Phond Phunchongharn

Resource allocation for device-to-device communications underlaying LTE-advanced networks

The Long Term Evolution-Advanced (LTEAdvanced) networks are being developed to provide mobile broadband services for the fourth generation (4G) cellular wireless systems. Deviceto- device (D2D) communications is a promising technique to provide wireless peer-to-peer services and enhance spectrum utilization in the LTE-Advanced networks. In D2D communications, the user equipments (UEs) are allowed to directly communicate between each other by reusing the cellular resources rather than using uplink and downlink resources in the cellular mode when communicating via the base station. However, enabling D2D communications in a cellular network poses two major challenges. First, the interference caused to the cellular users by D2D devices could critically affect the performances of the cellular devices. Second, the minimum quality-of-service (QoS) requirements of D2D communications need to be guaranteed. In this article, we introduce a novel resource allocation scheme (i.e. joint resource block scheduling and power control) for D2D communications in LTE-Advanced networks to maximize the spectrum utilization while addressing the above challenges. First, an overview of LTE-Advanced networks, and architecture and signaling support for provisioning of D2D communications in these networks are described. Furthermore, research issues and the current state-of-the-art of D2D communications are discussed. Then, a resource allocation scheme based on a column generation method is proposed for D2D communications. The objective is to maximize the spectrum utilization by finding the minimum transmission length in terms of time slots for D2D links while protecting the cellular users from harmful interference and guaranteeing the QoS of D2D links. The performance of this scheme is evaluated through simulations.

A cognitive radio system for e-health applications in a hospital environment

Wireless communications technologies are used to support a variety of electronic health applications to transfer medical data and patient information. However, using wireless communications technology in a healthcare environment poses two major challenges. First, the electromagnetic interference caused to bio-medical devices by wireless devices could critically affect their performance. Second, since different types of e-health applications have different priorities, access to the wireless channel by the corresponding devices needs to be prioritized. In this article we introduce a novel cognitive-radio-based approach to address these challenges in wireless communications for e-health applications in a hospital environment. First, the requirements for a wireless communications system to be used in a healthcare environment are identified, and potential applications of cognitive radio technology for e-health applications are discussed. Then a cognitive radio system is proposed for e-health applications in a hospital environment, which protects the medical devices from harmful interference by adapting the transmit power of wireless devices based on EMI constraints. An EMI-aware handshaking protocol is proposed for channel access by two different types of applications with different priorities. The performance of this cognitive radio system for e-health applications is evaluated through simulations.

Mode selection for energy-efficient D2D communications in LTE-advanced networks: A coalitional game approach

We consider the problem of mode selection for device-to-device (D2D) communications in LTE-advanced networks. We propose a solution based on a coalitional game among D2D links to select their communications modes. The solution is given by three coalitions which represent the groups of D2D links using cellular mode, reuse mode, and dedicated mode of transmission. The D2D links in the same coalition cooperatively select the subchannels and use the corresponding transmission mode such that the total power is minimized while their rate requirements are satisfied. The D2D links can make a decision to leave and join a coalition based on their individual transmission costs. The individual transmission cost of each D2D link is a function of the transmission power and the price of channel occupancy which depends on the D2D links communications mode. We find stable coalitions as the solution of the mode selection problem. The stable coalitions represent the system states in which no D2D link can change its communication mode and have lower transmission cost without making others worse off. A discrete-time Markov chain-based analysis and a distributed algorithm are presented to obtain the stable coalitions.

An EMI-Aware Prioritized Wireless Access Scheme for e-Health Applications in Hospital Environments

Wireless communications technologies can support efficient healthcare services in medical and patient-care environments. However, using wireless communications in a healthcare environment raises two crucial issues. First, the RF transmission can cause electromagnetic interference (EMI) to biomedical devices, which could critically malfunction. Second, the different types of electronic health (e-Health) applications require different quality of service (QoS). In this paper, we introduce an innovative wireless access scheme, called EMI-aware prioritized wireless access, to address these issues. First, the system architecture for the proposed scheme is introduced. Then, an EMI-aware handshaking protocol is proposed for e-Health applications in a hospital environment. This protocol provides safety to the biomedical devices from harmful interference by adapting transmit power of wireless devices based on the EMI constraints. A prioritized wireless access scheme is proposed for channel access by two different types of applications with different priorities. A Markov chain model is presented to study the queuing behavior of the proposed system. Then, this queuing model is used to optimize the performance of the system given the QoS requirements. Finally, the performance of the proposed wireless access scheme is evaluated through extensive simulations.

Electromagnetic Interference-Aware Transmission Scheduling and Power Control for Dynamic Wireless Access in Hospital Environments

We study the multiple access problem for e-Health applications (referred to as secondary users) coexisting with medical devices (referred to as primary or protected users) in a hospital environment. In particular, we focus on transmission scheduling and power control of secondary users in multiple spatial reuse time-division multiple access (STDMA) networks. The objective is to maximize the spectrum utilization of secondary users and minimize their power consumption subject to the electromagnetic interference (EMI) constraints for active and passive medical de vices and minimum throughput guarantee for secondary users. The multiple access problem is formulated as a dual objective optimization problem which is shown to be NP-complete. We propose a joint scheduling and power control algorithm based on a greedy approach to solve the problem with much lower computational complexity. To this end, an enhanced greedy algorithm is proposed to improve the performance of the greedy algorithm by finding the optimal sequence of secondary users for scheduling. Using extensive simulations, the tradeoff in performance in terms of spectrum utilization, energy consumption, and computational complexity is evaluated for both the algorithms.

Distributed Robust Scheduling and Power Control For Cognitive Spatial-Reuse TDMA Networks

We investigate the distributed robust transmission scheduling and power control problem in a cognitive spatial-reuse time division multiple access (STDMA) network. In particular, we address the problem of minimizing the transmission length (in terms of time-slots) of the secondary links under their minimum quality-of-service (QoS) requirements without violating the maximum tolerable interference limit for the primary receivers. Traditionally, the joint transmission scheduling and power control problem only considers the average link gains; therefore, QoS violation can occur due to improper power allocation with respect to instantaneous channel gain realization. To overcome this problem of QoS violation, our problem formulation takes the channel gain uncertainty into account. Since an optimal solution cannot be obtained due to the NP-completeness of the problem, we propose a novel distributed two-stage algorithm based on the distributed column generation method to obtain the near-optimal solution for the robust transmission schedules in an ad-hoc cognitive radio network. To demonstrate its relative efficiency, our algorithm is compared with previously proposed algorithms. For the proposed algorithm, we also derive the bounds on the probability of signal-to-interference-plus-noise ratio (SINR) constraint violation and the expected number of additional time-slots required to satisfy the traffic demand requirements of secondary links.

Robust Scheduling and Power Control for Vertical Spectrum Sharing in STDMA Wireless Networks

We study the robust transmission scheduling and power control problem for spectrum sharing between secondary and primary users in a spatial reuse time-division multiple access (STDMA) network. The objective is to find a robust minimum-length schedule for secondary users (in terms of time slots) subject to the interference constraints for primary users and the traffic demand of secondary users. We consider the fact that power allocation based on average (or estimated) link gains can be improper since actual link gains can be different from the average link gains. Therefore, transmission of the secondary links may fail and require more time slots. We also consider this demand uncertainty arising from channel gain uncertainty. We propose a column generation-based algorithm to solve the scheduling and power control problem for secondary users. The column generation method breaks the problem down to a restricted master problem and a pricing problem. However, the classical column generation method can have convergence problem due to primal degeneracy. We propose an improved column generation algorithm to stabilize and accelerate the column generation procedure by using the perturbation and exact penalty methods. Furthermore, we propose an efficient heuristic algorithm for the pricing problem based on a greedy algorithm. For the simulation scenario considered in this paper, the proposed stabilized column generation algorithm can obtain the optimal schedules with 18.85% reduction of the number of iterations and 0.29% reduction of the number of time slots. Also, the heuristic algorithm can achieve the optimality with 0.39% of cost penalty but 1.67×10-4 times reduction of runtime.

Joint cell selection and subchannel allocation for energy efficiency in small cell networks: A coalitional game

Due to the dramatic growth of mobile data traffics, small cell network concept has been considered as a promising technique for mobile data offloading and enhancing spectrum utilization in the Long Term Evolution (LTE)-Advanced networks. Small cell networks allow the user equipment (UE) to transmit data through small cells instead of using only macro cells by reusing the same radio spectrum of the macro cells. A UE can select to transmit data through either a small cell or a macro cell. Moreover, the network needs to allocate the appropriate subchannel and the transmission power for each UE in the macro cells and small cells in order to avoid the harmful interference. In this paper, we focus on the problem of joint cell selection and resource allocation (i.e., subchannels and transmission power) for energy efficiency in small cell networks. A coalitional game is formulated to select cell and allocate resources where UEs in the same cell are considered to be in the same coalition. Small cell UEs (SUEs) are rational to maximize their individual utilities which equals to minimize the transmission power while transmission rate requirement is satisfied. To select subchannel, a greedy algorithm is used to assign a subchannel for an SUE to approximate an optimal assignment with low complexity. A distributed algorithm is also proposed to find stable coalitions. The simulation results show that the proposed scheme can achieve 31 percent in terms of power saving compared to the static random scheme.

Robust Transmission Scheduling and Power Control for Spectrum Sharing in Spatial Reuse TDMA Wireless Networks

We consider the scheduling and power control problem for spectrum sharing between secondary users and primary users in a spatial reuse time-division multiple access (STDMA) network. The objective is to minimize the transmission length of secondary users in a frame subject to the interference constraints for primary users and the traffic demand of secondary users. The uncertainty of the channel gains is taken into account. Since the power allocation can be improper with respect to the link gain realization, transmissions in the secondary links may fail, and hence, require more time slots. Therefore, traffic demand uncertainty resulting from channel gain variation is also considered. We propose an efficient algorithm based on column generation for robust optimal scheduling and power control for secondary users in presence of channel gain and traffic demand uncertainty. Numerical results show that the proposed algorithm has high computation speed with very low penalty cost when compared to the optimal algorithm. By adjusting the degree of conservatism, we can balance the tradeoff between the robustness and the transmission length of secondary users in a frame.

File Type Classification for Adaptive Object File System

This paper gives an overview of a novel storage management concept, called, adaptive object file system (AOFS). The design of the file type classification module in AOFS is emphasized. The design attempts to increase the efficiency through the dynamic tuning technique, which automatically classifies files using attributes and access pattern. The file classification, thus, allows files to be stored in the most efficient way. The key idea is to utilize the file properties, such as access pattern, owner, size, and permissions to select adaptive file system policies (e.g. disk allocation, redundancy, and caching strategies). Moreover, a metadata store is maintained to provide the best possible dynamic tuning strategy for any given operating period. The static classification initial design is done based on decision tree, while the dynamic classification adapts the Hidden Markov model for prediction. The main goal of the AOFS design is to enhance system performance, storage efficiency and flexibility