3 research outputs found
Advances in parameter estimation, source enumeration, and signal identification for wireless communications
Parameter estimation and signal identification play an important role in modern wireless
communication systems. In this thesis, we address different parameter estimation
and signal identification problems in conjunction with the Internet of Things (IoT),
cognitive radio systems, and high speed mobile communications.
The focus of Chapter 2 of this thesis is to develop a new uplink multiple access
(MA) scheme for the IoT in order to support ubiquitous massive uplink connectivity
for devices with sporadic traffic pattern and short packet size. The proposed uplink
MA scheme removes the Media Access Control (MAC) address through the signal
identification algorithms which are employed at the gateway.
The focus of Chapter 3 of this thesis is to develop different maximum Doppler
spread (MDS) estimators in multiple-input multiple-output (MIMO) frequency-selective
fading channel. The main idea behind the proposed estimators is to reduce the computational
complexity while increasing system capacity.
The focus of Chapter 4 and Chapter 5 of this thesis is to develop different antenna
enumeration algorithms and signal-to-noise ratio (SNR) estimators in MIMO timevarying
fading channels, respectively. The main idea is to develop low-complexity
algorithms and estimators which are robust to channel impairments.
The focus of Chapter 6 of this thesis is to develop a low-complexity space-time
block codes (STBC)s identification algorithms for cognitive radio systems. The goal
is to design an algorithm that is robust to time-frequency transmission impairments
Recommended from our members
Adaptive Coded Modulation Classification and Spectrum Sensing for Cognitive Radio Systems. Adaptive Coded Modulation Techniques for Cognitive Radio Using Kalman Filter and Interacting Multiple Model Methods
The current and future trends of modern wireless communication systems place heavy demands on fast data transmissions in order to satisfy end users’ requirements anytime, anywhere. Such demands are obvious in recent applications such as smart phones, long term evolution (LTE), 4 & 5 Generations (4G & 5G), and worldwide interoperability for microwave access (WiMAX) platforms, where robust coding and modulations are essential especially in streaming on-line video material, social media and gaming. This eventually resulted in extreme exhaustion imposed on the frequency spectrum as a rare natural resource due to stagnation in current spectrum management policies. Since its advent in the late 1990s, cognitive radio (CR) has been conceived as an enabling technology aiming at the efficient utilisation of frequency spectrum that can lead to potential direct spectrum access (DSA) management. This is mainly attributed to its internal capabilities inherited from the concept of software defined radio (SDR) to sniff its surroundings, learn and adapt its operational parameters accordingly. CR systems (CRs) may commonly comprise one or all of the following core engines that characterise their architectures; namely, adaptive coded modulation (ACM), automatic modulation classification (AMC) and spectrum sensing (SS).
Motivated by the above challenges, this programme of research is primarily aimed at the design and development of new paradigms to help improve the adaptability of CRs and thereby achieve the desirable signal processing tasks at the physical layer of the above core engines. Approximate modelling of Rayleigh and finite state Markov channels (FSMC) with a new concept borrowed from econometric studies have been approached. Then insightful channel estimation by using Kalman filter (KF) augmented with interacting multiple model (IMM) has been examined for the purpose of robust adaptability, which is applied for the first time in wireless communication systems. Such new IMM-KF combination has been facilitated in the feedback channel between wireless transmitter and receiver to adjust the transmitted power, by using a water-filling (WF) technique, and constellation pattern and rate in the ACM algorithm. The AMC has also benefited from such IMM-KF integration to boost the performance against conventional parametric estimation methods such as maximum likelihood estimate (MLE) for channel interrogation and the estimated parameters of both inserted into the ML classification algorithm. Expectation-maximisation (EM) has been applied to examine unknown transmitted modulation sequences and channel parameters in tandem. Finally, the non-parametric multitaper method (MTM) has been thoroughly examined for spectrum estimation (SE) and SS, by relying on Neyman-Pearson (NP) detection principle for hypothesis test, to allow licensed primary users (PUs) to coexist with opportunistic unlicensed secondary users (SUs) in the same frequency bands of interest without harmful effects. The performance of the above newly suggested paradigms have been simulated and assessed under various transmission settings and revealed substantial improvements