Abstract: A retransmission response feedback method and apparatus are provided. The method includes: traversing serving cells to determine HARQ-ACK of an original downlink slot and a delayed downlink slot in each serving cell for which an uplink channel resource requires feedback, wherein the original downlink slot refers to a downlink slot corresponding to an ACK/NACK feedback determined based on a slot offset K1 from a PDSCH to the ACK/NACK feedback, and the delayed downlink slot refers to a downlink slot in which the ACK/NACK feedback determined based on the slot offset K1 is transmitted with a delay; acquiring HARQ-ACK information of the original downlink slot and HARQ-ACK information of the delayed downlink slot, respectively; generating a HARQ-ACK codebook based on the HARQ-ACK information of the original downlink slot and the HARQ-ACK information of the delayed downlink slot; and transmitting the HARQ-ACK codebook using an uplink channel.

Abstract: The disclosed embodiments provide a system for performing network A/B testing. During operation, the system obtains a graph of a social network and calculates a set of equally sized clusters of users in the social network by iteratively switching memberships of the nodes among the equally sized clusters to increase a number of edges in each of the equally sized clusters. Next, the system randomly selects a subset of the equally sized clusters for exposure to a treatment version of a message. The system then performs an A/B test by presenting the treatment version to the selected clusters and tracking a response of the selected clusters to the treatment version.

Abstract: The disclosed embodiments provide a method and system for performing network A/B testing. During operation, the system obtains, for a set of users in a social network, a set of treatment assignments of the users in an A/B test, wherein the treatment assignments indicate exposure of the users to a control version or a treatment version of a message. Next, the system obtains, for each of the users, a fraction of neighbors exposed to the treatment version in the A/B test. The system then applies a statistical model to the treatment assignments and the fraction of neighbors exposed to the treatment version to estimate an average treatment effect (ATE) for the set of users. Finally, the system selects, based on the ATE, a fraction of additional users in the social network for subsequent exposure to the treatment version and presents the treatment version to the fraction of additional users.