The present disclosure relates to a communication technique merging IoT technology with a 5th generation (5G) or pre-5G communication system for supporting a data transmission rate higher than that of a 4th generation (4G) communication system such as long term evolution (LTE), and a system therefor. The present disclosure may be applied to intelligent services (for example, smart homes, smart buildings, smart cities, smart cars or connected cars, health care, digital education, retail, and security and safety related services) on the basis of 5G communication technologies and IoT-related technologies. Various embodiments of the present disclosure may provide a method and a device for bandwidth part switching in consideration of a dormant bandwidth part.

A method and apparatus for a cell selection or a cell reselection considering a network slice in a wireless communication system is described. A wireless device configures multiple network slices. A wireless device receives a network slice information related to the multiple network slices. A wireless device establishes a PDU session for a specific service. A wireless device determines one or more frequencies that support the at least one network slice, used by the PDU session, from among the multiple network slices based on the frequency information. A wireless device performs a cell selection procedure and/or a cell reselection procedure based on the determined one or more frequencies.

A method and apparatus for a cell selection or a cell reselection considering a network slice in a wireless communication system is described. A wireless device configures multiple network slices. A wireless device receives a network slice information related to the multiple network slices. A wireless device establishes a PDU session for a specific service. A wireless device determines one or more frequencies that support the at least one network slice, used by the PDU session, from among the multiple network slices based on the frequency information. A wireless device performs a cell selection procedure and/or a cell reselection procedure based on the determined one or more frequencies.

Provided is a communication system that can be normally and efficiently operated in the case where existing carriers and new carrier types coexist. A base station device and a communication terminal device are configured to perform communication in cells of legacy carriers LC1 to LC3 being existing carriers. When the base station device starts operating new carrier types NCT1 and NCT2, the NCT1 and NCT2 are associated with legacy carriers belonging to the same frequency band. For example, the NCT2 is associated with the LC2 or the LC3 and is not associated with the LC1. The legacy carriers LC1 to LC3 associated with the NCT1 and the NCT2 notify the communication terminal device of the information on the NCT1 and NCT2. This allows the communication terminal device to communicate with the NCT1 and the NCT2.

Machine-learning based techniques are described herein for determining and modifying handover parameters within multilayer wireless networks. Various communication session data, such as key performance indicators, may be analyzed and compared at multiple frequency layers to determine sets of custom parameters associated with one or more wireless networks. The sets of custom parameters and network performance data may be used to train one or more machine-learned models to improve and/or optimize the handover parameters used by the network nodes. In some examples, different trained models may be associated with different network performance metrics, such as throughput optimization, network speed, and/or dropped call minimization, etc. A trained machine-learned model may be used to analyze the session data from a set of network nodes, and to determine or tune the handover parameters used by the network nodes.

Machine-learning based techniques are described herein for determining and modifying handover parameters within multilayer wireless networks. Various communication session data, such as key performance indicators, may be analyzed and compared at multiple frequency layers to determine sets of custom parameters associated with one or more wireless networks. The sets of custom parameters and network performance data may be used to train one or more machine-learned models to improve and/or optimize the handover parameters used by the network nodes. In some examples, different trained models may be associated with different network performance metrics, such as throughput optimization, network speed, and/or dropped call minimization, etc. A trained machine-learned model may be used to analyze the session data from a set of network nodes, and to determine or tune the handover parameters used by the network nodes.

A system for interference detection includes one or more processors are configured to receive from one or more access points within a wireless network information indicative of airtime usage of one or more client devices associated with the one or more access points, determine an amount of total interference for at least one of the access points on a first channel, the total interference including foreign interference and in-network interference, determine a correlation between the total interference and the airtime usage of the one or more client devices, determine an amount of the foreign interference or an amount of the in-network interference based on the correlation, and selectively switch the at least one of the access points from communicating on the first channel to communicating on a second channel based on the determined amount of the foreign interference or the determined amount of the in-network interference.

A system for interference detection includes one or more processors are configured to receive from one or more access points within a wireless network information indicative of airtime usage of one or more client devices associated with the one or more access points, determine an amount of total interference for at least one of the access points on a first channel, the total interference including foreign interference and in-network interference, determine a correlation between the total interference and the airtime usage of the one or more client devices, determine an amount of the foreign interference or an amount of the in-network interference based on the correlation, and selectively switch the at least one of the access points from communicating on the first channel to communicating on a second channel based on the determined amount of the foreign interference or the determined amount of the in-network interference.

Embodiments described herein relate to wireless communications, including methods and apparatus for dynamically selecting subscriber identity module (SIM) profiles, electronic SIM (eSIM) profiles, multi-SIM operating modes, and/or wireless access networks for network slice based applications of wireless devices. A wireless device can include at least two SIM/eSIM profiles and switch use of SIM/eSIM profiles, change between dual SIM operating modes, and/or select a registration mode based on requirements of one or more applications in use and availability of network slices via different wireless networks.

Embodiments described herein relate to wireless communications, including methods and apparatus for dynamically selecting subscriber identity module (SIM) profiles, electronic SIM (eSIM) profiles, multi-SIM operating modes, and/or wireless access networks for network slice based applications of wireless devices. A wireless device can include at least two SIM/eSIM profiles and switch use of SIM/eSIM profiles, change between dual SIM operating modes, and/or select a registration mode based on requirements of one or more applications in use and availability of network slices via different wireless networks.