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Started adding proximal decoder examination results

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Andreas Tsouchlos 2023-01-16 14:38:31 +01:00
parent 7f74df4af1
commit 8d6dcb2fc1
8 changed files with 498 additions and 7 deletions
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6
latex/presentations/midterm/presentation.tex
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@ -13,6 +13,12 @@
\usepackage{listings}
\usepackage{graphicx}
\usepackage{xcolor}
%\geometry{textheight=17.07cm,textwidth=6.9cm}
%\usepackage{pgfpages}
%\pgfpagesuselayout{resize to}[physical paper height=17.07cm,
% physical paper width=6.9cm]
%\setbeamertemplate{note page}[plain]
%\setbeameroption{show notes on second screen=right}
\usepgfplotslibrary{colorbrewer}
\setlength {\marginparwidth }{2cm}

171
latex/presentations/midterm/res/2d_ber_fer_dfr_20433484.csv Normal file
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@ -0,0 +1,171 @@
SNR,gamma,BER,FER,DFR,num_iterations
1.0,0.0,0.1341001747233547,1.0,0.5,101.0
1.0,0.01,0.10002912055911474,1.0,0.5,101.0
1.0,0.02,0.09634051640458163,1.0,0.5,101.0
1.0,0.03,0.09566103669190448,1.0,0.5,101.0
1.0,0.04,0.09675423567485246,0.9805825242718447,0.4950980392156863,103.0
1.0,0.05,0.097473604826546,0.9711538461538461,0.4926829268292683,104.0
1.0,0.06,0.09901960784313725,0.9619047619047619,0.49029126213592233,105.0
1.0,0.07,0.10030165912518854,0.9711538461538461,0.4926829268292683,104.0
1.0,0.08,0.10152714932126697,0.9711538461538461,0.4926829268292683,104.0
1.0,0.09,0.10204562594268476,0.9711538461538461,0.4926829268292683,104.0
1.0,0.1,0.10341251885369532,0.9711538461538461,0.4926829268292683,104.0
1.0,0.11,0.10419069588619762,0.9901960784313726,0.4975369458128079,102.0
1.0,0.12,0.11963696369636964,1.0,0.5,101.0
1.0,0.13,0.1822947000582411,1.0,0.5,101.0
1.0,0.14,0.255824111822947,1.0,0.5,101.0
1.0,0.15,0.31435643564356436,1.0,0.5,101.0
1.0,0.16,0.3663851679285576,1.0,0.5,101.0
1.5,0.0,0.12157833430401864,1.0,0.5,101.0
1.5,0.01,0.07974179770918269,1.0,0.5,101.0
1.5,0.02,0.07372164552095348,0.9901960784313726,0.4975369458128079,102.0
1.5,0.03,0.07240896358543418,0.9619047619047619,0.49029126213592233,105.0
1.5,0.04,0.07275059556532894,0.9439252336448598,0.4855769230769231,107.0
1.5,0.05,0.07421023965141613,0.9351851851851852,0.48325358851674644,108.0
1.5,0.06,0.07470951343500364,0.9351851851851852,0.48325358851674644,108.0
1.5,0.07,0.0680926916221034,0.9181818181818182,0.4786729857819905,110.0
1.5,0.08,0.06911764705882353,0.9181818181818182,0.4786729857819905,110.0
1.5,0.09,0.07027629233511587,0.9181818181818182,0.4786729857819905,110.0
1.5,0.1,0.07079137961490903,0.9099099099099099,0.47641509433962265,111.0
1.5,0.11,0.07316630355846042,0.9351851851851852,0.48325358851674644,108.0
1.5,0.12,0.08923384168482208,0.9351851851851852,0.48325358851674644,108.0
1.5,0.13,0.14840445982314496,0.9901960784313726,0.4975369458128079,102.0
1.5,0.14,0.22693194925028834,0.9901960784313726,0.4975369458128079,102.0
1.5,0.15,0.3169772859638905,1.0,0.5,101.0
1.5,0.16,0.36929722384003105,1.0,0.5,101.0
2.0,0.0,0.10556202679091438,1.0,0.5,101.0
2.0,0.01,0.06061929722384003,1.0,0.5,101.0
2.0,0.02,0.052521008403361345,0.9619047619047619,0.49029126213592233,105.0
2.0,0.03,0.05013368983957219,0.9181818181818182,0.4786729857819905,110.0
2.0,0.04,0.04746941511647394,0.8632478632478633,0.463302752293578,117.0
2.0,0.05,0.048447712418300655,0.8416666666666667,0.45701357466063347,120.0
2.0,0.06,0.048392156862745096,0.808,0.4469026548672566,125.0
2.0,0.07,0.048749421028253824,0.7952755905511811,0.44298245614035087,127.0
2.0,0.08,0.04882661726107766,0.7952755905511811,0.44298245614035087,127.0
2.0,0.09,0.0496031746031746,0.8015873015873016,0.44493392070484583,126.0
2.0,0.1,0.05075901328273245,0.8145161290322581,0.4488888888888889,124.0
2.0,0.11,0.05332376853180296,0.8211382113821138,0.45089285714285715,123.0
2.0,0.12,0.06984169814714877,0.926605504587156,0.48095238095238096,109.0
2.0,0.13,0.1265554298642534,0.9711538461538461,0.4926829268292683,104.0
2.0,0.14,0.2107843137254902,0.9711538461538461,0.4926829268292683,104.0
2.0,0.15,0.30348476024072996,1.0,0.5,101.0
2.0,0.16,0.35934769947582995,1.0,0.5,101.0
2.5,0.0,0.08920597942147156,1.0,0.5,101.0
2.5,0.01,0.044070055206548636,0.9805825242718447,0.4950980392156863,103.0
2.5,0.02,0.0335827313381493,0.8859649122807017,0.4697674418604651,114.0
2.5,0.03,0.030043077837195484,0.7651515151515151,0.4334763948497854,132.0
2.5,0.04,0.029513888888888888,0.7013888888888888,0.4122448979591837,144.0
2.5,0.05,0.029194589228096302,0.6392405063291139,0.38996138996138996,158.0
2.5,0.06,0.029628940183668403,0.6392405063291139,0.38996138996138996,158.0
2.5,0.07,0.029442594647922062,0.6352201257861635,0.38846153846153847,159.0
2.5,0.08,0.03135345047109753,0.6558441558441559,0.396078431372549,154.0
2.5,0.09,0.030182513256875078,0.6352201257861635,0.38846153846153847,159.0
2.5,0.1,0.03113063407181054,0.6558441558441559,0.396078431372549,154.0
2.5,0.11,0.03316781257957729,0.6558441558441559,0.396078431372549,154.0
2.5,0.12,0.0449806683236675,0.7112676056338029,0.4156378600823045,142.0
2.5,0.13,0.12628170534268754,0.926605504587156,0.48095238095238096,109.0
2.5,0.14,0.20740529221397297,0.9805825242718447,0.4950980392156863,103.0
2.5,0.15,0.2913511939429237,1.0,0.5,101.0
2.5,0.16,0.3571636575422248,1.0,0.5,101.0
3.0,0.0,0.08042127742185984,1.0,0.5,101.0
3.0,0.01,0.028912490922294844,0.9351851851851852,0.48325358851674644,108.0
3.0,0.02,0.02059555165349722,0.753731343283582,0.4297872340425532,134.0
3.0,0.03,0.017426201249730662,0.554945054945055,0.3568904593639576,182.0
3.0,0.04,0.016144766364257147,0.5024875621890548,0.3344370860927152,201.0
3.0,0.05,0.015299393299920866,0.452914798206278,0.3117283950617284,223.0
3.0,0.06,0.015491044902809609,0.43722943722943725,0.3042168674698795,231.0
3.0,0.07,0.01583868841654309,0.42436974789915966,0.29793510324483774,238.0
3.0,0.08,0.016332826233941854,0.4353448275862069,0.3033033033033033,232.0
3.0,0.09,0.016654097536450477,0.43162393162393164,0.30149253731343284,234.0
3.0,0.1,0.016147635524798153,0.42436974789915966,0.29793510324483774,238.0
3.0,0.11,0.015808823529411764,0.42083333333333334,0.2961876832844575,240.0
3.0,0.12,0.032132083602671835,0.554945054945055,0.3568904593639576,182.0
3.0,0.13,0.0940359477124183,0.8416666666666667,0.45701357466063347,120.0
3.0,0.14,0.2045154600301659,0.9711538461538461,0.4926829268292683,104.0
3.0,0.15,0.2978547854785479,1.0,0.5,101.0
3.0,0.16,0.36225975538730343,1.0,0.5,101.0
3.5,0.0,0.06823917685886235,1.0,0.5,101.0
3.5,0.01,0.01576797385620915,0.8416666666666667,0.45701357466063347,120.0
3.5,0.02,0.01018270944741533,0.4590909090909091,0.3146417445482866,220.0
3.5,0.03,0.009244182101906506,0.3494809688581315,0.258974358974359,289.0
3.5,0.04,0.007880033733923677,0.271505376344086,0.2135306553911205,372.0
3.5,0.05,0.007118214769203895,0.22494432071269488,0.18363636363636363,449.0
3.5,0.06,0.007706392249571223,0.23877068557919623,0.19274809160305342,423.0
3.5,0.07,0.007644467478621405,0.22246696035242292,0.18198198198198198,454.0
3.5,0.08,0.007928000357318325,0.23006833712984054,0.18703703703703703,439.0
3.5,0.09,0.008958755916159567,0.26790450928381965,0.2112970711297071,377.0
3.5,0.1,0.007654786052351975,0.24876847290640394,0.1992110453648915,406.0
3.5,0.11,0.007232040798382133,0.226457399103139,0.1846435100548446,446.0
3.5,0.12,0.015532719111741082,0.3042168674698795,0.23325635103926096,332.0
3.5,0.13,0.07970722535589578,0.6917808219178082,0.4089068825910931,146.0
3.5,0.14,0.19695708472068071,0.9528301886792453,0.48792270531400966,106.0
3.5,0.15,0.2849206349206349,0.9619047619047619,0.49029126213592233,105.0
3.5,0.16,0.3555843906189927,0.9901960784313726,0.4975369458128079,102.0
4.0,0.0,0.0553290623179965,1.0,0.5,101.0
4.0,0.01,0.010924369747899159,0.7214285714285714,0.4190871369294606,140.0
4.0,0.02,0.005332226934661012,0.3166144200626959,0.24047619047619048,319.0
4.0,0.03,0.003971451594336365,0.19765166340508805,0.1650326797385621,511.0
4.0,0.04,0.0030801090489671803,0.13502673796791445,0.11896348645465253,748.0
4.0,0.05,0.0027566182348239057,0.1211031175059952,0.10802139037433155,834.0
4.0,0.06,0.0026605853287723297,0.11516533637400228,0.1032719836400818,877.0
4.0,0.07,0.003047164811870694,0.11868390129259694,0.10609243697478991,851.0
4.0,0.08,0.00290082662653493,0.11234705228031146,0.101,899.0
4.0,0.09,0.0030920060331825036,0.11952662721893491,0.10676532769556026,845.0
4.0,0.1,0.0029757916834159633,0.11503416856492027,0.10316649642492338,878.0
4.0,0.11,0.003021112521747308,0.1211031175059952,0.10802139037433155,834.0
4.0,0.12,0.00988081507112649,0.1980392156862745,0.16530278232405893,510.0
4.0,0.13,0.06088181128805113,0.5580110497237569,0.35815602836879434,181.0
4.0,0.14,0.1857004062886416,0.9099099099099099,0.47641509433962265,111.0
4.0,0.15,0.2681687353840619,0.926605504587156,0.48095238095238096,109.0
4.0,0.16,0.34630911188004615,0.9901960784313726,0.4975369458128079,102.0
4.5,0.0,0.043826441467676176,1.0,0.5,101.0
4.5,0.01,0.006672826198542244,0.5287958115183246,0.3458904109589041,191.0
4.5,0.02,0.0021541225185726463,0.15683229813664595,0.13557046979865772,644.0
4.5,0.03,0.001657950072788052,0.0925756186984418,0.08473154362416108,1091.0
4.5,0.04,0.0014982254029208123,0.07492581602373888,0.06970324361628709,1348.0
4.5,0.05,0.0014828601816248056,0.0702364394993046,0.06562703053931124,1438.0
4.5,0.06,0.0013842324494564666,0.06541450777202072,0.06139817629179331,1544.0
4.5,0.07,0.0013921070334105689,0.06416772554002541,0.060298507462686564,1574.0
4.5,0.08,0.0015095550578280745,0.0674682698730795,0.06320400500625782,1497.0
4.5,0.09,0.00150252920317063,0.06715425531914894,0.06292834890965732,1504.0
4.5,0.1,0.0015441833569889082,0.06769436997319035,0.06340238543628374,1492.0
4.5,0.11,0.0010550979421951859,0.0499752597723899,0.04759660697455231,2021.0
4.5,0.12,0.00594988589259932,0.11995249406175772,0.1071049840933192,842.0
4.5,0.13,0.05700133456523971,0.5287958115183246,0.3458904109589041,191.0
4.5,0.14,0.16968325791855204,0.8632478632478633,0.463302752293578,117.0
4.5,0.15,0.2703477617462079,0.9528301886792453,0.48792270531400966,106.0
4.5,0.16,0.34789781297134237,0.9711538461538461,0.4926829268292683,104.0
5.0,0.0,0.03848767229664143,1.0,0.5,101.0
5.0,0.01,0.003257007500986972,0.3389261744966443,0.2531328320802005,298.0
5.0,0.02,0.0007909474785359817,0.06571242680546518,0.061660561660561664,1537.0
5.0,0.03,0.00039884720292316403,0.026509186351706036,0.02582459728969573,3810.0
5.0,0.04,0.0003238045023153455,0.019680436477007016,0.019300592394420026,5132.0
5.0,0.05,0.00032260095049255655,0.018013197788478687,0.01769446391030133,5607.0
5.0,0.06,0.00034767610748002905,0.018703703703703705,0.01836029812761316,5400.0
5.0,0.07,0.00034719665007838227,0.018064746914684314,0.017744202389318342,5591.0
5.0,0.08,0.0003588633613660106,0.0193560751245688,0.01898853167888701,5218.0
5.0,0.09,0.00039854135821328133,0.020175789053136238,0.019776776972782455,5006.0
5.0,0.1,0.000381363189191988,0.0205159455616494,0.020103503184713375,4923.0
5.0,0.11,0.00047299621603027174,0.023944997629208157,0.023385042833989348,4218.0
5.0,0.12,0.0023214669716341172,0.050348953140578266,0.04793545325106787,2006.0
5.0,0.13,0.0493999323867478,0.4353448275862069,0.3033033033033033,232.0
5.0,0.14,0.16054901960784312,0.808,0.4469026548672566,125.0
5.0,0.15,0.23911598537720172,0.8559322033898306,0.4611872146118721,118.0
5.0,0.16,0.3551038062283737,0.9901960784313726,0.4975369458128079,102.0
5.5,0.0,0.030430984274898078,1.0,0.5,101.0
5.5,0.01,0.0014344885240918073,0.19573643410852712,0.16369529983792544,516.0
5.5,0.02,0.0002301881264779124,0.021558164354322305,0.021103217718345172,4685.0
5.5,0.03,0.00014828355459466463,0.010427421020028908,0.01031981199550424,9686.0
5.5,0.04,0.00010747493009259079,0.00669007087500828,0.006645611264640084,15097.0
5.5,0.05,0.0001065844549344906,0.006347011877081632,0.0063069813912826275,15913.0
5.5,0.06,0.00011370939201249014,0.006581090766925132,0.006538063179699637,15347.0
5.5,0.07,0.00010887129822295129,0.006301079293780024,0.006261624302541848,16029.0
5.5,0.08,0.00011300352671730736,0.0064855840236306425,0.006443792267449279,15573.0
5.5,0.09,0.0001226603732986869,0.007059481372754596,0.00700999444752915,14307.0
5.5,0.1,0.0001347696580962301,0.007844050947499223,0.007783000693534715,12876.0
5.5,0.11,0.0001584723973134477,0.009171812568107519,0.009088454962656348,11012.0
5.5,0.12,0.0011407078554692316,0.023526671325413463,0.022985889849795174,4293.0
5.5,0.13,0.05012400217003798,0.39920948616600793,0.2853107344632768,253.0
5.5,0.14,0.18832054560954817,0.8782608695652174,0.4675925925925926,115.0
5.5,0.15,0.23803104575163397,0.8416666666666667,0.45701357466063347,120.0
5.5,0.16,0.3358072200842954,0.9439252336448598,0.4855769230769231,107.0
1 SNR gamma BER FER DFR num_iterations
2 1.0 0.0 0.1341001747233547 1.0 0.5 101.0
3 1.0 0.01 0.10002912055911474 1.0 0.5 101.0
4 1.0 0.02 0.09634051640458163 1.0 0.5 101.0
5 1.0 0.03 0.09566103669190448 1.0 0.5 101.0
6 1.0 0.04 0.09675423567485246 0.9805825242718447 0.4950980392156863 103.0
7 1.0 0.05 0.097473604826546 0.9711538461538461 0.4926829268292683 104.0
8 1.0 0.06 0.09901960784313725 0.9619047619047619 0.49029126213592233 105.0
9 1.0 0.07 0.10030165912518854 0.9711538461538461 0.4926829268292683 104.0
10 1.0 0.08 0.10152714932126697 0.9711538461538461 0.4926829268292683 104.0
11 1.0 0.09 0.10204562594268476 0.9711538461538461 0.4926829268292683 104.0
12 1.0 0.1 0.10341251885369532 0.9711538461538461 0.4926829268292683 104.0
13 1.0 0.11 0.10419069588619762 0.9901960784313726 0.4975369458128079 102.0
14 1.0 0.12 0.11963696369636964 1.0 0.5 101.0
15 1.0 0.13 0.1822947000582411 1.0 0.5 101.0
16 1.0 0.14 0.255824111822947 1.0 0.5 101.0
17 1.0 0.15 0.31435643564356436 1.0 0.5 101.0
18 1.0 0.16 0.3663851679285576 1.0 0.5 101.0
19 1.5 0.0 0.12157833430401864 1.0 0.5 101.0
20 1.5 0.01 0.07974179770918269 1.0 0.5 101.0
21 1.5 0.02 0.07372164552095348 0.9901960784313726 0.4975369458128079 102.0
22 1.5 0.03 0.07240896358543418 0.9619047619047619 0.49029126213592233 105.0
23 1.5 0.04 0.07275059556532894 0.9439252336448598 0.4855769230769231 107.0
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69 2.5 0.16 0.3571636575422248 1.0 0.5 101.0
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83 3.0 0.13 0.0940359477124183 0.8416666666666667 0.45701357466063347 120.0
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87 3.5 0.0 0.06823917685886235 1.0 0.5 101.0
88 3.5 0.01 0.01576797385620915 0.8416666666666667 0.45701357466063347 120.0
89 3.5 0.02 0.01018270944741533 0.4590909090909091 0.3146417445482866 220.0
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93 3.5 0.06 0.007706392249571223 0.23877068557919623 0.19274809160305342 423.0
94 3.5 0.07 0.007644467478621405 0.22246696035242292 0.18198198198198198 454.0
95 3.5 0.08 0.007928000357318325 0.23006833712984054 0.18703703703703703 439.0
96 3.5 0.09 0.008958755916159567 0.26790450928381965 0.2112970711297071 377.0
97 3.5 0.1 0.007654786052351975 0.24876847290640394 0.1992110453648915 406.0
98 3.5 0.11 0.007232040798382133 0.226457399103139 0.1846435100548446 446.0
99 3.5 0.12 0.015532719111741082 0.3042168674698795 0.23325635103926096 332.0
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101 3.5 0.14 0.19695708472068071 0.9528301886792453 0.48792270531400966 106.0
102 3.5 0.15 0.2849206349206349 0.9619047619047619 0.49029126213592233 105.0
103 3.5 0.16 0.3555843906189927 0.9901960784313726 0.4975369458128079 102.0
104 4.0 0.0 0.0553290623179965 1.0 0.5 101.0
105 4.0 0.01 0.010924369747899159 0.7214285714285714 0.4190871369294606 140.0
106 4.0 0.02 0.005332226934661012 0.3166144200626959 0.24047619047619048 319.0
107 4.0 0.03 0.003971451594336365 0.19765166340508805 0.1650326797385621 511.0
108 4.0 0.04 0.0030801090489671803 0.13502673796791445 0.11896348645465253 748.0
109 4.0 0.05 0.0027566182348239057 0.1211031175059952 0.10802139037433155 834.0
110 4.0 0.06 0.0026605853287723297 0.11516533637400228 0.1032719836400818 877.0
111 4.0 0.07 0.003047164811870694 0.11868390129259694 0.10609243697478991 851.0
112 4.0 0.08 0.00290082662653493 0.11234705228031146 0.101 899.0
113 4.0 0.09 0.0030920060331825036 0.11952662721893491 0.10676532769556026 845.0
114 4.0 0.1 0.0029757916834159633 0.11503416856492027 0.10316649642492338 878.0
115 4.0 0.11 0.003021112521747308 0.1211031175059952 0.10802139037433155 834.0
116 4.0 0.12 0.00988081507112649 0.1980392156862745 0.16530278232405893 510.0
117 4.0 0.13 0.06088181128805113 0.5580110497237569 0.35815602836879434 181.0
118 4.0 0.14 0.1857004062886416 0.9099099099099099 0.47641509433962265 111.0
119 4.0 0.15 0.2681687353840619 0.926605504587156 0.48095238095238096 109.0
120 4.0 0.16 0.34630911188004615 0.9901960784313726 0.4975369458128079 102.0
121 4.5 0.0 0.043826441467676176 1.0 0.5 101.0
122 4.5 0.01 0.006672826198542244 0.5287958115183246 0.3458904109589041 191.0
123 4.5 0.02 0.0021541225185726463 0.15683229813664595 0.13557046979865772 644.0
124 4.5 0.03 0.001657950072788052 0.0925756186984418 0.08473154362416108 1091.0
125 4.5 0.04 0.0014982254029208123 0.07492581602373888 0.06970324361628709 1348.0
126 4.5 0.05 0.0014828601816248056 0.0702364394993046 0.06562703053931124 1438.0
127 4.5 0.06 0.0013842324494564666 0.06541450777202072 0.06139817629179331 1544.0
128 4.5 0.07 0.0013921070334105689 0.06416772554002541 0.060298507462686564 1574.0
129 4.5 0.08 0.0015095550578280745 0.0674682698730795 0.06320400500625782 1497.0
130 4.5 0.09 0.00150252920317063 0.06715425531914894 0.06292834890965732 1504.0
131 4.5 0.1 0.0015441833569889082 0.06769436997319035 0.06340238543628374 1492.0
132 4.5 0.11 0.0010550979421951859 0.0499752597723899 0.04759660697455231 2021.0
133 4.5 0.12 0.00594988589259932 0.11995249406175772 0.1071049840933192 842.0
134 4.5 0.13 0.05700133456523971 0.5287958115183246 0.3458904109589041 191.0
135 4.5 0.14 0.16968325791855204 0.8632478632478633 0.463302752293578 117.0
136 4.5 0.15 0.2703477617462079 0.9528301886792453 0.48792270531400966 106.0
137 4.5 0.16 0.34789781297134237 0.9711538461538461 0.4926829268292683 104.0
138 5.0 0.0 0.03848767229664143 1.0 0.5 101.0
139 5.0 0.01 0.003257007500986972 0.3389261744966443 0.2531328320802005 298.0
140 5.0 0.02 0.0007909474785359817 0.06571242680546518 0.061660561660561664 1537.0
141 5.0 0.03 0.00039884720292316403 0.026509186351706036 0.02582459728969573 3810.0
142 5.0 0.04 0.0003238045023153455 0.019680436477007016 0.019300592394420026 5132.0
143 5.0 0.05 0.00032260095049255655 0.018013197788478687 0.01769446391030133 5607.0
144 5.0 0.06 0.00034767610748002905 0.018703703703703705 0.01836029812761316 5400.0
145 5.0 0.07 0.00034719665007838227 0.018064746914684314 0.017744202389318342 5591.0
146 5.0 0.08 0.0003588633613660106 0.0193560751245688 0.01898853167888701 5218.0
147 5.0 0.09 0.00039854135821328133 0.020175789053136238 0.019776776972782455 5006.0
148 5.0 0.1 0.000381363189191988 0.0205159455616494 0.020103503184713375 4923.0
149 5.0 0.11 0.00047299621603027174 0.023944997629208157 0.023385042833989348 4218.0
150 5.0 0.12 0.0023214669716341172 0.050348953140578266 0.04793545325106787 2006.0
151 5.0 0.13 0.0493999323867478 0.4353448275862069 0.3033033033033033 232.0
152 5.0 0.14 0.16054901960784312 0.808 0.4469026548672566 125.0
153 5.0 0.15 0.23911598537720172 0.8559322033898306 0.4611872146118721 118.0
154 5.0 0.16 0.3551038062283737 0.9901960784313726 0.4975369458128079 102.0
155 5.5 0.0 0.030430984274898078 1.0 0.5 101.0
156 5.5 0.01 0.0014344885240918073 0.19573643410852712 0.16369529983792544 516.0
157 5.5 0.02 0.0002301881264779124 0.021558164354322305 0.021103217718345172 4685.0
158 5.5 0.03 0.00014828355459466463 0.010427421020028908 0.01031981199550424 9686.0
159 5.5 0.04 0.00010747493009259079 0.00669007087500828 0.006645611264640084 15097.0
160 5.5 0.05 0.0001065844549344906 0.006347011877081632 0.0063069813912826275 15913.0
161 5.5 0.06 0.00011370939201249014 0.006581090766925132 0.006538063179699637 15347.0
162 5.5 0.07 0.00010887129822295129 0.006301079293780024 0.006261624302541848 16029.0
163 5.5 0.08 0.00011300352671730736 0.0064855840236306425 0.006443792267449279 15573.0
164 5.5 0.09 0.0001226603732986869 0.007059481372754596 0.00700999444752915 14307.0
165 5.5 0.1 0.0001347696580962301 0.007844050947499223 0.007783000693534715 12876.0
166 5.5 0.11 0.0001584723973134477 0.009171812568107519 0.009088454962656348 11012.0
167 5.5 0.12 0.0011407078554692316 0.023526671325413463 0.022985889849795174 4293.0
168 5.5 0.13 0.05012400217003798 0.39920948616600793 0.2853107344632768 253.0
169 5.5 0.14 0.18832054560954817 0.8782608695652174 0.4675925925925926 115.0
170 5.5 0.15 0.23803104575163397 0.8416666666666667 0.45701357466063347 120.0
171 5.5 0.16 0.3358072200842954 0.9439252336448598 0.4855769230769231 107.0

31
latex/presentations/midterm/res/2d_ber_fer_dfr_20433484_hybrid.csv Normal file
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@ -0,0 +1,31 @@
SNR,gamma,BER,FER,DFR,num_iterations
1.0,0.01,0.09643758493496409,1.0,0.5,101.0
1.0,0.05,0.09717416378316032,0.9901960784313726,0.4975369458128079,102.0
1.0,0.15,0.32425742574257427,1.0,0.5,101.0
1.5,0.01,0.07559592464436755,0.9901960784313726,0.4975369458128079,102.0
1.5,0.05,0.0707749766573296,0.9619047619047619,0.49029126213592233,105.0
1.5,0.15,0.2964958260531936,1.0,0.5,101.0
2.0,0.01,0.05896913220733838,1.0,0.5,101.0
2.0,0.05,0.04654837141468157,0.8347107438016529,0.45495495495495497,121.0
2.0,0.15,0.2969050365244137,0.9901960784313726,0.4975369458128079,102.0
2.5,0.01,0.04330858557015039,0.9805825242718447,0.4950980392156863,103.0
2.5,0.05,0.02880658436213992,0.6234567901234568,0.3840304182509506,162.0
2.5,0.15,0.29430988081507115,0.9901960784313726,0.4975369458128079,102.0
3.0,0.01,0.028516624040920716,0.8782608695652174,0.4675925925925926,115.0
3.0,0.05,0.015358301594018903,0.36330935251798563,0.26649076517150394,278.0
3.0,0.15,0.2875169869928169,1.0,0.5,101.0
3.5,0.01,0.018695850433196534,0.7829457364341085,0.4391304347826087,129.0
3.5,0.05,0.006135031332120477,0.20824742268041238,0.17235494880546076,485.0
3.5,0.15,0.2707861990950226,0.9711538461538461,0.4926829268292683,104.0
4.0,0.01,0.008812513769552765,0.5674157303370787,0.36200716845878134,178.0
4.0,0.05,0.0019857029388403494,0.08022239872915012,0.07426470588235294,1259.0
4.0,0.15,0.27478468022723107,0.9439252336448598,0.4855769230769231,107.0
4.5,0.01,0.004626022594468251,0.3344370860927152,0.2506203473945409,302.0
4.5,0.05,0.0006845708267509215,0.031911532385466033,0.030924678505817513,3165.0
4.5,0.15,0.2821402145763966,0.9528301886792453,0.48792270531400966,106.0
5.0,0.01,0.0017591120532297003,0.15165165165165165,0.1316818774445893,666.0
5.0,0.05,0.00021886964617101427,0.010585892464102296,0.010475005185646131,9541.0
5.0,0.15,0.2730257240510883,0.926605504587156,0.48095238095238096,109.0
5.5,0.01,0.0006439016284479894,0.061138014527845036,0.057615516257843696,1652.0
5.5,0.05,6.1029411764705884e-05,0.00305,0.0030407257863516277,20000.0
5.5,0.15,0.24632352941176472,0.8145161290322581,0.4488888888888889,124.0
1 SNR gamma BER FER DFR num_iterations
2 1.0 0.01 0.09643758493496409 1.0 0.5 101.0
3 1.0 0.05 0.09717416378316032 0.9901960784313726 0.4975369458128079 102.0
4 1.0 0.15 0.32425742574257427 1.0 0.5 101.0
5 1.5 0.01 0.07559592464436755 0.9901960784313726 0.4975369458128079 102.0
6 1.5 0.05 0.0707749766573296 0.9619047619047619 0.49029126213592233 105.0
7 1.5 0.15 0.2964958260531936 1.0 0.5 101.0
8 2.0 0.01 0.05896913220733838 1.0 0.5 101.0
9 2.0 0.05 0.04654837141468157 0.8347107438016529 0.45495495495495497 121.0
10 2.0 0.15 0.2969050365244137 0.9901960784313726 0.4975369458128079 102.0
11 2.5 0.01 0.04330858557015039 0.9805825242718447 0.4950980392156863 103.0
12 2.5 0.05 0.02880658436213992 0.6234567901234568 0.3840304182509506 162.0
13 2.5 0.15 0.29430988081507115 0.9901960784313726 0.4975369458128079 102.0
14 3.0 0.01 0.028516624040920716 0.8782608695652174 0.4675925925925926 115.0
15 3.0 0.05 0.015358301594018903 0.36330935251798563 0.26649076517150394 278.0
16 3.0 0.15 0.2875169869928169 1.0 0.5 101.0
17 3.5 0.01 0.018695850433196534 0.7829457364341085 0.4391304347826087 129.0
18 3.5 0.05 0.006135031332120477 0.20824742268041238 0.17235494880546076 485.0
19 3.5 0.15 0.2707861990950226 0.9711538461538461 0.4926829268292683 104.0
20 4.0 0.01 0.008812513769552765 0.5674157303370787 0.36200716845878134 178.0
21 4.0 0.05 0.0019857029388403494 0.08022239872915012 0.07426470588235294 1259.0
22 4.0 0.15 0.27478468022723107 0.9439252336448598 0.4855769230769231 107.0
23 4.5 0.01 0.004626022594468251 0.3344370860927152 0.2506203473945409 302.0
24 4.5 0.05 0.0006845708267509215 0.031911532385466033 0.030924678505817513 3165.0
25 4.5 0.15 0.2821402145763966 0.9528301886792453 0.48792270531400966 106.0
26 5.0 0.01 0.0017591120532297003 0.15165165165165165 0.1316818774445893 666.0
27 5.0 0.05 0.00021886964617101427 0.010585892464102296 0.010475005185646131 9541.0
28 5.0 0.15 0.2730257240510883 0.926605504587156 0.48095238095238096 109.0
29 5.5 0.01 0.0006439016284479894 0.061138014527845036 0.057615516257843696 1652.0
30 5.5 0.05 6.1029411764705884e-05 0.00305 0.0030407257863516277 20000.0
31 5.5 0.15 0.24632352941176472 0.8145161290322581 0.4488888888888889 124.0

8
latex/presentations/midterm/res/2d_ber_fer_dfr_20433484_metadata.json Normal file
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@ -0,0 +1,8 @@
{
"duration": 38.217501369000274,
"name": "2d_BER_FER_DFR_20433484",
"platform": "Linux-6.1.3-arch1-1-x86_64-with-glibc2.36",
"omega": 0.05,
"K": 100,
"end_time": "2023-01-07 09:31:11.720513"
}

BIN
latex/presentations/midterm/res/ber_paper.pdf Normal file
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Binary file not shown.

9
latex/presentations/midterm/sections/decoding_algorithms.tex
View File

@ -16,7 +16,12 @@
\underbrace{\sum_{j=1}^{n} \left( x_j^2 - 1 \right)^2}_{\text{Bipolar constraint}}
+ \underbrace{\sum_{i=1}^{m} \left[ \left(
\prod_{j\in\mathcal{A}\left( i \right)} x_j\right) -1 \right]^2}
_{\text{Parity constraint}}
_{\text{Parity constraint}},
\hspace{5mm}\mathcal{A}\left( i \right) \equiv \left\{
j | j\in \mathcal{J},
\boldsymbol{H}_{i,j} = 1
\right\},
i \in \mathcal{I}
\end{align*}
\item Approximation of prior PDF:
\begin{align*}
@ -54,7 +59,7 @@
= - \ln\left( f_{\boldsymbol{Y}}
\left( \boldsymbol{y} | \boldsymbol{x} \right) \right)
\end{align*}
\todo{Note about explaning notational difference between $f$ and $f_X$ or $f_Y$}
\note{Notational difference between $f$ and $f_X$ or $f_Y$}
\item Code proximal operator:
\begin{align*}
\text{prox}_{\gamma h} \left( \boldsymbol{x} \right) &\equiv

275
latex/presentations/midterm/sections/examination_results.tex
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@ -7,14 +7,283 @@
\label{sub:Ex Proximal Decoder}
\begin{frame}[t]
\frametitle{Proximal Decoder}
\frametitle{Proximal Decoder: Examination Results}
\begin{itemize}
\item AWGN Channel - LDPC Code with $n=204, k=102$:
\vspace{2mm}
\begin{figure}[H]
\centering
\todo{TODO}
\begin{subfigure}{0.5\textwidth}
\centering
\begin{tikzpicture}[scale=0.45]
\begin{axis}[
grid=both,
xlabel={SNR (dB)}, ylabel={Bit Error Rate},
ymode=log,
legend style={at={(0.05,0.05)},anchor=south west},
width=11.5cm,
height=8cm,
ytick={0, 10e-1, 10e-2, 10e-3, 10e-4},
xtick={1, 2, 3, 4, 5},
ymax=1.2, ymin=0.8e-4,
]
\addplot table [x=SNR, y=BER,
col sep=comma, discard if not={gamma}{0.15}]
{res/2d_ber_fer_dfr_20433484.csv};
\addlegendentry{$\gamma = 0.15$}
\addplot table [x=SNR, y=BER,
col sep=comma, discard if not={gamma}{0.01}]
{res/2d_ber_fer_dfr_20433484.csv};
\addlegendentry{$\gamma = 0.01$}
\addplot table [x=SNR, y=BER,
col sep=comma, discard if not={gamma}{0.05}]
{res/2d_ber_fer_dfr_20433484.csv};
\addlegendentry{$\gamma = 0.05$}
\end{axis}
\end{tikzpicture}
\caption{Simulation results for $\omega = 0.05, K=100$}
\label{fig:sim_results_prox}
\end{subfigure}%
\begin{subfigure}{0.5\textwidth}
\centering
\includegraphics[scale=0.6]{res/ber_paper}
\caption{Results from ``Proximal Decoding for LDPC Codes''}
\label{fig:sim_results_prox}
\end{subfigure}%
\end{figure}
\item Performance: $2800 \text{ transm.} / s$ - Intel Core i7-7700HQ @ 2.80GHz\\
($\sim 10s$ for the shown plot)
\end{itemize}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{ADMM}%
\begin{frame}[t]
\frametitle{Proximal Decoder: Choice of $\gamma$}
\begin{figure}[H]
\centering
\begin{subfigure}[c]{0.5\textwidth}
\centering
\begin{tikzpicture}[scale=0.52]
\begin{semilogyaxis}[xlabel={SNR},ylabel={BER}, grid=both, grid style={line width=.1pt}, legend style={at={(0.05,0.05)},anchor=south west}]
\foreach \gamma in {0.01, 0.05, 0.15}{
\addplot table [x=SNR, y=BER, col sep=comma, discard if not={gamma}{\gamma}] {res/2d_ber_fer_dfr_20433484.csv};
\legend{\gamma}
}
\legend{$\gamma=0.01$, $\gamma=0.05$, $\gamma=0.15$}
\end{semilogyaxis}
\end{tikzpicture}
\end{subfigure}%
\begin{subfigure}[c]{0.5\textwidth}
\centering
\begin{tikzpicture}[scale=0.7]
\begin{axis}[view={75}{60},
zmode=log,
xlabel={SNR},
ylabel={$\gamma$},
zlabel={BER}]
\addplot3[surf, mesh/rows=17, mesh/cols=10, colormap/viridis] table [col sep=comma, x=SNR, y=gamma, z=BER] {res/2d_ber_fer_dfr_20433484.csv};
\addlegendentry{$\gamma = \left[ 0\text{:}.01\text{:}.16 \right] $}
\addplot3[red, line width=1.5] table[col sep=comma, discard if not={gamma}{0.05}, x=SNR, y=gamma, z=BER] {res/2d_ber_fer_dfr_20433484.csv};
\addlegendentry{$\gamma = 0.05$}
\addplot3[blue, line width=1.5] table[col sep=comma, discard if not={gamma}{0.01}, x=SNR, y=gamma, z=BER] {res/2d_ber_fer_dfr_20433484.csv};
\addlegendentry{$\gamma = 0.01$}
\addplot3[brown, line width=1.5] table[col sep=comma, discard if not={gamma}{0.15}, x=SNR, y=gamma, z=BER] {res/2d_ber_fer_dfr_20433484.csv};
\addlegendentry{$\gamma = 0.15$}
\end{axis}
\end{tikzpicture}
\end{subfigure}
\caption{BER for $\omega = 0.05, K=100$}
\label{fig:ber_3d}
\end{figure}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\begin{frame}[t, fragile]
\frametitle{Proximal Decoder: Frame Error Rate}
\begin{minipage}{.4\textwidth}
\centering
\begin{algorithm}[caption={}, label={},
basicstyle=\fontsize{7.5}{9.5}\selectfont
]
$\boldsymbol{s}^{\left( 0 \right)} = \boldsymbol{0}$
for $k=0$ to $K-1$ do
$\boldsymbol{r}^{\left( k+1 \right)} = \boldsymbol{s}^{(k)} - \omega \nabla L \left( \boldsymbol{s}^{(k)}; \boldsymbol{y} \right) $
Compute $\nabla h\left( \boldsymbol{r}^{\left( k+1 \right) } \right)$
$\boldsymbol{s}^{\left( k+1 \right)} = \boldsymbol{r}^{(k+1)} - \gamma \nabla h\left( \boldsymbol{r}^{\left( k+1 \right) } \right) $
$\boldsymbol{\hat{x}} = \text{sign}\left( \boldsymbol{s}^{\left( k+1 \right) } \right) $
If $\boldsymbol{\hat{x}}$ passes the parity check condition, break the loop.
end for
Output $\boldsymbol{\hat{x}}$
\end{algorithm}
\end{minipage}%
\begin{minipage}{.6\textwidth}
\centering
\begin{figure}[H]
\centering
\begin{tikzpicture}[scale=0.45]
\begin{axis}[
grid=both,
xlabel={SNR}, ylabel={BER},
ymode=log,
legend style={at={(0.05,0.05)},anchor=south west},
ymax=1.5, ymin=0.8e-4,
]
\addplot table [x=SNR, y=BER, col sep=comma, discard if not={gamma}{0.15}]
{res/2d_ber_fer_dfr_20433484.csv};
\addlegendentry{$\gamma = 0.15$}
\addplot table [x=SNR, y=BER, col sep=comma, discard if not={gamma}{0.01}]
{res/2d_ber_fer_dfr_20433484.csv};
\addlegendentry{$\gamma = 0.01$}
\addplot table [x=SNR, y=BER, col sep=comma, discard if not={gamma}{0.05}]
{res/2d_ber_fer_dfr_20433484.csv};
\addlegendentry{$\gamma = 0.05$}
\end{axis}
\end{tikzpicture}\\
\begin{tikzpicture}[scale=0.45]
\begin{axis}[
grid=both,
xlabel={SNR}, ylabel={FER},
ymode=log,
legend style={at={(0.05,0.05)},anchor=south west},
ymax=1.5, ymin=0.8e-4,
]
\addplot table [x=SNR, y=FER, col sep=comma, discard if not={gamma}{0.15}]
{res/2d_ber_fer_dfr_20433484.csv};
\addlegendentry{$\gamma = 0.15$}
\addplot table [x=SNR, y=FER, col sep=comma, discard if not={gamma}{0.01}]
{res/2d_ber_fer_dfr_20433484.csv};
\addlegendentry{$\gamma = 0.01$}
\addplot table [x=SNR, y=FER, col sep=comma, discard if not={gamma}{0.05}]
{res/2d_ber_fer_dfr_20433484.csv};
\addlegendentry{$\gamma = 0.05$}
\end{axis}
\end{tikzpicture}
\begin{tikzpicture}[scale=0.45]
\begin{axis}[
grid=both,
xlabel={SNR}, ylabel={Decoding Failure Rate},
ymode=log,
legend style={at={(0.05,0.05)},anchor=south west},
ymax=1.5, ymin=0.8e-4,
]
\addplot table [x=SNR, y=DFR, col sep=comma, discard if not={gamma}{0.15}]
{res/2d_ber_fer_dfr_20433484.csv};
\addlegendentry{$\gamma = 0.15$}
\addplot table [x=SNR, y=DFR, col sep=comma, discard if not={gamma}{0.01}]
{res/2d_ber_fer_dfr_20433484.csv};
\addlegendentry{$\gamma = 0.01$}
\addplot table [x=SNR, y=DFR, col sep=comma, discard if not={gamma}{0.05}]
{res/2d_ber_fer_dfr_20433484.csv};
\addlegendentry{$\gamma = 0.05$}
\end{axis}
\end{tikzpicture}
\caption{Simulation results for $\omega = 0.05, K=100$}
\label{fig:simulation_results}
\end{figure}
\end{minipage}
\end{frame}
\begin{frame}[t]
\frametitle{title}
\begin{figure}[H]
\centering
\begin{tikzpicture}[scale=0.45]
\begin{axis}[
grid=both,
xlabel={SNR}, ylabel={BER},
ymode=log,
legend style={at={(0.05,0.05)},anchor=south west},
ymax=1.5, ymin=0.8e-5,
]
% \addplot table [x=SNR, y=BER, col sep=comma, discard if not={gamma}{0.15}]
% {res/2d_ber_fer_dfr_20433484.csv};
% \addlegendentry{$\gamma = 0.15$}
% \addplot table [x=SNR, y=BER, col sep=comma, discard if not={gamma}{0.01}]
% {res/2d_ber_fer_dfr_20433484.csv};
% \addlegendentry{$\gamma = 0.01$}
\addplot table [x=SNR, y=BER, col sep=comma, discard if not={gamma}{0.05}]
{res/2d_ber_fer_dfr_20433484.csv};
\addlegendentry{$\gamma = 0.05$}
\addplot table [x=SNR, y=BER, col sep=comma, discard if not={gamma}{0.05}]
{res/2d_ber_fer_dfr_20433484_hybrid.csv};
\addlegendentry{hybrid $\gamma = 0.05$}
\end{axis}
\end{tikzpicture}\\
\begin{tikzpicture}[scale=0.45]
\begin{axis}[
grid=both,
xlabel={SNR}, ylabel={FER},
ymode=log,
legend style={at={(0.05,0.05)},anchor=south west},
ymax=1.5, ymin=0.8e-5,
]
% \addplot table [x=SNR, y=FER, col sep=comma, discard if not={gamma}{0.15}]
% {res/2d_ber_fer_dfr_20433484.csv};
% \addlegendentry{$\gamma = 0.15$}
% \addplot table [x=SNR, y=FER, col sep=comma, discard if not={gamma}{0.01}]
% {res/2d_ber_fer_dfr_20433484.csv};
% \addlegendentry{$\gamma = 0.01$}
\addplot table [x=SNR, y=FER, col sep=comma, discard if not={gamma}{0.05}]
{res/2d_ber_fer_dfr_20433484.csv};
\addlegendentry{$\gamma = 0.05$}
\addplot table [x=SNR, y=FER, col sep=comma, discard if not={gamma}{0.05}]
{res/2d_ber_fer_dfr_20433484_hybrid.csv};
\addlegendentry{hybrid $\gamma = 0.05$}
\end{axis}
\end{tikzpicture}
\begin{tikzpicture}[scale=0.45]
\begin{axis}[
grid=both,
xlabel={SNR}, ylabel={Decoding Failure Rate},
ymode=log,
legend style={at={(0.05,0.05)},anchor=south west},
ymax=1.5, ymin=0.8e-5,
]
% \addplot table [x=SNR, y=DFR, col sep=comma, discard if not={gamma}{0.15}]
% {res/2d_ber_fer_dfr_20433484.csv};
% \addlegendentry{$\gamma = 0.15$}
% \addplot table [x=SNR, y=DFR, col sep=comma, discard if not={gamma}{0.01}]
% {res/2d_ber_fer_dfr_20433484.csv};
% \addlegendentry{$\gamma = 0.01$}
\addplot table [x=SNR, y=DFR, col sep=comma, discard if not={gamma}{0.05}]
{res/2d_ber_fer_dfr_20433484.csv};
\addlegendentry{$\gamma = 0.05$}
\addplot table [x=SNR, y=DFR, col sep=comma, discard if not={gamma}{0.05}]
{res/2d_ber_fer_dfr_20433484_hybrid.csv};
\addlegendentry{hybrid $\gamma = 0.05$}
\end{axis}
\end{tikzpicture}
\caption{Simulation results for $\omega = 0.05, K=100$}
\label{fig:simulation_results}
\end{figure}
\end{frame}
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{ADMM: Examination Results}%
\label{sub:Ex ADMM}
\begin{frame}[t]

5
latex/presentations/midterm/sections/theoretical_background.tex
View File

@ -56,7 +56,6 @@
\left(0,\frac{1}{2}\left(\frac{k}{n}\frac{E_b}{N_0}\right)^{-1}\right),
\hspace{2mm} \boldsymbol{y}, \boldsymbol{n} \in \mathbb{R}^n
\end{align*}
\todo{Why $\frac{1}{2}$}
\item All zeros assumption:
\begin{align*}
\boldsymbol{c} = 0
@ -171,6 +170,7 @@
\right\},
j \in \mathcal{J}
\end{align*}
\todo{Is this correct? Shouldn't i and j be switched around?}
\item ``Illegal configurations''
\begin{align*}
S \subseteq N\left( j \right), \left| S \right| \text{odd}
@ -237,7 +237,8 @@
\caption{Relaxed polytope for $n=3$}
\end{figure}
\end{minipage}
\todo{How is this a relaxation and not just an alternative formulation?}
\todo{How is this a relaxation and not just an alternative formulation?
We have just switched out valid codewords for invalid ones}
\todo{Is LP Relaxation relevant as theoretical background?}
\end{frame}