Abstract: An exercise intensity assessing system includes a physiological information sensor, a signal transmitter connected with the physiological information sensor, a central control host connected with the signal transmitter, and a cloud database connected with the central control host. The physiological information sensor senses physiological information of an exerciser before and after the exerciser exercises. The physiological information is transmitted by the signal transmitter to the central control host, and transmitted by the central control host to the cloud database for being diagnosed and analyzed by a fitness instructor. The cloud database obtains a forecasted watt value corresponding to the physiological information, and obtains a resistance level of different fitness apparatuses according to the forecasted watt value.

Abstract: An intelligent exercise intensity assessing system includes an exercise testing machine, a physiological information sensor, a signal transmitter connected with the physiological information sensor, a central control host connected with the signal transmitter, and a cloud database connected with the central control host. The physiological information sensor senses physiological information of an exerciser before and after the exerciser operates the exercise testing machine. The physiological information is transmitted by the signal transmitter to the central control host, and transmitted by the central control host to the cloud database. The cloud database analyzes the physiological information to obtain a corresponding forecasted watt value, and obtains a resistance level of different fitness apparatuses according to the forecasted watt value.

Abstract: Arrangements disclosed here provide an LTE E-RAN employing a hierarchical architecture with a central controller controlling multiple LTE radio nodes (RNs). The RNs may be clustered within the small cell network. A fractional frequency reuse (“FFR”) scheme is provided that dynamically computes the FFR allocations at individual RNs and configures the corresponding schedulers within each RN to improve cell-edge users' experience. Once an FFR pattern has been generated and frequencies allocated, UE throughput can be emulated to predict the resulting bit rates for each UE. Using the prediction, a scheduler emulation may be run to predict the behavior of the system. The results of each cell may then be collected to generate the performance of the entire system, which may in turn be used to generate a new or modified FFR pattern, or new or modified clustering. Optimization of the performance results in an optimized FFR pattern.

Abstract: Arrangements disclosed here provide an LTE E-RAN employing a hierarchical architecture with a central controller controlling multiple LTE radio nodes (RNs). The RNs may be clustered within the small cell network. A fractional frequency reuse (“FFR”) scheme is provided that dynamically computes the FFR allocations at individual RNs and configures the corresponding schedulers within each RN to improve cell-edge users' experience. Once an FFR pattern has been generated and frequencies allocated, UE throughput can be emulated to predict the resulting bit rates for each UE. Using the prediction, a scheduler emulation may be run to predict the behavior of the system. The results of each cell may then be collected to generate the performance of the entire system, which may in turn be used to generate a new or modified FFR pattern, or new or modified clustering. Optimization of the performance results in an optimized FFR pattern.

Abstract: Arrangements disclosed here provide an LTE E-RAN employing a hierarchical architecture with a central controller controlling multiple LTE radio nodes (RNs). The RNs may be clustered within the small cell network. A fractional frequency reuse (“FFR”) scheme is provided that dynamically computes the FFR allocations at individual RNs and configures the corresponding schedulers within each RN to improve cell-edge users' experience. Once an FFR pattern has been generated and frequencies allocated, UE throughput can be emulated to predict the resulting bit rates for each UE. Using the prediction, a scheduler emulation may be run to predict the behavior of the system. The results of each cell may then be collected to generate the performance of the entire system, which may in turn be used to generate a new or modified FFR pattern, or new or modified clustering. Optimization of the performance results in an optimized FFR pattern.

Abstract: Methods and systems are provided for allocating frequencies in a radio access network (RAN) that includes a plurality of radio nodes each associated with a cell and a services node operatively coupled to the radio nodes. In accordance with the method, the radio nodes (RNs) in the RAN are divided into a plurality of clusters of RNs. A fractional frequency reuse (FFR) pattern is generated for each cluster. Transmission resources are allocated to the radio nodes in each cluster in accordance with the respective FFR pattern that is generated for each cluster.

Abstract: Arrangements disclosed here provide an LTE E-RAN employing a hierarchical architecture with a central controller controlling multiple LTE radio nodes (RNs). The RNs may be clustered within the small cell network. A fractional frequency reuse (“FFR”) scheme is provided that dynamically computes the FFR allocations at individual RNs and configures the corresponding schedulers within each RN to improve cell-edge users' experience. Once an FFR pattern has been generated and frequencies allocated, UE throughput can be emulated to predict the resulting bit rates for each UE. Using the prediction, a scheduler emulation may be run to predict the behavior of the system. The results of each cell may then be collected to generate the performance of the entire system, which may in turn be used to generate a new or modified FFR pattern, or new or modified clustering. Optimization of the performance results in an optimized FFR pattern.

Abstract: Methods and systems are provided for allocating frequencies in a radio access network (RAN) that includes a plurality of radio nodes each associated with a cell and a services node operatively coupled to the radio nodes. In accordance with the method, the radio nodes (RNs) in the RAN are divided into a plurality of clusters of RNs. A fractional frequency reuse (FFR) pattern is generated for each cluster. Transmission resources are allocated to the radio nodes in each cluster in accordance with the respective FFR pattern that is generated for each cluster.