In this paper, a sequential stopping rule for the estimation of a probability p by means of Monte Carlo simulation is analyzed. It is shown that the proposed estimator is almost unbiased, and guarantees a given relative precision irrespective of p. Under very mild conditions, the method also guarantees a certain confidence level for a given relative estimation error, provided that p does not exceed a maximum value. An extension to importance sampling is discussed.

The estimation of a probability p from repeated Bernoulli trials is considered in this letter. A sequential approach is followed, using a simple stopping rule. A closed-form expression and an upper bound are obtained for the mean absolute error of the unbiased estimator of p. The results given permit the estimation of an arbitrary probability with a prescribed level of normalized mean absolute error.

This letter presents an improved result on the negative-binomial Monte Carlo technique analyzed in a previous paper (see IEEE Trans. Commun., vol.54, no.2,pp. 231-41, Feb. 2006) for the estimation of an unknown probability p. Specifically, the confidence level associated to a relative interval [p/mu ₂, pmu ₁], with mu ₁, mu ₂ > 1, is proved to exceed its asymptotic value for a broader range of intervals than that given in the referred paper, and for any value of p. This extends the applicability of the estimator, relaxing the conditions that guarantee a given confidence level.

Although the deployment of 5G networks has already started, there are still open questions regarding propagation at millimeter-wave frequency bands. Several propagation campaigns have been carried out at several bands previously identified by regulatory organizations, but due to the wide range of allocated segments of spectrum and the variety of possible propagation scenarios, more measurement campaigns are needed. In this regard, the Universidad Politécnica de Madrid (UPM) has taken millimeter-wave measurements at 26, 32, and 39 GHz bands in an indoor corridor scenario in line-of-sight (LOS) conditions with two antenna configurations (a horn antenna has been used in transmission whereas horn and omnidirectional antennas have been used in reception), and the main results are presented in this paper. The obtained path loss results have been compared with existing millimeter-wave propagation models.

Despite the growing trend towards the use of big data methodologies, there is still limited application of such techniques to understand how spectrum is used in mobile networks. In this paper we analyse how low (<; 1 GHz) and high (>1 GHz) frequency spectrum is used in 4G networks in urban areas, in relation to eNodeB density, available bandwidth, Reference Signal Received Power (RSRP) and Reference Signal Received Quality (RSRQ). We present a method to analyse the strategies used by Mobile Network Operators (MNOs) to deal with traffic congestion, and the degree to which they must densify their networks depending on their spectrum portfolio. Using crowdsourced data from 2017 from a popular mobile app, we apply this method to Greater London. We find that the fraction of sites that fully use all available bands to the MNO range from 2% to 20%. Additionally, MNOs with large bandwidth use 42% fewer sites on average in dense urban environments. This difference decreases in suburban areas to 23% fewer sites. The lowest frequencies in each eNodeB tend to exhibit lower RSRP values, as they are often used to serve cell-edge users. These frequencies also show lower RSRQ values because of higher interference caused by neighbouring cells. Similarly, large (high frequency) bandwidth improves RSRQ as it allows for fewer users per MHz, which reduces interference and enables larger cell sizes. We conclude that in dense urban environments, the available bandwidth, rather than propagation properties, determines the preferred band for network deployment by MNOs.

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