• Journal of the military operations research society of Korea
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• v.26 no.2
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• pp.90-100
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• 2000

This paper proposes a computer model for airline overbooking. The model is constructed in such as way that users feel easy to find the daily optimal number of additional customers given the reservations already bookded on that day by providing the appropriate cost of denied boarding. In this model, however, the reservations are restricted only up to 40 seats due to the limited P.C. capability. Heuristic approaches may be highly recommended for the larger number of reservations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4759-4780
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• 2017

Full Dimension multiple input multiple output (FD-MIMO) architecture employs a planar array design at the Base Station (BS) to provide high order multi-user MIMO (MU-MIMO) via simultaneous data transmission to large number of users. With FD-MIMO, the BS can also adjust the beam direction in both elevation and azimuth direction to concentrate the energy on the user of interests while minimizing the interference leakage to co-scheduled users in the same cell or users in the neighboring cells. In a typical highly populated macrocell environment, modelling the elevation angular characteristics of three-dimensional (3D) channel is critical to understanding the performance limits of the FD-MIMO system. In this paper, we study the throughput performance of FD-MIMO system with varying elevation angular spread and inter-element spacing using a 3D spatial channel model. Our results show that for a typical urban scenario, horizontal beamforming with correlated antenna spacing achieves optimal performance but by restricting the spread of elevation angles of departure, elevation beamforming achieves high array gain with wide inter-element spacing. We also realize significant gains due to spatial array processing via modelling the elevation domain and varying the inter-element spacing for both the transmitter and receiver.

• Journal of Communications and Networks
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• v.17 no.3
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• pp.267-274
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• 2015

Due to the difficulty of coordination in the cellular uplink, it is a practical challenge how to achieve the optimal throughput scaling with distributed scheduling. In this paper, we propose a distributed and opportunistic user scheduling (DOUS) that achieves the optimal throughput scaling in a single-input multiple-output interfering multiple-access channel, i.e., a multi-cell uplink network, with M antennas at each base station (BS) and N users in a cell. In a distributed fashion, each BS adopts M random receive beamforming vectors and then selects M users such that both sufficiently large desired signal power and sufficiently small generating interference are guaranteed. As a main result, it is proved that full multiuser diversity gain can be achieved in each cell when a sufficiently large number of users exist. Numerical evaluation confirms that in a practical setting of the multi-cell network, the proposed DOUS outperforms the existing distributed user scheduling algorithms in terms of sum-rate.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.6
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• pp.1101-1112
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• 2011

The Multiuser Eigenmode Transmission (MET) has generated significant interests in literature due to its optimal performance in linear precoding systems. The MET can simultaneously transmit several spatial multiplexing eigenmodes to multiple users which significantly enhance the system performance. The maximum number of users that can be served simultaneously is limited due to the constraints on the number antennas, and thus an appropriate user selection is critical to the MET system. Various algorithms have been developed in previous works such as the enumerative search algorithm. However, the high complexities of these algorithms impede their applications in practice. In this paper, motivated by the necessity of an efficient and effective user selection algorithm, a low complexity recursive user selection algorithm is proposed for the MET system. In addition, the fairness of the MET system is improved by using the combination of the proposed user selection algorithm and the adaptive Proportional Fair Scheduling (PFS) algorithm. Extensive simulations are implemented to verify the efficiency and effectiveness of the proposed algorithm.

• Journal of Communications and Networks
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• v.15 no.4
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• pp.398-406
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• 2013

In this paper, we investigate two types of in-phase and quadrature-phase (IQ) data transfer methods for cloud multiple-input multiple-output (MIMO) network operation. They are termed "after-precoding" and "before-precoding". We formulate a cloud massive MIMO operation problem that aims at selecting the best IQ data transfer method and transmission strategy (beamforming technique, the number of concurrently receiving users, the number of used antennas for transmission) to maximize the ergodic sum-rate under a limited capacity of the digital unit-radio unit link. Based on our proposed solution, the optimal numbers of users and antennas are simultaneously chosen. Numerical results confirm that the sum-rate gain is greater when adaptive "after/before-precoding" method is available than when only conventional "after-precoding" IQ-data transfer is available.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.464-466
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• 2018

In this paper, a dense cellular network is considered in which each base station equipped with multiple antennas simultaneously communicates with multiple single-antenna users. Based on limited feedback, each user feeds back its quantized channel state information (CSI) to its associated transmitter, and the transmitter broadcasts multiple data streams prepared for the scheduled users using a space-division multiple access scheme. As the amount of CSI is limited at the transmitter, the downlink throughput increases with the number feedback bits. However, the increased number of feedback bits requires the correspondingly increased amount of uplink resources. Thus, an appropriate balance between the downlink throughput and the uplink resource usage should be considered in realistic systems. A net spectral efficiency defined in this context is used in this paper, and the optimal number of feedback bits that maximizes the net spectral efficiency is analyzed. This paper particularly focuses on the case when the received signal power is much smaller than the noise power.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1998.10a
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• pp.182-185
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• 1998

In the two-tier CDMA cellular system with microcells and overlaying macrocells, slow-moving mobile user are assigned to microcells and those who move fast are assigned to overlaying macrocells in order to minimize the total number of handoffs. With this consideration the problem is how to find the thresholds by which the system distinguishes fast-moving user from those who move slowly based on the estimated speed of users. In this paper, two methods for the mobile speed estimation are proposed and two operations schemes for micro-macro cellular CDMA system are suggested. Based on these, Optimization models to find the optimal thresholds for micro-macrocell selection, which are subject to the constrains of QoS, are developed in view of minimizing the weighted total number of handoffs. And then algorithms to find optimal solutions of the models are devised.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.3
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• pp.542-559
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• 2011

Most game-theoretic works of Aloha have emphasized investigating Nash equilibria according to the system state represented by the number of network users and their decisions. In contrast, we focus on the possible change of nodes' utility state represented by delay constraint and decreasing utility over time. These foregone changes of nodes' state are more likely to instigate selfish behaviors in networking environments. For such environment, in this paper, we propose a repeated Bayesian slotted Aloha game model to analyze the selfish behavior of impatient users. We prove the existence of Nash equilibrium mathematically and empirically. The proposed model enables any type of transmission probability sequence to achieve Nash equilibrium without degrading its optimal throughput. Those Nash equilibria can be used as a solution concept to thwart the selfish behaviors of nodes and ensure the system stability.

• The Journal of the Korea Contents Association
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• v.19 no.4
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• pp.79-90
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• 2019

As an increasing number of cloud service providers begin to provide cloud computing services, they form a competitive market to compete for users. Due to different resource configurations and service workloads, users may observe different response times for their service requests and experience different levels of service quality. To compete for cloud users, it is crucial for each cloud service provider to determine an optimal price that best corresponds to their service qualities while also guaranteeing maximum profit. To achieve this goal, the underlying rationale and characteristics in this competitive market must be clarified. In this paper, we analyze price competition in the multimedia cloud service market with two service providers. We characterize the nature of non-cooperative games in a duopoly multimedia cloud service market with the goal of capturing how each cloud service provider determines its optimal price to compete with the other and maximize its own profit. To do this, we introduce a queueing model to characterize the service process in a multimedia cloud data center. Based on performance measures of the proposed queueing model, we suggest a price competition problem in a duopoly multimedia cloud service market. By solving this problem, we can obtain the optimal equilibrium prices.

• 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.