Effective Capacity Analysis over α-κ-μ/Gamma Composite Fading Channel

In this paper, a novel closed form expression for the effective capacity (EC) over the α‐η‐μ/Lognormal and α‐κ‐μ/Lognormal composite fading channels has been derived. Using the proposed expression, the influence of various fading and shadowing parameters on the composite fading channels is investigated. Moreover, to get more insight behavior of the channels, an asymptotic expression under high and low signal‐to‐noise ratio (SNR) regimes have also been derived.

ABSTRACTIn this paper, the performance analysis of wireless communication system over shadowed-Rice (SR) composite fading channel has been investigated and analysed. The unified analytical expressions of the average symbol error probability (ASEP) for several coherent and non-coherent modulation schemes separately with different constellation sizes are derived for composite fading channel under two different fade mitigation methods, with maximal ratio combining (MRC) microdiversity (Method 1) and MRC applied over the composite fading channel (Method 2). Furthermore, an asymptotic analysis is carried out and a closed-form expression of the ASEP with Method 2 is presented. Analytical expressions of the corresponding average probability of energy detection (PD) are formulated for both the methods. Finally, the derived PD expression is utilised to analyse the performance of cooperative system assuming erroneous feedback channel. Analysis of optimisation of detection threshold as well as number of cognitive users to minimise the total error rate is also carried out. The closed-form expressions are validated by comparing them with exact numerical results and Monte Carlo simulation.

Cell-free massive multiple-input multiple-output (MIMO) is considered as a promising technology for satisfying higher rate requirement of users in beyond-5G networks. This paper investigates the impact of phase noise on the performance of cell-free MIMO systems which employ a large number of multi-antenna access points (APs). For both downlink and uplink transmissions, we derive the tractable closed-form achievable rate expressions when signal detection with channel state information (CSI) and channel distribution information (CDI), respectively, by utilizing the extended deterministic equivalence method in random matrix theory. The closed-form achievable rate expressions provide efficient evaluation for the impact of phase noise on system performance. The analytical results indicate that the phase noise influences the power of the desired signal, the beamforming uncertainty gain, and the inter-user interference increment caused by the pilot contamination. In addition, the special cases that only APs have phase noise and only users have phase noise are discussed. It is found that the impact of phase noise at users is more severe than that at APs. Besides, we discuss the system performance at low and high SNR regimes. It demonstrates that the phase noise is the major limitation of the system performance in high SNR regime while it can be neglected in low SNR regime. Furthermore, we formulate the max-min power control problem for both downlink and uplink transmissions to guarantee the users' fairness and prove that the downlink max-min problem is a quasi-concave problem while the uplink max-min problem is a geometric programming (GP) problem. Then, the phase noise-aware max-min power control schemes for both downlink and uplink transmissions are proposed to ensure the users' fairness. Finally, numerical results are provided to validate the analytical results and the proposed phase noise-aware power control scheme.

Compressed sensing (CS) enables measurement reconstruction by using sampling rates below the Nyquist rate, as long as the amplitude vector of interest is sparse. In this paper, we first derive and analyze the Bayesian Cramer–Rao bound (BCRB) for the amplitude vector when the set of indices (the support) of its nonzero entries is known. We consider the following context: i) The dictionary is nonstochastic but randomly generated; ii) the number of measurements and the support cardinality grow to infinity in a controlled manner, i.e., the ratio of these quantities converges to a constant; iii) the support is random; and iv) the vector of nonzero amplitudes follow a multidimensional generalized normal distribution. Using results from random matrix theory, we obtain closed-form approximations of the BCRB. These approximations can be formulated in a very compact form in low and high SNR regimes. Second, we provide a statistical analysis of the variance and the statistical efficiency of the oracle linear mean-square-error (LMMSE) estimator. Finally, we present results from numerical investigations in the context of non-bandlimited finite-rate-of-innovation (FRI) signal sampling. We show that the performance of Bayesian mean-square error (BMSE) estimators that are aware of the cardinality of the support, such as OMP and CoSaMP, are in good agreement with the developed lower bounds in the high SNR regime. Conversely, sparse estimators exploiting only the knowledge of the parameter vector and the noise variance in form of a priori distributions of these parameters, like LASSO and BPDN, are not efficient at high SNR. However, at low SNR, their BMSE is lower than that of the former estimators and may be close to the BCRB.

In this paper, we propose a mixture gamma distribution based analytical framework for NOMA wireless systems over composite fading channels. We analyze the outage probability (OP), delay-limited throughput (TP) and effective capacity (EC) in uplink NOMA with imperfect successive interference cancellation (SIC) due to the presence of residual hardware impairments and delay constraints. A mixture gamma distribution is used to approximate the probability density functions of fading channels. Based on this, we obtain closed-form expressions in terms of Meijer-G functions for the OP, the TP and the EC. We also perform asymptotic analysis of these metrics to characterize system behaviors at the high signal-to-noise ratio regime. Moreover, upper-bounds for the EC is derived. Efficacy of NOMA over orthogonal multiple access is analytically examined. Unlike the existing works, our analytical expressions hold for NOMA systems with an arbitrary number of users per cluster over a wide range of channel models, including lognormal-Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$K_G$</tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\eta -\mu$</tex-math></inline-formula> , Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$q$</tex-math></inline-formula> (Hoyt), <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\kappa -\mu$</tex-math></inline-formula> , Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> (Rician), Nakagami- <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> , and Rayleigh fading channels. This unified analysis facilitates evaluations of impacts of the residual interference, the power allocation among users, the delay quality-of-service exponent as well as the shadowing and small-scale fading parameters on the performance metrics. Simulation results are provided to validate theoretical analysis.

The exact closed‐form expressions regarding the outage probability and capacity of distributed MIMO (DMIMO) systems over a composite fading channel are derived. This is achieved firstly by using a lognormal approximation to a gamma‐lognormal distribution when a mobile station (MS) in the cell is in a fixed position, and the so‐called maximum ratio transmission/selected combining (MRT‐SC) and selected transmission/maximum ratio combining (ST‐MRC) schemes are adopted in uplink and downlink, respectively. Then, based on a newly proposed nonuniform MS cell distribution model, which is more consistent with the MS cell hotspot distribution in an actual communication environment, the average outage probability and capacity formulas are further derived. Finally, the accuracy of the approximation method and the rationality of the corresponding theoretical analysis regarding the system performance are proven and illustrated by computer simulations.

This paper investigates the effects of hybrid co-channel interference (CCI) on integrated satellite-terrestrial networks (ISTN). The system performance is analyzed over the composite fading channel. More specifically, the outage probability (OP) formula is obtained for interference limited ISTN systems operating over shadowed-Rician and extended generalized-K (EGK) fading channels, for satellite and terrestrial links, respectively. The results are indicated on the basis of Fox's H function. This new formula is versatile and can serve as an effective way to estimate the performance of ISTN systems. The analysis is further illustrated by numerical evaluated results, clearly demonstrating effects of several key factors on the outage performance.

The $F$ composite fading model was recently proposed as an accurate and tractable statistical model for the characterization of the composite fading conditions encountered in realistic wireless communication scenarios. In the present contribution we capitalize on the distinct properties of this composite model to evaluate the achievable effective rate over $F$ composite fading channels. To this end, we derive an exact closed-form expression for the effective rate, which is subsequently used as a benchmark for the derivation of tight upper and lower bounds, as well as of an accurate approximation. The derived analytic expressions are provided in closed-form and benefit from being tractable both analytically and numerically. This enables the development of meaningful insights on the effect of fading conditions and/or latency on the overall system performance. Also, it allows the accurate quantification of the signal to noise ratio required in target quality of service requirements under different composite fading conditions.

In exciting recent work, Bertsimas, King and Mazumder (Ann. Statist. 44 (2016) 813–852) showed that the classical best subset selection problem in regression modeling can be formulated as a mixed integer optimization (MIO) problem. Using recent advances in MIO algorithms, they demonstrated that best subset selection can now be solved at much larger problem sizes than what was thought possible in the statistics community. They presented empirical comparisons of best subset with other popular variable selection procedures, in particular, the lasso and forward stepwise selection. Surprisingly (to us), their simulations suggested that best subset consistently outperformed both methods in terms of prediction accuracy. Here, we present an expanded set of simulations to shed more light on these comparisons. The summary is roughly as follows: •neither best subset nor the lasso uniformly dominate the other, with best subset generally performing better in very high signal-to-noise (SNR) ratio regimes, and the lasso better in low SNR regimes; •for a large proportion of the settings considered, best subset and forward stepwise perform similarly, but in certain cases in the high SNR regime, best subset performs better; •forward stepwise and best subsets tend to yield sparser models (when tuned on a validation set), especially in the high SNR regime; •the relaxed lasso (actually, a simplified version of the original relaxed estimator defined in Meinshausen (Comput. Statist. Data Anal. 52 (2007) 374–393)) is the overall winner, performing just about as well as the lasso in low SNR scenarios, and nearly as well as best subset in high SNR scenarios.

A novel expression for the probability of detection in spectrum-sensing cognitive radio systems is derived over the extended generalised-𝒦 composite fading channel assuming energy detection is employed. The new tractable expression subsumes several fading and shadowing environments as special cases. It can also be easily evaluated using commercial software packages such as MATHEMATICA© and MATLAB©.

The introduction to autonomous vehicles and intelligent transportation systems, as well as related work on autonomous vehicles and intelligent transportation systems, are described in this chapter. Aside from that, it has a chapter on autonomous intelligent vehicles and autonomous vehicles. Issues and challenges in autonomous intelligent vehicles, as well as the framework of an intelligent transportation system. In addition, the chapter discusses research opportunities in autonomous intelligent vehicles, IPv4 and IPv6 issues, and the future of IPv9 for vehicles, as well as a survey on autonomous vehicles in highway scenarios. During the last century, the automobile industry has excelled at producing cars that are both safe and affordable. Because of significant advancements in the Internet of things (IoT), the programmable logic controller (PLC), and other aspects of computing, autonomous vehicles (AVs) are becoming a reality. Cars, trucks, and other vehicles that don’t need to be plugged in to get power are referred to as self-sufficient vehicles. The multi-agent-based transportation system, which demonstrates how vehicles, drivers, roads, infrastructure, and vehicles all work together, is one of the most important intelligent transportation systems because it demonstrates how all of these things work together. To solve and manage traffic problems, an intelligent transportation systems (ITS) system employs a variety of technologies. Communication and control, vehicle sensing, and electronics are among these technologies. ITS has been used in the developed world for the past two decades. It is still a novel concept in the developing world. Sensors and computers work together in the Internet of things to store and process data, and data analytics is used to make the traffic system run more smoothly.KeywordsAutonomous vehicle (AV)Intelligent transportation system (ITS)Autonomous intelligent vehicle (AIV)Advanced driver assistance system (ADAS)Advanced driving system (ADS)

In this paper, the cross-layer design (CLD) performance of uplink massive MIMO system is studied by combining the discrete rate adaptive modulation with truncated automatic repeat request over composite Rayleigh fading channel. As shown in the performance analysis, each user's effective signal-to-noise ratio (SNR) and the corresponding conditional probability density function are, respectively, derived. Furthermore, we deduce the switching thresholds for CLD by means of the approximation of the PER with the constraint of the target packet error rate (PER). Based on switching thresholds, using the appropriate numerical integration method, average PER (APER) and overall average spectral efficiency (ASE) of massive MIMO with CLD are derived, and resultant closed-form expressions of APER and ASE can be achieved. The theoretical results above can agree with the corresponding simulations, and thus the system performance can be feasibly assessed. Simulation results illustrate that the CLD system is able to increase the ASE while maintaining the target PER, and thus the theoretical analysis is valid. Besides, with the increase of the maximum retransmission number, the ASE increment will decrease while the PER will increase. Moreover, the system performance will become better with the number of receive antennas increases, as expected.

In this Letter, the authors derive the closed‐form formulas for the generalised moment generating function of the Fisher–Snedecor (F–S ) distribution, which enable us to effectively evaluate the probability of successful secure transmission over F–S composite fading channels. Extensive numerical results are presented to validate the accuracy of their analytical framework.

SEP of rectangular QAM in composite fading channels

We investigated a simple Adaptive selection/maximal-ratio (ASM) combining cooperative system with output threshold over independent nonidentical composite Nakagami-lognormal fading channels using Mixture gamma (MG) distribution. Some novel closed-form expressions for the probability density function, the cumulative distribution function and the momentgeneration function of the output Signal-to-noise ratio (SNR) for the ASM system are derived, respectively. The average symbol error rate, outage probability and the diversity order for the ASM system are given based on the above expressions. For the purpose of comparison, we also derive the statistical characterizations of the conventional selection combining and maximal-ratio combining cooperative systems using MG distribution. Numerical and simulation results are shown to verify the accuracy of the analytical results under different scenarios, such as varying average SNR, fading parameters per hop, and the location of relaying nodes. These results show that ASM is a simple and flexible cooperative system, and can be useful in future practical deployment.