Channel Scenarios Research Articles

On the Convenience of Perfect Channel Knowledge for Spatial Equalization of Correlated MIMO-VLC Links: Is it Really Worth it?

Visible Light Communication has risen as a promising technology to support indoor wireless connectivity and provide illumination services as well. Moreover, high communication performance can be achieved by implementing Multiple-Input Multiple-Output architectures. However, the crosstalk characterizing spatially correlated channels scenarios may significantly impact on reliability. Such issue is commonly addressed by means of spatial equalization, with zero forcing and minimum mean square error representing the most widespread approaches. These methods allow the mitigation of crosstalk, even though they may cause undesired noise amplification as side effect, inducing errors during signal detection and decoding. In this regard, by focusing on a more realistic approach with respect to the literature, this work overturns the belief that having perfect channel knowledge is always better. Hence, we show that, when applying spatial equalization, imperfect channel knowledge is sometimes preferable to ideal channel state information. As a result, noise amplification is reduced at the expense of a reasonably less accurate crosstalk mitigation, with better communication performance being achieved.

Blind and Semi-Blind Channel Estimation/Equalization for Poisson Channels in Optical Wireless Scattering Communication Systems

The communication systems for Poisson channels require long pilot for channel estimation and may result in large percentage of overhead due to the signal-dependent noise of Poisson-distributed signal, i.e., both signal part and noise part experience random processes. In this paper, both blind and semi-blind channel estimation methods are studied to shorten the overhead and increase the transmission efficiency for Poisson channels. First, fractionally spaced equalizers are proposed based on modified constant modulus algorithm (CMA) and subspace (SS). Second, a data-aided iterative channel estimation (ICE) is designed and analyzed in terms of its asymptotic unbiasedness and convergence. The proposed methods are evaluated based on both a constant channel scenario and a varying channel scenario. Numerical simulation results show that the modified CMA has the worst bit-error rate performance but requires the lowest computational complexity. Besides, both SS based channel estimation and ICE have negligible overhead and comparable bit-error rate performances with respect to the conventional periodic pilot based channel estimation having 50% overhead.

Three-dimensional reconstruction of transmission channel hidden trouble distance measurement based on multi-eye monitoring equipment

With the popularization of transmission channel visualization and intelligent identification technology, effective management and control of hidden dangers in power transmission channels has become more and more important. The clearance distance of a hidden hazard is an important criterion for judging the degree of hazard of a hidden hazard. However, the currently applied ranging technology has a relatively large error. This paper proposes a technology suitable for feature extraction and feature matching of transmission channels. And image set is obtained by feature matching. We generate a depth map of the three-dimensional space scene of the transmission channel. Based on the reconstructed depth map, the hidden danger in the transmission channel is measured. It can improve the efficiency of daily inspections and operation and maintenance management of transmission line hidden dangers and channel scenarios. It can help operation and maintenance personnel find hidden dangers in time. Thereby, the safe and stable operation of the line can be guaranteed.

Collaborative Operation Model with Selling Profit Sharing Strategy for Direct Online Retailing

This study focuses on the operation mode choice issue on whether brand owners should use the autonomous or the collaborative operation mode in direct online retailing. Results demonstrate that when the service provider’s investment is relatively effective and the selling profit sharing ratio of the service provider is high, the brand owner should choose the collaborative operation mode. Surprisedly, we find that brand owners in a dual-channel scenario have more motivation to use the collaborative operation mode than those in a single e-commerce channel scenario. The e-commerce service market may have much potential in the future.

Distributed Machine Learning Based Downlink Channel Estimation for RIS Assisted Wireless Communications

The downlink channel estimation requires a huge pilot overhead in the reconfigurable intelligent surface (RIS) assisted communication system. By exploiting the powerful learning ability of the neural network, the machine learning (ML) technique can be used to estimate the high-dimensional channel from a few received pilot signals at the user. However, since the training dataset collected by the single user only contains the information of part of the channel scenarios of a cell, the neural network trained by the single user is not able to work when the user moves from one channel scenario to another. To solve this challenge, we propose to leverage the distributed machine learning (DML) technique to enable the reliable downlink channel estimation. Specifically, we firstly build a downlink channel estimation neural network shared by all users, which can be collaboratively trained by the BS and the users with the help of the DML technique. Then, we further propose a hierarchical neural network architecture to improve the channel estimation accuracy, which can extract different channel features for different channel scenarios. Simulation results show that compared with the neural network trained by the single user, the proposed DML based neural networks can achieve better estimation performance with the reduced pilot overhead for all users from different scenarios.

Channel estimation based on superimposed pilot and weighted averaging

Channel estimation based on superimposed pilot (SP) is a challenge in orthogonal frequency division multiplexing (OFDM) systems. To reduce the pilot data interference in the SP and estimate the channel state information accurately, a weighted averaging (WA) channel estimation method based on the superimposed pilot is proposed in this paper. At the transmitter, two signals with data symbols and pilot symbols superimposed at different subcarriers are transmitted. At the receiver, the elimination scheme is proposed to remove the pilot data interference. Based on the temporal correlation of wireless channels, the WA method is used to reduce the interference caused by additive white Gaussian noise in the channel. Simulation results verify that the proposed method can be applied to different channel scenarios. It has better normalized mean square error and bit error rate performance than other existing methods, and the superimposed pilot can improve the throughput of wireless communication systems.

Artificial Intelligence Enabled Radio Propagation for Communications—Part I: Channel Characterization and Antenna-Channel Optimization

To provide higher data rates, as well as better coverage, cost efficiency, security, adaptability, and scalability, the 5G and beyond 5G networks are developed with various artificial intelligence (AI) techniques. In this two-part article, we investigate the application of AI and, in particular, machine learning (ML) to the study of wireless propagation channels. It first provides a comprehensive overview of ML for channel characterization and ML-based antenna–channel optimization in this first part, and then, it gives a state-of-the-art literature review of channel scenario identification and channel modeling in Part II. Fundamental results and key concepts of ML for communication networks are presented, and widely used ML methods for channel data processing, propagation channel estimation, and characterization are analyzed and compared. A discussion of challenges and future research directions for ML-enabled next-generation networks of the topics covered in this part rounds off this article.

Enhanced feature selection method based on regularization and kernel trick for 5G applications and beyond

Prediction of wireless channel scenarios is fundamental for modern wireless communication systems with diverse propagation conditions. Moreover, the type of data extracted from a wireless communication channel impulse response (CIR) is complex. In recent research, machine learning (ML) techniques have proven their success in classification problems of wireless communication scenarios and provide reasonable results. In this paper, a new enhanced feature selection method is proposed to improve the training model and classification performance of the conventional model. This improvement is achieved based on the concept of regularization in which the selection of the best features is considered before training the model under any propagation environment. The adoption of regularization leads to a high Total Explained Variance (TEV) during the process of kernel Principal Component Analysis (k-PCA). As a consequence, two principal features are used instead of three. The proposed model has high generalization ability since it reduces the features dimensionality (computational complexity) and, generally, enhances the ML classification performance. Experimental simulation is executed to compare the proposed model and the conventional one in terms of accuracy, precision and recall. The accuracy is increased from 97% to 99%, from 96% to 99%, from 89% to 97% and from 90% to 98% for k-nearest neighbor (k-NN), support vector machine (SVM), k-Means and Gaussian mixture model (GMM), respectively.

An Overview of OTFS for Internet of Things: Concepts, Benefits, and Challenges

The Internet of Things (IoT) is envisioned to connect everything, spanning from terrestrial to nonterrestrial terminals, where reliable communication is expected to be allowed in both time-invariant and time-variant wireless channels. Since classic orthogonal frequency-division multiplexing (OFDM) modulation, which has been widely used in both the fourth-generation (4G) and the fifth-generation (5G) cellular systems, is sensitive to high Doppler effect, it is challenging to satisfy the ever-growing demands of future IoT. To circumvent this issue, the orthogonal time–frequency space (OTFS) scheme is proposed, which modulates the information bits in both the delay and the Doppler domains, and exhibits beneficial advantages in both static and high-mobility wireless channel scenarios. In this article, we present a comprehensive overview of OTFS for IoT, including the current transceiver design, the potential benefits, the challenge issues, as well as future design guidelines.

On Sensing Performance of Multi-Antenna Mobile Cognitive Radio Conditioned on Primary User Activity Statistics

In limited space scenarios, the antennas in a multiantenna cognitive radio (CR) system are closely spaced and often experience correlation among them. In this paper, the sensing performance of arbitrarily correlated antennas over the Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$m$ </tex-math></inline-formula> fading channel for the mobile CR user is analysed. In particular, the analytical expression for the average detection probability for a mobile CR user employing the selection combining with arbitrarily correlated triple diversity branches is derived as a special case. Moreover, to characterize the performance of an energy detector under mobility, the area under the curve of a receiver operating characteristic is analysed. Furthermore, as the sensing decisions can be utilized to improve the sensing performance, a comprehensive analysis of sensing performance of the mobile secondary user conditioned on the primary user (PU) activity statistics is carried out. We assume the PU traffic to be following the Discrete-Time Markov Chain (DTMC) model, and its <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$idle$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$busy$ </tex-math></inline-formula> periods to be following generalised Pareto distribution. The analytical framework is substantiated by Monte Carlo simulations. Results indicate that antenna correlation deteriorates detection performance. Moreover, the detection performance deteriorates further due to the mobility of CR users, especially in the deep fading channel scenarios. Furthermore, findings also suggest that under mobility, the sensing performance improves if the duty cycle of the PU channel is low. This work provides a realistic sensing framework for CR enabled vehicles.

A Low Complexity Symbol-Wise ML Detection Algorithm for User-Centric C-RAN

This letter considers an optimal signal detection problem for uplink user-centric cloud radio access network (C-RAN). In user-centric C-RAN, users communicate with nearby remote radio heads (RRHs), and RRHs are connected to baseband unit (BBU) pool. Then, the baseband processing functionalities are migrated to the BBU pool to provide joint signal detection for a number of users. However, joint signal processing will encounter high computational complexity as the network scale grows. Especially for optimal signal detection, the computational complexity increases exponentially as the number of users increases. To address this issue, we propose a symbol-wise maximum likelihood (ML) detection algorithm to achieve optimal performance with low complexity. The key to realize the symbol-by-symbol separate detection is that it makes full use of the sparsity of the channel matrix in user-centric C-RAN. We further provide the theoretical derivation to clarify the superiority of the proposed algorithm in this sparse channel scenario. Numerical experiments show that the proposed algorithm can achieve optimal performance with low complexity in user-centric C-RAN.

Quantum steering and quantum discord under noisy channels and entanglement swapping

Quantum entanglement, quantum discord, and EPR-steering are properties which are considered as valuable resources for fuelling quantum information-theoretic protocols. EPR-steering is a correlation weaker than nonlocality (in the Bell's sense) and yet stronger than entanglement. Quantum discord on the other hand, captures non-classical behaviour beyond that of entanglement, and its study has remained of active research interest during the past two decades. Exploring the behaviour of these quantum correlations in different physical scenarios, like those simulated by open quantum systems, is therefore of crucial importance for understanding their viability for quantum technologies. In this work, we analyse the behaviour of EPR-steering, entanglement, and quantum discord, for partially entangled two-qubit states with coloured noise, introduced by Ameida et al. [1], under various quantum processes. First, we consider the three noisy channel scenarios of; phase damping, generalised amplitude damping and stochastic dephasing channel. Second, we explore their behaviour in an entanglement swapping scenario. We quantify EPR-steering by means of an inequality with three-input two-output measurement settings, and address quantum discord as the interferometric power of quantum states. We discuss the sudden death of steering and entanglement induced by the noisy processes. Additionally, in the case of generalised amplitude damping, a death and revival behaviour can be interpreted in terms of one of the noise's parameters. Second, we contrast the fact that noisy channels in general reduce the amount of correlations present in the system, with the swapping protocol, which displays scenarios where these quantum correlations can be enhanced with respect to the correlations at the initial stage of the protocol. In particular, we present a trade-off between the post-swap amount of correlations and their probability of occurrence.

Spectrum sharing between wireless medical capsule endoscopy and LTE system

Wireless medical capsule endoscopy (WMCE) is an innovative technology that can be used to detect diseases of the human gastrointestinal tract. This technology utilizes a miniature camera capsule (CapCam) as a convenient alternative tool for treating small intestines with no pain and non-invasive method with very low levels of medical hazards compared to traditional methods. The CapCam technology operates in industrial, scientific, and medical (ISM) bands including 915 and 2400 MHz. However, co-channel and adjacent channel bands in the ISM bands are identified for mobile service, represented by long term evolution (LTE) system, on a primary basis (915 MHz) and a co-primary basis (2400 MHz) in the three ITU Regions. Such a condition creates a potential and mutual interference between the CapCam and LTE systems and it can highly affect human health. This paper proposes a model to study the potential interference scenarios between the CapCam and LTE systems in possible shared frequency bands for co-channel and adjacent channel scenarios based on a real urban macrocell (UMa) where line of sight (LOS) and non-LOS (NLOS) conditions are available. In this study, four propagation environment models are involved in estimating the total propagation losses including outdoor propagation, building penetration, indoor propagation, and propagation through a human body. The obtained results using the carrier to interference ratio (CIR), as a spectrum sharing criterion, showed that the interference from the LTE system is dominant. In addition, the concurrent operation of the CapCam and LTE systems with minimum distance seems more likely for the NLOS environment with higher frequency band (2400 MHz) and adjacent frequency scenario. Also, the channel capacity of the LTE and CapCam systems, under various CIR levels, displayed a good agreement with previous studies.

Channel Estimation and Equalization Using FIM for MIMO-OFDM on Doubly Selective Faded Noisy Channels

Orthogonal frequency division multiplexing (OFDM) plays an important role in wireless communication due to its high transmission rate. Information is conveyed across spatial and temporal dimensions through the space-time shift keying (STSK) technique which is basically used to handle multiplexing diversity and gains. On the other hand, index modulation integrated OFDM not only communicates information through conventional signal constellations as in classical OFDM, but also through indexes of the subcarriers. In index modulation, the subcarriers are transmitted over a particular index and can be implemented effectively. The active indices are selected and further information bits transmitted. In this paper, to handle such limitations, the deep neural network (DNN) has been proposed for end-to-end performance. Under the noisy and faded channel scenario, channel state information must be acquired to recover the transmitted signal correctly. To evaluate the channel distortion level, a deep learning model is trained offline from simulated data and then applied to online data to estimate and recover the channel state as well as the transmitted signal, respectively, in comparison to the traditional least minimum mean square error (LMMSE) channel estimation technique. The analysis results demonstrate superiority over the conventional LMMSE for channel estimation and signal detection in wireless communications with complex channel distortion and interference. The mean square error (MSE) is evaluated for carrying out performance information in each subcarrier block and to reduce the detector error rate.

Online Learning for Adaptive Video Streaming in Mobile Networks

In this paper, we propose a novel algorithm for video bitrate adaptation in HTTP Adaptive Streaming (HAS), based on online learning. The proposed algorithm, named Learn2Adapt (L2A) , is shown to provide a robust bitrate adaptation strategy which, unlike most of the state-of-the-art techniques, does not require parameter tuning, channel model assumptions, or application-specific adjustments. These properties make it very suitable for mobile users, who typically experience fast variations in channel characteristics. Experimental results, over real 4G traffic traces, show that L2A improves on the overall Quality of Experience (QoE) and in particular the average streaming bitrate, a result obtained independently of the channel and application scenarios.

On channel estimation and spectral efficiency for cell‐free massive MIMO with multi‐antenna access points considering spatially correlated channels

AbstractChannel estimation (CE) and spectral efficiency (SE) are two processes that have a major influence on system performance, especially in a more practical channel scenario with spatially correlated Rayleigh fading. During this work, we address the uplink phase CE process and the SE for spatially correlated Rayleigh fading in a cell‐free (CF) massive multiple‐input multiple‐output (M‐MIMO) network and in the conventional M‐MIMO network operating under the time‐division duplex protocol. Since practical channels are correlated spatially with Rayleigh fading; thereby, this work addresses spatially correlated Rayleigh fading channels in both networks using a combination of the exponential correlation model with large‐scale variation across the array. Moreover, this work addresses the correlated and uncorrelated channels, where the correlated one is investigated using two array arrangements, namely, a uniform linear array (ULA) and a uniform planar array (UPA). For the two networks, the CE and SE are addressed using the minimum mean square error (MMSE) estimator and the MMSE combining vector, respectively. We propose a novel array design for CF M‐MIMO using the Kronecker product of a ULA arrangement, where each access point has a UPA arrangement. In other words, we propose the covariance matrix generated by the UPA arrangement for CF M‐MIMO using the Kronecker product of the covariance matrix generated by the ULA arrangement for CF M‐MIMO. Numerical results are presented to validate our theoretical expressions, where we evaluate the performance of the MMSE estimator and MMSE combining vector, respectively, using the normalized mean square error metric and the quantity of information effectively transmitted per second.

Performance Analysis of Joint Active User Detection and Channel Estimation for Massive Connectivity

This paper considers joint active user detection (AUD) and channel estimation (CE) for massive connectivity scenarios with sporadic traffic. The state-of-art method under a Bayesian framework to perform joint AUD and CE in such scenarios is approximate message passing (AMP). However, the existing theoretical analysis of AMP-based joint AUD and CE can only be performed with a given fixed point of the AMP state evolution function, lacking the analysis of AMP phase transition and Bayes-optimality. In this paper, we propose a novel theoretical framework to analyze the performance of the joint AUD and CE problem by adopting the replica method in the Bayes-optimal condition. Specifically, our analysis is based on a general channel model, which reduces to particular channel models in multiple typical MIMO communication scenarios. Our theoretical framework allows ones to measure the optimality and phase transition of AMP-based joint AUD and CE as well as to predict the corresponding performance metrics under our model. To reify our proposed theoretical framework, we analyze two typical scenarios from the massive random access literature, i.e., the isotropic channel scenario and the spatially correlated channel scenario. Accordingly, our performance analysis produces some novel results for both the isotropic Raleigh channel and spatially correlated channel case.

5G New Radio Physical Downlink Control Channel Reliability Enhancements for Multiple Transmission-Reception-Point Communications

Non-coherent transmission from multiple transmission-reception-points (TRPs)/base stations or panels to a user equipment (UE) is exploited in 5G NR to improve downlink reliability and cell-edge throughput. Ultra reliable low-latency communications (URLLC) and enhanced Mobile BroadBand (eMBB) are prominent target use-cases for multi-TRP or multi-panel transmissions. In Third-Generation Partnership Project (3GPP) Release 17 specifications, multi-TRP-based transmissions were specified for the physical downlink control channel (PDCCH) specifically to enhance its reliability and robustness. In this work, a comprehensive account of various multi-TRP reliability enhancement schemes applicable for the 5G NR PDCCH, including the ones supported by the 3GPP Release 17 specifications, is provided. The impact of the specifications for each scheme, UE and network complexity and their utility in various use-cases is studied. Their error performances are evaluated via link-level simulations using the evaluation criteria agreed in the 3GPP proceedings. The 3GPP-supported multi-TRP PDCCH repetition schemes, and additionally proposed PDCCH repetition and diversity schemes are shown to be effective in improving 5G NR PDCCH reliability and combating link blockage in mmWave scenarios. The link-level simulations also provide insights for the implementation of the decoding schemes for the PDCCH enhancements in different channel scenarios. Analysis of the performance, complexity and implementation constraints of the proposed PDCCH transmission schemes indicate their suitability to UEs with reduced-capability or stricter memory constraints and flexible network scheduling.

Deep Learning for Massive MIMO Uplink Detectors

Detection techniques for massive multiple-input multiple-output (MIMO) have gained a lot of attention in both academia and industry. Detection techniques have a significant impact on the massive MIMO receivers&#x2019; performance and complexity. Although a plethora of research is conducted using the classical detection theory and techniques, the performance is deteriorated when the ratio between the numbers of antennas and users is relatively small. In addition, most of classical detection techniques are suffering from severe performance loss and/or high computational complexity in real channel scenarios. Therefore, there is a significant room for fundamental research contributions in data detection based on the deep learning (DL) approach. DL architectures can be exploited to provide optimal performance with similar complexity of conventional detection techniques. This paper aims to provide insights on DL based detectors to a generalist of wireless communications. We garner the DL based massive MIMO detectors and classify them so that a reader can find the differences between various architectures with a wider range of potential solutions and variations. In this paper, we discuss the performance-complexity profile, pros and cons, and implementation stiffness of each DL based detector&#x2019;s architecture. Detection in cell-free massive MIMO is also presented. Challenges and our perspectives for future research directions are also discussed. This article is not meant to be a survey of a mature-subject, but rather serve as a catalyst to encourage more DL research in massive MIMO.

An Image Steganography Algorithm Based on PSO and IWT for Underwater Acoustic Communication

To achieve fast and secure information steganography in underwater acoustic channel scenarios, this paper proposes a PSO (Particle Swarm Optimization) and IWT (integer wavelet transform) based image steganography algorithm for underwater acoustic communication. Firstly, the underwater image to be embedded is interleaved and packed to generate the secret information code-stream to improve the detection and recovery performance of transmission errors. Secondly, the cover image is divided into non-overlapping blocks and IWT is applied to each block, which can save PSO search space and improve the optimal solution performance. Thirdly, the embedding position matrix of the high frequency sub-band coefficients is chaotic scrambled, and PSO is used to select the optimal key to obtain the optimal matching position for the secret information to produce the best stego-image quality. Finally, transmission error detection and recovery measures are applied to the extracted secret information to improve the reconstruction quality in underwater acoustic channel scenarios. Experimental results show that the proposed algorithm outperforms existing approaches in terms of information embedding capacity and steganographic visual quality, meeting the requirements of safety and real-time performance of underwater communication. Moreover, it also has the ability of error detection and recovery for harsh underwater acoustic channel.