Downlink Analysis and Evaluation of Multi-Beam LEO Satellite Communication in Shadowed Rician Channels

The extension of wide area wireless connectivity to low-earth orbit (LEO) satellite communication systems demands a fresh look at the effects of in-orbit base stations, sky-to-ground propagation, and cell planning. A multi-beam LEO satellite delivers widespread coverage by forming multiple spot beams that tessellate cells over a given region on the surface of the Earth. In doing so, overlapping spot beams introduce interference when delivering downlink concurrently in the same area using the same frequency spectrum. To permit forecasting of communication system performance, we characterize desired and interference signal powers, along with SNR, INR, SIR, and SINR, under the measurement-backed Shadowed Rician (SR) sky-to-ground channel model. We introduce a minor approximation to the fading order of SR channels that greatly simplifies the PDF and CDF of these quantities and facilitates statistical analyses of LEO satellite systems such as probability of outage. We conclude this paper with an evaluation of multi-beam LEO satellite communication in SR channels of varying intensity fitted from existing measurements. Our numerical results highlight the effects satellite elevation angle has on SNR, INR, and SINR, which brings attention to the variability in system state and potential performance as a satellite traverses across the sky along its orbit

Error vector magnitude analysis of fading SIMO channels relying on MRC reception

We analytically characterize the data-aided Error Vector Magnitude (EVM) performance of a Single Input Multiple Output (SIMO) communication system relying on Maximal Ratio Combining (MRC) having either independent or correlated branches that are non-identically distributed. In particular, exact closed form expressions are derived for the EVM in -? fading and -? shadowed fading channels and these expressions are validated by simulations. The derived expressions are expressed in terms of Lauricella’s function of the fourth kind F(N) D (.), which can be easily computed. Furthermore, we have simplified the derived expressions for various special cases such as independent and identically distributed branches, Rayleigh fading, Nakagamim fading and -? fading. Additionally, a parametric study of the EVM performance of the wireless system is presented

On the connection between noncircularly-symmetric and noncentral fading models: univariate and multivariate analysis

This thesis provides new statistical connections between noncircularly-symmetric central and circularly-symmetric noncentral underlying complex Gaussian models. This is particularly interesting since it facilitates the analysis of noncircularly-symmetric models, which are often underused despite their practical interest, since their analysis is more challenging. Although these statistical connections have a wide range of applications in different areas of univariate and multivariate analysis, this thesis is framed in the context of wireless communications, to jointly analyze noncentral and noncircularly-symmetric fading models. We provide an unified framework for the five classical univariate fading models, i.e. the one-sided Gaussian, Rayleigh, Nakagami-m, Nakagami-q and Rician, and their most popular generalizations, i.e the Rician shadowed, η-µ, κ-µ and κ-µ shadowed. Moreover, we present new simple results regarding the ergodic capacity of single-input single-output systems subject to κ-µ shadowed, κ-µ and η-µ fadings. With applications to multiple-input multiple-output communications, we are interested in matrices of the form W=XX^H (or W=X^HX), where X is a complex Gaussian matrix with unequal variance in the real and imaginary parts of its entries, i.e., X belongs to the noncircularly-symmetric Gaussian subclass. By establishing a novel connection with the well-known complex Wishart ensemble, we facilitate the statistical analysis of W and give new insights on the effects of such asymmetric variance profile

Performance analysis of energy detector over generalised wireless channels in cognitive radio

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.This thesis extensively analyses the performance of an energy detector which is widely employed to perform spectrum sensing in cognitive radio over different generalised channel models. In this analysis, both the average probability of detection and the average area under the receiver operating characteristic curve (AUC) are derived using the probability density function of the received instantaneous signal to noise ratio (SNR). The performance of energy detector over an ŋ --- µ fading, which is used to model the Non-line-of-sight (NLoS) communication scenarios is provided. Then, the behaviour of the energy detector over к --- µ shadowed fading channel, which is a composite of generalized multipath/shadowing fading channel to model the lineof- sight (LoS) communication medium is investigated. The analysis of the energy detector over both ŋ --- µ and к --- µ shadowed fading channels are then extended to include maximal ratio combining (MRC), square law combining (SLC) and square law selection (SLS) with independent and non-identically (i:n:d) diversity branches. To overcome the problem of mathematical intractability in analysing the energy detector over i:n:d composite fading channels with MRC and selection combining (SC), two different unified statistical properties models for the sum and the maximum of mixture gamma (MG) variates are derived. The first model is limited by the value of the shadowing severity index, which should be an integer number and has been employed to study the performance of energy detector over composite α --- µ /gamma fading channel. This channel is proposed to represent the non-linear prorogation environment. On the other side, the second model is general and has been utilised to analyse the behaviour of energy detector over composite ŋ --- µ /gamma fading channel. Finally, a special filter-bank transform which is called slantlet packet transform (SPT) is developed and used to estimate the uncertain noise power. Moreover, signal denoising based on hybrid slantlet transform (HST) is employed to reduce the noise impact on the performance of energy detector. The combined SPT-HST approach improves the detection capability of energy detector with 97% and reduces the total computational complexity by nearly 19% in comparison with previously implemented work using filter-bank transforms. The aforementioned percentages are measured at specific SNR, number of selected samples and levels of signal decompositionMartyrs Foundatio

Diversity Gains in Random Line-Of-Sight and Rich Isotropic Multipath Environment

Antenna diversity gain for theoretical as well as measured antennas is studied in two extreme environments, the rich isotropic multipath environment (RIMP) and the random line-of-sight environment. The RIMP diversity gain was previously defined based on improved fading performance, here we equivalently consider it as a metric for the cumulative improvement of the 1% worst users randomly distributed in the RIMP environment. Similarly, we consider the diversity gain in the random line-of-sight environment to be the performance improvement of the 1% of the users which receives the weakest signal relative to a theoretical Rayleigh distribution of the signal levels among the users. The random line-of-sight environment is regarded as being caused by the statistics of an ensemble of users (or terminals) with arbitrary 3D orientations

Neural-Kalman Schemes for Non-Stationary Channel Tracking and Learning

This Thesis focuses on channel tracking in Orthogonal Frequency-Division Multiplexing (OFDM), a widely-used method of data transmission in wireless communications, when abrupt changes occur in the channel. In highly mobile applications, new dynamics appear that might make channel tracking non-stationary, e.g. channels might vary with location, and location rapidly varies with time. Simple examples might be the di erent channel dynamics a train receiver faces when it is close to a station vs. crossing a bridge vs. entering a tunnel, or a car receiver in a route that grows more tra c-dense. Some of these dynamics can be modelled as channel taps dying or being reborn, and so tap birth-death detection is of the essence. In order to improve the quality of communications, we delved into mathematical methods to detect such abrupt changes in the channel, such as the mathematical areas of Sequential Analysis/ Abrupt Change Detection and Random Set Theory (RST), as well as the engineering advances in Neural Network schemes. This knowledge helped us nd a solution to the problem of abrupt change detection by informing and inspiring the creation of low-complexity implementations for real-world channel tracking. In particular, two such novel trackers were created: the Simpli- ed Maximum A Posteriori (SMAP) and the Neural-Network-switched Kalman Filtering (NNKF) schemes. The SMAP is a computationally inexpensive, threshold-based abrupt-change detector. It applies the three following heuristics for tap birth-death detection: a) detect death if the tap gain jumps into approximately zero (memoryless detection); b) detect death if the tap gain has slowly converged into approximately zero (memory detection); c) detect birth if the tap gain is far from zero. The precise parameters for these three simple rules can be approximated with simple theoretical derivations and then ne-tuned through extensive simulations. The status detector for each tap using only these three computationally inexpensive threshold comparisons achieves an error reduction matching that of a close-to-perfect path death/birth detection, as shown in simulations. This estimator was shown to greatly reduce channel tracking error in the target Signal-to-Noise Ratio (SNR) range at a very small computational cost, thus outperforming previously known systems. The underlying RST framework for the SMAP was then extended to combined death/birth and SNR detection when SNR is dynamical and may drift. We analyzed how di erent quasi-ideal SNR detectors a ect the SMAP-enhanced Kalman tracker's performance. Simulations showed SMAP is robust to SNR drift in simulations, although it was also shown to bene t from an accurate SNR detection. The core idea behind the second novel tracker, NNKFs, is similar to the SMAP, but now the tap birth/death detection will be performed via an arti cial neuronal network (NN). Simulations show that the proposed NNKF estimator provides extremely good performance, practically identical to a detector with 100% accuracy. These proposed Neural-Kalman schemes can work as novel trackers for multipath channels, since they are robust to wide variations in the probabilities of tap birth and death. Such robustness suggests a single, low-complexity NNKF could be reusable over di erent tap indices and communication environments. Furthermore, a di erent kind of abrupt change was proposed and analyzed: energy shifts from one channel tap to adjacent taps (partial tap lateral hops). This Thesis also discusses how to model, detect and track such changes, providing a geometric justi cation for this and additional non-stationary dynamics in vehicular situations, such as road scenarios where re ections on trucks and vans are involved, or the visual appearance/disappearance of drone swarms. An extensive literature review of empirically-backed abrupt-change dynamics in channel modelling/measuring campaigns is included. For this generalized framework of abrupt channel changes that includes partial tap lateral hopping, a neural detector for lateral hops with large energy transfers is introduced. Simulation results suggest the proposed NN architecture might be a feasible lateral hop detector, suitable for integration in NNKF schemes. Finally, the newly found understanding of abrupt changes and the interactions between Kalman lters and neural networks is leveraged to analyze the neural consequences of abrupt changes and brie y sketch a novel, abrupt-change-derived stochastic model for neural intelligence, extract some neuro nancial consequences of unstereotyped abrupt dynamics, and propose a new portfolio-building mechanism in nance: Highly Leveraged Abrupt Bets Against Failing Experts (HLABAFEOs). Some communication-engineering-relevant topics, such as a Bayesian stochastic stereotyper for hopping Linear Gauss-Markov (LGM) models, are discussed in the process. The forecasting problem in the presence of expert disagreements is illustrated with a hopping LGM model and a novel structure for a Bayesian stereotyper is introduced that might eventually solve such problems through bio-inspired, neuroscienti cally-backed mechanisms, like dreaming and surprise (biological Neural-Kalman). A generalized framework for abrupt changes and expert disagreements was introduced with the novel concept of Neural-Kalman Phenomena. This Thesis suggests mathematical (Neural-Kalman Problem Category Conjecture), neuro-evolutionary and social reasons why Neural-Kalman Phenomena might exist and found signi cant evidence for their existence in the areas of neuroscience and nance. Apart from providing speci c examples, practical guidelines and historical (out)performance for some HLABAFEO investing portfolios, this multidisciplinary research suggests that a Neural- Kalman architecture for ever granular stereotyping providing a practical solution for continual learning in the presence of unstereotyped abrupt dynamics would be extremely useful in communications and other continual learning tasks.Programa de Doctorado en Multimedia y Comunicaciones por la Universidad Carlos III de Madrid y la Universidad Rey Juan CarlosPresidente: Luis Castedo Ribas.- Secretaria: Ana García Armada.- Vocal: José Antonio Portilla Figuera

Performance Analysis of Multi-Antenna Hybrid Satellite-Terrestrial Relay Networks in the Presence of Interference

Abstract—The integration of cooperative transmission into satellite networks is regarded as an effective strategy to increase the energy efficiency as well as the coverage of satellite communications. This paper investigates the performance of an amplifyand-forward (AF) hybrid satellite-terrestrial relay network (HSTRN), where the links of the two hops undergo Shadowed- Rician andRayleigh fadingdistributions, respectively.By assuming that a single antenna relay is used to assist the signal transmission between the multi-antenna satellite and multi-antenna mobile terminal, and multiple interferers corrupt both the relay and destination, we first obtain the equivalent end-to-end signal-to-interference-plus-noise ratio (SINR) of the system. Then, an approximate yet very accurate closed-form expression for the ergodic capacity of the HSTRN is derived. The analytical lower bound expressions are also obtained to efficiently evaluate the outage probability (OP) and average symbol error rate (ASER) of the system. Furthermore, the asymptotic OP and ASER expressions are developed at high signal-to-noise ratio (SNR) to reveal the achievable diversity order and array gain of the considered HSTRN. Finally, simulation results are provided to validate of the analytical results, and show the impact of various parameters on the system performance