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Monday, June 6, 2011

How Game Theory helps Resource Allocation in OFDMA


In 2001, the first IEEE 802.16 standard was approved, followed by several amendments years afterwards. The IEEE 802.16 specifies a non-line-of-sight environment for the frequency band of 2–11 GHz; this allows OFDM/OFDMA (orthogonal frequency division multiple access) to be included (WirelessMAN-OFDM and WirelessMAN-OFDMA), which can combat multipath propagation. However, the resource allocation schemes and algorithms remain an open issue.

Nowadays, wireless communication networks must support connectivity for not a single user but for multiple users, which can be technically referred to as multiple access. In multiple-access systems, several users who are connected to the network will have to share a common pool of system resources. Familiar examples include time division multiple access (TDMA), frequency-division multiple access (FDMA), and code division multiple access (CDMA), where users are assigned different timeslots, frequencies, or codes. More advanced technologies include OFDMA, where the common radio resource is a set of orthogonal frequency channels. Sharing of resources does not always mean that we divide the “cake” into different pieces and give one to each user. Resources (especially a spectrum) can also be reused to increase the utilization efficiency and accommodate more users, provided that careful designs are taken to ensure that conflict does not take place. Here, more than one user can share the same resource (e.g., frequency band) simultaneously. Obviously, they would cause interference to each other, but the effect of this
interference can be carefully controlled. Owing to this constraint, a performance limitation exists for every system. Another closely coupled issue in resource management is the need to support individual quality of service (QoS). This need stems from the fact that data traffic is becoming more and more complex, diversified, and application dependent. For example referring to the OSI seven-layer hierarchical model: on top lies the Application layer, which consists of various user application programs such as messaging, multimedia streaming, Internet browsing, and social blogging. Each type of traffic has its own profile, and supporting the various traffic profiles is known as provisioning of QoS. The evolution of these various traffic types makes it hard for us to predict the dominant ones in the future. That diversified trend of many emerging new traffic services will continue to grow. Future wireless networks, however, must be able to accommodate any type of application service, requiring a flexible and customizable network capable of quickly responding to market demand. In order to achieve this, it is extremely crucial to provide sufficient QoS to users and services.


Early systems such as first-generation mobiles employed fixed allocation, in which channels or timeslots are permanently assigned to users without overlapping. This is a simple design and does not require knowledge of the environment, but can result in a waste of resources. Unlike fixed resource allocation, dynamic allocation enables users to adapt parameters for the appropriate use and reuse of resources to minimize interference and increase overall network performance. In other words, it allocates resources according to users’ need and quality. The requirement for complex algorithms and detailed knowledge of\ the environment may prove to be a heavy burden, but can be overcome by today’s advances in hardware and computer technology. The types and nature of such complex algorithms are very diversified.

Game theory has long been exploited in microeconomics to deal with competition among selfish, intelligent decision makers. It is a useful tool for studying interactive behaviors individually and collectively under conflict of interest. Recently, it has been adopted by the communication engineering community to solve certain optimization problems, for example, CDMA power control, cognitive radio, and OFDMA resource allocation.

Some of the most attractive features of game theory that can be applied to the OFDMA resource allocation problem include;

- Distributed optimization: As communication systems grow larger and larger in scale, the traditional centralized allocation mechanism (in which a central authority monitors all the tasks) becomes more and more difficult and computationally infeasible. So one option is to allow each party to participate in the allocation process. Non-cooperative game theory that specializes in solving conflict in a distributed context can be very useful in this situation.

- Inherent risks and uncertainties: On very few occasions the outcome of a game can be predicted with exact certainty due to randomness and imperfect information; hence players have to face the risks of loss in utility. The same argument applies very well to wireless communication systems where users cannot have full knowledge of the channel conditions of others to avoid interference.

- Rational decision making: Intelligence and rationality are two basic assumptions in game theory. Ironically, human decision making hardly satisfies the two assumptions in the strict sense. However, if we consider a game among various computer stations, which are programmed to always aim at maximizing a predefined objective function, then we can expect the two assumptions to hold.

Game theory can therefore be used to adaptively allocate power and bandwidth resources in an OFDMA system in order to obtain optimized transmission rates as well as efficient power consumption. The OFDMA single-cell and multi-cell scenarios with distributed users competing for resources fit into the game theoretical models.

In the multi-cell scenario, players are the BSs of the various cells, who need to distribute the OFDMA subcarriers and power to their MSs scattered around the cell and at the same time try to avoid the potentially high co-channel interference from neighboring cells. The joint carrier power assignment problem is a generic non-cooperative game equivalent to a hard optimization problem, and is often broken into smaller games of power minimization and rate maximization in consequence.

For a single-cell scenario, the cooperative game theoretical approach is used. Distributed mobile users within the same cell act together to find the most efficient and fairest rate allocation, and the BS may serve as a network hub for users to share information and carry out their resource bargaining.

Due to the game theoretical framework, novel viewpoints were offered into the traditional optimization problem associated with resource allocation in wireless-access systems like OFDMA. However, game theory alone does not solve the optimization problem. Instead it provides rather theoretical solution concepts such as Nash equilibrium allocation points and the different bargaining solutions, and thus makes way for optimization techniques and algorithms, which are still the backbone of the problem in question, to compute those solutions effectively.


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