## 1 Introduction  
The ubiquity and high positioning performance of recent smartphones have widely expanded their primary application that was personal navigation [1]. As a consequence, the door to novel areas of market, industry, and science has been opened for smart, hand-held, and low-cost GNSS devices [2, 3, 4]. As it was reported by European GNSS (Global Navigation Satellite System) Agency, smartphones are now dominating the installed base of devices that are equipped with GNSS chips [5]. Such progress induced the scientific community to draw special attention to GNSS observations derived from smartphones. As a result, a great deal of effort has been put into the development of observational and stochastic models that are suited to process smartphone GNSS observations [6, 7]. These algorithms address the specific limitations of smartphone GNSS observations such as the low suppression to multipath and high observational noise highlighted by [PERSON] et al. [8], the carrier phase discontinuity that is driven by duty-cycle analyzed by [PERSON] et al. [9] and an existence of unwanted biases that destroy integer properties of phase ambiguities showed by [PERSON] et al. [10] and [PERSON] and [PERSON] [11]. Several researchers have already proved the feasibility of precise positioning with smartphone GNSS observations under these specific conditions. In one of the primary papers, [PERSON] et al. [12] evaluated a short-baseline positioning without an integer ambiguity resolution and concluded that it is achievable to reach a decimeter-level accuracy in a relative mode with observations collected by a Nexus 9 smart device. [PERSON] and [PERSON] [13] showed a centimeter-level accuracy of the relative positioning with an integer ambiguity; fixing this, however, required a priori calibration and correction of initial phase biases. [PERSON] et al. [14] demonstrated Precise Point Positioning with smartphone and survey-grade GNSS antenna. [PERSON] and [PERSON] [15] proved that it is feasible to reach a coordinate precision of a few centimeters in a static relative positioning with GPS L1 observations collected by Huawei P30 and an embedded antenna. More recently, [PERSON] et al. [16] presented a centimeter-level precision of the smartphone to smartphone positioning and [PERSON] et al. [17] reported a subentimeter accuracy of the short baseline RTK positioning with Xiaomi Mi 8 and Huawei P30 smartphones taking advantage of the linear fitting to restore the integer property of phase ambiguities. Nevertheless, there is still a great need to evaluate an accuracy level that may be reached with current smart devices in a single point positioning (SPP) fed with code pseudoranges [18]. This is justified by the fact that even now there are only a few smartphones that offer continuous high quality phase observations. On the other hand, we may recognize a number of real-time or post-processing position-based applications which do not require a centimeter-level precision that may be reached only with phase observations [19; 20]. Moreover, several manufacturers have recently released smart devices equipped with chipsets that acquire code pseudoranges not only on the first frequency but also on the second one, e.g., L5/E5a as in the case of GPS and Galileo system, respectively. As it was proved for geodetic receivers, code signals on L5 and E5a frequency bands are characterized with noticeably lower noise as compared to E1/L1 ones [21; 22]. In a view of these conditions and expectations, this paper aims at the evaluation of the single point positioning performance of the most recent smartphones that track dual-frequency multi-constellations GNSS signals. We analyze potential progress in this term with respect to preliminary studies performed with early smartphones. We investigate the benefit from the integration of multi-constellation signals and analyze selected observation weighting schemes. We verify the hypothesis that smartphone positioning with L5/E5a code observations may significantly outperform that with L1/E1 signals.