• 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.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.212-216
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• 2022

Space based constellation network is a kind of ad hoc network in which users are self-organized without center node. In space based constellation network, users are allowed to enter or leave the network at any given time. Thus, the number of active users is an unknown and time-varying parameter, and the performance of the network depends on how accurately this parameter is estimated. The so-called problem of active user identification, which consists of determining the number and identities of users transmitting in space based constellation network is discussed and a novel active user identification method is proposed in this paper. Active user identification code generated by transmitter address code and receiver address code is used to spread spectrum. Subspace-based method is used to process received signal and judgment model is established to identify active users according to the processing results. The proposed method is simulated under AWGN channel, Rician channel and Rayleigh channel respectively. Numerical results indicate that the proposed method obtains at least 1.16dB E_b/N₀ gains compared with reference methods when miss alarm rate reaches 10^-3.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.2C
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• pp.57-64
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• 2011

A two-way relay channel is a bidirectional cooperative communication channel between two nodes using a relay. In many cooperative communication schemes, a relay transmits its data to each node using separate channels. However, in the two-way relay channel, a relay can broadcast the network-coded signal to both nodes in a same time slot, which can increase the system throughput. In this paper, a new cooperative network error correcting scheme using distributed turbo code in a two-way relay channel is proposed. The proposed scheme not only increases the system throughput using network code but also improves the performance by utilizing the LLR information from relay node and other user node through distributed turbo code. Also, a power allocation scheme is investigated for various channel conditions to improve the system performance.

• Journal of the Institute of Electronics and Information Engineers
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• v.49 no.11
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• pp.132-138
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• 2012

VANET(Vehicular Ad-hoc Network), standardization of IEEE 802.11p specification is in process. 802.11 MAC protocol grants all nodes equal opportunity to acquire channel without regard to their bit-rates, making it possible for lower bit-rate nodes to occupy communication channel for a fair amount of time thus keeping the higher bit-rate nodes from acquiring connection channel which downward-equalize the overall network performance. Also with the 802.11p MAC protocol, the probability of collision occurring increases as the number of nodes grow. The proposed algorithm is a new MAC protocol that guarantees nodes with acquired channel a firm priority over other nodes for a fixed amount of time with TXOP concept added to 'packet burst' according to the current transmitting speed. This newly designed algorithm allows the construction of wireless network with enhanced network throughput, decreased probability of collisions as well as providing the means to grant each node a fair chance of acquiring connection according to their channel conditions. The algorithm sets the CW's (Contention Window) width wider than the standard's and modulates the continuous transmitting threshold value depending on channel acquired time, thus improving the overall performance of the network.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10B
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• pp.964-972
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• 2009

Wireless mesh network (WMN) is emerging a future core technology to resolve many problems derived from exist wireless networks by employing multi-interface and multi-channel. Ability to utilize multiple channels in WMNs substantially increases the effective bandwidth available to wireless network nodes. However, minimum interference channel assignment algorithms are required to use the effective bandwidth in multi-channel environments. This paper proposes a cluster-based minimum interference channel assignment (MI-CA) algorithm to improve the performance of WMN. The MI-CA algorithm is consists of Inter-Cluster and Intra-Cluster Intrchannel assignment between clusters and in the internal clusters, respectively. The Inter-Cluster channel assignment assigns a barebone channel to cluster heads and border nodes based on minimum spanning tree (MST) and the Intra-Cluster channel assignment minimizes channel interference by reassigning ortasgonal channels between cluster mespann. Our simheation results show that MI-CA can improve the performance of WMNs by minimizing channel interference.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.27 no.3B
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• pp.226-236
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• 2002

In this paper, we present the channel assignment and management methods for an efficient use of traffic channel resource for supporting 12.2 Kbps, 64 Kbps, and 384 Kbps traffic with the different quality of service (QoS) in the radio network controller (RNC) in asynchronous IMT-2000 system. We first describe two types of traffic channel block assignments for utilizing the traffic channel efficiently; the partially dedicated and partially shared channel (PDPSC) assignment and the completely shared channel (CSC) assignment. The former is that some traffic channel block is completely assigned to each traffic type and the other blocks are shared with some traffic type. The latter is that all traffic channel blocks are completely shared with all traffic types. Further, for efficiently assigning, releasing, and managing the channel resource, we present the traffic channel management method which consists of the block and task management step. Through numerical examples, we evaluate the blocking probability and the mean number of required search for fading the available channel when applying our proposed channel block assignment and resource management methods.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.3
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• pp.339-347
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• 1993

The channel assignment problem in a mobile communication system is a NP-complete combinatorial optimization problem, in which the calculation time increases exponentially as the range of the problem is extended. This paper adapts a conventional Hopfield neural network model to the channel assignment problem to relieve the calculation time by means of the parallelism supplied from the neural network. In the simulation study, we checked the feasability of such a parallel method for the fixed channel assignment with uniform, and nouniform channel requirements, and for the dynamic channel assignment with considering continously varying channel requirements.

• Journal of Mechanical Science and Technology
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• v.20 no.10
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• pp.1576-1589
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• 2006

Most of surface water ways in Egypt suffer from the infestation of aquatic weeds especially submerged ones which cause lots of problems for the open channels and the water structures such as increasing water losses, obstructing the water flow, and reducing the efficiency of the water structures. Accurate simulation of the water flow behavior in such channels is very essential for water distribution decision makers. Artificial Neural Network (ANN) has been widely utilized in the past ten years in civil engineering applications for the simulation and prediction of the different physical phenomena and has proven its capabilities in the different fields. The present study aims towards introducing the use of ANN technique to model and predict the impact of the existence of submerged aquatic weeds on the hydraulic performance of open channels. Specifically the current paper investigates utilizing the ANN technique in developing a simulation and prediction model for the flow behavior in an open channel experiment that simulates the existence of submerged weeds as branched flexible elements. This experiment was considered as an example for implementing the same methodology and technique in a real open channel system. The results of current manuscript showed that ANN technique was very successful in simulating the flow behavior of the pre-mentioned open channel experiment with the existence of the submerged weeds. In addition, the developed ANN models were capable of predicting the open channel flow behavior in all the submerged weeds' cases that were considered in the ANN development process.

• Korean Journal of Hydrosciences
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• v.5
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• pp.1-16
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• 1994

In this paper distributed models for simulating spatially and temporally varied moving storm in a watershed were developed. The complete simulation in a watershed is achieved through two sequential flow simulations which are overland flow simulation and channel network flow simulation. Two dimensional continuity equation and momentum equation of kinematic approximation were used in the overland flow simulation. On the other hand, in the channel network simulation two types of governing equations which are one dimensional continuity and momentum equations between two adjacent sections in a channel, and continuity and energy equations at a channel junction were applied. The finite difference formulations were used in the channel network model. Macks Creek Experimental Watershed in Idaho, USA was selected as a target watershed and the moving storm on August 23, 1965, which continued from 3:30 P.M. to 5:30 P.M., was utilized. The rainfall intensity fo the moving storm in the watershed was temporally varied and the storm was continuously moved from one place to the other place in a watershed. Furthermore, runoff parameters, which are soil types, vegetation coverages, overland plane slopes, channel bed slopes and so on, are spatially varied. The good agreement between the hydrograph simulated using distributed models and the hydrograph observed by ARS are Shown. Also, the conservations of mass between upstreams and downstreams at channel junctions are well indicated and the wpatial and temporal vaiability in a watershed is well simulated using suggested distributed models.

• Journal of the Korea Institute of Military Science and Technology
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• v.25 no.3
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• pp.219-230
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• 2022

In implementing a robust automatic target recognition(ATR) system with synthetic aperture radar(SAR) imagery, one of the most important issues is accurate classification of target variants, which are the same targets with different serial numbers, configurations and versions, etc. In this paper, a deep learning network with channel attention modules is proposed to cope with the recognition problem for target variants based on the previous research findings that the channel attention mechanism selectively emphasizes the useful features for target recognition. Different from other existing attention methods, this paper employs the channel attention modules without dimensionality reduction along the channel direction from which direct correspondence between feature map channels can be preserved and the features valuable for recognizing SAR target variants can be effectively derived. Experiments with the public benchmark dataset demonstrate that the proposed scheme is superior to the network with other existing channel attention modules.