\r\n\tThe objective of the proposed book is to give a multi-perspective view on role of autophagy in injury, infection and cancer diseases. The book chapters aim to elucidate autophagy pathways in sustaining the host defense mechanisms, adaptive homeostasis as well as in remodeling and regeneration events that are essential for recuperation of the affected tissues. A specific subject for discussion will be up-regulation and/or impairment of autophagy and crinophagy in phagocytes/granulocytes and adult stem cells.
\r\n
\r\n\tRationale: \r\n\tThe cell/tissue responses to acute stress, trauma/injury or pathogens are mediated by expression and release of plethora of paracrine and endocrine effectors including DAMPs, PAMPs and inflammatory cytokines, chemokines, defensins, and reactive intermediate species. These effectors drive the integrative interactome constituted by hubs of the acute phase response modules, the inflammatory response modules, the module of the adaptive homeostatic response in the damaged parenchymal cells, vascular cells, immunocompetent cells and emerging stem cells. Among these defense mechanisms is autophagy – the lysosomal pathway for processing of compromised cell constituents and/or bacterial and viral pathogens. In this light, explication of the role of autophagy in cellular pathology may arouse R&D of new modalities for management of devastating diseases such as injury, acute infections or cancer.
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\n
1. Role of membership functions
\n
Fuzzy logic systems are widely used for control, system identification, pattern recognition problems, and many more applications from industry to academia. The membership functions (MFs) play vital role in the overall performance of fuzzy representation. The MFs are the building blocks of fuzzy set theory, that is, fuzziness in a fuzzy set is determined by its MF. Accordingly, the shapes of MFs are important for a particular problem since they effect on a fuzzy inference system. They may have different shapes such as triangular, trapezoidal, Gaussian, and so forth. The only condition a MF must really satisfy is that it must vary between 0 and 1.
\n
\n
\n
2. How to choose an appropriate membership function?
\n
The MFs can be of any shape and form as long as it maps the given data with desirable degree of memberships. As far as choice of MFs is concerned, it is us to decide. This is where fuzzy system offers individual degrees of freedom. With experience, one will come to know which shape of MF is good for the application under consideration.
\n
As there are infinite number of ways to characterize fuzziness, there are infinite number of ways to graphically depict the MFs that describe this fuzziness. The choice of which of the methods to use depends entirely on the problem size and problem type. Instead of choosing the shape of MF, setting the interval and number of MFs are also very important. For instance, to model a temperature control system by fuzzy logic, it is really important to know how many MFs are needed (e.g., low, med, and high MF) and also choosing the intervals of MFs. These two factors also have a great impact on the outcome of a fuzzy logic system.
\n
In addition, looking at the distribution of the data is a good idea. Although, trial and error method is often used for MF shape, because there is no exact method for choosing the MFs. The shape of MFs depends on how one believes in a given linguistic variable. It is more a question of intuition then criteria. The only condition a MF must really satisfy is that it must vary between 0 and 1. The function itself can be an arbitrary curve whose shape we can define as a function that suits us from the point of view of simplicity, convenience, speed, and efficiency. Therefore, the type of MF doesnot play a crucial role in shaping how the model performs.
\n
However, the number of MF has greater influence as it determines the computational time. Hence, the optimum model can be determined by varying the number/type of MFs for achieving best system performance. Ref. [1] discusses which shape is best if one uses fuzzy logic as a universal approximator. Also, a constrained interpolations scheme was developed for fitting a MF to a finite number of known membership values [2].
\n
There are many references giving directions of how to choose MF [3, 4, 5, 6]. The basic problem with modeling a situation, is to break the 0–1 modeling. This can be done by using triangular MF. However, if the situation is complex and deep, we might need a special type of MF. For instance, if the problem at hand is a quantum mechanics problem, then a special MF is needed. In order to make the best choice, one needs a lot of “experience” with the given situation. This experience will tune up and best fit, the subjective choice of the researcher with the given reality. There is no objective way to do so. Thus, a high fidelity intuition based on sufficient experience will give an acceptable answer.
\n
Generally speaking, triangular MF is one of the most encountered MF in practice. Of highly applied MFs, the triangular MFs are formed using straight lines. These straight line membership functions have the advantage of simplicity. Gaussian MFs are popular methods for specifying fuzzy sets because of their smoothness and concise notation. These curves have the advantage of being smooth and nonzero at all points.
\n
It is advisable to use the symmetric triangular MF with 50% overlap, and then apply tuning procedure during which we can either change the left and/or right spread and/or overlapping. This is to be continued till we get satisfactory results. Same approach can be attempted for other shapes such as trapezoidal, bell-shape, and so forth.
\n
Triangular shapes represent fuzzy numbers, while trapezoid shapes represent fuzzy intervals. These are the simplest shapes. Other different shapes can be obtained from transformations of the triangle induced by linguistic modifiers, truth-functional modifiers, compositions, projections, and other operations.
\n
In fact, the selection of MF shape is problem specific. Based on extensive review on many literatures, it can be concluded that the triangular MF is widely used because of its simplicity. Using various MF for given problems, usually Gaussian and triangular MFs are found to be closely performing well and better than other types of MF. In specific, the triangular MF is found to be better than Gaussian MF. Zhao and Bose [7] compared the response of the system with various MFs and conveyed that the triangular MF is superior to any other MFs.
\n
Indeed, if one has no priority on the shape of MFs, triangular or trapezoidal shapes are simple to implement and fast for computation. However, if one has some priorities on their shapes (e.g., from histograms on sampled data), it may be interested to build MFs with shapes derived from these a priori shapes after some smoothing if needed.
\n
Afterwards, the question is what the optimal values of initial parameters are that we need to be aware of to make a sensible choice of the chosen MF?
\n
In order to maximize their performance, it is often necessary to undertake a design optimization process in which the adjustable parameters defining a particular fuzzy system are tuned to maximize a given performance criterion. Using metaheuristic optimization methods and evolutionary optimization algorithms, fuzzy logic possesses the great flexibility toward its initial parameters regarding MFs [8].
\n
Interested reader can find some useful information about MFs and some procedures (e.g., GA and neural network) to assign memberships to fuzzy variables [4]. There are many articles, which have used optimization algorithms such as particle swarm optimization and genetic algorithm to find the optimal set of parameters for fuzzy models [9, 10, 11, 12].
\n
\n\n',keywords:null,chapterPDFUrl:"https://cdn.intechopen.com/pdfs/62600.pdf",chapterXML:"https://mts.intechopen.com/source/xml/62600.xml",downloadPdfUrl:"/chapter/pdf-download/62600",previewPdfUrl:"/chapter/pdf-preview/62600",totalDownloads:1152,totalViews:589,totalCrossrefCites:8,totalDimensionsCites:16,hasAltmetrics:0,dateSubmitted:"May 29th 2018",dateReviewed:"June 15th 2018",datePrePublished:null,datePublished:"October 31st 2018",dateFinished:null,readingETA:"0",abstract:null,reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/62600",risUrl:"/chapter/ris/62600",book:{slug:"fuzzy-logic-based-in-optimization-methods-and-control-systems-and-its-applications"},signatures:"Ali Sadollah",authors:[{id:"147215",title:"Dr.",name:"Ali",middleName:null,surname:"Sadollah",fullName:"Ali Sadollah",slug:"ali-sadollah",email:"ali_sadollah@yahoo.com",position:null,institution:{name:"University of Science and Culture",institutionURL:null,country:{name:"Iran"}}}],sections:[{id:"sec_1",title:"1. Role of membership functions",level:"1"},{id:"sec_2",title:"2. How to choose an appropriate membership function?",level:"1"}],chapterReferences:[{id:"B1",body:'Kosko B, Mitaim S. What is the best shape for a fuzzy set in function approximation? Proceedings of the 5th IEEE International Conference on Fuzzy Systems (FUZZ-96); September 1996. pp. 1237-1243\n'},{id:"B2",body:'Chen JE, Otto KN. Constructing membership functions using interpolation and measurement theory. Fuzzy Sets and Systems. 1995;73(3):313-327\n'},{id:"B3",body:'Wu D. Twelve considerations in choosing between Gaussian and trapezoidal membership functions in interval type-2 fuzzy logic controllers. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE); 2012; Brisbane, QLD, Australia\n'},{id:"B4",body:'Ross TJ. Fuzzy Logic with Engineering Applications. 3ed. John Wiley & Sons; 2010\n'},{id:"B5",body:'Rutkowska A. Influence of membership function’s shape on portfolio optimization results. 2016;6 (1):45-54\n'},{id:"B6",body:'Czekalski P. Evolution-fuzzy rule based system with parameterized consequences. International Journal of Applied Mathematics and Computer Science. 2006;16(3):373-385\n'},{id:"B7",body:'Zhao J, Bose BK. Evaluation of membership functions for fuzzy logic controlled induction motor drive. 28th Annual IEEE Conference of the Industrial Electronics Society 2002; Sevilla; Spain\n'},{id:"B8",body:'El-Zonkoly AM, Khalil AA, Ahmied NM. Optimal tuning of lead-lag and fuzzy logic power system stabilizers using particle swarm optimization. Expert Systems with Applications. 2009;36(2):2097-2106\n'},{id:"B9",body:'Zhang W, Liu Y. Fuzzy logic controlled particle swarm for reactive power optimization considering voltage stability. IEEE International Conference on Power Engineering, Singapore. 2005;2005:1-5\n'},{id:"B10",body:'Esmin AAA, Aoki AR, Lambert-Torres G. Particle swarm optimization for fuzzy membership functions optimization. In: IEEE International Conference on Systems, Man and Cybernetics, Yasmine Hammamet, Tunisia; Tunisia; 2002\n'},{id:"B11",body:'Arslan A, Kaya M. Determination of fuzzy logic membership functions using genetic algorithms. Fuzzy Sets and Systems. 2001;118(2):297-306\n'},{id:"B12",body:'Zhang W, Liu Y. Fuzzy logic controlled particle swarm for reactive power optimization considering voltage stability. In: The 7th International Conference on Power Engineering; Singapore; January 2005. DOI: 10.1109/IPEC.2005.206969\n'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Ali Sadollah",address:"ali_sadollah@yahoo.com",affiliation:'
School of Mechanical Engineering, Sharif University of Technology, Tehran, Iran
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1. Background and introduction
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Recently, the space industry has pointed out that in the past 5 years, the commercial market has been driving the advancement of satellite technology. Lockheed Martin is building commercial satellites (e.g., Hellas-sat series) with advanced on-board processing capabilities for the Saudi Arabian [1]. Hellas satellites probably will be the first commercial HTS with a very advanced digital processor on-board. The focus of this chapter will be on commercial satellite systems for communication applications, and a comparison study between commercial HTS and typical satellites systems conducted by Inmarsat will be provided [2].
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For communication applications, commercial satellite systems have been categorized as mobile satellite services (MSSs), fixed satellite services (FSSs), broadcast satellite services (BSSs), and high-throughput satellite (HTS) services. Depending on the services, satellite payload architecture will be designed to meet the specified requirements for that service. Basically, satellite payload architecture can be classified into four categories: (1) analog bent-pipe satellite (ABPS); (2) digital bent-pipe satellite (DBPS); (3) advanced digital bent-pipe satellite using digital channelizer and beamformer (AdDBPS-DCB); and (4) advanced regenerative on-board processing satellite (AR-OBPS). This chapter provides an overview of these payload architectures and presents two satellite system architectures using AdBPS-DCBS and AR-OBPS payloads for the fifth-generation cellular phone (5G) applications.
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The chapter is organized as follows: Section 2 provides a comparison between commercial HTS and typical satellite systems; Section 3 discusses the typical satellite network topologies; Section 4 presents an overview of legacy ADPS transponder, existing DBPS transponder, AdBPS-DCBS transponder, and AR-OBPS satellite system; Section 5 discusses the use of AdBPS-DCBS transponder and AR-OBPS payloads for the fifth-generation cellular phone (5G) applications; and Section 6 concludes the chapter with a summary and brief discussion of way forward.
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2. Typical commercial satellites and HTS comparison
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Typical and regular commercial satellites are operating in C-band, Ku-band, and Ka-band with downlink frequencies approximately at 4, 12, and 40 GHz, respectively. For C-band, Ku-band, and Ka-band, the spectrum bandwidths available by geostationary orbital position are 500 MHz, 500 MHz, and 3.5 GHz, respectively. Typical antenna types for these regular commercial satellites are pointed antenna type with a single beam. Typical diameters for these pointed antennas are (a) greater than 1.8 m for C-band; (b) 0.9–1.2 m for Ku-band; and (c) 0.6–1.2 m for Ka-band satellite. Figure 1(a) illustrates a typical regular commercial satellite.
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Figure 1.
Typical commercial satellites and HTS configurations.
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Typical HTSs are usually also operating in Ku-band and Ka-band with the same downlink frequencies as the regular satellites except that they employ multiple pointed beam as oppose to a single-pointed beam. Figure 1(b) describes a multiple beam HTS system. The salient feature of multiple beams is the frequency reuse. The frequency reuse is defined as the number of times a satellite can reuse the same spectrum and frequencies. However, high frequency reuse factor can cause potential cochannel interference or an increase in carrier-to-interference power ratio (CIR or C/I). IMMARSAT has reported that a reuse factor of 5–30 is possible with multiple spot beams employed by commercial HTS. Depending on the number of beams implemented on-board of the satellite, the cost for HTS can be twice of the cost for a regular satellite. But, the cost per bit for HTS is much lower than the regular satellite. HTS is a preferred option for point-to-point services, for example, beyond line-of-sight (BLOS) cellular phone services. Table 1 provides a summary of the comparison of HTS and regular commercial satellites [2].
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Comparison factor
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Typical regular commercial satellite
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Typical high-throughput satellite (HTS)
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Remark
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\n\n\n
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Operational frequency band
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C-band, Ku-band, Ka-band
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Ku-band, Ka-band
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It should be noted that for data presented here, all satellites and supply are not equal; various technical, regulatory, and commercial parameters come into play when comparing the two-type satellites. Data collected from IMMARSAT. Source: see [2]
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Throughput capability (Gbps)
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~1–10
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~5–300+ (with frequency reuse in multiple spot beam)
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Typical cost including launch (USD)
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~200–300
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~300–500 (cost can be twice of regular satellite)
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Advantages
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Wide coverage; preferred solution for point-to-multipoint communication
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Higher bandwidth/lower cost per bit; preferred option for point-to-point services
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Disadvantages
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Limited supply available; lower spectrum efficiency for an equivalent frequency
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Higher upfront costs; difficult to find enough customers to fill each of the beams
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\n\n
Table 1.
Comparison of typical commercial satellites and HTS.
This section describes the most commonly used satellite network topologies, namely “Star” satellite network (Section 3.1) and “Mesh” satellite network (Section 3.2).
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3.1 Typical “star” satellite network
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A typical commercial satellite network topology consists of an uplink from a central anchor station (aka satellite Gateway or satellite Hub) to a satellite and a downlink from the satellite to users. Users can be mobile or fixed users. Mobile users can be located in an airplane, a boat, or a car. Fixed users can be located in a building or a cellular base station. The “star” satellite network is derived from a spoke-hub distribution paradigm in computer networks, where one central hub serves as a conduit to transmit messages among network users [3]. Thus, for star satellite networks, all communications will be passed through a satellite gateway. As shown in Figure 2, if Mobile User 1 wants to talk to Mobile User 2, Mobile User 1 needs to send its messages to the satellite gateway (yellow lines), and satellite gateway relays that messages to Mobile User 2 (red lines).
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Figure 2.
Typical “star” satellite network.
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3.2 Typical “mesh” satellite network
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The “mesh” satellite network topology is derived from a local network topology, where the network nodes are corrected to each other directly, dynamically, and nonhierarchically to as many other nodes as possible [4]. In this network topology, the network nodes can cooperate with one another to route data from one user to another user efficiently. Hence, for mesh satellite network, Mobile User 1 can talk to fixed user directly without going through the satellite gateway (solid lines), and Mobile User 2 can also talk to the fixed user directly (dash lines). Any one of the user within the network can send the messages to a terrestrial network through the red lines representing uplink and downlink between the satellite gateway and the satellite (Figure 3).
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Figure 3.
Typical “mesh” satellite network.
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Star satellite network topology does not require advanced satellite payload processing on-board and multiple beam, but mesh satellite network requires advanced on-board processing and multiple beam allowing one user to communicate to another user automatically and effectively. Section 4 discusses various satellite payload architectures used in regular satellite and HTS for star and mesh satellite network applications.
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4. Legacy, existing, and advanced satellite payload architectures
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This section presents an overview of legacy, existing, and advanced satellite payload architectures. Section 4.1 presents legacy ABPS payload architecture, Section 4.2 provides a description of a typical existing DBPS payload architecture, Section 4.3 discusses AdDBPS-DCB payload architecture, and Section 4.4 provides an overview of AR-OBPS payload architecture.
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4.1 Legacy analog bent-pipe satellite (ABPS) payload architecture
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A typical legacy ABPS payload architecture is depicted in Figure 4, where the payload has multiple beam antennas (MBAs) using parabolic dishes. For this architecture, the RF signal is received at the satellite payload and amplifies by a low noise amplifier (LNA) for increased received signal-to-noise power ratio (SNR). The RF signal with increased SNR is downconverted (D/C) to an intermediate frequency (IF) and processed by an IF filter to clean up the signal from adjacent interference and out-of-band noise. The clean-up signal is then (a) routed to the proper downlink port by an IF analog switching circuit and upconverted (U/C) to RF, (b) combined by a multiplexer (MUX), and (c) amplified by a high-power amplifier (HPA) for downlink transmission.
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Figure 4.
Legacy ABPS payload architecture.
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As illustrated in Figure 5, there are two options for the D/C, namely Option 1 (see Figure 5(a)) is a double downconverter using two local oscilators (LOs) to downconvert RF signal to IF signal with stable and low phase noise, and Option 2 (see Figure 5(b)) is single downconverter using a LO downconverting RF signal directlty to an IF signal. Option 1 is being used in many legacy, existing, and advanced satellite payloads. Option 2 is mostly used in advanced satellite payloads.
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Figure 5.
Options for RF downconversion and associated LO’s phase noise.
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\nFigure 5(c) shows commercial-of-the-shelf (COTS) phase noise characteristics for typical LOs operating at X-band, Ku-band, and Ka-band. X-band, Ku-band, and Ka-band illustrated in this figure correspond to 7–11.2, 12–18, and 26.5–40 GHz, respectively. The main advantages of Option 2 using single downconversion are its low cost, small size, and low power consumption (also known as small SWAP-C). This option uses the smallest number of external components as compared to Option 1 using double downconversion, which is also known as super heterodyne receiver [5]. However, Option 2 suffers amplitude and phase imbalances caused by imperfect references associated with I-Q components, direct current (DC) signal due to self-mixing, and flicker noise.1 Option 1 does not suffer from these problems and offers excellent selectivity and sensitivity, that is, better rejection of adjacent interferences. Option 1’s disadvantages are the integration complexity and high SWAP-C.
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In satellite electronic communications, MUX is a multiplexer, which is a device that selects several (multiple) analog (or digital) input signals and outputs a single signal. Figure 6(a) describes a functional MUX (aka multiplexer) circuit. On the contrary, Figure 6(b) depicts a DEMUX (aka demultiplexer), which is an electronic device that sends a single input signal to multiple signal outputs.
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Figure 6.
Functional block diagrams of MUX and DEMUX.
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4.2 Existing digital bent-pipe satellite (DBPS) payload architecture
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\nFigure 7 presents an existing DBPS payload architecture using on-board digital channelizer. Similar to analog payload, there are two options for the RF-to-IF downconversion process. Double-downconversion process is typically used for digital bent-pipe payload architecture.
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Figure 7.
Existing DBPS payload architecture.
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\nFigure 8 depicts typical RF-to-IF (or baseband) downconversion and digitization and sampling processes for a commercial DBPS payload architecture. The RF-to-IF process shown in this figure uses Option 1, double downconversion, and the digitization and sampling process employing bandpass sampling with digital quadrature technology [6]. The RF bandwidth (BW) associated with the RF bandpass filter (BPF) is selected to match with an over channel bandwidth (e.g., a maximum of 500 MHz for Ku-band). The automated gain control (AGC) is designed to maintain a constant power over the specified channel bandwidth. There are several advantages associated with bandpass sampling with digital quadrature techniques, including (a) no phase and amplitude imbalances; (b) digital finite impulse response (FIR) filters are flexible and computational complexity with linear phase introducing a constant group delay; (c) only one A/D converter is required (less weight and power); and (d) when the sampling period is set at one-quarter of the carrier frequency, the reference in-phase and quadrature components reduce to an alternating sequence between I-channel and Q-channel [6].
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Figure 8.
Typical R/F downconversion and digitization processing approach.
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As shown in Figure 9, the key design issue associated with the digitization and sampling processing is the selection of required number of bits of the analog-to-digital (A/D) conversion to (1) achieve optimum loading factor (LF) and (2) minimize the quantization noise. The LF is defined as the root mean square (RMS) of the total input signal voltage-to-A/D converter saturation voltage ratio. The total input signal voltage includes desired signal voltage (S) plus noise voltage (N) plus interference voltage (I). Figure 10 illustrates an optimum LF as a function of number of bit of a typical A/D converter. As an example, for 4-bit, the optimum LF is about 0.4. In conjunction with LF, the number of bit should be selected to maximize the signal-to-quantization noise ratio (SQNR) using the following relationship:2\n
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Figure 9.
Existing digitization and sampling processing using bandpass sampling with digital quadrature technique.
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Figure 10.
Optimum LF as a function of number of bit of A/D converter.
As an example, when N = 4 bits, signal-to-quantization noise ratio is about 25.84 dB.
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The key feature of DBPS payloads is the flexibility of the digital channelizer. Current digital technologies allow for the implementation of robust and reconfigurable digital channelizer adapting to require the number of users and associated users’ data rates. A typical flexible digital channelizer using polyphase/discrete Fourier transform (DFT) technology is shown in Figure 11.
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Figure 11.
Typical digital channelizer using polyphase/DFT technology.
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As shown in Figure 11, the heart of a typical digital channelizer is a polyphase-filter network (or simply a polyphase network) and a DFT processor. A typical polyphase network with a DFT processor is described in Figure 12. The polyphase network consists of a set of NC digital filters with transfer function H0, H1..., HNc-1, which is obtained by shifting a basic low pass complex filter function along the frequency axis [7]. As an example, for a typical 500 MHz channel bandwidth, assuming for a typical user data rate of 4 MHz and a guardband of 1 MHz, digital channelizer, NC = 500/(4 + 1) = 100, that is, the number of filter is 100, and each has a total of 5 MHz bandwidth. A change in sampling frequency by a factor of NC can be introduced, thus allowing the circuit in different paths of the polyphase network to operate at lower frequency than the original sampling frequency. A practical implementation of a high-throughput low-latency polyphase channelizer can be found in [8, 9].
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Figure 12.
Typical Polyphase/DFT Technology.
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\nFigure 12 shows an example of five input signals, namely S1, S2, S3, S4, and S5, and the channelizer will select signal interest by filtering out the other signals. As an example, the signal line with the filter transfer function of H0 filters out S2, S3, S4, and S5 and sends S1 as an output signal.
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4.3 Advanced digital bent-pipe satellite using digital channelizer and beamformer (AdDBPS-DCB) payload architecture
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For a typical commercial HTS system architecture, it usually requires on-board multiple beam phase array (PA) antenna with associated adaptive digital beamformer network (DBF) for spot beamforming and frequency reusing of the spot beams when the beams are not located near each other. Figure 13 describes a typical AdDBPS-DCB payload architecture, where the digital channelizer is combined with a DBF to make a “digital channelizer and beamformer” (DCB) [10, 11, 12]. For this payload architecture, the key feature that differentiates this architecture with the ones discussed above is the combined digital channelizer using polyphase network/DFT processor and DBF (PolyN/DFT-DBF).
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Figure 13.
AdDBPS-DCB payload architecture.
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As pointed out in [10, 11, 12], DCB architecture shown in Figure 13 can be designed to (1) form individual beams for each active receive and transmit communication channels; (2) adaptively generate channel beam steering weights to dynamically vary the bandwidth, location, and shape of each beam based on traffic demands and the locations of other, potentially interfering beams avoiding adjacent channel interference; (3) use digital beamforming weight calibration to compensate for the temporal and thermal phase and amplitude response variations inherent in analog multibeam phased array antennas; and (4) adjust the gain of individual receive-and-transmit channel beams automatically to compensate for propagation path and analog payload response variations. In general, there are two possible DCB implementation approaches, namely DCB Approach 1 and DCB Approach 2 [13]. Figure 14 describes the DCB Approach 1 for processing the uplink signals, where the uplink signals are individually processed by the digital channelizer (i.e., PolyN/DFT processing) and DBF independently and separately. DCB Approach 1 requires a larger computational load because each DBF processes all the user link bandwidth (e.g., S1, S2, S3, S4, and S5 in Figure 12) at all times to form multiple beams.
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Figure 14.
DCB Approach 1: PolyN/DFT and DBFN individual processing.
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DCB Approach 2 is shown in Figure 15, where DCB utilizes an unified processing approach with each DBF processes only the bandwidth corresponding to a beam (S1 in Figure 12) at normal times. During anomaly operation condition (e.g., natural disaster event), when the bandwidth has to be reassigned to specific areas, the arithmetic load on DBF can be reduced by implementing multiple DBFs, with each capable of processing a bandwidth narrower than that assigned to a beam (i.e., smaller channel unit). This approach enables a reduction in wasteful arithmetic resource usage on bandwidth.
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Figure 15.
DCB Approach 2: Unified and combined PolyN/DFT and DBFN individual processing.
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If one defines the number of multipliers, D implemented in each Tx/Rx DBF as C/fop, where C is the computational load of a DBF (multiplications/sec), and fop is the operation frequency of the multiplier. Let us compare D calculations between DCB Approach 1 and DCB Approach 2. Let us assume the following parameters: n is the number of array elements, m is the number of beams, an userlink processing bandwidth of 28 MHz, 5 frequency repetitions of the userlink, and an operating frequency of multiplier of 256 MHz. Using these values, D for the DBF/channelizer of the DCB Approach 1 configuration becomes [13]:
The latter calculation assumes an ideal case in which DBF network (DFBN) processing is performed on a channel-by-channel basis. The complexity of DCB Approach 2 configuration is 10 times less complex than DCB Approach 1.
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As pointed out in [12], the DBFN when coupled with a digital channelizer (aka DCB) offered more capabilities with many advantages. Nguyen et al. [14] developed a computer simulation model of a typical DBFN in MATLAB and presented simulation results for X, Ku, and Ka BFNs using 60-element, 104-element, and 149-element, respectively. Figure 16 is an extracted Ka-band BFN result showing the achievable antenna gain of 45.5 dB at 3-dB beamwidth of 0.9°. For practical applications, the DBFN will shape the beam size depending on the coverage area and desired number of beams. Nguyen et al. [14] pointed out that for 2.5° coverage area and the desired number of beams of 7, the minimum 3-dB beamwidth of 1.1° is required. Nguyen et al. [14] also pointed out that DCB can provide a significant increase in frequency reuse, where the frequency reuse is defined as the number of times a satellite can reuse the same spectrum and frequencies. High frequency reuse factor can cause potential cochannel interference (CCI) that results in a decrease in carrier-to-interference power ratio [aka (C/I) CCI]. As pointed out in [14], for dynamic allocation using real-time allocation of beams so that the coverage radius of a cell is equal to the satellite pointing error, assuming satellite pointing error of 0.02 degree pointing error, the (C/I)CCI is about 25 dB for frequency reuse factor 40 [14].
\n
Figure 16.
Antenna beamwidth and gain of a notional Ka-band DBFN with 12-bit quantization [14].
\nFigure 17 depicts a potential future AR-OBPS payload architecture [10]. The payload includes (1) a typically set of digitized analog multiple beam antenna (MBA) input signals, digitally frequency division demultiplex each input signal to produce single carrier per channel (SCPC) signal data and demodulate and decode individual traffic channels to recover the original information bits transmitted on the uplink; (2) a set of digitized analog multibeam phase array antenna (MB-PAa) input signals, digitally frequency division demultiplex each input signal to produce SCPC signal data and demodulate and decode individual traffic channels to recover the original information bits transmitted on the uplink; and (3) fast packet switches are typically employed at the AR-OBPS payload’s core to realize statistical multiplexing gains by efficiently packing and moving data through the switch and onto the downlink in bursty uplink transmission applications. Moreover, the digital bandwidth (in Hz) through the AR-OBPS switch is at least 25 times less3 than that supported by an equivalent (pre-demodulation) digital baseband switch at the center of a DC- or DCB-based system. AR-OBPS payload can also support digital beamforming, following the frequency division demultiplexing operation, if a phased array is employed in place of the analog MBA. On the secondary (output) side of the switch, each user’s binary information is channel encoded and modulated onto a carrier. The modulated carrier data thus produced are multiplexed, digital-to-analog converted, and passed through an analog reconstruction filter to generate output signals for the transmit portion of the communication payload. The channel codes and modulations employed on the uplink (input) communication channels clearly do not need to be the same as the channel codes and modulations used on the transmitted downlink channels. Hence, an AR-OBPS payload can serve as a “translator” facilitating single-hop communications between terminals employing different link protocols. However, if either the digital multichannel demultiplexer (DMCD), demodulator, decoder, or digital multichannel multiplexer (DMCM) encoder modulator, multiplexer (MCEM2) functions are implemented in ASICs to minimize size-weight-and-power (SWaP), then the AR-OBPS system becomes somewhat inflexible, unable to support either uplink or downlink terminals, respectively, using communication protocols differing from those for which the AR-OBPS was specifically designed. For this reason, AR-OBPS systems are typically employed in support of “private networks” in which the communication satellite service provider only accommodates terminals designed to work on the provider’s network. Iridium and Spaceway are two examples of commercial AR-OBPS-based communication satellite systems.
\n
Figure 17.
AR-OBPS payload architecture.
\n
\n
\n
\n
5. Satellite system architectures for 5G cellular phone applications
\n
Sections 5.1 and 5.2 present a notional satellite system architecture using AdBPS-DCBS satellite payload and AR-OBPS satellite system architecture for 5G cellular phone applications, respectively.
\n
\n
5.1 AdBPS-DCBS satellite system architecture for 5G applications
\n
AdBPS-DCBS satellite payload can be used to support 5G users. There are potentially two satellite system architecture options for using AdBPS-DCBS satellite payload to support 5G mobile user equipment (aka 5G-UE), namely AdBPS-DCBS Option 1 and AdBPS-DCBS Option 2. For AdBPS-DCBS Option 1, the AdBPS-DCBS satellite provides communication services directly to 5G-UEs. While in AdBPS-DCBS Option 2, the satellite provides services to 5G-UEs through the 5G relay nodes (RNs). Figure 18 illustrates the AdBPS-DCBS satellite system architecture for (a) AdBPS-DCBS Option 1 and (b) AdBPS-DCBS Option 2 [15].
\n
Figure 18.
AdDBPS-DCB satellite system architectures for supporting 5G users.
\n
\nFigure 18(a) shows that the AdBPS-DCBS satellite requires new radio (NR) interfaces between (1) AdBPS-DCBS satellite and terrestrial gateway (GW) and (2) AdBPS-DCBS satellite and 5G-UEs. In addition, it is also required a 5G narrow-band (gNB) processing station to process the 5G signals from the next generation core (NGC) network before passing the 5G data to public data network.
\n
\n
\n
5.2 AR-OBPS satellite system architecture for 5G applications
\n
Similar to AdBPS-DCBS satellite payload, AR-OBPS satellite payload can also be used to support 5G users. There are also two satellite system architecture options for using AR-OBPS payload to support 5G mobile user equipment, namely AR-OBPS Option 1 and AR-OBPS Option 2. For AR-OBPS Option 1, the AR-OBPS satellite provides communication services directly to 5G-UEs. For AR-OBPS Option 2, the satellite provides services to 5G-UEs through the 5G RNs. Figure 19 describes these two AR-OBPS architecture options, namely (a) for AR-OBPS Option 1 and (b) for AR-OBPS Option 2. For these two system architecture options, the gNB processing is now incorporated into the AR-OBPS satellite payload and no longer required for the ground system. The GW now can pass the 5G data directly to the NGC. The decoding-demodulation and encoding-modulation processing on-board of the satellite will be designed to align with the 5G waveform specifications, including 5G modulation and coding schemes.
\n
Figure 19.
AR-OBPS satellite system architectures for supporting 5G users.
\n
\nFigure 19(a) shows that the AR-OBPS satellite also requires NR interfaces between (1) AR-OBPS satellite and GW and (2) AR-OBPS satellite and 5G-UEs. Similar to AdBPS-DCBS satellite system architecture options, the NR interfaces between the AR-OBPS satellite and 5G-UEs are new. Since the gNB processing is now placed at AR-OBPS satellite payload, the NR interfaces between AR-OBPS satellite and 5G-UEs are not the same as the AdBPS-DCBS satellite and 5G-UEs. To show the differences between the two, Figures 19(a) and (b) use Sat-NG-C and Sat-NG-U to indicate the new radio interface between (1) terrestrial GW-NGC-and-AR-OBPS satellite and (2) AR-OBPS satellite-and-terrestrial GW-NGC, respectively.
\n
\n
\n
\n
6. Conclusion
\n
This chapter uses a top-down approach for providing an overview of legacy, existing, and future advanced satellite payload architectures for future wireless communication applications. The chapter focuses on the commercial satellite technologies based on the research results presented in [1, 2]. Section 2 provides the comparison results performed by Inmarsat describing the technical characteristics and associated advantages and disadvantages between commercial HTS and typical satellite systems currently available in commercial satellite market. In Section 3, two most commonly satellite network topologies used by existing commercial satellite networks are presented, and the concept of satellite uplink and downlink associated with star satellite network and mesh satellite network is discussed. The satellite network topologies presented lead to Section 4, where four satellite payload architectures are discussed. The legacy analog ABPS payload architecture is shown to be more appropriate for star satellite network than mesh network. Existing digital DBPS and AdDBPS-DCB payload architectures are designed for supporting mesh satellite network with large number of mobile users. Future advanced digital satellite payload architecture, namely AdDBPS-DCB, is also presented in this section. With decoding-demodulating and encoding-modulating processing on-board of the satellite, AR-OBPS allows for packet switching on-board and higher quality of service (QOS) than existing DBPS and AdDBPS-DCB at the expense of higher SWAP-Cost (SWAP-C). Section 4 of the chapter discusses the applications of AdBPS-DCBS and AR-OBPS payloads for supporting 5G users. Four satellite system architecture options are presented for supporting the future 5G users.
\n
\n
Conflict of interest
The preparation of this chapter was not funded by Gulfstream, and it was done by the author using his own time and resources; thus, it does not represent the Gulfstream’s view on the results presented in this chapter.
\n
Notes/Thanks/Other declarations
\n
The author wishes to thank his wife, Annie Luu-Nguyen, for her immense patience and support.
\n
\n',keywords:"high-throughput satellite, analog bent-pipe satellite, digital bent-pipe satellite, digital channelizer and beamformer, advanced regenerative on-board processing satellite, cellular phone",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/72839.pdf",chapterXML:"https://mts.intechopen.com/source/xml/72839.xml",downloadPdfUrl:"/chapter/pdf-download/72839",previewPdfUrl:"/chapter/pdf-preview/72839",totalDownloads:84,totalViews:0,totalCrossrefCites:0,dateSubmitted:"December 7th 2019",dateReviewed:"June 19th 2020",datePrePublished:"July 17th 2020",datePublished:null,dateFinished:null,readingETA:"0",abstract:"This chapter presents an overview of legacy, existing, and future advanced satellite systems for future wireless communications. The overview uses top-down approach, starting with a comparison between a typical commercial regular satellite system and a high-throughput satellite (HTS) system, following by a discussion on commonly used satellite network topologies. A discussion on the design of satellite payload architectures supporting both typical regular satellite and HTS with associated network topologies will be presented. Four satellite payload architectures will be discussed, including legacy analog bent-pipe satellite (ABPS); existing digital bent-pipe satellite (DBPS) and advanced digital bent-pipe satellite using digital channelizer and beamformer (AdDBPS-DCB); and future advanced regenerative on-board processing satellite (AR-OBPS) payload architectures. Additionally, various satellite system architectures using AdBP-DCBS and AR-OBPS payloads for the fifth-generation (5G) cellular phone applications will also be presented.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/72839",risUrl:"/chapter/ris/72839",signatures:"John Nguyen",book:{id:"7030",title:"Satellite Systems - Design, Modeling, Simulation and Analysis",subtitle:null,fullTitle:"Satellite Systems - Design, Modeling, Simulation and Analysis",slug:null,publishedDate:null,bookSignature:"Dr. Tien Manh Nguyen",coverURL:"https://cdn.intechopen.com/books/images_new/7030.jpg",licenceType:"CC BY 3.0",editedByType:null,editors:[{id:"210657",title:"Dr.",name:"Tien",middleName:"Manh",surname:"Nguyen",slug:"tien-nguyen",fullName:"Tien Nguyen"}],productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"}},authors:null,sections:[{id:"sec_1",title:"1. Background and introduction",level:"1"},{id:"sec_2",title:"2. Typical commercial satellites and HTS comparison",level:"1"},{id:"sec_3",title:"3. Typical commercial satellite network topologies",level:"1"},{id:"sec_3_2",title:"3.1 Typical “star” satellite network",level:"2"},{id:"sec_4_2",title:"3.2 Typical “mesh” satellite network",level:"2"},{id:"sec_6",title:"4. Legacy, existing, and advanced satellite payload architectures",level:"1"},{id:"sec_6_2",title:"4.1 Legacy analog bent-pipe satellite (ABPS) payload architecture",level:"2"},{id:"sec_7_2",title:"4.2 Existing digital bent-pipe satellite (DBPS) payload architecture",level:"2"},{id:"sec_8_2",title:"4.3 Advanced digital bent-pipe satellite using digital channelizer and beamformer (AdDBPS-DCB) payload architecture",level:"2"},{id:"sec_9_2",title:"4.4 Future advanced regenerative on-board processing satellite (AR-OBPS) payload architecture",level:"2"},{id:"sec_11",title:"5. Satellite system architectures for 5G cellular phone applications",level:"1"},{id:"sec_11_2",title:"5.1 AdBPS-DCBS satellite system architecture for 5G applications",level:"2"},{id:"sec_12_2",title:"5.2 AR-OBPS satellite system architecture for 5G applications",level:"2"},{id:"sec_14",title:"6. Conclusion",level:"1"},{id:"sec_18",title:"Conflict of interest",level:"1"},{id:"sec_15",title:"Notes/Thanks/Other declarations",level:"1"}],chapterReferences:[{id:"B1",body:'\nMagnuson S. Commercial space—Not military—Driving satellite innovation. National Defense Magazine. 2018. Available from: https://www.nationaldefensemagazine.org/articles/2018/4/17/commercial-space-not-military-driving-satellite-innovation\n\n'},{id:"B2",body:'\nRevillon P. Fundamentals and Dynamics of the Satellite Communications Business. Presented at the Euroconsult for Inmarsat Capital Markets Day in 2016. Available from: www.euroconsult-ec.com\n\n'},{id:"B3",body:'\nStar Network. Wikipedia. Available from: https://en.wikipedia.org/wiki/Star_network\n\n'},{id:"B4",body:'\nMesh Network. Wikipedia. Available from: https://en.wikipedia.org/wiki/Mesh_networking\n\n'},{id:"B5",body:'\nSuperheterodyne Receiver. Wikipedia. Available from: https://en.wikipedia.org/wiki/ Superheterodyne_receiver\n\n'},{id:"B6",body:'\nSadr R, Shahshahani M. On Sampling Band-Pass Signals, TDA Progress Report 42-96. Pasadena, California: NASA-Jet Propulsion Laboratory; 1988\n'},{id:"B7",body:'\nvan der Veldt K, van Nieuwpoort R, Varbanescu AL, Jesshope C. A polyphase filter for GPUs and multi-core processors. In: Proceedings of the Workshop on High-Performance Computing for Astronomy; Delft, Netherlands; 18 June, 2012. pp. 33-40\n'},{id:"B8",body:'\nKim SC, Bhattacharyya SS. Implementation of a high-throughput low-latency polyphase channelizer on GPUs. In: EURASIP Journal on Advances in Signal Processing; 2014. Available from: http://asp.eurasipjournals.com/content/2014/1/141\n\n'},{id:"B9",body:'\nEjima F, Akita M, Fujimura A. Digital Channelizer for High Throughput Satellite Communications, Technical Paper. Mitsubishi Electric Advance (Technical Journal published by Mitsubishi); 2014. pp. 7-10\n'},{id:"B10",body:'\nButash TC, Marshall JR. Leveraging digital on-board processing to increase communications satellite flexibility and effective capacity. In: 28th AIAA ICSSC. AIAA 2010-8715; Anaheim, CA; 30 August–2 September 2010\n'},{id:"B11",body:'\nSichi SF, Ziegler HE. Beamforming architectures for advanced MSS network deployment. In: 29th AIAA ICSSC; AIAA 2011-8021; Nara, Japan; 28 November–01 December, 2011\n'},{id:"B12",body:'\nFreedman JB, Marshack DS, et al. Advantages and capabilities of a beamforming satellite with a space-based digital processor. In: 32nd AIAA International Communications Satellite Systems Conference (ICSSC); AIAA 2014-4321; San Diego, CA; 4-7 August 2014\n'},{id:"B13",body:'\nKomiyama N, Miura A, Orikasa T, Fujino Y. Development of resource allocation re-construction technology (digital beam former and digital channelizer). Journal of the National Institute of Information and Communications Technology. 2015. pp. 151-163\n'},{id:"B14",body:'\nNguyen TM, Guillen A, Matsunaga S. Practical achievable capacity for advanced SATCOM on-the-move. In: 2016 IEEE MILCOM Conference Proceedings; 2016\n'},{id:"B15",body:'\nGuidotti A, Vanelli-Coralli A, Conti M, Andrenacci S, Chatzinotas S, Maturo N, et al. Architectures and key technical challenges for 5G systems incorporating satellites. IEEE Transactions on Vehicular Technology. 2019. pp. 2624-2639\n'}],footnotes:[{id:"fn1",explanation:"Flicker noise is a type of electronic noise with a 1/frequency power spectral density."},{id:"fn2",explanation:"Quantization (signal processing). Available from: https://en.wikipedia.org/wiki/Quantization_(signal_processing)."},{id:"fn3",explanation:"Assumes 1 bps/Hz modulation efficiency, 10 bit signal data quantization, and 2.5× practical Nyquist sampling rate."}],contributors:[{corresp:"yes",contributorFullName:"John Nguyen",address:"johndncva@gmail.com",affiliation:'
JohnDTN Consulting Services, Huntington Beach, California, USA
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