A Bayesian Game-Theoretic Approach for Distributed Resource Allocation in Fading Multiple Access Channels

The fading multiple access channel (MAC) is a basic wireless channel model that allows several transmitters connected to the same receiver to transmit over it and share its capacity. The capacity region of the fading MAC and the optimal resource allocation algorithms have been characterized and well studied in many pioneering works with different information assumptions [1–4]. However, in order to achieve the full capacity region, it usually requires a central computing resource (a scheduler with comprehensive knowledge of the network information) to globally allocate the system resources. This process is centralized, since it involves feedback and overhead communication whose load scales linearly with the number of transmitters in the network. In addition, with the fast evolution of wireless techniques, this centralized network infrastructure begins to expose its weakness in many aspects, for example, slow reconfiguration against varying environment, increased computational complexity, and so forth. This is especially crucial for femto-cell networks where it is quite difficult to centralize the information due to a limited capacity backhaul. Moreover, the high density of base stations would increase the cost of centralizing the information.

In recent years, increased research interest has been given to self-organizing wireless networks in which mobile devices allocate resources in a decentralized manner [5]. Tools from game theory [6] have been widely applied to study the resource allocation and power control problems in fading MAC [7], as well as many other types of channels, such as orthogonal frequency division multiplexing (OFDM) [8], multiple input and multiple output (MIMO) channels [9, 10], and interference channels [11]. Typically, the game-theoretic models used in these previous works assume that the knowledge, for example, channel state information (CSI), about other devices is available to all devices. However, this assumption is hardly met in practice. In practical wireless scenarios, mobile devices can have local information but can barely access to global information on the network status.

A static noncooperative game has been introduced in the context of the two-user fading MAC, known as "waterfilling game" [7]. By assuming that users compete with transmission rates as utility and transmit powers as moves, the authors show that there exists a unique Nash equilibrium [12] which corresponds to the maximum sum-rate point of the capacity region. This claim is somewhat surprising, since the Nash equilibrium is in general inefficient compared to the Pareto optimality. However, their results rely on the fact that both transmitters have complete knowledge of the CSI, and in particular, perfect CSI of all transmitters in the network. As we previously pointed out, this assumption is rarely realistic in practice.

Thus, this power allocation game needs to be reconstructed with some realistic assumptions made about the knowledge level of mobile devices. Under this consideration, it is of great interest to investigate scenarios in which devices have "incomplete information" about their components, for example, a device is aware of its own channel gain, but unaware of the channel gains of other devices. In game theory, a strategic game with incomplete information is called a "Bayesian game." Over the last ten years, Bayesian game-theoretic tools have been used to design distributed resource allocation strategies only in a few contexts, for example, CDMA networks [13, 14], multicarrier interference networks [15]. The primary motivation of this paper is therefore to investigate how Bayesian games can be applied to study the resource allocation problems in the fading MAC. In some sense, this study can help to design a self-organizing femto-cell network where different frequency bands or subcarriers are used for the femto-cell coverage, for example, different femto-cells operate on different frequency bands to avoid interference.

In this paper, we introduce a Bayesian game-theoretic model to design and analyze the resource allocation problem in a fading MAC, where users are assumed to selfishly maximize their ergodic capacity with incomplete information about the fading channel gains. In such a game-theoretic study, the central question is whether a Bayesian equilibrium exists, and if so, whether the network operates efficiently at the equilibrium point. We prove that there exists exactly one Bayesian equilibrium in our game. Furthermore, we study the network sum-rate maximization problem by assuming that all users coordinate to an optimization-based symmetric strategy. This centralized strategy is important when the fading processes for all users are relatively stationary and the global system structure is fixed for a long period of time. This result also serves as an upper bound for the unique Bayesian equilibrium.

The paper is organized in the following form: In Section 2, we introduce the system model and state important assumptions. In Section 3, the -user MAC is formulated as a static Bayesian game. In Section 4, we characterize the Bayesian equilibrium set. In Section 5, we give a special discussion on the optimal symmetric strategy. Some numerical results are provided to show the efficiency of the Bayesian equilibrium in Section 6. Finally, we close with some concluding remarks in Section 7.

We model the -user fading MAC as a Bayesian game, in which users do not have complete information. In a -user MAC, to have "complete information" means that, at each time , the channel gain realizations are known at all the transmitters, denoted by . Any other condition corresponds to a situation of incomplete information. In this paper, the "incomplete information" particularly refers to a situation where each only knows its own channel gain realization , but does not know the channel gains of other transmitters . We will denote by the channel gain variable of user , whose distribution is assumed to be stationary and ergodic in this section.

In such a communication system, the natural object of each user is to maximize its ergodic capacity subject to an average power constraint, that is,

(4)

where and are transmit power strategy and average power constraint of user , respectively. Under the assumption that each user has incomplete information about the channel gains, user 's strategy is defined as a function of its own channel gain , that is, . Note that (4) implies that user should know at least the statistics of other users' channels.

For a given set of power strategies , , the single-user maximization problem (4) is a convex optimization problem [18]. Via Lagrangian duality, the solution is given by the following equation:

(5)

where the dual variable is chosen such that the power constraint in (4) is satisfied with equality. However, the solution of (5) depends on which user does not know, and the same holds for all other users. Thus, in order to obtain the optimal power allocation, each user must adjust its power level based on the guess of all other users' strategies. Now, given the following game model, each user is able to adjust its strategy according to the belief it has about the strategy of the other user.

The -player MAC Bayesian game can be completely characterized as

(6)

1. (i)

   Player set: .

    
2. (ii)

   Type set: (" " stands for the Cartesian product) where . A player's type is defined as its channel gain, that is, .

    
3. (iii)

   Action set: where . A player's action is defined as its transmit power, that is, .

    
4. (iv)

   Probability set: where , we have and .

    
5. (v)

   Payoff function set: where is chosen as player 's achievable rate

    

(7)

In games of incomplete information, a player's type represents any kind of private information that is relevant to its decision making. In our context, the fading channel gain is naturally considered as the type of user , since its decision (in terms of power) can only rely on . Note that this is a continuous game (a continuous game extends the notion of a discrete game (where players choose from a finite set of pure strategies), it allows players to choose a strategy from a continuous pure strategy set) with discrete states, since each player's action can take any value satisfying the constraint and the channel state is finite .

The Bayesian game-theoretic approach provides us a better understanding of the wireless resource competition existing in the fading MAC when every mobile device acts as a selfish and rational decision maker (this means a device always chooses the best response given its information). The advantage of this model is that it mathematically captures the behavior of selfish wireless entities in strategic situations, which can automatically lead to the convergence of system performance. The introduced Bayesian game-theoretic framework fits very well the concept of self-organizing networks, where the intelligence and decision making is distributed. Such a scheme has apparent benefits in terms of operational complexity and feedback load.

However, from the global system performance perspective, it is usually inefficient to give complete "freedom" to mobile devices and let them take decisions without any policy control over the network. It is very interesting to note that a similar situation happens in the market economy, where consumers can be modeled as players to complete for the market resources. In the famous literature The Wealth of Nations, Adam Smith (a Scottish moral philosopher, pioneer of political economy, and father of modern economics) expounded how rational self-interest and competition can lead to economic prosperity and well-being through macroeconomic adjustments. For example, all states today have some form of macroeconomic control over the market that removes the free and unrestricted direction of resources from consumers and prices such as tariffs and corporate subsidies.

In particular, wireless service providers would like to design an appropriate policy to efficiently manage the system resource so that the global network performance can be optimized or enhanced to a certain theoretical limit, for example, Shannon capacity or capacity region [20]. Apparently, a centralized scheduler with comprehensive knowledge of the network status can globally optimize the resource utility. However, this approach usually involves sophisticated optimization techniques and a feedback load that grows with the number of wireless devices in the network. Thus, the optimization-based centralized decision has to be frequently updated as long as the wireless environment varies, or the system structure changes, for example, a user joins or exits the network.

In this section, we consider that the channel statistics (fading processes) for all wireless devices are jointly stationary for a relatively long period of signal transmission, and the global system structure remains unchanged. In addition, we neglect the problem of computational complexity at the scheduler and the impact of feedback load to the useful data transmission rate. In this case, the network service provider would strictly prefer to use a centralized approach, that is, a scheduler assigns some globally optimal strategies to the wireless devices, guiding them how to react under all kinds of different situations. Based on the Bayesian game settings, we provide a special discussion on the optimal symmetric strategy design. Note that this result can be treated as a theoretical upperbound for the performance measurement of Bayesian equilibrium.

We now introduce a necessary assumption.

Assumption.

Mobile devices are designed to use the same power strategies, that is, they send the same power if their observations on the channel states are symmetric. In addition, we assume that the mobile devices have the same average power constraint, that is, .

5.1. Two Channel States

For simplicity of our presentation, We first consider the scenario of two users with two channel states. In fact, the analysis of multiuser MAC can be extended in a similar way. According to Assumption 6, we define

(20)

and we have . Write user 's average payoff as (Without loss of generality, we consider user in the following context, since the problem is symmetric for user )

(21)

Now, is transformed into a function of , write it as . To maximize the average achievable rate, user needs to solve the following optimization problem, as mentioned in (4)

(22)

Under Assumption 6, it can be shown that (due to the symmetric property) this single-user maximization problem is equivalent to the multiuser sum average rate maximization problem, that is, , which is our object in this section.

But unfortunately, may not be a convex function [18], so the single-user problem may not be a convex optimization problem. It can be further verified that is convex under some special conditions, depending on all the parameters , and . Here, we will not discuss all the convex cases, but only focus on the high SNR regime (meaning that the noise can be omitted compared to the signal strength). In this case, we have

(23)

This function is strict convex. To be more precise, it is decreasing on and increasing on , and the solution is given by

(24)

Note that in this setting the choice of the optimal symmetric strategy is to concentrate the full available power on a single channel state. The selection of the channel state on which to transmit depends not only on the channel conditions but also on the probability of the channel states. This result implies that, in the high SNR regime, the optimal symmetric power strategy is to transmit information in an "opportunistic" way. For a better understanding of the "opportunistic" transmission, the interested reads are referred to [2].

5.2. Multiple Channel States

In this subsection, we discuss the extension to arbitrary ( ) channel states. Note that the result of this subsection can also be applied to the case of two channel states.

Assumption.

Each user's channel gain has positive states, which are with probability , respectively (Without loss of generality, ), and we have .

Based on Assumption 6, we define , as the transmit power when a user's channel gain is . As previously mentioned, our object in this part is to maximize the sum ergodic capacity of the system, that is, . Under the symmetric assumption, this sum-ergodic-capacity maximization problem is equivalent to the following single-user maximization problem

(25)

where is now defined as . This optimization problem is difficult, since the objective function is again nonconvex in . However, we can consider a relaxation of the optimization by introducing a lower bound [21]

(26)

where and are chosen specified as

(27)

we say that the lower bound (26) is tight with equality at a chosen value .

Let us consider the lower bound (denoted as ) by using the relaxation (26) to the objective function in  (25)

(28)

which is still nonconvex, and so it is not concave in . However, with a logarithmic change of the following variables and constants: and , we can turn the geometric programming [18] associated with the objective function (28) into the following problem:

(29)

where is defined as

(30)

Now, it is easy to verify that the lower bound is concave in the transformed set , since the log-sum-exp function is convex. The constraints of the optimization problem are such that Slater's condition is satisfied [18]. So, the Karush-Kuhn-Tucker (KKT) condition of the optimization is sufficient and necessary for the optimality. Given the following Lagrangian dual function (denoted by ):

(31)

the KKT conditions are

(32)

where , and is a dual variable associated with the power constraint in (29).

Define , the equivalent KKT conditions can be simply written as a quadratic equation

(33)

where the parameters are expressed as

(34)

Note that and are functions of , we can write them as and . Since , the solution to the KKT conditions can only be one of the roots to the quadratic equation, that is,

(35)

where is chosen such that . Thus, for some fixed value of , we can directly apply (35) to maximize the lower bound (28). Then, it is natural to improve the bound periodically. Based on the discussion above, we propose the following algorithm, namely Lower Bound Tightening (LBT) algorithm

The algorithm convergence can be easily proved, since the objective is monotonically increasing at each iteration. However, the global optimum is not always guaranteed, due to the nonconvex property.

In this section, numerical results are presented to validate our theoretical claims. For Figures 1 and 2, the network parameters are chosen as and .

First, we show the existence and uniqueness of Bayesian equilibrium in the scenario of two-user fading MAC. In Figure 1(a), we assume the channel gains are ; in Figure 1(b), we assume . On both and axis, and represent the power allocated by user and user when the channel gain is . The curves and represent the best-response functions of user and user , respectively. As expected, the Bayesian equilibrium is unique in both cases, that is, (0.6,0.6) and (0.5,0.5).

Second, we investigate the efficiency of Bayesian equilibrium from the viewpoint of global average network performance. The axis, SNR is defined as the ratio between the power constraint and the noise variance . In Figure 2(a), again, we assume ; in Figure 2(b), we assume . The curve "Pareto" represents the Nash equilibrium in the waterfilling game, in which users have complete information. This gives the upper bound for our Bayesian equilibrium, since it is also the Pareto optimal solution [7]. The curve "Uniform" represents the time-domain uniform power allocation. Since this is the strategy when users have no information about the channel gains, it corresponds obviously to a lower bound. The curve "Symmetric" represents the optimal symmetric strategy presented in Section 5. This can be treated as a weaker upper bound (inferior to the Pareto optimality) for the Bayesian equilibrium. From the slopes of these curves, we can clearly observe the inefficiency of the Bayesian equilibrium, especially in the high SNR regime. This can be explained as follows: in our game , users with incomplete information improve the global network performance (comparing to the scenario in which the users have no information), however, it does not improve the performance slope.

Finally, we show the convergence behavior of the lower bound tightening (LBT) algorithm. In Figure 3, we choose the parameters as . The sum capacity versus the SNR are plotted for five iterations. The upper bound is achieved by exhaustive search. As expected, one can easily observe the convergence behavior. In the low SNR regime, we can find that the algorithm converges to the local instead of the global maximum. However, we also find that the performance of the local optimum is improved while the SNR is increasing.

We presented a Bayesian game-theoretic framework for distributed resource allocation in fading MAC, where users are assumed to have only information about their own channel gains. By introducing the assumption of finite channel states, we successfully found a analytical way to characterize the Bayesian equilibrium set. First, we proved the existence and uniqueness. Second, the inefficiency was shown from numerical results. Furthermore, we analyzed the optimal symmetric power strategy based on the practical concerns of resource allocation design. Future extension is considered to improve the efficiency of Bayesian equilibrium through pricing or cooperative game-theoretic approaches.

Algorithm 1: Lower Bound Tightening (LBT).

Initialize   ; ; , for ,

repeat

  repeat

    for   to   do

       update using  (34)

    end for

  until  

  for   to and to   do

  end for

until converge