Thank you very much for downloading table of integrals series and products. Maybe you have knowledge that, people have look hundreds times for their chosen books like this table of integrals series and products, but end up in harmful downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some harmful virus inside their laptop. table of integrals series and products is available in our book collection an online access to it is set as public so you can get it instantly. Our book servers saves in multiple locations, allowing you to get the most less latency time to download any of our books like this one. Merely said, the table of integrals series and products is universally compatible with any devices to read.

Table Of Integrals Series And Products

In this chapter we deal with the following nonlinear fractional programming problem: 

$$P:\mathop{{\max }}\limits_{{x \in s}} q(x) = (f(x) + \alpha )/((x) + \beta )$$

where f, g: R n → R, α, β ∈ R, S ⊆ R n . To simplify things, and without restricting the generality of the problem, it is usually assumed that, g(x) + β + 0 for all x ∈ S,S is non-empty and that the objective function has a finite optimal value.

Nonlinear Fractional Programming

Deep neural network models for computational histopathology: A survey

Mobile network is evolving from a communication-only network towards the one with joint communication and radio/radar sensing (JCAS) capabilities, that we call perceptive mobile network (PMN). Radio sensing here refers to information retrieval from received mobile signals for objects of interest in the environment surrounding the radio transceivers. In this paper, we provide a comprehensive survey for systems and technologies that enable JCAS in PMN, with a focus on works in the last ten years. Starting with reviewing the work on coexisting communication and radar systems, we highlight their limits on addressing the interference problem, and then introduce the JCAS technology. We then set up JCAS in the mobile network context, and envisage its potential applications. We continue to provide a brief review for three types of JCAS systems, with particular attention to their differences on the design philosophy. We then introduce a framework of PMN, including the system platform and infrastructure, three types of sensing operations, and signals usable for sensing, and discuss required system modifications to enable sensing on current communication-only infrastructure. Within the context of PMN, we review stimulating research problems and potential solutions, organized under eight topics: mutual information, waveform optimization, antenna array design, clutter suppression, sensing parameter estimation, pattern analysis, networked sensing under cellular topology, and sensing-assisted secure communication. This paper provides a comprehensive picture for the motivation, methodology, challenges, and research opportunities of realizing PMN. The PMN is expected to provide a ubiquitous radio sensing platform and enable a vast number of novel smart applications.

Enabling Joint Communication and Radio Sensing in Mobile Networks -- A Survey

This paper considers the problem of power minimization-based robust orthogonal frequency division multiplexing (OFDM) radar waveform design, in which the radar coexists with a communication system in the same frequency band. Recognizing that the precise characteristics of target spectra are impossible to capture in practice, it is assumed that the target spectra lie in uncertainty sets bounded by known upper and lower bounds. Based on this uncertainty model, three different power minimization-based robust radar waveform design criteria are proposed to minimize the worst-case radar transmitted power by optimizing the OFDM radar waveform, which are constrained by a specified mutual information requirement for target characterization and a minimum capacity threshold for communication system. These criteria differ in the way the communication signals scattered off the target are considered: 1) as useful energy, 2) as interference, or 3) ignored altogether at the radar receiver. Numerical simulations demonstrate that the radar transmitted power can be efficiently reduced by exploiting the communication signals scattered off the target at the radar receiver. It is also shown that the robust waveforms bound the worst-case power-saving performance of radar system for any target spectra in the uncertainty sets.

/pdf/power-minimization-based-robust-ofdm-radar-waveform-design-25bbydu5y4.pdf

Power Minimization-Based Robust OFDM Radar Waveform Design for Radar and Communication Systems in Coexistence

In this paper, we consider multiple input multiple output (MIMO) radar waveform design in colored noise. Two information theoretic measures are used as criterions for optimal waveform design under transmitted power constraint. The first one is by maximizing the mutual information between target impulse response and target echoes; the second one is by maximizing the relative entropy between two hypotheses: in the first hypothesis we assume the target is not present in the echoes while in the second hypothesis we assume the target exists in the echoes. We derive optimal solutions for both cases. Interestingly, both optimal solutions require that transmitted waveform should “match” with the target and noise. However, the optimal solutions of the two problems lead to different power allocation strategies.

MIMO Radar Waveform Design in Colored Noise Based on Information Theory

This paper considers the joint design of transmit waveforms and receive filters for airborne multiple-input-multiple-output (MIMO) radar systems. The aim is to maximize the output signal-to-interference-plus-noise ratio (SINR) such that we can achieve enhanced detection performance for a slow-moving target that might be obscured by clutter and jamming. We devise a cyclic algorithm to tackle the non-convex joint design problem. Considering practical implementation issues, we extend the algorithm to deal with constrained designs (i.e., the design of waveforms under a constant-envelope constraint and a similarity constraint, respectively). The convergence of all the devised algorithms is guaranteed. We provide several numerical examples to demonstrate the effectiveness of the proposed algorithms.

Joint Design of Transmit Waveforms and Receive Filters for MIMO Radar Space-Time Adaptive Processing

We address the waveform design problem for multiple-input multiple-output (MIMO) radar in spectrally crowded environments. We exploit the mutual information between the target reflections and the target responses as the design metric. To tackle the associated nonconvex optimization problem, we propose an algorithm based on first-order Taylor series approximation as well as minorization–maximization (MM) based algorithms to design the spectrally constrained waveforms. Interestingly, for some scenarios, we can synthesize the globally optimal (spectrally constrained) waveforms with the maximum mutual information. We also show that, through intelligent waveform design, MIMO radar can coexist more efficiently with other communication systems occupying the same spectrum, while suffering from insignificant mutual information losses.

Spectrally Constrained MIMO Radar Waveform Design Based on Mutual Information

Due to its long-standing importance, the problem of designing the receive filter and transmit sequence for clutter/interference rejection in active sensing has been studied widely in the last decades. In this letter, we propose a cyclic optimization of the transmit sequence and the receive filter. The proposed approach can handle arbitrary peak-to-average-power ratio (PAR) constraints on the transmit sequence, and can be used for large dimension designs (with ~ 103 variables) even on an ordinary PC.

https://www.it.uu.se/katalog/mojso279/p6.pdf

Bo Tang

Papers

MIMO Radar Waveform Design in Colored Noise Based on Information Theory

Joint Design of Transmit Waveforms and Receive Filters for MIMO Radar Space-Time Adaptive Processing

Spectrally Constrained MIMO Radar Waveform Design Based on Mutual Information

Joint Design of the Receive Filter and Transmit Sequence for Active Sensing

Maximum likelihood estimation of DOD and DOA for bistatic MIMO radar