A distributed algorithm for instantaneous allocation of discrete resources in heterogeneous networks

The objective of this paper is two fold: proposes a distributed resource optimization framework in the context of heterogeneous systems and optimizes allocation of discrete resources and bit rates to users within each system (network). In fact, most of existing wireless techniques use Adaptive Modulation and Coding technique where a finite set of Modulation and Coding schemes is available in the system. The bit rates to attribute to users are then selected among a finite set of available discrete rates. More specifically, the objective in this paper is to minimize to overall transmit power of the whole system while ensuring to each user a minimum data rate and fulfilling a maximum power constraint within each system. The use of centralized optimization frameworks to handle Radio Resource Management (RRM) problem in heterogeneous systems requires huge signaling overhead. In order to obtain distributed resource allocation, we approach the problem by using a convex optimization framework and thus split it into different subproblems each one performed by a given agent of the network(base station or user). We propose then a distributed algorithm based on successive iterations (negotiations) between the base stations and users. In each iteration, each network node (user or base station) has to handle a discrete optimization problem using an efficient (low complexity) algorithm described in the paper. By using in each iteration an appropriate update method, based on the subgradient of the dual Lagrangian objective function, our approach converges to the optimal solution with zero duality gap and after a finite number of iterations. Simulation results show good performance of the proposed algorithm and the convergence to the global optimal solution.