A los gobiernos nacionales, regionales y locales

Conclusion

Today’s world is experiencing an unprecedented proliferation of wireless devices and services. This fact, coupled with the further significant anticipated growth, has led to a continuously increasing demand for higher transmission rates in wireless communications and networks. This is a great challenge given the limited availability of radio spectrum and the problem becomes even more acute by a rigid regulatory framework and large spectrum underutilisation. The latter has prompted regulators to promote the concept of opportunistic spectrum access, which allows secondary users (SU) to access unoccupied bands licensed to primary users (PU). Cognitive radio (CR) technology offers an attractive solution to the spectrum underutilisation, but encounters major challenges like high sampling rates and low number of available samples due to the stringent timing requirements. Compressive sensing (CS) is a new signal acquisition paradigm for signals being sparse in some domain, which has the potential to address the above issues by acquiring few measurements at sampling rates well below Nyquist. However, existing CS architectures, such as the random demodulator (RD) and the compressive multiplexer (CM), have limitations pertaining to the applicable signal types, the spectrum estimation methods employed, the identification of the occupancy status of spectral bands and the exclusive reliance on frequency sparse signals. This provided the context for the research question addressed in this project.

This thesis has presented a novel, generic, flexible and robust CS framework that enhances CS performance and addresses the above mentioned limitations of the CS architectures. The framework is the most important scientific contribution being able to process a wide range of signals encountered in wireless networks. It employs the

discrete Fourier transform (DFT) matrix as the sparsity basis which is accordingly modified to increase frequency sparsity and thereby enhance performance pertaining to a diverse range of digital and analog modulations as well as access schemes like the

orthogonal frequency division multiple access (OFDMA). It expands the concept of sparsity beyond the frequency domain by utilising the Walsh-Hadamard transform (WHT) matrix as the sparsity basis so to include sequency sparse signals within a

code division multiple access (CDMA) scheme. The new framework has been critically evaluated for a range of test signals as well as signals whose parameter settings are wireless network compliant such as those defined in current CR network standards such as LTE-Advance and IEEE 802.11af. The new framework integrates the following suite of innovative models:

a. A new autoregressive with CS (AR-CS) model capable of operating in both single and multi-channel modes and is applicable to both RD and CM architectures. It employs AR filtering to precolour the signal thereby increasing frequency sparsity and consequently enhancing CS performance. The precolouring process is seamlessly integrated to the model with the modification of the DFT matrix to form a new frequency sparsity basis. While the AR method is parametric and the model assumes a priori knowledge of the AR coefficients, this is not a major obstacle since such coefficients can be easily derived by the PU for speech coding purposes. Rigorous simulations with various digital modulation schemes, confirm that the AR-CS model exhibits superior spectral leakage and robustness performance compared with the classical RD and CM architectures.

b. A new multitaper with CS (MT-CS) model which embeds a variant of the MT method. The model employs the parameters of the MT method to modify the DFT matrix and create a new MT-based Fourier basis. The non-parametric nature of the MT method permits its seamless integration into the existing CS architectures without imposing either any computational burden or requiring cooperation from the PU. While simulation results have shown the MT-CS model provides both improved CS and robustness performance, these benefits are achieved for analogue modulation schemes, while for digital modulation schemes equivalent CS performance of signals is achieved compared with the

c. Given their signal sparsity enhancements focus on different modulation classes, the AR-CS and MT-CS models in (a) and (b) are combined to create the unified CS (UNI-CS) model. This embraces the advantages of both individual models by seamlessly integrating the AR and MT methods. UNI-CS has also been critically evaluated as a spectral classifier, and its ability to identify the occupancy status of a frequency band which is vital for spectrum sensing in CR applications. Moreover UNI-CS is a more robust spectral classifier, since it consistently provides more accurate spectral classification compared to its classical counterparts across a range of input SNR values.

d. Finally, the novel Walsh-Hadamard with CS (WH-CS) model considers signals that are sparse in domains other than frequency. In particular, sequency sparse signals, such those arising in the CDMA scheme. To this end, the WH-CS model exploits sequency sparsity using a WHT matrix as a sparse basis instead of a DFT. Simulation results have shown that while the frequency-based models are not able to recover the sequency sparse CDMA signals, the WH-CS model is capable of achieving channel sensing by successfully recovering CDMA signals and thereby more accurate identification of CDMA channel status.

In summarising, the new generic CS framework makes a notable contribution to the field of efficient spectral/channel sensing for the diverse range of signals encountered in today’s wireless networks and CR-based standards. It offers a flexible and robust solution for enhanced CR spectrum sensing, which is both seamlessly extendible to other potential sparsity domains as well as also being embedded into different CS architectures to enhance their performance.