• Proceedings of the IEEK Conference
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• summer
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• pp.15-18
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• 2004

In this paper we present a linear discrete bit-loading algorithm that maximizes the transmit bit rate using the channel informations to optimize the performance of the very high-speed digital subscriber line(VDSL) system. It will be useful under the constraint of a maximum transmit power for each user. When the level of crosstalk is high, the power allocation of a user changes the noise experienced by the other users in the same binder. In this case, the performance of DSL modems can be improved by jointly considering the bit and power allocation of all users.

• Journal of Communications and Networks
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• v.9 no.2
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• pp.136-140
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• 2007

In this paper, an adaptive minimum transmit power modulation scheme under constant data rate and fixed bit error rate (BER) for the multiple-input multiple-output (MIMO) system is proposed. It adjusts the modulation order and allocates the transmit power to each spatial sub-channel when meeting the user's requirements at the cost of minimum transmission power. Compared to the other algorithm, it can obtain good performance with lower computational complexity and can be applied to the wireless communication system. Computer simulation results present the efficiency of the proposed scheme. And its performance under different channel condition has been compared with the other algorithm.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.218-220
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• 2004

Orthogonal frequency division multiplexing(OFDM) is one of the most promising technique for next generation wireless broadband communication systems. In this paper, we propose a new bit allocation algorithm in multiuser OFDM. The proposed algorithm is derived from the geometric progression of the additional transmit power of subcarriers and the arithmetic-geometric means inequality. The simulation shows that this algorithm has similar performance to the conventional adaptive bit allocation algorithm and lower computational complexity than the existing algorithms.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.5
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• pp.1286-1302
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• 2012

In this paper, the resource allocation problem with proportional fairness rate in cognitive OFDM-based wireless network is studied. It aims to maximize the total system throughput subject to constraints that include total transmit power for secondary users, maximum tolerable interferences of primary users, bit error rate, and proportional fairness rate among secondary users. It is a nonlinear optimization problem, for which obtaining the optimal solution is known to be NP-hard. An efficient bio-inspired suboptimal algorithm called immune clonal optimization is proposed to solve the resource allocation problem in two steps. That is, subcarriers are firstly allocated to secondary users assuming equal power assignment and then the power allocation is performed with an improved immune clonal algorithm. Suitable immune operators such as matrix encoding and adaptive mutation are designed for resource allocation problem. Simulation results show that the proposed algorithm achieves near-optimal throughput and more satisfying proportional fairness rate among secondary users with lower computational complexity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.6
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• pp.2282-2303
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• 2021

To solve the problems of heavy computing load and system transmission pressure in energy internet (EI), we establish a three-tier cloud-edge integrated EI network based on a cloud-edge collaborative computing to achieve the tradeoff between energy consumption and the system delay. A joint optimization problem for resource allocation and task offloading in the threetier cloud-edge integrated EI network is formulated to minimize the total system cost under the constraints of the task scheduling binary variables of each sensor node, the maximum uplink transmit power of each sensor node, the limited computation capability of the sensor node and the maximum computation resource of each edge server, which is a Mixed Integer Non-linear Programming (MINLP) problem. To solve the problem, we propose a joint task offloading and resource allocation algorithm (JTOARA), which is decomposed into three subproblems including the uplink transmission power allocation sub-problem, the computation resource allocation sub-problem, and the offloading scheme selection subproblem. Then, the power allocation of each sensor node is achieved by bisection search algorithm, which has a fast convergence. While the computation resource allocation is derived by line optimization method and convex optimization theory. Finally, to achieve the optimal task offloading, we propose a cloud-edge collaborative computation offloading schemes based on game theory and prove the existence of Nash Equilibrium. The simulation results demonstrate that our proposed algorithm can improve output performance as comparing with the conventional algorithms, and its performance is close to the that of the enumerative algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.12
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• pp.3018-3036
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• 2013

In this paper, we investigate power allocation scheme and outage performance for a physical-layer network coding (PNC) relay based secondary user (SU) communication in cognitive multi-antenna relay networks (CMRNs), in which two secondary transceivers exchange their information via a multi-antenna relay using PNC protocol. We propose an optimal energy-efficient power allocation (OE-PA) scheme to minimize total energy consumption per bit under the sum rate constraint and interference power threshold (IPT) constraints. A closed-form solution for optimal allocation of transmit power among the SU nodes, as well as the outage probability of the cognitive relay system, are then derived analytically and confirmed by numerical results. Numerical simulations demonstrate the PNC protocol has superiority in energy efficiency performance over conventional direct transmission protocol and Four-Time-Slot (4TS) Decode-and-Forward (DF) relay protocol, and the proposed system has the optimal outage performance when the relay is located at the center of two secondary transceivers.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.4A
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• pp.360-370
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• 2010

In this paper, a distributed beamforming problem is considered in an amplify-and-forward (AF) wireless relay network consist of multiple source-destination pairs and relaying nodes. To exploit degree of freedom of the number of beamformers, in the first step, we proposed that the sources transmit their signals through orthogonal channels. During the second step, the relays transmit their received signals multiplied by complex weights to amplify and compensate for phase changes introduced by the backward channels through one common channel. The optimal beamforming vectors are obtained through minimization of the total relay transmit power while the signal-to-interference-plus-noise ratios (SINRs) at the destinations are above certain thresholds to meet a quality of services (QoSs) level. In the numerical example, it is shown that the proposed scheme needs less transmit power for moderate network data rates than other schemes, such as space division multiplexing or time-division multiplexing scheme.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.10A
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• pp.974-983
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• 2007

In this paper, a low complexity subcarrier allocation scheme is proposed for multiuser MIMO-OFDM systems with zero-forcing beamformer (ZFBF) so that the total transmit power can be minimized satisfying given target data rate. Since the optimal method requires very high computational complexity, we propose a low complextiy suboptimal method. Using the fact that the effective channel gain is proportional to the orthogonallity of channels of multiplexed users, a user set with the highest orthogonality of channel among users is assigned to each subcarrier in order to minimize required transmit power. The numerical results show that the proposed suboptimal method can reduce computational complexity with little performance loss.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.6
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• pp.98-104
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• 2012

We consider the optimum power allocation problem for downlink system throughput maximization in a 2 time slotted relay interference channel. Base station (BS) transmits power to Mobile Station (MS) and Relay Station (RS) in time slot 1 (orthogonal channel). In time slot 2, BS and RS transmit power to each MS, while causing cochannel interference to each other. The obtained optimum power allocation scheme allows simultaneous transmissions of BS and RS when the interference level in time slot 2 is low. However, when the interference level is high, RS shuts down its power. Numerical results are provided to support our analysis.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.26 no.10
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• pp.1517-1524
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• 2022

In this paper, we consider a heterogeneous network (HetNet) consisting of one macro base station and multiple small base stations, and assume the coordinated multi-point transmission between the base stations. In addition, we assume that the channel between the base station and the user consists of path loss and Rayleigh fading. Under these assumptions, we present the energy efficiency (EE) achievable by the user for a given base station and we formulate an optimization problem of dynamic cell selection and transmit power allocation to maximize the total EE of the HetNet. In this paper, we propose an unsupervised deep learning method to solve the optimization problem. The proposed deep learning-based scheme can provide high EE while having low complexity compared to the conventional iterative convergence methods. Through the simulation, we show that the proposed dynamic cell selection scheme provides higher EE performance than the maximum signal-to-interference-plus-noise ratio scheme and the Lagrangian dual decomposition scheme, and the proposed transmit power allocation scheme provides the similar performance to the trust region interior point method which can achieve the maximum EE.