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Analog integrated circuit design is widely considered a time-consuming task due to the acute dependence of analog performance on the transistors’ and passives’ dimensions. An important research effort has been conducted in the past decade to reduce the front-end design cycles of analog circuits by means of various automation approaches. On the other hand, the significant progress in high-performance computing hardware has made machine learning an attractive and accessible solution for everyone. The objectives of this paper were: (1) to provide a comprehensive overview of the existing state-of-the-art machine learning techniques used in analog circuit sizing and analyze their effectiveness in achieving the desired goals; (2) to point out the remaining open challenges, as well as the most relevant research directions to be explored. Finally, the different analog circuits on which machine learning techniques were applied are also presented and their results discussed from a circuit designer perspective.

International audience; The spatial data infrastructures (SDIs) which constitute a direct link between spatial data users and the large Earth observation industry, have a leading role in establishing market opportunities in the space sector. The spatial information supplied through various forms of SDI platforms exhibits large increases in demand volatility. The users' demand is unpredictable and the market is vulnerable to high evolution shifts. We study the effect of extreme demands for a particular type of spatial information, the satellite images. Drawing on two French SDIs, GEOSUD and PEPS, we examine the shifts occurring on their platforms and assess the probability of witnessing a spike/drop in the short term of different satellite imagery schemes: the high resolution images through GEOSUD; the Landsat (U.S.), Sentinel (Europe) and SPOT (France) images through PEPS. We analyze the market stability through the two SDIs and evaluate the probability of future records by using the Records theory. The results show that the high resolution images demand through GEOSUD, for which the classical i.i.d. model fits the most, is stable. Moreover, the Yang-Nevzorov model fits to the Landsat data, due to more records concentrated beyond the first observations. The Landsat demand is the less stable out of the other three satellite images series, and the probability of having a record in the coming years is the highest. While the use of Records theory drops mathematical constraints, it offers an alternative solution to the non-applicability of the machine learning techniques and long-term memory models.

International audience; This paper proposes a framework for assessing the economic impacts of Spatial Data Infrastructures (SDIS), based on a case study of GEOSUD and the clear-cut maps related to forest management. The valuation is based on an online survey covering the state services responsible for the clear-cutting operations control. The main impacts are the cost and productivity savings, the added value created across the value chain related to the clear-cut mappings and the innovation processes within the GEOSUD SDI members. Other qualitative impacts on the forest policies' properties and more generally on territorial development dynamics are also considered. In addition, for every euro invested in the functioning of the SDI (excluding initial investment), the direct and indirect added value created amounts to a maximum of 63€ and 24€ as avoided transaction costs.; Cet article propose un cadre d'évaluation des impacts économiques des Infrastructures de Données Géographiques et Spatiales (IDGS), à partir de l'exemple de GEOSUD et des cartes de coupes rases relatives à la gestion des forêts. L'évaluation est réalisée à partir d'une enquête en ligne auprès des services de l'État chargés du contrôle des coupes rases. Les effets portent sur les économies de coûts et de productivité, la valeur ajoutée créée à l'échelle de la chaine de valeur liée à ces cartes et sur les processus d'innovation au sein des adhérents de GEOSUD. D'autres effets qualitatifs sur les propriétés des politiques forestières et plus généralement sur les dynamiques de développement territorial sont aussi appréhendés. Au total il apparaît que pour un euro dépensé pour le fonctionnement de l'IDGS (hors investissement initial), la valeur ajoutée créée directement et indirectement s'élève au maximum à 63€ et les coûts de transactions évités à 24 €.

Réalisé dans le cadre d’un contrat du programme TOSCA financé par le CNES, ce guide vise à familiariser les opérateurs et usagers divers liés au monde de l’information satellitaire aux dimensions socio-économiques requises pour caractériser et mesurer les effets résultant des IDS. Il s’agit d’offrir un niveau minium de compréhension de ces processus de façon, selon les types de lecteurs, à:Prendre conscience de la diversité des effets et des composantes de la valeur des IDS ;Orienter les choix méthodologiques pour des études futures en fonction des questions posées, en particulier s’agissant de la rédaction de futurs cahiers des charges d’études dans ce domaine ; Comprendre et éventuellement vérifier la pertinence méthodologique de travaux d’évaluation réalisés dans ce domaine ;Aider des équipes désireuses de réaliser des enquêtes pour évaluer divers dispositifs relevant des IDS.

In this work, a novel approach to reduce the complexity of memory polynomial model is presented. The proposed technique, named the coefficient decomposition (CD) technique, is based on reducing the search space for the memory polynomial coefficients based on memory effects generation mechanism. In this paper, the technique is demonstrated for power amplifier of classes A and AB. In this case, the physical analysis shows that the conventional two-dimensional coefficient architecture of the memory polynomial could be simplified into a one dimensional architecture while keeping almost the same accuracy. The performance of the proposed approach is demonstrated and compared with other models using a 30 dBm commercial power amplifier with 20 MHz and 60 MHz LTE signals. The measurements and simulations showed that, the presented model achieves −34.2 dB of NMSE while needing only 13 coefficients compared with 25 needed for a memory polynomial model. Also, the DPD performance of the proposed model is the same as memory polynomial model and comparable with high complexity models like the PLUME and GMP.

To the authors'best knowledge, the concept of post-distorted receivers appeared in the early 1990s [1,2]. Initially, post-distortion was thought as a solution for compensating intermodulation distortions resulting from the AM-AM and AM-PM non-linear behaviour of a mobile terminal power amplifier at the base-station receiver side. It was then considered as an alternative to pre-distortion that aimed to relax out-of-band emission specifications of portable transmitters while improving their power efficiency.

Nowadays, there are is an increasing need for multimedia communication standards offering multi-access services and high data rates. Downscaling of integrated circuit technologies has made efficient and cost-effective digital signal processing (DSP) hardware possible in many modern wireless communication systems. This trend has contributed in the development of reconfigurable and flexible radio frequency (RF) systems in order to push the analog-to-digital interface close to the antenna by facilitating DSP.

The Direct RF-to-Digital $\Delta\Sigma$ receiver has emerged as an attractive solution for multi-band multi-standard wireless applications. This architecture is a direct RF to baseband digitizer with RF feedback from baseband to RF stages. The presence of the RF blocks such as the low noise amplifier and down-conversion mixer inside the loop filter significantly relaxes their linearity requirements and also improves the noise transfer function of the loop filter. However, the RF feedback poses several challenges, especially making the design methodology more complex. In contrast to classical receiver architectures, the blocks of the Direct RF-to-Digital $\Delta\Sigma$ receiver cannot be designed, analyzed, and simulated independently. Besides, conventional design methodologies for $\Delta\Sigma$ Modulators cannot be applied for this kind of receiver due to the presence of the RF blocks and the RF feedback inside the loop. This paper presents a new design methodology suited for this family of receivers. The proposed methodology covers simulation approaches, system design, and non-ideality impacts on the receiver. A design example of a multi-band multi-standard receiver for GSM/WCDMA/LTE is considered throughout the paper to demonstrate this design methodology.

In this study, a new behavioural model named memory aware physically enhanced polynomial model is proposed. This modelling approach is based on using a physical analysis of the power amplifier (PA) operation and takes into account the electro-thermal and electrical memory effects as main sources of long-term memory. The physical analysis-based approach allows to select the most relevant coefficients for the model and discards the less significant ones in order to minimise the complexity. In this study, the proposed methodology is demonstrated on a single-stage class-AB PA. Nevertheless, it is applicable for multi-stage PAs. The model accuracy and complexity are evaluated using measurement results from two commercial PAs. Compared to the conventional memory polynomial (MP) model, for, respectively, 20 and 80 MHz long-term evolution signals, the proposed model shows 3 and 5 dB improvement in the normalised mean square error with fewer coefficients 18 instead of 20 for a memory depth of 3. Also, with same accuracy the proposed model has considerably fewer coefficients compared with the PLUME and generalised MP (GMP) models. Applying digital predistortion (DPD), the proposed model outperforms the MP model in terms of performance and the PLUME and GMP models in terms of complexity.