• Journal of Communications and Networks
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• v.16 no.2
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• pp.183-192
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• 2014

This paper proposes a payment-based power control scheme using non-cooperative game with a novel pricing function in cognitive radio networks (CRNs). The proposed algorithm considers the fairness of power control among second users (SUs) where the value of per SU' signal to noise ratio (SINR) or distance between SU and SU station is used as reference for punishment price setting. Due to the effect of uncertainty fading environment, the system is unable to get the link gain coefficient to control SUs' transmission power accurately, so the quality of service (QoS) requirements of SUs may not be guaranteed, and the existence of Nash equilibrium (NE) is not ensured. Therefore, an alternative iterative scheme with sliding model is presented for the non-cooperative power control game algorithm. Simulation results show that the pricing policy using SUs' SINR as price punishment reference can improve total throughput, ensure fairness and reduce total transmission power in CRNs.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.13 no.5
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• pp.71-76
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• 2013

This paper considers an approach of secondary user selection method in cooperative spectrum sensing, which two users with the best SNR in sensing channel and in reporting channel, respectively, are selected to cooperate with each other in the spectrum sensing. The sensing results reported by two users are then combined to detect PU signal operation. A comparison between this proposed method with conventional selection technique in which only the user having the best sensing channel SNR is selected shows that the proposed method outperforms. We make an assumption that sensing channels experience identical, independent distributed (i.i.d) Rayleigh fading and the reporting channels are invariant and non-identical. Simulation results are derived for demonstration.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3433-3446
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• 2019

Aiming at the high complexity of traditional single-channel demodulation algorithm for PCMA signals, a new demodulation algorithm based on neural network is proposed to reduce the complexity of demodulation in the system of non-cooperative PCMA communication. The demodulation network is trained in this paper, which combines the preprocessing module and decision module. Firstly, the preprocessing module is used to estimate the initial parameters, and the auxiliary signals are obtained by using the information of frequency offset estimation. Then, the time-frequency characteristic data of auxiliary signals are obtained, which is taken as the input data of the neural network to be trained. Finally, the decision module is used to output the demodulated bit sequence. Compared with traditional single-channel demodulation algorithms, the proposed algorithm does not need to go through all the possible values of transmit symbol pairs, which greatly reduces the complexity of demodulation. The simulation results show that the trained neural network can greatly extract the time-frequency characteristics of PCMA signals. The performance of the proposed algorithm is similar to that of PSP algorithm, but the complexity of demodulation can be greatly reduced through the proposed algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.6
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• pp.1743-1758
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• 2023

Automatic modulation classification is a critical algorithm for non-cooperative communication systems. This paper addresses the challenging problem of closed-set and open-set signal modulation classification in complex channels. We propose a novel approach that incorporates a self-learning filter and center-loss in Deep Residual Shrinking Networks (DRSN) for closed-set modulation classification, and the Opendistance method for open-set modulation classification. Our approach achieves better performance than existing methods in both closed-set and open-set recognition. In closed-set recognition, the self-learning filter and center-loss combination improves recognition performance, with a maximum accuracy of over 92.18%. In open-set recognition, the use of a self-learning filter and center-loss provide an effective feature vector for open-set recognition, and the Opendistance method outperforms SoftMax and OpenMax in F1 scores and mean average accuracy under high openness. Overall, our proposed approach demonstrates promising results for automatic modulation classification, providing better performance in non-cooperative communication systems.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.5
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• pp.1755-1777
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• 2022

The development of wireless communication technology has led to the underutilization of radio spectra. To address this limitation, an intelligent cognitive radio network was developed. Specific emitter identification (SEI) is a key technology in this network. However, in realistic non-cooperative scenarios, the system may detect signal classes beyond those in the training database, and only a few labeled signal samples are available for network training, both of which deteriorate identification performance. To overcome these challenges, a meta-learning-based open-set identification system is proposed for SEI. First, the received signals were pre-processed using bi-spectral analysis and a Radon transform to obtain signal representation vectors, which were then fed into an open-set SEI network. This network consisted of a deep feature extractor and an intrinsic feature memorizer that can detect signals of unknown classes and classify signals of different known classes. The training loss functions and the procedures of the open-set SEI network were then designed for parameter optimization. Considering the few-shot problems of open-set SEI, meta-training loss functions and meta-training procedures that require only a few labeled signal samples were further developed for open-set SEI network training. The experimental results demonstrate that this approach outperforms other state-of-the-art SEI methods in open-set scenarios. In addition, excellent open-set SEI performance was achieved using at least 50 training signal samples, and effective operation in low signal-to-noise ratio (SNR) environments was demonstrated.

• ETRI Journal
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• v.46 no.3
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• pp.446-460
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• 2024

In this paper, we mathematically investigate a downlink non-orthogonal multiple access (NOMA) system for short-packet communications (SPC) in which the near users are used as full-duplex (FD) relays to forward intended signals from the source to a far user. In addition, partial relay selection is employed to enhance the performance of the FD relays under the impact of imperfect interference cancellation. At the far user, selection combining (SC) or maximal ratio combining (MRC) is employed to combine the signals received from the source and the selected FD relay. The analytical expressions for the average block error rate (BLER) of two users over flat Rayleigh fading channels are derived. Furthermore, closed-form asymptotic expressions of the average BLERs at the near and far users in high signal-to-noise ratio (SNR) regimes are obtained. The numerical results show that the analytical BLERs of the near user and far user closely match the simulation results.

• International Journal of Control, Automation, and Systems
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• v.5 no.4
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• pp.444-455
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• 2007

An approach to construct multiple interpretable and precise fuzzy systems based on the Pareto Multi-objective Cooperative Coevolutionary Algorithm (PMOCCA) is proposed in this paper. First, a modified fuzzy clustering algorithm is used to construct antecedents of fuzzy system, and consequents are identified separately to reduce computational burden. Then, the PMOCCA and the interpretability-driven simplification techniques are executed to optimize the initial fuzzy system with three objectives: the precision performance, the number of fuzzy rules and the number of fuzzy sets; thus both the precision and the interpretability of the fuzzy systems are improved. In order to select the best individuals from each species, we generalize the NSGA-II algorithm from one species to multi-species, and propose a new non-dominated sorting technique and collaboration mechanism for cooperative coevolutionary algorithm. Finally, the proposed approach is applied to two benchmark problems, and the results show its validity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.11
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• pp.3008-3025
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• 2012

In this paper, the downlink resource allocation of OFDMA system with decode-and-forward (DF) relaying is investigated. A non-convex optimization problem maximizing system throughput with users' satisfaction constraints is formulated with joint relay selection, subcarrier assignment and power allocation. We first transform it to a standard convex problem and then solve it by dual decomposition. In particular, an Optimal resource allocation scheme With Time-sharing (OWT) is proposed with combination of relay selection, subcarrier allocation and power control. Due to its poor adaption to the fast-varying environment, an improved version with subcarrier Monopolization (OWM) is put forward, whose performance promotes about 20% compared with that of OWT in the fast-varying vehicular environment. In fact, OWM is the special case of OWT with binary time-sharing factor and OWT can be seen as the tight upper bound of the OWM. To the best of our knowledge, such algorithms and their relation have not been accurately investigated in cooperative OFDMA networks in the literature. Simulation results show that both the system throughput and the users' satisfaction of the proposed algorithms outperform the traditional ones.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.6
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• pp.2686-2708
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• 2020

This paper investigates the energy efficiency of energy harvesting (EH) bidirectional cooperative sensor networks, in which the considered system model enables the uplink information transmission from the sensor (SN) to access point (AP) and the energy supply for the amplify-and-forward (AF) relay and SN using power-splitting (PS) or time-switching (TS) protocol. Considering the minimum EH activation constraint and quality of service (QoS) requirement, energy efficiency is maximized by jointly optimizing the resource division ratio and transmission power. To cope with the non-convexity of the optimizations, we propose the low complexity iterative algorithm based on fractional programming and alternative search method (FAS). The key idea of the proposed algorithm first transforms the objective function into the parameterized polynomial subtractive form. Then we decompose the optimization into two convex sub-problems, which can be solved by conventional convex programming. Simulation results validate that the proposed schemes have better output performance and the iterative algorithm has a fast convergence rate.

• The Journal of Korea Robotics Society
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• v.11 no.3
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• pp.127-132
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• 2016

For a practical mobile robot team such as carrying out a search and rescue mission in a disaster area, the localization have to be guaranteed even in an environment where the network infrastructure is destroyed or a global positioning system (GPS) is unavailable. The proposed architecture supports localizing robots seamlessly by finding their relative locations while moving from a global outdoor environment to a local indoor position. The proposed schemes use a cooperative positioning system (CPS) based on the two-way ranging (TWR) technique. In the proposed TWR-based CPS, each non-localized mobile robot act as tag, and finds its position using bilateral range measurements of all localized mobile robots. The localized mobile robots act as anchors, and support the localization of mobile robots in the GPS-shadow region such as an indoor environment. As a tag localizes its position with anchors, the position error of the anchor propagates to the tag, and the position error of the tag accumulates the position errors of the anchor. To minimize the effect of error propagation, this paper suggests the new scheme of full-mesh based CPS for improving the position accuracy. The proposed schemes assuring localization were validated through experiment results.