Evaluation of cell load of the LTE network based on control information decoding

Cell load evaluation is the one of the tools used for the development of methods to increase quality of service (QoS) in cellular networks. For instance, the analysis of mobile traffic usage can be used for network optimization and management in terms of radio resources. Existing assessing methods, such as drive tests or passive evaluations based on analysis of physical channel indicators are either unreliable, inaccurate, or inconvenient. A cell load evaluation method based on decoding and analysing the control channels is presented. The method can reliably monitor the resource allocations and the throughput in a public mobile cell. A real LTE signal recorded from the eNodeB using software defined radio was analysed. The main indicators of the cell were determined, including the efficiency of using resource blocks, the number of active users, and the cell’s throughput. The accuracy of the algorithm was also evaluated in this paper.

Dynamic input loads evaluation of a recreational vehicle frame using multibody dynamics hybrid modeling validated with experimental and full analytical modeling data

Knowledge of frame loads at the limits of the intended driving conditions is important during the design process of a vehicle structure. Yet, retrieving these loads is not trivial as the load path between the road and the frame mounting point is complex. Fortunately, recent studies have shown that multibody dynamic (MBD) simulations could be a powerful tool to estimate these loads. Two main categories of MBD simulations exist. Firstly, full analytical simulations, which have received great attention in the literature, are run in a virtual environment using a tire model and a virtual road. Secondly, hybrid simulations, also named semi analytical, uses experimental data from Wheel Force Transducers and Inertial Measurement Units to replace the road and tire models. It is still unclear how trustworthy semi analytical simulations are for frame load evaluation. Both methods were tested for three loads cases. It was found that semi analytical simulations were slightly better in predicting vehicle dynamic and frame loads than the full analytical simulations for frequencies under the MF-Tyre model valid frequency range (8 Hz) with accuracy levels over 90%. For faster dynamic maneuvers, the prediction accuracy was lower, in the 50%–80% range, with semi analytical simulations showing better results.

Training Load in Different Age Category Soccer Players and Relationship to Different Pitch Size Small-Sided Games

This study sought to evaluate the training load in different age category soccer players associated with distinct pitch size small-sided games (SSGs). Twenty-four soccer players (eight in each age category: U-12, U-15, and U-23) performed three consecutive 4 vs. 4 ball possession SSGs (SSG1: 16 × 24 m; SSG2: 20 × 30 m; and SSG3: 24 × 36 m) all with 3 min duration and 3 min rest. Subjects carried ultra-wideband-based position-tracking system devices (WIMU PRO, RealTrack System). Total distance covered increased from SSG1 to SSG3 in all age categories and predominantly in running speeds below 12 km∙h−1. Moreover, distance covered in 12–18 km∙h−1 running speed was different in all performed SSGs and age categories. Residual or null values were observed at 18–21 km∙h−1 or above running speed, namely in U-12, the only age category where metabolic power and high metabolic load distance differences occurred throughout the performed SSGs. Edwards’ TRIMP differences between age categories was only observed in SSG2 (U-12 < U-15). The design of SSGs must consider that the training load of the players differs according to their age category and metabolic assessment should be considered in parallel to external load evaluation in SSGs. Wearable technology represents a fundamental support in soccer.