Turbo Bayesian Compressed Sensing

Compressed sensing (CS) theory specifies a new signal acquisition approach, potentially allowing the acquisition of signals at a much lower data rate than the Nyquist sampling rate. In CS, the signal is not directly acquired but reconstructed from a few measurements. One of the key problems in CS is how to recover the original signal from measurements in the presence of noise. This dissertation addresses signal reconstruction problems in CS. First, a feedback structure and signal recovery algorithm, orthogonal pruning pursuit (OPP), is proposed to exploit the prior knowledge to reconstruct the signal in the noise-free situation. To handle the noise, a noise-aware signal reconstruction algorithm based on Bayesian Compressed Sensing (BCS) is developed. Moreover, a novel Turbo Bayesian Compressed Sensing (TBCS) algorithm is developed for joint signal reconstruction by exploiting both spatial and temporal redundancy. Then, the TBCS algorithm is applied to a UWB positioning system for achieving mm-accuracy with low sampling rate ADCs. Finally, hardware implementation of BCS signal reconstruction on FPGAs and GPUs is investigated. Implementation on GPUs and FPGAs of parallel Cholesky decomposition, which is a key component of BCS, is explored. Simulation results on software and hardware have demonstrated that OPP and TBCS outperform previous approaches, with UWB positioning accuracy improved by 12.8x. The accelerated computation helps enable real-time application of this work

Decentralized Turbo Bayesian ompressed Sensing with application to UWB Systems

In many situations, there exist plenty of spatial and temporal redundancies in original signals. Based on this observation, a novel Turbo Bayesian Compressed Sensing (TBCS) algorithm is proposed to provide an efficient approach to transfer and incorporate this redundant information for joint sparse signal reconstruction. As a case study, the TBCS algorithm is applied in Ultra-Wideband (UWB) systems. A space-time TBCS structure is developed for exploiting and incorporating the spatial and temporal a priori information for space-time signal reconstruction. Simulation results demonstrate that the proposed TBCS algorithm achieves much better performance with only a few measurements in the presence of noise, compared with the traditional Bayesian Compressed Sensing (BCS) and multitask BCS algorithms

Towards 5G wireless systems: A modified Rake receiver for UWB indoor multipath channels

This paper presents a modified receiver based on the conventional Rake receiver for Ultra-Wide Band (UWB) indoor channels of femtocell systems and aims to propose a new solution to mitigate the multipath phenomenon. Furthermore, this work proposes an upgrade for the conventional Rake receiver to fulfill the needs of 5G wireless systems through a new concept named “hybrid femtocell” that joins UWB with millimeter wave (mmWave) signals. The modified receiver is considered to be a part of the UWB/mmWave hybrid femtocell system, where it is developed for confronting the indoor multipath channels and to ensure a flexible transmission based on an Intelligent Controlling System (ICS). Hence, we seek to exploit the circumstances when the channel is less complex to switch the transmission to a higher data rate through higher M-ary Pulse Position Modulation (PPM). Furthermore, an ICS algorithm is proposed and an analytical model is developed followed by performance studies through simulation results. The results show that using the UWB technology through the modified receiver in femtocells could aid in mitigating the multipath effects and ensuring high throughputs. Thus, the UWB based system promotes Internet of Things (IoT) devices in indoor multipath channels of future 5G

Anchor Self-Calibrating Schemes for UWB based Indoor Localization

Traditional indoor localization techniques that use Received Signal Strength or Inertial Measurement Units for dead-reckoning suffer from signal attenuation and sensor drift, resulting in inaccurate position estimates. Newly available Ultra-Wideband radio modules can measure distances at a centimeter-level accuracy while mitigating the effects of multipath propagation due to their very fine time resolution. Known locations of fixed anchor nodes are required to determine the position of tag nodes within an indoor environment. For a large system consisting of several anchor nodes spanning a wide area, physically mapping out the locations of each anchor node is a tedious task and thus makes the scalability of such systems difficult. Hence it is important to develop indoor localization systems wherein the anchors can self-calibrate by determining their relative positions in Euclidean 3D space with respect to each other. In this thesis, we propose two novel anchor self-calibrating algorithms - Triangle Reconstruction Algorithm (TRA) and Channel Impulse Response Positioning (CIRPos) that improve upon existing range-based implementations and solve existing problems such as flip ambiguity and node localization success rate. The localization accuracy and scalability of the self-calibrating anchor schemes are tested in a simulated environment based on the ranging accuracy of the Ultra-Wideband modules

CRLB-based positioning performance of indoor hybrid AoA/RSS/ToF localization

Fingerprinting indoor localization provides high positioning accuracy with low cost and easy deployment. Considering the unsatisfying precision of received signal strength (RSS)-based fingerprinting, hybrid metrics including angle-of-arrival (AoA) and time-of-flight (ToF), are incorporated to the RSS fingerprinting system. To evaluate the positioning performance of hybrid metrics, the closed-form Cramér-Rao lower bound (CRLB) is derived in this paper. The existence conditions of CRLBs, as well as the relationship of the CRLBs between single and hybrid metrics is revealed. Numerical results based on an office building scenario show that hybrid metrics greatly improve the positioning performance and the robustness to measured standard deviations compared to the single metric's case. Furthermore, hybrid schemes of the AoA/RSS/ToF metrics are also investigated, and simulations reveal that the scheme of AoA/ToF-supporting access points (AP) enhanced with single RSS-supporting APs achieves the best positioning accuracy among all hybrid schemes

Intelligent Processing in Wireless Communications Using Particle Swarm Based Methods

There are a lot of optimization needs in the research and design of wireless communica- tion systems. Many of these optimization problems are Nondeterministic Polynomial (NP) hard problems and could not be solved well. Many of other non-NP-hard optimization problems are combinatorial and do not have satisfying solutions either. This dissertation presents a series of Particle Swarm Optimization (PSO) based search and optimization algorithms that solve open research and design problems in wireless communications. These problems are either avoided or solved approximately before. PSO is a bottom-up approach for optimization problems. It imposes no conditions on the underlying problem. Its simple formulation makes it easy to implement, apply, extend and hybridize. The algorithm uses simple operators like adders, and multipliers to travel through the search space and the process requires just five simple steps. PSO is also easy to control because it has limited number of parameters and is less sensitive to parameters than other swarm intelligence algorithms. It is not dependent on initial points and converges very fast. Four types of PSO based approaches are proposed targeting four different kinds of problems in wireless communications. First, we use binary PSO and continuous PSO together to find optimal compositions of Gaussian derivative pulses to form several UWB pulses that not only comply with the FCC spectrum mask, but also best exploit the avail- able spectrum and power. Second, three different PSO based algorithms are developed to solve the NLOS/LOS channel differentiation, NLOS range error mitigation and multilateration problems respectively. Third, a PSO based search method is proposed to find optimal orthogonal code sets to reduce the inter carrier interference effects in an frequency redundant OFDM system. Fourth, a PSO based phase optimization technique is proposed in reducing the PAPR of an frequency redundant OFDM system. The PSO based approaches are compared with other canonical solutions for these communication problems and showed superior performance in many aspects. which are confirmed by analysis and simulation results provided respectively. Open questions and future Open questions and future works for the dissertation are proposed to serve as a guide for the future research efforts