Receiver Autonomous Integrity Monitoring (RAIM) has been certified to provide lateral guidance in flight operations ranging from En-route to Non-Precision Approach (NPA). Recent developments in the RAIM algorithm science, namely Advanced RAIM (ARAIM), have suggested a future role in vertically guided operations down to LPV with a decision height of 200ft [EU-U.S., 2012]. However, more stringent requirements as a result of the vertical guidance application question the external risk or trust that is placed on the constellation service provision and may require the partial reduction of this risk through the use of a ground segment, identifying and removing threats and providing data through an ISM (Integrity Support Message). This ground segment should ideally be light and low-cost so not to replicate that implemented for SBAS. In addition the ISM latency [Walter et al, 2012] should ideally be allowed as long as possible to obviate the challenging and expensive communications requirements as imposed, for example, on SBAS (6 sec Time to Alert). Furthermore, the ISM should be as simple as possible to ensure the data broadcast requirements can be met with a number of solutions from ATC, to local ground communications to GEO relay. Finally, the network should be light, in the sense of a sparse and global distribution of stations. In order to meet the defined role of the ground segment and its monitoring capability; three possible methodologies were identified [Milner et al, 2014]: No ground monitoring, Offline Monitoring, Real-Time Monitoring. The parameters of interest to this monitoring are the input parameters defined for the ARAIM baseline airborne algorithm as given below: • URA/SISA: Standard deviation of ranging measurement for integrity • URE/SISE: Standard deviation of ranging measurement for nominal accuracy/continuity • Bnom: Maximum nominal bias on ranging measurement • Psat: Prior probability of fault in satellite per approach • Pconst : Prior probability of fault affecting more than one satellite in constellation per approach This list of parameters contains the maximum nominal bias Bnom. A nominal bias is a fault-free bias, both to account for near-constant uncorrected errors (signal deformation and antenna bias) and non-Gaussian behaviour. However, some small nominal bias may be included in this Bnom parameter. Indeed, after application of all possible corrections, ionofree smoothed code ranges are affected by residual ephemeris plus satellite clock and payload group delay errors w.r.t constellation reference frame and clock. In the context of ARAIM, the residual ephemeris plus clock errors, residual tropospheric error, and multipath plus noise errors, are all assumed to be random errors overbounded by zero mean gaussian errors with known modeled variance. However, it is noted that the residual ephemeris plus satellite clock errors may include a long term bias. These ionofree smoothed code ranges are also affected by the receiver clock offset, defined as the common propagation delays from antenna to signal processing stages, also defined as the error identical to all measurements of the same constellation, which varies across constellations (time reference, signal) and the receiver design. Note that the receiver clock offset may include residual payload plus ephemeris delays identical to all satellites used in the navigation solution computation, so may vary depending on the set of satellites used in this computation. The iono-free nominal bias may then be defined as the permanent bias in excess of the residual error identical to all measurements of the same constellation, and from this definition may therefore depend on the receiver clock offset. A first paper has been issued to define properly the nominal bias and to characterize over the globe those biases for an ARAIM user [Macabiau et al, 2014]. Three possible types of sources of nominal bias were identified: nominal signal deformation, variation of SV antenna group delay with nadir angle, variation of user antenna group delay with Azimuth (Az) and Elevation (El) angles. Models used to characterize theses nominal bias contributions were proposed and fully defined. Assumptions were made at several levels of these models to try and reflect possible nominal situations of signal distortion, SV or user receiver antenna group delay variation. Initial work was presented on the ARAIM reference algorithm integrity monitoring performance to protect the ARAIM user against the impact of these nominal biases, driven by the ISM input Bnom value transmitted by the ground segment. The aim of this paper is therefore to update the analysis done on nominal bias affecting the ARAIM user, on the capacity of the ground monitoring network to provide a pertinent Bnom, and on the impact on the ARAIM user performance. This methodology allows determining possible restrictions on ARAIM user receiver characteristics. Based on the definition of the ARAIM user nominal bias expressed in [Macabiau et al, 2014] identifyng three possible sources of nominal bias (signal deformations, SV antenna, and user antenna), assumption and models definition are set in the first part of the paper. Then, we determine the impact of that defined nominal bias on the ARAIM user receiver range measurement and position estimate. Impact of nominal signal deformations is evaluated using a models derived from the bounding ICAO EWF threat model The evaluation considers different receiver configurations in terms of bandwidth and chip spacing, representing the regions that are proposed at RTCA/EUROCAE [Phelts et al., 2014b] plus regions identified to induce a maximum ranging error due to nominal signal deformation. The analysis of the nominal bias obtained for the different configuration leads to the identification of suitable design requirements for the ARAIM user receiver. Impact of user antenna group delay variation as a function of Azimuth and Elevation is then addressed, considering several models for user antenna, including recent results for the model of a dualfrequency civil aviation antenna mounted on aircraft. Impact of SV antenna group delay variation is also assessed based on mathematical analysis of antenna group delay. Then, through simulation, we analyze the results of the implementation of these ground monitoring techniques for estimation of the Bnom bounds and we analyze the performance of these bounds with respect to the possible distribution of the ARAIM user nominal bias. Situations leading to extreme integrity situations have been identified and are analyzed with respect to current monitoring concept used in the ISM. This analysis will assess the sensitivity of the results with respect to the model used to define each nominal bias contributor. From these simulations, we finally conclude on the capacity of the ground to provide efficient Bnom coupled with some possible restrictions for the characteristics of the ARAIM user receiver.