@PhdThesis{diss_mods_00020861, author = {Yaqot, Abdullah}, title = {Adaptive Precoding and Resource Allocation in Cognitive Radio Networks}, year = {2017}, publisher = {Christian-Albrechts-Universit{\"a}t zu Kiel}, address = {Kiel}, keywords = {Cognitive radio; next generation networks; adaptive precoding; precoding diversity; multiuser MIMO; OFDMA; resource allocation; convex optimization theory; Lagrange multiplier theory}, abstract = {In this thesis, we develop efficient resource allocation and adaptive precoding schemes for two scenarios: multiuser MIMO-OFDM and multiuser MIMO based CR networks. In the context of the multiuser MIMO-OFDM CR network, we have developed resource allocation and adaptive precoding schemes for both the downlink (DL) and uplink (UL). The proposed schemes are characterized by both computational and spectral efficiencies. The adaptive precoder operates based on generating degrees of freedom (DoF). The resource allocation has been formulated as a sum-rate maximization problem subject to the upper-limit of total power and interference at primary user constraints. The formulated optimization problem is a mixed integer programming having a combinatorial complexity which is hard to solve, and therefore we separated it into a two-phase procedure to elaborate computational efficiency: Adaptive precoding (DoF assignment) and subcarrier mapping. From the implementation perspective, the resource allocation of the DL is central based processing, but the UL is semi-distributed based. The DL and UL problems are sorted out using the Lagrange multiplier theory which is regarded as an efficient alternative methodology compared to the convex optimization theory. The solution is not only characterized by low-complexity, but also by optimality. Numerical simulations illustrate remarkable spectral and SNR gains provided by the proposed schemes.}, url = {https://macau.uni-kiel.de/receive/diss_mods_00020861}, file = {:https://macau.uni-kiel.de/servlets/MCRFileNodeServlet/dissertation_derivate_00007079/AP_and_RA_in_CR_Networks.pdf:PDF}, language = {en} }