Minimum Sum-rate

The condition for the nonemptiness of B(f#

α,≤) can be easily derived based on the submodularity of this base polyhedron.

Lemma 5.3.2. If 0 ≤ α < H(V), f_α# is intersecting submodular; If α ≥ H(V), f_α# is

submodular.

Proof. For function f_α#, we have

f_α#(X) + f_α#(Y)− f_α#(X∪Y)− f_α#(X∩Y) =      H(X) +H(Y)−H(X∪Y)−H(X∩Y) +α−H(V) X∩Y=∅ H(X) +H(Y)−H(X∪Y)−H(X∩Y) otherwise .

Due to the submodularity of the entropy function H, ifα≥ H(V), f_α#(X) + f_α#(Y)−

f#

α(X∪Y)− fα#(X∩Y)≥0,∀X,Y⊆ V, i.e., fα# is submodular; ifα< H(V), fα#(X) + f_α#(Y)− f_α#(X∪Y)− f_α#(X∩Y) ≥ 0,∀X,Y ⊆ V: X∩Y 6= ∅, i.e., f_α# is intersecting submodular.

For X⊆V, denote byΠ(X)the set that contains all partitions ofX. A partitionP

0 1 2 3 4 0 1 0 1

r

1

r

2

r

3

r(V) =

4

B(f

₄#

,≤)

P(f

# 4

,≤)

R

∗ NCO

(V) =

B(f

4#

,≤)∩Z

3

Figure 5.4: For the system in Example 5.2.1, when α = 4, the polyhedron P(f₄#,≤)

and the plane{rV∈R3: r(V) =4}intersect, i.e., B(f4#,≤) ={rV ∈RCO(V): r(V) =

4} 6= ∅. Also, R_NCO∗ (V) = B(f#

4,≤)∩Z3 = {(2, 1, 1),(3, 0, 1),(3, 1, 0)}. In this case,

there are three optimal rate vectors for the non-asymptotic model.

C,C0 ∈ P; (c) ∪_C∈PC = X. Denote Π0(X) = Π(X)\ {X} = {P ∈ Π(X): |P |> 1}. For a partitionP ∈Π(X), let

f_α#[P] =

∑

C∈P f_α#(C)

and ˆf_α#be theDilworth truncationof f_α# that is defined as [100] ˆ

f_α#(X) = min P ∈Π(X)f

#

α[P], ∀X⊆V. (5.5) We have ˆf_α# being a submodular function due to the intersecting submodularity of f_α#[164]. It is shown in [167] that, for a given value ofα, the minimal/finest and max-

imal/coarsest partitions that minimize minP ∈Π(X) fα#[P]exist.

12 _{Dilworth truncation}

is an important concept in CO. We will show in the following context that a condi- tion on the Dilworth truncation determines the nonemptiness of the base polyhedron B(f#

α,≤). In Section 5.5.2, we will show that this maximization problem can be solved in polynomial time by efficient algorithms for solving theDilworth truncation problem, the minimization problem in (5.5).

12_{In [167], it is shown that the minimizers of min}

P ∈Π(X)fα#[P]form a partition lattice, which is called the Dilworth truncation lattice, where the minimal/finest and maximal/coarsest minimizers uniquely exist.

Theorem 5.3.3. B(f_α#,≤)is nonempty, i.e.,αis achievable, and B(f_α#,≤) = B(fˆ_α#,≤)if and

only if

α= fˆ_α#(V). (5.6)

Proof. Whenα≥ H(V), f_α#is submodular according to Lemma 5.3.2. So,α= f_α#(V) =

ˆ

f_α#(V)andB(f_α#,≤)6=∅ [49]. When 0≤ α< H(V), due to the intersecting submod-

ularity of f_α#, B(f_α#,≤) 6= ∅ if and only if α = fˆ_α#(V) based on Lemma 5.A.1 in

Appendix 5.A.

In Theorem 5.3.3, ˆf#

α(V)determines the maximum sum-rate of all rate vectors in polyhedronP(f_α#,≤),13 whileαis the sum-rate for all rate vectors in the hyperplane

{rV ∈ R|V|: r(V) = α}. There are two situations: if α > fˆ_α#(V), P(f_α#,≤) does

not intersect with the hyperplane {rV ∈ R|V|: r(V) = α}; if α = fˆ_α#(V), P(f_α#,≤)

intersects with the hyperplane {rV ∈ R|V|: r(V) = α} at B(f_α#,≤). In the latter

case, B(f#

α,≤)6= ∅. Theorem 5.3.3 can also be interpreted by the principal sequence of partitions (PSP) in 5.5.1, where the comparison between the maximum sum-rate in P(f#

α,≤) and the sum-rate α of the hyperplane {rV ∈ R

|V|_{: r(V) =}

α}, or the

nonemptiness of the base polyhedron B(f_α#,≤), is fully characterized by a ˆf_α#(V)vs.

αplot.

Example 5.3.4. For the system in Example 5.2.1, it can be shown that: when α < 7₂, we

haveα> fˆ_α#(V); whenα≥ 7₂, we haveα= fˆ_α#(V).14 For example, in Fig. 5.2 whenα= 16₅,

one can show thatmax{r(V): rV ∈ P(f16/5# ,≤)}= fˆ16/5# (V) = 135 < α. So, P(f16/5# ,≤)

does not intersect with hyperplane{rV ∈R|V|:r(V) = 16₅}, i.e., B(fα#,≤) =∅. In Fig. 5.4, whenα=4, we have f₄# being

f₄#(∅) =0,f₄#({1}) =3,f₄#({2}) =1,f₄#({3}) =1,

f₄#({1, 2}) =4,f₄#({1, 3}) =4, f₄#({2, 3}) =3, f₄#({1, 2, 3}) =4 and the Dilworth truncation fˆ₄# being

ˆ

f₄#(∅) =0, ˆf₄#({1}) =3, ˆf₄#({2}) =1, ˆf₄#({3}) =1, ˆ

f₄#({1, 2}) =4, ˆf₄#({1, 3}) =4, ˆf₄#({2, 3}) =2, ˆf₄#({1, 2, 3}) =4. 13_{For determining the maximum sum-rate inP(f}#

α,≤), we have max{r(V):rV∈P(fα#,≤)}=r[P

∗_{] =}

∑C∈Pr(C) = fˆα#(V), whereP

∗ _{is the minimizer of min}

P ∈Π(V) fα#[P][49]. The detailed explanation is shown in Appendix 5.C.

14_{The two situations can be seen from the ˆf}#

One can show that max{r(V): rV ∈ P(f₄#,≤)} = fˆ₄#(V) = 4 = α and B(f₄#,≤) =

B(fˆ₄#,≤)6= ∅.

By comparing the values of f#

4 and fˆ4#, we can see that the Dilworth truncation tight-

ens the constraints in the polyhedron P(f₄#,≤). For example, the inequality r({2, 3}) ≤

f#

4({2, 3}) = 3 in P(f4#,≤) can be tightened by r({2}) ≤ f4#({2}) = 1 and r({3}) ≤

f₄#({3}) = 1 so that we have r({2, 3}) ≤ fˆ₄#({2, 3}) = 2 in P(fˆ₄#,≤). It also ex- plains that fˆ#

α(V)determines the maximum sum-rate over all rate vectors in the polyhedron P(f_α#,≤)[49].

Theorem 5.3.3 and the geometric relationship between P(f#

α,≤) and the hyper- plane {rV ∈ R|V|: r(V) = α} can also be interpreted by the concept of principal

sequence of partitions (PSP) in Section 5.5.1.

Corollary 5.3.5. ForP ∈Π0(V), define

ϕ(P) =

∑

C∈P

H(V)−H(C)

|P | −1 .

The minimum sum-rate in the asymptotic and non-asymptotic models are respectively RACO(V) = max P ∈Π0_(V)ϕ(P), (5.7a) RNCO(V) = max P ∈Π0_(V)ϕ(P) . (5.7b)

Proof. (5.6) in Theorem 5.3.3 is equivalent to α ≤ f_α#[P],∀P ∈ Π(V), which can be

converted toα≥ ϕ(P),∀P ∈ Π0(V). It gives rise to the expressions ofRACO(V)and

RNCO(V)in (5.7a) and (5.7b), respectively.

Corollary 5.3.5 states that the minimum sum-rate can be determined by a maxi- mization over all multi-way cuts ofV. Any partitionP ∈Π0(V)can be considered as a multi-way cut of the user setV. For anyC∈ P, the cut{C,V\C}imposes the SW constraintr(V\C)≥ H(V\C|C) = H(V)−H(C). By applying this to eachC ∈ P, we have ∑_C∈Pr(V\C) = (|P | −1)r(V) ≥ ∑C∈P H(V)−H(C)

, which imposes requirement or lower bound

r(V)≥

∑

C∈P

H(V)−H(C)

|P | −1 = ϕ(P)

Since the SW constraint applies to all the subset of V, an achievable sum-rate must satisfy the highest requirement imposed by ϕ(P)over all multi-way cuts, i.e., ϕ(P)

should be maximized over all P ∈ _Π0_{(V). Therefore, we have (5.7a) and (5.7b). We} call the mininal/finest maximizer of (5.7a) thefundamental partitionand denote it by

P∗_{. The fundamental partition is of great importance in many problems, e.g., the} secrecy capacity problem [151, 160, 161], the clustering problem [163, 168] and the optimal network attack problem [169].

Example 5.3.6. For the system in Example 5.2.1, by applying (5.7a)and (5.7b), we have RACO(V) = 7₂ and RNCO(V) = 4, which is consistent with the results in Examples 5.2.1

and 5.3.1. In addition, we haveP∗ _{={{1},{2},{3}}being the fundamental partition.}

In document Submodularity and Its Applications in Wireless Communications (Page 103-107)