35 results about "Cellular traffic" patented technology

Disclosed are methods and system for dynamically switching at least a base station in ON/OFF mode in a cellular network. The method comprises: determining traffic load information of BSs; exchanging the traffic load information between BSs; determining at least a preferred BS for one of a switched OFF mode and a switched ON mode. The system comprises: at least a coverage BS with a master controller; at least a capacity BS with a slave controller adapted to be in one of the switched ON mode and the switched OFF mode depending on network conditions and to satisfy cellular traffic loads, and at least a communication interface adapted to provide interface between at least the coverage BS and the capacity BS.

Systems and methods for estimating bandwidth. A first probe flow is sent into cellular traffic, and a first bandwidth quantity achieved by the first probe flow is measured. A second probe flow is sent into the cellular traffic, and a second bandwidth quantity achieved by the first probe flow while the second probe flow is in the cellular traffic is measured. The first bandwidth quantity and the second bandwidth quantity are compared, and at least one result from the comparing is determined.

Providing an inter-femto Base Station (fBS) network to facilitate low interference, low power cellular access utilizing two or more fBSs is provided herein. For example, a group of fBSs can be inter-connected by a wired and/or wireless communication network. Multiple fBSs then can link with a mobile device and coordinate cellular traffic amongst the fBS network to facilitate hand-off related communication. Additionally, cellular traffic can be forwarded from one or more fBSs to an appropriate fBS designated to carry cellular traffic for each mobile device. Furthermore, by inter-connecting multiple fBSs, multi-base station cellular-type hand-off can be supported by the fBS network, while preserving predetermined cellular interface constraints associated with such mobile devices.

The invention discloses a wireless cellular network flow prediction method based on deep transfer learning and cross-domain data fusion, and belongs to the technical field of intelligent communication. Similarity among three services of short messages, telephones and the Internet and the similarity among different regions are analyzed, a plurality of cross-domain data sets is fused, and a space-time cross-domain neural network model is adopted to predict the wireless cellular traffic; a cross-service and region fusion transfer learning strategy based on a space-time cross-domain neural networkmodel (STC-N) is provided, and the prediction precision of a target domain is improved according to data characteristics of a source domain. The method can verify that the more comprehensive the considered data set is, the higher the prediction precision of the model is; in addition, the proposed transfer learning strategy can reduce the training data, calculation capability and generalization capability required for constructing the deep learning model.

Embodiments of the invention disclose a cellular network traffic-pulling method, device and apparatus, and a storage medium. The method includes steps: obtaining resource data through point-to-point traffic pulling; obtaining a local network state; and restricting uploading of the obtained resource data if the local network state satisfies a preset condition, wherein the preset condition refers tothat the network state is a cellular data network and the traffic is limited. The problem of traffic consumption brought by point-to-point traffic pulling for cellular data users is solved, the cellular network users are allowed to participate in the point-to-point network to improve the playing smoothness of live-broadcasting videos, and the traffic and the cost are reduced.

An emergency mobile call agent includes a mobile originating call agent and a mobile terminating call agent. The mobile originating call agent responds to receiving an emergency notification and the emergency being above an emergency threshold condition, by notifying a contact list associated with a user that the user is impacted by the emergency. The mobile originating call agent may notify the contact list if the emergency is at or above a threshold emergency level set by the user. The mobile terminating call agent controls cellular traffic by evaluating one or more aspects of radio access network (RAN) loading and diverting incoming traffic to a user impacted by the emergency to another form of communication if RAN loading is too high for the traffic.

The invention discloses a dynamic sorting-based device and base station connection method in a cellular flow unloading network. The method comprises the following steps: (1) multiple APs are deployed near a user for providing service for the user, the user can select to be accessed to an AP or a BS, and a benefit function is given to evaluate current benefits of the user; (2) through calculation, after a problem is layered, once the user access mode is found to be determined, the transmitting power of the user is determined accordingly, the benefit function is only related to the user access condition, and thus, the optimal user access only needs to be found for determining the optical system benefits; and (3) according to restrictive conditions C1 and C2 and in combination with a target function given in the second step, the user access mode is selected to maximize the benefits of the overall system. Superior user access can be quickly and effectively found, the power distribution of the user is determined at the same time, and the efficiency is high.

Methods and systems for reducing the cost of cellular backhaul are disclosed. The methods and systems include using the public Internet (138) to backhaul cellular traffic given the wide availability and relatively low cost of Internet connections compared with dedicated T1 lines. Re-distribution of switching functionality between a base station (136) and a base station controller (140) is achieved by moving some of the switching functionality from the base station controller to the base station.

The invention discloses a mobile network traffic anomaly detection method and system based on feature dimensionality reduction, and the method comprises the steps: dividing a city region into M * N grid regions according to the distribution of city base stations, and aggregating the cellular traffic value of each grid region by using pandas to obtain a cellular traffic total value with an hour as a unit; dividing a detection time period into K time slots to form a time sequence vector, and taking the time sequence vector as an original cellular flow vector xj; extracting low-dimensional traffic features cj from the original cellular traffic vectors xj of all grid areas by using an LSTM auto-encoder; determining suspicious abnormal low-dimensional traffic features in the low-dimensional traffic features corresponding to all the grid regions; using the K-means clustering for carrying out anomaly confirmation on the suspicious and abnormal low-dimensional traffic features, completing mobile network traffic anomaly detection based on feature dimension reduction. The method and the system can realize anomaly detection of the mobile network traffic and have the advantages of being large in number of processing areas and short in data processing time.

The invention discloses a mobile cellular traffic efficient prediction method based on a space-time aggregation graph convolutional network, which comprises the following steps of: firstly, dividing a prediction region into a plurality of sub-regions by the aggregation graph convolutional network, taking the sub-regions as nodes in the network, and performing modeling on a daily historical mode and a current mode per hour of mobile traffic, and capturing the complex space-time correlation of all nodes across different time. And then, the outputs of the K layers of aggregation graph convolutional network modules are connected through an embedded module. And then, fusing the prediction information with external features extracted by an external module by using a regression module to obtain a final mobile traffic prediction result, and updating model parameters to obtain a minimum loss function. According to the invention, the prediction performance of the mobile cellular traffic is effectively improved.

A broadband cellular network device comprises a cellular controller means adapted to control the distribution of cellular traffic consisting of asynchronous transfer mode cells in trunking mobile communication networks based on ATM technology, and an asynchronous transfer mode switching means controlled by said cellular controller means and adapted to multicast said cellular traffic to downlink base transceiver stations using multicast properties of the asynchronous transfer mode switching means.

The invention discloses a multi-data-set joint prediction method based on an attention mechanism, and the method comprises the following steps: 1) carrying out the anomaly detection of different types of activity data in a CDR data set through employing a mobile network anomaly detection method based on feature extraction, removing abnormal activity data, and then respectively putting each type of activity data into a recurrent neural network with the same structure; 2) inputting an output result of each recurrent neural network into an attention unit, and inputting an output result of the attention unit into the recurrent neural network for flow data prediction; and 3) taking the output of the recurrent neural network used for traffic data prediction as a prediction result of the cellular traffic. The method can accurately predict the traffic data at the future moment.