Kae Won Choi

Hybrid Random Access and Data Transmission Protocol for Machine-to-Machine Communications in Cellular Networks

To address random access channel (RACH) congestion and high signaling overhead problems of machine-to-machine (M2M) communication in cellular networks, we propose a new design of a random access procedure that is exclusively engineered for the M2M communication. Our design has two prominent features. One is a fast signaling process that allows M2M user equipment to transmit data right after preamble transmission on a physical RACH to reduce the signaling overhead. The other is a self-optimization feature that allows the cellular system to produce optimal M2M throughput by adaptively changing resource block (RB) composition and an access barring parameter according to the amount of available RBs and the M2M traffic load. We derive a closed-form analytic formula for the M2M traffic throughput and propose a joint adaptive resource allocation and access barring scheme based on the analytic results. By simulation, we show that the proposed scheme exhibits a near-optimal performance in terms of the capacity.

Opportunistic Access to Spectrum Holes Between Packet Bursts: A Learning-Based Approach

We present a cognitive radio (CR) mechanism for opportunistic access to the frequency bands licensed to a data-centric primary user (PU) network. Secondary users (SUs) aim to exploit the short-lived spectrum holes (or opportunities) created between packet bursts in the PU network. The PU traffic pattern changes over both time and frequency according to upper layer events in the PU network, and fast variation in PU activity may cause high sensing error probability and low spectrum utilization in dynamic spectrum access. The proposed mechanism learns a PU traffic pattern in real-time and uses the acquired information to access the frequency channel in an efficient way while limiting the probability of collision with the PUs below a target limit. To design the channel learning algorithm, we model the CR system as a hidden Markov model (HMM) and present a gradient method to find the underlying PU traffic pattern. We also analyze the identifiability of the proposed HMM to provide a condition for the convergence of the proposed learning algorithm. Simulation results show that the proposed algorithm greatly outperforms the traditional listen-before-talk algorithm which does not possess any learning functionality.

Two-Stage Semi-Distributed Resource Management for Device-to-Device Communication in Cellular Networks

In cellular networks, the device-to-device (D2D) communication increases the network capacity by spatial reuse of radio resources and prolongs the battery life of devices by reducing the transmission power. In this paper, we propose a two-stage semi-distributed resource management framework for the D2D communication. At the first stage of the framework, the base station (BS) allocates resource blocks (RBs) to BS-to-user device (B2D) links and D2D links, in a centralized manner. At the second stage, the BS schedules the transmission using the RBs allocated to B2D links, while the primary user device of each D2D link carries out link adaptation on the RBs allocated to the D2D link, in a distributed fashion. The proposed framework has the advantages of both centralized and distributed design approaches, i.e., high network capacity and low control/computational overhead, respectively. We formulate the problems of RB allocation to maximize the radio resources efficiency, taking account of two different policies on the spatial reuse of RBs. To solve these problems, we suggest a greedy algorithm and a column generation-based algorithm. By simulation, it is shown that the proposed scheme achieves the near-optimal performance while reducing the control/computational overhead.

Resource allocation scheme for device-to-device communication for maximizing spatial reuse

Recently, device-to-device (D2D) communication in cellular networks has gained much attention for enabling a base station (BS) to offload traffic to direct D2D links and for facilitating new network services. In this paper, we propose a semi-distributed resource allocation scheme for the D2D communication where the BS allocates radio resources for cellular user links and D2D links in a centralized manner while the modulation and coding scheme (MCS) level and the transmission power of D2D links are distributively decided by the device of each D2D link. We first formulate a resource allocation problem of which the objective is to maximize the spatial reuse of radio resources by allowing the simultaneous transmission of D2D links on the same resources. To solve the problem, we present a suboptimal greedy algorithm. By simulation, we show that the cellular networks with the proposed scheme achieve a higher network throughput by maximizing the spatial reuse of radio resources.

Downlink Subchannel and Power Allocation in Multi-Cell OFDMA Cognitive Radio Networks

We propose a novel subchannel and transmission power allocation scheme for multi-cell orthogonal frequency-division multiple access (OFDMA) networks with cognitive radio (CR) functionality. The multi-cell CR-OFDMA network not only has to control the interference to the primary users (PUs) but also has to coordinate inter-cell interference in itself. The proposed scheme allocates the subchannels to the cells in a way to maximize the system capacity, while at the same time limiting the transmission power on the subchannels on which the PUs are active. We formulate this joint subchannel and transmission power allocation problem as an optimization problem. To efficiently solve the problem, we divide it into multiple subproblems by using the dual decomposition method, and present the algorithms to solve these subproblems. The resulting scheme efficiently allocates the subchannels and the transmission power in a distributed way. The simulation results show that the proposed scheme provides significant improvement over the traditional fixed subchannel allocation scheme in terms of system throughput.

Device-to-Device Discovery for Proximity-Based Service in LTE-Advanced System

In this paper, we propose a device-to-device (D2D) discovery scheme as a key enabler for a proximity-based service in the Long-Term Evolution Advanced (LTE-A) system. The proximity-based service includes a variety of services exploiting the location information of user equipment (UE), for example, the mobile social network and the mobile marketing. To realize the proximity-based service in the LTE-A system, it is necessary to design a D2D discovery scheme by which UE can discover another UE in its proximity. We design a D2D discovery scheme based on the random access procedure in the LTE-A system. The proposed random-access-based D2D discovery scheme is advantageous in that 1) the proposed scheme can be readily applied to the current LTE-A system without significant modification; 2) the proposed scheme discovers pairs of UE in a centralized manner, which enables the access or core network to centrally control the formation of D2D communication networks; and 3) the proposed scheme adaptively allocates resource blocks for the D2D discovery to prevent underutilization of radio resources. We analyze the performance of the proposed D2D discovery scheme. A closed-form formula for the performance is derived by means of the stochastic geometry-based approach. We show that the analysis results accurately match the simulation results.

Cooperative Spectrum Sensing Under a Random Geometric Primary User Network Model

We propose a novel cooperative spectrum sensing algorithm for a cognitive radio (CR) network to detect a primary user (PU) network that exhibits some degree of randomness in topology (e.g., due to mobility). We model the PU network as a random geometric network that can better describe small-scale mobile PUs. Based on this model, we formulate the random PU network detection problem in which the CR network detects the presence of a PU receiver within a given detection area. To address this problem, we propose a location-aware cooperative sensing algorithm that linearly combines multiple sensing results from secondary users (SUs) according to their geographical locations. In particular, we invoke the Fisher linear discriminant analysis to determine the linear coefficients for combining the sensing results. The simulation results show that the proposed sensing algorithm yields comparable performance to the optimal maximum likelihood (ML) detector and outperforms the existing ones, such as equal coefficient combining, OR-rule-based and AND-rule-based cooperative sensing algorithms, by a very wide margin.

Distributed and Centralized Hybrid CSMA/CA-TDMA Schemes for Single-Hop Wireless Networks

The strength of carrier-sense multiple access with collision avoidance (CSMA/CA) can be combined with that of time-division multiple access (TDMA) to enhance the channel access performance in wireless networks such as the IEEE 802.15.4-based wireless personal area networks. In particular, the performance of legacy CSMA/CA-based medium access control scheme in congested networks can be enhanced through a hybrid CSMA/CA-TDMA scheme while preserving the scalability property. In this paper, we present distributed and centralized channel access models that follow the transmission strategies based on Markov decision process (MDP) to access both contention period and contention-free period in an intelligent way. The models consider the buffer status as an indication of congestion provided that the offered traffic does not exceed the channel capacity. We extend the models to consider the hidden node collision problem encountered due to the signal attenuation caused by channel fading. The simulation results show that the MDP-based distributed channel access scheme outperforms the legacy slotted CSMA/CA scheme. The centralized model outperforms the distributed model but requires the global information of the network.

Estimation of Primary User Parameters in Cognitive Radio Systems via Hidden Markov Model

For a cognitive radio (CR) system, we investigate the estimation problem in which a secondary user (SU) estimates the channel parameters such as the sojourn times on the active and the inactive states of the primary user (PU) as well as the PU signal strength on the basis of the sequence of the sensing results. By modeling the CR system as a hidden Markov model (HMM), the channel parameters are estimated by the standard expectation-maximization (EM) algorithm. We focus on mathematically analyzing the condition under which the EM algorithm can find the true channel parameters. For this, we apply the theory of the equivalence and the identifiability to the proposed HMM model for a CR system. Based on the identifiability analysis, we propose a parameter estimation algorithm for our problem by extending the EM algorithm. The numerical results show that the proposed algorithm successfully estimates the true channel parameters as long as the condition for finding the channel parameters is satisfied.

Stochastic Optimal Control for Wireless Powered Communication Networks

In this paper, we propose a stochastic optimal control algorithm for the wireless powered communication networks (WPCNs), in which the access point (AP) supplies energy to wireless nodes by means of the RF energy transfer technology. The energy beamforming is used to enhance the RF energy transfer efficiency by concentrating the radiated power on target nodes. Each wireless node is equipped with an energy queue and a data queue. We propose an algorithm that minimizes the expected energy transmission power from the AP while stabilizing the data queues of all nodes. The proposed algorithm is an online algorithm that adaptively decides the beamforming vector, the data scheduling, and the data transmission power, only based on the current state of the energy and the data queues. The proposed algorithm dynamically steers the energy beam to nodes that currently have low energy in the energy queue. We apply the Lyapunov optimization technique to design such an algorithm. We mathematically prove that the proposed algorithm achieves the optimal performance.