Classical Lattices: , , , ,
The Parade of Classical Lattices
Lattice theory has a small cast of very important characters. In dimension the integer lattice is always there as the trivial baseline. The root lattices and come from the Cartan classification of simple Lie algebras and give the best low-dimensional packings. The exceptional root lattice is the densest in (proved by Viazovska in 2017). The Coxeter–Todd lattice holds the 12-D record. And the Leech lattice , constructed by Leech in 1967, is optimal in (proved by Cohn, Kumar, Miller, Radchenko, and Viazovska in 2017). This section introduces each of these lattices and tabulates their parameters; later sections use the tabulated values in density and theta-series calculations.
Definition: The Integer Lattice
The Integer Lattice
The integer lattice is Its generator matrix is , so , , , and (the ). The kissing number grows linearly with dimension; the packing density tends to zero very quickly with : , , , . The cube is the benchmark every other lattice beats.
Definition: The Root Lattice
The Root Lattice
The root lattice lives in the hyperplane of of vectors with integer coordinates summing to zero: It has minimum-norm vectors of squared length (permutations of ), kissing number , and fundamental volume . The 2D case is the hexagonal lattice discussed in s01–s02 (after a rotation into ), which is the densest 2D lattice.
The root lattice takes its name from the root system of the Lie algebra — its minimum vectors are the roots. The isomorphism between lattice theory and Lie theory is an old mystery that is still unfolding; Viazovska's proof uses the theta series, which is a modular form (Lie theory in disguise).
Definition: The Checkerboard Lattice
The Checkerboard Lattice
The checkerboard lattice is i.e., the sublattice of consisting of "even-sum" vectors. It has index in , so . The minimum-norm vectors are the permutations and sign choices of with squared length : . Packing radius , covering radius for (equal to the half-diagonal of the "deep hole").
is the densest lattice in dimensions . In it is the face-centred cubic (FCC) packing, long conjectured optimal among all 3D packings and proved by Hales in 1998 (Kepler conjecture).
In dimensions there is a denser lattice: , where is the all-ones vector. For , .
Definition: The Gosset Lattice
The Gosset Lattice
The lattice (Gosset, Korkine–Zolotarev, Leech) is the densest 8D lattice. One construction is , i.e., Equivalently, consists of all with coordinate sum an even integer. It has , minimum-norm squared , kissing number , and packing density .
is self-dual and even (all squared norms are even integers). In 2017 Viazovska proved that is the densest sphere packing in — lattice or otherwise — closing a fifty-year-old conjecture. Her proof constructs a modular form that witnesses the bound using the linear-programming method of Cohn and Elkies.
shows up everywhere in mathematics and physics: string theory uses the heterotic model; error-correcting codes use the extended Hamming code, which is the first-order Reed–Muller code and also the mod-2 reduction of .
Example: The Kissing Number of
Compute the kissing number and the packing density directly from the definition. Verify and . Compare to to read off the coding gain of over the cube.
Minimum-norm vectors
Minimum-norm vectors of have squared length . They are the permutations and sign-choices of . Counting: ways to pick which two coordinates are nonzero, and sign choices for each. Total . Equivalently, for .
Packing radius and volume
, so . The volume is (index 2 in ).
Packing density
(volume of the unit 4-ball).
Coding gain over $\mathbb{Z}^4$
. The density ratio is exactly , so the fundamental coding gain of over is dB per lattice point, or dB per dimension. This 3-dB-per-two-dimensions gain appears throughout the TCM literature.
Definition: The Leech Lattice
The Leech Lattice
The Leech lattice is the densest 24D lattice (Conway–Sloane attribute this folklore to Leech, ) and was proved optimal among all 24D sphere packings by Cohn, Kumar, Miller, Radchenko, and Viazovska in . It has (self-dual), minimum-norm squared (so with a conventional normalisation), kissing number , and packing density (equivalently, center density ).
One construction: let be the extended binary Golay code. Then — up to the exact normalization, the Leech lattice is built by stacking Golay-code cosets in 24D. Conway–Sloane Ch. 12 gives several other constructions (via , via , via the Niemeier lattices).
The kissing number 196560 is the largest known kissing number in dimension 24 and is now proved optimal: no sphere packing in can have more than neighbours of any given sphere.
The Leech Lattice : Structure and Optimality
Definition: The Coxeter–Todd Lattice
The Coxeter–Todd Lattice
The Coxeter–Todd lattice is the densest known -D lattice (also believed to be the densest -D sphere packing, but unlike and this is not proved). It has (up to scale), minimum-norm squared , kissing number , and center density . It can be constructed as a ternary Golay code over lifted into .
In the unsolved dimensions the optimal packing is known (Hales for , Gauss for , Viazovska and collaborators for , trivial for , and known via ). In every other dimension including , the conjecture remains open.
Parameters of the Classical Lattices
| Lattice | Dim | Kissing | Packing density | Gain over [dB] | ||
|---|---|---|---|---|---|---|
| any | ||||||
| (hex) | ||||||
| /dim | ||||||
| /dim | ||||||
| /dim | ||||||
| /dim | ||||||
| (total) | ||||||
| (total) | ||||||
| (Leech) | (total) |
Coding Gains of Classical Lattices
Bar chart of the fundamental coding gain (in dB, normalised to per dimension) of the classical lattices relative to . The gain scales roughly like , where is the Hermite constant. Three spikes stand out: (where is known optimal), (where delivers more per dimension than adjacent dimensions), and (Leech). These exceptional dimensions are the cases where the algebraic structure of the lattice is "tight".
Parameters
Theorem: Viazovska 2017: and Are Optimal Sphere Packings
No sphere packing in has density greater than , and no sphere packing in has density greater than . In both cases the extremiser is the lattice packing — and respectively — and is unique up to isometry.
The Cohn–Elkies linear-programming method converts "density of a sphere packing" into "value of a linear programme on functions with positive Fourier transform". Viazovska constructed, for (and with the others for ), a "magic" function whose value at and Fourier evaluation at the minimum vectors exactly witness the bound. The function is built from modular forms — objects first studied by Jacobi and Eisenstein in the 19th century — which is why the proof reads like a piece of 19th-century mathematics suddenly solving a 20th-century problem.
We state the theorem; the proof uses modular forms and the Cohn–Elkies bound, beyond the scope of this book.
For a sketch, see Viazovska's Fields-lecture video; for the full argument, the 2017 Annals papers.
Statement only
The proof is too long and specialised to include here; we refer to Viazovska's Annals of Mathematics paper for and the Cohn–Kumar–Miller–Radchenko–Viazovska paper for . Both proofs use the Cohn–Elkies linear-programming method: for each dimension they construct an explicit Schwartz function with , for at least the minimum-norm of (resp. ), and for all . Such a function witnesses the bound via a Poisson-summation argument.
Why $n = 8$ and $n = 24$?
The Cohn–Elkies bound is never exactly tight: for most dimensions the optimal LP function does not match any known lattice. What makes special is that the eigenvalue structure of the Eisenstein series at these dimensions produces a modular form with just the right zero structure. In other dimensions the known packings (BW lattices, , various "Mordell–Weil" lattices) are believed to be optimal but provably not — no closed-form LP magic function exists.
Physical interpretation
For the coded-modulation practitioner: in every dimension, some lattice is the densest, and using that lattice as the coding lattice in the coset-code construction of Ch. 4 (or the AWGN lattice code of Ch. 16) gives the maximum coding gain. The Viazovska theorem says that in dimensions and the density achievable by any code (lattice or otherwise) is no better than what and already provide.
Historical Note: Viazovska 2017: The Sphere-Packing Breakthrough
2017For years — since Cohn and Elkies proposed the linear-programming method in and the Conway–Sloane tables had catalogued the and Leech packings in the – — the optimality of and remained the flagship open problem in discrete geometry. The breakthrough came in March , when Maryna Viazovska, then a postdoc at Humboldt University in Berlin, posted a -page preprint on arXiv proving the case. The proof's central object is a "magic modular form", explicitly constructed from Eisenstein series and theta functions of half-integral weight. A week later, Viazovska joined Henry Cohn, Abhinav Kumar, Stephen D. Miller, and Danylo Radchenko to adapt the same method to dimension 24, proving the Leech-lattice case.
Viazovska was awarded the Fields Medal in for this work. For our purposes, the practical import is that in and the coding gain of the Conway–Sloane lattice tables is not only the best known but provably the best achievable. This closes the coding-gain side of the shaping/coding decomposition of Ch. 4 in these two exceptional dimensions.
High-Dimensional Lattices Are Not Directly Implementable
The Leech lattice has minimum-norm vectors; at the error-floor end of a decoding curve the union bound has 196560 pairwise terms. A maximum-likelihood decoder must perform a nearest- neighbour search over , which — even with the fastest known Leech-lattice decoder (the soft-decision -level trellis of Forney & Vardy, ) — costs on the order of multiplications per received vector. That is tractable for modems but prohibitive for -Gb/s optical or 400-Gb/s Ethernet.
In practice, modern coded-modulation systems do NOT use Leech directly. They use (QAM) for coding, finite block codes (LDPC, polar) for the gain, and probabilistic shaping (Ch. ) for the -dB shaping gap. The Leech lattice lives on as a theoretical benchmark, the source of coding-gain upper bounds for any 24D system, and the benchmark our designs are measured against.
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ML decoding complexity: – multiplications per Leech vector — too high for 100 Gb/s+ systems.
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High-dimensional constellation addressing in firmware: no standard ISA maps to 24-D integer arithmetic.
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Higher-dimensional lattices earn diminishing coding gain per added dimension; the Leech gain of 6 dB is close to the asymptotic limit set by the Minkowski–Hlawka bound.
Partition Chains Through the Classical Lattices
The classical lattices fit together into nested chains that Ch. 4 called partition chains. The -D chain runs , where is a rotation-by- and scale by . The -D chain runs , used by Forney's "coset codes Part II" for TCM gain up to dB. The -D chain runs , giving room for a large multi-level code. Each step of the chain is a coset partition of index , and the binary code on top of the chain selects which coset at each level.
Common Mistake: Best Lattice Depends On Dimension — No Universal Winner
Mistake:
Assuming that what works in dimension (Leech) tells you anything specific about dimensions . The densest lattice changes with dimension, often discontinuously: dominates for ; root lattices for ; at ; at ; and nothing much is certain in between.
Correction:
Look up the best known lattice for your specific dimension in the Nebe–Sloane database (online at www.math.rwth-aachen.de/~Gabriele.Nebe/LATTICES) or in Conway–Sloane's Table 1.2. Do not extrapolate from isolated cases. The dimension-dependent pattern is one of the most surprising features of sphere-packing theory.
Root lattice
A lattice whose minimum vectors form a root system in the Lie-theoretic sense. The , , , , lattices are root lattices; their kissing numbers match the number of roots in the corresponding Lie algebra.
Related: The Gosset Lattice , The Checkerboard Lattice , The Root Lattice
lattice
The Gosset lattice in : the unique (up to isometry) densest-packing lattice in 8D, proved optimal by Viazovska in 2017. Self-dual, even, , .
Related: Root lattice, The Leech Lattice , Viazovska's Theorems: and Close the Gap
Leech lattice
The unique (up to isometry) densest-packing lattice in 24D, constructed by Leech in 1967 and proved optimal by Cohn–Kumar–Miller–Radchenko– Viazovska in 2017. Self-dual, even, kissing number 196560.
Related: The Gosset Lattice , Golay Code, Viazovska's Theorems: and Close the Gap
Hermite constant
The supremum , taken over all -dimensional lattices. Measures the best achievable ratio of minimum-squared distance to fundamental volume; known exactly only for and .
Related: Packing Density and Center Density, Viazovska's Theorems: and Close the Gap
Quick Check
What is the kissing number of ?
has 240 minimum-norm nonzero vectors. In the construction, 112 of these come from (vectors of the form ) and 128 come from (vectors of the form with an even number of minus signs). The minimum vectors are exactly the roots of the Lie algebra.
Quick Check
What is the kissing number of ?
Minimum vectors of have the form . Choose nonzero positions: ways. For each, sign choices: .
Key Takeaway
The classical lattices — , , , , , , , — are the benchmarks every lattice code is measured against. In each dimension some specific lattice is densest; the table of , , , and controls what coding gain is available. The exceptional dimensions (Gosset ) and (Leech ) are uniquely optimal (Viazovska 2017); in other dimensions the best known lattice is believed optimal but not proven. Ch. 16 will build AWGN codes on these lattices; Ch. 17 will build LAST codes that shape over them.