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5.4 Proofs

5.4.4 Proof of Theorem 5.2

In general, Theorem 5.2 is a wider and stronger generalization of Theorem 5.1.

In what follows, we first sketch the main steps in the proof, which is similar to that of Theorem 5.1, and more details will be given afterwards.

1. As Ge2[M] is the induced message conflict subgraph with message set M, the symmetric DoF value achieved by orthogonal schemes is the inverse of the fractional chromatic number of this conflict graph, i.e., dsym= 1

f(Ge2[M]).

2. Since G is a chordal bipartite graph, which is weakly chordal [108], then the conflict graph Ge2 is also weakly chordal [114] and therefore perfect. According to the definition of perfect graphs, for any induced subgraph with vertex set M, we have (Ge2[M]) =!(Ge2[M]).

3. For every clique in induced conflict subgraph G2e[M], the involved messages form an acyclic demand graph. The clique with largest size dominates the symmetric DoF. As such, the symmetric DoF are outer bounded by the inverse of clique number ofGe2[M], i.e.,dsym!(G21

e[M]). 4. Due to the relation ! !f = f  and =! for perfect graphs

Ge2[M], it follows that ! = !f = f = and thus the achievability coincides with the outer bound.

The detailed proofs are presented as follows.

Step I

As proved in Lemma 5.1, Ge2[M] is the message conflict graph of G with regard to message set M. Following the same arguments in the proof of Theorem 5.1, the adjacent vertices (messages) inGe2[M] should be assigned with di↵erent colors. A proper color assignment strategy corresponds to an orthogonal scheduling scheme. As such, the maximum symmetric DoF achieved by orthogonal schemes turn out to be the inverse of fractional chromatic number, i.e., 1

f(Ge2[M]). Thus,dsym = 1

f(Ge2[M]) is best achievable symmetric DoF by the orthogonal scheme.

Step II

First, the topology graphGis a chordal bipartite graph, and thus it is weakly chordal [108]3.

Then, we show that the message conflict graph Ge2 of chordal cellular networks is weakly chordal as well with aid of the following lemma.

Lemma 5.6 ( [114]). Given a graph G, if G is weakly chordal, then Ge2 is also weakly chordal.

Proof. We sketch the proof in short and the detailed proof can be found in [114]. The proof is based on the following two facts:

3Note that the chordal bipartite graph is bipartite and weakly chordal, but not chordal.

Although this terminology has caused confusion, it becomes well-accepted by the graph theory community.

THE OPTIMALITY OF ORTHOGONAL ACCESS

• IfG has no induced cycles on at leastkvertices, where k 4, then Ge2

has no induced cycles on at least kvertices.

• If G is weakly chordal, then G2e does not contain the antihole as an induced subgraph.

If G is weakly chordal, thenG contains neither a hole nor an antihole, and thusGe2 is also weakly chordal according to the above two facts.

Lastly, weakly chordal graphs are perfect, such that for any induced subgraphGe2[M] by M, we have (Ge2[M]) =!(Ge2[M]).

Step III

For a given induced subgraph Ge2[M], every clique with involved messages forms an acyclic demand graph, whose cardinality yields an outer bound for the symmetric DoF according to Lemma 5.3. Thus, among all cliques, the tightest outer bound is given by the clique with the maximum size, i.e., clique number. As such, we have the outer bound

dsym  1

!(Ge2[M]) (5.36)

This is corroborated by the following lemma.

Lemma 5.7. Given a chordal bipartite graph G with message set M and its induced conflict subgraph Ge2[M], the demand graph of the associated messages of every clique is acyclic, such that the sum DoF value of these involved messages is no more than 1.

Proof. Let us consider the whole conflict graph Ge2 first. Given a set of messages WL = {Wi1i2, Wi3i4, . . . , Win 1in}, the associated vertices form a clique in Ge2. Suppose there exist a shortest chordless cycle in demand graph, which is corresponding to the following connectivity in network topology

Si1 !Di2 9Si3 !Di4 9Si5 !· · ·!Din 9Si1. (5.37) Because of the chordless cycle, all the sources are distinct, and so are the destinations. Otherwise, there should exist a shorter cycle. It is readily verified thatnhas to be even andn 6. It is because the demand graphs are bipartite graph, and for any two messages Wikik+1 and Wijij+1 in WL, Dij+1 9Sik andDik+1 9Sij should not present simultaneously, i.e., n6= 4, otherwise two messages Wikik+1 and Wijij+1 are not adjacent in Ge2, which results in a contradiction.

When n= 6, according to the cycle (5.37), it follows that

Si1 ⇢Di4, Si3 ⇢Di6, Si5 ⇢Di2. (5.38) Together with the desired links

Si1 !Di2, Si3 !Di4, Si5 !Di6 (5.39) we conclude that there is a chordless cycle in G, namely,

Si1 !Di2 Si5 !Di6 Si3 !Di4 Si1 (5.40) with length six.

When n 8, according to the cycle (5.37), it follows that

Si1 ⇢Di4, Di6, Di8, . . . , Si3 ⇢Di6, Di8, . . . (5.41) Si5 ⇢Di8, . . . , Di2, Si7 ⇢. . . , Di2, Di4. (5.42) ConsideringDi2, Si3, Di4, Si5, Di6, Si7, we have a chordless cycle with length six, i.e.,

Si3 !Di4 Si7 ⇢Di2 Si5 !Di6 Si3. (5.43)

Figure 5.6: Illustration of the proof for Lemma 5.7. The black and red dashed arrows form a cyclic demand graph, the black arrows and lines are edges in topology graph, and the purple lines indicate a cycle with length six in topology graph.

Thus, if there is a cycle in the demand graph corresponding to a clique inGe2 as shown in Fig. 5.6, then there is at least a cycle with length six in

THE OPTIMALITY OF ORTHOGONAL ACCESS

topology graph G. By contraposition, for a chordal bipartite graph G where no cycles with length greater than four exist, every clique with messagesWL in the conflict graph Ge2 leads to an acyclic demand graph with regard to WL.

In any subgraph induced byM, e.g.,Ge2[M], the clique inGe2[M] is still a clique in Ge2. Thus, the above argument still holds, i.e., each clique inGe2[M] corresponds to an acyclic demand graph. By [104, Lemma 1], it follows that the sum DoF value of the corresponding messages involved in the clique is no more than 1. This completes the proof.

In fact, based on the proof of Lemma 5.7, we can make a stronger claim.

Lemma 5.8. Any clique Q in Ge2 serves as an outer bound, i.e., X

q2Q

dq1, (5.44)

if the network topological graph G is C6-free, i.e., there do not exist induced subgraphs with length-six cycles in G.

Proof. The proof of this lemma can be directly extended from the above proof. Since if there exists a cycle in demand graphs, then these should exist a length-six cycle in G. By contraposition, ifG isC6-free, then cliques in Ge2

correspond to acyclic demand graphs, and thus serve as outer bounds.

By Lemma 5.3 and Lemma 5.7, the tightest outer bound on symmetric DoF is determined by the clique with the largest size. Thus, the clique number o↵ers an outer bounddsym!(Ge21[M]).

Step IV

Since Ge2 is weakly chordal and therefore perfect, every induced subgraph Ge2[M] is also perfect [108]. By the definition of perfect graph, we have (Ge2[M]) =!(Ge2[M]). Because fractional chromatic (resp. clique) number

f(Ge2[M]) [resp. !f(Ge2[M])] is the linear programming relaxation of chro-matic number (Ge2[M]) [resp. !(Ge2[M])], and f(Ge2[M]) and!f(Ge2[M]) are solutions of the dual problems [106], we have

!(Ge2[M])!f(Ge2[M]) = f(Ge2[M]) (Ge2[M]) (5.45) For the perfect graph Ge2[M], all these four quantities are equal. As such, orthogonal schemes achieve the optimal symmetric DoFdsym = (G21

e[M]) =

1

!(Ge2[M]).

Proof of Sum DoF

The proof of sum DoF is similar to that of Theorem 5.1. Given an induced conflict subgraphGe2[M], the messages in any independent set can be sched-uled at a single time slot without causing any mutual interference. As such, the independent set number yields the maximum achievable sum DoF

dsum=↵(Ge2[M]). (5.46) On the other hand, by Lemma 5.7, each clique in Ge2[M] leads to an acyclic demand graph outer bound, such that for the messages involved in the clique, the sum DoF are bounded by 1, i.e.,:

X

i2Cj

di 1, 8 Cj 2C(Ge2[M]) (5.47) whereC(Ge2[M]) is the collection of all possible cliques inGe2[M]. Suppose there exists a set of cliquesCc(Ge2[M]) such that each vertex is included at least once. Thus, we have

dsum= X

Cj2Cc

X

i2Cj

di |Cc|. (5.48) According to the definition of clique covering, the tightest outer bound is given by the clique covering number, i.e.,

dsum= X

Cj2Cc

X

i2Cj

di ✓(Ge2[M]) (5.49) Due to the duality between the maximum independent set (resp. maximum clique) problem and minimal clique covering (resp. minimum proper coloring) problem, in the induced subgraphGe2[M], we have

↵(Ge2[M]) =!(Ge2[M]), ✓(Ge2[M]) = (Ge2[M]) (5.50) whereGe2[M] is the complement graph ofGe2[M]. BecauseGe2 is perfect, then its complementGe2 is also perfect [108], such that

(Ge2[M]) =!(Ge2[M]) (5.51) whereGe2[M] is the induced subgraph of Ge2. It is obvious that Ge2[M] and Ge2[M] yield the same subgraph. Thus,↵(Ge2[M]) =✓(Ge2[M]), showing that the achievability and outer bound coincide. This completes the proof of sum DoF.

THE OPTIMALITY OF ORTHOGONAL ACCESS

Proof of DoF Region

Consider the conflict graph Ge2[M] with a message setM. BecauseGe2 is a weakly chordal graph and thus perfect, its induced subgraphGe2[M] is perfect as well [108]. The achievability and outer bound proofs are similar to those of Theorem 5.1.

Achievability

Regarding the graphs Ge2[M], given an independent (or stable) setS with S 2S(Ge2[M]), whereS(Ge2[M]) is the collection of all independent sets in

Similarly, we have the independent set polytope [112]

K(Ge2[M]) =

From Lemma 5.7, for every clique inG, the involved messages form an acyclic demand graph, and thus o↵er an outer bound. The outer bound region is exactly a relaxation of the independent set polytope by replacing 0 1 with real numbers, i.e., fractional independent set polytope,

Kf(Ge2[M]) = i.e., all the corner points have integral coordinates.

Clearly, it follows immediately that achievability coincides with outer bound. As such, the orthogonal schemes achieve the optimal DoF region.

All in all, orthogonal access has been shown to achieve the optimal symmetric DoF, optimal sum DoF and optimal DoF region, and therefore it is DoF optimal for all the chordal cellular networks.