Channel Coherence Research Articles

Spatial and frequency dressed multimode of two spontaneous cascaded four-wave mixing processes

The frequency and spatial multimode of correlated fields are seen as essential resources for future multimode entanglement and quantum networks. Here, we present multimode properties from the process of parametric amplified cascaded four-wave mixing through a rubidium atomic ensemble under dressing and Kerr nonlinear effects. The experimental results complement the theoretical simulations. Three frequency modes and 34 spatial spots were experimentally observed due to the double-dressing and Kerr effect of the two pump fields. Further, it is verified that changing the pump field frequency has a larger line shift over pump field power and the angle between the two pump fields, which can be realized as a multicontact switch. Through a dressing effect, interestingly the number of coherent channels also increases by the order of dressing, indicating a large possibility of frequency and spatial multimode. These results will have important applications in quantum communications.

An experimental study of mobile underwater acoustic channels and their impacts on mobile acoustic communications

Most underwater acoustic communication schemes have implicitly assumed limited platform mobility and their performance tends to suffer considerably when the platform is in high motion. To gain insight into the effects of high platform mobility, a sea-going experiment was recently conducted. In the experiment, two channels are set up. One is a stationary channel between a fixed source and a fixed receiver. The other is a highly mobile channel between a fixed receiver and a source towed in a circle at a speed of 6 knots. The two channels are directly compared in terms of four key channel parameters changes that matter most to the performance of acoustic communications, namely, channel coherence, path loss, Doppler scaling factor and carrier frequency offsets. A channel probing signal is judiciously designed to enable reliable estimation of those parameters at high resolution in time. This paper will provide the details of this sea experiment, including its design, setups and execution, and report interesting findings.

Noncoherent Demodulation and Decoding via Polynomial Zeros Modulation for Pilot-Free Short Packet Transmissions over Multipath Fading Channels

With the advent of the Internet of Things (IoT), short packet transmissions will dominate the future wireless communication. However, traditional coherent demodulation and channel estimation schemes require large pilot overhead, which may be highly inefficient for short packets in multipath fading scenarios. This paper proposes a novel pilot-free short packet structure based on the association of modulation on conjugate-reciprocal zeros (MOCZ) and tail-biting convolutional codes (TBCC), where a noncoherent demodulation and decoding scheme is designed without the channel state information (CSI) at the transceivers. We provide a construction method of constellation sets and demodulation rule for M-ary MOCZ. By deriving low complexity log-likelihood ratios (LLR) for M-ary MOCZ, we offer a reasonable balance between energy and bandwidth efficiency for joint coding and modulation scheme. Simulation results show that our proposed scheme can attain significant performance and throughput gains compared to the pilot-based coherent modulation scheme over multipath fading channels.

Multi-user transmit beamforming for broadband acoustic systems

Multi-user transmit beamforming for acoustic systems based on orthogonal frequency division multiplexing (OFDM) modulation is studied and demonstrated. Transmit beamforming consists of assigning weight coefficients to multiple transmit elements, such that the signals transmitted from the array add constructively at the users' receive sides. In an optimal setting, the transmitter assigns weights that are proportional to the channel transfer functions to the users. However, the assumption of complete channel knowledge is not entirely accurate, as the transmitter obtains the channels via feedback from the users. Thus, the transmitter has partial knowledge of the channels, as their estimates are not only noisy, but can be outdated if the channels have changed during the time it took to close the feedback loop. These adversities are notably pronounced in time-varying acoustic channels, as the feedback delay is large as compared to the channel coherence time due to the low speed of sound propagation. To counteract this problem, a technique that targets only those features of the channels that can withstand the feedback delay is proposed. Such features are the users' angles of arrival of the principal propagation paths. The principal paths are stable paths that do not experience rapid fluctuations and are thus varying sufficiently slowly that they can tolerate long feedback delays. The design concepts are demonstrated using experimental over-the-air transmissions in an indoor environment of an acoustic communications testbed.

DRL-Based Offloading for Computation Delay Minimization in Wireless-Powered Multi-Access Edge Computing

Wireless power transfer (WPT) and edge computing have been validated as effective ways to solve the energy-limited problem and computation-capacity-limited problem of wireless devices (WDs), respectively. This paper studies the wireless-powered multi-access edge computing (WP-MEC) network, where WDs conduct either local computing or task offloading for their individable computation tasks. We aim to minimize total computation delay (TCD) when each WD has a computation task to execute, referred to as the total computation delay minimization (TCDM) problem, by jointly optimizing the offloading-decision, WPT duration, and transmission durations of offloading WDs. The TCDM problem is a mixed integer programming (MIP) problem that is challenging to efficiently obtain the optimal or near-optimal solution. To tackle this challenge, we decompose the TCDM problem into the sub-problem of optimizing the WPT duration and transmission durations, and the top-problem of optimizing the offloading decision. For the nonconvex sub-problem, we design a worst-WD-adjusting (WDA) algorithm to efficiently obtain its optimal solution. For the top-problem, under the time-varying channel conditions, traditional optimization methods are hard to determine the optimal or near-optimal offloading decision within the channel coherence duration. To fast obtain the near-optimal offloading decision, we propose a deep neural networks (DNN)-based deep reinforcement learning (DRL) model, which takes the sub-problem solving as one component for utility evaluation. Finally, numerical results demonstrate that the proposed online DRL-based offloading algorithm achieves the near-minimal TCD with low computational complexity, and is suitable for the fast-fading WP-MEC network.

Experimental verification of fiber coupling characteristics for FSO downlinks from the International Space Station.

Free-space optical (FSO) systems are compulsory to realize high capacity and interference-free communication links from low-Earth orbit (LEO) satellite constellations as well as spacecraft and space stations to the Earth. To be integrated with high-capacity ground networks, the collected portion of the incident beam should be coupled into an optical fiber. To accurately evaluate the signal-to-noise ratio (SNR) and bit-error rate (BER) performance metrics, the probability density function (PDF) of fiber coupling efficiency (CE) must be determined. Previous studies have experimentally verified the CE PDF for a single-mode fiber, however, there is no such investigation for the CE PDF of a multi-mode fiber (MMF) in a LEO-to-ground FSO downlink. In this paper, for the first time, the CE PDF for a 200-μm MMF is experimentally investigated using data from an FSO downlink from the Small Optical Link for International Space Station (SOLISS) terminal to a 40-cm sub-aperture optical ground station (OGS) supported by a fine-tracking system. An average CE of 5.45 dB was also achieved given that the alignment between SOLISS and OGS was not optimal. In addition, using the angle-of-arrival (AoA) and received power data, the statistical characteristics such as channel coherence time, power spectral density, spectrogram, and PDFs of AoA, beam misalignments, and atmospheric turbulence-induced fluctuations are revealed and compared with the state-of-the-art theoretical background.

Lead Halide Perovskite Nanocrystals with Low Inhomogeneous Broadening and High Coherent Fraction through Dicationic Ligand Engineering.

Lead halide perovskite nanocrystals (LHP NCs) are an emerging materials system with broad potential applications, including as emitters of quantum light. We apply design principles aimed at the structural optimization of surface ligand species for CsPbBr3 NCs, leading us to the study of LHP NCs with dicationic quaternary ammonium bromide ligands. Through the selection of linking groups and aliphatic backbones guided by experiments and computational support, we demonstrate consistently narrow photoluminescence line shapes with a full-width-at-half-maximum below 70 meV. We observe bulk-like Stokes shifts throughout our range of particle sizes, from 7 to 16 nm. At cryogenic temperatures, we find sub-200 ps lifetimes, significant photon coherence, and the fraction of photons emitted into the coherent channel increasing markedly to 86%. A 4-fold reduction in inhomogeneous broadening from previous work paves the way for the integration of LHP NC emitters into nanophotonic architectures to enable advanced quantum optical investigation.

Distributed Transmit Beamforming: Design and Demonstration From the Lab to UAVs

Cooperating radios can extend their communication range by adjusting their signals to ensure coherent combining at a destination radio. This technique is called distributed transmit beamforming. Beamforming (BF) relies on the BF radios having frequency synchronized carriers and phases adjusted for coherent combining. Both requirements are typically met by exchanging preambles with the destination. However, since BF aims to increase the received power, the individually transmitted preambles are typically at low SNR and their lengths are constrained by the channel coherence time. These noisy preambles lead to errors in frequency and phase estimation, which result in randomly changing BF gains. To build reliable distributed BF systems, the impact of estimation errors on the BF gains need to be considered in the design. In this work, assuming a destination-led BF protocol and Kalman filter for frequency tracking, we optimize the number of BF radios and the preamble lengths to achieve reliable BF gain. To do that, we characterize the relations between the BF gains distribution, the channel coherence time, and design parameters like the SNR, preamble lengths, and the number of radios. The proposed relations are verified using simulations and via experiments using software-defined radios in a lab and on UAVs.

Statistical Beamforming for Multi-Set Space–Time Shift-Keying-Based Full-Duplex Millimeter Wave Communications

Full-duplex (FD) communication has been shown to provide an increased achievable rate, while millimeter wave (mmWave) communications benefit from a large available bandwidth that further improves the achievable rate. On the other hand, the concept of multi-set space-time shift keying (MS-STSK) has been proposed to provide a flexible design trade-off between throughput and performance. Hence, in this work, we consider the design of an FD-aided MS-STSK transceiver for millimeter wave communications. However, a major challenge is that channel reciprocity is not valid in mmWave communications due to shorter channel coherence time. Thus, the uplink (UL) pilots cannot be utilized to estimate the downlink (DL) channel. To overcome this challenge, we propose a beamforming technique based on channel statistics without assuming channel reciprocity. For this purpose, a closed-form expression for the outage probability of the system is derived by employing the characterization of the ratio of the Indefinite Quadratic Form (IQF). The derived analytical expression is then utilized to design optimum beamforming weights using the Sequential Quadratic Programming (SQP)-based heuristic method. Moreover, an Iterative Statistical Method (ISM) of joint transmit and receive beamforming algorithm is also developed by utilizing Principle Eigenvector (PE) and Generalized Rayleigh Quotient (G-RQ) optimization techniques. Finally, we verify our simulation results with the theoretical analysis.

Channel Estimation in RIS-Assisted MIMO Systems Operating Under Imperfections

The promising gains of reconfigurable intelligent surface (RIS)-assisted multiple-input multiple-output (MIMO) systems, in terms of extended coverage and enhanced capacity, are critically dependent on the accuracy of the channel state information. However, traditional channel estimation (CE) schemes are not applicable in RIS-assisted MIMO networks, since passive RISs typically lack the signal processing capabilities that are assumed by CE algorithms. This becomes problematic when physical imperfections or electronic impairments affect the RIS due to its exposition to different environmental effects or caused by hardware limitations from the circuitry. While these real-world effects are typically ignored in the literature, in this paper we propose efficient CE schemes for RIS-assisted MIMO systems taking different imperfections into account. Specifically, we propose two sets of tensor-based algorithms, based on the parallel factor analysis decomposition schemes. First, assuming a long-term model-where the RIS imperfections, modeled as unknown phase shifts, are static within the channel coherence time-we formulate an iterative alternating least squares (ALS)- based algorithm for the joint estimation of the unknown phase deviations and the communication channels. Then, we develop the short-term imperfection model, which allows both amplitude and phase RIS imperfections to be non-static with respect to the channel coherence time. We propose two iterative ALS-based and closed-form higher-order singular value decomposition-based algorithms for jointly estimating the channels and the unknown impairments. We also investigate the computational complexity and the identifiability of the proposed algorithms and study the effect of various imperfections on the CE quality. Simulation results show the effectiveness of our proposed tensor-based algorithms in terms of estimation accuracy and computational complexity.

Physical Layer Authentication Based on Channel Polarization Response in Dual-Polarized Antenna Communication Systems

This study presents a novel approach for physical layer authentication based on channel polarization response (CPR). CPR is sensitive to variation in the physical properties of scatterers, and the CPR difference between various channels is higher than the channel frequency response (CFR) under rich scattering scenarios. Additionally, the estimation of CPR is continuous, the authentication interval can be adjusted according to the channel coherence time, then the proposed scheme can be applied to any rich scattering scenarios, including highly dynamic scenarios. Since the received polarization state is fixed during the channel coherence time, we can coherently stack the received polarization state to improve the signal to noise ratio (SNR) and the estimation accuracy of CPR, thereby achieving high authentication accuracy under ultra-low SNR. Moreover, since the transmitted polarization state of various transmitters is different, because of their unique hardware deficiencies, and since the CPR is dependent on the transmitted polarization state, the CPR of other transmitters is different, allowing the resolution of co-located attacks. We theoretically drive the false alarm probability, detection probability, optimal discriminant threshold, computational complexity, optimal stacking numbers, and optimal CPR points for authentication. Furthermore, extensive simulations and experiments are performed to verify the validity and effectiveness of the proposed scheme.

Generalized Channel Probing and Generalized Pre-Processing for Secret Key Generation

The paper considers secret key generation (SKG) from a channel between Alice and Bob against Eve all with multiple antennas. A generalized channel probing (GCP) method and a generalized pre-processing (GPP) method are proposed as two major steps before quantization, information reconciliation and privacy amplification for SKG. The degree-of-freedom (DoF) of the secret key capacity (SKC), relative to power or signal-to-noise ratio, based on the data sets at Alice, Bob and Eve after the execution of GCP is shown. The SKC-DoF is also shown to be the same as that based the new data sets after the application of GPP except for modifications chosen for computational efficiency. In particular, if Eve has a less number of antennas than either Alice and/or Bob, the SKC-DoF grows with the number of random transmissions from either Alice and/or Bob within each channel coherence period. This SKC-DoF property for GCP and GPP does not require the reciprocity of the channel. A reciprocal channel simply adds a product of the numbers of antennas at Alice and Bob towards the total SKC-DoF. Also shown in this paper is GCP with embedded public pilots, which results in no loss of SKC-DoF. Two prior methods for channel probing are also reviewed, and it is shown that none of them has the above mentioned SKC-DoF property despite previous claims. The potential application areas of GCP and GPP include both wireless and wireline networks.

Deep Neural Network-Aided Soft-Demapping in Coherent Optical Systems: Regression Versus Classification

We examine here what type of predictive modelling, classification, or regression, using neural networks (NN), fits better the task of soft-demapping based post-processing in coherent optical communications, where the transmission channel is nonlinear and dispersive. For the first time, we present possible drawbacks in using each type of predictive task in a machine learning context, considering the nonlinear coherent optical channel equalization/soft-demapping problem. We study two types of equalizers based on the feed-forward and recurrent NNs, for several transmission scenarios, in linear and nonlinear regimes of the optical channel. We point out that even though from the information theory perspective the cross-entropy loss (classification) is the most suitable option for our problem, the NN models based on the cross-entropy loss function can severely suffer from learning problems. The latter translates into the fact that regression-based learning is typically superior in terms of delivering higher Q-factor and achievable information rates. In short, we show by empirical evidence that loss functions based on cross-entropy may not be necessarily the most suitable option for training communication systems in practical scenarios when overfitting- and vanishing gradients-related problems come into play.

Optimization of resource allocation in FDD massive MIMO systems

The performance of massive MIMO systems relies heavily on the availability of Channel State Information at the Transmitter (CSIT). A large amount of work has been devoted to reducing the CSIT acquisition overhead at the pilot training and/or CSI feedback stage. In fact, the downlink communication generally includes three stages, i.e., pilot training, CSI feedback, and data transmission. These three stages are mutually related and jointly determine the overall system performance. Unfortunately, there exist few studies on the reduction of CSIT acquisition overhead from the global point of view. In this paper, we integrate the Minimum Mean Square Error (MMSE) channel estimation, Random Vector Quantization (RVQ) based limited feedback and Maximal Ratio Combining (MRC) precoding into a unified framework for investigating the resource allocation problem. In particular, we first approximate the covariance matrix of the quantization error with a simple expression and derive an analytical expression of the received Signal-to-Noise Ratio (SNR) based on the deterministic equivalence theory. Then the three performance metrics (the spectral efficiency, energy efficiency, and total energy consumption) oriented problems are formulated analytically. With practical system requirements, these three metrics can be collaboratively optimized. Finally, we propose an optimization solver to derive the optimal partition of channel coherence time. Experiment results verify the benefits of the proposed resource allocation schemes under three different scenarios and illustrate the tradeoff of resource allocation between three stages.

A study on coherence between virtual signal and physical signals in remote acoustic sensing.

Remote acoustic sensing can be used to estimate the error signals in human ears without placing any physical microphones there. In this paper, the coherence between the signals picked up by physical microphones over a sphere surface and the signal obtained at the sphere center is investigated. Based on the multiple channel coherence formulas in the time domain and frequency domain, the relationship between the coherence and the placement of physical microphones is analyzed by numerical simulations first, then the experimental results obtained in a reverberation chamber and a car cabin are presented to verify the simulation results. Finally, a placement of physical microphones for active control of road noise in car cabins is discussed. Both the numerical and experimental results show that an upper limit frequency exists for accurate sound pressure estimation at the center of a sphere with the sound pressure on the sphere surface. For a sufficiently complex sound field such as that in a reverberation room or in a car, half the wavelength of the upper limit frequency is about the average distance among the physical microphones.

Underwater Acoustic Channel Tracking with Cluster Variation Learning for Acoustic Mobile OFDM Communication

In this paper, we propose a channel tracking method with cluster variation learning in multicarrier communications, by exploiting the cluster-specific channel coherence for time-varying underwater acoustic (UWA) channels. We develop a cluster variation model due to the cluster-specific channel coherence. In the cluster variation model, the path delay and the Doppler scale assemble the range and the velocity in the target movement. Inspired by the target tracking for its movement, we propose a channel tracking method which can incorporate the cluster variation model with the measurement model efficiently through Kalman filtering. The cluster variation model can learn its parameters block by block through pilots, therefore reduce its model mismatch adaptively. The proposed method has a moderate computational complexity, since it eliminates the explicit Doppler scale estimation and Doppler variation model comparing to the conventional channel estimation. Using simulated data as well as experimental data in mobile UWA communications with one phone element, we study the system performance in term of mean square error (MSE) and symbol error rate (SER) performance, and show that the proposed channel tracking method improves the performance significantly.

Atomic-coherence-assisted multipartite entanglement generation with dressing-energy-level-cascaded four-wave mixing

Multipartite entanglement plays an important role in quantum information processing and quantum metrology. Here, the dressing-energy-level-cascaded four-wave mixing (FWM) processes are proposed to generate all-optical controlled multipartite entanglement within a single device. The entanglement characteristics of the produced states of light are characterized by applying the Duan criterion and the positivity under partial transposition criterion. Moreover, by using an internal dressing field to modulate atomic coherence, multiple quantum coherent channels of FWM are simultaneously constructed, which result in a great extension of entanglement mode number and quantum information capacity. We find that the violation of the entanglement criteria inequalities is coherent-channel dependent, and the produced states can be directly modulated via atomic coherence. Our system can integrate the generation and modulation of the entangled states in one process. It may help provide a compact method for realizing large-scale quantum networks.

Joint Task Offloading and Resource Allocation for IoT Edge Computing With Sequential Task Dependency

Incorporating mobile-edge computing (MEC) in the Internet of Things (IoT) enables resource-limited IoT devices to offload their computation tasks to a nearby edge server. In this article, we investigate an IoT system assisted by the MEC technique with its computation task subjected to sequential task dependency, which is critical for video stream processing and other intelligent applications. To minimize energy consumption per IoT device while limiting task processing delay, task offloading strategy, communication resource, and computation resource are optimized jointly under both slow and fast-fading channels. In slow fading channels, an optimization problem is formulated, which is nonconvex and involves one integer variable. To solve this challenging problem, we decompose it as a 1-D search of task offloading decision problem and a nonconvex optimization problem with task offloading decision given. Through mathematical manipulations, the nonconvex problem is transformed to be a convex one, which is shown to be solvable only with the simple Golden search method. In fast-fading channels, optimal online policies depending on the instant channel state are derived even though they are entangled. In addition, it is proved that the derived policy will converge to the offline policy when the channel coherence time is low, which can help save extra computation complexity. Numerical results verify the correctness of our analysis and the effectiveness of our proposed strategies over the existing methods.

A Counter-Eavesdropping Technique for Optimized Privacy of Wireless Industrial IoT Communications

The industrial Internet of Things (IIoTs) is a key component of the fourth industrial revolution (Industry 4.0) which is faced with privacy issues as the scale and sensitivity of user and system data constantly increases. Eavesdropping attack is one of such privacy issue of the IIoT system especially when the number of transmitting antennas is increased. Thus, the focus of this article is on establishing efficient privacy in an IIoT-multiple-input–multiple-output–multiple-antenna eavesdropping communications scenario. To achieve this, a closed-form derivation for asymptotic regularized prompt privacy rate is first formulated for IIoT network system. Then, the study further examines the design of optimal jamming parameters by proposing a model referred as optimal counter-eavesdropping channel approximation technique for tackling eavesdropping attack in IIoT. The simulated performance of the proposed model clearly shows that provided that the channel coherence time is less than two times the number of transmitting nodes, a high privacy precision is achieved even without deploying any artificial noise.

Physical Layer Security in Multi-Antenna Cellular Systems: Joint Optimization of Feedback Rate and Power Allocation

This paper comprehensively studies the physical layer security in frequency division duplex multi-antenna cellular systems, where the multi-antenna base stations (BSs), legitimate users (LUs), and eavesdroppers are all randomly located. Each BS employs artificial-noise (AN)-aided multi-user linear beamforming with limited channel state information feedback. Based on the stochastic geometry theory, we first derive an analytical expression of a lower bound on the ergodic secrecy rate (ESR) of the typical LU without assuming asymptotes for any system parameter. We then develop a tight closed-form approximation on the optimal number of feedback bits to maximize a lower bound on the per-user net ESR, which takes into consideration the cost of uplink spectral efficiency for limited feedback. Moreover, the power allocation coefficient between message-bearing signals and AN can be optimized by using a bisection search method. Our main finding is that, the optimum number of feedback bits scales linearly with the path-loss exponent and the number of antennas, and scales logarithmically with the channel coherence time. The derived analytical results can also provide system-level insights into the ESR performance of the multi-antenna random cellular networks with limited feedback and the optimal system design. Numerical results are also presented to verify the obtained results.