Mathematical optimisation and signal processing techniques in wireless relay networks

With the growth of wireless networks such as sensor networks and mesh networks, the challenges of sustaining higher data rates and coverage, coupled with requirement for high quality of services, need to be addressed. The use of spatial diversity proves to be an attractive option due to its ability to significantly enhance network performance without additional bandwidth or transmission power. This thesis proposes the use of cooperative wireless relays to improvise spatial diversity in wireless sensor networks and wireless mesh networks. Cooperation in this context implies that the signals are exchanged between relays for optimal performance. The network gains realised using the proposed cooperative relays for signal forwarding are significantly large, advocating the utilisation of cooperation amongst relays. The work begins with proposing a minimum mean square error (MMSE) based relaying strategy that provides improvement in bit error rate. A simplified algorithm has been developed to calculate the roots of a polynomial equation. Following this work, a novel signal forwarding technique based on convex optimisation techniques is proposed which attains specific quality of services for end users with minimal transmission power at the relays. Quantisation of signals passed between relays has been considered in the optimisation framework. Finally, a reduced complexity scheme together with a more realistic algorithm incorporating per relay node power constraints is proposed. This optimisation framework is extended to a cognitive radio environment where relays in a secondary network forward signals without causing harmful interferences to primary network users.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

A GMD-based precoding scheme for downlink multiuser multistream MIMO channels

In order to obtain a good balance of bit error rate (BER) across channels, the geometric mean decomposition (GMD) is introduced to replace the singular value decomposition (SVD) for precoding in the downlink of a multiuser multistream multiple-input multiple-output (MIMO) system. By combining GMD with a block diagonalization method, we obtain two kinds of precoding schemes: iterative nullspace-directed GMD and non-iterative nullspace-directed GMD. Considering their respective advantages and disadvantages, a mixed nullspace-directed GMD is proposed to solve the convergence related problems of the iterative method. Furthermore, the computational complexity of the mixed scheme is similar to the iterative scheme under the same conditions. The simulation results show that the average BER performance of the block diagonalization method based on GMD is better than the same method based on SVD, and the mixed nullspace-directed GMD outperforms the iterative nullspace-directed GMD and the non-iterative nullspace-directed GMD

Joint Beamforming and User Maximization Techniques for Cognitive Radio Networks Based on Branch and Bound Method

We consider a network of cognitive users (also referred to as secondary users (SUs)) coexisting and sharing the spectrum with primary users (PUs) in an underlay cognitive radio network (CRN). Specifically, we consider a CRN wherein the number of SUs requesting channel access exceeds the number of available frequency bands and spatial modes. In such a setting, we propose a joint fast optimal resource allocation and beamforming algorithm to accommodate maximum possible number of SUs while satisfying quality of service (QoS) requirement for each admitted SU, transmit power limitation at the secondary network basestation (SNBS) and interference constraints imposed by the PUs. Recognizing that the original user maximization problem is a nondeterministic polynomial-time hard (NP), we use a mixed-integer programming framework to formulate the joint user maximization and beamforming problem. Subsequently, an optimal algorithm based on branch and bound (BnB) method has been proposed. In addition, we propose a suboptimal algorithm based on BnB method to reduce the complexity of the proposed algorithm. Specifically, the suboptimal algorithm has been developed based on the first feasible solution it achieves in the fast optimal BnB method. Simulation results have been provided to compare the performance of the optimal and suboptimal algorithms