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Due to the fast changing wireless communication standards coupled with strict performance constraints, the demand for flexible yet high-performance architectures is increasing. To tackle the flexibility requirement, Software-Defined Radio (SDR) is emerging as an obvious solution, where the underlying hardware implementation is tuned via software layers to the varied standards depending on power-performance and quality requirements leading to adaptable, cognitive radio. To design the hardware architecture for SDR is an interesting challenge, which involves determining the perfect balance of flexibility and performance for the target algorithmic kernels. In this paper, we conduct such a design case study for representatives of two complexity classes of WCDMA channel estimation algorithms. The two algorithms, polynomial channel estimation and weighted multi-slot averaging, differ also significantly in their algorithmic performance, difference which can be exploited in cognitive radio. Furthermore, we propose new design guidelines for highly specialised architectures that provide just enough flexibility to support multiple applications, targeting adaptability, low power and high-performance. Our experiments with various design points show that the resulting architecture meets the performance constraints of WCDMA and offers weak programmability to tune the architecture depending on power/performance constraints of SDR.