Naver Corporation

About: Naver Corporation is a company organization based out in Seongnam-si, South Korea. It is known for research contribution in the topics: Terminal (electronics) & Computer science. The organization has 4038 authors who have published 4294 publications receiving 35045 citations. The organization is also known as: NAVER Corporation & NAVER.

Base Station Switching and Sleep Mode Optimization With LSTM-Based User Prediction

Abstract: The base station (BS) switching technique has recently attracted considerable attention for reducing power consumption in wireless networks. In this paper, we propose a novel BS switching and sleep mode optimization method to minimize the power consumption, while ensuring that the arriving user traffic is sufficiently covered. First, the user traffic in multiple time slots was predicted using the long-short term memory (LSTM) prediction model. Subsequently, we solved the Lyapunov optimization problem to obtain the optimal BS switching solution from the trade-off relationship between the reduced power consumption by BS switching and the user traffic handled in time series. Finally, we selected the sleep mode for the switched result by calculating the wake-up time and the power consumption ratio of each sleep mode. Simulation results confirm that the proposed algorithm successfully reduces the total power consumption by approximately 15% while preventing the user data queue from diverging in multiple time slots.

On KSGNS representations on Krein C∗-modules

Abstract: Motivated by the notion of P-functional, we introduce a notion of α-completely positive map between ∗-algebras which is a Hermitian map satisfying a certain positivity condition, and then a α-completely positive map which is not completely positive is constructed. We establish the Kasparov-Stinespring-Gelfand-Naimark-Segal constructions of C∗-algebra and ∗-algebra on Krein C∗-modules with α-completely positive maps.

Self-Supervised and Controlled Multi-Document Opinion Summarization

Abstract: We address the problem of unsupervised abstractive summarization of collections of user generated reviews through self-supervision and control. We propose a self-supervised setup that considers an individual document as a target summary for a set of similar documents. This setting makes training simpler than previous approaches by relying only on standard log-likelihood loss and mainstream models. We address the problem of hallucinations through the use of control codes, to steer the generation towards more coherent and relevant summaries.

Efficient Beam-Training Technique for Millimeter-Wave Cellular Communications

Abstract: In this paper, a beam ID preamble (BIDP) technique, where the beam ID is transmitted in the physical layer, is proposed for efficient beam training in millimeter-wave (mmWave) cellular communication systems. In order to facilitate beam ID detection in a multicell environment with multiple beams, the BIDP is designed such that the beam ID is mapped onto a Zadoff–Chu sequence in association with its cell ID. By analyzing the correlation property of the BIDP, it is shown that multiple beams can be transmitted simultaneously with the proposed technique with minimal interbeam interference in a multicell environment, where beams have different time delays due to propagation delay or multipath channel delay. Through simulation with the spatial channel model (SCM), it is shown that the best beam pairs can be found with a significantly reduced processing time of beam training in the proposed technique.

Conversation based search system and method

Abstract: A conversation based search method includes the steps of: proposing an utterance phrase corresponding to a query input by a user; and reforming the query by means of an answer from the user and offering a search result corresponding to the reformed query.