Telefonaktiebolaget Lm Ericsson (publ)

Junaid Shaikh

Jing Fu

Gunnar Heikkilä

Tomas Lundberg

Lackis Eleftheriadis

Daniel Lindström

Konstantinos Vandikas

Rafia Inam

David Lindegren

Yifei Jin

Selim Ickin

Farnaz Moradi

Jawwad Ahmed

David Lindero

Valentin Kulyk

Xiaoyu Lan

A network node for handling operation of the network node in a communication network. The network node includes at least one power supply unit and one or more additional power units for supplying power to the network node. The network node is configured to obtain an output from a computational model. The network node is configured to set an output voltage from the at least one power supply unit based on the obtained output.

Power management using rectifier circuits TN wireless networks

Methods and systems automatically protect radio network equipment in extreme weather conditions such as sand-storms or snow-storms. A pair of phase-shift material grids having different orientations is installed at an air intake of the radio network equipment. One of the grids is powered to open or close the air intake according to a current amount of air-carried particles entering the radio network equipment and a temperature inside the radio network equipment. The current amount of air-carried particles entering the radio network equipment may be detected by a piezoelectric sensor.

Method and system for automatically protecting radio network equipment in extreme environmental conditions

A method and a scoring node for estimating a user's Quality of Experience, QoE, when a service is delivered in a media stream to the user by means of a communication network. Quality-related parameters pertaining to the service delivery are extracted, and an individual opinion score is determined for each of a set of quality models by applying each respective quality model in the set on the quality-related parameters. An aggregated opinion score is then determined as a weighted average of the individual opinion scores. The aggregated opinion score can then be used as an estimation of the user's QoE for the service delivery, e.g. when evaluating the service and/or the communication network.

Method and scoring node for estimating a user's quality of experience for a delivered service

Mechanisms for predicting a multimedia session MOS are provided. The multimedia session has a video session and an audio session. The video quality and the audio quality are represented by vectors of per-time-unit scores of video quality and audio quality, respectively. The multimedia session is represented by a vector of the rebuffering start times of each rebuffering event, and a vector of the rebuffering durations of each rebuffering event. Audiovisual quality features are generated from the vectors of per-time-unit scores of video and audio quality. Buffering features are generated from the vector of rebuffering start times of each rebuffering event and the vector of rebuffering durations of each rebuffering event. A multimedia session MOS is then estimated based on the generated audiovisual quality features and the generated buffering features.

Quality estimation of adaptive multimedia streaming

A method and radio base station for handling a power disturbance caused by a radio unit of the radio base station. The method comprises: obtaining scheduling information of data traffic associated with the RU; comparing the power disturbance with one or more first thresholds, wherein the power disturbance is caused by the RU adapted to handle the data traffic according to the scheduling information; and adjusting a switching frequency of a direct current to direct current, DC/DC, converter based on the comparison, wherein the DC/DC converter is comprised in the RU to power the RU.

Method and radio base station for handling power disturbance in the radio base station

Systems and methods are provided for generating a predicted number of viewers in support of selecting an advertisement to be displayed during an advertisement break. The method includes: generating, by an advertisement break class identifier module, a plurality of advertisement break classes based on historical advertisement break information and prediction errors generated by a regression model analysis of advertisement break data samples; evaluating, by the advertisement break class identifier module, the plurality of advertisement break classes; redefining and rearranging, by the advertisement break class identifier module, advertisement break classes from the plurality of advertisement break classes based on the step of evaluating which results in an updated set of advertisement break classes; forwarding, by the advertisement break class identifier module, the updated set of advertisement break classes and trained model information to a prediction module; receiving, modifying and returning prediction model information to the prediction module by a training function in the advertisement break class identifier module; generating, by the prediction module, the prediction of a number of viewers for the advertisement break, wherein a regression model uses the updated set of advertisement break classes and data associated with a specific advertisement break to generate the prediction; and sending, by the prediction module, the prediction of a number of viewers for the advertisement break to a scheduler of advertisements.

Dynamic viewer prediction system for advertisement scheduling

It is provided a method, performed by a MOS, Mean Opinion Score, estimator, for predicting a multimedia session MOS. The multimedia comprises a video and an audio, wherein video quality is represented by a list of per time unit scores of a video quality, an initial buffering event and rebuffering events in the video, and wherein audio quality is represented by a list of per time unit scores of audio quality. The method comprises: generating video features from the list of per time unit scores of the video quality; generating audio features from the list of per time unit scores of the audio quality; generating buffering features from the initial buffering event and rebuffering events in the video; and estimating a multimedia session MOS from the generated video features, generated audio features and generated buffering features by using machine learning technique.

Predicting multimedia session MOS

There are provided mechanisms for predicting a multimedia session MOS. The multimedia session comprises a video session and an audio session, wherein video quality is represented by a vector of per-time-unit scores of video quality and wherein audio quality is represented by is a vector of per-time-unit scores of audio quality. The multimedia session is represented by a vector of rebuffering start times of each rebuffering event, a vector of rebuffering durations of each rebuffering event, and an initial buffering duration being the time between an initiation of the multimedia session and a start time of the multimedia session. The method comprises generating audiovisual quality features from the vector of per-time-unit scores of video quality and the vector of per-time-unit scores of audio quality. The audiovisual quality features comprise: a vector of per-time-unit scores of audiovisual quality, calculated as a polynomial function of the vector of per-time-unit scores of video quality and the vector of per-time-unit scores of audio quality; a weighted combination of the per-time-unit scores of audiovisual quality, wherein the weights are exponential functions of a time since the start time of multimedia session and a multimedia session duration; a negative bias representing how a sudden drop in per-time-unit scores of audiovisual quality affects the multimedia session MOS; and a term representing a degradation due to oscillations in the per-time-unit-scores of audiovisual quality. The method comprises generating buffering features from the vector of rebuffering start times of each rebuffering event, calculated from the start time of multimedia session, and the vector of rebuffering durations of each rebuffering event. The method comprises estimating a multimedia session MOS from the generated audiovisual quality features and the generated buffering features.

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Sundbyberg, Sweden

Junaid Shaikh