Minhae Kwon

An improved approximate decoding with correlated sources

We consider ad hoc sensor network topologies that aim for distributed delivery of correlated delay-sensitive data. In order for efficient data delivery, network coding technique in conjunction with approximate decoding algorithm is deployed. The approximate decoding algorithm enables receivers to recover the original source data even when the number of received data packets is not sufficient for decoding. Therefore, it leads to significantly improved decoding performance and enhanced robustness for delay-sensitive data. In this paper, we further improve the approximate decoding algorithm by explicitly considering the characteristics of the correlation. Specifically, we study the case where the source data are correlated by a simple linear correlation, which is quantified by a similarity factor. We investigate several properties of the proposed algorithm and analyze the impact of the similarity factor on the decoding performance. Our experimental results confirm the properties of the proposed approximate decoding algorithm with linear correlation.

Approximate decoding for network coded inter-dependent data

In this paper, we consider decoding of loss tolerant data encoded by network coding and transmitted over error-prone networks. Intermediate network nodes typically perform the random linear network coding in a Galois field and a Gaussian elimination is used for decoding process in the terminal nodes. In such settings, conventional decoding approaches can unfortunately not reconstruct any encoded data unless they receive at least as many coded packets as the original number of packets. In this paper, we rather propose to exploit the incomplete data at a receiver without major modifications to the conventional decoding architecture. We study the problem of approximate decoding for inter-dependent sources where the difference between source vectors is characterized by a unimodal distribution. We propose a mode-based algorithm for approximate decoding, where the mode of the source data distribution is used to reconstruct source data. We further improve the mode-based approximate decoding algorithm by using additional short information that is referred to as position similarity information (PSI). We analytically study the impact of PSI size on the approximate decoding performance and show that the optimal size of PSI can be determined based on performance requirements of applications. The proposed approach has been tested in an illustrative example of data collection in sensor networks. The simulation results confirm the benefits of approximate decoding for inter-dependent sources and further show that 93.3% of decoding errors are eliminated when the approximate decoding uses appropriate PSI.

Improved approximate decoding based on position information matrix

This paper proposes a robust decoding algorithm in delivery of network coded data which is in particular correlated and delay-sensitive. We consider ad-hoc sensor network topologies, where a correlated data is delivered based on network coding techniques in conjunction with approximate decoding algorithm in order for efficient and robust data delivery. The approximate decoding algorithm has been developed as a decoding solution to ill-posed problems for network coded correlated data sources. In this paper, we improve the performance of approximate decoding algorithm by explicitly considering more information, which is used to additionally refine the recovered data. The information includes potential results that are from finite field operations and the set of such information is referred to as position information matrix in this paper. We deploy the position information matrix into approximate decoding algorithm and investigate its corresponding properties. We then analytically show that this improves the performance of approximate decoding algorithm. Our simulation results confirm the properties of the proposed approximate decoding algorithm with position information matrix and improved performance.

Distributed Network Formation Strategy for Network Coding Based Wireless Networks

In this letter, we propose a distributed network formation solution for network coding deployed wireless networks which includes multisource multicast flows. This is an attempt to solve an open problem of network coding based multisource multicast flow design based on a game theoretic approach, which can eventually form a network in a distributed way. The network is in particular constructed by individual decision makings of the nodes, while taking advantages of network coding techniques. The decisions made by the nodes include the transmission powers and the use of network coding operations. In each stage game, nodes update the parameters based on feedbacks such as rewards, penalties, and evaluate their prior actions, which enables the nodes to make best responses in the next stage game. Our simulations confirm that the resulting network can reduce overall power consumption compared to direct transmission, and improve system throughput with less power consumption compared to no coding strategy.

Compressed network coding: Overcome all-or-nothing problem in finite fields

In this paper, we consider a delay-sensitive data transmission strategy based on network coding technique in finite fields over error-prone networks. In order to solve all-or-nothing problem inherited from network coding, compressed network coding is proposed by jointly considering network coding techniques and compressed sensing technique. While network coding techniques have been jointly used with the compressed sensing techniques, network coding operations are performed in the field of real numbers, and thus, the payload of transmitted data can be enlarged as the data traverse more hops in networks. In this paper, however, we propose to use network coding techniques in finite fields, such that the size of payload does not increase as more hops are traversed. With the help of compressed sensing technique, a destination node is able to approximately recover the source data based on l1-norm minimization approach, in case of innovative packet loss. It is analytically shown that the payload size of the proposed approach is always smaller than that of the conventional approach, while the proposed approach can achieve comparable decoding performances. We evaluate the effectiveness of the proposed approach based on an illustrative application of image delivery system.

Approximate recovery of network coded real-time information

In this paper, we consider real-time voice transmission or speech communication systems, where voice information is encoded based on network coding techniques. For real-time delivery of data encoded by network coding techniques, the All-Or-Nothing problem of network coding is one of the most important challenges in order to guarantee quality of service (QoS) requirements. In order to overcome the problem, approximate decoding is used for immediate data recovery. In this paper, we focus on optimizing parameters for the best performance of approximate decoding algorithm by explicitly considering the information about source correlation. In particular, we consider the case where consecutive source data sets have symmetric distributions. We analytically show that the best strategy for the approximate decoding algorithm is to use mean of the distributions. Moreover, the performance of the proposed algorithm can improve as the variance of the distributions becomes lower.

Analysis on decoding error rate of systematic network coding

In this paper, we consider a real-time multimedia broadcasting system with systematic network coding over packet erasure channel. We focus on studying a theoretical decoding error rate and shows that it is a function of encoding number, packet loss rate of wireless channel and code rate of system. Specifically, we show that the decoding error rate is inversely proportional to the encoding number if packet loss rate is low, while it is proportional to encoding number if packet loss rate is high. The simulation results confirm theoretical analysis.

An architecture of IPTV networks based on network coding

In this paper, we propose a novel architecture for IPTV networks, where network coding is deployed in the backbone networks. As the number of subscribers to IPTV services increases, the size of backbone network is correspondingly grows. Therefore, it is challenging to manage network faults in a centralized manner. Moreover, IPTV services that provide larger video stream sizes in a limited network capacity becomes a problem of current networks. In order to overcome such challenges of IPTV services in the current networks, we propose to deploy network coding technique, which can increase network capacity while improving robustness against network faults.

Network coding-based distributed network formation game for multi-source multicast networks

In this paper, we propose a distributed solution based on game-theoretic approaches to the topology formation problem for mobile wireless sensor networks with multi-source multicast flows. Our solution significantly reduces computational complexity by taking advantage of network coding. Finding an optimal topology for network coding in multi-source multicast flows is NP-hard problem, so the proposed algorithm provides a suboptimal solution with low computational complexity. We formulate the problem of distributed network topology formation as a network formation game by considering the nodes in the network as players that can take actions for making outgoing links. The proposed game, which consists of multiple players and multicast flows, can be decomposed into independent link formation games played by only two players with a unicast flow. The proposed algorithm is also guaranteed to converge, i.e., a stable network topology can be always formed. Our simulation results confirm that the computational complexity of the proposed solution is low enough for practical deployment in large-scale mobile, wireless sensor networks.

The cluster formation strategies for approximate decoding in IoT networks

In this paper, we consider delay-constrained data transmission based on network coding techniques over error-prone IoT networks. While network coding approaches can provide various advantages, there is a critical drawback referred to as all-or-nothing problem; the encoded source data cannot be recovered if a set of required number of data is not entirely received by a decoding deadline. As a solution, an approximate decoding approach has been proposed. In this paper, we quantify the performance of approximate decoding and show that the performance is determined only by the insufficient number of packets. Moreover, we analytically show the fundamental tradeoff between the performance of the approximate decoding and data transfer rate improvement; as the cluster size increases, data transfer rate is improved while the decoding performance is degraded. This tradeoff can lead to an optimal cluster size of network coding based networks that achieves the target decoding performance of applications. The analysis is confirmed by a set of experiments.