The Wireless Network Coding Optimization (PDF/DOC)
ABSTRACT
Network coding (NC) is an emerging technique of packet forwarding that encodes packets at relay node in order to increase network throughput. It is understood that the performance of NC is strongly dependent on the physical layer as well as the MAC layer, and greedy coding method may in fact reduce the network throughput owing to the reduction in the spatial reuse. In this paper, we propose a NC-aware scheduling method combining link aggregation to improve the network throughput by considering the interplay between NC and spatial reuse. Simulation results demonstrate the effectiveness of our proposed link aggregation method compared with the unicast transmission model.
LIST OF ACRONYMS
- EM – Electromagnetic
- IM – Instant Messaging
- LNC – Linear Network Coding
- MAC – Media Access Control
- NC – Network Coding
- ONC – Opportunistic Network Coding
- PNC – Physical Network Coding
- RLNC – Random Linear Network Coding
- SNR – Signal Noise Ratio
- TNC – Triangular Network Coding
- TWRC – Two-Way Relay Channel
TABLE OF CONTENTS
COVER PAGE
TITLE PAGE
APPROVAL PAGE
DEDICATION
ACKNOWELDGEMENT
ABSTRACT
LIST OF ACRONYMS
CHAPTER ONE
INTRODUCTION
- BACKGROUND OF THE PROJECT
- PROBLEM STATEMENT
- AIM OF THE PROJECT
- OBJECTIVE OF THE PROJECT
- SCOPE OF THE PROJECT
- PROJECT ORGANISATION
CHAPTER TWO
LITERATURE REVIEW
- INTRODUCTION
- DEFINITION OF NETWORK CODING
- THEORY BEHIND NETWORK CODING
- NETWORK CODING SCHEMES
- RANDOM LINEAR NETWORK CODING
- TRIANGULAR NETWORK CODING
- OPPORTUNISTIC NETWORK CODING
- PHYSICAL LAYER NETWORK CODING
- THEORY BEHIND PNC
- APPLICATIONS OF NC TO WIRELESS NETWORKS
- NETWORK CODING IN COMMUNICATION
- RELATED WORKS
CHAPTER THREE
- METHODOLOGY
CHAPTER FOUR
- RESULT
CHAPTER FIVE
- CONCLUSION
- REFERENCES
CHAPTER ONE
1.0 INTRODUCTION
1.1 BACKGROUND OF THE STUDY
The notion of coding at the packet level—commonly called network coding—has attracted significant interest since the publication of [1], which showed the utility of network coding for multicast in packet networks. But the utility of network coding reaches much further. And, in particular, it reaches to include various wireless applications. In fact, wireless packet networks are a most natural setting for network coding because the very characteristics of wireless links that complicate routing, namely, their unreliability and broadcast nature, are the very characteristics for which coding is a natural solution. Couple this with the fact that we are not nearly as constrained in our protocol design choices in the wireless case as we are in the wireline one, and applying network coding to wireless packet networks seems an ideal way of achieving appreciable efficiency gains.
Overlapped signals are always considered to be harmful in wireless communication systems. However, the emergence of network coding (NC) has shifted the design method of network communication. In conventional network coding (CNC) scheme, the relay node encodes packets after receiving them in separate communication phases. Physical layer network coding (PNC) was first proposed in 2006 and then received widely research. It encodes packets through simultaneous transmissions. It is a simple fact in physics that when multiple electromagnetism (EM) waves come together within the same physical space, they can add. The mixing of EM waves is a form of NC performed by nature. Although CNC and PNC schemes can convey the same number of packets with less transmission time, they may not always be the optimal transmission methods. One reason is that the relay node has to broadcast with higher transmission power to guarantee all the receiving nodes can receive the packet successfully in the broadcasting stage, which may decrease concurrent transmissions. Another reason is performing NC in a greedy method may lower the spectrum spatial reuse, and decline network throughput. Therefore, NC and scheduling for spatial reuse should be jointly considered for network design.
It has been demonstrated in [1] that the NC performance has a closely relationship with the joint decision between physical layer and MAC layer. Because greedy NC may decrease spatial reuse and lower network throughput, a trade-off should be conducted between spatial reuse and NC gain. Due to the time varying characteristics in wireless network, there may be a huge gap among different links, nodes should adopt scheduling scheme in order to optimize network performance. Since the applications (such as video gaming, video conferencing), whose content involves multicasting data to a set of receivers, increase sharply nowadays, handling multicast traffic is undoubtedly becoming of significant interest. In this paper, we study the interplay between scheduling and NC, and present a NC-aware link scheduling scheme to improve network throughput by adding virtual links for link aggregation in wireless network.
1.2 PROBLEM STATEMENT
In today’s wireless networks, diversity is regarded as an efficient and established means to combat multipath fading. Moreover, user cooperation has emerged lately as an elegant technique to achieve spatial diversity over wireless channels, where the installation of multiple antennas on handheld, battery-powered, mobile terminals is often impractical. Recently, the application of network coding in cooperative wireless networks has gained increasing interest with its potential to further boost the network performance, such as in terms of the achievable throughput. With network coding, the relaying nodes are allowed to linearly combine packets from multiple source nodes, and then forward the combined packets for better resource utilization. — We propose mutual user pairing in amulti-user infrastructure-based network-coded cooperative wireless network to realize network coding, in the absence of dedicated relay nodes. We propose an optimal user pairing algorithm, and tailor it to maximize the network capacity. Next, we develop heuristic pairing algorithms which approach the optimal performance at a reduced complexity. Performance analysis is conducted in terms of the average capacity per user, average outage probability per user, and user-fairness. — For energy-constrained network-coded cooperative networks, we subsequently address the problem of transmission power minimization. A joint optimization problem is formulated and solved to find the pairing which maximizes the network capacity, and minimizes the transmission power, such that certain performance constraints in terms of the average capacity per user or average outage probability per user are satisfied.
1.3 AIM OF THE PROJECT
The main aim of this work is to propose a joint NC-aware link scheduling method to optimize the throughput gain by considering the interplay between NC and spatial reuse with power control for unicast transmission.
1.4 OBJECTIVES OF THE STUDY
At the end of this study, we shall overview some of the main features of network coding that are most relevant to wireless networks. In particular, we discuss, first, the asymptotic optimality of random distributed network coding for wireless networks with and without packet erasures; and, second, the superior performance and relative ease of cost optimization in coded wireless networks as opposed to traditional routed wireless networks.
1.5 SCOPE OF THE STUDY
Based on an information model that classifies intermediate nodes in multicast networks into network coding, routing and replicating nodes, multicast max-flow and minimum cost optimization frameworks are formulated to solve optimization problems in wireless networks with or without network coding. Two special properties of wireless transmissions are taken into consideration, i.e., multi-hop cooperation and mutual interference among nodes. Such expressions are then used to modify the edge capacity in the optimization frameworks. The proposed method is helpful to the study of node cooperation and competition in multi-hop wireless communication networks.
1.6 PROJECT ORGANISATION
The rest of this paper is organized as follows. Section 2 reviews some related works, and the optimal NC-aware link scheduling scheme is presented in Section 3. Simulation results are illustrated in Section 4, and some concluding remarks are provided in Section 5.
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