Cognitive radios is one of the hot topics for emerging and future wireless commu- nication. It has been proposed as a suitable solution for the spectrum scarcity caused by the increase in frequency demand. The concept is based on allowing unlicensed users, called cognitive or secondary users, to share the unoccupied frequency bands with their owners, called the primary users, under constraints on the interference they cause to them. The objective of our work is to propose some enhancements to cognitive radio systems while taking into account practical constraints. Cogni- tive radios requires a capability to detect spectrum holes (spectrum sensing) and a scheduling flexibility to avoid the occupied spectrum and selectively use the empty spectrum (dynamic resource allocation). Thus, the work is composed of two main parts. The first part focuses on cooperative spectrum sensing. We compute in this part the analytical performance of cooperative spectrum sensing under non identical and imperfect channels. Different schemes are considered for the cooperation between users such as hard binary, censored information, quantized, and soft information. The second part focuses on the dynamic resource allocation. We first propose low-cost re- source allocation algorithms that use location information to estimate the interference to primary users to replace absence of instantaneous channel state information. We extend these algorithms to handle practical implementation constraints such as dis- 5 crete bit-loading and collocated subcarriers allocations. We then propose a reduced dimension approach based on the grouping of subcarriers into clusters and performing the resource allocation over clusters of subcarriers instead of single subcarriers. This approach is shown to reduce the computational complexity of the algorithm with lim- ited performance loss. In addition, it is valid for a generic set of resource allocation problems in presence of co-channel interference between users.