• ETRI Journal
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• v.36 no.1
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• pp.62-68
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• 2014

This paper presents a novel user selection method based on the signal-to-interference-plus-noise ratio (SINR), which is approximated using limited feedback data at the base stations (BSs) of multiple user multiple-input multiple-output (MU-MIMO) systems. In the proposed system, the codebook vector index, the quantization error obtained from the correlation between the measured channel and the codebook vector, and the measured value of the largest singular value are fed back from each user to the BS. The proposed method not only generates precoding vectors that are orthogonal to the precoding vectors of the previously selected users and are highly correlated with the codebook vector of each user but also adopts the quantization error in approximating the SINR, which eventually provides a significantly more accurate SINR than the conventional SINR-based user selection techniques. Computer simulations show that the proposed method enhances the sum rate of the conventional SINR-based methods by at least 2.4 (2.62) bps/Hz when the number of transmit antennas and number of receive antennas per user terminal is 4 and 1(2), respectively, with 100 candidate users and an SNR of 30 dB.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.1
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• pp.32-35
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• 2018

Massive multiple-input multiple-output (MIMO) transmission is an essential technique for achieving the high bandwidth efficiency required in 5G mobile communication systems. Various forms of arrays can be used as the number of antenna elements increases for massive MIMO transmission. In this letter, we propose a beamforming algorithm applicable to multiuser MIMO transmission using uniform circular arrays. By employing quasi-orthogonal beam pairs obtained from the inter-beam correlation information, we minimize inter-user interference and evaluate the resulting performance gain.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.5C
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• pp.601-610
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• 2004

In this paper, the performance of an adaptive array antenna using Constant Modulus Algorithm (CMA) based on the signal structure for the IMT-2000 3GPP specification reverse link of an asynchronous direct sequence code division multiple access (DS-CDMA) system are evaluated. In addition, the performance is compared with the array antenna using Least Mean Square (LMS) based on the training signal. The simulation parameters such as the number of multipath, mu10pa1h intensity profiles between path, spreading gain and multiuser etc., are considered in the Monte Carlo simulation. Simulation results demonstrate an adaptive array antenna using CMA may give more capacity gain than the amy antenna using LMS in the case of multipath fading channel.

• Journal of Communications and Networks
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• v.10 no.1
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• pp.28-37
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• 2008

In this paper, we investigate the problem of minimizing the average transmission power of users while guaranteeing the average delay constraints in time-varying uplink channels. We design a scheduler that selects a user for transmission and determines the transmission rate of the selected user based on the channel and backlog information of users. Since it requires prohibitively high computation complexity to determine an optimal scheduler for multi-user systems, we propose a low-complexity scheduling scheme that can achieve near-optimal performance. In this scheme, we reduce the complexity by decomposing the multiuser problem into multiple individual user problems. We arrange the probability of selecting each user such that it can be determined only by the information of the corresponding user and then optimize the transmission rate of each user independently. We solve the user problem by using a dynamic programming approach and analyze the upper and lower bounds of average transmission power and average delay, respectively. In addition, we investigate the effects of the user selection algorithm on the performance for different channel models. We show that a channel-adaptive user selection algorithm can improve the energy efficiency under uncorrelated channels but the gain is obtainable only for loose delay requirements in the case of correlated channels. Based on this, we propose a user selection algorithm that adapts itself to both the channel condition and the backlog level, which turns out to be energy-efficient over wide range of delay requirement regardless of the channel model.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.3A
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• pp.238-244
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• 2010

We propose a user selection method in multi-cell multiuser uplink system with the subspace interference alignment to maintain the high sum-rate capacity. The proposed method selects users whose interference to adjacent cells are strong and apply the subspace interference alignment to the users. The proposed method provides that the number of users performing the interference alignment reduces and the high total sum-rate is maintained. Simulation results exhibit the proposed method reduces the number of users who would decompose the channel for the subspace interference alignment by 50% when the required sum-rate is 90% of the maximal achievable sum-rate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.9
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• pp.2120-2133
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• 2013

In this paper, we address the problem of linear minimum mean-squared error (MMSE) transmitter design for the cognitive radio (CR) multi-user multiple-input single-output (MU-MISO) broadcasting channel (BC), where the cognitive users are subject to not only a sum power constraint, but also a interference power constraint. Evidently, this multi-constraint problem renders it difficult to solve. To overcome this difficulty, we firstly transform it into its equivalent formulation with a single constraint. Then by utilizing BC-MAC duality, the problem of BC transmitter design can be solved by focusing on a dual MAC problem, which is easier to deal with due to its convexity property. Finally we propose an efficient two-level iterative algorithm to search the optimal solution. Our simulation results are provided to corroborate the effectiveness of the proposed algorithm and show that this proposed CR MMSE-based scheme achieves a suboptimal sum-rate performance compared to the optimal DPC-based algorithm with less computational complexity.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.7
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• pp.1257-1265
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• 2015

In this paper, we describe a greedy user selection scheme for multiuser multiple-input multiple-output (MIMO) systems. We propose a new metric which has significantly improved performance compared to the Frobenius norm metric. The approximation of projection matrix is applied to increase the accuracy of Frobenius norm of effective channel matrix. We analyze the computational complexity of two metrics by using flop counts, and also verify the achievable sum rate through numerical simulation. Our simulation result shows that the proposed metric can achieve the improved sum rate as the number of user antenna increases.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.10C
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• pp.929-936
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• 2009

In this paper, we consider designing a multi-input multi-output (MIMO) overlay system for fixed MIMO wireless link, where a frequency flat narrowband channel is shared by multiple transmitter and receiver pairs. Assuming the perfect knowledge of the second-order statistics of the received legacy signals and the composite channels from the overlay transmitter to the legacy receivers, the jointly optimal linear precoder and decoder matrices of the MIMO overlay system is derived to minimize the total mean squared error (MSE) of the data symbol vector, subject to total average transmission power and zero interference induced to legacy MIMO systems already existing in the frequency band of interest. Furthermore, the necessary and sufficient condition for the existence of the optimal solution is also derived.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.3
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• pp.463-468
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• 2016

The overall performance of multiuser systems significantly depends on how effectively and fairly manage resources shared by them. The efficient resource management strategies are even more important for multimedia users since multimedia data is delay-sensitive and massive. In this paper, we focus on resource allocation based on a game-theoretic approach, referred to as Nash bargaining solution (NBS), to provide a quality of service (QoS) guarantee for each user. While the NBS has been known as a fair and optimal resource management strategy, it is challenging to find the NBS efficiently due to the computationally-intensive task. In order to reduce the computation requirements for NBS, we propose an approach that requires significantly low complexity even when networks consist of a large number of users and a large amount of resources. The proposed approach linearizes utility functions of each user and formulates the problem of finding NBS as a convex optimization, leading to nearly-optimal solution with significantly reduced computation complexity. Simulation results confirm the effectiveness of the proposed approach.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.12
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• pp.3061-3080
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• 2012

To improve the sensing performance, cooperation among secondary users can be utilized to collect space diversity. In this paper, we focus on the optimization of cooperative spectrum sensing in which multiple cognitive users efficiently cooperate to achieve superior detection accuracy with minimum sensing error probability in multiple cross-over cognitive radio networks. The analysis focuses on two fusion strategies: soft information fusion and hard information fusion. Under soft information fusion, the optimal threshold of the energy detector is derived in both noncooperative single-user and cooperative multiuser sensing scenarios. Under hard information fusion, the optimal randomized rule and the optimal decision threshold are derived according to the rule of minimum sensing error (MSE). MSE rule shows better performance on improving the final false alarm and detection probability simultaneously. By simulations, our proposed strategy optimizes the sensing performance for each cognitive user which is randomly distributed in the multiple cross-over cognitive radio networks.