Studies on fade mitigation control for microwave satellite signal propagation

Transcription

1 Studies on fade mitigation control for microwave satellite signal propagation

2 Objective of the project 2 The implementation of the mitigation techniques to overcome the atmospheric impairments in the higher frequency bands(ka-band). The tracking of the channel fades so that the transmission can be done using the advantages of the good channel conditions. In other words, it is the implementation of the link adaptation in satellite communication.

3 Contents Introduction Fade mitigation techniques Implementation of FMT Block diagram of system Block diagram of simulator Algorithms used for FMT Control Logic Interim Results Results Update The experimental plan and set-up Status update Future work 3

4 Introduction 4 Challenges in Satellite communication 1. Saturated conventional(c, Ku) bands 2. Higher capacity-cost efficiency-good availability The possible solutions could be Going for higher frequency bands 1. Larger bandwidth 2. Reduced equipment size 3. Severe propagation impairments 4. Limited cost availability Multibeam coverage with large number of narrow beams

5 5 Fade Mitigation Techniques

6 Fade mitigation techniques 6 Power Control : Transmitting power level changed in accordance with propagation impairments Adaptive waveform : Fade compensated by a more efficient modulation and coding scheme Diversity : Fade avoided by the use of another less impaired link Layer 2 : To Cope with the temporal dynamics of the fade

7 Different FMTs 7 POWER CONTROL: Four types of Power Control FMT can be considered : 1. Up-Link Power Control (ULPC) 2. End-to-End Power Control(EEPC) 3. Down-Link Power Control (DLPC) 4. On-Board Beam Shaping (OBBS). ADAPTIVE WAVEFORM: These FMTs could be split into 1. Adaptive Coding (AC) 2. Adaptive Modulation (AM) 3. Data Rate Reduction (DRR). DIVERSITY: Three types of diversity techniques can be considered: 1. site diversity 2. satellite diversity 3. frequency diversity LAYER 2: Two different techniques can be envisaged at layer 2 1. Automatic Repeat Request (ARQ) 2. Time Diversity (TD).

8 8 Implementation of fade mitigation techniques

9 FMT control logic 9 Monitored signal Detection Decision FMT activation FMT no active FMT active level 1 FMT active level 2 FMT active level n

10 Block diagram of the system Satellite 10 Earth station Earth station MODEM MODEM TX Processing RX Processing DATA BITS

11 Block diagram of the system Satellite 11 Earth station Earth station MODEM MODEM TX Processing DATA BITS Modem Interfacing PER Estimation SNR Estimation Fade Prediction Data Rate Selection Tx-Stn Control Cmd. RX Processing Modem Interfacing

12 Block diagram of the system Satellite 12 Earth station Earth station MODEM MODEM TX Processing DATA BITS TX-Modem Interfacing RX-Stn. Interfacing Modem Interfacing PER Estimation SNR Estimation Fade Prediction Data Rate Selection Tx-Stn Control Cmd. RX Processing Modem Interfacing

13 Block diagram of the simulator 13 Rain Fade Model h N(h) MODULATION X HPA X*h Y=X*h+n Y=1/h CODING TX DEMODULATION RX M and C SELECTION DECODING CRC CRC CHECK DATA BITS SNR Estimation PER Estimation RECEIVED DATA BITS INPUT BITS Margin Correction Prediction of Fade OUTPUT BITS

14 Excerpt from Literature Survey: Channel 14 Ref.1: M.M.J.L van de Kamp: Rain Attenuation As a Markov Process: How To Make an Event, ONERA-DEMR, Toulouse, France. Describes the rain attenuation probability distribution is predicted, dependent on two sample values measured shortly earlier. The model equations and its parameter derived empirically from measurement results. Probability distribution function of the attenuation of any sample is predicted as the hyperbolic secant distribution.

15 15 Ref.2 :-M.M.J.L van de Kamp: Rain Attenuation as a Markov Process: The Meaning of Two Samples, COST Action 280, Propagation Impairment Mitigation for Millimetre Wave Radio Systems Described the standard deviation of attenuation as the function of the previous two value of attenuation. The average and standard deviation of next rain attenuation being known, its probability distribution function can be plotted a short time after a measured value.

16 Ref.3 : Maseng, T. and P. M. Bakken, A Stochastic Dynamic Model of Rain Attenuation,IEEE Trans. Commun., COM-29(5), , The model is based on the log normal distributions of the rain attenuation and utilizes a non-linear device to transform attenuation and rain intensity into a one dimensional Gaussian Stationary Markov process. The channel used in the simulation is a slow varying rayleigh faded channel with a standard deviation of 9.The channel is sampled at every 10secs.

17 17 Algorithms used for Detection and Decision of SNR

18 FMT control logic 18 Monitored signal Detection Decision FMT activation FMT no active FMT active level 1 FMT active level 2 FMT active level n

19 Algorithms used for Detection and Decision of SNR 19 Detection : CRC based Embedded Pilots : 1) Distributed Pilot (2) Continued Pilot CRC : A cyclic redundancy check (CRC) or polynomial code checksum is a non-secure hash function designed to detect accidental changes to raw computer data. It is commonly used in digital networks and storage devices such as hard disk drives. The CRC bits are added to the data bits or to a block and transmitted. At the receiver, when a block is read or received the device repeats the calculation to check the CRC. If the new CRC does not match the one calculated earlier, then the block contains a data error and then subsequently, device may take corrective action such as rereading or requesting the block be sent again, otherwise the data is assumed to be error free.

20 Embedded Pilots : 20 1) Continuous Pilot (2) Distributed Pilot 1) Continuous Pilot : The pilot bits are transmitted together as a single long sequence after sending a specified number of raw data packets.

21 21 2) Distributed Pilot : The pilot bits are added to each packet i.e. the pilot bits are distributed in time. Advantage: The channel condition can be captured in much detail, as bits are distributed in time.

22 Decision 22 Decision Using the information of the detected attenuation, the decision function will decide if the considered link performs according to specifications, i.e.: System margin > =0 Where, System margin = SNR - SNR required For a given BER, modulation and coding scheme

23 To cope with possible estimation and prediction errors, an additional margin can be included in the equation: 23 System Margin >= Control logic Margin If the state of our channel is close to the detection threshold, small fluctuations may cause the FMT switch from one level of activation to the following. System Margin >= Detection Margin + Hysteresis

24 Decision Flow Chart 24

25 Delay Calculation T up > 120msec T down > 120msec Informat ion bits Transmitter MODEM(Tp) Receiver MODEM(Rp) Informat ion bits Decision (T dec) Prediction (T pr) T feed back Detection (T det) Total time delay = Tp+Tup+Tdown+Rp+Tdet+Tfeedback+Tpr+Tdec 25

26 Delay Compensation Strategies 26 Asymmetric Back-off: with this strategy, if the channel is rising, the estimated channel value is increased by 1 db, whereas if the channel is falling, it is decreased by 2 db. This strategy selects a considerably pessimistic Mod- Cod in case of falling channel. Symmetric Back-off: with this strategy, the same margin, 1 db, is applied in case of falling and rising channel, whereas for rising channel this value is added to the measurement and for falling channel the margin is subtracted. Adaptive back-off: with this strategy the calculated difference between both compared measurements is directly applied as back-off. This allows for adaptation to the steepness of the channel tendency.

27 Delay Compensation Flow Chart 27

28 Delay Compensation Flow Chart For Adaptive Back off 28

29 Results of the detection schemes and Back-off Algorithm

30 Estimated SNR with and without back off for CRC comparison of SNR curves for crc SNR estimated with adaptive BACKOFF SNR calculated SNR estimated with NBF DATA RATE OPTIMUM DATA RATE Adaptive back off DATA RATE with no back off 8 6 SNR in db time unit in sec

31 Comparison of data rate curves with and without back off for CRC 31 Comparison of DATA RATE curves for crc 2.2 DATA RATE OPTIMUM DATA RATE Adaptive back off DATA RATE with no back off data rate time unit in sec

32 Comparison of SNR curves for CRC 32 comparison of SNR curves for crc SNR in db SNR estimated with adaptive BACKOFF SNR calculated SNR estimated with 1 db symmetric back off SNR estimated with ASBF time unit in sec

33 Estimated SNR with and without back off for Continuous pilot snr and data rate curves for continuous pilot with and without backoff SNR estimated with no back off SNR calculated SNR estimated with adaptive back off DATA RATE OPTIMUM DATA RATE WITH nbf DATA RATE WITH ADAPTIVE BACK OFF 10 8 SNR in db time unit in sec

34 Comparison of SNR curves for Continuous pilot 34 snr and data rate curves for continuous pilot with and without backoff all 10 SNR calculated SNR estimated with adaptive back off SNR estimated with asbf SNR estimated with SBF 8 6 SNR in db time unit in sec

35 Estimated SNR with and without back off for Distributed pilot snr curves for distributed pilot with and without back off SNR calculated DATA RATE OPTIMUM SNR estimated with NBF SNR estimated with adaptive back off DATA RATE WITH NBF DATA RATE with adaptive back off 10 8 SNR in db time in sec

36 Comparison of SNR curves for Distributed pilot 36 snr curves for distributed pilot with and without back off SNR in db SNR calculated SNR estimated with adaptive back off SNR estimated with SBF SNR estimated with ASBF time unit in sec

37 37 Interim RESULTS

38 PER versus SNR curves for different modulation schemes PER performance for different ACM schemes 10-1 PER 10-2 QPSK simulated QPSK-1/2 simulated QPSK-1/3 simulated QPSK theoretical 16-QAM simulated 16-QAM-1/2 simulated 16-QAM-1/3 simulated 16-QAM theoretical 64-QAM simulated 64-QAM-1/2 simulated 64-QAM-1/3 simulated 64-QAM theoretical SNR (in db)

39 Switching between different ACM schemes with time SNR calculated SNR estimated PER decision store data rate M/C SNR calculated in db time i unit=10sec

40 Switching between different ACM schemes with time PER decision store SNR calculated SNR estimated data rate M/C PER decision time i unit=10sec

41 Results Update Results obtained by July 10

42 Without SNR Moving Average and Adaptive-Back off 42 BLER(packets/sec) and SNR(dB)

43 Without SNR Moving Average and Adaptive-Back off 43 THROUGHPUT v/s SNR(dB)

44 Without SNR Moving Average and Adaptive-Back off 44 Cross correlation between the estimated SNR(dB) and Calculated SNR(dB)

45 Without SNR Moving Average and Adaptive-Back off 45 Total Cross Correlation Plot

46 Without SNR Moving Average and Adaptive-Back off 46

47 Without SNR Moving Average and Adaptive-Back off 47

48 Without SNR Moving Average with no back-off 48 BLER(packets/sec) and SNR(dB)

49 Without SNR Moving Average with no back-off 49 THROUGHPUT v/s SNR(dB)

50 Without SNR Moving Average with no back-off 50 Cross correlation between the estimated SNR(dB) and Calculated SNR(dB)

51 Without SNR Moving Average with no back-off 51 Total Cross Correlation Plot

52 Without SNR Moving Average with no back-off 52

53 Without SNR Moving Average with no back-off 53

54 With SNR Moving Average and no back-off 54 BLER(packets/sec) and SNR(dB)

55 With SNR Moving Average and no back-off 55 THROUGHPUT v/s SNR(dB)

56 With SNR Moving Average and no back-off 56 Cross correlation between the estimated SNR(dB) and Calculated SNR(dB)

57 With SNR Moving Average and no back-off 57 Total Cross Correlation Plot

58 With SNR Moving Average and no back-off 58

59 With SNR Moving Average and no back-off 59

60 With SNR Moving Average and Adaptive back off 60 BLER(packets/sec) and SNR(dB)

61 With SNR Moving Average and Adaptive back off 61 THROUGHPUT v/s SNR(dB)

62 With SNR Moving Average and Adaptive back off 62 Cross correlation between the estimated SNR(dB) and Calculated SNR(dB)

63 With SNR Moving Average and Adaptive back off 63 Total Cross Correlation Plot

64 64 Results Update(November 2010)

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66 66 Detection Back Off Schemes Adaptive Back off Symmetric Back off Asymmetric Back off No Back off CRC Margin added/subtracted by adapting according to channel fluctuation Margin = 1dB (Rising channel) Margin = -1dB (Falling channel) (Channel constant) Margin = 1dB (Rising channel) Margin = -2dB (Falling channel) No Margin Distributed pilots Margin added/subtracted by adapting according to channel fluctuation Margin = 1dB (Rising channel) Margin =- 1dB (Falling channel) (Channel constant) Margin = 1dB (Rising channel) Margin = -2dB (Falling channel) No Margin Continuous pilots Margin added/subtracted by adapting according to channel fluctuation Margin = 1dB (Rising channel) Margin =- 1dB (Falling channel) (Channel constant) Margin = 1dB (Rising channel) Margin = -2dB (Falling channel) No Margin

67 67 The Results are shown in two parts based on the margin that helps to select the Back-off: Version I contains the results based on the calculated margin, thus choosing the optimum Mod-Cod rates. Version II contains the results based on the estimated margin.

68 VERSION- I 68

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74 Summary (VERSION-I): The margin is taken as the difference between the calculated SNR of the previous sample and the present SNR. The SNR offset for the CRC is taken as 2 db and for the pilots it is 3 db. The SNR offset depends on the rate of fluctuation(in other words, depends on the velocity). Adaptive Back-off scheme performs better for both the cases as this strategy adapts to the slope of the channel more accurately compared to others. Issues (VERSION-I): 74 The margin taken is calculated which is always not feasible because the correct SNR may not be known always. So, consideration of estimated margin is better. The offset and the margin has to be decided accurately.

75 VERSION- II 75

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82 82 Summary (VERSION II): The margin is the difference between the estimate and calculated SNR. The ADBF performs better in terms of error performance and throughput. The performance of CRC is better than the pilots. Remarks: The margin for simulation has to be decided correctly. As the SNR offset depends on the channel variation and doppler, so a proper channel characteristics is required.

83 Experimental Set-up 83

84 Implementation with Modem SRM6100 Specifications: 84 This modem is a transceiver modem with a data communications in the range of GHz. Frequency hopping and error detection technology. The operating range of the modem is in the range of 24 km in optimal conditions of line-of-sight

85 Experiments to be done with the modem SRM To measure the unlock-to-lock delay performance of the modem. Implementation of the FMT algorithms and verification of the same.

86 Experiments done with the modem SRM Performance of the loop back bench test The test was done by following the instructions from the manual. In this case, the receiver terminal was shorted with the transmitter terminal, so that what ever data was being transmitted was received at the same time. Interfacing with Matlab : Program written to send data through the modem and receive the same at the receiver-end. Data can be sent through the keyboard or can be sent as an string through the code. The short was removed and the two modem were connected to two different computers as transmitter and receiver, respectively.

87 Continued Configuration of the modem: 87 The data can be sent as mentioned earlier. The modem is configured, where different parameters like transmit power, data-rate can be changed. Then, the data can be sent with the new parameters. At first, data is transmitted with fixed data rate and transmit power. Secondly we change the configuration settings of the modem to send the data with different transmit power.

88 Limitations of the Modem-SRM The modem should always be triggered manually to show the configuration screen every time, even though we program it through matlab. The actual data rate is not known to the user and thus there are not many data rate options. There are not enough options in SRM6100 to change the modulation scheme and coding rate in accordance with the channel.

89 Experimental Plan and Set-up

90 The experimental plan Objective of the experiment To measure the unlock-to-lock delay performance of the high-speed satellite modem Scope The boundary conditions of the experiment are: The modem used in the experiment is the CDM-700 Satellite Modem. 90 We will test the unlock-to-lock delay, mainly, by changing the Modulation and Coding (M&C) scheme. This will be done by giving the corresponding command to the two modems. We will also try to study the effect of changing the attenuation value in the attenuator on the delay value. The switching delay occurring while sending data to the Tx-modem and getting data from the Rx-modem will be considered negligible for the purpose of our experiment. Basically, we will try to find an upper-bound to the time the system takes to change the M&C scheme and get ready to send and receive data.

91 BLOCK DIAGRAM OF THE EXPERIMENTAL SET- UP The significance of the numbers are given in the following slide. 91

92 92 1. The data-bits to be transmitted are sent from the computer to the transmitter-modem 2. The Rx-modem sends the total received bits to the computer for BER calculation 3. Connection for the attenuator with the computer 4. Connection for transmitting the M&C decision 5. Connection to the receiver-modem regarding the M&C that has been selected at the transmitter-modem. 6. IF/RF Connection for transmission of the modulated bits from the transmitter to the attenuator. 7. IF/RF Connection for transmission of the modulated bits from the attenuator to the receiver modem.

93 Resources required Computer Transmitter and Receiver Modems both CDM-700 Satellite Modems Programmable Attenuator interfaced to the computer (optional) Data interface (for the two modems) Expected Results 93 Estimation of the average and maximum values of delay between sending the M&C scheme information to the modems and getting the modems ready to send and receive data after M&C switching. Finding whether changing the channel attenuation has any effect on the unlock-to-lock delay. If it has, what is the effect like? Implementation of the FMT loop operating in real-time. This will be possible only if the attenuator is programmable using the computer.

94 Status Update

95 Module Wise Status Update 95 System Model (Framework) development (75%) Most of components already considered as described To include HPA effects Modem Specifications necessary for system design Simulation Framework development (75%) Channel Model parameters necessary for algorithm design Modem-abstraction model simulation complete Receiver noise as a function of attenuation implemented. Fade Mitigation modules developed. Needs refinement Synchronization to be included (need Modem specs)

96 Future Work Plan w.r.t. modules 96 Rain Fade Model h N(h) MODULATION X HPA X*h Y=X*h+n Y=1/h CODING TX DEMODULATION RX Data rate SELECTION DECODING CRC CRC CHECK DATA BITS SNR Estimation PER Estimation RECEIVED DATA BITS INPUT BITS Margin Correction Prediction of Fade OUTPUT BITS

97 Future Work Plan w.r.t. modules 97 Rain Fade Model h N(h) MODULATION X HPA X*h Y=X*h+n Y=1/h CODING TX DEMODULATION RX Data rate SELECTION DECODING CRC CRC CHECK DATA BITS SNR Estimation PER Estimation RECEIVED DATA BITS INPUT BITS Margin Correction Prediction of Fade OUTPUT BITS

98 Development of Future Work Plan Fade detection algorithm ( Channel Model parameters) Modem interfacing and control logic ( Modem Specs) Refinement of Channel Model PER Estimation SNR Estimation Data rate selection Simulation with real time data h 98 N(h) Y=1/h DEMODULATION DECODING RECEIVED DATA BITS OUTPUT BITS

99 Budget 99 As per discussion in the last meeting at IIT Kharagpur Rs 5 Lacs in total for each year. Recruit one more personnel

100 Budget Salary HEADS AMOUNT Rs.1,20,000 Equipment Rs.2,50,000 Books Contingencies Outsourcing Travel Consumable SRIC Overhead 15% Total Amount Rs.15,000 Rs.1,00,000 Rs.15,000 Rs.75,000 Rs

101 Status update (in details) 101 Earlier, for deciding the optimum ACM scheme, the channel attenuation value was assumed to be known at transmitter at every instant. This assumption has been relaxed now and a SNR detection module has been added to the system which uses CRC32 for estimating the SNR. Channel attenuation not only influences the signal level but also influences the system noise temperature which increases the noise power. This was not considered in earlier simulator. Till date, the simulation has being done using a slow rayleigh faded channel, but simulation with real time attenuation values is to be done for the validation of the results

102 Future work (in details) 102 Increasing the accuracy of SNR estimation using better SNR search algorithms Increasing the accuracy of the channel model Incorporating intelligence into the decision module for prevention of frequent switching between ACM schemes Addition of the ability for short-term prediction of the channel condition for further accuracy in choice of the optimum ACM scheme Faster SNR estimation for compensation of the delay associated with the FMT loop

103 103 THANK YOU