• The Journal of Korean Institute of Communications and Information Sciences
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• v.37A no.12
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• pp.1076-1084
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• 2012

In the CS/CB(Coordinated Scheduling/Beamforming) scheme, the cell edge user throughput is increased by selecting MIMO (Multiple Input Multiple Output) precoders which can minimize the interferences from adjacent base stations (BSs). However, in current LTE(Long Term Evolution) systems, the serving cell is selected in the initialization stage by using the synchronization signals and cell specific reference signals transmitted by adjacent BSs with a single antenna. The selected BS in the initialization stage may not be the best one since the MIMO precoding gain has not been considered in the cell selection stage. In this paper, a new cell selection technique is proposed for LTE systems with MIMO precoder by taking into account the effect of the precoder in the initialization stage. The proposed technique enables a user equipment (UE) in the cell boundary to select the serving BS by using the information (channel rank, effective channel capacity, and effective SINR(Signal to Interference plus Noise Ratio)) acquired from cell specific reference signals of candidate BSs. It is verified by computer simulation that the proposed technique can increase the channel capacity significantly in the multi-cell environments, compared with the conventional CS/CB scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.1
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• pp.261-279
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• 2023

In this article, the uplink achievable rate is investigated for massive multiple-input multiple-output (MIMO) under correlated Ricean fading channel, where each base station (BS) and user are both deployed multiple antennas. Considering the availability of prior knowledge at BS, two different channel estimation approaches are adopted with and without prior knowledge. Based on these channel estimations, a two-layer decoding scheme is adopted with maximum ratio precoding as the first layer decoder and optimal second layer precoding in the second layer. Based on two aforementioned channel estimations and two-layer decoding scheme, the exact closed form expressions for uplink achievable rates are computed with and without prior knowledge, respectively. These derived expressions enable us to analyze the impacts of line-of-sight (LoS) component, two-layer decoding, data transmit power, pilot contamination, and spatially correlated Ricean fading. Then, numerical results illustrate that the system with spatially correlated Ricean fading channel is superior in terms of uplink achievable rate. Besides, it reveals that compared with the single-layer decoding, the two-layer decoding scheme can significantly improve the uplink achievable rate performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.33 no.3A
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• pp.286-295
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• 2008

In this paper, we consider the use of orthogonal multiple beams (OMBs) to simultaneously achieve multi-user diversity and multiplexing gain in a packet-based wireless system. Previous OMB scheme considers the use of a fixed number of multiple beams equal to the number of transmit antennas. However, unless the number of active users is sufficiently large, the use of a fixed number of multiple beams may not provide desired performance due to the interference signal transmitted through other beams, being even worse than the use of a single beam. To alleviate this problem, we consider the adjustment of the number of beams in use to maximize the spectral efficiency according to the operating condition. Simulation results show the validity of the proposed scheme.

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.191-192
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• 2008

We give new definition of the effective-leakage and the signal to effective-leakage plus noise ratio (SELNR) to consider receiver combining motivated by the leakage. We propose a method to find jointly beamforming vector and combining vector for the two linear receivers (maximal ratio combining (MRC) receiver and minimum mean square error (MMSE) receiver) based on the SELNR.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.46 no.6
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• pp.86-93
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• 2009

We investigate lattice-reduction-aided precoding techniques for multi-user multiple-input multiple-output (MIMO) channels. When assuming full knowledge of the channel state information only at the transmitter, a vector perturbation (VP) is a promising precoding scheme that approaches sum capacity and has simple receiver. However, its encoding is nondeterministic polynomial time (NP)-hard problem. Vector perturbation using lattice reduction algorithms can remarkably reduce its encoding complexity. In this paper, we propose a vector perturbation scheme using Seysen's lattice reduction (VP-SLR) with simultaneously reducing primal basis and dual one. Simulation results show that the proposed VP-SLR has better bit error rate (BER) and larger capacity than vector perturbation with Lenstra-Lenstra-Lovasz lattice reduction (VP-LLL) in addition to less encoding complexity.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.5
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• pp.346-348
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• 2016

Coordinated multi-point transmission is a candidate technique for next-generation cellular communications systems. We consider a system with multiple cells in which base stations coordinate with each other by sharing user channel state information, which mitigates inter-cell interference (ICI), especially for users located at the cell edge. We introduce a new user scheduling method that considers both ICI and intra-cell orthogonality. Due to the influence of ICI cancellation and the loss reduction of effective channel gain during the beamforming process, the proposed method improves the system sum rate, when compared to the conventional method, by an average of 0.55bps/Hz for different numbers of total users per cell.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.47 no.7
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• pp.36-44
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• 2010

When multi-user MIMO (Multiple-Input Multiple-Output) systems utilize a sum-rate maximization (SRM) scheduler, the throughput of the systems can be enhanced. However, fairness problems may arise because users located near cell edge or experiencing poor channel conditions are less likely to be selected by the SRM scheduler. In this paper, a weighted sum-rate maximization (WSRM) scheduler is used to enhance the fairness performance of the MIMO systems. Closed-form expressions for the optimal transmit power allocation of WSRM and corresponding weighted sum-rate (WSR) are derived in the 6-sector collaborative transmission system. Using the derived results, we propose an algorithm which searches the optimal power allocation for WSRM in the 3-sector collaborative transmission system. Based on the derived closed-form expressions and the proposed algorithm, we perform computer simulations to compare performance of the WSRM scheduler and the SRM scheduler with respect to the sum-rate and the log-sum-of-average rates. We further verify that the WSRM scheduler efficiently improves fairness performance by showing the enhanced performance of average transmission rates in low percentile region.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.34 no.8A
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• pp.592-599
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• 2009

In this paper, we consider multiuser multi-input/multi-output antenna systems with zero-forcing receivers in downlink. In this case, to exploit multiuser diversity, spatial-division multiple access (SDMA) system allows to assign different users to a part of transmit antennas at the base station whereas spatial-division multiplexing (SDM) system assigns all antennas to single user's data stream. In this paper, we present analytical frameworks to evaluate performance of these systems. We first analyze the performance of these two systems by deriving closed-form expressions of achievable throughput. Numerical results show that the derived expressions are very tight. In addition, we approximate the capacity expression of SDM and SDMA systems and compare the SDM with the optimal case.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.794-815
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• 2023

The rapid development of mobile communication not only has made the industry gradually diversified, but also has enhanced the service quality requirements of users. In this regard, it is imperative to consider jointly network slicing and mobile edge computing. The former mainly ensures the requirements of varied vertical services preferably, and the latter solves the conflict between the user's own energy and harsh latency. At present, the integration of the two faces many challenges and need to carry out at different levels. The main target of the paper is to minimize the energy consumption of the system, and introduce a multi-slice joint task offloading and resource allocation scheme for massive multiple input multiple output enabled heterogeneous networks. The problem is formulated by collaborative optimizing offloading ratios, user association, transmission power and resource slicing, while being limited by the dissimilar latency and rate of multi-slice. To solve it, assign the optimal problem to two sub-problems of offloading decision and resource allocation, then solve them separately by exploiting the alternative optimization technique and Karush-Kuhn-Tucker conditions. Finally, a novel slices task offloading and resource allocation algorithm is proposed to get the offloading and resource allocation strategies. Numerous simulation results manifest that the proposed scheme has certain feasibility and effectiveness, and its performance is better than the other baseline scheme.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.4
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• pp.1504-1526
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• 2018

For uplink multi-user massive MIMO systems, conventional minimum mean square error (MMSE) linear detection method achieves near-optimal performance when the number of antennas at base station is much larger than that of the single-antenna users. However, MMSE detection involves complicated matrix inversion, thus making it cumbersome to be implemented cost-effectively and rapidly. In this paper, we first summarize in detail the state-of-the-art simplified MMSE detection algorithms that circumvent the complicated matrix inversion and hence reduce the computation complexity from ${\mathcal{O}}(K^3)$ to ${\mathcal{O}}(K^2)$ or ${\mathcal{O}}(NK)$ with some certain performance sacrifice. Meanwhile, we divide the simplified algorithms into two categories, namely the matrix inversion approximation and the classical iterative linear equation solving methods, and make comparisons between them in terms of detection performance and computation complexity. In order to further optimize the detection performance of the existing detection algorithms, we propose more proper solutions to set the initial values and relaxation parameters, and present a new way of reconstructing the exact effective noise variance to accelerate the convergence speed. Analysis and simulation results verify that with the help of proper initial values and parameters, the simplified matrix inversion based detection algorithms can achieve detection performance quite close to that of the ideal matrix inversion based MMSE algorithm with only a small number of series expansions or iterations.