Top PDF Applying Physical Layer Network Coding in Wireless Networks

Applying Physical Layer Network Coding in Wireless Networks

Applying Physical Layer Network Coding in Wireless Networks

A main distinguishing feature of a wireless network compared with a wired network is its broadcast nature, in which the signal transmitted by a node may reach several other nodes, and a node may receive signals from several other nodes, simultaneously. Rather than a blessing, this feature is treated more as an interference-inducing nuisance in most wireless networks today (e.g., IEEE 802.11). This paper shows that the concept of network coding can be applied at the physical layer to turn the broadcast property into a capacity-boosting advantage in wireless ad hoc networks. Specifically, we propose a physical-layer network coding (PNC) scheme to coordinate transmissions among nodes. In contrast to “straightforward” network coding which performs coding arithmetic on digital bit streams after they have been received, PNC makes use of the additive nature of simultaneously arriving electromagnetic (EM) waves for equivalent coding operation. And in doing so, PNC can potentially achieve 100% and 50% throughput increases compared with traditional transmission and straightforward network coding, respectively, in 1D regular linear networks with multiple random flows. The throughput improvements are even larger in 2D regular networks: 200% and 100%, respectively.
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PHY-MAC Cross-Layer Cooperative Protocol Supporting Physical-Layer Network Coding

PHY-MAC Cross-Layer Cooperative Protocol Supporting Physical-Layer Network Coding

Nowadays, the increase in the number of people using mobile devices has leveraged the development of wireless networks. With the increased requirements in the quality of service for various applications, technical solutions need to be developed to improve the network performance such as the channel capacity, end-to-end throughput, transmission reliability, energy e ffi ciency, and the network coverage. Cooperative transmission has been known as an effective method to exploit spatial diversity to enhance the quality of wireless channels at the physical layer. In the cooperative transmission multiple single-antenna devices can collaborate with one another to share their antennas with neighbouring partners in order to form a virtual multiple-input multiple-output (MIMO) system.
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The Performance of Network Coding at the Physical Layer with Imperfect Self Information Removal

The Performance of Network Coding at the Physical Layer with Imperfect Self Information Removal

Next generation wireless communication networks are expected to provide gigabit ubiquitous access and coverage over a large area for wireless users. To enable gigabit broadband services for wireless end-users, a higher capacity is required. Direct application of network coding [1] in a wireless relaying [2] at the physical layer increases the capacity of bi-directional communication in a flat (i.e., frequency-nonselective) fading channel (called bi-directional amplification of the throughput (BAT-relaying) in [3] and physical layer network coding (PNC) in [4]). The ana- log network coding (ANC) proposed in [5] is essentially another variation of BAT-relaying and PNC schemes in a flat fading channel with a simpler implementation. Recently, broadband ANC based on orthogonal frequency division multiplexing (OFDM) and single carrier with frequency domain equalization (SC-FDE) was investigated in a multipath (i.e., frequency-selective) fading channel [6].
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Joint Power Allocation for Multicast Systems with Physical Layer Network Coding

Joint Power Allocation for Multicast Systems with Physical Layer Network Coding

Since the lattice plays a very important role in the perfor- mance of network coding for the wireless communication, its construction method is of great importance to achieve the potential performance of network coding while having quite low complexity. The famous algorithm of Construction A has widely been employed to design the lattice [18]. Upon completing the design of lattice offline, the messages are modulated to this lattice online and then transmitted to all relays. With the mixed lattice codes at each relay, the relay amplifies the mixed lattice codes and then retransmits them to the destinations. Note that the mixed lattice codes received at each relay are still in the lattice due to the closed property of the lattice. At the destination, each message is demodulated by using the lattices. The detailed flow chart is presented in Figure 2.
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ENERGY EFFICIENT DISTRIBUTED IMAGE COMPRESSION USING JPEG2000 IN WIRELESS SENSOR 
NETWORKS (WSNS)

ENERGY EFFICIENT DISTRIBUTED IMAGE COMPRESSION USING JPEG2000 IN WIRELESS SENSOR NETWORKS (WSNS)

Wireless sensor networks (WSN) are faced with the problem that network size is inversely proportional to network lifetime because the number of data packets transmitted is equal to the number of communication nodes for recovering original signal. Compressed sensing(CS) compresses and samples in sparse domain of network source to reduce the amount of transmitted data to accurately reconstruct original signal which is far less than the number of network nodes, and thereby reducing total energy consumption of network. Network coding (NC) is applied to intermediate node to encode data before forwarding it rather than simply store and forward it, achieving balance of network load. This paper introduces research status of two emerging technologies, proposing combined method by researching their inner contact and applying them to WSN to expand network size while reducing energy consumption to prolong life of network. Furthermore, we conclude our work and point out future research directions.
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Data Dissemination in Wireless Sensor Networks with Network Coding

Data Dissemination in Wireless Sensor Networks with Network Coding

Our data dissemination process is conducted at each cluster head so as to make sure that finally all the sensors obtain the updating packets. In a multihop cluster hierarchy, if a cluster head in an intermediate layer starts to transmit the received packet immediately after receiving one fresh packet, the gain of network coding cannot be fully utilized. On the other hand, if a cluster head waits and starts to transmit packets until it receives all packets from the cluster head in the upper layer, it will waste bandwidth and introduce extra delay. In order to achieve the balance between bandwidth efficiency and network coding gain, we propose to use a threshold α to determine when the current cluster head starts to transmit the packets to its member nodes. Specifically, for each cluster head, after obtaining αM fresh native packets, where 0 < α ≤ 1 and M is the number of native packets available
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Master Synchronization in Physical Layer Communications of Wireless Sensor Networks

Master Synchronization in Physical Layer Communications of Wireless Sensor Networks

Shown in Figure 1, there are two examples including a large amount of sprinkled sensor tags exchanging infor- mation with master node in aircraft, and tag nodes in warehouse reporting humidity and temperature to anchors. The hierarchical or cluster structure is designed for the large- scale WSNs [1], and its master nodes have remarkable ability for communication and information processing. Most sensor tags, whose major task is to collect sensing information, work under the low-power mode even in a sleep state. To wake up the nodes along with communications, each tag node has to equip full-function wireless communication module [11]. The nodes communicate small amount of data in low burst rate in numerous situations, including various-distance links from tens of meters to a kilometer. There is no requirement for establishing a link within a few microseconds. Meanwhile, the master nodes need no more consideration for their power consumption and complexity due to their dominant roles in WSNs. To simplify the receiver in tags, this paper develops a synchronization mechanism using the feedback control principles to reduce the complexity and power consumption in tag nodes, where there is no or little timing recovery circuits required. The method can not only be applied to the interaction among low-complexity and high-power e ffi ciency nodes, but also provides a flexible communication by combining the advantages of WPSN.
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Relay Optimization for ANC and PNC over Rician Fading Channels

Relay Optimization for ANC and PNC over Rician Fading Channels

Co-operation and network coding are two such powerful techniques that heightened the proficiency of wireless Networks. This paper mainly focuses on Physical-layer Network Coding (PNC) and Analog Network Coding (ANC), as they are the most studied variants of network coding in literature and represent radically different strategies between themselves, thus shedding light into which problems may hinder the scalability of network coding to more better wireless network topologies. Also, performance of cooperative networks can be enhanced with optimum relay selection. In this paper, we present the simulation results for both Analog Network Coding and Physical-layer Network Coding. We have evaluated the bit error rate (BER) over Rician fading channelsfor ANC and PNC with relay optimization by taking path loss in our system model. BPSK modulation is used for simulation results.
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Design of adaptive constellations and error protection coding for wireless network coding in 5 node butterfly networks

Design of adaptive constellations and error protection coding for wireless network coding in 5 node butterfly networks

In this paper, we focus on the design of a reliable communication scheme for generic 5-node wireless butterfly networks (WBN). Since the WBN represents a natural extension of the basic two-way relay communication channel, we follow the paradigm of Wireless Physical Layer Network Coding and we show that the design of novel adaptive constellations is desirable to exploit the promising performance of wireless network coding in WBN. We introduce a systematic constellation design algorithm, and we show that the proposed constellations are capable of outperforming conventional point-to-point modulations in WBN over the whole range of channel signal to noise ratios. To further support the applicability of the proposed constellations in a practical system, we integrate a simple binary channel coding scheme, providing a robust adaptive modulation and coding scheme for relaying in symmetric WBNs with arbitrary channel conditions.
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On robustness of physical layer network coding to pollution attack

On robustness of physical layer network coding to pollution attack

In this work, the effects of pollution attack on the perfor- mance of the three schemes ADNF, AF, and LLNC at the physical layer are investigated. The analytical approxima- tion results for the SER performance of the three schemes with and without an intruder have been illustrated as well. From an end-to-end SER perspective, it has been shown that LLNC scheme outperforms the ADNF and AF schemes regardless of the presence of the intruder. With the end-to-end throughput perspective, it has been shown that with an intruder in the network, and with reason- ably high ASRs, the AF outperforms ADNF and LLNC schemes at high SNRs. It has also been observed that ADNF scheme does outperform the other schemes if the ASRs are kept low (for a realistic wireless environment). In order for the ADNF scheme to perform better, complexity of the system has to be increased, where the denois- ing maps need to be redesigned for a larger network. A future direction is to evaluate the network performance with a channel that experiences large-scale fading, where the distance between nodes (or the intruder) becomes an important factor in network behavior. One can evaluate other types of attacks. For instance, the intruder may use
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Evaluation of MAC in vehicular networks

Evaluation of MAC in vehicular networks

toward safe and smooth driving without delay. Wireless communications systems are expected to play a pivotal role in ITS safety-related applications. Dedicated short- range communication (DSRC) radio technology [5] with a 75-MHz bandwidth at the 5.9-GHz [2] band is projected to support low-latency wireless data communications among vehicles and from vehicles to roadside units in North America. DSRC-based communication devices are expected to be installed in future vehicles and to work with sensors in the vehicles to enhance road safety. The draft of the upcoming 802.11p standard, defining specifications of the physical layer and the medium access control (MAC) layer of the vehicular wireless communication networks based on DSRC [4], has been created and distributed for discussions. The DSRC spectrum consists of seven ten-megahertz channels that include one control channel and six service channels. Channel 178 is the control channel, which is generally restricted to safety communications only [2, 3]. The DSRC physical layer uses an orthogonal frequency division multiplex (OFDM) modulation scheme to multiplex data. The DSRC physical layer follows exactly the same frame structure, modulation scheme, and training sequences specified by IEEE 802.11a physical layer. However, DSRC applications require reliable communication between OBEs and from OBE to RSUs when vehicles are moving up to 120 miles/hour and having communication ranges up to 1000 meters. According to the updated version of the DSRC standard, the MAC layer of the DSRC adopts 802.11a layer specification with minor modifications. This is a random access scheme for all associated devices in a cluster based on carrier sense multiple accesses with collision avoidance (CSMA/CA). In the 802.11 MAC protocols, the fundamental mechanism for medium access is the distributed coordination function (DCF). DCF is meant to support an ad hoc network without the need of any infrastructure element such as an access point, but DCF is not able to provide predictable quality of service (QoS). The development of a robust and efficient MAC protocol will be central to the new generation DSRC devices.
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Power Saving Strategies In Wireless Sensor 
                      Networks Using Cross Layer Design

Power Saving Strategies In Wireless Sensor Networks Using Cross Layer Design

Assume that sensor nodes 1, 2, 3, 4, and sink node are distributed in area “A” and area “B” as shown in Figure 5. All these nodes are gathering environmental data and sending them to the sink node. Node 1 and 2 can send data directly to the sink node while node 3 and 4 use node 2 as a relay node to send their data. Assume that time-to-death of any of the nodes is the network lifetime. In area “A”, as node 2 sends its own data as well as relays data from node 3 and node 4 towards the sink node, so node 2 will drain its energy earlier, resulting in network lifetime of say T. If network layer of node 4 or node 3 gets to know about the energy level of node 2 frequently for routing decisions, it can notify link layer to increase the transmit power. In this way, node 2 will not be used as a relay node for node 3 and 4 and would save energy that was supposed to be utilized by node 2 for signal processing and relaying of messages. Node 3 and node 4 would directly send data to the sink node at cost of higher transmit power. In this way, T can be extended by cross layer information exchange between the routing and physical layers. In the second scenario, area “B” assumes that there is temporarily some noise and interference from external sources (e.g., microwaves).
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Network Coding Opportunity of the Encyclopaedia Discovery Scheme in Wireless Networks

Network Coding Opportunity of the Encyclopaedia Discovery Scheme in Wireless Networks

network within the stream can This results in increased productivity using advanced control error Coding technology to address the issue of reliability, which is different Network coding between the flow. Others [15] exploit Network coding within the flow and direction of opportunism, Another method that achieves a high productivity in the face Loss of wireless connections. It seems that the idea of the network within the flow can be codified We are accustomed to address the problem discussed at COPE Before, which leads to the proposal of our plan. Prior to transmission, Kobe's first use to code things will flow knot Packages in batches, followed by the encoding of the network within the stream For the formation of a new set of packets for the end of the transmission. East On the issue of the reliability of COPE can be effective Addressed. He referred to the scheme that resulted in C & M, for A new network coding system that takes advantage of both COPE and the network within the coding technique such as flow More. As a result, it can dramatically increase Performance of wireless networks. As Kobe, C & M sits Between MAC and IP layer, which keeps the architecture clear Abstraction and can be implemented easily. to the As we know, on paper they only refer to the idea of We present here are [16], which is a
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Adaptive Scalable Cross Layer Framework for Multi-hop Wireless Sensor Networks

Adaptive Scalable Cross Layer Framework for Multi-hop Wireless Sensor Networks

Wireless Sensor Networks (WSNs) represent a new generation of distributed systems to support a broad range of applications. A WSN consists of a large number of microsensors which are typically powered by small energy- constrained batteries. In many application scenarios, these batteries can not be replaced or recharged, making the sensor useless once battery life is over. Thus, minimizing the total energy consumption (i.e., circuits and signal transmission energies) is a critical factor in designing a WSN. In sensor networks, nodes are deployed into an infrastructure free environment. Without any a priori information about the network topology or the global, even local view, sensor nodes must self-configure and gradually establish the network infrastructure from the scratch during the initialization phase. With the support of this infrastructure, nodes are able to accept queries from remote sites, interact with the physical environment, actuate in response to the sensor readings, and relay sensed information through the multi-hop sensor networks.
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Error Performance Analysis Of Physical Layer Network Coding With Transmit Diversity At Relay

Error Performance Analysis Of Physical Layer Network Coding With Transmit Diversity At Relay

Abstract---We analyze the error performance of the physical layer network coding (PNC) protocol with transmit diversity in bidirectional relay networks for binary phase shift keying (BPSK) over Rayleigh fading channels. It is assumed that a bidirectional relay network consists of two sources at transmitter and two sources at receiver and a relay consists of two antennas, where each source node has a single antenna and operates in a half duplex mode, and the PNC over finite GF(2) is employed. In this system, since the signal estimation of the multiple access channel (MAC) at the relay is given by the sum of two exponential functions. Then relay transmit in the form of transmit diversity (alamouti) to the source. Then finally we derive the capacity for system model. Finally we obtain outage probability, outage capacity for the end to end average bit error rate.
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On the performance of bi directional cognitive radio system with network coding at the physical layer

On the performance of bi directional cognitive radio system with network coding at the physical layer

Recently, a variant of network coding at the physical layer named wireless network coding (WNC) has gained much attention due to it’s simplicity and capacity improvement of a bi-directional link. In this paper, we design and analyze a bi-directional cognitive radio (CR) system with multiple pairs based on WNC while taking into account the imperfect spectrum sensing and interference from/to the CR system. In addition, we design a resource allocation framework consisting of a subcarrier allocation strategy with different priority assignments and optimal power allocation algorithm. We show that the quality of service within the CR system highly depends on a proper design of the spectrum sensing process to minimize the probability of missed detection, while the spectrum efficiency of the CR system increases with the number of pairs within the system to which we assign priorities.
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Iterative Channel Coded Physical Layer Network Coding on Impulsive Noise Channels

Iterative Channel Coded Physical Layer Network Coding on Impulsive Noise Channels

Physical-layer network coding (PNC) is a technique for wireless two-way relay communications [1], which exploits interference at a relay node to boost the throughput. In [2], Hausl introduced an extension of the conventional two-way relay communication with a joint network-channel coding method for PNC. Zeng et al. [3] presented the noncoherent detection of iterative differential phase-shift keying (DPSK) demodulation for PNC combined with turbo codes on a conventional two-way relay communications. There are several studies on trellis BICM combined with PNC on a conventional two-way relay communications. In [4], Xu et al. showed that trellis BICM can significantly improve the BER performance of a PNC system by applying a suitable iterative demapping and decoding framework and proper constellation mapping schemes specially designed for PNC.
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Applying Clustering Strategies to Improve the Efficiency of Network in Wireless Sensor Networks

Applying Clustering Strategies to Improve the Efficiency of Network in Wireless Sensor Networks

Wireless Sensor Networks (WSN), are spatially dispersed self-governing sensors to screen physical or ecological conditions, for example, temperature, sound, pressure and so forth. WSN is framed by hundreds or thousands of nodes that speak with each other and pass information along starting with one then onto the next and obligatorily associated with no less than one base station.

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Cooperative network-coding system for wireless sensor networks

Cooperative network-coding system for wireless sensor networks

In this paper, we propose two practical power- and bandwidth-efficient systems based on amplify-and-forward (AF) and decode-and-forward (DF) schemes to address the problem of information exchange via a relay. The key idea is to channel encode each source’s message by using a high-performance non-binary turbo code based on Partial Unit Memory (PUM) codes to enhance the bit-error-rate performance, then reduce the energy consumption and increase spectrum efficiency by using network coding (NC) to combine individual nodes’ messages at the relay before forwarding to the destination. Two simple and low complexity physical layer NC schemes are proposed based on combinations of received source messages at the relay. We also present the theoretical limits and numerical analysis of the proposed schemes. Simulation results under Additive White Gaussian Noise, confirm that the proposed schemes achieve significant bandwidth savings and fewer transmissions over the benchmark systems which do not resort to NC. Theoretical limits for capacity and Signal to Noise Ratio behaviour for the proposed schemes are derived. The paper also proposes a cooperative strategy that is useful when insufficient combined messages are received at a node to recover the desired source messages, thus enabling the system to retrieve all packets with significantly fewer retransmission request messages.
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Title: DISTRIBUTED ENCODE MESSAGE TOWARDS MANET’S USING NETWORK CODING

Title: DISTRIBUTED ENCODE MESSAGE TOWARDS MANET’S USING NETWORK CODING

In physical layer network coding only used in the static networks, so we didn’t used in dynamic system. The node creation must be in the static system. Decoding loss can be desired based on the correlated networking packet. Different lemmas are need to analyze the throughput and the decoding loss.

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