General Strong Polarization
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1 General Strong Polarization Madhu Sudan Harvard University Joint work with Jaroslaw Blasiok (Harvard), Venkatesan Gurswami (CMU), Preetum Nakkiran (Harvard) and Atri Rudra (Buffalo) December 4, 2017 IAS: General Strong Polarization 1 of 21
2 Shannon and Channel Capacity Shannon [ 48]: For every (noisy) channel of communication, capacity CC s.t. communication at rate RR < CC is possible and RR > CC is not. Needs lot of formalization omitted. Example: XX FF 2 BBBBBB(pp) XX w.p. 1 pp 1 XX w.p. pp Acts independently on bits Capacity = 1 HH(pp) ; HH(pp) = binary entropy! HH pp = pp log 1 pp + 1 pp log 1 1 pp December 4, 2017 IAS: General Strong Polarization 2 of 21
3 Achieving Channel Capacity Commonly used phrase. Many mathematical interpretations. Some possibilities: How small can nn be? Get RR > CC εε with polytime algorithms? [Luby et al. 95] Make running time poly 1 εε? Open till 2008 Shannon 48 : nn = OO CC RR 2 Forney 66 :time = pppppppp nn, 2 1 εε 2 Arikan 08: Invented Polar Codes Final resolution: Guruswami+Xia 13, Hassani+Alishahi+Urbanke 13 Strong analysis December 4, 2017 IAS: General Strong Polarization 3 of 21
4 Context for today s talk [Arikan 08]: General class of codes. [GX 13, HAU 13]: One specific code within. Why? Bottleneck: Strong Polarization Arikan et al.: General class polarizes [GX,HAU]: Specific code polarizes strongly! This work/talk: Introduce Local Polarization Local Strong Polarization Thm: All known codes that polarize also polarize strongly! December 4, 2017 IAS: General Strong Polarization 4 of 21
5 This talk: What are Polar Codes Local vs. Global (Strong) Polarization Analysis of Polarization December 4, 2017 IAS: General Strong Polarization 5 of 21
6 Lesson 0: Compression Coding Linear Compression Scheme: HH, DD for compression scheme for bern pp nn if Linear map HH: FF 2 nn FF 2 mm Pr ZZ bern pp nn DD HH ZZ ZZ εε Hopefully mm HH(pp) nn Compression Coding Let HH be such that HH HH = 0; Encoder: XX HH XX Error-Corrector: YY = HH XX + ηη YY DD HH YY = ww.pp. 1 εε HH XX December 4, 2017 IAS: General Strong Polarization 6 of 21
7 Question: How to compress? Arikan s key idea: Start with 2 2 Polarization Transform : UU, VV UU + VV, VV Invertible so does nothing? If UU, VV independent, then UU + VV more random than either VV UU + VV less random than either Iterate (ignoring conditioning) End with bits that are completely random, or completed determined (by others). Output totally random part to get compression! December 4, 2017 IAS: General Strong Polarization 7 of 21
8 Information Theory Basics Entropy: Defn: for random variable XX Ω, HH XX = pp xx log 1 where pp pp xx = Pr XX = xx xx xx Ω Bounds: 0 HH XX log Ω Conditional Entropy Defn: for jointly distributed XX, YY HH XX YY = pp yy yy HH(XX YY = yy) Chain Rule: HH XX, YY = HH XX + HH(YY XX) Conditioning does not increase entropy: HH XX YY HH(XX) Equality Independence: HH XX YY = HH XX XX YY December 4, 2017 IAS: General Strong Polarization 8 of 21
9 Iteration by Example: AA, BB, CC, DD AA + BB + CC + DD, BB + DD, CC + DD, DD EE, FF, GG, HH EE + FF + GG + HH, FF + HH, GG + HH, HH BB + DD HH BB + DD AA + BB + CC + DD) BB + DD + FF + HH HH BB + DD + FF + HH AA + BB + CC + DD, EE + FF + GG + HH FF + HH HH FF + HH EE + FF + GG + HH) HH FF + HH FF + HH AA + BB + CC + DD, EE + FF + GG + HH, BB + DD + FF + HH December 4, 2017 IAS: General Strong Polarization 9 of 21
10 Algorithmics Subliminal channel: details in appendix. Summary: Iterating tt times yields nn nn-polarizing transform for nn = 2 tt : (ZZ 1,, ZZ nn ) (WW 1,, WW nn ) WW SS ( SS HH pp nn) Encoding/Compression: Clearly poly time. Decoding/Decompression: Successive cancellation decoder : implementable in poly time. Can compute Pr WW ii = 1 WW <ii recursively ZZ bern(pp) Can reduce both run times to OO(nn log nn) Only barrier: Determining SS Resolved by [Tal-Vardy 13] December 4, 2017 IAS: General Strong Polarization 10 of 21
11 Analysis: The Polarization martingale Martingale: Sequence of r.v., XX 0, XX 1, ii, xx 0,, xx ii 1, EE XX ii xx 0,, xx ii 1 Polarization martingale: = xx ii 1 XX ii = Conditional entropy of random output at iith stage. Why martingale? At iith stage two inputs take (UU, AA); (VV, BB) Output = (UU + VV, A BB) and VV, AA BB UU + VV Input entropies = xx ii 1 (HH UU AA = HH VV BB = xx ii 1 ) Output entropies average to xx ii 1 HH UU + VV AA, BB + HH VV AA, BB, UU + VV = 2xx ii 1 December 4, 2017 IAS: General Strong Polarization 11 of 21
12 Quantitative issues How quickly does this martingale converge? (λλ) How well does it converge? εε Formally, want: Pr XX tt λλ, 1 λλ εε εε: Gives gap to capacity λλ: Governs decoding failure nn Reminder: nn = 2 tt For useable code, need: λλ 4 tt λλ For poly 1 εε block length, need: εε αα tt, αα < 1 December 4, 2017 IAS: General Strong Polarization 12 of 21
13 Regular and Strong Polarization (Regular) Polarization: γγ > 0, Lim tt Pr XX tt γγ tt, 1 γγ tt 0. Strong Polarization: γγ > 0 αα > 0 tt, Pr XX tt γγ tt, 1 γγ tt αα tt Both global properties (large tt behavior) Construction yields: Local Property How does XX ii relate to XX ii 1? Local vs. Global Relationship? Regular polarization well understood. Strong understood only UU, VV (UU + VV, VV) What about UU, VV, WW UU + VV, VV, WW?, more generally? December 4, 2017 IAS: General Strong Polarization 13 of 21
14 General Polarization Basic underlying ingredient MM FF 2 kk kk Local polarization step: Pick UU jj, AA jj jj [kk] i.i.d. One step: generate LL 1,, LL kk = MM UU 1,, UU kk XX ii 1 = HH UU jj l AA l, UU <jj = HH UU jj AA jj XX ii = HH LL jj l AA l, LL <jj Examples HH 2 = 1 0 ; HH 3 = Which matrices polarize? Strongly? December 4, 2017 IAS: General Strong Polarization 14 of 21
15 Mixing matrices Observation 1: Identity matrix does not polarize. Observation 1 : Upper triangular matrix does not polarize. Observation 2: (Row) permutation of nonpolarizing matrix does not polarize! Corresponds to permuting UU 1,, UU kk Definition: MM mixing if no row permutation is upper-triangular. [KSU]: MM mixing martingale polarizes. [Our main theorem]: MM mixing martingale polarizes strongly. December 4, 2017 IAS: General Strong Polarization 15 of 21
16 Key Concept: Local (Strong) Polarization Martingale XX 0, XX 1, locally polarizes if it exhibits Variance in the middle: ττ > 0 σσ > 0, Var XX ii XX ii 1 ττ, 1 ττ > σσ Suction at the end: θθ > 0 cc <, ττ > 0 Pr XX ii < XX ii 1 cc (similarly with 1 XX ii ) Definition local: compares XX ii with XX ii 1 Implications global: XX ii 1 ττ θθ Thm 1: Local Polarization Strong Polarization. Thm 2: Mixing matrices Local Polarization. December 4, 2017 IAS: General Strong Polarization 16 of 21
17 Mixing Matrices Local Polarization 2 2 case: Ignoring conditioning, we have 1 HH 2pp 1 pp w. p. HH pp 2 1 2HH pp HH 2pp 1 pp w. p. 2 Variance, Suction Taylor series for log. kk kk case: Reduction to 2 2 case: 1 2 probs. 1 kk December 4, 2017 IAS: General Strong Polarization 17 of 21
18 Local (Global) Strong Polarization Step 1: Medium Polarization: αα, ββ < 1 Pr XX tt ββ tt, 1 ββ tt αα tt Proof: Potential Φ tt = EE min XX tt, 1 XX tt Variance+Suction ηη < 1 s.t. Φ tt ηη φφ tt 1 (Aside: Why XX tt? Clearly XX tt won t work. And XX tt 2 increases!) Φ tt ηη tt Pr XX tt ββ tt, 1 ββ tt ηη ββ tt December 4, 2017 IAS: General Strong Polarization 18 of 21
19 Local (Global) Strong Polarization Step 1: Medium Polarization: αα, ββ < 1 Pr XX tt ββ tt, 1 ββ tt αα tt Step 2: Medium Polarization + tt-more steps Strong Polarization: Proof: Assume XX 0 αα tt ; Want XX tt γγ tt w.p. 1 OO αα 1 tt Pick cc large & ττ s.t. Pr XX ii < XX ii 1 cc XX ii 1 ττ θθ 2.1: Pr ii ss. tt. XX ii ττ ττ 1 αα tt (Doob s Inequality) 2.2: Let YY ii = log XX ii ; Assuming XX ii ττ for all ii YY ii YY ii 1 log cc w.p. θθ; & kk w.p. 2 kk. EExp YY tt tt log cc θθ + 1 2tt log γγ Concentration! Pr YY tt tt log γγ αα 2 tt QED! December 4, 2017 IAS: General Strong Polarization 19 of 21
20 Conclusion Simple General Analysis of Strong Polarization But main reason to study general polarization: Maybe some matrices polarize even faster! This analysis does not get us there. But hopefully following the (simpler) steps in specific cases can lead to improvements. December 4, 2017 IAS: General Strong Polarization 20 of 21
21 Thank You! December 4, 2017 IAS: General Strong Polarization 21 of 21
22 Algorithmics: Encoding Upto permutation on input variables, the polarizing transform is given by EE 2nn UU, VV = EE nn UU VV, EE nn VV where UU, VV FF 2 nn Obviously takes OO nn log nn time. December 4, 2017 IAS: General Strong Polarization 22 of 21
23 Algorithmics: Decoding Key idea: Strengthen algorithm to work when inputs are independent, but not identical!! Decompressor s input: pp 1,, pp 2nn ; ww 0,1,? 2nn Notations: ww 1 = ww 1,, ww nn ; ww 2 = ww nn+1,, ww 2nn pp aa, bb = expectation of XX YY where XX bern(aa) and YY bern(bb) [XX, YY are single bits!] pp aa, bb cc = expectation of YY XX YY = cc DD pp 1,, pp 2nn ; ww : If nn = 1 2 output ww 1 if ww 1 {0,1}; else output 1 if pp 1.5 and 0 o.w. Else Let qq ii = pp pp ii, pp nn+ii for ii nn Let XX = DD qq 1,, qq nn ; ww 1 Let rr ii = pp pp ii, pp nn+ii XX ii for ii nn Let YY = (DD( rr 1,, rr nn ; ww 2 ) Output (XX YY, YY) December 4, 2017 IAS: General Strong Polarization 23 of 21
24 Algorithmics: Decoding Analysis Inductive claim: Recursion is correct! i.e., If UU, VV ii bern pp ii then UU VV ii bern qq ii & VV UU VV = XX ii bern(rr ii ) So eventually get down to guessing individual bits. Let WW = EE 2nn (UU, VV) ; and ww 1,, ww 2nn be the 2nn individual bits output in line 1 of decoder. Claim: When guessing WW ii, pp-value = Pr WW ii = 1 WW <ii = ww <ii ] Claim: If HH WW ii WW <ii = ττ then Pr HH WW ii ww <ii ττ ττ ; and so Pr ww ii WW ii ww <ii = WW <ii 2 ττ Back to talk December 4, 2017 IAS: General Strong Polarization 24 of 21
General Strong Polarization
General Strong Polarization Madhu Sudan Harvard University Joint work with Jaroslaw Blasiok (Harvard), Venkatesan Gurswami (CMU), Preetum Nakkiran (Harvard) and Atri Rudra (Buffalo) May 1, 018 G.Tech:
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