Network using parameters provided from user equipment for access traffic steering, switching and splitting rule selection and associated wireless communication method
A network (NW) includes a wireless communication circuit and an access traffic steering, switching and splitting (ATSSS) policy decision circuit. The wireless communication circuit receives parameters from a user equipment (UE), and transmits a network-decided ATSSS policy to the UE, wherein the parameters do not include performance of a 3rd generation partnership project (3GPP) access and performance of a non-3GPP access. The ATSSS policy decision circuit performs ATSSS rule selection for generating the network-decided ATSSS policy, wherein the ATSSS rule selection is assisted by at least a portion of the parameters provided by the UE.
1 . A network (NW) comprising:
a wireless communication circuit, arranged to receive parameters from a user equipment (UE) and transmit a network-decided access traffic steering, switching and splitting (ATSSS) policy to the UE, wherein the parameters do not include performance of a 3rd generation partnership project (3GPP) access and performance of a non-3GPP access; and
an ATSSS policy decision circuit, arranged to perform ATSSS rule selection for generating the network-decided ATSSS policy, wherein the ATSSS rule selection is assisted by at least a portion of the parameters provided by the UE;
wherein after the wireless communication circuit receives the parameters from the UE, the ATSSS policy decision circuit generates the network-decided ATSSS policy according to the ATSSS rule selection assisted by said at least a portion of the parameters, and the wireless communication circuit transmits the network-decided ATSSS policy generated by the ATSSS policy decision circuit to the UE.
2 . The NW of claim 1 , wherein the ATSSS policy decision circuit is arranged to generate the network-decided ATSSS policy through machine learning.
3 . The NW of claim 2 , wherein the parameters comprise neural-network (NN) parameters transmitted from the UE, and the ATSSS policy decision circuit uses an NN model indicated by the NN parameters to generate the network-decided ATSSS policy.
4 . The NW of claim 1 , further comprising:
an access performance prediction circuit, arranged to perform access performance prediction to obtain predicted performance of the 3GPP access and predicted performance of the non-3GPP access that are referenced by the ATSSS rule selection;
wherein the access performance prediction is assisted by at least a portion of the parameters provided by the UE.
5 . The NW of claim 4 , wherein the access performance prediction circuit is arranged to obtain the predicted performance of the 3GPP access and the predicted performance of the non-3GPP access through machine learning.
6 . The NW of claim 5 , wherein the parameters comprise neural-network (NN) parameters transmitted from the UE, and the access performance prediction circuit uses an NN model indicated by the NN parameters to obtain the predicted performance of the 3GPP access and the predicted performance of the non-3GPP access.
7 . A network (NW) comprising:
a wireless communication circuit, arranged to receive parameters from a user equipment (UE) and transmit a network-decided access traffic steering, switching and splitting (ATSSS) policy to the UE;
an access performance prediction circuit, arranged to perform access performance prediction to obtain predicted performance of the 3GPP access and predicted performance of the non-3GPP access, wherein the access performance prediction is assisted by at least a portion of the parameters; and
an ATSSS policy decision circuit, arranged to generate the network-decided ATSSS policy according to the predicted performance of the 3GPP access and the predicted performance of the non-3GPP access;
wherein after the wireless communication circuit receives the parameters from the UE, the ATSSS policy decision circuit generates the network-decided ATSSS policy according to the predicted performance of the 3GPP access and the predicted performance of the non-3GPP access that are obtained by the access performance prediction assisted by said at least a portion of the parameters, and the wireless communication circuit transmits the network-decided ATSSS policy generated by the ATSSS policy decision circuit to the UE.
8 . The NW of claim 7 , wherein the access performance prediction circuit is arranged to obtain the predicted performance of the 3GPP access and the predicted performance of the non-3GPP access through machine learning.
9 . The NW of claim 8 , wherein the parameters comprise neural-network (NN) parameters transmitted from the UE, and the access performance prediction circuit uses an NN model indicated by the NN parameters to obtain the predicted performance of the 3GPP access and the predicted performance of the non-3GPP access.
10 . A wireless communication method applicable to a network (NW), comprising:
receiving parameters transmitted from a user equipment (UE), wherein the parameters do not include performance of a 3rd generation partnership project (3GPP) access and performance of a non-3GPP access;
performing access traffic steering, switching and splitting (ATSSS) rule selection for generating a network-decided ATSSS policy, wherein the ATSSS rule selection is assisted by at least a portion of the parameters provided by the UE; and
transmitting the network-decided ATSSS policy to the UE;
wherein after the parameters are received from the UE,
the network-decided ATSSS policy is generated by the ATSSS rule selection assisted by said at least a portion of the parameters, and
the network-decided ATSSS policy is transmitted to the UE.
11 . The wireless communication method of claim 10 , wherein the ATSSS rule selection generates the network-decided ATSSS policy through machine learning.
12 . The wireless communication method of claim 11 , wherein the parameters comprise neural-network (NN) parameters transmitted from the UE, and the ATSSS rule selection uses an NN model indicated by the NN parameters to generate the network-decided ATSSS policy.
13 . The wireless communication method of claim 10 , further comprising:
performing access performance prediction to obtain predicted performance of the 3GPP access and predicted performance of the non-3GPP access that are referenced by the ATSSS rule selection;
wherein the access performance prediction is assisted by at least a portion of the parameters provided by the UE.
14 . The wireless communication method of claim 13 , wherein the access performance prediction obtains the predicted performance of the 3GPP access and the predicted performance of the non-3GPP access through machine learning.
15 . The wireless communication method of claim 14 , wherein the parameters comprise neural-network (NN) parameters transmitted from the UE, and the access performance prediction uses an NN model indicated by the NN parameters to obtain the predicted performance of the 3GPP access and the predicted performance of the non-3GPP access.
16 . A wireless communication method applicable to a network (NW), comprising:
receiving parameters transmitted from a user equipment (UE);
performing access performance prediction to obtain predicted performance of the 3GPP access and predicted performance of the non-3GPP access, wherein the access performance prediction is assisted by at least a portion of the parameters;
generating a network-decided access traffic steering, switching and splitting (ATSSS) policy according to the predicted performance of the 3GPP access and the predicted performance of the non-3GPP access; and
transmitting the network-decided ATSSS policy to the UE;
wherein after the parameters are received from the UE,
the network-decided ATSSS policy is generated according to the predicted performance of the 3GPP access and the predicted performance of the non-3GPP access that are obtained by the access performance prediction assisted by said at least a portion of the parameters, and
the network-decided ATSSS policy is transmitted to the UE.
17 . The wireless communication method of claim 16 , wherein the access performance prediction obtains the predicted performance of the 3GPP access and the predicted performance of the non-3GPP access through machine learning.
18 . The wireless communication method of claim 17 , wherein the parameters comprise neural-network (NN) parameters transmitted from the UE, and the access performance prediction uses an NN model indicated by the NN parameters to obtain the predicted performance of the 3GPP access and the predicted performance of the non-3GPP access.