Low Energy Efficient Wireless Communication Network Design
#1

Low Energy Efficient Wireless Communication Network Design

Energy efficient wireless communication network design is an important and challenging problem. It is important because mobile units operate on batteries with energy supply. It is challenging because there are many different issues that must be dealt with when designing a low energy wireless communication system (such as amplifier design, coding, and modulation design), and these issues are coupled with one another. Furthermore, the design and operation of each component of a wireless communication system present trade-offs between performance and energy consumption. Therefore, the challenge is to exploit the coupling among the various components of a wireless communication system, and understand trade-offs between performance and energy consumption in each individual component, in order to come up with an overall integrated system design that has optimal performance and achieves low energy (power). The key observation is that constraining the energy of a node imposes a coupling among the design layers that cannot be ignored in performing system optimization. In addition, the coupling between layers requires simulation in order to accurately determine the performance. The purpose of this power is to present a methodology for the design, simulation and optimization of wireless communication networks for maximum performance with an energy constraint.

Before we proceed, we illustrate, through simple examples, a couple of issues that need to be addressed. To highlight the trade-offs between performance and energy consumption at individual components, consider the design and operation of an amplifier. The amplifier boosts the power of the desired signal so that the antenna can radiate sufficient power for reliable communications. However, typical power amplifiers have maximum efficiency in converting DC power into RF power when the amplifier is driven into saturation. In this region of operation, the amplifier voltage transfer function is nonlinear. Because of this non linearity, the amplifier generates unwanted signals (so called intermodulation products) in the band of the desired signal and in adjacent bands. When the amplifier drive level is reduced significantly (large back off) the amplifier voltage transfer characteristic becomes approximately linear. In this case it does not generate intermodulation products. However, with large back off the amplifier is not able to efficiently convert DC power into RF power. Thus, there is considerable wasting of power at low drive levels, but at high drive levels more interfering signal are generated.

To highlight the coupling among the design of individual components of a wireless system, consider packet routing in a wireless network that contain no base station (i.e. an ad hoc network). For simplicity consider a network with nodes A, B and C shown in figure. If Node A wants to transmit a message to Node C, it has two options. Transmit with power sufficient to reach Node C in a single transmission, or transmit first from A to B with smaller power, and then B to C. since the received signal power typically decays with distance as d4, there is significantly smaller power loss due to propagation in the second option because d^4ac>d^4ab+d^4bc.however even though Node A transmits with smaller output power, it does not necessarily proportionally decreases the amount of actually consumed because of the amplifier's effect discussed above.

Furthermore, besides the energy required for packet transmission, there are energy requirements for packet reception and information decoding. The probability of packet error reception that is achieved depends on energy allocated to the receiver. Consequently, there is a coupling among amplifier design, coding and modulation design, and decoding design as well as routing protocol.
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#2
hi sir.. i like this topic plz send me the full report..plz.. as early as possible
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#3
i want documentation and ppt on low energy efficient wireless communication network design
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#4
Energy efficient communication in wireless networks
The article describes the energy efficient communication in the wireless communiccation networks using multihop communication or cooperative beamforming. The role of transmit power on the energy efficiency of the multihop network where the nodes communicate using DS-CDMA is studied using the game theory. The games are proposed in which the user chooses the transmit powers and recievers try to maximise the number of bits successfully transmitted per unit of energy consumed. This game is analysed for its nash equilibrium Then the optimal transmit powers are derived. The recieved SNR is increased by collaborating the nodes to retransmit the local signals to multiply the recieved analog waveform
Clusters of collaborating nodes is studied using the game theory for their interactions between the clusters. The limits on the energy consumed by the multihop network are defined to stud\y the optimal network design criteria.

For full report, visit this page:
http://scribddoc/19157166/Energy-efficient-communication-in-wireless-networks#fullscreen:on
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#5
I,amisha,am in urgent need of complete report $ ppt of the topic "low energy efficient wireless communication network design".
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#6
[attachment=11955]
1. INTRODUCTION
Energy efficient wireless communication network design is an important and challenging problem. It is important because mobile units operate on batteries with energy supply. It is challenging because there are many different issues that must be dealt with when designing a low energy wireless communication system (such as amplifier design, coding, and modulation design), and these issues are coupled with one another. Furthermore, the design and operation of each component of a wireless communication system present trade-offs between performance and energy consumption. Therefore, the challenge is to exploit the coupling among the various components of a wireless communication system, and understand trade-offs between performance and energy consumption in each individual component, in order to come up with an overall integrated system design that has optimal performance and achieves low energy (power). The key observation is that constraining the energy of a node imposes a coupling among the design layers that cannot be ignored in performing system optimization. In addition, the coupling between layers requires simulation in order to accurately determine the performance. The purpose of this power is to present a methodology for the design, simulation and optimization of wireless communication networks for maximum performance with an energy constraint.
Before we proceed, we illustrate, through simple examples, a couple of issues that need to be addressed. To highlight the trade-offs between performance and energy consumption at individual components, consider the design and operation of an amplifier. The amplifier boosts the power of the desired signal so that the antenna can radiate sufficient power for reliable communications. However, typical power amplifiers have maximum efficiency in converting DC power into RF power when the amplifier is driven into saturation. In this region of operation, the amplifier voltage transfer function is nonlinear. Because of this non linearity, the amplifier generates unwanted signals (so called intermodulation products) in the band of the desired signal and in adjacent bands. When the amplifier drive level is reduced significantly (large back off) the amplifier voltage transfer characteristic becomes approximately linear. In this case it does not generate intermediation products. However, with large back off the amplifier is not able to efficiently convert DC power into RF power. Thus, there is considerable wasting of power at low drive levels, but at high drive levels more interfering signal are generated.
To highlight the coupling among the design of individual components of a wireless system, consider packet routing in a wireless network that contain no base station (i.e. an ad hoc network). For simplicity consider a network with nodes A, B and C shown in figure. If Node A wants to transmit a message to Node C, it has two options. Transmit with power sufficient to reach Node C in a single transmission, or transmit first from A to B with smaller power, and then B to C. since the received signal power typically decays with distance as d4, there is significantly smaller power loss due to propagation in the second option because d^4ac>d^4ab+d^4bc.however even though Node A transmits with smaller output power, it does not necessarily proportionally decreases the amount of actually consumed because of the amplifier’s effect discussed above.
Furthermore, besides the energy required for packet transmission, there are energy requirements for packet reception and information decoding. The probability of packet error reception that is achieved depends on energy allocated to the receiver. Consequently, there is a coupling among amplifier design, coding and modulation design, and decoding design as well as routing protocol.
Finally, to highlight the coupling and tradeoff between energy consumption and system wide performance, we consider the situational awareness problem in a mobile wireless network. In this problem, the objective of each node is to be aware of the position of every other node. If energy consumption is ignored, and the overall performance metric is an average (overall mobiles) mean square position error, this error is minimized when all nodes continuously communicate their positions with one another. Such a strategy requires significant energy. If, on the other hand, the objective is to minimize a weighted sum of the expected energy consumed and the average (overall mobiles) mean squared position error, then the nodes have to jointly decide when to communicate and whom to communicate to. Qualifying the tradeoff between information flow and performance is a nontrivial task, and it is one of our main goals. Such quantification requires the understanding of all the issues highlighted by all the previous examples. The remainder of the paper is organized as follows. In section 2 we present a methodology for system design incorporating the effect of the amplifier, propagation, coding, modulation and network protocol. In section 4 we present the result obtained for the situational awareness application. Section 5 concludes the paper.
2. SYSTEM DESIGN METHODOLOGY
We consider a wireless network consisting of interacting mobiles or nodes. The mobiles need to communicate in order to take some action or to share information, such as their respective positions. In this section, a stimulation-based design methodology that simultaneously optimizes a number of different parameters including, amplifier dry level, quantization levels at the receiver for demodulation and decoding, and network protocol parameters is shown. The overall goal is to characterize the performance as a function of energy. In order to achieve the desired design and optimization we divide the problem into interacting design layers as shown in fig.2.1. Each layer has a local optimization but also interacts with layers above and below in a well-defined manner. In addition, there is a global optimization that encompasses the different layers. The lowest layer is the devise layer. This layer contains the antenna model and the amplifier model. The next layer is the processing layer. This layer contains a model for the physical layer communication system including the coding algorithm and the demodulation. The top layer is the network layer which accounts for the routing protocol and contains a model of the mobile location. From an overall perspective the design methodology is described as follows:
1. Divide the system into hierarchical layers.
2. Identify the direct interactions between layers. In our case, for example, the amplifier drive affects the packet error probability through the transmitted power and the energy consumed through the DC power. Indirect interactions will “trickle through” the model.
3. Optimize each layer as a function of its interactions with the other layers. In our example, we select the bit allocations for minimum packet error rate subject to particular energy consumption constraints.
4. Using simplified models of the layers based on the individual optimizations, optimize the complete system. These simplified models could be based on table lookups. This step captures complex interactions between the layers, with the multi-input, multi-output character of each layer capturing the complex trade-off in each element’s design.
5. The optimizations at the different layers may have to be stimulation-based in the absence of complete analytical expressions for performance or of necessary convexity assumptions for the available analytical expressions.
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