Toshiro Nakahira’s research while affiliated with Nippon Telegraph and Telephone and other places

... Furthermore, it should also be considered that in the context of network operators, key features such as radio-related key performance indicators, GPS data, and mobility estimation can also be used for finding out about the network infrastructure as well as user mobility data to improve accuracy. The video player can then leverage information provided by the network leading to higher throughput prediction accuracy as described in Cradio [27]. In this paper, the impact of throughput prediction accuracy on the long-term chunk selection mechanism will be shown by comparing six different baselines with different degrees of accuracy: ...

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Long-Term Adaptive Bitrate Control Mechanism

... Consider a system shown in Figure 1. In this system [1], h helper terminals and d detection terminals are prepared in a terminal collaboration group. The helper terminal forwards the detection terminals a digitized received waveform after receiving the signal from the base station. ...

Reference:

Impact of Multiple Detection Terminals on the Error Performance in Collaborative MIMO Reception Systems

... We improve our prior approach [10] and propose an error-control scheme with reduced collaboration traffic on the MS side. In the proposed scheme, multiple detection MSs are used to enhance detection performance, similar to the approach in [10], while keeping the collaboration traffic volume as small as possible by using on-demand DBS exchange based on reliability information [11]. For simplicity, we assume that one of the detection MSs is a target information sink. ...

Reference:

Error Control on Mobile Station Sides in Collaborative Multiple-Input Multiple-Output Systems

... Against the previous methods, Sotiroudis et al. [19] developed a long short-term memory neural network for the prediction of PL with the frequency of 2-26 GHz. Other relevant works adopted DL or ML to predict PL over a channel [20]- [22]. Some of the methods are support vector machine (SVM) [23], random forest [24], and K-nearest neighbors (KNN) [25]. ...

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PATH LOSS PREDICTION IN THE TROPICS USING BROADCAST SIGNAL: A DEEP LEARNING FRAMEWORK

... Measurement campaigns were undertaken in [2]- [10], RT simulations were used in [11]- [13], and propagation models were derived in [4], [8], [14]. Moreover, the geometry of the stadiums also varied, from small, open-track soccer stadiums [3] and closed basketball courts [9], to larger venues, such as multi-level stadiums, with capacities of tens of thousands of spectators [6], [7], [10]. However, the role of parameters such as the transmitter (Tx) configuration (position, orientation and pattern) and the geometry of the stadium have not been included in existing models. ...

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Generalizable Machine-Learning Based Modeling of Radiowave Propagation in Stadiums

... The authors in [10] focused on the broadcast of wireless media and proposed distributed detection schemes to improve detection performance with the same number of forwarded waveforms. In these schemes, a neighboring MS (seconddetection MS) overhears the forwarded waveforms and then independently decodes the MIMO signals transmitted from the BS. ...

Reference:

Error Control on Mobile Station Sides in Collaborative Multiple-Input Multiple-Output Systems

... Early stopping (ES) can reduce the computational complexity of iterative signal processing. In [15], it was revealed that the ES can improve the error performance in experimental performance evaluation. In this study, the residual interference coefficients given by (8) are exploited as a stopping metric. ...

Reference:

Error Control on Mobile Station Sides in Collaborative Multiple-Input Multiple-Output Systems