worsen when either the transmitter or receiver end is in motion, or they both are in motion. To sustain high quality of links, MIMO operates, in particular, on channel state information by using orthogonal pilot sequences.Objective. The goal is to estimate the bit-error-rate performance of 2×2, 3×3, and 4×4 MIMO systems with orthogonal pilot sequences for a range of Doppler shifts by the Rician fading distribution modeling the scenario of direct line-of-sight path. The estimation is believed to answer the question on MIMO efficiency by ascertaining whether increasing the number of antennas mitigates the Doppler effect in 2×2 to 4×4 MIMO systems with direct line-of-sight path.Methods. To achieve the goal, 2×2 to 4×4 MIMO systems with channel state estimation are simulated. The simulation is configured and carried out by using MATLAB® R2019a Communications System ToolboxTM functions. The 35 subcases of the frame length and number of pilot symbols per frame are considered.Results. The near-exponential-type decreasing of the bit-error-rate performance is almost not violated by no motion, whereas further speed increments destroy it for 2×2 MIMO systems. It is revealed that too long packet transmissions by short orthogonal pilot sequences (with respect to the packet length) are prone to errors. This especially concerns 2×2 MIMO systems, where channel state estimation by short orthogonal pilot sequences appears to be ineffective or weakly effective.Conclusions. In general, to maintain an appropriate MIMO link data rate, the packet length should be shortened as the motion speed increases. Increasing the number of antennas additionally mitigates Doppler effects. S o, 4 ×4 M IMO s ystem transmissions are indeed efficient. The shortest possible packet transmissions are not affected by Doppler effects if three or four antennas are used.

Deep learning‐based pilot‐assisted channel state estimator for OFDM systems

Deep learning‐based pilot‐assisted channel state estimator for OFDM systems

Development and analysis of downlink scheduling algorithm in LTE system with imperfect channel quality indicator

Long-term evolution (LTE) is the broadband technology, introduced by third generation partnership project (3GPP) to support variety of multimedia services. Scheduling in LTE plays an important role to meet the system performance targets. In this paper, a new scheduling method is proposed, which considers the achievable rate based on the estimated channel condition of user in the priority metric calculation. It is highly uncertain to have perfect channel quality indicator (CQI) report at scheduler end due to poor quality of the channel and the variation of current channel condition with that of received CQI report. So, the channel condition of the user is ascertained with imperfect CQI, where the Kalman filter is used to estimate the channel condition and effectively recover the correct CQI from imperfect CQI to improve the system performance. The proposed scheduling provides better performance in terms of throughput, delay and packet loss rate (PLR) when compared with the already proposed service based scheduler (SBS) method.

On Pilot Spoofing Attack in Massive MIMO Systems: Detection and Countermeasure

Massive MIMO systems are vulnerable to pilot spoofing attacks (PSAs) since the estimated channel state information can be contaminated by the eavesdropping link, thus incurring severe information leakage in downlink transmission. To safeguard legitimate communications, this paper proposes a PSA detection method which relies on pilot manipulation. Specifically, users randomly partition pilot sequences into two parts, where the first part remains unchanged and the second one is multiplied with a diagonal matrix. Although a malicious node may follow the same way to send pilots, this makes it more likely to be detected. According to the principle of the likelihood-ratio test, the proposed detector is designed based on a decision metric that does not include the legitimate channel. This feature differentiates our scheme from existing ones and remarkably improves the detection accuracy. Besides, the possibility of performance enhancement by joint detection is discussed. Furthermore, based on pilot manipulation, a jamming-resistant receiver is designed. The key of this receiver is a new channel estimator that is robust to the PSA. Finally, extensive simulations are carried out to validate our proposed algorithms.

State Leakage and Coordination With Causal State Knowledge at the Encoder

We revisit the problems of state masking and state amplification through the lens of empirical coordination. Specifically, we characterize the rate-equivocation-coordination trade-offs regions of a state-dependent channel in which the encoder has causal and strictly causal state knowledge. We also extend this characterization to the cases of two-sided state information and noisy channel feedback. Our approach is based on the notion of core of the receiver’s knowledge, which we introduce to capture what the decoder can infer about all the signals involved in the model. Finally, we exploit the aforementioned results to solve a channel state estimation zero-sum game in which the encoder prevents the decoder to estimate the channel state accurately.

A Machine Learning Approach for 5G SINR Prediction

Artificial Intelligence (AI) and Machine Learning (ML) are envisaged to play key roles in 5G networks. Efficient radio resource management is of paramount importance for network operators. With the advent of newer technologies, infrastructure, and plans, spending significant radio resources on estimating channel conditions in mobile networks poses a challenge. Automating the process of predicting channel conditions can efficiently utilize resources. To this point, we propose an ML-based technique, i.e., an Artificial Neural Network (ANN) for predicting SINR (Signal-to-Interference-and-Noise-Ratio) in order to mitigate the radio resource usage in mobile networks. Radio resource scheduling is generally achieved on the basis of estimated channel conditions, i.e., SINR with the help of Sounding Reference Signals (SRS). The proposed Non-Linear Auto Regressive External/Exogenous (NARX)-based ANN aims to minimize the rate of sending SRS and achieves an accuracy of R = 0.87. This can lead to vacating up to 4% of the spectrum, improving bandwidth efficiency and decreasing uplink power consumption.

Overcoming the Channel Estimation Barrier in Massive MIMO Communication via Deep Learning

A new wave of wireless services, including virtual reality, autonomous driving and Internet of Things, is driving the design of new generations of wireless systems to deliver ultra-high data rates, massive numbers of connections and ultra-low latency. Massive multiple-input multiple-output (MIMO) is one of the critical underlying technologies that allow future wireless networks to meet these service needs. This article discusses the application of deep learning (DL) for massive MIMO channel estimation in wireless networks by integrating the underlying characteristics of channels in future high-speed cellular deployment. We develop important insights derived from the physical radio frequency (RF) channel properties and present a comprehensive overview on the application of DL for accurately estimating channel state information (CSI) with low overhead. We provide examples of successful DL application in CSI estimation for massive MIMO wireless systems and highlight several promising directions for future research.

Joint Channel Estimation and Precoding for Faster-Than-Nyquist Signaling

The performance of existing frequency-domain channel estimation and equalization algorithms for faster-than-Nyquist (FTN) signaling is seriously hampered with the noise enhancement phenomenon that results from their inverse or pseudo-inverse operations. Through simulations, we show that the linear precoding can effectively mitigate the above-mentioned noise enhancement phenomenon. At first, a low complexity precoding-based channel estimation (PCE) algorithm is proposed for FTN signaling over frequency-selective fading channels. In contrast with most existing frequency-domain channel estimation algorithms, the proposed PCE algorithm has much better mean square error performance, and the performance improvement enlarges with the increase of signal to noise ratio. Furthermore, a joint channel estimation and precoding (JCEP) algorithm is proposed to perform data detection for FTN signaling over frequency-selective fading channels. On the one hand, compared with the existing frequency-domain channel estimation and equalization algorithms, the JCEP algorithm greatly reduces the complexity of signal processing at receivers since it performs the linear precoding processing at transmitters. On the other hand, even with estimated channel state information (CSI), the proposed JCEP algorithm can approach the bit error rate (BER) performance of the Nyquist signaling for all the modulation types adopted in digital video broadcasting-satellite-second generation extension (DVB-S2X). More precisely, the BER performance degradation with perfect and estimated CSI is 0.06 dB and 0.07 dB respectively when the time acceleration parameter equals to 0.7 and the rolling factor is 0.45.

Evaluation of Rate Adaptation Algorithms in IEEE 802.11 Networks

Wireless technologies are being used in various applications for their ease of deployment and inherent capabilities to support mobility. Most wireless standards supports multiple data rates that may vary between few Mbps to few Gbps. Reaching the maximum supported data rate is what most application seek for. Nevertheless, the choice of data rates is very closely related to the quality of communication links and their stability. IEEE 802.11 standard introduced multi-rate support, since then, a lot of research has been done on rate adaptation, dealing with the different parameters that lead to an estimation of the channel conditions and the metrics that affect the network performance. In this paper, we present some of the popular rate adaptation schemes and summarize their characteristics. We categorize them as well into different categories according to their design and functionalities in terms of the strategies that are used to estimate channel conditions and decision making. We implemented some algorithms from the different categories in the network simulator NS-3 in order to evaluate their performance under different scenarios in Ad hoc and infrastructure modes. We present the lessons learned as well as our insights for future research work that can enhance the current approaches in the literature.