Fading Channel Model Research Articles

PERFORMANCE OF NETWORK CODING SCHEMES FOR 5G SYSTEM

Huge data rates have been provided by 5G wireless communication systems using millimeter wave (mmWave) band that have frequencies ranging from 30 to 300 GHz. mmWave provides much wider bandwidth than the existing 4G band. The 5G network deals with massive number of devices. This presents many challenges including capacity, end to end delay, data rate, and very large number of connections. In this paper, the main task is to apply network coding to 5G mmWave communication system to increase the throughput of the communication links. Simple packet-based network coding schemes using butterfly network topology are simulated. The two network coding schemes considered here are Physical Layer Network Coding (PLNC) and Network Layer Network Coding (NLNC). Models of Additive White Gaussian Noise (AWGN) and mmWave indoor fading channels are considered in the work using Quadrature Phase Shift Keying (QPSK) modulation. The results of the tests showed that the use of both NLNC and PLNC improved throughput in comparison to uncoded system. Using PLNC increased the Bit Error Rate (BER) and the Packet Error Rate (PER), while NLNC scheme showed almost identical error performance to uncoded system over mmWave fading channel. The results show that network coding improved throughput when compared.

Latency Optimization of UAV-Enabled MEC System for Virtual Reality Applications Under Rician Fading Channels

This letter considers an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) system, where UAV base stations (UBSs) are deployed to cache, process, and deliver virtual reality (VR) content from a cloud server to VR users (VRUs). Under Rician fading channel model, we optimize various resource allocation parameters, e.g., association of VRUs with UBSs, caching policy, computing-capacity allocation, and location of UBSs, with the objective of minimizing the maximum latency, subject to computing, caching, and power constraints at the UBSs. The problem is non-convex and solved by using alternating optimization and successive convex approximation techniques. Our simulation results clearly show the importance of the proposed joint optimization algorithm, in terms of performance improvement, over different benchmark schemes.

Transmit Power and Rate Control for Spatial Reuse in Dense Wireless LANs

Spatial reuse is an approach to better utilize the wireless medium in dense networks. In contrast to time multiplexed channel access, the idea is to allow a certain level of interference between parallel transmitting links. Depending on transmit power and rate settings, such parallel transmissions may result in increased overall system performance. This paper presents a systematic approach to model dense networks and optimize transmit power and rate settings for best total system performance. It discusses driving factors and limitations for spatial reuse. The paper introduces two algorithms for concurrent optimal transmit power and rate selection. System simulations using a fading channel model compare the performance of the algorithms against CSMA/CA. The simulation results show that average system performance can be increased by 100% and more.

Tobit Kalman filter with channel fading and dead-zone-like censoring

In complex control systems, the channel fading and the measurement censoring are often unavoidable due to sudden environmental changes, intermittent transmission congestions, random failures and repairs of components. To this end, the Tobit Kalman filtering problem for discrete-time systems with the dead-zone-like censoring and channel fading is studied where the censoring and fading phenomena are, respectively, described by the Tobit measurement model and the -order Rice fading channel model. The design of the filter is carried out in two stages: (1) the state augmentation technique is firstly adopted to remove the time-delay effect caused by the fading channel; and (2) the statistical information of censored measurements is used to determine the optimal filter structure in the minimum mean-square error sense, where the orthogonal projection principle is utilised to avoid the deep correlations among the multiple random variables induced by measurement censoring and channel fading. Besides, the desired filter is also devised based on the approach of the conditional expectation. For the convenience of calculation, the filter with the dead-zone-like censoring is converted into an equivalent one with one-side censoring. Finally, a numerical example of estimating the ballistic ball rates is given to verify the effectiveness of the proposed filters.

Average quality description of energy detection for generalised multipath shadowed κ – μ fading channels

The research addresses the problem of analytical description of energy detection procedure being one of the most favourable mechanism of white-space detection for cognitive radio and Internet-of-Things as well as unknown signal detection in radars and Unmanned Ground/Aerial Vehicle applications. A novel closed-form analytic solutions for average quality metrics (average receiver operating characteristic and area under the AROC curve) are derived for a generic shadowed fading channel model. Numerical simulations verify the computational effectiveness of the derived expressions compared to straightforward numerical integration and analytical treatment demonstrated their consistency with the several specific simplified cases widely used in engineering practice. The results enable one to estimate signal detection quality on the stage of wireless communications' system design.

Conceiving Inferential Prototypes of MIMO Channel Models via Buckingham’s Similitude Principle for 30+ GHz through THz Spectrum

Facilitating newer bands of ‘unused’ segments (windows) of RF spectrum falling in the mm-wave range (above 30+ GHz) and seeking usable stretches across unallocated THz spectrum, could viably be considered for Multiple Input Multiple Output (MIMO) communications. This could accommodate the growing needs of multigigabit 3G/4G applications in outdoor-based backhauls in picocellular networks and in indoor-specific multimedia networking. However, in contrast with cellular and Wi-Fi, wireless systems supporting sub-mm wavelength transreceive communications in the outdoor electromagnetic (EM) ambient could face “drastically different propagation geometry”; also, in indoor contexts, envisaging pertinent spatial-multiplexing with directional, MIMO links could pose grossly diverse propagation geometry across a number of multipaths; as such, channel-models based on stochastic features of diverse MIMO-specific links in the desired test spectrum of mm-wave/THz band are sparsely known and almost non-existent. To alleviate this niche, a method is proposed here to infer sub-mm band MIMO channel-models (termed as “prototypes”) by judiciously sharing “similarity” of details available already pertinent to traditional “models” of lower-side EM spectrum, (namely, VLF through micro-/mm-wave). Relevant method proposed here relies on the “principle of similitude” due to Edgar Buckingham. Exemplar set of “model-to-(inferential)-prototype” transformations are derived and prescribed for an exhaustive set of fading channel models as well as, towards estimating path-loss of various channel statistics in the high-end test spectrum.

Self-Interference Channel Training for Full-Duplex Massive MIMO Systems.

Full-duplex (FD) is a promising technology for increasing the spectral efficiency of next-generation wireless communication systems. A major technical challenge in enabling FD in a real network is to remove the self-interference (SI) caused by simultaneous transmission and reception at the transceiver, and the SI cancellation performance depends significantly on the estimation accuracy of the SI channel. In this study, we proposed a novel partial SI channel training method for minimizing the residual SI power for FD massive multiple-input multiple-output (MIMO) systems. Based on an SI channel training framework under a limited training overhead, using the proposed scheme, the BS estimates only a part of the SI channel vectors, while skipping the channel training for the other remaining SI channel vectors by using their last estimates. With this partial training framework, the proposed scheme finds the optimal partial SI channel training strategy for pilot allocation to minimize the expected residual SI power, considering the time-varying Rician fading channel model for the SI channel. Therefore, the proposed scheme can improve the sum-rate performance compared with other simple partial training schemes for FD massive MIMO systems under a limited training overhead. Numerical results confirm the effectiveness of the proposed scheme for FD massive MIMO systems compared with the full training scheme, as well as other partial training schemes.

A Method for Detecting Amplitude-Phase Joint Characteristic Parameters of Wireless Channel for Generating Key Parameters

Aiming at the problems of poor adaptability and low secret key generation rate of secret key generation scheme based on single characteristic parameter of wireless channel, a secret key generation method based on joint characteristic parameters of amplitude and phase of wireless channel is proposed. This method is based on the single-eavesdropping wireless fading channel model; the joint characteristic model of amplitude and phase of wireless fading channel is established; it detects and extracts the joint characteristic parameters of amplitude and phase of channel, and then the proposed characteristics are quantified by using the equal probability joint quantization strategy of amplitude and phase to generate the secret key random parameters. In this paper, the amplitude and phase joint feature parameter detection method of wireless channel can not only improve the generation rate of random secret key parameters but also make the eavesdropping party’s eavesdropping error rate closer to 0.5. The test results show that the proposed scheme can significantly improve the rate, reliability, and security of generating key random parameters.

ADAPTIVE REDUCED PATHS SUCCESSIVE CANCELLATION LIST DECODING FOR POLAR CODES

Adaptive Reduced Path Successive Cancellation List (ARP-SCL) decoding for polar codes is proposed in this paper. The aim here is to reduce the complexity of the conventional Successive Cancellation List (SCL) and a previously proposed Reduced Path SCL (RP-SCL) decoders. The complexity reduction is obtained at low and moderate signal to noise power ratio (SNR) regions. The operation of the proposed ARP-SCL decoder relied on the calculation of different optimum pruning parameters according to the operating SNR, while RP-SCL used single parameters set. Simulation tests are carried out to determine both the complexity and Bit Error Rate (BER) performances of the proposed ARP-SCL decoder in comparison with SCL and RP-SCL decoders. Additive White Gaussian Noise (AWGN) channel and two fading channel models simulating indoor 5G environment using millimeter Wave (mmWave) with carrier frequency of 28GHz and having either 50 or 400 MHz bandwidths are used. The results revealed that the proposed ARP-SCL decoder reduced the average processed paths up to 46.88%. The largest complexity reductions are obtained in the case of fading channel having 400 MHz bandwidth at high SNR regions. In most test conditions, the mentioned reduction in complexity is achieved without greatly sacrificing the required BER.

Wireless Channel Modeling Based on Machine Learning in the High-speed Railway Scenario

In the high-speed railway (HSR) scenario, there is a problem of poor generalization ability caused by the overfitting phenomenon through wireless channel modeling. In this paper, a precise modeling method based on machine learning is proposed to address this issue under the wireless channel of HSR. According to the K-means clustering algorithm, the typical values of the K-factor in a multitude of scenarios are obtained through Rician K-factor clustering in the wireless channel fading model. Founded on the theory of model evaluation and selection in machine learning, the measured values of path loss are fitted by least squares regression using the cross-validation method. Then, we obtain the formulas of the path loss models and the expected generalization errors of the models in different scenarios. The mathematical relationship of the ergodic capacity depending on the Rician K-factor, the signal-noise ratio of the receiver, and the path loss generalization error are established after the ergodic capacity analysis of the HSR wireless channel. Finally, the lower boundary curves of the ergodic capacity are obtained by simulation experiments in different scenarios.

Fading Channel Models for mm-Wave Communications

A realistic performance assessment of any wireless communication system requires the use of a fading channel model that reflects its main characteristics. The traditional Rayleigh and Nakagami-m models have been (and still are) the basis of most theoretical research on wireless technologies today, even for emerging technologies, such as millimeter-wave communications (mm-Wave). In this article, we show that the fluctuating multiple-ray (FMR) and κ-μ shadowed models had a better fit (i.e., lowest mean square error statistical test) to field measurements in outdoor environments at 28 GHz than the conventional channel models. Therefore, these generalized models are feasible alternatives that can be used as a benchmark when evaluating communication performance in mm-Wave scenarios.

Composite Fading Model for Aerial MIMO FSO Links in the Presence of Atmospheric Turbulence and Pointing Errors

In this letter, we introduce accurate approximation expressions for composite fading channel model and outage probability of multiple-input multiple-output (MIMO) free space optical (FSO) link between two unmanned aerial vehicles (UAVs) in hovering state. In our derivations, we consider the effects of weather attenuation, random fluctuations in UAVs' positions and orientations, and atmospheric turbulence. Our proposed model is unified with respect to turbulence effect, which means it is valid for different atmospheric turbulence models. We show the accuracy of the proposed model through comparisons with Monte Carlo simulations under different channel conditions.

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

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 $K_a<<K_\text{tot}$ of them are active. Previous results, based on compressed sensing, require that $K_a\leq L$, which is a bottleneck in massive deployment scenarios such as Internet-of-Things and unsourced random access. In this work we show that such limitation can be overcome when the number of base station antennas $M$ is sufficiently large. We also provide two algorithms. One is based on Non-Negative Least-Squares, for which the above scaling result can be rigorously proved. The other consists of a low-complexity iterative componentwise minimization of the likelihood function of the underlying problem. Finally, we use the discussed approximated ML algorithm as the decoder for the inner code in a concatenated coding scheme for unsourced random access, a grant-free uncoordinated multiple access scheme where all users make use of the same codebook, and the massive MIMO base station must come up with the list of transmitted messages irrespectively of the identity of the transmitters. We show that reliable communication is possible at any $E_b/N_0$ provided that a sufficiently large number of base station antennas is used, and that a sum spectral efficiency in the order of $\mathcal{O}(L\log(L))$ is achievable.

Interference-Based QoS and Capacity Analysis of VANETs for Safety Applications

Whether the current IEEE 802.11p communication system meets the stringent quality of service (QoS) requirements for safety applications or not is still not very clear. Signal-to-interference-plus-noise ratio (SINR) distribution plays a primary role in quantifying the QoS as well as link capacity of IEEE 802.11p in one-dimensional (1-D) highway and two-dimensional (2-D) intersection road. Most of the analytical models based on stochastic geometry assumed ALOHA access to analyze the SINR distribution, while few of the works developed the stochastic geometry based model considering CSMA access but derived the SINR distribution utilizing statistical estimator. On the other hand, the current interference based probability analytic model for the SINR distribution limit the 1-D extension to 2-D, due to the very high numerical computational complexity at 2-D. In this paper, we propose an analytic model under more general non-homogeneous Poisson process (NHPP) node distribution, more general channel fading model ( Nakagami ) with path loss, and noise, for the study of QoS and capacity of VANET for BSM based safety applications in both 1-D highway and 2-D intersection road. The proposed model derives QoS and capacity of VANET BSM broadcast through evaluation of SINR distribution using probability theory and ordered statistics, which has much lower computational complexity compared with the unordered statistic model. The proposed model is validated by NS2 simulation and extended to the derivation of other QoS metrics such as packet reception probability, packet reception ratio, and broadcast link capacity, etc. The performance comparisons between 1-D and 2-D are implemented, and the QoS sensitivity analyses regarding the extension to multi-intersection as well as enlarging interference range are discussed.

Jamming Aided Covert Communication With Multiple Receivers

We consider that a transmitter covertly communicates with multiple receivers under the help of a friendly jammer. The messages intended for different receivers are transmitted in mutually orthogonal frequency bands. An adversary observes all these frequency bands aiming at detecting whether or not communication occurs, while the friendly jammer broadcasts jamming signals to degrade the detection performance of the adversary. We consider a block Rayleigh fading channel model and evaluate the performance of covert communication in two situations: 1) the wireless channels vary slowly such that the transmission ends within one channel coherent time block, and 2) the wireless channels vary fast such that the wireless channels have changed several times before the whole transmission is finished. In the former case, subject to a covertness constraint, we maximize the sum of the effective rates by optimizing the transmit power allocation and the transmission rate for each receiver. In the latter case, we take the channel training process into consideration, and subject to a covertness constraint, we maximize the sum of the ergodic rates by optimizing the power allocation and the pilot length. Though both of the two optimization problems are non-convex, we presented methods to find their global optimal solutions. Besides, we also present methods to find sub-optimal solutions with lower computational complexities. Numerical results are presented to evaluate the performance under the two situations.

A Theoretical Analysis of Mobility Detection in Connectivity-Based Localization for Short-Range Networks

This paper presents a theoretical analysis of mobility detection in connectivity-based localization, which exploits connectivity information as range measurements to anchors at a known location, to investigate how well and how precise mobility can be detected with connectivity in short-range networks. We derive mobility detection, miss detection, and false alarm probabilities in terms of a mobility detection threshold, defined as the minimum distance to detect the mobility, under the shadow fading channel and arbitrary mobility models to take into account practical and general scenarios. Based on the derivations, we address the threshold determination with the criteria in the sense of the minimum average error from miss detection and false alarm. Numerical and simulation evaluations are performed to verify our theoretical derivations, to show that increasing anchor numbers can improve the mobility detection probability with a smaller detection threshold, and that the probabilities are bounded by the weights of miss detection and false alarm. This work is the first attempt at addressing the performance of mobility detection using connectivity, and it can be utilized as a baseline for connectivity-based mobility tracking.

Estimation of $$\alpha -\kappa -\mu $$ mobile fading channel parameters using evolutionary algorithms

This paper proposes the use of evolutionary algorithms (EAs) to estimate the physical parameters of the generalized $$\alpha -\kappa -\mu $$ mobile fading channel model. The estimation of parameters is a fundamental step that allows for the statistical model to adjust to the real experimental data. The The maximum likelihood estimation (MLE) method that is traditionally used for estimating parameters of the $$\alpha -\kappa -\mu $$ channel uses nonlinear numerical methods. In some cases, the use of nonlinear numerical methods may lead the MLE to make physically unacceptable estimations, or even to not be able to obtain a result. Our proposal is to innovate the existing EAs by incorporating an adaptive approach, a new mutation strategy and an adequate fitness function for the estimation of $$\alpha -\kappa -\mu $$ parameters. Experimental results are presented to confirm that parameters estimated by the EAs (genetic algorithms, differential evolution algorithms, and differential evolution algorithms with an adaptive guiding mechanism) are all physically acceptable. These experiments show that the EAs outperform MLE estimation results.

Nonstationary Filtering for Markov Jumping Systems with Fading Channel and Multiplicative Noises

This paper studies the nonstationary filtering problem of Markov jump system under l2 - l∞ performance. Due to the difference in propagation channels, signal strength and phase will inevitably change randomly and cause the waste of signals resources. In response to this problem, a channel fading model with multiplicative noise is introduced. And then a nonstationary filter, which receives signals more efficiently is designed. Meanwhile Lyapunov function is constructed for error analysis. Finally, the gain matrix for filtering is obtained by solving the matrix inequality, and the results showed that the nonstationary filter converges to the stable point more quickly than the traditional asynchronous filter, the stability of the designed filter is verified.

Effect of Generalized Improper Gaussian Noise and In-Phase/Quadrature-Phase Imbalance on Quadrature Spatial Modulation

Quadrature spatial modulation (QSM) isa recently proposed multiple-input multiple-output (MIMO) wireless transmission paradigm that has garnered considerable research interest owing to its relatively high spectral efficiency. QSM essentially enhances the spatial multiplexing gain while maintaining all the inherent advantages of spatial modulation (SM). This work studies the effects of in-phase/quadrature-phase (I/Q) imbalance and improper Gaussian noise (IGN) on the performance of QSM. Considering a scenario where both receiver and transmitter operate under the effects of I/Q imbalance, we propose a novel receiver design that optimizes the system bit error rate (BER) when there is IGN at the receiver. Closed forms of the average pairwise error probability (APEP) and upper bound of the average BER formulas are derived. These formulas are derived considering the Beckmann fading channel model, where most well-known fading channel models can be considered special cases. The proposed designs demonstrate solid performance despite the effects of I/Q imbalance. In fact, these effects can be entirely eliminated if they exist at the receiver and significantly reduced at the transmitter. All analytical results were verified by computer simulations.

Channel Measurement and Modeling Prototype for IEEE 802.22-Based Regional Area Networks

During the design and development of a reliable transceiver, its performance and robustness are evaluated using channel models that simulate the propagation channel characteristics in a real-world environment. In the case of wide-area communication systems in rural areas such as those based on TV white spaces, long delay multipath fading is one of the dominant factors impairing communication quality, and it is important to apply realistic multipath fading channel models in various application scenarios. Therefore, it is necessary to develop channel measurement and modeling systems that adaptively obtain channel factors and reflect multipath propagation characteristics in various wide-area communication environments. In this study, we developed a flexible channel measurement and modeling prototype applicable to mobile IEEE 802.22-based wide-area communication systems, which can be simply implemented by using programmable hardware and software. The prototype includes a new channel modeling framework that applies an improved double exponential slope model to extract the essential factors of multipath propagation characteristics. To validate the developed prototype, a fading emulator is applied to it to measure the raw data used for modeling. As a result, the multipath fading characteristics measured with the prototype were successfully modeled and reproduced by the proposed channel modeling framework with a high fitness of root mean square delay spread distribution. The derived bit error rate characteristics agreed better with the simulation results compared to the conventional channel model.