Jeongsik Choi

PSO-FastSLAM: An improved FastSLAM framework using particle swarm optimization

FastSLAM is a framework which solves the problem of simultaneous localization and mapping using a Rao-Blackwellized particle filter. Conventional FastSLAM is known to degenerate over time in terms of accuracy due to the particle depletion in resampling phase. In this work, a new FastSLAM framework is proposed to prevent the degeneracy by particle cooperation. First, after resampling phase, a target that represents an estimated robot position is computed using the positions of particles. Then, particle swarm optimization is performed to update the robot position by means of particle cooperation. Computer simulations revealed that the proposed framework shows lower RMS error in both robot and feature positions than conventional FastSLAM.

3-D terrain covering and map building algorithm for an AUV

In order to improve the efficiency of a terrain covering algorithm for a robot(AUV), an artificial island(AI) technique is developed. Such an algorithm is necessary to acquire information and make a terrain map in an unknown three dimensional underwater environment. This algorithm considers that the three dimensional environment consists of a number of planes, at various ocean depths. The artificial island technique has the advantage of reducing the covering path length and the time cost for the robot, because it takes the correlation between every plane. In this paper, the concept of the artificial island technique is presented and its validity is proved under certain conditions. Through various simulations, we validate the efficiency in terms of the total path length and the running time of the AUV. An example of a complete three dimensional map obtained using this technique is provided.

Roadmap-based stealth navigation for intercepting an invader

This paper deals with the stealth target-interception problem based on the active prediction planning execution (APPE) strategy. In order to cope with the evasive feature of the invader, we make the robot move stealthily by hiding behind obstacles. We adopt a roadmap-based decoupled approach, which plans the path and the trajectory separately by developing new concept; the detection map. Simulation results demonstrate the efficiency of the proposed algorithm. It has an enough practical running time to be applied to the real-time situation.

An improved throughput estimation method and dynamic user association in multi-cell networks

In this paper, we propose an effective user association rule for resolving the unbalanced loading problem in multi-cell cellular networks. We first derive an improved user throughput estimation formula for the proportional fair (PF) scheduling algorithm under the Rayleigh fading environment. Our simple and accurate throughput estimation can be regarded as an extended version of the conventional scheme which is only valid when the data rate function is linear. Based on this result, we also develop a centralized user association framework, where a central node periodically determines the overall association of the system. The simulation results show that our load balancing algorithm achieves almost 90% of the upper limit of the system.

Dynamic user association and eICIC management in heterogeneous cellular networks

In heterogeneous cellular networks, users are sometimes forcibly redirected into a low power base station (BS) for the purpose of data offloading. In order to guarantee an acceptable performance for such users, who suffer from severe inter-tier interference, enhanced inter-cell interference coordination (eICIC) was proposed. Using this technique, macro BSs periodically mute their data transmission by using an almost blank subframe (ABS). In this paper, we formulate a joint optimization problem incorporating user association and ABS portion tuning to increase network-wide utility. In the development of our algorithms, we particularly consider the time-varying characteristics of wireless propagation channels in order to reflect practical signal transmission environments, and we derive a throughput estimation equation that is compatible with the eICIC operation. Based on this analysis, we separately develop algorithms for user association and ABS tuning, and the performance enhancement achieved by our proposed methods is verified through extensive system-level simulations.

Deep Learning Based NLOS Identification With Commodity WLAN Devices

Identifying line-of-sight (LOS) and non-LOS channel conditions can improve the performance of many wireless applications, such as signal strength-based localization algorithms. For this purpose, channel state information (CSI) obtained by commodity IEEE 802.11n devices can be used, because it contains information about channel impulse response (CIR). However, because of the limited sampling rate of the devices, a high-resolution CIR is not available, and it is difficult to detect the existence of an LOS path from a single CSI measurement, but it can be inferred from the variation pattern of CSI over time. To this end, we propose a recurrent neural network (RNN) model, which takes a series of CSI to identify the corresponding channel condition. We collect numerous measurement data under an indoor office environment, train the proposed RNN model, and compare the performance with those of existing schemes that use handcrafted features. The proposed method efficiently learns a nonlinear relationship between input and output, and thus, yields high accuracy even for data obtained in a very short period.

Measurement and Analysis on Land-to-Ship Offshore Wireless Channel in 2.4 GHz

In this letter, measurement results of land-to-ship offshore wireless channels are presented. Large-scale and small-scale fading characteristics of 2.4 GHz frequency band are investigated by implementing narrowband channel measurement system between quay and a sailing coast patrol boat. For large-scale fading, free space path loss model, ITU-R P.1546 model, and two-ray model are compared with the empirical PL data. The results of an extensive measurement were used to determine that the existing ITU-R P.1546 model is inconsistent with the empirical PL data for offshore environment, whereas the two-ray model considering sea wave height agrees well with overall empirical data. Destructive and constructive interferences around distances of 200–500 m are observed from the empirical PL data, which ensures the validity of the two-ray model for offshore environment. For small-scale fading, the Kolmogorov–Smirnov test is utilized to verify goodness of fit, and it is shown that the result agrees best with the Rician distribution, compared with Nakagami-m or Rayleigh distributions.

Distributed Power Control-Based Connectivity Reconstruction Game in Wireless Localization

In geographically dense and energy-limited wireless sensor networks, connectivity-based localization with full power transmission can be inefficient in terms of energy consumption. In this letter, we propose a distributed power control-based connectivity reconstruction game, which considers both energy efficiency and the quality of localization. The proposed scheme results in a better performance with an improved 61.9% reduction in energy consumption while maintaining the performance of localization at a level similar to the conventional algorithm with full-power transmission.

Throughput Estimation Based Distributed Base Station Selection in Heterogeneous Networks

Small cells are considered an emerging technology for increasing the potential capacity of cellular networks. However, as the density of infrastructure increases, users have many choices for connection, and therefore, selecting an appropriate base station (BS) becomes an important issue. This study aims to provide an improved user association rule, where each user autonomously chooses the best among all the BSs in the vicinity, while considering their congestion levels. As the first step, an optimization problem is formulated, which emphasizes both the time-varying nature of the wireless channel and fairness among users. On the basis of this formulation, the influence of a specific handover event on system performance is investigated, and then, two versions of the handover frameworks are developed. Simulation results show that the proposed algorithms increase the throughput of every user in the network by 2.8-12% compared to the best conventional scheme. Furthermore, these schemes especially enhance the performance of users having low service quality by 5.0-85%, through efficient utilization of the pre-installed infrastructures.

Cooperative Limited Feedback Precoding in Interference-Limited MIMO Networks

In this paper, we propose new cooperative precoder selection technique for interference limited MIMO networks. Our proposed method gives weighting to precoders in the codebook according to each precoder`s performance priority. By applying our proposed method to precoder selection sequence, performance of entire system can be improved in terms of sumrate, stability, and feedback rate.