Abstract Pricing is often used in noncooperative games or Nash equilibrium problems (NEPs) to meet global constraints in cognitive radio networks. In this paper, we analyze the pricing mechanism for a class of solvable NEPs with global constraints, called monotone NEPs. In contrast to the ideal assumption of perfect measure of pricing functions, in practice pricing functions are often imperfectly known and subject to uncertainty. We theoretically analyze the impacts of bounded uncertainty and price-updating step sizes of imperfect pricing in globally constrained NEPs for cognitive radio networks.