Abstract
This paper investigates how to perform optimal cooperative power control for the coexistence of heterogeneous multihop networks. Although power control on the node level in multihop networks is a difficult problem due to its large design space and the coupling relationship of power control with scheduling and routing, we formulate a multiobjective optimization problem for the total power consumption of the two heterogeneous multihop networks with discretized power level. We reformulate the nonlinear constraint (relationship between power and capacity) into the linear one by piecewise linearization procedure and offer an in-depth study of cooperative power control in terms of its optimal power—the minimum power consumption with discretized power level for both heterogeneous multihop networks. Through a novel approach based on adaptive weighted-sum method, we transform the multiobjective optimization problem into a single-objective optimization problem and find the set of Pareto-optimal points iteratively. Using the Pareto-optimal points, we construct the minimum power curve. Using numerical results, we demonstrate that it can save more energy with cooperative power control than the case without cooperative power control.
Highlights
The ever-increasing number of wireless systems leads to the scarcity of available spectrum
We explored the optimal power curve with discretized power level for multiple heterogeneous multihop networks
We formulated a multiobjective optimization problem based on power control on node level and developed a novel approach based on adaptive weighted-sum method
Summary
The ever-increasing number of wireless systems leads to the scarcity of available spectrum. In order to avoid interference and achieve optimal network performance under the paradigm of spectrum sharing, it inevitably leads to cooperative power control between multiple heterogeneous multihop networks, which is based on the node level. For a Pareto-optimal solution α†, the corresponding objective pair (U†, V†) is called a Pareto-optimal point. For a Pareto-optimal point (U†, V†), there does not exist another feasible solution α with objective pair (U, V) such that U ≤ U† and V < V†, or U < U† and V ≤ V†. This means that it is impossible to further decrease any one objective while holding up the other. For a feasible solution α∗ with corresponding objective pair (U∗, V∗), if there does not exist any other solution α with its objective pair (U, V) such that U < U∗ and V < V∗, solution α∗ is called a weakly
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