NUST Institutions Library Catalogue

This thesis presents research contributions to the developing field of Compressed Sensing
(CS) and its applications in wireless communications. The novel approach of CS
has opened new venues to tackle wireless communication challenges. Peculiar nature
of problems while communicating through wireless medium provides opportunities for
CS based solutions.
In the first contribution, a detailed survey on sensing matrices and wireless applications
specific recommendation is provided. An analysis of its main characteristics and
sparse signal recovery guarantees based on these properties is provided. Moreover, a
qualitative comparison of sensing matrices in real and complex generated through different
techniques is carried out. Furthermore, an analysis based on their application
specific desirable features is performed.
Algorithms for coherence optimization of Rank-1 Grassmannian codebooks are provided
in the second contribution. The contribution reduces processing time for state
of the art algorithms, namely Best Complex Antipodal Spherical Code (BCASC) and
Coherence Based Grassmannian Codebook (CBGC) for generating deterministic sensing
matrices achieving coherence lower bound. The trimming of runtime is performed
by preventing the algorithms to remain stagnant or divergent during the process. The
proposed modifications preserve the low coherence quality of the generated matrices
in orders of magnitude lesser time. Reduction in processing time allows generation of
larger codebooks which are required in many wireless applications.
The third contribution provides coherence optimized channel estimation for mmwave
massive multiple input multiple output (MIMO) communication. Millimeterwave
(mm-wave) communication is inherently sparse due to its peculiar propagation
characteristics with limited diffraction, penetration and small number of scatterers.
Based on these characteristics, a 2-dimensional non-uniform quantized azimuth and
elevation angle grid antenna array response is proposed. Later coherence minimized
training vectors generation algorithm is proposed by extending the CBGC algorithm.
The proposed training vectors minimize the coherence with respect to the antenna array
response. Open-loop channel estimation of the mm-wave channel performed by the
proposed method improves normalized mean squared error performance and spectral
efficiency in comparison to the existing techniques.
Frequency hopping (FH) radio network identification in a wideband using CS is performed
in the fourth contribution. Radio networks operating in overlapping wideband
spectrum are sensed using an established CS technique of multi-coset sampling. The
acquired features are then used to isolate and differentiate between different radio networks
based on their respective attributes.