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Abstract-In orthogonal frequency-division multiple access (OFDMA), closely spaced multiple subcarriers are assigned to different users for parallel signal transmission. An interleaved subcarrier-assignment scheme is preferred because it provides maximum frequency diversity and increases the capacity in frequency-selective fading channels. The subcarriers are overlapping, but orthogonal to each other such that there is no intercarrier interference (ICI). Carrier-frequency offsets (CFOs) between the transmitter and the receiver destroy the orthogonality and introduces ICI, resulting in multiple-access interference. This paper exploits the inner structure of the signals for CFO estimation in the uplink of interleaved OFDMA systems. A new uplink signal model is presented, and an estimation algorithm based on the signal structure is proposed for estimating the CFOs of all users using only one OFDMA block. Diversity schemes are also presented to improve the estimation performance. Simulation results illustrate the high accuracy and efficiency of the proposed algorithm.Index Terms-Carrier-frequency offset (CFO), matrix decomposition, multiple access, orthogonal frequency-division multiplex (OFDM), parameter estimation.

China's performance in economic growth and poverty the population aged 15 to 64) was quite rapid and reduction has been remarkable. There is an ongoing contributed significantly to growth and welfare. debate about whether this growth is mainly driven by After incorporating human capital, they also find that productivity or factor accumulation. But few past studies the growth of total factor productivity still plays a had incorporated information on China's human capital positive and significant role during the reform period. In stock, and thus contained an omission bias.contrast, productivity growth was negative in the pre-Wang and Yao construct a measure of China's human reform period. The results are robust to changes in labor capital stock from 1952 to 1999 and, using a simple shares in GDP. growth accounting exercise, incorporate it in theirThe recent declining rate of human capital analysis of the sources of growth during the pre-reform accumulation is a cause for concern, if China is to sustain and the reform period .its improvements in growth and welfare in the coming They find that the accumulation of human capital in decade. Funding for basic education is unevenly China (as measured by the average years of schooling for distributed and insufficient in some poor regions.This paper-a product of the Economic Policy and Poverty Reduction Division, World Bank Institute-is part of a larger effort in the institute to examine country experience on globalization and growth. Copies of the paper are available free from the World Bank,

Abstract-We investigate the problem of distributed channel selection using a game-theoretic stochastic learning solution in an opportunistic spectrum access (OSA) system where the channel availability statistics and the number of the secondary users are apriori unknown. We formulate the channel selection problem as a game which is proved to be an exact potential game. However, due to the lack of information about other users and the restriction that the spectrum is time-varying with unknown availability statistics, the task of achieving Nash equilibrium (NE) points of the game is challenging. Firstly, we propose a genie-aided algorithm to achieve the NE points under the assumption of perfect environment knowledge. Based on this, we investigate the achievable performance of the game in terms of system throughput and fairness. Then, we propose a stochastic learning automata (SLA) based channel selection algorithm, with which the secondary users learn from their individual action-reward history and adjust their behaviors towards a NE point. The proposed learning algorithm neither requires information exchange, nor needs prior information about the channel availability statistics and the number of secondary users. Simulation results show that the SLA based learning algorithm achieves high system throughput with good fairness.Index Terms-Cognitive radio networks, opportunistic spectrum access, distributed channel selection, exact potential game, stochastic learning automata.

Abstract-In cognitive radio networks, spectrum sensing is a critical to both protecting the primary users and creating spectrum access opportunities of secondary users. Channel sensing itself, including active probing and passive listening, often incurs cost, in terms of time overhead, energy consumption, or intrusion to primary users. It is thus not desirable to sense the channel arbitrarily. In this paper, we are motivated to consider the following problem. A secondary user, equipped with spectrum sensors, dynamically accesses a channel. If it transmits without/with colliding with primary users, a certain reward/penalty is obtained. If it senses the channel, accurate channel information is obtained, but a given channel sensing cost incurs. The third option for the user is to turn off the sensor/transmitter and go to sleep mode, where no cost/gain incurs. So when should the secondary user transmit, sense, or sleep, to maximize the total gain? We derive the optimal transmitting, sensing, and sleeping structure, which is a threshold-based policy. Our work sheds light on designing sensing and transmitting scheduling protocols for cognitive radio networks, especially the in-band sensing mechanism in 802.22 networks.

Deep learning (DL) is a new machine learning (ML) methodology that has found successful implementations in many application domains. However, its usage in communications systems has not been well explored. This paper investigates the use of the DL in modulation classification, which is a major task in many communications systems. The DL relies on a massive amount of data and, for research and applications, this can be easily available in communications systems. Furthermore, unlike the ML, the DL has the advantage of not requiring manual feature selections, which significantly reduces the task complexity in modulation classification. In this paper, we use two convolutional neural network (CNN)-based DL models, AlexNet and GoogLeNet. Specifically, we develop several methods to represent modulated signals in data formats with gridlike topologies for the CNN. The impacts of representation on classification performance are also analyzed. In addition, comparisons with traditional cumulant and ML-based algorithms are presented. Experimental results demonstrate the significant performance advantage and application feasibility of the DL-based approach for modulation classification.

Abstract-In orthogonal frequency-division multiplexing, the total spectral resource is partitioned into multiple orthogonal subcarriers. These subcarriers are assigned to different users for simultaneous transmission in orthogonal frequency-division multiple access (OFDMA). In an unsynchronized OFDMA uplink, each user has a different carrier frequency offset (CFO) relative to the common uplink receiver, which results in the loss of orthogonality among subcarriers and thereby multiple access interference. Hence, OFDMA is very sensitive to frequency synchronization errors. In this paper, we construct the received signals in frequency domain that would have been received if all users were frequency synchronized. A generalized OFDMA framework for arbitrary subcarrier assignments is proposed. The interference in the generalized OFDMA uplink due to frequency synchronization errors is characterized in a multiuser signal model. Least squares and minimum mean square error criteria are proposed to construct the orthogonal spectral signals from one OFDMA block contaminated with interference that was caused by the CFOs of multiple users. For OFDMA with a large number of subcarriers, a low-complexity implementation of the proposed algorithms is developed based on a banded matrix approximation. Numerical results illustrate that the proposed algorithms improve the system performance significantly and are computationally affordable using the banded system implementation.Index Terms-Banded matrix, carrier frequency offset (CFO), least squares (LS), minimum mean-square error (MMSE), multiple access, orthogonal frequency-division multiplexing (OFDM), synchronization.

Drones, also known as mini-unmanned aerial vehicles, have attracted increasing attention due to their boundless applications in communications, photography, agriculture, surveillance and numerous public services. However, the deployment of amateur drones poses various safety, security and privacy threats. To cope with these challenges, amateur drone surveillance becomes a very important but largely unexplored topic. In this article, we firstly present a brief survey to show the state-of-the-art studies on amateur drone surveillance. Then, we propose a vision, named Dragnet, by tailoring the recent emerging cognitive internet of things framework for amateur drone surveillance. Next, we discuss the key enabling techniques for Dragnet in details, accompanied with the technical challenges and open issues.Furthermore, we provide an exemplary case study on the detection and classification of authorized and unauthorized amateur drones, where, for example, an important event is being held and only authorized drones are allowed to fly over. Index TermsGuoru Ding is with the National is an associate professor at college of information and communication engineering, Harbin Engineering University, China. He received the B.S. degree in Dalian Maritime University in 2003, the M.S. degree in Harbin Institute of Technology in 2005, and the Ph.D degree in Harbin engineering university in 2010. He was a visiting scholar in Wright State University, USA, from 2014 to 2015. His research interests include machine learning, information fusion, cognitive and software defined radio. Theodoros A. Tsiftsis (theodoros.tsiftsis@nu.edu.kz) is an Associate Professor of communication technologies with the School of Engineering, Nazarbayev University, Astana, Kazakhstan. His research interests include the broad areas of cooperative communications, cognitive radio, communication theory, wireless powered communication systems, and optical wireless communication systems. He is currently an Area Editor for Wireless Communications II of the IEEE TRANSACTIONS ON COMMUNICATIONS and an Associate Editor of the IEEE TRANSACTIONS ON MOBILE COMPUTING.Yu-Dong Yao (yyao@stevens.edu) has been with Stevens Institute of Technology, Hoboken,