Satellite Communication Col John Keesee Satellite Communications Architecture ? Identify Requirements ? Specify Architectures ? Determine Link Data Rates ? Design & Size each link ? Document your rationale Definition ? Uplinks ? Downlinks ? Crosslinks ? Relays ? TT & C Uplink Downlink Intersatellite links Relay satellite Relay satellite Relay satellite Sensor satellite Sensor satellite Crossover or Intersatellite links Mission data Launch phase TT&C TT&C Satellite Ground station TT&C Tracking, Telemetry and Control The communications architecture consists of satellites and ground stations interconnected with communications links. (Adapted from SMAD.) Architectures: Defined by Satellite-Ground Geometry ? Store & Forward ? Geostationary ? Molniya ? Geostationary/ Crosslink ? LEO/ Crosslink Adapted from SMAD. Architectures: Defined by Function ? System Function – Tracking Telemetry & Command – Data Collection – Data Relay ? Satellite Design – Onboard Processing – Autonomous Satellite Control – Network Management Communications Architecture: Selection Criteria ?Orbit ? RF Spectrum ? Data Rate ? Duty Factor ? Link Availability ? Link Access Time ? Threat Advantages of Digital Communication ? Less distortion and interference ? Easy to regenerate ? Low error rates ? Multiple streams can be easily multiplexed into a single stream ? Security ? Drift free, miniature, low power hardware Tracking Telemetry & Control ? Telemetry – Voltages, currents, temperatures, accelerations, valve and relay states ? Commanding – Low data rate – Store, verify, execute or execute on time – Programmable control ? Range or Range Rate – Round trip delay yields range – Doppler shift yields range rate – Pseudo-random code ? Existing TT&C Systems – AFSCN (SGLS) - AF Satellite Control Network (Space Ground Link System) – NASA DSN - Deep Space Network – Intelsat/ COMSAT – TDRS - Tracking and Data Relay Satellite Data Collection Mission cycleduty SecondSamples sample Pixels pixel Bits imagerDR b Y Vn X Sw pushbroomDR _ / )( )( Adapted from SMAD. Variable Definitions Chart 9 Variable Definition Units DR Data Rate Bits/second SW Swath Width Meters X Across track pixel dimension Meters Vn Ground track velocity Meters/second Y Along track pixel dimension Meters b Bits/pixel Bits Reducing the Data Rate ? Increase the Duty Cycle ? Collect only above-threshold data ? Amplitude changes only ? Data compression Link Design Process 1. Define Requirements for each link 2. Design Each Link – Select frequency – Select modulation & coding – Apply antenna size & beam width constraints – Estimate atmospheric, rain attenuation – Estimate received noise, interference power – Calculate required antenna gain & transmitter power 3. Size the Payload – Payload antenna configuration, size & mass – Estimate transmitter mass & power – Estimate payload mass & power Link Equation E b N o P L l G t L s L a G r kT s R Energy/bit to noise-density ratio Variable Definitions Chart 12 Variable Definition Units Units dB E b Energy per bit Watt-seconds dB N o Noise spectral density Watts/hertz dB P Transmitter power Watts dBW L l Line loss dB G t Transmitter antenna gain db L s Space loss DB Variable Definitions Chart 12 continued Variable Definition Units Units (dB) L a Transmission path loss dB G r Receiver gain dB kBoltzman constant J/K dBW/(Hz-K) T s System noise temperature K R Data rate Bits/ second Power Flux Density W f PL l G t L a 4SS 2 (EIRP)L a 4SS 2 EIRP - Effective Isotropic Radiated Power Variable Definitions for Chart 16 Variable Definition Units Units (dB) W f Power flux density W/m 2 SPath lengthM EIRP Effective Isentropic Radiated Power WDBW Received Power C Wf ? SDr 2 K 4 PL l G t L a Dr 2 K 16S 2 G r ( SDr 2 K 4 ) 4S O 2 S 2 Dr 2 K O 2 Space Loss L s ( O 4SS ) 2 C EIRP * L s * L a * G r Variable Definitions Chart 18 Variable Definition Units Units (dB) CRcved power W D r Receiver antenna diameter mdB K Antenna efficiency O Wavelength m L s Space loss Link Equation Concluded E b energy/bit C R N o noise spectral density N total received noise power B receiver noise bandwidth N o = kT s N / B E b N o P u L l u G t u L a u G r u L s k ?T s ?R Link Equation in dB E b N o P L l  G t  L s  L a  G r  228 .6 10 log T s 10 log R EIRP  L s L a G r  228 .6 10 log T s 10 log R C N o EIRP L s  L a  G r T s  228.6 C N EIRP L s  L a  G r T s  228.6 10log B RIP E b N o  G r T s  228.6 10log R (Received isentropic power) Gain in dB G r S 2 D r 2 K O 2 f c O G 20logS  20log D 20 log f  10logK 20log c (dB) 159.59  20log D 20log f 10logK (dB) Beamwidth T [degrees] f [GHz] D [m] T 21 f ? D G 27,000 T 2 L T  12(e /T) 2 (dB) Antenna gain Offset beam loss Space loss in dB L s O 4SS § ? ¨ · 1 ? 2 (ratio) L s = 147.55- 20 log S - 20 log f (dB) System Noise Temperature - External to Antenna ? Galactic noise ? Clouds, rain in path ? Solar noise (in mainbeam or sidelobe) ? Earth (290K) ? Man-made noise ? Nearby objects ? Satellite structure (See SMAD Fig 13-7) System Noise Temperature - Internal to System ? Transmission lines and filters F is a figure of merit for a receiver T r (1  L)T L P o P i ? Low noise amplifier T r F 1 290K T s T ant  T o 1 L r L r § ? ¨ ¨ · 1 ? ?  T o F 1 L r § ? ¨ ¨ · 1 ? ? Variable Definitions Chart 21 Variable Definition Units T r Receiver noise temperature K L Power ratio T Component temperature K P o Output power W P I Input power W FNoise figu T o Reference temperature (usually 290 K) K Modulation ? Modulation modifies an RF Carrier signal so that it contains input signal information – Amplitude – Frequency –Phase – Polarization Modulation Techniques ? BPSK - Binary Phase Shift Keying ? QPSK - Quadriphased Phase Shift Keying ? FSK - Frequency Shift Keying ? MFSK - Multiple FSK ? DPSK - Differential Shift Keying Bit Error Rate ? Primary Figure of Merit for Digital Link Performance ? Energy/bit (Eb) must exceed the noise spectral density (N o ) to achieve a required BER Coding ? Forward Error Correction sends additional data to help detect and correct errors. – Reduces the Eb/No requirement – Reduces required transmitter power – Reduces antenna size – Increases margin – Increases data rate and bandwidth Convolutional Coding with Viterbi Decoding ? Extra bits sent with each block of data bits ? Receiver examines string of bits, generates possible code sequences, selects most likely ? Shannon limit E b /N o = -1.6 dB ? Double coding necessary on deep space probes Attenuation ? Atmosphere absorbs some frequencies ? Divide zenith attenuation by sin(elevation angle) ? Oxygen absorption at 60 GHz ? Scintillation disrupts below 200 MHz Rain and Cloud Attenuation ? Crane model for world’s climatic data ? Important above 10 GHz ? Worst for elevation angles < 20 degrees ? Rain reduces availability Rain and Cloud Attenuation Adapted from SMAD. Com’l. K MILSTAR Uplink ACTS Uplink DSCS Downlink 500 MHz GBS Uplink ACTS Downlink Commercial SATCOM Services Commercial SATCOM Services Government / Military SATCOM Services Government / Military SATCOM Services VHF UHF L S X K V VHF EHF AF / FLT SATCOM UFO Military UHF Band Government S-Band (SGLS) US Government X-Band 1GHz 8GHz 18GHz 40GHz 75GHz DSCS Uplink 500 MHz Military EHF (44/20) inmarsat, odyssey, iridium, globalstar odyssey, inmarsat, globalstar INTELSAT, inmarsat INTELSAT TELEDESIC, COMERCIAL, iridium,odyssey (gateway links) SPACEWAY, CYBERSTAR, ASTROLINK TELEDESIC iridium, odyssey (gateway links) Com’l. L Com’l. S Com’l. C Com’l. Ku Com’l. Ka 3 GHz 30 GHz UHF CKu SHF 300 MHz SATCOM Frequencies Usage ALL CAPS = Fixed Satellite Service (FSS) small case = Mobile Satellite Service (MSS)/Personal Comm Services (PCS) 1.61 1.62 2.4 2.5 4 6 12 14 17.3 20.2 225 Mhz 400 Mhz 1.761- 1.842 2.200- 2.290 7.25 7.75 7.9 8.4 78 19.2 20.2 29 30 20.2 MILSTAR, GBS Downlink 21.2 30 31 43 45 SATCOM users are secondary in UHF: subject to interference from terrestrial users Heavy orbital/terrestrial congestion: much coordination with terrestrial users needed 1 GHz 1 GHz 1 GHz 1 GHz 2 GHz 27.5 30 800 Mhz 900 “regular” cellular (Land Mobile Radio) Freq at Risk: Int’l & US Commercial encroachment GPS L2: 1227.6 Mhz L1: 1575.42 12GHz Ka 9090 0 10 EAST WEST LONGITUDELONGITUDE 20 30 40 50 60 70 80 10 20 30 40 50 60 70 80 10 20 30 40 50 60 70 80 100 110 120 130 140 150 160 170 180 100 110 120 130 140 150 160 170 DEGREES DEGREES 0 0 10 20 30 40 50 60 70 80 NORTHERN HEMISPHERE LEGEND LOCATIONS OF CURRENT & PROPOSED GEOSTATIONARY SATELLITES WITH 17.3-GHz THRU 20.2-GHz DOWNLINKS = SKYSAT (PROPOSED) PROPERTY OF: JOINT SPECTRUM CENTER REVISED 6-27-96 = SAMSAT (PROPOSED) = EUROSKYWAY(PROPOSED) = SOUTH AFRICASAT (PROPOSED)= ARTEMIS (PROPOSED) = ASTROLINK (PROPOSED) = EDRSS (PROPOSED) = MORNINGSTAR (PROPOSED) = ORION (PROPOSED) = PANAMSAT (PROPOSED) = SARIT (PROPOSED) = INFOSAT (PROPOSED) =HISPASAT (PROPOSED) = GALAXY/SPACEWAY (PROPOSED) = GE STAR (PROPOSED) = MALTASAT (PROPOSED) = KASTAR (PROPOSED) = MILLENIUM (PROPOSED) = INTELSAT-KA (PROPOSED) = RADIOSAT (PROPOSED) = EASTSAT (PROPOSED) (PROPOSED)= CANSAT = DACOMSAT (PROPOSED) = CYBERSTAR (PROPOSED) = ECHOSTAR (PROPOSED) = DIAMONDSAT (PROPOSED) = USASAT (PROPOSED) = TOR (OPERATIONAL/PROPOSED) = USABSS (OPERATIONAL/PROPOSED) = TONGASAT (PROPOSED) = VISIONSTAR (PROPOSED) = VOICESPAN (PROPOSED) (PROPOSED)= VIDEOSAT = DFS (OPERATIONAL/PROPOSED) = BSB (PROPOSED) = EUTELSAT (OPERATIONAL/PROPOSED) = AFRISAT (PROPOSED) = LUX\KA (PROPOSED) (PROPOSED)= USCSID = ACTS (PROPOSED) =ITALSAT (PROPOSED) = N STAR (PROPOSED) = PAKSAT (PROPOSED) = MEGASAT (PROPOSED) = SUPERBIRD (PROPOSED) = DRTS (PROPOSED) = ASIASAT (PROPOSED) = CHINASAT (PROPOSED) = ARABSAT (PROPOSED) = KYPROS (PROPOSED) = TURKSAT (PROPOSED) =THIACOM (PROPOSED) = YAMAL (PROPOSED) Frequency Selection Drivers ? Spectrum availability and FCC allocation ? Relay/Ground Station frequency ? Antenna size ? Atmospheric/Rain attenuation ? Noise temperature ? Modulation and coding Communication Payload Antennas ? Parabolic ?Helix ?Horn ? Phased Arrays – Multiple beams – Hopping beams Milstar Satellite Layout ? Weight: 10,000 lb ? Length: 51 ft (across payload) 116 ft (across solar arrays) ? Array Power: 5,000 W ? Orbit Altitude: 22,500 miles geosynchronous ? Launch Vehicle: Titan IV/Centaur upper stage Z+ +X NSB #2 SHF THRUSTERS +X WING PAYLOAD (LDR) +X WING CROSSLINK ANTENNASHF AGILE RCV UHF EHF AGILE WSB XMT UHF EC EHF EC NSB #1 SPACECRAFT BUS REACTION WHEEL ASSEMBLIES (SCS) HORIZON SENSORS PROPELLANT TANKS FLEXIBLE SUBSTRATE SOLAR ARRAY PANELS Z- X- THRUSTERS -X WING CROSSLINK ANTENNA -X WING PAYLOAD MDR NULLING ANTENNAS CROSSLINK MDR DUCA ANTENNAS (MDR*, CROSSLINK) UPLINK: 5 AGILES, 2 NARROW SPOTS, 1 WIDE SPOT, 1 EARTH COVERAGE DOWNLINK: SINGLE DOWNLINK TIME-SHARED BY: 1 AGILE, 2 NARROW SPOTS, 1 WIDE SPOT, 1 EARTH COVERAGE UPLINK: 2 NULLING SPOTS 6 DISTRIBUTED USER COVERAGE (DUCs) DOWNLINK: SINGLE DOWNLINK TIME-SHARED BY: 2 SPOTS AND 6 DUCs (Image removed due to copyright considerations.) (Image removed due to copyright considerations.) Multiple Access Strategies ? FDMA - Frequency Division Multiple Access ? TDMA - Time Division Multiple Access ? CDMA - Code Division Multiple Access – Phase Modulation plus pseudo-random noise Antijam Techniques ? Spread Spectrum ? Narrow beamwidths ? On board processing ? Nulling antennas Special Topics ? Data security through encryption ? Spatial, time and satellite diversity ? Frequency hopping ? Interleaving Why Compress Data ? Need to send more data than bandwidth accommodates – Digital image files in particular are very large ? Bandwidth is limited by the link equation and international regulation ? Concept inseparable from data encoding Early Development -- Huffman codes ? Assign different number of bits to each possible symbol to minimize total number of bits – Example: Encode letters of alphabet – 26 symbols, each with equal chance of occurring => 5bits/symbol (2 5 = 32 = lowest power of 2 above 26) – If R occurs 50% of time, use fewer bits to encode R. Compression Algorithms ? Lossless compression – Ensures data recovered is exactly same as original data – Used for executable code, numeric data -- cannot tolerate mistakes ? Lossy compression – Does not promise that data received is the same as data sent – Removes information that cannot later be restored – Used for still images, video, audio - Data contains more info than human can perceive – Data may already contain errors/imperfections – Better compression ratios than Lossless (order of magnitude) When does Compression Pay Off? ? Compression/decompression algorithms involve time- consuming computations ? Compression beneficial when x / B c + x / (r B n ) < x / B n Where B c = data bandwidth through compress/decompressprocess B n = network bandwidth for uncompressed data r = average compression ratio x / B n = time to send x bytes of uncompressed data x / B c + x / (rB n ) = time to compress and send ? Simplified: B c > + r / (r - 1) * B n Lossless Compression Algorithms ? Run Length Encoding ? Differential Pulse Code Modulation - DPCM ? Dictionary-Based Methods Run Length Encoding ? Replace consecutive occurrences of symbol with 1 copy plus count of how many times symbol occurs: AAABBCDDDD => 3A2B1C4D ? Can be used to compress digital imagery – Compare adjacent pixel values and encode only changes ? Scanned text can achieve 8-to-1 compression due to large white space ? Key compression algorithm used to transmit faxes ? Large homogeneous regions -- effective ? Small degree of local variation increases image byte size – 2 bytes represent 1 symbol when not repeated Differential Pulse Code Modulation - DPCM ? Represent differences between data – Output reference symbol – For each symbol in data, output difference between it and reference symbol: AAABBCDDDD -> A0001123333 ? When differences are small, encode with fewer bits (2 bits vs 8 bits) ? Takes advantage of fact that adjacent pixels are similar - 1.5-to-1 ? Delta encoding encodes symbol as difference from previous one: AAABBCDDDD -> A001011000. ? Works well when adjacent pixels are similar ? Can combine delta encoding and RLE Dictionary-Based Methods ? Lempel-Ziv (LZ) most well known, used by Unix compress command – Build dictionary of expected data strings – Replace strings with index to dictionary ? Example: "compression" (77-bits of 7-bit ASCII) has index 4978 (15 bits) in /usr/share/dict/words -- 5-to-1 compression ratio ? How is the dictionary built? – A priori, static, tailored to data – Adaptively define based on contents of data. However, dictionary must be sent with data for proper decompression Graphical Interchange Format (GIF) ? Variation of LZ algorithm used for digital images – Reduce 24-bit color to 8-bit-color – Store colors in table which can be indexed by an 8-bit number – Value for each pixel replaced by appropriate index – Run LZ over result and create dictionary by identifying common sequences of pixels ? If picture contains << 256 colors, can achieve 10-to-1 compression ? If picture contains > 256 colors, Lossy! (e.g., natural scenes) Image Compression ? JPEG (Joint Photographic Experts Group) defines an algorithm and a format – Apply discrete cosine transform (DCT) to 8 x 8 block (transform into spatial frequency domain). Lossless. – Low frequency = gross features; high frequency = detail – Quantize result, losing least significant info. Lossy – Encode result - RLE applied to coefficients. Lossless. Color Images ? Three components used to represent each pixel - 3D – RGB - red, green, blue – YUV - luminance (Y) and two chrominance (U and V) ? To compress, each component is processed independently ? Three components used to represent each pixel - 3D ? JPEG can also compress multi-spectral images ? Compress 24-bit color images by 30-to-1 ratio – 24 bits -> 8 bits (GIF) gives 3-to-1 – 3D JPEG compression gives 10-to-1 Video Compression x Moving Picture Experts Group (MPEG) x Succession of still images displayed at video rate – Each frame compressed using DCT technique (JPEG) – Interframe redundancy ? Typically, can achieve 90-to-1 ratio; 150-to-1 possible ? Involves expensive computation, typically done offline. References ? Wertz, James R. and Wiley J.Larson, Space Mission Analysis and Design, Microcosm Press, El Segundo CA 1999, pg 533-586 ? Morgan and Gordon, Communication Satellite Handbook, 1989 ? Peterson and Davie, on reserve in Barker Library ? http://www-isl.stanford.edu/people/gray/fundcom.pdf