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An example of two PDPs collected at the same location and frequency carrier of 3649 MHz. The first PDP (ID = 1) includes three paths, while the second PDP (ID = 2) includes seven paths.
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Understanding radio propagation characteristics and developing channel models is fundamental to building and operating wireless communication systems. Among others uses, channel characterization and modeling can be used for coverage and performance analysis and prediction. Within this context, this paper describes a comprehensive dataset of channel...
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Context 1
... a reference example, an excerpt of the dataset containing two collected PDPs is shown in Table 2. Note that each PDP is identified by a unique ID. ...Citations
... Likewise, the authors in [13] analyzed commercial 5G mid-band networks in China, focusing on coverage, throughput, latency, and device energy consumption aspects. Finally, our previous work in [9] focused on the analysis of coverage, deployment, and performance (throughput and latency) aspects for 5G NSA mid-band networks in Italy, and was complemented by further work in [19,20,21], where handover implications on performance [19] and outdoor-to-indoor propagation in the mid-band frequency [20,21] were discussed. ...
... Likewise, the authors in [13] analyzed commercial 5G mid-band networks in China, focusing on coverage, throughput, latency, and device energy consumption aspects. Finally, our previous work in [9] focused on the analysis of coverage, deployment, and performance (throughput and latency) aspects for 5G NSA mid-band networks in Italy, and was complemented by further work in [19,20,21], where handover implications on performance [19] and outdoor-to-indoor propagation in the mid-band frequency [20,21] were discussed. ...
... We used a subset of the available features, including spatial and temporal fields, carrier frequency identifiers, and signal strength/quality indicators, i.e., Reference Signal Received Power (RSRP), Reference Signal Received Quality (RSRQ), and Signal to Interference and Noise Ratio (SINR). For 4G, such indicators were measured on the Reference Signal (RS) sent by 4G PCIs; for 5G, they were measured on the Secondary Synchronization Signal (SSS), sent by 5G PCIs in the SSBs [20,21]. Hence, for 5G, the dataset includes Secondary Synchronization Reference Signal Received Power (SS-RSRP), Secondary Synchronization Reference Signal Received Quality (SS-RSRQ), and Secondary Synchronization Signal to Interference and Noise Ratio (SS-SINR) at SSB beam level. 2 The 4G and 5G passive datasets consist of approximately 527K and 8.48M samples, respectively (see Table 1). ...
... The 5G mobile initiative is a tremendous collective effort to specify, standardize, design, manufacture, and deploy the next cellular network generation. 5G is characterized by three key features: faster speeds, lower latency, and the ability to connect many devices at the same time [2]. It will support demanding services such as enhanced Mobile Broadband, Ultra-Reliable, Low Latency Communications, and massive Machine-Type Communications, which will require high data rates and latencies of a few milliseconds [3]. ...
New frequency bands, such as the mmWave at 28 GHz for fifth Generation networks in Frequency Range 2, are being introduced to accomplish the required throughput for new services such as remote surveillance, object tracking, and factory automation. These high-frequency bands are sensible to reflections and diffraction. Therefore, accurate path loss calculation relies on advanced models and ray tracing. However, this is time-consuming, and evaluating a large set of candidate solutions is no longer possible when planning the optimal number and location of base stations in the network planning process. This paper investigates the use of machine learning to approximate a complex mmWave ray-tracing-based path loss model in indoor scenarios. The much lower calculation time allows us to approximate the ray-tracer path loss estimation well and to apply a genetic algorithm for realizing network planning. The machine learning model is trained and validated for two buildings and tested with another, with an average of the Mean Absolute Error of 2.8 dB over all cases. It is shown that the combination of Machine Learning and Genetic Algorithm is able to find a network deployment in the FR2 band accounting for the minimum number of access points and minimum electromagnetic exposure, while still providing a predefined coverage percentage inside the building.
... In the Oslo campaign the setup included an Exelonix NB-IoT USB device for the collection of power consumption and active data for NB-IoT [32], while in the Rome campaign, this device was replaced with a Samsung S20 5G mobile phone, which was used to collect 5G active data. The setup used in the Rome campaign is presented in Figure 1 and is drawn from [33]. In this work, only passive data collected by the built-in RF receiver were used. ...
... In addition, the TSMA6 features an advanced autocalibration algorithm, which was independently verified to be highly accurate by multiple research groups [34,35]. Additional information on the measurement campaigns and on both passive and active data availability can be found in [3,36,37] for the Oslo campaign and [33,[38][39][40] for the Rome campaign. ...
... The setup adopted for the measurement campaign in Rome, Italy: R&S TSMA6 mobile network scanner (a), GPS antenna (b), Samsung S20 5G mobile phone (c), RF antenna (d), and tablet used to remotely access the ROMES software running in the embedded PC within the TSMA6 scanner (e) (image drawn from[33]). ...
Narrowband Internet of Things (NB-IoT) has quickly become a leading technology in the deployment of IoT systems and services, owing to its appealing features in terms of coverage and energy efficiency, as well as compatibility with existing mobile networks. Increasingly, IoT services and applications require location information to be paired with data collected by devices; NB-IoT still lacks, however, reliable positioning methods. Time-based techniques inherited from long-term evolution (LTE) are not yet widely available in existing networks and are expected to perform poorly on NB-IoT signals due to their narrow bandwidth. This investigation proposes a set of strategies for NB-IoT positioning based on fingerprinting that use coverage and radio information from multiple cells. The proposed strategies were evaluated on two large-scale datasets made available under an open-source license that include experimental data from multiple NB-IoT operators in two large cities: Oslo, Norway, and Rome, Italy. Results showed that the proposed strategies, using a combination of coverage and radio information from multiple cells, outperform current state-of-the-art approaches based on single cell fingerprinting, with a minimum average positioning error of about 20 m when using data for a single operator that was consistent across the two datasets vs. about 70 m for the current state-of-the-art approaches. The combination of data from multiple operators and data smoothing further improved positioning accuracy, leading to a minimum average positioning error below 15 m in both urban environments.
... The BER results are provided for all modulations types for both transmitters and for both current and 100 ms future CSI amplitude prediction of PEACH. 8 To further elaborate the advantages of PEACH, we demonstrate the performance under interference scenario. Finally, we provide an evaluation summary for completeness, since the performance of PEACH shows similar trends on both static and mobile Tx. ...
... A 5G-related dataset is presented in [51], which provides data only with KPIs such as CQI or bitrate. The most recent and closest dataset to our work is [8], where commercial data for the n78 band, collected in an outdoor-to-indoor setup, are provided, including those of power delay profile, the received power for each multipath. However, the dataset includes neither raw I/Q samples nor data obtained over SDRs. ...
... Note that the sample input images inFig. 6are not pre-processed for ease of understanding to human vision.8 Note that for the reliability evaluations the CSI phase for PEACH estimations are obtained from the DMRS phase estimations. ...
Up-to-date and accurate prediction of Channel State Information (CSI) is of paramount importance in Ultra-Reliable Low-Latency Communications (URLLC), specifically in dynamic environments where unpredictable mobility is inherent. CSI can be meticulously tracked by means of frequent pilot transmissions, which on the downside lead to an increase in metadata (overhead signaling) and latency, which are both detrimental for URLLC. To overcome these issues, in this paper, we take a fundamentally different approach and propose PEACH, a machine learning system which utilizes environmental information with depth images to predict CSI amplitude in beyond 5G systems, without requiring metadata radio resources, such as pilot overheads or any feedback mechanism. PEACH exploits depth images by employing a convolutional neural network to predict the current and the next $100$\:ms CSI amplitudes. The proposed system is experimentally validated with extensive measurements conducted in an indoor environment, involving two static receivers and two transmitters, one of which is placed on top of a mobile robot. We prove that environmental information can be instrumental towards proactive CSI amplitude acquisition of both static and mobile users on base stations, while providing an almost similar performance as pilot-based methods, and completely avoiding the dependency on feedback and pilot transmission for both downlink and uplink CSI information. Furthermore, compared to demodulation reference signal based traditional pilot estimation in ideal conditions without interference, our experimental results show that PEACH yields the same performance in terms of average bit error rate when channel conditions are poor (using low order modulation), while not being much worse when using higher modulation orders, like 16-QAM or 64-QAM. More importantly, in the realistic cases with interference taken into account, our experiments demonstrate considerable improvements introduced by PEACH in terms of normalized mean square error of CSI amplitude estimation, up to $6$\:dB, when compared to traditional approaches.
... As detailed later, parts of the dataset were already used to support specific analyses [2]- [4]; other relevant work can be found in [5] for 4G, [6]- [8] for 5G, and [9]- [11] for NB-IoT, where data collection and analyses were carried out on each technology separately, in some cases finalized to propose AI/ML solutions for specific aspects (e.g., 5G throughput prediction [6]- [8] and NB-IoT access protocol optimization [10]). ...
... The dataset open-sourced in this paper, and the smaller companion dataset disclosed in [4], can provide a key contribution in this context, thanks to the large quantity of heterogeneous data, e.g., in terms of scenarios, frequencies, and technologies, that can be used for model training, validation, and refinement. To give an example referring to Table I, features in Spatial and mobility information class (e.g., UE and cell coordinates), Mobile network information class (e.g., carrier frequency), and Cell site information class (e.g., cell/beam identifiers) can be used as input for deriving a ML model for the features in the Radio coverage information class (e.g., RSRP). ...
Mobile networks are highly complex systems. Therefore, it is crucial to examine them from an empirical perspective to better understand how network features affect performance, so to suggest additional improvements. To this aim, this paper presents a large-scale dataset of measurements collected over fourth generation (4G) and fifth generation (5G) operational networks, providing Long Term Evolution (LTE), Narrowband Internet of Things (NB-IoT), and 5G New Radio (NR) connectivity. We collected our dataset during seven weeks in Rome, Italy, by performing several tests on the infrastructures of two major mobile network operators (MNOs). The open-sourced dataset has enabled multi-faceted analyses of network deployment, coverage, and end-user performance, and can be further used for designing and testing artificial intelligence (AI) and machine learning (ML) solutions for network optimization.
... The works carried out earlier to study the interference modeling in the mmWave frequency band take into consideration the single link communication system [6]. Therefore, the realistic measurements in operational 5G cellular networks are lacking in the literature. ...
... As mentioned above, the analysis presented in this paper focuses on the channel samples for the PCIs/SSBs detected with the highest power (one per operator); however, aiming at allowing further investigation and analysis by the research community, the open-sourced dataset includes channel samples for all the PCIs/SSBs pairs detected during each campaign [56]. ...
... Future work will focus on the tuning of a channel model at 3.5 GHz for outdoor-toindoor propagation based on the dataset partially used in this work and described in [56], and on the design of site-specific channel models, which are recently gaining interest due to the always increasing application of data-driven machine learning approaches to channel propagation modeling and prediction, thanks to the availability in the dataset of both the estimated position of the PCIs and the map of the environment. We would also take into account the channel characterization based on the building material characterization and entry loss. ...
... Data Availability Statement: Data have been released under an open access license; an ad hoc paper [56], published in MDPI Data, covers the description of the dataset. ...
The successful rollout of fifth-generation (5G) networks requires a full understanding of the behavior of the propagation channel, taking into account the signal formats and the frequencies standardized by the Third Generation Partnership Project (3GPP). In the past, channel characterization for 5G has been addressed mainly based on the measurements performed on dedicated links in experimental setups. This paper presents a state-of-the-art contribution to the characterization of the outdoor-to-indoor radio channel in the 3.5 GHz band, based on experimental data for commercial, deployed 5G networks, collected during a large scale measurement campaign carried out in the city of Rome, Italy. The analysis presented in this work focuses on downlink, outdoor-to-indoor propagation for two operators adopting two different beamforming strategies, single wide-beam and multiple synchronization signal blocks (SSB) based beamforming; it is indeed the first contribution studying the impact of beamforming strategy in real 5G networks. The time and power-related channel characteristics, i.e., mean excess delay and Root Mean Square (RMS) delay spread, path loss, and K-factor are studied for the two operators in multiple measurement locations. The analysis of time and power-related parameters is supported and extended by a correlation analysis between each pair of parameters. The results show that beamforming strategy has a marked impact on propagation. A single wide-beam transmission leads, in fact, to lower RMS delay spread and lower mean excess delay compared to a multiple SSB-based transmission strategy. In addition, the single wide-beam transmission system is characterized by a smaller path loss and a higher K-factor, suggesting that the adoption of a multiple SSB-based transmission strategy may have a negative impact on downlink performance.
... The authors in [6] introduced the "Large-scale dataset for the analysis of outdoor-toindoor propagation for 5G mid-band operational networks", a dataset of measurements performed over commercial 5G networks. In particular, the dataset included measurements of channel power delay profiles from two 5G networks in Band n78, i.e., 3.3-3.8 ...
The evolution of modern cyber-physical systems and the tremendous growth in the number of interconnected Internet of Things (IoT) devices are already paving new ways for the development of improved data collection and processing methods [...]
