Seong Jun Oh

Beamforming in a Multi-User Cognitive Radio System with Partial Channel State Information

This paper focuses on a downlink beamforming problem in the cognitive radio (CR) communication system where primary users (PUs) coexist with secondary users (SUs). It is assumed that the SU transmitter has multiple antennas and transmits data to another SUs single-antenna receiver by employing the beamforming. By properly designing a beamforming vector, a SU can maximize its channel gain while maintaining an interference with PU below a predefined level. Designing a beamforming vector includes steering beamforming vector direction as well as adjusting transmit power. In order to construct the optimal beamforming vector, the SU transmitter requires knowledge of the downlink channel information of both PU and SU receivers sent on the feedback channel. However, it is impractical to assume that the transmitter has the perfect channel state information (CSI) considering the tremendous feedback overhead. One way to tackle this feedback overhead problem is to use a finite number of feedback bits indicating the index of a predefined codeword in the codebook. We analyze interference to the PU receiver and propose an algorithm to design a beamforming vector considering an error attributed to the partial CSI. The simulation shows that the analysis is quite accurate and that the beamforming vector, designed according to the proposed algorithm, keeps interference to PU below a predefined level while taking into account the error owing to the partial CSI. In addition, we propose a feedback bit allocation mechanism, in order to maximize the gain of the SU link. By using the proposed scheme, the CR system become robust against errors attributed to the partial CSI.

Distributed SC-FDMA Resource Allocation Algorithm Based on the Hungarian Method

In this paper we focus on a SC-FDMA systems resource allocation problem considering the uplink of the 3GPP Long Term Evolution system. We first define a utility function at each sector aiming at maximizing sum of average SINR, and implement the fairness factor which enables the resource allocator to schedule resources fairly among the users. Then, for the fairness-aware utility function, the optimization problem is solved using the iterative Hungarian. In multi-cell system, as the users suffer a uncorrelated inter-cell interference, it is difficult to find the globally optimal radio resource allocation. We propose a distributed allocation method which avoids the drastic interference level changes. Results exhibit that the proposed allocation method has a robustness to the interference variation and can be close to the globally optimal allocation. Index Terms—OFDMA, SC-FDMA, Resource Allocation, Multi-Cell, Inter-Cell Interference

Optimal Resource Allocation for Data Service in CDMA Reverse Link

The optimal resource allocation policy is studied for non-real-time users in CDMA reverse link. The resource allocation policy of interest includes channel coding, spreading gain control and power allocation under the conventional receiver operation. The constraints in the optimization include peak transmit power of the mobile station, total received power at the base station and QoS in the form of minimum SINR for each user. The coding and spreading gain control can be separated from the power allocation strategy. Our results show that the optimal power allocation policy depends on the objective function: a greedy policy is optimal to maximize the sum of throughput from each user, whereas a fair policy is optimal to maximize the product of throughput from each user. A unified approach is taken to derive the optimal policies, and it can also be applied to other power allocation problems in CDMA reverse link. Numerical results of the channel capacity are presented for both objectives along with the effect of QoS constraints.

Cognitive Radio Channel with Cooperative Multi-Antenna Secondary Systems

Recently, a cooperative cognitive radio network (CCRN) and multiple-input multiple-output (MIMO) cognitive radio (CR) network have been proposed and investigated individually as promising paradigms for CR networks. Herein, we consider applying the MIMO techniques to a CCRN in order to achieve increased spectral efficiency. Assuming that the system comprises a single-input single-output (SISO) primary user (PU) pair and a multi-input single-output (MISO) secondary user (SU) pair, we propose jointly optimizing the beamforming vector and power allocation for the SU transmitter in order to maximize the rate for the SU while meeting the rate requirement for the PU. As the problem is nontrivial, we circumvent it using the uplink and downlink duality and equality of the left and right eigenvalues. Further, we introduce an iterative algorithm with a proof of convergence. From the numerical results, the proposed algorithm converges in a small number of iterations and outperforms the zero-forcing (ZF) beamforming algorithm while providing an upper bound for the CCRN-MIMO performance. Furthermore, the application of the proposed algorithm is not limited to CCRN-MIMO; it can also be extended to the conventional relay MIMO and/or MU-MIMO with a quality-of-service (QoS) requirement.

Local and Global Information Exchange for Enhancing Object Detection and Tracking

Object detection and tracking using visual sensors is a critical component of surveillance systems, which presents many challenges. This paper addresses the enhancement of object detection and tracking via the combination of multiple visual sensors. The enhancement method we introduce compensates for missed object detection based on the partial detection of objects by multiple visual sensors. When one detects an object or more visual sensors, the detected object’s local positions transformed into a global object position. Local and global information exchange allows a missed local object’s position to recover. However, the exchange of the information may degrade the detection and tracking performance by incorrectly recovering the local object position, which propagated by false object detection. Furthermore, local object positions corresponding to an identical object can transformed into nonequivalent global object positions because of detection uncertainty such as shadows or other artifacts. We improved the performance by preventing the propagation of false object detection. In addition, we present an evaluation method for the final global object position. The proposed method analyzed and evaluated using case studies.

A queuing model with random interruptions for electric vehicle charging systems

We consider a queuing model with applications to electric vehicle (EV) charging systems in smart grids. We adopt a scheme where Electric Service Company (ESCo) broadcasts one bit signal to consumers indicating on-peak periods for the grid. EVs randomly suspend/resume charging based on the signal. To model the dynamics of the population of EVs we analyze an M/M/∞ queue with random interruptions, and propose estimates using time-scale decomposition. Using the estimates we show how ESCo can optimally adjust the indicator signal so as to minimize the mean number of charging EVs during the actual on-peak periods. Next we consider the case where EVs respond to the signal based on the individual loads. Simulation results show that performance is improved if the EVs carrying higher loads are less sensitive to the on-peak indicator signal.

VPAL: Video Packet Adaptation Layer for reliable video multicast over IEEE 802.11n WLAN

In this paper, we propose a scheme, called Video Packet Adaptation Layer (VPAL), for reliable video multicast over the IEEE 802.11n WLAN. VPAL is composed of (1) Raptor coding for reliable video transmission, (2) header compression and (3) packet aggregation, both for efficient video transmission. Most of the VPAL functionalities reside above the emerging IEEE 802.11n Medium Access Control (MAC) layer while the packet aggregation requires some changes in the MAC functionalities. The reliability of the video multicast under a strict delay requirement, is provided by achieving the target error probability of video packets, which is done by controlling both the Raptor code rate and the physical (PHY) layer transmission rate. This strategy can provide a satisfactory quality of multicast video service irrespective of the channel condition with a minimum bandwidth use. New features of the 802.11n MAC are utilized for the channel status feedback from the users. Redundant header fields in the video packets are compressed, and then these packets are aggregated to further reduce the protocol overheads. We also consider a reduced version of VPAL which does not require any change in the MAC functionalities and simply works with the IEEE 802.11n MAC. The performance of the proposed systems is comparatively evaluated in terms of the perceived video quality, i.e., peak signal-to-noise ratio (PSNR), as well as the amount of required resources via both numerical analysis and simulations.

Object Association and Identification in Heterogeneous Sensors Environment

An approach for dynamic object association and identification is proposed for heterogeneous sensor network consisting of visual and identification sensors. Visual sensors track objects by a 2D localization, and identification sensors (i.e., RFID system, fingerprint, or iris recognition system) are incorporated into the system for object identification. This paper illustrates the feasibility and effectiveness of information association between the position of objects estimated by visual sensors and their simultaneous registration of multiple objects. The proposed approach utilizes the object dynamics of entering and leaving the coverage of identification sensors, where the location information of identification sensors and objects is available. We investigate necessary association conditions using set operations where the sets are defined by the dynamics of the objects. The coverage of identification sensor is approximately modeled by the maximum sensing coverage for a simple association strategy. The effect of the discrepancy between the actual and the approximated coverage is addressed in terms of the association performance. We also present a coverage adjustment scheme using the object dynamics for the association stability. Finally, the proposed method is evaluated with a realistic scenario. The simulation results demonstrate the stability of the proposed method against nonideal phenomena such as false detection, false tracking, and inaccurate coverage model.

Receiver Cooperation in Topology Control for Wireless Ad-Hoc Networks

We propose employing receiver cooperation in centralized topology control to improve energy efficiency as well as network connectivity. The idea of transmitter cooperation has been widely considered in topology control to improve network connectivity or energy efficiency. However, receiver cooperation has not previously been considered in topology control. In particular, we show that we can improve both connectivity and energy efficiency if we employ receiver cooperation in addition to transmitter cooperation. Consequently, we conclude that a system based both on transmitter and receiver cooperation is generally superior to one based only on transmitter cooperation. We also show that the increase in network connectivity caused by employing transmitter cooperation in addition to receiver cooperation is at the expense of significantly increased energy consumption. Consequently, system designers may opt for receiver-only cooperation in cases for which energy efficiency is of the highest priority or when connectivity increase is no longer a serious concern.

Acoustic sensor-based multiple object tracking with visual information association

Object tracking by an acoustic sensor based on particle filtering is extended for the tracking of multiple objects. In order to overcome the inherent limitation of the acoustic sensor for the simultaneous multiple object tracking, support from the visual sensor is considered. Cooperation from the visual sensor, however, is better to be minimized, as the visual sensors operation requires much higher computational resources than the acoustic sensor-based estimation, especially when the visual sensor is not dedicated to object tracking and deployed for other applications. The acoustic sensor mainly tracks multiple objects, and the visual sensor supports the tracking task only when the acoustic sensor has a difficulty. Several techniques based on particle filtering are used for multiple object tracking by the acoustic sensor, and the limitations of the acoustic sensor are discussed to identify the need for the visual sensor cooperation. Performance of the triggering-based cooperation by the two visual sensors is evaluated and compared with a periodic cooperation in a real environment.