A high performance digital channel simulator for satellite transmissions

A good understanding of the wireless channel, its key physical parameters and the modeling issues, lays the foundation for the rest of the book. This is the goal of this chapter. A defining characteristic of the mobile wireless channel is the variations of the channel strength over time and over frequency. The variations can be roughly divided into two types (Figure 2.1): Large-scale fading , due to path loss of signal as a function of distance and shadowing by large objects such as buildings and hills. This occurs as the mobile moves through a distance of the order of the cell size, and is typically frequency independent. Small-scale fading , due to the constructive and destructive interference of the multiple signal paths between the transmitter and receiver. This occurs at the spatial scale of the order of the carrier wavelength, and is frequency dependent. We will talk about both types of fading in this chapter, but with more emphasis on the latter. Large-scale fading is more relevant to issues such as cell-site planning. Small-scale multipath fading is more relevant to the design of reliable and efficient communication systems – the focus of this book. We start with the physical modeling of the wireless channel in terms of electromagnetic waves. We then derive an input/output linear time-varying model for the channel, and define some important physical parameters. Finally, we introduce a few statistical models of the channel variation over time and over frequency.

Testing of a read channel for hard disk drives (HDD) requires an extremely high-speed arbitrary waveform generator (AWG) to emulate Lorentzian waveforms for read functional tests. If a sampling rate required to the AWG exceeds the maximum available rate of the instrument, a possible solution is to use two AWG's together running at the same rate synchronously and generating signal alternately to make their equivalent sampling rate doubled. One of the key components of such interleaving AWG is a high-speed analog selector. However it is not easy to find a good selector in ultra high frequency range. An application note proposes a practical solution that is a pseudo interleaving AWG. It is a variation of traditional signal combining method using a power combiner instead of a high-speed selector. This paper describes about performance analysis and experimental results of the pseudo interleaving compared to a true interleaving. Data stored in two AWG's are created with the same method as the true interleaving AWG. The data are simply distributed alternately into each AWG. The results show that the pseudo interleaving works well in spite of its simple structure and can be useful for generating Lorentzian waveforms

There are two kinds of fading models for large and small scale fading in the propagation process of electromagnetic waves, and this paper analyzes the root-mean-square delay expansion and Les K factor of two kinds of small scale fading characteristics under line-of-sight and non-line-of-sight scenarios channels by combining the statistical method with the fitting. A channel model library is established, and multiple large-scale fading models are used to compare the residual sum of squares and the goodness of fit to analyze the large-scale fading characteristics in HF signal transmission. Combined with the performance of the characteristics existing in HF signal transmission, we propose a wireless channel multipath identification algorithm based on D-S evidence theory, analyze the algorithm operation flow, and test the algorithm for multi-channel scene identification. The CDF of the rms delay extensions under the line-of-sight and non-line-of-sight scene channels can be fitted well with shape parameters of 0.727 and 0.272 and scale parameters of 655.33 and 1173.83, respectively. The CDFs of the Rice K factors for the 2 scenarios fit the normal distribution well, with standard deviations less than 3 dB. The wireless channel multipath identification algorithm based on D-S evidence theory can improve the wireless signal transmission identification and optimize the frequency response of wireless signal transmission.

In many applications, it is necessary to transmit images at a remote station, where wired Internet service is not available. In this case, wireless local loop (WLL) can help in making wireless link between one end node of the internet and remote service center. In such link, the communication is heavily affected by large and small scale fading; hence the received signal experiences huge distortion in case of forward error correction. Otherwise, huge service delay arises due to frequent negative acknowledgements. To combat the situation, we can choose Alamouti channel of full rate and fully orthogonal space-time block code (OSTBC). Our aim is to transmit images through Alamouti channel and to observe the quality of the recovered image, in context of bit error rate (BER). We have also observed the impact of fading and additive white Gaussian noise (AWGN) on the image without application of error correction or detection technique of channel coding. To ensure security, we apply the RSA algorithm on each pixel prior transmitting and decrypt them at the receiving end, where we found no impairment from the algorithm. Finally, we observe that the relative performance of the system changes digital modulation schemes.

The accurate simulation of multipath fading channels is a crucial issue in the development and evaluation of modern wireless communication networks. Since the received signal depends on many fast-changing factors, statistical models are typically used to simulate fading. Many fading models have been developed over the last four decades, making use of several statistical distributions (Rice, Rayleigh, Nakagami-m, etc.) and with different degrees of complexity. In this chapter, we describe some of the most important statistical models developed for the simulation of wireless fading channels, paying special attention to the accuracy–complexity trade-off and discussing the application domain where each of them is more appropriate. Indeed, the main purpose of this chapter is providing clear algorithms for the efficient simulation of each of the channels described (which are often hard to find in the literature), rather than focusing only on the theoretical aspects (which are also covered). In order to achieve this purpose, several case studies are introduced throughout the chapter, showing how to simulate real-world channels (at different degrees of detail and complexity) for practical applications. The chapter is structured as follows. Firstly, we provide an introduction to mobile wireless communication networks, motivating the use of statistical tools to simulate multipath fading channels in Section 10.1. In Section 10.2, we describe the general framework of the statistical models used for the simulation of wireless fading channels, emphasizing the different types of channels that can be encountered and the key elements that have to be modelled. A brief description of a digital communications transmitter and receiver is also provided in this section. Then, Section 10.3 focuses on largescale fading, which is typically simulated using a fixed average loss plus a log-normal distribution to describe local variations. Section 10.4, which is the main section of the chapter, concentrates on the simulation of small-scale fading for the two most common situations found in modern communication systems: slow flat fading and slow frequency-selective fading. Finally, Section 10.5 closes the chapter. 10.

Capacity maximizing in massive MIMO with linear precoding for SSF and LSF channel with perfect CSI

The paper presents an analysis of the impact of Local Oscillators (LO) phase noise on the performance of digital transmission systems. Using an efficient phase noise model, we study degradations induced by phase noise and Additive White Gaussian Noise (AWGN) on a carrier recovery system combined to high order Quadrature Amplitude Modulations (QAM). Simulation results show that an optimum trade-off between AWGN and phase noise robustness for the loop bandwidth can be determined. In order to compare simulated and analytical performance, a new general expression of the bit error probability is developped for square QAM in the presence of phase noise over Gaussian channel. Furthermore, novel decision areas related to Quadrature Amplitude Modulations are determined in order to improve the performance of the carrier recovery algorithm in the presence of phase noise and frequency offset.

UAV(Unmanned aerial vehicles)는 현재 많은 분야에서 관심을 받고 있으며 독특한 장점 덕분에 기존 무선 네트워크를 보조하기 위한 대안으로서 떠오르고 있다. UAV의 가장 큰 장점은 3차원 공간의 높은 자유도(Degree of freedom, DoF)를 적극적으로 활용할 수 있다는 점이다. 구체적으로, 3차원 공간에서 UAV의 높은 고도는 LoS(Line of sight)가 지배적인 환경을 형성하므로 무선 네트워크에 넓은 커버리지(Coverage)를 제공한다. 또한, 고정적인 배치나 한정적인 이동만이 가능한 기존 기지국이나 릴레이(Relay)와 달리 UAV는 수평이나 수직의 이동이 자유롭다. UAV의 3차원 공간 위에서 자유로운 궤적은 기존 셀룰러 네트워크의 성능을 위해 최적화되어 셀룰러 네트워크에 큰 이득을 제공한다. 본 논문에서는 위의 이점들을 살려 UAV를 기존 무선 네트워크와 결합하기 위한 연구들, 즉 UAV 채널 모델링과 UAV 궤적 최적화를 살펴보도록 한다. 첫 번째로, UAV 채널 모델링을 크게 대규모 페이딩(Large scale fading)과 소규모 페이딩(Small scale fading) 채널로 나누어 정리하고 세부적인 부분들로 현재 UAV 채널에 관한 연구 동향을 파악하도록 한다. 두 번째로, UAV의 3D에서 높은 자유도를 활용하기 위한 UAV 궤적 최적화 문제를 일반적인 수식으로 나타내본다. 더불어, UAV 궤적 최적화에 관한 최신 연구들로 일반적인 수식에 대한 구체적인 이해를 돕고 동시에 전반적인 연구 동향을 살펴본다. 마지막으로 UAV 기반 셀룰러 네트워크의 중요한 연구 주제들을 살펴보는 것으로 후속적으로 이루어질 연구를 논의하며 UAV 기반 네트워크의 잠재력을 짚어보며 마무리한다.

We consider an energy harvesting (EH) relay-assisted, two hop, cooperative communication system. For it, we analytically characterize EH relay outage probability when the source device transmissions undergo small scale fading as well as large scale fading. In the analysis, we develop simple yet insightful upper bound for the EH relay outage probability. Furthermore, we investigate asymptotic outage probability of EH relay. We validate our analytical results with Monte-Carlo simulations. Assuming other system parameters fixed, our results show that the EH relay outage probability decreases with the increase in the harvesting power.

Radio resource allocation in device-to-device (D2D) communications can be optimized by minimizing outage probability or maximizing throughput. In this letter, we derive two closed-form expressions for the outage probability of underlay D2D communications, by considering an additive white Gaussian noise, and large-scale and small-scale fading components. We jointly model small and large scale fading components by the Suzuki distribution and derive non-analytic integrals for the outage probability, which can be solved by Gauss–Laguerre quadrature and Gauss–Legendre quadrature numerical methods. We show that these formulas for the outage probability closely approach the simulation results. Our comparative study further shows that a low-complexity approximation belongs to the three-point Gauss–Legendre-based formula. Finally, random-based and Hungarian-based resource allocations are investigated.

Wireless communication technology profoundly affects each person's life; channel model has become one of the research hot spots in the trade union in the wireless communication system. This article firstly expounds three kinds of fading channels: large-scale fading, small scale fading, and SISO channel model in wireless communication system, then simulates the impact of distances to the path loss in different channel models through MATLAB, and lastly concludes the full text.

Classical data compression focuses on the most accurate reproduction of data. However, modern applications, particularly in artificial intelligence, increasingly impose new statistical or behavioral constraints. This thesis extends compression theory and practice to these new settings. Our main tool is channel simulation—a generalization of data compression for remotely generating samples from a specified distribution. We first establish a framework for universal channel simulation, analogous to universal source coding for when the target distribution is unknown, and provide a provably near-optimal algorithm. We then develop a computationally efficient algorithm for simulating the Gaussian channel, a ubiquitous model for which no such practical method existed. In distributed learning, we formalize remote reinforcement learning under communication constraints and propose a general, highly efficient solution employing channel simulation. Furthermore, we uncover a fundamental connection between speculative decoding—a method to accelerate large language models—and channel simulation, yielding new algorithms and speed-up guarantees. Finally, we introduce a goal-oriented compression framework for agents whose actions and communications are concurrent, for which we derive performance bounds and design practical coding schemes. The contributions in this thesis highlight the fundamental nature of channel simulation as a novel and effective tool to address a variety of the challenges in federated learning, reinforcement learning, realistic image compression, and large language model inference.

The channel measurement and modeling of fading characteristics of 25.5 and 39.5 GHz bands in corridor environment are investigated. An automatic measurement system owning a sufficient dynamic range is designed to overcome the prominent attenuation in the millimeter-wave band. After conducting extensive measurements, large-scale and small-scale fading characteristics are obtained and compared. Good-of-fit test is applied to validate whether the assumed log-normal distribution can describe the shadow fading characteristics. The closed-in log-distance path loss model and the least-square fitted model are respectively employed to analyze the effects of frequency and antenna polarization on large-scale fading. Besides, typical small-scale fading parameters such as root-mean square (RMS) delay spread and in-cluster RMS angle spread are extracted utilizing the bisecting k-means clustering algorithm.

This paper elaborates on the performance of orthogonal space-time block codes (STBCs) for multiple-input multiple-output (MIMO) systems operating in generalized-K fading conditions. The considered fading model is generic since it intimately encompasses both small-scale fading (modeled via the Nakagami-m distribution) and large-scale fading (modeled via the gamma distribution). In the following, new exact analytical expressions are derived for the Shannon capacity along with asymptotic expressions in the high and low Signal-to-Noise ratio (SNR) regimes. We also present exact tractable expressions followed by first-order expansions for the marginal outage probability and symbol error rate (SER); further, we quantify the performance of STBCs in terms of diversity order and coding gain. The derived analytical expressions are validated via a set of Monte-Carlo simulations which also demonstrate the implications of the model parameters on the overall performance.

This paper investigates the performance of Multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) systems with randomly deployed users, where the randomly deployed NOMA users follow Poisson point process (PPP), the spatial correlation between MIMO channels are characterized by using Kronecker model, and the composite channel model is used to capture large-scale fading as well as small-scale fading. The spatial randomness of users' distribution, the spatial correlation among antennas and large-scale fading will severally impact the system performance, but they are seldom considered in prior literature for MIMO-NOMA systems, and the consideration of all these impact factors challenges the analysis. Based on zero-forcing (ZF) detection, the exact expressions for both the average outage probability and the average goodput are derived in closed-form. Moreover, the asymptotic analyses are conducted for both high signal-to-noise ratio (SNR) (/small cell radius) and low SNR (/large cell radius) to gain more insightful results. In particular, the diversity order is given by $\delta = {{N_r} - {M} + 1}$, the average outage probability of $k$-th nearest user to the base station follows a scaling law of $O\left({D^{\alpha \left( {{N_r} - {M} + 1} \right) + 2k}}\right)$, the average goodput scales as $O({D^{2}})$ and $O({D^{-2}})$ as $D \to 0$ and $D \to \infty$, respectively, where $N_r$, $M$, $\alpha$ and $D$ stand for the number of receive antennas, the total number of data streams, the path loss exponent and the cell radius, respectively. The analytical results are finally validated through the numerical analysis.