Ching-Chun Chou

Bio-Inspired Proximity Discovery and Synchronization for D2D Communications

In this paper, a distributed mechanism for application-aware proximity services (ProSe) in device to device (D2D) communications is presented. The method, which is derived from the bio-inspired firefly algorithm, can achieve proximity discovery and synchronization at one time. To be precise, this mechanism not only enables neighbour discovery and service discovery simultaneously, but achieves synchronization in physical communication timing and service interests in the meanwhile. However, the basic firefly algorithm is not perfectly suitable for larger scale systems such as LTE-A D2D. In most network topologies, the property that each node may not be able to hear all its neighbours makes the basic version fail. Thus, we further propose the firefly spanning tree (FST) algorithm which can solve the topology-dependent divergence problems.

Pseudo Random Network Coding Design for IEEE 802.16m Enhanced Multicast and Broadcast Service

Applying network coding on broadcasting service is known to reduce times of transmission in the process of recovering the loss packets. In previous design, coding coefficients are put in the packet headers so that MSs can decode the coded packets. However, it causes extra overhead. Moreover, since many mechanisms depend on feedback information to encode packets, they may cease operating once they are out of feedback support. To address these problems, we propose a codebook-based network coding scheme. The codebook defines the coding coefficients of the transmission packets; therefore, we only need to put the index of the codebook in the packet header to decode packets. Furthermore, it can operate without feedback. The simulation result shows that PRNC decodes more packets and has higher decode ratio compared with other network coding schemes.

Device-to-Device Communication in LTE-Advanced System: A Strategy-Proof Resource Exchange Framework

Device-to-device (D2D) communication could improve the efficiency of resource utilization in cellular networks by allowing nearby devices to communicate directly with each other. Nevertheless, one main challenge in D2D communication is resource allocation. We observed that the transmission quality in D2D communications can be significantly improved through a proper resource exchange. Based on this observation, we propose a novel D2D resource-allocation framework for a Long-Term Evolution Advanced (LTE-Advanced) system. We theoretically prove that any arbitrary algorithm, either distributed or centralized, will converge in the proposed framework whenever all performed exchanges are beneficial. Based on the concept of beneficial exchange, we propose a trader-assisted resource exchange (T-REX) mechanism, which is an exchange-based mechanism that converges in polynomial time and achieves Pareto optimality as an efficient and flexible solution to the D2D resource-allocation problem. The Evolved NodeB (eNodeB) regulates the D2D resource allocation through designing the trader preference functions in the T-REX mechanism. By applying game-theoretic analysis to the D2D communication system, we prove that all rational D2D pairs will truthfully report their information when the trader preference functions are properly designed. Finally, our simulation results show that the proposed T-REX mechanism significantly mitigates the interference experienced by D2D devices in LTE-Advanced systems.

Network Coding Based Data Distribution in WiMAX

This paper proposes a network coding based data distribution mechanism in WiMAX. Packets are sent by the BS and then a network coded packet is sent for the data reliability. Different schemes of coding and transmission method are also provided for the data distribution mechanism.

An on-demand QoE resource allocation algorith for multi-flow LTE eMBMS

Recent multimedia studies show that the Quality-of-Experience(QoE) more accurately represents the users level of satisfaction than the Quality-of-Service (QoS) does. Long Term Evolution (LTE) Multimedia Broadcast Multicast Operation (MBMS), via MBMS Operation On-Demand (MooD), enables dynamic resource configuration of multicast flows. In order to maximize the QoE of all users in a LTE MooD system we propose a resource allocation method which efficiently allocates resources based on both the demand of each live video stream and the channel conditions of the users. We compare our method against other methods; our method achieves the highest QoE and demonstrates efficient resource allocation regardless of whenever resources are sufficient or not.

Synchronous multicast and broadcast service in multi-rate IEEE 802.16j WiMAX relay network

IEEE 802.16j Mobile Multihop Relay Standard defines multi-hop relay operation in a WiMAX system. It uses a novel synchronous multicast and broadcast transmission mechanism to achieve macro diversity. With the newly introduced synchronous delivery constraint, the multicast data delivery algorithm should be designed differently to enhance system performance. This paper provides Multi-Rate Selection Algorithm (MRSA) for multicast and broadcast (MBS) data delivery. It could reduce the data distribution delay from the BS to all the RSs. Besides, we also propose the path selection algorithm to further improve the effectiveness of MRSA. Our simulation results show that using MRSA with our shortest-path path selection algorithm, the delay for data delivery in 802.16j MBS system could be greatly reduced. The proposed scheme could achieve the performance closed to the optimal solutions. To the authors’ best knowledge, this is the first research work to investigate the IEEE 802.16j multicast and broadcast problem.

Modeling and analysis of applying adaptive modulation coding in wireless multicast and broadcast systems

Traditional wireless communications only utilize fixed-rate multicast and broadcast. In other words, only the most robust modulation and coding scheme can be applied for data transmission. Such a scheme fails to sufficiently exploit the potential gains of multicast and broadcast, resulting in bandwidth waste. To overcome such a problem, investigating the rate adaptation of multicast and broadcast wireless systems is the primary task. Unlike the traditional wireless systems, this paper presents an analytical model with rate adaptation for both multicast and broadcast. Adaptive modulation and coding are applied to achieve rate adaptation. We construct a stochastic model by using Finite State Markov chains for the multicast broadcast system modeling. The model’s outputs are shown to approximate to the results of our system level simulations. The model derives the performance of rate adaptation in multicast and broadcast. With the deduced modeling results, we can predict the system throughput providing the channel states, and the modulation and coding schemes variations.

Multi-Group Wireless Multicast Broadcast Services Using Adaptive Modulation and Coding: Modeling and Analysis

Adaptive modulation and coding (AMC) is attracting research interest in wireless multicast broadcast services (MBS). AMC increases the system throughput by utilizing possible good channel states instead of using robust modulation and coding schemes. A model for the AMC in an MBS system with multiple MBS groups is provided, and stochastic analysis using Markov chain is also applied for modeling analysis. Simulation results show extremely high correspondence to the proposed mathematical model.

Dynamic Resource Allocation Framework for MooD (MBMS Operation On-Demand)

The demand for mobile video is increasing every year. To address the strain on long term evolution (LTE) networks 3GPP introduced multimedia broadcast multicast operation (MBMS). As of LTE release 12, support for MBMS operation on-demand (MooD) was also added (MooD enables dynamic resource configuration of multicast flows). While multicast algorithms assuage the demands on the network, quality-of-service performance metrics no longer are considered an accurate measure of a users satisfaction with the network; recent multimedia studies show that quality-of-experience (QoE) is more accurate. In order to maximize the QoE of all users in a LTE MooD system, we propose two resource allocation algorithms, both of which efficiently allocate resource blocks (RBs) based on both the demand for each live video stream and the channel conditions of the users within each group. We also compare our resource allocation algorithms against four other commonly used resource allocation algorithms. Both of our algorithms achieve a higher QoE and video quality, when compared to other commonly used resource allocation algorithms. Furthermore, our algorithms demonstrate efficient resource allocation regardless of whether or not the RBs are sufficient.