Energy Efficiency Of Cellar Network Computer Science Essay

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Global warming and the impact of the recession and also researches which show that the communication industry contributes about one percent in the total energy used in countries which will results in increasing the CO2 emission brings interest to improve the energy efficiency of cellular networks. Besides environmental concerns, it is also an important economic incentive to decrease consumption in the system and the increasing cost of electricity in the world market in recent years. Also other factors such as energy taxation are becoming more important in future.

each cellular network usually needs many base stations in order to provide a good coverage  . Since each base station requires a lot of energy, depending on different sites and their different settings the total consumption of energy for coverage nationally will be in order of MW. Mobile phone networks are such a system that the benefits of greater efficiency can be seen in them .         

. a network with Macro cells will provide a stable coverage, but not effective enough for high data rate transmission , also Testing the energy efficiency of cellular network is a important task as well .

The aim of this project is to first , study the different ways of improving the energy efficiency of cellular network and then effect of different parameters on the efficiency of a network , and how these parameter will effect the power consumption and energy efficiency , in order to improve networks energy efficiency by using different simulation and models .[6,7,11]

Literature review

A very important aspect is the efficiency of virtual multiple input output MIMO communication architecture based on the transformation of V blast receiver. This in terms of energy efficiency in communication. Looking over many papers by different universities one can see which results illustrate that the energy provides significant savings by the architecture of virtual MIMO that has distributed networks of wireless sensors. Also, another aspects which is supported by the results is that while optimizing the speed of transmission distance, in some cases, it can offer greater amount of energy efficiency; this is not necessary to achieve as the energy savings compared scheme called Alamouti- based virtual MIMO implementations. Consequently, in most scenarios a fixed rate virtual MIMO system with binary phase- shift- keying (BPSK) can accomplish this performance very close to that of variable rate system optimum. Despite this, the results in the papers that have been reviewed also show demonstrate that MIMO system can result in delay penalties compared to a traditional SISO communication network of sensors based on the order of virtual MIMO architecture growth.[13,14]

Now days the concept of making wireless networks more energy efficient became interesting in telecommunication industry , which means reducing the power consumption of mobile wireless networks . as known , recently and in recent decade wireless network plays a key role in communication so the design of it become very important [14,15].so by designing a low energy consumer network the energy efficiency of Wireless network could be improved [15]

Also the maximum lifetime accumulative broadcast (MLAB) algorithm and the way user can reduce the transmit power level by using this algorithm been studied in literature of this report [14] and it was proven that way that using algorithm can make the network more efficient [14].

Framework

Overview of Picocells & Macrocells.

Establishment of scenario & its input parameter.

Design a Model with different deployment of Macro and Micro cells

Calculation of network power consumption for a scenario and their results.

Writing a complete Matlab program to produce result of power consumption for different deployment of Macro and Micro cells

Comparison of network power consumption for different deployment

Discussion of results

Conclusions.

Refrences

Appendix

Objective / Motivation

For the last couple of decades the problems that has increasingly risen of global warming has taken an important role in the international politics. Information and communication ( ICT's) have been identified to be a great significant contributor towards global emissions of the greenhouse gas emission. Therefore, in orde to increase the effciency of communication technologies, reducing the environmental impac of ICT needs to take place. Mobile radio networks represent roughly .2% of world emissions as one of the sectors of ICT, with contribution to a figuratively small amount of the general assembly carbon footprint of ICT today. Despite all of this, with increase in demand for communication services in countries that are developed, serious issues regarding energy requirements of mobile networks are expected in the near future. In addition to minimixing the environmental impact of industry, all mobile operators distress and try their best in decreasing the energy consumption of their networkss mainly for economical reasons which plays an important role from their viewpoint. Costs for operating a network is greatly influenced y energy costs and great savings. At the moment, over 80% of the power of mobile consumed in the radio access networkover particular base stations. Acknowledging this, there are two levers to reduce energy consumption of networks:

the optimization of individual sites, for instance, using charge more efficient and scalable hardware components and software modules.

using good strategies to improve implementaton, as a result effectively reducingthe number of sites in the network required to meet certain performance indicators such as coverage and spectral efficiency.

As a whole, progress in one are are complementary, if the deployment is optimized in comparison to some coverage, the additional energy saving could be gained by optimizing the site. Interdepencencies, on the other hand, without site optimization affecs the link budget. For instance, f the receiver sensitivity is reduced by using RF components. Concerning the deployment of the network, most often deployment of topologies with high density of low base statons considered to improve network efficiency implementations compared to basic low power density of some stations. This report examins the energy efficieny aspects of base station deployment strategies and introduces concepts to evaluate and optimize energy consumption of a standard cellular network which consists of a combination of regulated Macro sites and a series of smaller devices referred to as Micro base stations. In comparison with the first, second cover an area much smaller hence with low power consumption. Moreover, areas covered by micro base stations are in general of higher proportions of the average signal and noise ratio ( SINR) due to the loss of conditions and shortest path propogation distances. In the implementation of previous strategies, commonly contributions are an investigation with respect to spectral efficiency, coverage or the liklihood of interruption.

Previous research on profitability and cost structure of mixed topologies, consisting of Macro, Micro and pico cells that are held in the concept of spectral efficiency per unit area is introduced as a measure for the performance of cellular mobile systems.

In this case, this principle is used for frequency reuse Group. Additionally, the power of network consumption is characterized in watts per unit area of coverage and determined requirements of spectral efficency and optimize base station density. The final year project also offers simple models of energy consumption based on different types of staitons to obtain certain characteristic of micro stations to improve the figures of the total energy consumption network.

The rest of the report is organized as follows.

Introduces the system model and network performance

In the final year project the plan is to model the Macro base station network as infinity regular grid of sites is characterized by the distance between the site which has been defined as ''X '' generation of structures the size of hexagonal cells of side length

Which is also shown in Figure 1, Each cell can be divided into several sectors.

Well, given the distance between the sites X, which the cell size can be, calculates as:

And Throughout the document it's been assumed that traffic density, to be distributed uniformly in the Euclidean plane.

Figure . Regular grid of macro sites and corresponding cell geometry [11]

The first stage in this project is to do propagation modelling

Usually, the deterioration of signal quality due to the spreading and propagation is linked to three different causes:

the path loss,

slow fading (or shadow)

Faster (or multi-path) fading.

The formula for a basic signal propagation model to capture path loss and shadowing

has been shown:

Where:

: Received power

: Transmitted Power

λ : path Loss exponent

r : propagation distance

Ψ : is used to model slow mitigation generally follows a lognormal distribution,

,K : These parameters indicate to further adapt the model

This model is suitable for laboratory evaluation and simulation investigations. Moreover,

we also consider the user terminal and height of the base station.

Base Station types and models of energy

Conventional macro sites are designed to provide more areas with certain minimal coverage. The energy consumption of a site It depends on the size of the area covered and the degree of coverage required. In urban areas Radios cells generally range from about 1000 m to 5000 m with coverage of more than 90%. For the relationship between the Average energy and average power radiated per site, the formula like bottom will be use

Where

:average consumed power per site

:average power radiated per site

:

:the power consumption that scale with the average radiated power due to amplifier and feeder losses as well as cooling of the site

: shows the offset of site power that is independently consumed of the average transmit power due to signal processing ,battery backup as well as site cooling .

Site cooling devices may be considered an impact on both and as both depend on the power transmission and independent components

contribute thermal radiation. Both Am and Bm scale with the number of sectors and number of antennas per sector. Note that this project consider that the average power consumption in terms of average transmit power. This assumption is justified because energy Currently, sites operations Macro is virtually independent of instantaneous traffic load . In addition to conventional Macro sites its been considered the deployment small sites, which called Micro base stations. These devices offer only one omni-directional antenna and cover a much smaller area with a cell radius of 100 and 250m. In turn, Micro base stations are much lower energy consumption. In analogy with their counterparts at the Macro model the power of Micro base stations is assumed to be given by (where Pn is power consumption) :

A major advantage of Micro-sites is its ability to expand its energy consumption with the current level of activity, which is reflected in the factor L, the average device modelling activity level. Note that the expansion of the total energy of L If something is an ideal.[4,5,6,11]

Cell Coverage

The coverage area of the cell is defined as below. The cell cover C for a minimum received power which is Pmin :

Where :

Csize : defines the cell are as shown in Fig1

r : propagation distance

The cellular coverage overall

calculated with usage of below formula In site deployment scenarios cover Macro sites with coverage of Cm and also an average of Micro-sites N for the delivery of each cell an area of An and the Cn as coverage for Micro cell,

Where

This defines the maximum number of micro cell in one Macro cell.

Area spectral efficiency

The sector spectral efficiency is defined as the average the levels achieved in one network per unit bandwidth per unit area, usually measured in bits per second per Hertz per kilometre square. Which define as below also in this equation let Ā denote to a large area cantered around a reference site and let ץ(r, Ф, Ψ) denote SINR at (r, Ф) which varies by shadowing of Ψ:

Area Power Consumption

Observing the power consumption in single site it is seen that is it's not possible to compare networks of different density of place increasing distance from the areas to generate more coverage. To evaluate the energy consumption of network related to its size, area of energy consumption as the average power consumed in a cell divided by the corresponding area in the average cell measured in watts per square kilometre been introduced . The area power consumption is defined as below for a given power consumption of the cell (where Pc is cell power consumption).

At the time If there is an average of N Micro cell per Macro site power is the power gained by the use of Macro and Micro-sites.

Figure . Micro site position within the Macro grid [11]

Network Optimization

This section examines how the energy field affected by two factors related to deployment, namely the distance between the site and the average number of micro-sites per cell. This project uses Monte Carlo simulations for downlink scenarios to estimate the area spectral efficiency and energy consumption of different implementations. The plans is to First focus on the power consumption of a pure Macro scenario and then continue the investigation to the hybrid case with a certain number of macro site per cell. The next stage in the report is to couple observations with system performance measured in terms area spectral efficiency and research on the impact of Bm and Bn model parameters and energy consumption.

Considering a hexagonal network of Macro sites where the Distance from site ranges X is 1000 m to 3500 m. and Following a common procedure is defined as a reference site surrounded Two levels of interference sites. Mobiles are randomly arranged in cell area following a uniform distribution. In case of considering a OFDMA using a frequency reuse of one, mainly same time and frequency resources are used for transmitting in each cell. And in Monte Carlo, a mobile is served only by the reference cell. In the case of an OFDMA system, which serves multiple users per cell is equivalent to multiply the number of Monte Carlo iterations[17]. All sites are expected to convey the maximum level. Given that mobile operators typically apply Network tools and site planning to maximize SINR, assuming inner cell and outer cell path loss of 3 dB less, which is respectively, higher than the value proposed by the propagation model. Cell coverage should be 99% for each distance and each base station site.

Micro sites that are supposed to support a circular area with a radius of 200 m and placed on the edges of the cell where the signal macro sites can be expected to be low .

Performance Assessment Network

Typically, the distance of the site is obtained by specifying required coverage, and base station transmits power. But in this project it will be assumed that the coverage and inter site distance is fixed and for that fixed area the respective transmitted power will be calculated using previous defined formulas and If the implementation includes Micro-sites, the overall cellular coverage is the weighted sum of Micro-and Macro-coverage, a power of Macro site transmitter is reduced with increasing the number of Micro-sites. Energy consumption for different area deployments (increased number of Micro base stations per cell) . As explained before, a Performance on the optimal distance power of the smallest area of consumption can be seen in the plots which will be made to show the result. Although there will be an optimum the distance between the site for each additional micro site, it is the minimum distance achieved by a display of pure Macro cells. This result will be based on the fact that the power of a macro site due to a lower surface coverage does not offset the savings additional energy consumption of micro-sites. Of course, all of these result will be different by changing main parameters However, it is clear that the minimum surface energy consumption can not be the main indicator to assess energy efficiency of the system. To find the minimum power for the effectiveness of a certain spectral range minimum area proposes new method by using the Given the spectral resolution yield curves for different implementations, in this project a target area spectral efficiency will be established to a number which must be met. And then distances determine between the sites that achieve spectral efficiency target area to find the distances that maximize the area of energy consumption within these groups. Naturally, the results depend on site design parameters of the Am, Bm, An and Bn, and the average traffic load L.

Where:

An : the power consumption that scale with the average radiated power due to amplifier and feeder losses as well as cooling of the site for Micro site

Bn : shows the offset of site power that is independently consumed of the average transmit power due to signal processing ,battery backup as well as site cooling for micro site

Mathematical proof of transmitted power formula

By looking back at the received power formulas given in previous part it can be seen that :

In this case instead of can be written because received power will be minimum at the edge with maximum R , also can be taken as 1 so the top formula can be written as :

Also in this case can be written as , so :

for a fixed coverage requirements we obtain the relation :

So :

Where is the maximum distance from the cell center where the signal level is . also by reffering to Fig1 and applyin simple geometery it can be seen that :

So

So in this case we can write that :

And

And it can be seen that :

Effects of , C and X on the transmitted power

In this part the Macro base station network has been modelled with the Inter site distance of X ,Path Loss exponent , Cell Coverage of C , minimum power of Pmin and constant of K and the aim is to discuss the effect of three different parameter of ,C and X on the Transmitted power .

Inorder to get the useful and precise results its been chosen to use matlab modelling which can be really helpful in this case , so three different Matlab models has been made to show how effective transmitted power is for ,C and X..

By using a Matlab program written [Appendix 1] the plot which its been shown in Fig3 can be seen , which is the effect of on the transmitted power based on the formula given in top and the numbers in Table 1 .

Parameters

Value

P min ( minimum Power )

6.309e-17 W

C ( Coverage )

99%

X ( inter site distance )

200 m

K ( adaptation constant)

5.47e-4

Table . parameters value for effect of λ program in Matlab

Figure . plot of effect of λ on transmitted power

Fig3 shows that by increasing the value of which is path loss exponent the value for transmitted power increases exponentially , so inorder to decrease the transmitted power the should decrease and inorder to increase the transmitted power the should be increased.so in ideal world Path Loss exponent need to decrease cause the Power transmitted needs to get lower and a model with less transmitted power is an Ideal one .

For looking at the effects of Coverage on the Transmitted power the Matlab program [Appendix2] was written and the values in the Table2 were used

Parameters

Value

P min ( minimum Power )

6.309e-17 W

( Path Loss Exponent )

2.0

X ( inter site distance )

200 m

K ( adaptation constant)

5.47e-4

Table . parameters value for effect of Coverage program in Matlab

Figure . plot of effect of Coverage on transmitted power

Here there is a different approach to the transmitted power , in this case the Path loss exponent ,minimum power, Inter site distance were assumed to be as constant to calculate the transmitted power for different Cell Coverage area .

By looking at the graph on top it can be seen that by increasing or decreasing the Cell Coverage area the transmitted power will increase or decrease linearly so in a larger cell area where the fraction of cell area with received power the transmitted power is higher than a smaller Cell Coverage area so by increasing the Cell Coverage the transmitted Power will be increased and vise versa by decreasing the Coverage transmitted power will decrease .

The effect of inter site distance is another important parameter in calculating the transmitted power , in this case to see how effective the transmitted power will be by different inter site distance in the coverage area , a Matlab model has been designed and its been assumed that Path loss exponent ,minimum power and Coverage are constant and their values has been shown in Table 4.

By using a Matlab model in [Appendix3] the graph in the Fig 5 can be plotted and the results for effect of varying a Inter site Distance for a Cell Coverage Area on Transmitted Power can be seen in Fig5.

Parameters

Value

P min ( minimum Power )

1000W

( Path Loss Exponent )

2.0

C (Coverage )

99%

K ( adaptation constant)

5.47e-4

Table . parameters value for effect of Inter site Distance program in Matlab

Figure . plot of effect of Inter site Distance on transmitted power

The Fig5. is showing that for a constant parameter such as Path loss exponent ,minimum power and Coverage used in Table 3 by increasing the inter site distance ,Transmitted Power will exponentially increase and by decreasing it, the Power Transmitted decreases , so the other way of decreasing power transmitted is to decrease the inter site distance between cells which is helpful in case of using less power .

Network Persormance

This part shows that how the Area Power Consumption is effected by factors such as Inter site Distance X and Number of Micro site per cell in order to calculate Area Power consumption an Area Spectral Efficiency of different deployments, Monte Carlo Simulation [class of computational algorithms that rely on repeated random sampling to compute their results [16] ] been used , this report first studies the area power consumption of pure Macro Scenario and then talks about a different scenario which will have a number of different Micro sites per cell and then investigate the effect of Bm (shows the offset of site power that is independently consumed of the average transmit power due to signal processing ,battery backup as well as site cooling) and Bn (shows the offset of site power that is independently consumed of the average transmit power due to signal processing ,battery backup as well as site cooling for micro site ) on the Power Consumption model .

Model Simulation

For the simulation o , a hexagonal grid of Macro sites with the Inter site distance of X between 1000 and 3000 meters been modelled .the model contains randomly placed mobiles in the Cell Area with a uniform distribution . the OFDMA system has been employed in this model (Orthogonal Frequency-Division Multiple Access (OFDMA) is a multi-user version of the popular Orthogonal frequency-division multiplexing (OFDM) digital modulation scheme. Multiple access is achieved in OFDMA by assigning subsets of subcarriers to individual users as shown in the illustration below. This allows simultaneous low data rate transmission from several users.[17]) , also there isnt any frequency reuse patterns between sites in the model and each mobile sereves by the refference cell .and in case of OFDMA system the number of Monte Carlo iteration can be multiplied for multiple users .

In this model Matlab programming software has been used in order to model a simulation which the Matlab code has been shown in [Appendix4] .

The Cell coverage been chosen to be 99% during simulation for each base station type and site distance. Also the area with radius of 200 (m) was chosen for Micro sites to support .

Usually the Inter site Distance of (X) is calculated by using Transmitted power and Coverage , but in this case the Coverage and Base Stations transmitted power are assumed to be fixed . In Scenarios of mixed Micro and Macro base stations the overall Coverage is the sum of the all Micro and Macro Stations which can be seen that Macro sites Transmitted Power will be decreases by increasing the Micro sites numbers.

Matlab programming software were used to Model the top scenario , and the code written for this scenario has been shown in the [Appendix4].and the written Matlab code contains a series of different variables which Table4 in below shows the values which been used in the model in [Appendix4 ] inorder to get expected results to demonstrate the Area Power consumption as a function of Inter site Distance for different deployment of Macro and Micro site scenarios ( Macro Base station only,one Micro Base station ,two Micro Base station, three Micro Base station and five Micro base station )

The results for the modelling has been shown in the Fig6. which can explain that :

Parameter

Value

Pmin (minimum Power )

0.98 W

C ( Coverage )

99%

X ( Inter site Distance )

346 m

K (Constant)

5.47e-4

λ

2.6

L ( Current activity Level )

1

Am

21.45

Bm

354.44

An

7.84

Bn

71.5

N (Number of Micro sites )

0,1,2,3,5

Table .Value of different parameter used in area power consumption model in Matlab program

Figure .Area power consumption vs. Inter site Distance

By reviewing the graph shown in top , can be seen that in this case and by using these values the power consumption didn't improve by using Scenarios with Micro base station as well as Macro station , the top(gray) plot is the one with the scenario with Micro base station and the bottom (red) plot is the power consumption to inter site distance in case of using just Macro base station and its clearly can be seen that the Macro station only scenario has a lower power consumption than scenario with five ,three,two or one Micro base station .

The reason is that not all the time the used scenario is Ideal , and Power consumption reduces by adding more Micro station , because by adding Micro base station to the model the consumption of power can increases as well , as its obvious each Micro cell uses power and by adding their used power them up , the total power consumption of all of them add up to the original power consumption so the consumption will increases , and in this case by viewing the power consumption with the scenarios with more Micro base stations , for the given range of inter site distance between cells can be seen that power consumption doesn't reduces , and power consumption of the scenario with Macro base station only is less than four other scenarios .

Referring to the plots given in Fig6 , for the same inter site distance between cells such as 1500 meters the results in Table5 in below can be seen .

Scenarios with different Number of Macro and Micro Station

Power consumed in 1500 (m)

Macro Base station only

()

1 Micro Base station

()

2 Micro Base station

()

3 Micro Base station

()

5 Micro Base station

()

Table .Power consumption for different scenarios for a defined inter site Distance of 1500 (m)

Looking at the Table above one can acknowledge that power consumption for a scenarios with more Micro Base station is higher than the scenario involving Macro Base station only or less Micro Base stations . in the top case, 5 Micro station model has power consumption of and the model with Macro Base station has consumption , so these results reflex that not at all time the outcome is Ideal, which in this case results are not Ideal and power consumption doesn't decrease by involving more Micro Base stations in the system for a given Inter site distance .

Figure .Area spectral efficiency vs. Inter site distance

Micro sites Deployment is considered to primarily increase the Area Spectral efficiency of the model ,as its been shown in Fig7 . the aim is to find the lowest Area power consumption for a given spectral Efficiency .

To reach the goal ,first area spectral efficiency as function of Inter site distance for different deployment was calculated as shown in Fig7 , and the next step is to set a target for Area Spectral Efficiency which in here the target has been chosen to be ( which needs to be achieved .

The next step was to calculate the inter site distance which each different Deoployment scenario reaches the required Area Spectral Efficiency and from that , and by referring to the Fig6 the power consumption of those Inter site distances could be found as its been shown below .

Figure .Area spectral efficiency vs. Inter site distance

( to find Inter site Distance for given Spectral efficiency ) [11]

Figure . Area power consumption vs. Inter site Distance

( to find Area power consumption for given Inter site distance )

Scenarios with different Number of Macro and Micro Station

Inter site Distance to achieve (

Area Spectral Efficiency

Power consumed

Macro Base station only

1000 (m)

()

1 Micro Base station

1100 (m)

()

2 Micro Base station

1250 (m)

()

3 Micro Base station

1300 (m)

()

5 Micro Base station

1400 (m)

()

Table . output results for power consumption find from inter site distance

By looking at the Fig8 the values of Inter site distance ,for different scenarios which each scenario reaches the required Area spectral efficiency can be found , in this case by looking at the Fig8 all Inter site Distances for different scenarios been calculated and from the calculated Inter site distance of them and referring to Fig9 the power consumed in order to get the requested spectral efficiency for different deployed scenarios been measured and the Table6 shows these results .

From the Table6 one can see that for the scenarios with more Micro base stations the power that been consumed hasn't decreased and its increased , for example in a deployment of 5 Micro Base stations the power consumed is () and in the other hand , in the model with Macro Base station only the power consumption is () , so as mentioned before not always the system is Ideal and using Micro station reduces the power consumed and as seen in this model using Micro station increases the power consumption .

Offset power consumption Dependency

In this part the effects of offset power of Macro and Micro sites been studied ,the dependency of previous results on the Bm and Bn (Offset Powers for Macro and Micro station) in the linear power model is important and in here its been trying to make the contest clear to understand .

Micro sites are normally made from low cost components and for this reason the transmitted power loss in each antenna is almost %50 higher for Micro sites rather than Macro sites so the main advantage of Micro deployments is the low offset per antenna about (%55).[11]

In order to show the dependency of power consumption by different offsets in different Micro and Macro Base station deployment , a matlab program [Appendix5] has been modelled .

The Matlab model [Appendix5] tries to show the Area power consumption as function of number of Micro sites with Various Bm and Bn values . and for this reason the model uses the variable showed in the Table7 . to produce the wanted results .

The outcome of the Matlab model describe in the top can be seen in the Fig10 ,which helps to understand the role of offset power in power consumption of different scenarios .

Parameter

Value

Pmin (minimum Power )

6.309e-17W

C ( Coverage )

9%

X ( Inter site Distance )

346 m

K (Constant)

5.47e-4

λ

2.6

L ( Current activity Level )

0.2

Am

21.45

An

7.84

N (Number of Micro sites )

0,1,2,3,5

Table . Value of different parameter used in area power consumption to Number of Micro sites model in Matlab program

Figure . Area Power consumption as a function of number of Micro sites deployed in the modelling with various Bm and Bn constellation .

By looking at the plots in Fig10 it can be acknowledge( by considering the plots for Bm=0 and Bm=9 Bn ), incase of Bm=0 by deploying number of Micro sites the energy efficiency increases , the Table below shows the results for power consumption of different scenarios in case of Macro offset to be zero , this table clearly shows that for example, the power consumption for one Micro Base station is () and in the other hand ,Consumption of five Micro cell increase the power consumption to ().

Scenarios with different Number of Macro and Micro Station

Power consumed

Macro Base station only

()

1 Micro Base station

()

2 Micro Base station

()

3 Micro Base station

()

5 Micro Base station

()

Table . the results for power consumption in Bm=0 scenario

The results for Bm=9Bn are similar to the results for Bm=0, because power consumptions increase by deploying more Micro stations in the designed model , Table9 shows the results for Area power consumption as function of number of Micro stations deploys for Bm=9bn .

By looking at the Table9 it can be seen that , the results for Area power consumption in case of using just Macro sites is about () , and in the same scenario the results for deploying five Micro sites is about () so clearly by deploying more Micro sites the power consumption increases in this model .so in here its been proved that its not true that by increasing the number of Micro sites ,power consumption will decrease.

Scenarios with different Number of Macro and Micro Station

Power consumed

Macro Base station only

()

1 Micro Base station

()

2 Micro Base station

()

3 Micro Base station

()

5 Micro Base station

()

Table . the results for power consumption Bm=9Bn scenario

Discussion of Results

In this final year project, the influence of deployin different stategies using variable number of micro sites as well as the conventional macro has been examined.To start with, area power consumption as a metric system in performance is defined. Then the next part of the project was to create a model and simulation to support the aim of the project which is that area power consumption will vary with deploying different scenarios using macro and micro sites.

Inorder to simulate a helpful model to show the affectivity of Area Power consumption with different deployment and scenarios , a model was designed to show the Area power consumption as a function of Inter site distance for different deployment of Macro and Micro cells . designing this model was with using Matlab programming software , which first a code was written which its aim was to show different plots to show if Area power consumption varies by deploying Micro cells ,which from the graph it could be seen that deploying number of Micro cells doesn't always decrease the Area power consumption and in some scenarios such as here it could increase the Area power consumption .

The next step was to model a scenario to show that how effective the Area Spectral efficiency is as a function of Inter site distance , then a target Area spectral efficiency was chosen . by calculating different Inter site distance for different scenarios which they all reached the targeted Area spectral efficiency and using the calculated Inter site distance of the point reaching the target from the second model to find the Area power consumption of those points in the first model to show that in this case , the area power consumption decreases by deploying number of Micro sites in the scenario rather than decreasing .

In order to show the impact of Bm and Bn ( Offsets of power for Macro and Micro cells ) on Area power consumption of different deployment a model was made , and by use of Matlab Programming it had to plot different graphs of the Area power consumption as function of number of the Micro sites with variable Bm and Bn constellation , which by reviewing these graphs , again the results showed that for the same offset power ,by increasing the number of Micro sites the power consumption increased instead of decreasing which at the beginning of project was expected to happen .

Conclusion

The impact of deploying different strategies using Variable number of Micro sites in addition to the conventional Macro site has been studied in this final year project . first the concept of Area power consumption as a metric in system performance were defined . and then by using the simulation model it was found that in contrary to the first idea in the beginning not only did the Area power consumption decrease but on the other hand Area power consumption it did increase by adding Micro sites . this project tried to show that not in all scenarios using Micro base station will decrease the Area power consumption , and in some scenarios the power consumption will increase , but in all scenarios the coverage will be improved by using Micro cells which is really important . sometimes the Area power consumption of the model doesn't decrease because each one of the use micro stations, consumes separate power which they will add up together and will add an extra power consumption to the system . also the investigation in this project shows that Area power consumption depends strongly on offset power of Macro and Micro Base station as well , but again in some scenarios by using Micro sites the power consumption doesn't decrease even under these two important factors .

Reference

[1] McKinsey & Company, "The impact of ICT on global emissions,"on behalf of the Global eSustainability Initiative (GeSI), Tech. Rep.,November 2007.

[2] R. Kumar and L. Mieritz, "Conceptualizing 'Green IT' and data centre power and cooling issues," Gartner, Research paper G00150322,September 2007.

[3] Ericsson, "Sustainable energy use in mobile communications," August2007, White paper.

[4] G. P. Fettweis and E. Zimmermann, "ICT energy consumption - trends and challenges," in Proceedings of the 11th International Symposiumon Wireless Personal Multimedia Communications, Lapland, Finland,September 2008.

[5] J. Grivolas, "Greener wireless networks," Ovum, Tech. Rep., September 2008.

[6] M. S. Alouini and A. J. Goldsmith, "Area spectral efficiency of cellular mobile radio systems," IEEE Trans. Veh. Technol., vol. 48, no. 4, pp.1047-1066, July 1999.

[7] S. Hanly and R. Mathar, "On the optimal base-station density for CDMA cellular networks," IEEE Trans. Commun., vol. 50, no. 8, pp. 1274-1281,Aug. 2002.

[8] K. Johansson, "Cost effective deployment strategies for heterogeneous wireless networks," Ph.D. dissertation, KTH Information and Communication Technology, Stockholm, Sweden, November 2007.

[9] A. Goldsmith, "Wireless Communications''. New York, NY, USA: Cambridge University Press, 2005.

[10] IST-4-02-7756 WINNER II, D1.1.2 V1.2 WINNER II Channel Models, September 2007.

[11]Fred Richter, Albrecht J. Fehske, and Gerhard P. Fettweis Vodafone Stiftungslehrstuhl, Technische Universit¨at Dresden "Energy Efficiency Aspects of Base Station Deployment Strategies for Cellular Networks''2009 Dresden Germany.

[12] A. Corliano and M. Hufschmid, "Energieverbrauch der mobilen ommunikation," Bundesamt f¨ur Energie, Ittigen, Switzerland, Tech. Rep., February 2008, in German.

[13] Sudharman K. Jayaweera "An Energy-efficient Virtual MIMO Communications

Architecture Based on V-BLAST Processing for Distributed Wireless Sensor Networks''Department of Electrical and Computer Engineering Wichita State University, Wichita, KS, 67226, USA.2003

[14] Ivana Maric Member, IEEE and Roy D. Yates Member, IEEECooperative "multicast for Maximum Network Lifetime''.2005

[15]Christine E. Jones,Krishna M. Sivalingam, Prathima Agrawal and Jyh Cheng Chen, "A Survey of Energy Efficient Network Protocols for Wireless Networks'',Springer Netherlands 2004 .

[16] http://en.wikipedia.org/wiki/Monte_Carlo_method

[17] http://en.wikipedia.org/wiki/OFDMA

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