Tài liệu Matlab based performance replication in high altitude platforms (haps)

See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/301299009 MATLAB BASED PERFORMANCE REPLICATION IN HIGH ALTITUDE PLATFORMS (HAPs) COMMUNICATION SYSTEM Conference Paper · March 2016 CITATIONS READS 0 11 1 author: Fakir Mashuque Alamgir East West University (Bangladesh) 5 PUBLICATIONS 0 CITATIONS SEE PROFILE All in-text references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. Available from: Fakir Mashuque Alamgir Retrieved on: 27 September 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT) - 2016 MATLAB BASED PERFORMANCE REPLICATION IN HIGH ALTITUDE PLATFORMS (HAPs) COMMUNICATION SYSTEM. B. M. Adnan Sopan Chakma East West University Department of Electrical & Electronic Engineering Dhaka, Bangladesh E-mail: [email protected] East West University Department of Electrical & Electronic Engineering Dhaka, Bangladesh E-mail: [email protected] M. M. Jahazeb Alam East West University Department of Electrical & Electronic Engineering Dhaka, Bangladesh E-mail: [email protected]; Abstract— This thesis considers the examination by simulation of a digital narrow-band communication system for a scenario which consists of a High-Altitude aeronautical/ spacecraft Platform (HAP) and fixed/ mobile terrestrial transceivers. The aeronautical channel is modeled considering geometrical (angle of elevation vs. horizontal distance of the terrestrial reflectors) and statistical arguments and under these circumstances a serial concatenated coded digital transmission is investigated for several assumptions related to radio-electric coverage areas. Different flaws which can affect the signal strength are investigated in this particular case. We have considered specific modulation type (Differential Phase Shift Keying Model) and three basic channel types to model the aeronautical channel. Specific Bit Error Rate and Signal to Noise ratio per bit are considered to evaluate channel performance. The results indicate a good viability for the communication system proposed and analyzed. Keywords— High altitude platforms (HAP’s);Aeronautical channel model; Angle of elevation; Rice factor; Coverage; Concatenated communication systems; Differential phase shift keying (DPSK); Bit error rate; Signal to noise ratio per bit Ι. INTRODUCTION When high altitude platform (HAP’s) and terrestrial transceivers are taken into consideration, possibilities of point-to-multipoint radio communications emerges. In abroad, researches about the use of HAP’s for narrow and broadband communication system is developing [1], [2]. The performance of these systems for various telecommunications services-mobile and/or fixed terrestrial surroundings are considered as an open problem. In terms of digital transmission theory and looking from its point of view, the designing of aeronautical channel is an important feature that 978-1-4673-9939-5/16/$31.00 ©2016 IEEE Fakir Mashuque Alamgir Senior Lecturer, East West University Department of Electrical & Electronic Engineering Dhaka, Bangladesh E-mail: [email protected]; needs to be taken into consideration [3], [4], [5]. This paper mainly focuses and handles the theoretical derivation of a channel model that links the communication system between the platform and terrestrial mobile/fixed users and stations. Analysis of the channel modeling will be done by considering the geometrical and statistical arguments and the paper follows this reasoning. The architecture of HAP´s can be considered as hybrids; they have some common zones related to terrestrial communications, particularly Fixed Wireless Access, but are identical to the satellites when it comes to power limitations and general network architecture. In a mobile communication context is the fact it could substitute or support the terrestrial network, keeping away from problems with environmental impact and electromagnetic pollution. Platform design has several restrictions, relating to the applications to achieve: power available for the payload, stability, and maximum transmit power of the transmitters, link availability and many more. What is HAP? High Altitude Platforms (HAPs) are quasi-stationary platforms that are situated in the stratosphere (17-22 Km above from sea level). It is usually positioned between the active area of the terrestrial and satellite communication equipments. The platforms or vehicles are usually solar-powered (power supply from ground possible) airplanes, airships or balloons. They are considered to do the work in the role of HAPs. The HAPs and their applications provide potential benefits. The benefits are specified in the area of internet multimedia, remote sensing and navigation system support, military surveillance, delivering of communications services, etc. The prospects and promises of applications of HAPs from terrestrial and satellite broadcasting are much higher. A small delay and undisturbed line-of-sight link between the customer and base station is one of the examples. People have been researching, implementing, developing for civil and military interest and of international projects for the past few years. As it is found out that this could be the next revolutionary platform for communication, so HAPs turn for attention towards it is of greater possibilities. Day by day interests on aerial platforms are rising. So its development comes into consideration. The advancement of technologies has created better, stronger and effective materials which are UV resistant, leak proof to helium and long durable. These platforms are capable to travel the desired area and to make their return path to the world. Wireless delivery problems can be solved through aerial platforms. Aerial platforms will carry communications relay payloads and it will operate in a quasi-stationary position at altitudes up to some 17 km to 22 km. A payload can act as a complete base-station using aerial platforms or a transparent transponder similar to satellites. Line-of-sight propagation paths can be provided to most users with a modest FSPL, thus enabling services that take advantage of the best features of both terrestrial and satellite communications. A single aerial platform can be a substitute to a large number of terrestrial masts. With its striking qualities, it can reduce the associated costs, environmental impact, backhaul constraints, site allocation problems, etc. Regarding the above factors, HAPs can be a more beneficial and helpful option for wireless communications around the globe. The second chapter presents the scenario about the analyzation of radio communication system and under these conditions, the variables/parameters are the major part . The third chapter reflects on the relationship between geometrical and statistical aspects related to angle of elevation vs. Rice coefficient vs. horizontal distance of the terrestrial reflectors. The fourth chapter reflects on the concatenated coded digital transmission and simulation models. Results and conclusions are presented in the chapter six and seven. ΙΙ. RADIO ELECTRIC SCENARIO A.General Aspects The propagation of radio signals from/to HAP to/from ground antennae is affected by the aeronautical channel in several ways, but the most important effect is related to the multipath phenomena and therefore with the availability of the radio link. The scenario to consider for this aeronautical channel may define three areas: an urban zone, a suburban or opened zone and a rural zone. This whole scene will be represented by the figure given in Figure 1. The scene consists of an airborne which can be a globe, airplane or a helicopter. The scene also consists of a transceiver on the ground. In order for the scene to execute successfully, three aspects must be taken into consideration. • • • The direct ray or the line of sight could be adjusted from the deployed platform. Any changes of elevation angle “α” are able to create variations in the delays of the received signals. It also does the same to an increase of the multipath. In addition to that, the elevation angle depends on the flight height “h” and horizontal distance “r”. So any change on “h” and “r” will change “α” and this will create change in multipath and signals. The displacement of the receivers which makes the elevation angle is less than 45 degrees or the less than half height, it will cause shadows of the order of half or 50% at the time of connection. Figure 1. Platform air terminal. (Direct ray and reflected ray or echo) In the figure above, the transmitter (Tx) is situated on Ariel platform or HAP and the receiver (Rx) is situated on the ground. The figure shows a Line-Of-Sight ray or the direct ray. The figure also shows a reflected ray in the receiver. So, in the first approach the reception will consist of two signals. From the figure, these parameters can be considered: dLOS = h/ sin(α) r = h/ tan(α) r = horizontal distance receiver Δr = delta r (horizontal distance reflector) dLOS= Line of Sight distance deco= Echo distance B. Echo delay Echo delay mainly occurs when radio echoes return to the sender after a couple of seconds of radio transmission. Along with that, an unexpected second radio echoes with a significant time delay occurs after the primary radio echo had ended. The signals may pass the ionosphere and then it will be deducted in the magnetosphere out to a distance of several earth radii over to the opposite hemisphere where they will be reflected on top to the ionosphere. In addition to that, the 2 0.014 80 deg 70 deg 60 deg 0.013 0.012 0.011 Δ FS L dB round trip time varies with the geomagnetic latitude of the transmitter. The further the station, the larger the delay. As a result, echo delay is directly proportional with the geomagnetic latitude. The propagation times are proportional to the corresponding slant ranges plus detours, and then the echo delay (∆τ) may be formulated as: 0.01 0.009 0.008 2 0.007 -2 10 1.9 -1 0 10 1 10 2 10 3 10 10 h/Δr 1.8 Figure 3. Distortion of amplitude of the echo affecting the delay power spectrum, ΔFSL dB. 1.7 Δ τ.c /Δ r 1.6 1.5 -3 13 x 10 150 deg 140 deg 130 deg 120 deg 110 deg 100 deg 90 deg 1.4 12 1.3 1.1 1 -2 10 90 deg 45 deg 22.5 deg 11 10 -1 10 0 1 10 10 2 10 3 10 h/Δr Figure 2. Graph Δ τ ·c/|Δr| v/s h/|Δr|, Echo delay 9 Δ FS L dB 1.2 8 7 C. Amplitude of echo At the point of view of the radar, it is defined as an empirical measure of the strength of a target signal as determined from the appearance of the echo. In addition to that, the amplitude of the echo is measured by the deflection of the beam. Distortion of the echo amplitude is considered to be the main drawback because it generates signal degradation. Signal degradation causes inter symbol interference and phase noise at the sampling point. Amplitude distortion is one type of linear distortion. Linear distortion is considered to be a kind of impairments when a signal generates poor frequency response. This distortion affects the delay power system. There is a way to detect amplitude distortions. It can be done by changing the amplitude of the desired signal. If the impairments amplitude relative to the desired signal becomes identical, then the signal is distorted. As a result, the amplitude of the echo is distorted. Following the considerations of the equation in the echo delay, the echo signal can be related to the Free Space Loss (FSL) by the formula: 6 5 4 -2 10 -1 10 0 1 10 10 2 10 3 10 h/Δr Figure 4. Distortion of amplitude of the echoes taking range from 90° to 150° ΙΙΙ. GEOMETRICAL-STATISTICAL RELATIONSHIP CHANNEL The characterization is mainly distributed into two parts. One is Geometric Characterization and the other is Statistical Characterization. A. Geometrical Characterization From the geometrical point of view, in order to enhance or develop the channel model, there must be a good approximation of both the physical dimension and the type of environment where the system is located. The physical characteristics, mainly play a vital role when it comes to channel quality. There is a strong relationship between the receiver’s location on Earth and the HAPs and the channel characterization. The HAPs are placed in a good quasistationary condition at an altitude of around 20-25 km and the whole geometrical characteristics are specifically shown in the figure given below 3 Figure 5. HAPs based system geometry In the above figure, the parameters are α: User angle elevation in degrees h: Platform height in meters r: Horizontal distance receiver After certain precise calculation, it is found out that the best possible channel quality for elevation angles must be around 30° to 90° ( provided in a specific area), and this is where the control and gateway ground stations needs to be located. B. Statistical Characterization For the development and improvement of the channel models for HAP based systems, Geometrical and Statistical parameters are surely needed. If a user is located just below the platform i.e elevation angle is close to 90° then the channel is assumed to be Gaussian with very large values of the Rice factor (K>20). As the elevation angle decreases to a certain value, the channel can be modeled using a Rice distribution with smaller K values. From a statistical point of view, the amplitude probability density is mathematically expressed as: Figure 6. Rice Coefficient K in function of the angle of elevation (α) and horizontal distance of the reflectors (Δr, delta r). [8] Then : [8] α →90°⇒K→ ∞ Gaussian Channel α → [12°<α<90°] ⇒Rice Channel α <12° ⇒ K→ 0, Rayleigh Channel ΙV. COMMUNICATION SYSTEM SIMULATION MODEL The communication system simulation model is mainly used to understand the effects of the aeronautical channel and mainly to bridge the gap between the communication systems of the platform and a fixed/mobile terrestrial receiver. Below a simulation model is provided for further clarity. It is used for modeling and simulation of both proposed and actual spacecraft communication systems. Here, is defined as amplitude direct ray and Io is the Bessel function of first type and order zero. The rice distribution factor, K, can be mathematically expressed as: K can also be formulated as from a geometrical point of view: Here, “a” is related to the direct ray: dLOS = h/ sin(α) and “c” is related as the reflected ray: Relating all the information, an equation can be generated The following figure 6 indicates this relationship Figure 7. Generic Spacecraft Communication System Simulation model of the channel. From the above generic spacecraft communication system simulation model of the channel we can write an equation which is given below: ỹ(t)=ñ(t)+(x(̃ t)*K(α))+(x(̃ t)*g̃(t)) Here, x̃(t) is defined as the complex envelope of the serial concatenated coded signal. The complex envelope mainly creates an analytical signal from a signal that contains no 4 imaginary component.The analytical signal is a complexvalued function that contains no negative frequency components. The serial concatenated coded signal is used to encode the transmission of data over a noisy channel. Encoding of data can be done by putting a sequence of characters in a specialized format for efficient transmission or storage. Overall, the task of x̃(t) is to shift the frequency of the analytical signal as well as the serial concatenated coded signal. Then comes g̃(t), which is defined as the unitary power fading process. It arises when there is a deviation of the attenuation affecting a signal over certain propagation media. The fading process occurs due to multipath scattering effects, time dispersion, which all arises due to the relative motion between the transmitter and the receiver. simulates the system, BERTool iterates over the choice of Eb/N0 values and collects the results. • Plots one or more BER data sets on a single set of axes. For example, one can graphically compare simulation data with theoretical results or simulation data from a series of similar models of a communication system. • Fits a curve to a set of simulation data. • Sends BER data to the MATLAB workspace or to a file for any further processing one might want to perform. Then it is K(α), which mainly multiplicates function which allows the whole signal to vary the ratio between the average powers of the line of sight and diffuse signals. Here, line of sight is mainly a type of propagation that can transmit and receive data when the transmitter and the receiver are in face to face with each other without any obstacles present between them. Diffuse signals mainly occur due to interference or obstacles. The overall ratio mainly defines K(α). BER= (Bits in Error) / (Total bits received) Finally comes ñ(t), which represents the white noise process. A white noise process is one with a mean zero and no correlation between its values at different times. It mainly approximates noises in practical situations. Then ỹ(t) is the desired signal which is recovered from the noise and other fading process. V. SIMULATION TOOL, MODULATION TYPE AND CHANNEL MODEL A. Simulation Tool: BERTool [12] BERTool is a command in MATLAB platform that launches the Bit Error Rate Analysis Tool (BERTool) application. This application is capable to analyze the bit error rate (BER) performance of communications systems. It calculates the BER as a function of signal-to-noise ratio. It evaluates performance either with Monte-Carlo simulations of MATLAB functions and Simulink models or with theoretical closed-form expressions for selected kinds of communication systems. Using BERTool one can: • BER data generated for a communication system • Closed-form expressions for theoretical BER performance of selected types of communication systems. • The semianalytic technique. • Simulations contained in MATLAB simulation functions or Simulink models. After creating a function or model that 1. Bit Error Rate (BER) The Bit Error Rate (BER) or quality of the digital link is measured using the number of bits received in error divided by the transmitted number of bits. In digital transmission, the number of bit errors is the number of receiving bits of a data stream over a communication channel that has been altered due to noise, interference, distortion or bit synchronization errors. The BER is the number of bit errors divided by the total number of bits transmitted in a particular time interval. BER has no unit as it’s a ratio for measuring the performance and usually expressed as a percentage. For instance, N number of erroneous bits out of 100000 bits transmitted would be expressed as N*10^-5. In this paper, we assume that one erroneous bit out of 100000 bits would be transmitted. That is, the bit error rate is 1*10^-5. As we know even 1% BER is too much higher so it is always trying to keep it as low as possible, for this case we also followed this issue. In this case we have taken 100000 bits out of which only 1 bit is error which is .001% error. That means it is acceptable as it is very much lower than 1% BER. 2. Signal to Noise Ratio (SNR) The ratio between the received signal strength over the noise strength in the frequency range of the operation is known as Signal to Noise Ratio (SNR). For the physical layer of Local Area Wireless Network (LAWN) it is a vital parameter. Usually noise strength, able to include the noise in the environment and other unwanted signals (interference). BER is inversely linked to SNR that is high BER causes low SNR and vice versa. However, high BER is the reason that increases packet loss, increase in delay and decreases throughout. In the multichannel environment the actual relationship between BER and SNR is not easy to determine. Signal to noise ratio (SNR) is an index that generally used to test the quality of a communication link. It is measured in decibels (dB) and the expression is given below: SNR = 10 log10 (Signal Power / Noise Power) dB SNR tests the quality of a transmission over a network channel. The greater the signal to noise ratio, the easier it is to 5 identify and subsequently isolate and eliminate the source of noise. If the value of SNR is zero, then it means that the expected signal is virtually indistinguishable from the noise signal. In this paper, we have examined different fading channels according to SNR with the constant bit error rate, for our case that is 10^-5. B. Modulation Type: Differential Phase Shift Keying(DPSK) Differential phase shift keying (DPSK), is one kind of phase shift keying (PSK) or general type of phase modulation that transmits or transfers data by changing the phase of carrier signals/waves. As we know in Phase shift keying (PSK), only one cycle is contained by High state, however DPSK contains one and half cycle. Differential Phase Shift Keying (DPSK) is such type of modulation system or technique which codes information using the phase difference between two close neighboring symbols. So, each symbol in the transmitter is modulated relative to the immediate prior symbol and modulating signal, for example, in Binary Phase Shift Keying (BPSK), where 0 and 1 represent “no change” and “+180 degrees” respectively. Again in the receiver, the current symbol is demodulated, where the previous symbol used as a reference. The previous symbol serves as an estimate of the channel. A no change condition causes the modulated signal to remain at the same 0 or 1 state of the previous symbol. Usually this modulation system is 3dB poorer than coherent based on theoretical measurement. The reason behind this poorness because the differential system has 2 sources of error: one is corrupted symbol, and another one is corrupted reference. In DPSK, usually in the transmitter where each symbol is modulated relative to the phase of the immediately preceding signal component and then the data being transmitted. Usually DPSK is expressed as M-DPSK where M is the number of modulation order such as M = 2, 4, 6…. [11] Different channels can be assembled perfectly as the performance and the design of the channels depend upon the precision of the simulation. Fading is the very influential factor in the wireless communication system as it indicates about the fading formats in different conditions. No model can tell about the environment. A chosen signal should be error free, or very close to error free. The quality of voice and data transmission depend on the error of the signal. Selecting the fading model is the main factor while development of the application. The examination depending on the DPSK will provide the idea which helps in the application development in the market. Mainly three basic fading channel models are considered here, they are: Additive White Gaussian Noise (AWGN), Line of Sight (Rician) and Non Line of Sight (Rayleigh) Fading Channel models. 1. Rayleigh Fading Channel Non Line of Sight fading or Rayleigh fading occurs due to the multilink reception. The effect of the environment spreads to a wider area on a radio signal in this model. This one is the cheapest model of the signal propagation (i.e. For ionosphere and troposphere). This model is most relevant while there is no dominant propagation between transmitter and receiver. If the channel signal response is modeled as a Gaussian process with respect to the distribution of the individual elements and if the process has zero mean and phase lie between 0 to 2π radians, then the equation of probability density function is: 2. Rician Fading Channel Line of Sight or Rician Fading model is a part of Rayleigh fading, but it has a strong line of sight path in the Rayleigh fading environment. In satellite communications and for some urban scenarios this fading model is acceptable and applicable. This fading model is considered as small-scale fading due to the probability of deep fades is lower than that in the Rayleigh-fading model. The mathematical expression of probability density function of the amplitude (which also a Rician distribution) is: 3. Additive White Gaussian Noise Channel Figure 8. Block Diagram of DPSK Modulation. C. Channel Model Now-a-days wireless communication is one of the most important communication systems, which is also vastly used in the technology development and improvement areas. Additive White Gaussian Noise (AWGN) is used when the Doppler Effect occurs between a moving source and stationary receiver. To model the received signal at the antenna arrays narrowband data model is used. It assumes that the enclosure of the signal wave front inseminating across the antenna array usually leftovers constant. This model is applicable for the signals which have bandwidth much smaller than the carrier 6 frequency. As per above assumption or hypothesis, the received signal can be written as: H(t) = A(Ө)b(t) + N(t) Where, A(Ө) is the array manifold vector and N(t) is AWGN with zero mean and two-sided power spectral density given by No/2. From the above table, we can see that AWGN channel provides 10dB SNR for 10^-5 Bit Error Rate, on the other hand Rayleigh channel provides the >20dB SNR for the same BER, while Rician channel provides different SNR for different values of K for same BER. Result Graph 2: 0 VΙ. RESULT ANALYSIS 10 Result Graph 01: 0 10 AWGN Rayleigh K=10 K=15 K=20 K=25 K=35 -1 10 -1 10 -2 10 BER To evaluate the performance simulation we used a source of 32 Kb/s along with the concatenated coding (R-S, interleaving and convolution codes). We used a special tool in MATLAB named “BER TOOL” to get the output where we used above instructions to find the output. This information protected by this FEC modulates a DPSK and is transmitted over an aeronautical channel. We used several values of “K” to evaluate the performance simulation. Again, we measured the received data considering in Bit error rate (BER) vs. signal to noise ratio per bit (Eb/No). For several circumstances the results are presented in the figure 9 and 10 A1 DPSK,Uncoded,ch.Rayleigh A2 DPSK,Uncoded,ch.Rayleigh (higher order) B1 DPSK, Uncoded, ch. AWGN B2 DPSK, Convolutional coded (Hard), ch. AWGN B3 DPSK, Convolutional coded (Soft), ch. AWGN B4 DPSK, Reed-Solomon coded, ch. AWGN -3 10 -4 10 0 5 10 15 Eb/N0 (dB) 20 25 30 Figure 10. Channel codifications: A1, A2 worse case uncoded Rayleigh Channel; B1 uncoded better case, Gaussian channel; B2, B3, B4 concatenated coding better case, Gaussian channel. From the graph, A1, A2 curve shows that the analytical bound for worst case (Uncoded Rayleigh channel with angle o o elevations in the 0 -12 degrees range, with height, h=25 km). Therefore B1and B4 curves (encoded and RS coded respectively) better case Gaussian channel (channel with angle -2 BER 10 o o elevations in the 50 -90 degrees range). Again B2, B3 curves (convolutional coded hard and soft decision respectively, with Gaussian channel) also showing better channel with concatenated coding. -3 10 -4 10 0 2 4 6 8 10 Eb/N0 (dB) 12 14 16 18 20 Figure 9: Platform channel v/s angle of elevation modulations DPSK. Gain Rice K(α). From the figure we can see that the AWGN channel provides the low SNR where Rayleigh channel provides the worst that means highest SNR. Therefore Rician channel provides the in between SNR for different values of K. Table Ι. BER vs. SNR Comparison for AWGN, Rayleigh and Rician Channel with Gain Rice K (α) BER AWGN Rician (SNR Rayleigh (SNR (dB)) (dB)) (SNR (dB)) 10^-5 10.5 18.9 (K=10) > 20 10^-5 - 13.1 (K=20) - 10^-5 - 11.9 (K=35) - Table ΙΙ. BER vs. SNR Comparison for AWGN and Rayleigh Channel with (Coded and uncoded) BER Rayleigh Gausian Gausian Channel Channel Channel (Uncoded) (Uncoded) (concatenated [SNR(dB)] coded) [SNR(dB)] [SNR(dB)] 10^-5 26.5 10.2 8 (Convolutional, Hard) 10^-5 16 (higher 4.5 order) (Convolutional, Soft) 10^-5 10.5 (RS coded) From the above table, we can see that included Rayleigh channel provides higher SNR, which is around 26.5 dB than both coded and uncoded Gaussian channels. However, this channel provides lower SNR 16 dB for higher modulation 7 order of 4 and higher diversity order of 6, but both cases is not better than a Gaussian channel. On the other hand, both coded and uncoded Gaussian channels provide very lower SNR than a Rayleigh channel. For instance uncoded Gaussian channel provides 10.2 dB, convolutional coded, hard provides 8dB, convolutional coded, soft provides 4.5 dB, and RS coded provides 10.5dB which are way better than a Rayleigh channel. Here one thing needs to remember that all SNR are found against 10^-5 BER. VΙΙ. CONCLUSION [4] Peter Hoeher, Axel Jahn, Hermann Bischl, ”On Satellite Emulation by an Airborne Platform, Institute for Communications Technology(DLR) Global Telecommunications Conference, 1996. GLOBECOM '96. 'Communications: The Key to Global Prosperity , Volume: 2 , 1996 Page(s): 995-1000 vol.2 IEEE,1996. [5] Axel Jahn, Hermann Bischl, ”Channel Characterisation for Spread Spectrum Satellite Communications”, Proc. IEEE Int. Symp. On Spread SpectrumTechn. And Appl.,Mainz,Germany,Sept.1996. In this paper, a geometrical-statistical channel model is used. Communication System Simulation Model is used to analyze the different outcomes which can affect the signals of HAPs. Based on the Communication System Simulation Model different graphs are generated which can indicate the distortions and other flaws that can affect the performance of HAPs Communication Systems. It is seen that Gaussian channel always provides less signal to noise ratio whether it is coded or uncoded for the same modulation order, the same modulation system as well. On the other hand Rayleigh channel provides the poor output, which is higher signal to noise ratio. So, the proposed communication system has a good feasibility of implementation with Gaussian channel. The Communication System Simulation Model was further analyzed in several circumstances following authors referenced and the overall results were precise and accurate and provided a good feasibility for the proposed communication system. [6] J.A.Delgado-Penín, H.Carrasco, F. Ulloa-Vasquez, E.Bertran, ”Space- Time coding and processing to improve radio communication coverage from High Altitude Platforms (HAPs)-An approach”, DASIA 2001 –SP483,Niza,France.ESA Publication, ISBN 92-9092-773-9 References [10]http://www.nasa.gov/centers/goddard/news/student_balloo n.html [1] G. Djuknic, Lucent Technologies, "Wireless Communications Services via High-Altitude Aeronautical Platforms", IEEE Communications Magazine , Sep 1997, pp. 128-135. [2] D. Grace, N. E. Daly, T.C. Tozer, A.G. Burr, ”LMDS from High Altitude Aeronautical Platform”, in IEEE Globecom´99, December 1999. [3] P. Hoeher , E. Haas, "Aeronautical Channel Modelling at VHF-Band", Institute for Communications Technology, Germany Aerospace Center, IEEE, Veh. Techn. Conf., vol. 4, 1999, pp.1961-1966 [7] T. C. Tozer and D. Grace : ‘High-altitude platforms for wireless communications’ from ELECTRONICS & COMMUNICATION ENGINEERING JOURNAL JUNE 2001. [8] Ulloa-Vasquez, Fernando; Delgado-Penin, J.A ‘Performance simulation in High Altitude Platform (HAPs) communication system. ‘Proceedings of DASIA 2002,13-16 May 2002, Dublin, Ireland. Ed: R.A Harris . ESA SP-509, Noordwijk, Netherlands: ESA Publications Division, ISBN 92-9092-819-0, 2002, id.48.1-48.9, published on CD-ROM, Session 8B [9] Google loon project [11] Deepak K. Chy, Md. Khaliluzzaman. Evaluation of SNR for AWGN, Rayleigh and Rician Fading Channels Under DPSK Modulation Scheme with Constant BER. International Journal of Wireless Communications and Mobile Computing. 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