Abstract
In a multiuser communication system such as cognitive radio or digital subscriber lines, the transmission rate of each user is affected by the channel background noise and the crosstalk interference from other users. This paper presents an efficient ant colony optimization algorithm to allocate each user’s limited power on different channels for maximizing social utility (i.e., the sum of all individual utilities). The proposed algorithm adopts an initial solution that allocates more power on the channel with a lower background noise level. Besides, the cooling concept of simulated annealing is integrated into the proposed method to improve the convergence rate during the local search of the ant colony optimization algorithm. A number of experiments are conducted to validate the effectiveness of the proposed algorithm.
Highlights
In a modern communication system such as cognitive radio or digital subscriber lines (DSL), multiple users share the same frequency band and how to mitigate interference is a major design and management concern
We propose an efficient ant colony optimization algorithm to allocate each user’s limited power on different channels for maximizing social utility
We find that the proposed simulated annealing-based ant colony optimization (SAACO) algorithm spends less CPU time but obtains better solutions with higher social utilities than the simulated annealing (SA) approach Lin et al [12]
Summary
In a modern communication system such as cognitive radio or digital subscriber lines (DSL), multiple users share the same frequency band and how to mitigate interference is a major design and management concern. The frequency division multiple access (FDMA) mechanism is a standard approach to eliminate interference by dividing the spectrum into multiple tones and preassigning them to the users on a nonoverlapping basis. This approach may lead to high system overhead and low bandwidth utilization. Ye [2] proposed a competitive economy equilibrium solution that may achieve both social economic efficiency and individual optimality in dynamic spectrum management. Xie et al [13] proposed a decentralized tatonnement process for adjusting the prices to achieve a competitive equilibrium They showed that competitive equilibrium is the solution of a linear complementarity problem and can be computed efficiently. To evaluate each user’s utility, a commonly recognized utility for user i in communication is the Shannon utility [2, 16]: ui (xi, xi)
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