이웅섭 교수

In this letter, deep power control (DPC), which is the first transmit power control framework based on a convolutional neural network (CNN), is proposed. In DPC, the transmit power control strategy to maximize either spectral efficiency (SE) or energy efficiency (EE) is learned by means of a CNN. While conventional power control schemes require a considerable number of computations, in DPC, the transmit power of users can be determined using far fewer computations enabling real-time processing.

In this paper, we investigate cooperative spectrum sensing (CSS) in a cognitive radio network (CRN) where multiple secondary users (SUs) cooperate in order to detect a primary user, which possibly occupies multiple bands simultaneously. Deep cooperative sensing (DCS), which constitutes the first CSS framework based on a convolutional neural network (CNN), is proposed. In DCS, instead of the explicit mathematical modeling of CSS, the strategy for combining the individual sensing results of the SU

Integration of distributed generation based on renewable energy sources into the power system has gained popularity in recent years. Many small-scale electricity suppliers (SESs) have recently entered the electricity market, which has been traditionally dominated by a few large-scale electricity suppliers. The emergence of SESs enables direct trading (DT) of electricity between SESs and end-users (EUs), without going through retailers, and promotes the possibility of improving the benefits to bo

In this paper, we study the price competition among electric vehicle charging stations (EVCSs) with renewable power generators (RPGs). As electric vehicles (EVs) become more popular, a competition among EVCSs to attract EVs is inevitable. Thereby, each EVCS sets its electricity price to maximize its revenue by taking into account the competition with neighboring EVCSs. We analyze the competitive interactions between EVCSs using game theory, where relevant physical constraints such as the transmi

In this letter, a resource allocation strategy based on a deep neural network (DNN) is proposed for multi-channel cognitive radio networks, where the secondary user (SU) opportunistically utilizes channels without causing excessive interference to the primary user (PU). In the proposed scheme, the allocation of transmit power in each channel for SUs is found by utilizing the newly proposed DNN model, which separately determines the overall transmit power of individual SUs and the proportion of t

A transmit power control strategy using a deep neural network (DNN) is proposed for underlay device-to-device (D2D) communication where D2D user equipment (DUE) shares radio resources with cellular user equipment (CUE). In this scheme, a transmit power control strategy for DUE is found with the aid of a newly proposed DNN structure. Both the spectral efficiency (SE) of the DUE and the amount of interference at the CUE are taken into account, such that the SE of the DUE can be improved while alle

In this paper, we investigate the price competition among electric vehicle charging stations (EVCSs) with renewable power generators (RPGs). Both a large-sized EVCS (L-EVCS) and small-sized EVCSs (S-EVCSs), which have different capacities, are considered. Moreover, the price elasticity of electric vehicles (EVs), the effect of the distance between an EV and the EVCSs, and the impact of the number and type of charging outlets at the EVCSs on the behavior of EVs are taken into account. The electri

In this paper, a deep learning (DL) framework for the optimization of the resource allocation in multi-channel cellular systems with device-to-device (D2D) communication is proposed. Thereby, the channel assignment and discrete transmit power levels of the D2D users, which are both integer variables, are optimized for maximization of the overall spectral efficiency whilst maintaining the quality-of-service (QoS) of the cellular users. Depending on the availability of channel state information (C

Cognitive radio (CR) is a promising technology for overcoming the lack of available communication bands. In CR technology, spectrum sensing is an important issue, which has recently been extensively studied. We provide a solution to the spectrum-sensing problem for multiple cognitive terminals (CTs) that takes into account the difference among CTs with respect to the probabilities of a false detection and a missed detection. We optimize the spectrum-sensing performance by differentiating the num

In this letter, we propose a hybrid resource allocation scheme for multi-channel underlay device-to-device (D2D) communications. In our proposed scheme, the transmit power of D2D user equipment (DUE) allocated to each channel is controlled in order to maximize the sum rate of the DUEs for a given Quality of Service (QoS) constraints. We consider two QoS constraints such that the interference caused on cellular user equipment (CUE) is kept to be less than a predefined level and the rate of indivi

In this paper, a means of transmit power control for underlaid device-to-device (D2D) communication is proposed based on deep learning technology. In the proposed scheme, the transmit power of D2D user equipment (DUE) is autonomously learned via a deep neural network such that the weighted sum rate (WSR) of DUEs can be maximized by considering the interference from cellular user equipment. Unlike conventional transmit power control schemes in which complex optimization problems have to be solved

Given the ever increasing use of near field communication (NFC), the security of this system is becoming increasingly important. Recently, radio frequency (RF) fingerprinting, where the physical RF characteristics of a communication device are used as a means to provide guarantees of authenticity and security, has received serious consideration due to the uniqueness of these characteristics, making cloning difficult. In this article, we discuss the feasibility of RF fingerprinting assisted by de

The emergence of DL represents a potential paradigm shift in the design of WCS, from conventional handcrafted schemes based on mathematical models with assumptions, to autonomous schemes based on DL using large sets of data. In this article, we discuss the essential elements of DL and investigate an intelligent RA scheme based on a DNN, in which multiple goals with various constraints can be satisfied through DL. Having confirmed the optimality and feasibility of DNN-based RA through simulation,

Due to the increasing popularity of device-to-device (D2D) services, it is becoming increasingly important to find efficient means of identifying nearby users, i.e., by peer discovery. Herein, we propose a low-power peer discovery scheme, in which we exploit the tradeoff between the power consumption and the accuracy/scope of peer discovery. In our proposed scheme, the transmission of a proximity beacon is scheduled based on channel values, such that users in close proximity are likely to transm

In this paper, we consider a wireless-powered two-way communication, called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">transmit-harvest-respond</i> , with co-channel interference. The two-way communication considered here comprises three steps: i) transmitters send data signals, ii) receivers decode information and harvest energy simultaneously from the received signals using a policy of time switching (TS) or power splitting (PS), and iii)