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In this paper, we propose four transmit power control strategies for the underlay deviceto-device (D2D) communications, in which the spectral efficiency (SE) of the D2D communications is maximized while the amount of interference caused to a base station (BS) is kept less than a predefined threshold. To this end, we first propose a centralized power control strategy based on instantaneous and global channel state information (CSI) by formulating a convex optimization problem. Then, three distributed power control strategies are taken into account in which each D2D pair adjusts its transmit power in a distributed manner based on interference price and its local CSI, which significantly reduces the signaling overhead. In the distributed strategies, the interference price can be determined based on 1) the instantaneous local CSI; 2) the statistics of the local CSI (average power); and 3) the number of the D2D pairs without any CSI knowledge. Through extensive computer simulations, we show that the performances of the proposed strategies optimally adjust the transmit power of the D2D communications. Especially, we find that the distributed power control strategies can achieve almost the same SE with the centralized strategy with much lower signaling and control overhead.INDEX TERMS D2D communications, distributed transmit power control, interference management, cellular networks.

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As multimedia traffic has been increasing and is expected to grow more sharply, various technologies using caches have been attracting lots of attention. As one breakthrough technology to deal with the explosively growing traffic, exclusive OR (XOR)-based index coding has been widely investigated because it can greatly enhance the efficiency of network resource by reducing the number of transmissions. In this paper, we investigate how to apply XOR-based index coding to large-scaled practical streaming systems for video traffic that accounts for more than 70% of total Internet traffic. Contrary to most previous studies that have focused on theoretical analysis of optimal performance or development of optimal index coding schemes, our study proposes a new XOR coding-based video streaming (XC). We also propose a new grouping algorithm for creating XC groups while guaranteeing the complete backward compatibility of XC with existing streaming schemes such as unicast (UC), multicast (MC), and broadcast (BC). The performance of the proposed scheme is analyzed in two steps. First, the behavior of video contents in caches at clients is modeled as a Markov chain, and the steady-state probabilities and caching probabilities for each piece of video content are derived. Based on the probabilities, the performance of the proposed system is then analyzed in terms of the average number of connections that each client requires in order to receive one video content. Our numerical results show that the proposed video streaming scheme using XC can reduce the average number of transmissions by up to 18%, compared to the conventional scheme.

Estimating the growth performance in pigs is important in order to achieve a high productivity of pig farming. We herein analyze and verify the machine learning based estimations for the growth performance in swine which includes the daily gain of body weight (DG), feed intake (FI), required growth period for growing/finishing phase (GP), and marketed-pigs per sow per year (MSY), based on the farm specific data and climate, i.e., temperature, humidity, initial age (IA), initial body weight (IBW), number of pigs (NU) and stocking density (SD). The growth data used in our work is collected from 55 pig farms which are located across South Korea for the period between October 2017 and September 2018. In the estimation of growth performance, four machine learning schemes are applied, which are the logistic regression, linear support vector machine (SVM), decision tree, and random forest. Through the evaluation, we confirm that the accuracy of estimation for growth performance can be improved by 28% using machine learning techniques compared to the base line performance which is obtained by the ZeroR classifier. We also find that the accuracy of estimation is heavily dependent on the pre-process of growth data.

The need for drone traffic control management has emerged as the demand for drones increased. Particularly, in order to control unauthorized drones, the systems to detect and track drones have to be developed. In this paper, we propose the drone position tracking system using multiple Bluetooth low energy (BLE) receivers. The proposed system first estimates the target’s location, which consists of the distance and angle, while using the received signal strength indication (RSSI) signals at four BLE receivers and gradually tracks the target based on the estimated distance and angle. We propose two tracking algorithms, depending on the estimation method and also apply the memory process, improving the tracking performance by using stored previous movement information. We evaluate the proposed system’s performance in terms of the average number of movements that are required to track and the tracking success rate.

Cellular networks are becoming dense due to deployment of small cells and a number of user devices. Such networks are called ultra-dense networks (UDNs). In this paper, we propose a novel distributed scheduling with interferenceaware power control for an uplink of the UDN operating with time-division duplex (TDD). In the proposed technique, each user adjusts transmit power according to a pre-determined threshold of generating interference to other cell base stations (BSs) and each BS selects the users having the highest effective channel gains adjusted according to the transmit power of users. We assume that each user has a single transmit antenna and each BSs have M receive antennas. It is shown that the proposed technique with a carefully chosen threshold significantly outperforms the existing distributed user scheduling schemes through extensive simulations. In addition, we prove that the optimal multiuser diversity gain, i.e., log log N is achieved by the proposed technique in each cell even in the presence of intercell interference when S = 1, if the number of users in a cell, N , scales faster than SNR K−1 1− for a constant ∈ (0, 1), where S denotes the number of scheduled users.

Recently, device-to-device (D2D) communications have been attracting substantial attention because they can greatly improve coverage, spectral efficiency, and energy efficiency, compared to conventional cellular communications. They are also indispensable for the mobile caching network, which is an emerging technology for next-generation mobile networks. We investigate a cellular overlay D2D network where a dedicated radio resource is allocated for D2D communications to remove cross-interference with cellular communications and all D2D devices share the dedicated radio resource to improve the spectral efficiency. More specifically, we study a problem of radio resource management for D2D networks, which is one of the most challenging problems in D2D networks, and we also propose a new transmission algorithm for D2D networks based on deep learning with a convolutional neural network (CNN). A CNN is formulated to yield a binary vector indicating whether to allow each D2D pair to transmit data. In order to train the CNN and verify the trained CNN, we obtain data samples from a suboptimal algorithm. Our numerical results show that the accuracies of the proposed deep learning based transmission algorithm reach about 85%∼95% in spite of its simple structure due to the limitation in computing power.