Showing papers on "Fading published in 2019"

Deep Learning-Based Channel Estimation

Abstract: In this letter, we present a deep learning algorithm for channel estimation in communication systems. We consider the time–frequency response of a fast fading communication channel as a 2D image. The aim is to find the unknown values of the channel response using some known values at the pilot locations. To this end, a general pipeline using deep image processing techniques, image super-resolution (SR), and image restoration (IR) is proposed. This scheme considers the pilot values, altogether, as a low-resolution image and uses an SR network cascaded with a denoising IR network to estimate the channel. Moreover, the implementation of the proposed pipeline is presented. The estimation error shows that the presented algorithm is comparable to the minimum mean square error (MMSE) with full knowledge of the channel statistics, and it is better than an approximation to linear MMSE. The results confirm that this pipeline can be used efficiently in channel estimation.

Capacity Characterization for Intelligent Reflecting Surface Aided MIMO Communication

Abstract: Intelligent reflecting surface (IRS) is a promising solution to enhance the wireless communication capacity both cost-effectively and energy-efficiently, by properly altering the signal propagation via tuning a large number of passive reflecting units. In this paper, we aim to characterize the fundamental capacity limit of IRS-aided point-to-point multiple-input multiple-output (MIMO) communication systems with multi-antenna transmitter and receiver in general, by jointly optimizing the IRS reflection coefficients and the MIMO transmit covariance matrix. First, we consider narrowband transmission under frequency-flat fading channels, and develop an efficient alternating optimization algorithm to find a locally optimal solution by iteratively optimizing the transmit covariance matrix or one of the reflection coefficients with the others being fixed. Next, we consider capacity maximization for broadband transmission in a general MIMO orthogonal frequency division multiplexing (OFDM) system under frequency-selective fading channels, where transmit covariance matrices can be optimized for different subcarriers while only one common set of IRS reflection coefficients can be designed to cater to all subcarriers. To tackle this more challenging problem, we propose a new alternating optimization algorithm based on convex relaxation to find a high-quality suboptimal solution. Numerical results show that our proposed algorithms achieve substantially increased capacity compared to traditional MIMO channels without the IRS, and also outperform various benchmark schemes. In particular, it is shown that with the proposed algorithms, various key parameters of the IRS-aided MIMO channel such as channel total power, rank, and condition number can be significantly improved for capacity enhancement.

3D Trajectory Optimization in Rician Fading for UAV-Enabled Data Harvesting

Abstract: Dispatching unmanned aerial vehicles (UAVs) to harvest sensing-data from distributed sensors is expected to significantly improve the data collection efficiency in conventional wireless sensor networks (WSNs). In this paper, we consider a UAV-enabled WSN, where a flying UAV is employed to collect data from multiple sensor nodes (SNs). Our objective is to maximize the minimum average data collection rate from all SNs subject to a prescribed reliability constraint for each SN by jointly optimizing the UAV communication scheduling and three-dimensional (3D) trajectory. Different from the existing works that assume the simplified line-of-sight (LoS) UAV-ground channels, we consider the more practically accurate angle-dependent Rician fading channels between the UAV and SNs with the Rician factors determined by the corresponding UAV-SN elevation angles. However, the formulated optimization problem is intractable due to the lack of a closed-form expression for a key parameter termed effective fading power that characterizes the achievable rate given the reliability requirement in terms of outage probability. To tackle this difficulty, we first approximate the parameter by a logistic (“S” shape) function with respect to the 3D UAV trajectory by using the data regression method. Then, the original problem is reformulated to an approximate form, which, however, is still challenging to solve due to its non-convexity. As such, we further propose an efficient algorithm to derive its suboptimal solution by using the block coordinate descent technique, which iteratively optimizes the communication scheduling, the UAV’s horizontal trajectory, and its vertical trajectory. The latter two subproblems are shown to be non-convex, while locally optimal solutions are obtained for them by using the successive convex approximation technique. Finally, extensive numerical results are provided to evaluate the performance of the proposed algorithm and draw new insights on the 3D UAV trajectory under the Rician fading as compared to conventional LoS channel models.

Federated Learning over Wireless Fading Channels

Abstract: We study federated machine learning at the wireless network edge, where limited power wireless devices, each with its own dataset, build a joint model with the help of a remote parameter server (PS). We consider a bandwidth-limited fading multiple access channel (MAC) from the wireless devices to the PS, and propose various techniques to implement distributed stochastic gradient descent (DSGD). We first propose a digital DSGD (D-DSGD) scheme, in which one device is selected opportunistically for transmission at each iteration based on the channel conditions; the scheduled device quantizes its gradient estimate to a finite number of bits imposed by the channel condition, and transmits these bits to the PS in a reliable manner. Next, motivated by the additive nature of the wireless MAC, we propose a novel analog communication scheme, referred to as the compressed analog DSGD (CA-DSGD), where the devices first sparsify their gradient estimates while accumulating error, and project the resultant sparse vector into a low-dimensional vector for bandwidth reduction. Numerical results show that D-DSGD outperforms other digital approaches in the literature; however, in general the proposed CA-DSGD algorithm converges faster than the D-DSGD scheme and other schemes in the literature, and reaches a higher level of accuracy. We have observed that the gap between the analog and digital schemes increases when the datasets of devices are not independent and identically distributed (i.i.d.). Furthermore, the performance of the CA-DSGD scheme is shown to be robust against imperfect channel state information (CSI) at the devices. Overall these results show clear advantages for the proposed analog over-the-air DSGD scheme, which suggests that learning and communication algorithms should be designed jointly to achieve the best end-to-end performance in machine learning applications at the wireless edge.

Deep Learning-Based Channel Estimation for Doubly Selective Fading Channels

Abstract: In this paper, online deep learning (DL)-based channel estimation algorithm for doubly selective fading channels is proposed by employing the deep neural network (DNN). With properly selected inputs, the DNN can not only exploit the features of channel variation from previous channel estimates but also extract additional features from pilots and received signals. Moreover, the DNN can take the advantages of the least squares estimation to further improve the performance of channel estimation. The DNN is first trained with simulated data in an off-line manner and then it could track the dynamic channel in an online manner. To reduce the performance degradation from random initialization, a pre-training approach is designed to refine the initial parameters of the DNN with several epochs of training. The proposed algorithm benefits from the excellent learning and generalization capability of DL and requires no prior knowledge about the channel statistics. Hence, it is more suitable for communication systems with modeling errors or non-stationary channels, such as high-mobility vehicular systems, underwater acoustic systems, and molecular communication systems. The numerical results show that the proposed DL-based algorithm outperforms the existing estimator in terms of both efficiency and robustness, especially when the channel statistics are time-varying.

UAV-Aided MIMO Communications for 5G Internet of Things

Abstract: The unmanned aerial vehicle (UAV) is a promising enabler of the Internet of Things (IoT) vision, due to its agile maneuverability. In this paper, we explore the potential gain of UAV-aided data collection in a generalized IoT scenario. Particularly, a composite channel model, including both large-scale and small-scale fading is used to depict typical propagation environments. Moreover, rigorous energy constraints are considered to characterize IoT devices as practically as possible. A multiantenna UAV is employed, which can communicate with a cluster of single-antenna IoT devices to form a virtual MIMO link. We formulate a whole-trajectory-oriented optimization problem, where the transmission duration and the transmit power of all devices are jointly designed to maximize the data collection efficiency for the whole flight. Different from previous studies, only the slowly varying large-scale channel state information is assumed available, to coincide with the fact that practically it is quite difficult to predictively acquire the random small-scale channel fading prior to the UAV flight. We propose an iterative scheme to overcome the nonconvexity of the formulated problem. The presented scheme can provide a significant performance gain over traditional schemes and converges quickly.

Unified Statistical Channel Model for Turbulence-Induced Fading in Underwater Wireless Optical Communication Systems

Abstract: A unified statistical model is proposed to characterize turbulence-induced fading in underwater wireless optical communication (UWOC) channels in the presence of air bubbles and temperature gradient for fresh and salty waters, based on experimental data. In this model, the channel irradiance fluctuations are characterized by the mixture exponential–generalized gamma (EGG) distribution. We use the expectation–maximization algorithm to obtain the maximum likelihood parameter estimation of the new model. Interestingly, the proposed model is shown to provide a perfect fit with the measured data under all channel conditions for both types of water. The major advantage of the new model is that it has a simple mathematical form making it attractive from a performance analysis point of view. Indeed, we show that the application of the EGG model leads to closed-form and analytically tractable expressions for key UWOC system performance metrics such as the outage probability, the average bit-error rate, and the ergodic capacity. To the best of our knowledge, this is the first-ever comprehensive channel model addressing the statistics of optical beam irradiance fluctuations in underwater wireless optical channels due to both air bubbles and temperature gradient.

Analytical Performance Assessment of THz Wireless Systems

Abstract: This paper is focused on providing the analytical framework for the quantification and evaluation of the joint effect of misalignment fading and hardware imperfections in the presence of multipath fading at terahertz (THz) wireless fiber extenders. In this context, we present the appropriate system model that incorporates the different operation, design, and environmental parameters. In more detail, it takes into account the transceivers antenna gains, the operation frequency, the distance between the transmitter (TX) and the receiver (RX), the environmental conditions, i.e., temperature, humidity, and pressure, the spatial jitter between the TX and RX antennas that results to antennas misalignment, the level of transceivers' hardware imperfections, and the stochastic characteristics of the wireless channel. Based on this model, we analyze and quantify the joint impact of misalignment and multipath fading by providing novel closed-form expressions for the probability and cumulative density functions of the composite channel. Moreover, we derive exact closed-form expressions for the outage probability for both cases of ideal and non-ideal radio frequency (RF) front-end. In addition, in order to quantify the detrimental effect of misalignment fading, we analytically obtain the outage probability in the absence of misalignment cases for both cases of ideal and non-ideal RF front-end. In addition, we extract the novel closed-form expressions for the ergodic capacity for the case of the ideal RF front-end and tight upper bounds for both the cases of ideal and non-ideal RF front-end. Finally, an insightful ergodic capacity ceiling for the non-ideal RF front-end case is provided.

Performance Analysis of UAV Relay Assisted IoT Communication Network Enhanced With Energy Harvesting

Abstract: This paper investigated the multiple unmanned aerial vehicle (UAV) relays' assisted network in the Internet of Things (IoT) systems enhanced with energy harvesting in order to overcome the large-scale fading between source and sink as well as achieve the green cooperative communications, where time switch (TS) and power splitting (PS) strategies were typically applied for UAV relays to implement energy harvesting transmission, which was also selected via signal to noise ratio (SNR) maximization criterion so that the terminal node can obtain the optimal signal. Meanwhile, it was worth noting that the terminal node may be disturbed by aggregated interference caused by dense network signaling interaction in the future 5G/B5G systems. Therefore, after TS and PS protocols designing and utilizing, the closed-form expressions of outage probability and bit error rate (BER) for UAV relay assisted IoT systems suffered from aggregated interference were derived in detail. In addition, the throughput and delay limited state of UAV relay assisted transmission were also analyzed thoroughly. The derivations and analysis results showed that the proposed multi-parameter joint optimization of transmitting power, scaling factor, and UAV relay selection could effectively improve the system throughput and reduce the system outage probability and BER. The simulation experiments verified the effectiveness of the proposed schemes and the correctness of theoretical analysis.

Two Years of Nonthermal Emission from the Binary Neutron Star Merger GW170817: Rapid Fading of the Jet Afterglow and First Constraints on the Kilonova Fastest Ejecta

Abstract: We present Chandra and VLA observations of GW170817 at ~521-743 days post merger, and a homogeneous analysis of the entire Chandra data set. We find that the late-time non-thermal emission follows the expected evolution from an off-axis relativistic jet, with a steep temporal decay $F_{\ u}\\propto t^{-1.95\\pm0.15}$ and a simple power-law spectrum $F_{\ u}\\propto \ u^{-0.575\\pm0.007}$. We present a new method to constrain the merger environment density based on diffuse X-ray emission from hot plasma in the host galaxy and we find $n\\le 9.6 \\times 10^{-3}\\,\\rm{cm^{-3}}$. This measurement is independent from inferences based on the jet afterglow modeling and allows us to partially solve for model degeneracies. The updated best-fitting model parameters with this density constraint are a fireball kinetic energy $E_0 = 1.5_{-1.1}^{+3.6}\\times 10^{49}\\,\\rm{erg}$ ($E_{iso}= 2.1_{-1.5}^{+6.4}\\times10^{52}\\, \\rm{erg}$), jet opening angle $\\theta_{0}= 5.9^{+1.0}_{-0.7}\\,\\rm{deg}$ with characteristic Lorentz factor $\\Gamma_j = 163_{-43}^{+23}$, expanding in a low-density medium with $n_0 = 2.5_{-1.9}^{+4.1} \\times 10^{-3}\\, \\rm{cm^{-3}}$ and viewed $\\theta_{obs} = 30.4^{+4.0}_{-3.4}\\, \\rm{deg}$ off-axis. The synchrotron emission originates from a power-law distribution of electrons with $p=2.15^{+0.01}_{-0.02}$. The shock microphysics parameters are constrained to $\\epsilon_{\\rm{e}} = 0.18_{-0.13}^{+0.30}$ and $\\epsilon_{\\rm{B}}=2.3_{-2.2}^{+16.0} \\times 10^{-3}$. We investigate the presence of X-ray flares and find no statistically significant evidence of $\\ge2.5\\sigma$ of temporal variability at any time. Finally, we use our observations to constrain the properties of synchrotron emission from the deceleration of the fastest kilonova ejecta with energy $E_k^{KN}\\propto (\\Gamma\\beta)^{-\\alpha}$ into the environment, finding that shallow stratification indexes $\\alpha\\le6$ are disfavored.

A Novel Communication Paradigm for High Capacity and Security via Programmable Indoor Wireless Environments in Next Generation Wireless Systems

Abstract: Wireless communication environments comprise passive objects that cause performance degradation and eavesdropping concerns due to anomalous scattering. This paper proposes a new paradigm, where scattering becomes software-defined and, subsequently, optimizable across wide frequency ranges. Through the proposed programmable wireless environments, the path loss, multi-path fading and interference effects can be controlled and mitigated. Moreover, the eavesdropping can be prevented via novel physical layer security capabilities. The core technology of this new paradigm is the concept of metasurfaces, which are planar intelligent structures whose effects on impinging electromagnetic waves are fully defined by their micro-structure. Their control over impinging waves has been demonstrated to span from 1 GHz to 10 THz. This paper contributes the software-programmable wireless environment, consisting of several HyperSurface tiles (programmable metasurfaces) controlled by a central server. HyperSurfaces are a novel class of metasurfaces whose structure and, hence, electromagnetic behavior can be altered and controlled via a software interface. Multiple networked tiles coat indoor objects, allowing fine-grained, customizable reflection, absorption or polarization overall. A central server calculates and deploys the optimal electromagnetic interaction per tile, to the benefit of communicating devices. Realistic simulations using full 3D ray-tracing demonstrate the groundbreaking performance and security potential of the proposed approach in 2.4 GHz and 60 GHz frequencies.

Secrecy Outage Analysis for Cooperative NOMA Systems With Relay Selection Schemes

Abstract: This paper considers the secrecy outage performance of a multiple-relay assisted non-orthogonal multiple access (NOMA) network over Nakagami- $m$ fading channels. Two time slots are utilized to transmit signals from the base station to destination. At the first time slot, the base station broadcasts the superposition signal of the two users to all decode-and-forward relays by message mapping strategy. Subsequently, the selected relay transmits superposition signal to the two users via power-domain NOMA technology. Three relay selection schemes, i.e., optimal single relay selection (OSRS) scheme, two-step single relay selection (TSRS) scheme, and optimal dual relay selection (ODRS) scheme are proposed and the secrecy outage performance is analyzed. As a benchmark, we also examine the secrecy outage performance of the NOMA systems with traditional multiple relay forwarding (TMRF) scheme in which all the relay that successfully decode signals from the source forward signals to the NOMA users with equal power. Considering the correlation between the secrecy capacity of two users and different secrecy requirement for two NOMA users, the analytical expressions for the security outage probability (SOP) of the proposed OSRS, TSRS, and ODRS schemes along with the TMRF scheme are derived and validated via simulations. To get more insights, we also derive the analytical expressions for the asymptotic SOP for all the schemes with fixed and dynamic power allocations. Furthermore, the secrecy diversity order (SDO) and secrecy array gain of cooperative NOMA systems are obtained. The results demonstrate that our proposed schemes can significantly enhance the secrecy performance compared to the TMRF scheme and that all the schemes with fixed power allocation obtain zero SDO and the OSRS scheme with dynamic power allocation obtains the same SDO as TMRF.

Coverage and Rate Analysis for Vertical Heterogeneous Networks (VHetNets)

Abstract: In this paper, we leverage concepts from stochastic geometry to investigate the downlink performance of a vertical heterogeneous network (VHetNet) comprising aerial base stations (ABSs) and terrestrial base stations (TBSs). We model the ABSs as a 2D Poisson point process (PPP) deployed at a particular altitude while the TBSs are modelled as a 2D PPP deployed on the ground. The proposed analytical framework adopts an appropriate air-to-ground (A2G) channel model that incorporates line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions. We begin the main technical part of the analysis by deriving analytical expressions for the distribution of the distances between a typical user and the closest LoS ABS, NLoS ABS, and TBS. After that, we derive expressions for the probabilities that a typical user is associated with a NLoS ABS, LoS ABS, or TBS. Under the assumption that A2G and terrestrial channels experience Nakagami- $m$ fading with different $m$ parameters, we derive an expression for the Laplace transform of interference power. Furthermore, we derive exact and approximate analytical expressions for the coverage probability and achievable rate. We show that these approximations match the simulations with negligible errors for small SINR thresholds and $m$ parameters of Nakagami- $m$ fading.

Performance of Cell-Free Massive MIMO With Rician Fading and Phase Shifts

Abstract: In this paper, we study the uplink (UL) and downlink (DL) spectral efficiency (SE) of a cell-free massive multiple-input-multiple-output (MIMO) system over Rician fading channels. The phase of the line-of-sight (LoS) path is modeled as a uniformly distributed random variable to take the phase-shifts due to mobility and phase noise into account. Considering the availability of prior information at the access points (APs), the phase-aware minimum mean square error (MMSE), non-aware linear MMSE (LMMSE), and least-square (LS) estimators are derived. The MMSE estimator requires perfectly estimated phase knowledge whereas the LMMSE and LS are derived without it. In the UL, a two-layer decoding method is investigated in order to mitigate both coherent and non-coherent interference. Closed-form UL SE expressions with phase-aware MMSE, LMMSE, and LS estimators are derived for maximum-ratio (MR) combining in the first layer and optimal large-scale fading decoding (LSFD) in the second layer. In the DL, two different transmission modes are studied: coherent and non-coherent. Closed-form DL SE expressions for both transmission modes with MR precoding are derived for the three estimators. Numerical results show that the LSFD improves the UL SE performance and coherent transmission mode performs much better than non-coherent transmission in the DL. Besides, the performance loss due to the lack of phase information depends on the pilot length and it is small when the pilot contamination is low.

Physical Layer Security of Hybrid Satellite-FSO Cooperative Systems

Abstract: In this paper, we study the physical layer secrecy performance of a hybrid satellite and free-space optical (FSO) cooperative system. The satellite links are assumed to follow the shadowed-Rician fading distribution, and the channel of the terrestrial link between the relay and destination is assumed to experience the gamma-gamma fading. For the FSO communications, the effects of different types of detection techniques (i.e., heterodyne detection and intensity modulation with direct detection) as well as the pointing error are considered. We derive exact analytical expressions for the average secrecy capacity and secrecy outage probability (SOP) for both cases of amplify-and-forward (AF) and decode-and-forward (DF) relaying. The asymptotic analysis for the SOP is also conducted to provide more insights on the impact of FSO and satellite channels on secrecy performance. It is found that with the AF with fixed gain scheme, the secrecy diversity order of the investigated system is only dependent on the channel characteristics of the FSO link and the FSO detection type, whereas the secrecy diversity is zero when the relay node employs DF or AF with variable-gain schemes.

Millimeter-Wave Base Station Diversity for 5G Coordinated Multipoint (CoMP) Applications

Abstract: Millimeter-wave (mmWave) will be used for fifth-generation (5G) wireless systems. While many recent empirical studies have presented propagation characteristics at mmWave bands, macrodiversity and Coordinated Multipoint (CoMP) have not been carefully studied. This paper describes a large-scale mmWave base station diversity measurement campaign at 73 GHz in an urban microcell (UMi) in downtown, Brooklyn, NY, USA, and provides the first detailed analysis of CoMP and macrodiversity performance based on extensive measurements. The research employed nine different base station locations in a 200 m by 200 m area and considered 36 individual transmitter–receiver combinations for extensive co- and cross-polarized varying directional beam channel impulse response measurements. From the measured data, hypothesis testing with cross-validation shows that large-scale shadow fading of directional path loss at an RX from multiple base stations can be modeled as being independent. To consider life-like human blockage in CoMP and macrodiversity analysis, simulated human blockage traces are superimposed on the directional measurements to quantitatively show that a user that is served by multiple base stations undergoes dramatically less outage in the presence of rapid fading events, compared to a single serving base station. Moreover, the base station diversity measurements are used to determine the effectiveness of downlink precoding techniques for mmWave CoMP. While results show that the coordination can improve network performance by suppressing interference when it exists, nearly half of the 680 000 directional CoMP measurements (~43%) result in no interference for either user, meaning that macrodiversity alone may offer sufficient link and capacity improvement and that CoMP may not be necessary for interference coordination at mmWave when narrow directional beams are used.

One-Bit OFDM Receivers via Deep Learning

Abstract: This paper develops novel deep learning-based architectures and design methodologies for an orthogonal frequency division multiplexing (OFDM) receiver under the constraint of one-bit complex quantization. Single bit quantization reduces greatly the complexity and power consumption but makes accurate channel estimation and data detection difficult. This is particularly true for multicarrier waveforms that have high peak-to-average power ratio in the time domain and fragile subcarrier orthogonality in the frequency domain. The severe distortion for one-bit quantization typically results in an error floor even at moderately low signal-to-noise-ratio (SNR) such as 5 dB. For channel estimation (using pilots), we design a novel generative supervised deep neural network that can be trained with a reasonable number of pilots. After channel estimation, a neural network-based receiver—specifically, an autoencoder—jointly learns a precoder and decoder for data symbol detection. Since quantization prevents end-to-end training, we propose a two-step sequential training policy for this model. With synthetic data, our deep learning-based channel estimation can outperform least squares channel estimation for unquantized (full-resolution) OFDM at average SNRs up to 14 dB. For data detection, our proposed design achieves lower bit error rate (BER) in fading than unquantized OFDM at average SNRs up to 10 dB.

Error Probability Analysis of Non-Orthogonal Multiple Access Over Nakagami- $m$ Fading Channels

Abstract: Non-orthogonal multiple access (NOMA) is currently considered as a promising technology for the next-generation wireless networks. In this paper, the error rate performance of NOMA systems is investigated over Nakagami- $m$ fading channels, while considering imperfect successive interference cancelation. In particular, this paper focuses on the pairwise error probability (PEP) analysis, where exact PEP expressions are derived to characterize the performance of all users under different fading conditions. The obtained PEP expressions are then used to derive an exact union bound on the bit error rate (BER). Through the derived PEP expressions, the asymptotic PEP analysis is presented to investigate the maximum achievable diversity gain of NOMA users. Moreover, using the derived BER bound, the power allocation problem for all users in NOMA systems is considered under average power and users BER constraints, which allows realizing the full potential of NOMA. Monte Carlo simulation and numerical results are presented to corroborate the derived analytical expressions and give valuable insights into the error rate performance of each user and the achievable diversity gain.

Large-Scale-Fading Decoding in Cellular Massive MIMO Systems With Spatially Correlated Channels

Abstract: Massive multiple-input–multiple-output (MIMO) systems can suffer from coherent intercell interference due to the phenomenon of pilot contamination. This paper investigates a two-layer decoding method that mitigates both coherent and non-coherent interference in multi-cell Massive MIMO. To this end, each base station (BS) first estimates the channels to intra-cell users using either minimum mean-squared error (MMSE) or element-wise MMSE estimation based on uplink pilots. The estimates are used for local decoding on each BS followed by a second decoding layer where the BSs cooperate to mitigate inter-cell interference. An uplink achievable spectral efficiency (SE) expression is computed for arbitrary two-layer decoding schemes. A closed form expression is then obtained for correlated Rayleigh fading, maximum-ratio combining, and the proposed large-scale fading decoding (LSFD) in the second layer. We also formulate a sum SE maximization problem with both the data power and LSFD vectors as optimization variables. Since this is an NP-hard problem, we develop a low-complexity algorithm based on the weighted MMSE approach to obtain a local optimum. The numerical results show that both data power control and LSFD improve the sum SE performance over single-layer decoding multi-cell Massive MIMO systems.

Non-Bayesian Activity Detection, Large-Scale Fading Coefficient Estimation, and Unsourced Random Access with a Massive MIMO Receiver

Abstract: In this paper, we study the problem of user activity detection and large-scale fading coefficient estimation in a random access wireless uplink with a massive MIMO base station with a large number $M$ of antennas and a large number of wireless single-antenna devices (users). We consider a block fading channel model where the $M$-dimensional channel vector of each user remains constant over a coherence block containing $L$ signal dimensions in time-frequency. In the considered setting, the number of potential users $K_\text{tot}$ is much larger than $L$ but at each time slot only $K_a<

On the Performance Gain of NOMA over OMA in Uplink Communication Systems

Abstract: In this paper, we investigate and reveal the ergodic sum-rate gain (ESG) of non-orthogonal multiple access (NOMA) over orthogonal multiple access (OMA) in uplink cellular communication systems. A base station equipped with a single-antenna, with multiple antennas, and with massive antenna arrays is considered both in single-cell and multi-cell deployments. In particular, in single-antenna systems, we identify two types of gains brought about by NOMA: 1) a large-scale near-far gain arising from the distance discrepancy between the base station and users; 2) a small-scale fading gain originating from the multipath channel fading. Furthermore, we reveal that the large-scale near-far gain increases with the normalized cell size, while the small-scale fading gain is a constant, given by $\gamma$ = 0.57721 nat/s/Hz, in Rayleigh fading channels. When extending single-antenna NOMA to $M$-antenna NOMA, we prove that both the large-scale near-far gain and small-scale fading gain achieved by single-antenna NOMA can be increased by a factor of $M$ for a large number of users. Moreover, given a massive antenna array at the base station and considering a fixed ratio between the number of antennas, $M$, and the number of users, $K$, the ESG of NOMA over OMA increases linearly with both $M$ and $K$. We then further extend the analysis to a multi-cell scenario. Compared to the single-cell case, the ESG in multi-cell systems degrades as NOMA faces more severe inter-cell interference due to the non-orthogonal transmissions. Besides, we unveil that a large cell size is always beneficial to the ergodic sum-rate performance of NOMA in both single-cell and multi-cell systems. Numerical results verify the accuracy of the analytical results derived and confirm the insights revealed about the ESG of NOMA over OMA in different scenarios.

Secrecy Analysis for Cooperative NOMA Networks With Multi-Antenna Full-Duplex Relay

Abstract: In a downlink non-orthogonal multiple access (NOMA) system, the reliable transmission of cell-edge users cannot be guaranteed due to severe channel fading. On the other hand, the presence of eavesdroppers can severely threaten the secure transmission due to the open nature of wireless channel. Thus, a two-user NOMA system assisted by a multi-antenna decode-and-forward relay is considered in this paper, and a two-stage jamming scheme, full-duplex-jamming (FDJam), is proposed to ensure the secure transmission of NOMA users. In the FDJam scheme, using full-duplex, the relay transmits the jamming signal to the eavesdropper while receiving confidential messages in the first stage, and the base station generates the jamming signal in the second stage. Furthermore, we eliminate the self-interference and the jamming signal at the relay and the legitimate node, respectively, through relay beamforming. To measure the secrecy performance, analytical expressions for secrecy outage probability (SOP) are derived for both the cell-center and cell-edge users, and the asymptotic SOP analysis at high transmit power is presented as well. Moreover, two benchmark schemes, half-duplex-jamming and full-duplex-no-jamming, are also considered. Simulation results are presented to show the accuracy of the analytical expressions and the effectiveness of the proposed scheme.

Neural Network-Based Fading Channel Prediction: A Comprehensive Overview

Abstract: By adapting transmission parameters such as the constellation size, coding rate, and transmit power to instantaneous channel conditions, adaptive wireless communications can potentially achieve great performance. To realize this potential, accurate channel state information (CSI) is required at the transmitter. However, unless the mobile speed is very low, the obtained CSI quickly becomes outdated due to the rapid channel variation caused by multi-path fading. Since outdated CSI has a severely negative impact on a wide variety of adaptive transmission systems, prediction of future channel samples is of great importance. The traditional stochastic methods, modeling a time-varying channel as an autoregressive process or as a set of propagation parameters, suffer from marginal prediction accuracy or unaffordable complexity. Taking advantage of its capability on time-series prediction, applying a recurrent neural network (RNN) to conduct channel prediction gained much attention from both academia and industry recently. The aim of this article is to provide a comprehensive overview so as to shed light on the state of the art in this field. Starting from a review on two model-based approaches, the basic structure of a recurrent neural network, its training method, RNN-based predictors, and a prediction-aided system, are presented. Moreover, the complexity and performance of predictors are comparatively illustrated by numerical results.

An Empirical Air-to-Ground Channel Model Based on Passive Measurements in LTE

Abstract: In this paper, a recently conducted measurement campaign for unmanned-aerial-vehicle channels is introduced. The downlink signals of an in-service long-time-evolution network, which is deployed in a suburban scenario were acquired. Five horizontal and five vertical flight routes were considered. The channel impulse responses (CIRs) are extracted from the received data by exploiting the cell-specific signals, and the underlying physical propagation mechanisms are interpreted by exploiting the propagation graph modeling approach. Based on the CIRs, the parameters of multipath components are estimated by using a high-resolution algorithm derived according to the space-alternating generalized expectation-maximization (SAGE) principle. Based on the SAGE results, channel characteristics including the path loss, shadow fading, fast fading, delay spread, and Doppler frequency spread are thoroughly investigated for different heights and horizontal distances, which constitute a stochastic model.

Physical Layer Security of Multiuser Multirelay Hybrid Satellite-Terrestrial Relay Networks

Abstract: In this paper, we study the physical layer security of a downlink hybrid satellite-terrestrial relay network (HSTRN), where a multi-antenna satellite communicates with multiple terrestrial destinations via multiple cooperative relays in the presence of multiple eavesdroppers. We investigate the secrecy performance of the considered HSTRN by employing two classic cooperative protocols, namely, amplify-and-forward and decode-and-forward for two intercepting scenarios at eavesdroppers, i.e., non-colluding and colluding eavesdroppers. For both these cooperative protocols, we present opportunistic user-relay selection criteria and then derive novel and accurate expressions of the secrecy outage probability (SOP) by adopting Shadowed-Rician fading for satellite links and Nakagami- m fading for terrestrial links. Furthermore, in order to extract useful insights on the system design, we obtain the tractable asymptotic SOP expressions at high signal-to-noise ratio regime. Finally, we provide the numerical and simulation results to validate our analysis and highlight the impact of various channel/system parameters on the secrecy performance of the considered HSTRN.

Exact BER Performance Analysis for Downlink NOMA Systems Over Nakagami-m Fading Channels

Abstract: In this paper, the performance of a promising technology for the next generation wireless communications, non-orthogonal multiple access (NOMA), is investigated. In particular, the bit error rate (BER) performance of downlink NOMA systems over Nakagami-m flat fading channels, is presented. Under various conditions and scenarios, the exact BER of downlink NOMA systems considering successive interference cancellation (SIC) is derived. The transmitted signals are randomly generated from quadrature phase shift keying (QPSK) and two NOMA systems are considered; two users' and three users' systems. The obtained BER expressions are then used to evaluate the optimum power allocation for two different objectives, achieving fairness and minimizing average BER. The two objectives can be used in a variety of applications such as satellite applications with constrained transmitted power. Numerical results and Monte Carlo simulations perfectly match with the derived BER analytical results and provide valuable insight into the advantages of optimum power allocation which show the full potential of downlink NOMA systems.

3D Trajectory Optimization in Rician Fading for UAV-Enabled Data Harvesting

Abstract: In this paper, we consider a UAV-enabled WSN where a flying UAV is employed to collect data from multiple sensor nodes (SNs). Our objective is to maximize the minimum average data collection rate from all SNs subject to a prescribed reliability constraint for each SN by jointly optimizing the UAV communication scheduling and three-dimensional (3D) trajectory. Different from the existing works that assume the simplified line-of-sight (LoS) UAV-ground channels, we consider the more practically accurate angle-dependent Rician fading channels between the UAV and SNs with the Rician factors determined by the corresponding UAV-SN elevation angles. However, the formulated optimization problem is intractable due to the lack of a closed-form expression for a key parameter termed effective fading power that characterizes the achievable rate given the reliability requirement in terms of outage probability. To tackle this difficulty, we first approximate the parameter by a logistic ('S' shape) function with respect to the 3D UAV trajectory by using the data regression method. Then the original problem is reformulated to an approximate form, which, however, is still challenging to solve due to its non-convexity. As such, we further propose an efficient algorithm to derive its suboptimal solution by using the block coordinate descent technique, which iteratively optimizes the communication scheduling, the UAV's horizontal trajectory, and its vertical trajectory. The latter two subproblems are shown to be non-convex, while locally optimal solutions are obtained for them by using the successive convex approximation technique. Last, extensive numerical results are provided to evaluate the performance of the proposed algorithm and draw new insights on the 3D UAV trajectory under the Rician fading as compared to conventional LoS channel models.

Reduced-Dimension Design of MIMO Over-the-Air Computing for Data Aggregation in Clustered IoT Networks

Abstract: One basic operation of Internet-of-Things (IoT) networks is to acquire a function of distributed data collected from sensors over wireless channels, called wireless data aggregation (WDA). In the presence of dense sensors, low-latency WDA poses a design challenge for high-mobility or mission critical IoT applications. A promising solution is a low-latency multi-access scheme, called over-the-air computing (AirComp), that supports simultaneous transmission such that an access point (AP) can estimate and receive a summation-form function of the distributed sensing data by exploiting the waveform-superposition property of a multi-access channel. In this work, we propose a multiple-input-multiple-output (MIMO) AirComp framework for an IoT network with clustered multi-antenna sensors and an AP with large receive arrays. The framework supports low-complexity and low-latency AirComp of a vector-valued function . The contributions of this work are two-fold. Define the AirComp error as the error in the functional value received at AP due to channel noise. First, under the criterion of minimum error, the optimal receive beamformer at the AP, called decomposed aggregation beamformer (DAB), is shown to have a decomposed architecture: the inner component focuses on channel-dimension reduction and the outer component focuses on joint equalization of the resultant low-dimensional small-scale fading channels. In addition, an algorithm is designed to adjust the ranks of individual components of the DAB for a further performance improvement. Second, to provision DAB with the required channel state information (CSI), a low-latency channel feedback scheme is proposed by intelligently leveraging the AirComp principle to support simultaneous channel feedback by sensors. The proposed framework is shown by simulation to substantially reduce AirComp error compared with the existing design without considering channel structures.

Online Extreme Learning Machine-Based Channel Estimation and Equalization for OFDM Systems

Abstract: Machine learning-based channel estimation and equalization methods may improve the robustness and bit error rate (BER) performance of communication systems. However, the implementation of these methods has been blocked by some limitations, mainly including channel model-based offline training and high-computational complexity for training deep neural network (DNN). To overcome those limitations, we propose an online fully complex extreme learning machine (C-ELM)-based channel estimation and equalization scheme with a single hidden layer feedforward network (SLFN) for orthogonal frequency-division multiplexing (OFDM) systems against fading channels and the nonlinear distortion resulting from an high-power amplifier (HPA). Computer simulations show that the proposed scheme can acquire the information of channels accurately and has the ability to resist nonlinear distortion and fading without pre-training and feedback link between receiver and transmitter. Furthermore, the robustness of the proposed scheme is well investigated by extensive simulations in various fading channels, and its excellent generalization ability is also discussed and compared with the DNN.

On the Performance of LMS Communication With Hardware Impairments and Interference

Abstract: This paper investigates the performance of a dual-hop decode-and-forward (DF) relaying-aided land mobile satellite communication over Shadowed-Rician (SR) fading channels. A practical model for the satellite relaying system is first developed, where the impacts of satellite multi-beam antenna, radio propagation loss, and random shadowing are taken into account. Next, by assuming that the multi-beam satellite suffers from hardware impairments (HIs) and is perturbed by interference signals, we derive an equivalent end-to-end signal-to-interference-plus-noise-and-distortion-ratio of the system, and justify that the maximum ratio transmission at the source and the maximum ratio combining at the destination are the optimal transmit-receive beamforming schemes on the proposed HIs model. Then, closed-form expressions for the probability density function (PDF) of the sum of independent and identically distributed (i.i.d) squared SR random variables in the case of integer and rational Nakagami- $m$ fading parameters are derived. Based on the derived PDF, new analytical expressions for the outage probability (OP) and average throughput are obtained in the presence of HIs and interference. Moreover, the asymptotic OP and average throughput at high signal-to-noise ratio are investigated to reveal the achievable diversity order of the system. Finally, Monte Carlo simulation results are provided to corroborate the analytical results.