IV) or Direct Radiating Arrays (e.g. Globalstar and Spaceway) with complex beam forming and power amplifications sections. Additionally, the use of advanced ...
Overview of payload and system simulation tools at ESA F. M. Vanin, P. Angeletti, S. d’Addio, J. Lizarraga, F. Coromina European Space Agency, ESA-ESTEC, Noordwijk, 2201AZ, The Netherlands This paper presents some recent works and software tools developed internally in ESA-ESTEC addressing problems inherent to telecommunication payloads and systems. In particular, CoDe, which has been developed to study the load-balancing capabilities of nonregular beam layouts and APS, which has been developed for simulating very complex payloads. CoDe shows the advantages gained in terms of throughput and payload simplicity by non-regular beam layouts with respect to conventional regular lattices. APS instead shows great potential for the design and for the optimization of advanced payload architectures and sub-systems specification from an end-to-end design perspective. For these tools, the paper describes the basic concepts and methodology used. Examples of the capabilities and results obtained are provided. Nomenclature
APS BSS C(x,y) CoDe D DT 𝐷 ESA ESTEC Fle-TWT HPAs MPA N ROI SNIR SFSB
= = = = = = = = = = = = = = = =
Advanced Payload Simulator Broadband Satellite Services Capacity demand density [ Mbps/Km2] Coverage designer Demanded capacity in a single beam Total System demanded capacity 𝐷! = ! !!! 𝐷! Average Demanded Capacity 𝐷 = 𝐷! 𝑁 European Space Agency European Space Research and Technology Center Flexible Traveling Wave Tubes High Power Amplifiers Multi-Port Amplifiers Total number of beams Region of Interest Signal-to-Noise-plus-Interference Ratio Single Feed per Single Beam
I. Coverage Designer (CoDe)
Introduction On-going internal research in ESA-ESTEC on Broadband Satellite Communications has found that multi-beam systems throughput is strongly affected by the load imbalance among beams as this contradicts a cornerstone assumption of conventional multi-beam system design: the uniformity of the demand. In order to maximize operator’s revenue, the offered capacity should match the demand; in other words, it should not be larger than requested (wasting resources) nor should be less (leaving demand unmet, i.e. losing revenue). Hence, one of the major challenges being faced by operators is how to maximize their revenues coping with the highly uneven traffic demand distribution over their coverage regions. Recent developments allow dealing with this problem by introducing certain flexibility at payload level. The resulting architecture depends on the resource to be allocated (e.g. time in Beam-Hopped payloads). If it is power, the use of Flex-TWTAs or MPAs allows each beam to draw the necessary amount according to its traffic needs. However, using the latter approach, more power does not always imply an increase of capacity for which an upper limit exists. An alternative way to solve this problem is provided by CoDe: a software tool developed within ESA that transforms a classic regular load-unbalanced coverage into an irregular load-balanced one by matching the offer to the demand. CoDe adapts the beam sizes according to the geographical distribution of the traffic, hence maximizing the system throughput and operators’ revenue. Software As shown in Fig. 1, the traffic demand (load) D experienced by each beam is defined as the cumulative sum of the traffic demand density C (i.e. the traffic demand for unit surface) over its coverage area A: 𝐷=
𝐶 (𝑥, 𝑦)
!
𝐶 𝑥, 𝑦 𝑑𝐴
𝐴
Figure 1. The load D experienced by certain beam corresponds to the aggregated demand over its coverage region A.
(1)
The standard coverage design of multi-beam satellite communication systems assumes a uniform traffic demand on which a regular coverage lattice is overlaid as depicted in Fig. 2. However, in these systems with regular beam layouts, a spatially uniform traffic demand is a rather unrealistic scenario.
Figure 2. Regular lattice (70 beams) over the ROI with uniform traffic demand.
Instead, as shown in Fig. 3, a realistic non-spatially-uniform traffic demand creates traffic load imbalances among the different beams. Fig. 4 shows for each beam the load resulting from using a regular lattice over the non-uniform traffic demand scenario.
Figure 3. Regular lattice (70 beams) over the ROI with realistic non-uniform traffic demand scenario.
5 4.5 4 3.5 3 2.5 2 1.5 1 0.5 0 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69
Beam # Figure 4. Beam load resulting from using a regular lattice over the non-uniform traffic demand scenario (bars are normalized with the coefficient 𝑫 = 𝑫𝑻 𝑵).
If a system offers the same capacity across beams, as conventionally done, this translates into a situation in which part of the system capacity is left unused while there is still demand to be met. Assuming a uniform system capacity per beam equal to 1, Fig. 5 puts in evidence the case of unused system capacity as well as unmet. As it can be noted, the overall effect is a serious limitation to the full use of the system (i.e. overall throughput).
4 3.5
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Figure 5. Beam load resulting from using a regular lattice over the non-uniform traffic demand scenario. A uniform system capacity equal to 1 is assumed (bars are normalized with the coefficient). 𝑫 = 𝑫𝑻 𝑵).
Intuitively, it would be desirable to leverage the unmet demand with the unused capacity. CoDe uses the following approach: it transfers the unmet demand to beams that are still able to provide capacity. The effect is shaping the coverage so that the beams are all evenly loaded, but with different sizes. Fig. 6 shows the regular conventional coverage over a geographical area, while Fig. 7 depicts the same coverage after using CoDe.
Figure 6. Regular coverage beam layout (3 dB cuts).
Figure 7. Non-regular coverage beam layout (3dB cuts).
Note that Fig. 6 and Fig. 7 assume colored beams and it will be discussed in the next section. Fig. 8 shows the associated beam load, which corresponds to an increase of throughput of about 35% with respect to the conventional case shown in Fig. 5. 1.2
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Beam # Figure 8. Beam load resulting from using the non-regular coverage designed for the realistic nonuniform traffic demand scenario.
As an important result, CoDe is able to achieve load-balanced systems by design that have significantly higher throughput and would allow using conventional payload
architectures without flexible elements, yet at the expense of added complexity on the antenna system as shown in Fig. 9.
Figure 9. Flexible (complex) payload with classic antenna front-end vs. standard (simple) payload with complex antenna front-end.
Color Assignment The basic principle of multi-beam satellite communication systems is to cover a specified ROI with a number of independent beams each using a portion of the available bandwidth. The isolation resulting from antenna directivity and the spatial separation between beams is exploited to re-use the same frequency band in separate beams. In this way the same spectrum can be re-used multiple times over the ROI increasing the total capacity of the satellite system without increasing the allocated bandwidth. Moreover, using polarization diversity – i.e. of two orthogonal polarization states – allows doubling frequency re-use at the expense of a slight increase of the interference. In practice, frequency re-use is limited by the achievable beam to beam isolation and interference rejection. The frequency band-polarization pair assigned to a beam is commonly known as the color of the beam and the process of assigning one to each beam in the system is known as coverage coloring. CoDe colors the coverage using 4 colors, as shown in Fig. 10, to exploit the so-called ‘four-color theorem’. The theorem states that, given any plane (i.e. ROI) partitioned into contiguous regions, four colors are sufficient to ensure that adjacent regions (i.e. beams) have different color (i.e. frequency and/or polarization). Note that in Fig 9. blue and green (or, red and yellow) colors share only one point and they are by definition not contiguous, while the other pairs correspond to contiguous cases. The results for the regular and irregular beam layouts are shown in Figs. 6 and 7 respectively.
Figure 10.
Partitioning of the system spectral resources into 4 colors.
II. Modeling of complex telecom payloads with APS
Introduction Upcoming telecom missions are likely to exhibit complex payload RF front-ends. It is expected that standard SFSB solutions will be less common and that telecommunication payloads will use complex architectures including Array Fed Reflectors (e.g. Inmarsat IV) or Direct Radiating Arrays (e.g. Globalstar and Spaceway) with complex beam forming and power amplifications sections. Additionally, the use of advanced HPAs (e.g. Flex-TWTs and MPAs) further enforces this tendency. Such complex front-ends will provide a high degree of payload flexibility and re-configurability. To make optimum and flexible use of satellite resources satellite communication systems will likely make extensive use of bandwidth and power efficient coding and modulation transmission schemes (e.g. DVB-S2 [1]). With increased complexity of systems and payloads, it becomes difficult to provide sound requirements at the subsystem level and to estimate the real impact of the payload to the end-to-end signal degradation and to the overall system performance. The design phases of complex telecom systems are typically supported by end-to-end system performance analyses based on simulation tools. However, the simulation of SATCOM architectures in a comprehensive manner represents a complex task that, for an accurate end-to-end performance assessment, should rely on a joint payload and signal modeling. To reduce the complexity of the problem, simulation-based performance analyses are typically done at two levels: 1) end-to-end performance analyses are carried out by implementing a simplified payload model, whereas more effort is put on a precise description and modeling of the signal set; 2) accurate payload performance assessment is instead typically made in the presence of simplified stimuli such as Continuous Waves (CWs) or Additive White Gaussian Noise (AWGN) signals. The results of these assessment exercises are typically put together at system level in link budget analysis. However, this approach can result in inaccurate results leading to inefficient payload and system designs. In fact, in most cases detailed modulator/demodulator simulators are not manageable in payload design tools, and the use of simple signals is not representative of realistic payload operation conditions (e.g. HPAs operation in the non-linear region). On the other hand, typical end-to-end performance evaluation tools tend to simplify interference processes and approximate their effects onto the overall performance. As a consequence, over-dimensioned specifications typically arise on elementary RF subsystems and/or over-dimensioned margins at system level. The availability of a comprehensive and detailed end-to-end system performance simulation tool appears to be of critical importance for the analysis of the most promising payload architectures and for the derivation of not over-dimensioned specifications at subsystems level. In this paper, APS, recently developed internally at ESA, is described. This tool is based on a time-domain correlation method [2], [3]. This accurate, whilst efficient, simulation technique allows assessing different operational conditions and performing linearity versus efficiency trade-offs. APS allows the analysis of the payload impact on the end-to-end link performance.
ASP objectives and design guidelines APS is a tool able assess the impairments of the payload constitutive elements (e.g. frequency dependencies, non-linearities) on the signal quality, considering end-to-end system performance. It accurately predicts payload performance and supports payload designers with dimensioning of payload subsystems based on system level end-to-end optimization. The following main features were considered while realizing APS: 1) accurate modeling and implementation of a set of realistic traffic scenarios; 2) accurate modeling and implementation of all major payload and antenna subsystem elements, including the possible sources of impairments; 3) careful selection of a suitable figure of merit for the assessment of the end-to-end system performance. Concerning the first feature, it was included the possibility to generate a variable number of DVB-S2-based modulated carriers in the APS signal generation module (implementing all possible modulation/coding options with different symbol rates and flexible carrier spacing/frequency plan, together with carrier power unbalances). Regarding the second one, the payload/antenna module was designed to include, among others, multi-carrier non-linear HPA operation and distributed amplification. Being the payload performance a key design-driving element, its assessment in a realistic operational scenario is able to avoid excessive system margins (e.g. reducing HPA OBO with a favorable increase the DC-to-EIRP conversion efficiency). Therefore, a detailed implementation of high power amplification section modeling was performed, in terms of AM/AM AM/PM transfer characteristics, DC-to-RF conversion, frequency dispersion of such characteristics, isolation losses and unbalances in the MPA input and output networks. In addition, both in MPA-based and/or active antenna-based architectures, the knowledge of the actual spatial/frequency distribution of the intermodulation products were exploited with an adequate system frequency planning to reduce the carrier-tointermodulation interference ratio. Antenna architectures based on distributed amplification and different RF-front-end (including Single-Feed-Per-Beam as benchmarking solution, Array Fed Reflectors, and Direct Radiating Arrays) were accurately simulated, providing far-field emulation of the spatial dependency of the payload performances within the satellite coverage, both in reception and in transmission, modeling also different port-to-beam connection schemes for multi-beam systems. Regarding the third feature, the SNIR was selected as suitable figure-of-merit for the signal quality evaluation. In fact, in complex multi-beam systems, the overall SNIR takes into consideration inter-system interference, inter-beam and intra-beam interference, adjacent and co-channel interference, cross-polarization interference, overall payload NPR, intermodulation products, spectral regrowth due to HPA non-linearities, and multi beam antenna effects. Additionally, under certain hypothesis, the overall SNIR can be directly related to the end-to-end link quality in terms of (coded/uncoded) Bit Error Rate (BER) or Frame/Packet Error Rate (FER/PER). The selection of the SNIR as figure of merit was made having considered all these aspects relevant at system level both in the requirement and in the design phase. Within the APS signal quality evaluation module, the SNIR is measured on a specific carrier received in a specific location within the coverage by means of a Data-Aided Maximum Likelihood Estimation (DA-MLE)
method, which accurately computes the power contribution of the useful signal component and of the different interference processes. Simulator architecture The payload simulator consists of: •
a time domain simulation of the complete transmission chain, including the generation of the real modulated and filtered signals (M-PSK, M-QAM with Square-Root Raised-Cosine shaping and proper power levels),
•
an accurate simulation of the payload architecture, including linear and nonlinear effects,
•
an antenna and far field emulation and a receiver block. The latter contains an ad-hoc signal analyzer able to discriminate at each satellite footprint location the wanted signal from interferers (intra and inter beam interference) and to measure undesired noise power (including linear and non-linear distortion, intermodulation, interference and thermal noise).
The power measurement and the discrimination of the different interference contributors are based on the so-called correlation method. The methodology based on data-aided correlation techniques has been introduced for accurately assessing the impact of the payload’s sub-systems on the end-to-end system performance [2]. The objective of the correlation method is to estimate the impact of the payload non-idealities on the end-toend performance of a satellite link in a non-perturbative way. The methodology has been applied to the analysis of active antennas, single TWTAs operated in multicarrier scenarios [3] and MPAs. The detailed functional representation of the payload simulator is depicted in Fig.11. The signal generator module generates N uncorrelated binary data sequences and modulates them by applying DVB-S2-like modulation schemes. Each modulated signal is then frequency-converted and filtered with a Square Root Raised Cosine to avoid inter-symbol interference. All the signals are up-converted and appropriately spaced in the frequency domain to be grouped in different beam signals. The resulting beam signals B are multiplexed and passed trough the non-linear payload model. The payload module allows modular and scalar interconnection of basic payload building-blocks representing traditional payload elements (e.g. IMUX, OMUX, filters, frequency converters, amplifiers, microwave passive multiport networks, etc.). Ideal transfer characteristics are included in order to by-pass single payload subsystems or components. The high power amplifier models are described by means of the amplitude/amplitude (AM/AM) and amplitude/phase (AM/PM) complex transfer characteristics [4]. Frequency dispersion of characteristics is taken into account for distributed amplification. The distorted output signals are then fed at the antenna inputs. The Far-Field Emulator (FFE) allows simulating the spatial dependency of the payload performances both in reception and in transmission. It is based on the array manifold matrix representation [5] of the antenna system response consisting in a transfer matrix defining the amplitude and phase relationship between an antenna port and a far-field user location. The received distorted signal at a generic position on the coverage is then passed through the SRRC filter matched, for instance, to the k-th signal. Therefore, the
output signal is composed by a distorted replica of the k-th input signal plus the additional in-band component of adjacent channel interference, in-band intermodulation products and multi-beam antenna co-channel interference. In order to evaluate the ratio of the useful power to the interfering power at the demodulator, the received signal is crosscorrelated with the expected ideal replica of the k-th signal. Computational complexity and memory requirements are reduced if the generated sequence is split in shorter subsequences on which the cross-correlation and the power estimation are performed. The final power estimation is carried out by averaging the power values obtained from the single sub-sequences. The core of the simulator is developed in Matlab™. In addition, the Matlab™ core can be interfaced with external payload modules (e.g. Agilent ADS). The proposed methodology features many key benefits: • is applicable to any complex waveform, • can be adapted to any payload architecture, • it is capable of estimating the power of in band intermodulation distortion when the HPAs are operated with a real signals, hence accounting for all possible non-linear effects (not limited in intermodulation product order), • does not modifies the signal operational configuration, i.e., the signal is not affected by any artefacts/perturbations due to the measurements conditions, • allows for discriminating co-frequency signals, • measurement accuracy can be taken under control, • allows for frame based simulation (memory relaxation).
Figure 11.
Simulator model.
Results examples In this section examples of application of the APS to two typical telecommunication payloads performance analyses are presented. In the first case, the level of intermodulation distortion generated by a TWTA transponder in a typical channelized payload in multi-carrier (MC) operation is analyzed. For simplicity, in this case the far field emulator module has been by-passed. A typical Ka-band communication LTWTA, modeled by means of its AM/AM and AM/PM
transfer characteristics has been considered. The results shown in terms of carrier-tointermodulation power (C/I) curves as a function of IBO are provided in Fig 12. The IBO is considered as difference in dB between the single-tone saturated input power and the total power of the input modulated signal (single- or multi-carrier). In case of multicarrier operation with more than 2 carriers, a higher level of intermodulation is expected on the carriers internally positioned as compared to the external ones; the C/I values we report are those corresponding to the worst case (i.e., most internal carriers). Fig. 11 shows the analysis for MC operation with 8-PSK modulation, and compares it with standard NPR calculations. A significant C/I degradation occurs when the number of carriers increases from 1 to 8, and, in addition, the NPR appears to overestimate the level of IM of a LTWTA operating in realistic conditions. In the second case, a payload output section based on an 8x8 multiport amplifier (MPA) was analyzed in conjunction with a multi-beam reflector antenna based on single feed per beam. A typical Ka-band LTWTA operated at 1dB of OBO has been assumed in the analysis. The useful power C and the C/I on ground have been measured by adopting the cross-correlation method as mentioned in the previous paragraph. The joint impact of TWTA non-linearity and non-ideal channel isolation in the MPA is assessed in cochannel beams by comparison with an ideal case where ideal MPA and TWTA characteristics are considered (Fig.13). 45 8PSK 1C 8PSK 2C
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Estimated C/I and NPR curves as a function of IBO for 8-PSK-modulated carriers.
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Figure 13. ground.
Example of impact of TWTA non-linearity and MPA non-ideal isolation on C and C/I on
III.
Conclusion
In this paper two simulators recently developed at the European Space Agency have been presented. CoDe has been developed to study the load-balancing capabilities that non-regular beam layouts would provide to systems with not-uniform traffic demand distributions over their ROI. It has shown that it is possible to achieve load balanced systems by design which have a significantly higher throughput and simpler payloads than unbalanced systems at the expense of added complexity on the antenna system. APS has been developed for simulating complex payload showing great potential for the design and for the optimization of advanced payload architecture and sub-systems specification in an end-to-end design perspective. References [1] ETSI EN 302 307 v1.1.1. (2004-06), Digital Video Broadcasting; second generation framing structure, channel coding and modulation systems for Broadcasting, Interactive services, News gathering and other broadband satellite applications. [2] E. Braunschwig, E. Casini, P. Angeletti, “Co-channel signal power measurement methodology in a communication and payload joint simulator”, 24th AIAA International Communications Satellite Systems Conference (ICSSC 2006), San Diego (California), USA, 11-14 Jun 2006 [3] M. Aloisio, E. Casini, P. Angeletti, R. Oliva-Balague, E. Colzi, S. D’Addio, “Accurate Characterization of TWTA Distortion in Multicarrier Operation by Means of a Correlation-based Method”, in Proceedings of IVEC 2008, pp: 409-410. [4] O. Shimbo, “Effects of intermodulation, AM/PM conversion and additive noise in multicarrier TWT systems”, Proceedings of the IEEE, Vol. 59, Feb. 1971 [5] H. L. Van Trees, Detection, Estimation, and Modulation Theory, Part IV, Optimum Array Processing, Wiley, 2002
