Correlated Shadow Fading Research Articles

Experimental study on low-altitude UAV-to-ground propagation characteristics in campus environment

In recent years, unmanned aerial vehicle (UAV)-to-ground wireless channels have become focused areas. Thus a better understanding of UAV-to-ground wireless channel characterizations is critical. The large-scale fading characterization is widely analyzed in many research works. Nevertheless, the small-scale fading characterization of low-altitude UAV-to-ground propagation has not been fully modeled. In this work, the close-in (CI) free space and floating-intercept (FI) path loss models are implemented to characterize large-scale fading. The large-scale parameters, i.e., path loss exponent (PLE), shadow fading standard deviation (STD), and shadow fading correlation coefficient, are carefully extracted, analyzed, and compared. Regarding small-scale fading, Akaike’s Information Criteria (AIC) is used to find the most suitable fading distribution. In addition, fade depth (FD), level crossing rate (LCR), and average fade duration (AFD) are also calculated. The quantitative results and comparisons show a noticeable difference with ground propagation. The parameterized models are helpful for further understanding and deployment of UAV-to-ground wireless communication systems.

On channel estimation in cell‐free massive MIMO for spatially correlated channels with correlated shadowing under Rician fading

SummaryThis work addresses channel estimation (CE) in the uplink phase for a cell‐free massive multiple‐input multiple‐output system operating under the time division duplex protocol. Considering that, channels are spatially correlated under the Rician fading model, where the investigated model is composed of two components: deterministic and nondeterministic, with the deterministic component describing the line‐of‐sight paths and the nondeterministic component describing the non‐line‐of‐sight paths. Additionally, we dealt with correlated shadow fading that represents the most realistic situation. On the other hand, this work introduces a dynamic cooperation cluster framework in which the user is not served with the whole network ( i.e., all access points [APs]) but only the APs that present the best channel conditions regarding that user. In other words, this work proposes partial CE for each user because only APs with the best channel conditions are allowed to compute channel estimates. Consequently, we proposed partial channel estimators that perform the CE process with low complexity, namely, a partial minimum mean square error estimator and a partial element‐wise minimum mean square error estimator. In addition, a simple pilot assignment technique is proposed in order to reduce interference signals so that each user experiences low interference from other users. Furthermore, the computational complexity required by each estimator is derived, where it is represented by the number of complex multiplications that each estimator requires in each consistency block. Theoretical and simulated results are provided, where the performance of each estimator is evaluated and analyzed using the normalized mean‐square error metric.

Radio Environment Map Construction Based on Spatial Statistics and Bayesian Hierarchical Model

Constructing an infrastructure for spectrum sensing and spectrum sharing on the cloud is a promising technology. In this paper, we propose an analysis framework based on spectrum data gathered by distributed sensors to construct the radio environment map (REM). To the best of our knowledge, this is the first attempt to leverage the power of Bayesian analysis and Markov chain Monte Carlo (MCMC) in REM. Specifically, a three-stage Bayesian hierarchical model (BHM) is established to imitate the spectrum data generation process under spatially correlated shadow fading. Parameters of BHM are estimated with the MCMC algorithm from data collected by the sensor network. Then, we address the space-dimension spectrum inference problem with the aim to interpolate the signal strength where there is no sensor node by composition sampling. At each point in the area of interest, the posterior predictive distribution of the receive signal strength can be obtained by kernel density smoother. We make spatial inference under two sensor location modes (square lattice located sensors and randomly located sensors) and two scenarios (with and without the information about the signal source), respectively. Simulation results demonstrate that although the randomly located sensors mode is suitable for parameter estimation, the inference performance is not better than the square lattice located sensors mode. Quantitative analysis of the inference performance confirms that the effectiveness of our data analysis framework is compelling.

Wireless Signal Propagation Prediction Based on Computer Vision Sensing Technology for Forestry Security Monitoring.

In this paper, Computer Vision (CV) sensing technology based on Convolutional Neural Network (CNN) is introduced to process topographic maps for predicting wireless signal propagation models, which are applied in the field of forestry security monitoring. In this way, the terrain-related radio propagation characteristic including diffraction loss and shadow fading correlation distance can be predicted or extracted accurately and efficiently. Two data sets are generated for the two prediction tasks, respectively, and are used to train the CNN. To enhance the efficiency for the CNN to predict diffraction losses, multiple output values for different locations on the map are obtained in parallel by the CNN to greatly boost the calculation speed. The proposed scheme achieved a good performance in terms of prediction accuracy and efficiency. For the diffraction loss prediction task, 50% of the normalized prediction error was less than 0.518%, and 95% of the normalized prediction error was less than 8.238%. For the correlation distance extraction task, 50% of the normalized prediction error was less than 1.747%, and 95% of the normalized prediction error was less than 6.423%. Moreover, diffraction losses at 100 positions were predicted simultaneously in one run of CNN under the settings in this paper, for which the processing time of one map is about 6.28 ms, and the average processing time of one location point can be as low as 62.8 us. This paper shows that our proposed CV sensing technology is more efficient in processing geographic information in the target area. Combining a convolutional neural network to realize the close coupling of a prediction model and geographic information, it improves the efficiency and accuracy of prediction.

A Green Downlink Power Allocation Scheme for Cell-Free Massive MIMO Systems

In this article, we consider the problem of downlink power allocation in a cell-free massive multiple-input multiple-output (m-MIMO) communication system under spectral efficiency (SE) constraints for the users. From the perspective of green communications, the power allocation is formulated as an optimization problem where the aim is to maximize the sum SE as the objective function, while limiting the transmission power of each access point (AP) and imposing lower and upper bounds on the achievable SEs of different users. The resulting optimization problem is non-convex since the objective function is non-concave and the upper bounding constraints on user SEs are non-convex. To address these difficulties, we first derive a closed-form lower bound on the sum SE (objective function) and prove that it is a quasi-concave function. Then, we relax the unwieldy upper bounding constraints on the user SEs by replacing them with linear functions, which renders the optimization problem convex. An optimal solution to the relaxed problem is finally obtained by solving a sequence of convex feasibility programs. We evaluate the performance of the proposed downlink power allocation scheme through Monte Carlo simulations under the uncorrelated and correlated shadow fading models. The results show that for both models, the proposed algorithm can lead to a significant reduction in total power consumption compared to a benchmark approach, while accurately allocating power to the APs so that the SE constraint of each user is satisfied within the imposed bounds.

Joint Power Allocation Scheme for Distributed Secure Spatial Modulation in High-Speed Railway

In this article, a distributed secure spatial modulation (DSSM) is proposed to improve the secrecy performance of transmission system in high-speed railway (HSR) scenario. N-remote antenna units are connected with central processing unit to build a cooperative DSSM system. Considering the influences of correlated shadow fading, spatial-temporal correlated fading and Doppler shift, the expressions of secrecy capacity (SC) and bit error rate (BER) are derived. Based on the design of artificial noise, two power allocation algorithms are jointly proposed as security schemes in HSR scenario. At first, a joint optimal secrecy capacity algorithm (JOSCA) is proposed to maximize SC. Another Lagrange constraint extreme algorithm (LCEA) is proposed to maximize inhibition of eavesdropper's BER performance. The numerical results show that compared to conventional spatial modulation, the proposed DSSM with JOSCA brings higher SC and keeps a high value of cooperative SC as the advance of train. Moreover, the proposed LCEA brings better BER performance of legitimate receiver while greatly limiting that of eavesdropper. In terms of the tradeoff between SC and BER, the DSSM gets better balance at the viaduct of high-speed railway scenario.

Large-Scale Fading Characteristics and Models for Vehicle-to-Pedestrian Channel at 5-GHz

Vehicle-to-Pedestrian (V2P) communication is essential to enable a reliable collision avoidance system for vehicles and vulnerable road users (VRUs). In order to develop a reliable communication system accurate channel models are required. However, no dedicated channel model for V2P communication in critical scenarios has been proposed so far. To develop a channel model, we conducted wideband channel sounding measurements considering a typical collision scenario between a vehicle and a pedestrian in line-of-sight (LoS) and non-LoS (NLoS) situations. The wideband measurement data is used to study the characteristics of large-scale or shadow fading, pathloss and diffraction loss. The spatial correlation of shadow fading is examined and different models are proposed. For NLoS scenario, a 3D scenario model is designed to simulate the diffraction at a row of parked vehicles. The diffraction loss due to parked vehicles is analyzed and modeled based on multiple knife-edge diffraction model combined with a two-ray pathloss model. The results show that the proposed diffraction model provides a good match to the measured pathloss.

Mobility tracking in unknown correlated shadow fading wireless networks

In this study, the authors propose an algorithm for tracking of a mobile user and jointly estimate fading parameters of the wireless environment, e.g. the path-loss exponent. The authors use a state space dynamic model for the mobility of the device with a discrete Markov process for its acceleration and exploit only the received signal strength indication (RSSI) from multiple anchor points. They use particle filters for tracking, a maximum likelihood estimator for the path-loss exponent, and a Kalman filter for estimating of the shadow fading. The sequence of estimation procedures is done in one time step. The simulation comparison with similar methods illustrates a significant performance improvement by estimating the unknown fading parameters of RSSI.

Probability of outage due to self-interference in indoor wireless environments

In this letter, we derive a mathematically tractable expression for the outage probability in the presence of self-interference over indoor wireless environments modeled through the recently proposed joint fading and two-path shadowing channel model. The effect of self-interference is studied under two different conditions encountered in single-frequency network architectures: 1) long propagation delay and correlated shadow fading in a densely deployed single access point to single mobile user communication scenario and 2) interference between radiated and received signal power in full-duplex radios equipped with multiple antennas both for transmission and reception of data. The analysis is validated numerically both for single-channel and multiple-channel receivers.

Characterization of Quasi-Stationarity Regions for Vehicle-to-Vehicle Radio Channels

We analyze the nonwide-sense-stationarity (non-WSS) of vehicle-to-vehicle (V2V) radio channels using three metrics: 1) the correlation matrix distance (CMD); 2) the wideband spectral divergence (SD); and 3) the shadow fading correlation. The analysis is based on measurements carried out at 5.3 GHz using a ${\mathbf 30} \times {\mathbf 4}$ MIMO system in suburban, urban, and underground parking areas. Several factors such as the existence of a line-of-sight (LOS), the speed of cars, and the antenna array size and configuration are considered in the analysis of non-WSS. It is found that quasi-stationarity region ranges from 3 to 80 m in different V2V scenarios, and is strongly affected by the above factors. Based on the comparison of the equivalent quasi-stationarity region size estimated by the three metrics, it is suggested to use SD and shadowing correlation metrics for systems with small electrical array apertures and to use CMD metric only for arrays with large electrical apertures.

Shadow Fading Correlation Based on the Additive Model

In mobile wireless communications, the received signal power is modeled as a combination of path loss, shadow fading and small-scale fading. In previous studies, the lognormality of the shadow fading is generally explained as a multiplicative model. In [1] the author proposed an additive model as a physical basis for shadowing within an "extended local area", where the shadowing can be written as the logarithm of the power of the plane waves. In this paper, based on the additive model, we focus on the correlation of the shadow fading in two extended local areas, the analytical expressions are presented for the correlation coefficient (ρ) of the shadow fading.

Shadow Fading Correlation in High-Speed Railway Environments

In the design of diversity and fast handover schemes for high-speed railways (HSRs), a better understanding of the shadow fading correlation properties is required. In this paper, both auto- and cross-correlation models of shadow fading in HSR environments are proposed based on a large set of measurements with 6146 cells in China. The measurements are conducted using a practically deployed and operative Global System for Mobile Communications-Railway (GSM-R) system at 930 MHz to reflect the real conditions of shadow fading in HSR environments. Seven typical HSR environments and the realistic base station (BS) parameters are considered in the analysis. It is found that the decorrelation distance is correlated with the standard deviation (STD) of shadow fading and is generally larger than 100 m in most HSR environments. The cross correlation of shadow fading between two neighboring BSs depends on the BS height and the tilt angle of the antenna, and a heuristic truncated Gaussian distribution model of cross correlation is proposed. The model is validated by the measurements conducted in another operative HSR line. The results can be used to exploit the simulation algorithms to analyze the signal-to-interference-ratio (SIR) properties and to develop new and better handover schemes for HSRs.

Robust Tracking of Small-Scale Mobile Primary User in Cognitive Radio Networks

In cognitive radio networks (CRNs), secondary users must be able to accurately and reliably track the location of small-scale mobile primary users/devices (e.g., wireless microphones) in order to efficiently utilize spatial spectrum opportunities, while protecting primary communications. However, accurate tracking of the location of mobile primary users is difficult due mainly to the CR-unique constraint, i.e., localization must rely solely on reported sensing results (i.e., measured primary signal strengths), which can easily be compromised by malicious sensors (or attackers). To cope with this challenge, we propose a new framework, called Sequential mOnte carLo combIned with shadow-faDing estimation (SOLID), for accurate, attack/fault-tolerant tracking of small-scale mobile primary users. The key idea underlying SOLID is to exploit the temporal shadow fading correlation in sensing results induced by the primary user's mobility. Specifically, SOLID augments conventional Sequential Monte Carlo (SMC)-based target tracking with shadow-fading estimation. By examining the shadow-fading gain between the primary transmitter and CRs/sensors, SOLID 1) significantly improves the accuracy of primary tracking regardless of the presence/absence of attack, and 2) successfully masks the abnormal sensing reports due to sensor faults or attacks, preserving localization accuracy and improving spatial spectrum efficiency. Our extensive evaluation in realistic wireless fading environments shows that SOLID lowers localization error by up to 88 percent in the absence of attacks, and 89 percent in the presence of the challenging slow-poisoning” attack, compared to the conventional SMC-based tracking.

Secure Cooperative Sensing in IEEE 802.22 WRANs Using Shadow Fading Correlation

Cooperative (or distributed) sensing has been recognized as a viable means to enhance the incumbent signal detection by exploiting the diversity of sensors. However, it is challenging to secure such distributed sensing due mainly to the unique features of dynamic spectrum access networks-openness of low-layer protocol stacks in software-defined radio devices and the absence of interactions/coordination between primary and secondary devices. To meet this challenge, we propose an attack-tolerant distributed sensing protocol (ADSP) for DTV signal detection in IEEE 802.22 WRANs, under which sensors in close proximity are grouped as a cluster, and sensors within a cluster cooperate to safeguard the integrity of sensing. The heart of ADSP is a novel filter based on shadow-fading correlation, by which the fusion center cross-validates reports from the sensors to identify and penalize abnormal sensing reports. By realizing this correlation filter, ADSP significantly reduces the impact of an attack on the performance of distributed sensing, while incurring minimal processing and communication overheads. ADSP also guarantees the detectability requirements of 802.22 to be met even with the presence of sensing report manipulation attacks by scheduling sensing within the framework of sequential hypothesis testing. The efficacy of ADSP is validated on a realistic 2D shadow-fading field. Our extensive simulation-based study shows that ADSP reduces the false-alarm rate by 99.2 percent while achieving 97.4 percent of maximum achievable detection rate, and meets the detection requirements of IEEE 802.22 in various attack scenarios.

Leakage-based precoding with power allocation for multicellular multiuser MIMO downlink

To enhance the sum rate of maximising the signal-to-leakage-and-noise ratio (Max-SLNR), a signal-to-interference-and-noise ratio (SINR) based power allocation (PA) is proposed for Max-SLNR per substream of users in multiuser MIMO systems. Simulation shows that the rate gain of SINR based PA over equal PA is about 0.5bit/s/Hz per user at low and medium signal-to-noise ratios in the presence of shadow fading and antenna correlation.

DECODE: Exploiting Shadow Fading to DEtect COMoving Wireless DEvices

We present the DECODE technique to determine whether a set of transmitters are comoving, i.e., moving together in close proximity. Comovement information can find use in applications ranging from inventory tracking to social network sensing and to optimizing mobile device localization. The positioning errors from indoor RSS-based localization systems tend to be too large, making it difficult to detect whether two devices are moving together based on the interdevice distances. DECODE achieves accurate comovement detection by exploiting the correlations in positioning errors over time. DECODE can not only be implemented in the position space but also in the signal space where a correlation in shadow fading due to objects blocking the path between the transmitter and receiver exists. This technique requires no change in or cooperation from the tracked devices other than sporadic transmission of packets. Using experiments from an office environment, we show that DECODE can achieve near-perfect comovement detection at walking speed mobility using correlation coefficients computed over approximately 60-second time intervals. We further show that DECODE is generic and could accomplish detection for mixed mobile transmitters of different technologies (IEEE 802.11b/g and IEEE 802.15.4), and our results are not very sensitive to the frequency at which transmitters communicate.

Beamforming and power control for interference reduction in wireless communications using particle swarm optimization

We consider joint beamforming (BF) and power control to minimize cochannel interference in a CDMA wireless uplink, where an antenna array of different geometries receives from single-antenna wireless users. The problem is to find the optimum beamforming weight vector and the minimum transmission power of each terminal such that the quality of service (QoS) requirements of the users are fulfilled and the so-called near–far effect is alleviated. This will increase the channel capacity and further prolongs the battery lives of the mobile terminals. The particle swarm optimization method is used to determine the optimum beamforming weight vector and the minimum transmission power of each terminal. Uniform linear and circular array geometries are considered, with and without correlated shadow fading. Simulation results illustrate the advantage of using BF jointly with power control and the robustness of the optimization technique used.

Analysis of Correlated Shadow Fading in Dual-Link Indoor Radio Wave Propagation

In this letter, we analyze the results of dynamic, double-directional, dual-link channel sounding, where the measurement system consists of one transmitter moving along a continuous route and two fixed receivers. The results show that despite a large terminal separation at one side of the link, the fading behaviors of the two links can be strongly correlated. In this special case, correlated shadow fading was found to be caused by the similar initial signal directions at the transmitter side and due to pillars that shadowed the signals at that side of the link. The fading phenomenon has been analyzed by dividing the received power into groups of multipath components revealing similar propagation mechanisms and by investigating the properties of those multipath components that are most severely shadowed by the pillars.

Distributed measurements for estimating and updating cellular system performance

We investigate the use of distributed measurements for estimating and updating the performance of a cellular system. Specifically, we discuss the number and placement of sensors in a given cell for estimating its signal coverage. Here, an "outage" is said to occur at a location if a mobile receiver there has inadequate signal-to-noise ratio (SNR -based outage) or, using another criterion, inadequate signal-to-interference ratio (SIR- based outage); and the "outage probability" is the fraction of the cell area over which outage occurs. A design goal is to improve measurement efficiency (i.e., minimizing the required number of measurement sensors) while accurately estimating the outage probability and mapping the coverage holes. The investigation uses a generic path loss model incorporating distance effects and spatially correlated shadow fading. Our emphasis is on the performance prediction accuracy of the sensor network, rather than on cellular system analysis per se. Through analysis and simulation, we assess several approaches to estimating the outage probability. Applying the principle of importance sampling to the sensor placement, we show that a cell outage probability of P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sub> can be accurately estimated using ~ 10/P <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">o</sub> power-measuring sensors distributed in a random uniform way over the area with base-sensor distances from 50% to 100% of the cell radius. This result applies to both SNR-based and SIR-based outage estimation for both indoor and outdoor environments.

Sensor-assisted localization in cellular systems

We investigate the use of an auxiliary network of sensors to locate mobiles in a cellular system, based on the received signal strength at the sensor receivers from a mobile's transmission. The investigation uses a generic path loss model incorporating distance effects and spatially correlated shadow fading. We describe four simple localization schemes and show that they all meet E-911 requirements in most environments. Performance can be further improved by implementing the MMSE algorithm, which ideally reaches the Cramer-Rao bound. We compare the MMSE algorithm and the four simple schemes when the model parameters are estimated via inter-sensor measurements.