The inventors have studied a cross-layer optimization aimed to provide a strategy for the optimal allocation of network resources (such as sub-carriers assignments and data rates) to a set of users in consideration of system-wide fairness and spectral efficiency.
A radio resource scheduler in a base station typically performs the allocation of subcarriers and data transmission rate to users connected to the base station for each time-slot or multiple time-slots (an epoch).
In a multi-user system, maximum spectral efficiency (bit/s/Hz) can be achieved by first measuring or estimating the instantaneous channel gain between each sub-carrier and each user, mapping each such value to an achievable data rate and then allocating each sub-carrier to the particular user for which the throughput (over that sub-carrier) will be maximized. When repeated for each sub-carrier in the transmission band, this simple approach maximizes the total throughput but fails to take into account the user-perceived notion of fairness, since those users who may systematically experience poor channel conditions (such as those who are close to the cell edge) will always receive fewer network resources (e.g., sub-carriers) than those who may generally experience more favorable channel conditions. On the other hand, complete fairness (whereby users are simply allocated an equal number of sub-carriers regardless of actual channel capacity) results in a loss of spectral efficiency for the entire network.
Cross-layer optimization is a tool that can be used to balance the competing interests of spectral efficiency and user-perceived fairness in the allocation of network resources. It is an optimization approach whereby the objective function of the optimization problem is a utility function.
In almost all wireless applications, a reliable data transmission rate, r, is the most important factor in determining the satisfaction of users. Therefore, a utility function of the data rates, U(r), should be a non-decreasing function of its argument. In particular, in a traditional network optimization, where the objective is to maximize the aggregate throughput, the utility is a linear function of the data rate, such as, U(r)=r. A utility function is typically a convex nonlinear function of the data rate or other, measurable network resources. Therefore, the utility optimization framework can be regarded as a general extension of the traditional problem of network optimization.
The utility functions can serve as an optimization objective for the adaptive physical and MAC layer techniques. Consequently, they can be used to optimize radio resource allocation for different applications and to build a bridge amongst the physical, medium access control (MAC) layer, and higher layers. In practice, utility functions cannot always be obtained through theoretical derivations, but may be estimated from subjective surveys. For example, for best effort traffic, a well-accepted utility function that is derived based on survey material isU(r)=0.16+0.8 ln(r−0.3)  (1)where r is in units of k b/s.
In order to prevent assigning too many resources to a user that already has good channel conditions, the utility function is typically selected such that its slope decreases when data rate becomes large, in accordance with the characteristic behavior of (1). Consequently, the fairness is easily taken into account.
In the conventional case, for which U(r)=r and the goal is to maximize the sum throughput for all users that are active at a given moment, the slope of the utility curve is a constant value of one for each user (i.e., U′(r)=1). Consequently, max-utility is achieved by assigning each sub-carrier to the user with the best channel quality, which may result in a long-term policy that unfairly allocates resources to users who typically have good channel quality and starve those users whose channel conditions are systematically less favorable (e.g., those user who are located at the cell's edge). However, the derivative of the utility function is what actually controls the threshold of comparison for the distribution of resources. Hence, a non-linear utility function whose slope decreases when the data rate becomes large may constitute a basis for a more equitable resource distribution policy.
The allocation and management of resources is critical for wireless networks in which a scarce collection of resources is shared by multiple users. In the current layered networking architecture, each layer is designed and operated independently. However, wireless channels suffer from time-varying multi-path fading conditions and the statistical channel characteristics of different users are different. The sub-optimality and inflexibility of this architecture result in inefficient resource use in wireless networks. Hence, there is a need for an integrated adaptive design across different layers.
Fairness and efficiency are two important issues in resource allocation for wireless networks. Traditionally, spectral efficiency is evaluated in terms of the aggregate throughput, which favors those users who are either close to the base station or who have good channel conditions. Consequently, users who are far away from the base station or who systematically have poor channel conditions may be given fewer resources. However, absolute fairness—which allocates the same quantity of system resources to all users—may lead to low spectral efficiency. Therefore, there is a tradeoff between the issues of fairness and efficiency when designing the strategies for wireless resource allocation.
These issues of efficient and fair resource allocation have been long studied in economics, where utility functions are used to quantify the benefit of using certain resources. Recently, utility theory has also been studied for its use in communication networks as a mechanism to evaluate the degree to which a network satisfies service requirements of users' applications. In fixed, wire-line networks, utility and pricing mechanisms have been used for flow control, congestion control and routing. In wireless networks, the pricing of uplink power control in code division multiple access (CDMA) has been investigated. Utility-based power allocation in CDMA downlinks for both voice and data applications have been proposed.