4.1. Definition of Bayesian Equilibrium
What we can expect from the outcome of a Bayesian game if every selfish and rational (rational player means a player chooses the best response given its information) participant starts to play the game? Generally speaking, the process of such players' behaviors usually results in a Bayesian equilibrium, which represents a common solution concept for Bayesian games. In many cases, it represents a "stable" result of learning and evolution of all participants. Therefore, it is important to characterize such an equilibrium point, since it concerns the performance prediction of a distributed system.
Now, let
denote the strategy profile where all players play
except player
who plays
, we can then describe player
's payoff as
Definition 2 (Bayesian equilibrium).
The strategy profile
is a (pure strategy) Bayesian equilibrium, if for all
, and for all
and
where we define
.
From this definition, it is clear that at the Bayesian equilibrium no player can benefit from changing its strategy while the other players keep theirs unchanged. Note that in a strategic-form game with complete information each player chooses a concrete action, whereas in a Bayesian game each player
faces the problem of choosing a set or collection of actions (power strategy
), one for each type (channel gain
) it may encounter. It is also worth to mention that the action set of each player is independent of the type set, that is, the actions available to user
are the same for all types.
4.2. Characterization of the Bayesian Equilibrium Set
It is well known that, in general, an equilibrium point does not necessarily exist [
6]. Therefore, our primary interest in this paper is to investigate the
existence and
uniqueness of a Bayesian equilibrium in
. We now state our main result.
Theorem 3.
There exists a unique Bayesian equilibrium in the
-user
game
.
Proof.
It is easy to prove the existence part, since the strategy space
is convex, compact, and nonempty for each
; the payoff function
is continuous in both
and
;
is concave in
for any
[
6].
In order to prove the uniqueness part, we should rely on a sufficient condition given in [
19]: a non-cooperative game has a unique equilibrium, if the nonnegative weighted sum of the payoff functions is
diagonally strictly concave. We firstly give the definition.
Definition 4 (diagonally strictly concave).
A weighted nonnegative sum function
is called diagonally strictly concave for any vector
and fixed vector
, if for any two different vectors
, we have
where
is called pseudogradient of
, defined as
We start with the following lemma.
Lemma 5.
The weighted nonnegative sum of the average payoffs
in
is diagonally strictly concave for
, where
is a positive scalar,
is a vector whose every entry is
.
Proof.
Write the weighted nonnegative sum of the average payoffs as
where
is the transmit power vector and
is a nonnegative vector assigning weights
to the average payoffs
, respectively. Similar to (11), we let
be the pseudogradient of
. Now, we define
the transmit power of player
when its channel gain is
. Since we have shown from the Lagrangian that, at the equilibrium, the power constraint is satisfied with equality, that is,
, we can write
, as the transmit power when its channel gain is
. Therefore, it is easy to find that the average payoff
can be actually transformed into a weighted sum-log function as follows:
where
represents the index for different jointly probability events,
represents the corresponding probability for event
that are related to the probabilities
, and
and
represent some positive and nonzero real numbers that are related to the channel gains
. Note that the following conditions hold for all
:
Now, we can write the pseudogradient
as
where the function
is defined as
To check the diagonally strictly concave condition (10), we let
be two different vectors satisfying the power constraint, and define
where
and
are defined as
Since
are assumed to be two different vectors, we must have
. Now, we can draw a conclusion from the equation above:
. This is because:
the first part
for all
, since
and
for all
;
the second part
for all
, and there exists at least one nonzero term
, due to
and
for all
. Therefore, the summation of all the products of the first and the second terms must be positive. From Definition 4, the sum-payoff function
satisfies the condition of diagonally strictly concave. This completes the proof of this lemma.
Since our sum-payoff function
given in (12) is diagonally strictly concave, the uniqueness of Bayesian equilibrium in our game
follows directly from [
19, Theorem
].