A Layered Decomposition Framework for Resource Allocation in Multiuser Communications

The resource allocation problem for multiuser channels is decomposed into two layers. The lower layer is the weighted sum rate maximization, which is widely considered for many different multiuser channels. The weighted sum rate maximization is employed as a subroutine and called the upper layer. The upper layer is the optimization of dual variables for maximizing a joint utility function, which is very general and includes proportional fairness and max-min fairness as special cases. This layered approach decouples the physical-layer technologies from system-layer consideration. For example, if we want to evaluate a new objective in resource allocation, changes are required only in the outer loop, whereas the inner loop remains the same. This induces flexibility in the software structure. To numerically obtain the solution, a Gauss-Seidel-type algorithm is proposed, and its effectiveness in achieving proportional fairness in the parallel Gaussian broadcast channel is demonstrated by computer simulation.

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