Reduced-rank adaptive least bit-error-rate detection in hybrid direct-sequence time-hopping ultrawide bandwidth systems

Design of high-efficiency low-complexity detection schemes for ultrawide bandwidth (UWB) systems is highly challenging. This contribution proposes a reduced-rank adaptive multiuser detection (MUD) scheme operated in least bit-errorrate (LBER) principles for the hybrid direct-sequence timehopping UWB (DS-TH UWB) systems. The principal component analysis (PCA)-assisted rank-reduction technique is employed to obtain a detection subspace, where the reduced-rank adaptive LBER-MUD is carried out. The reduced-rank adaptive LBERMUD is free from channel estimation and does not require the knowledge about the number of resolvable multipaths as well as the knowledge about the multipaths’ strength. In this contribution, the BER performance of the hybrid DS-TH UWB systems using the proposed detection scheme is investigated, when assuming communications over UWB channels modeled by the Saleh-Valenzuela (S-V) channel model. Our studies and performance results show that, given a reasonable rank of the detection subspace, the reduced-rank adaptive LBER-MUD is capable of efficiently mitigating the multiuser interference (MUI) and inter-symbol interference (ISI), and achieving the diversity gain promised by the UWB systems

Novel Fine-Tuned Attribute Weighted Na\"ive Bayes NLoS Classifier for UWB Positioning

In this paper, we propose a novel Fine-Tuned attribute Weighted Na\"ive Bayes (FT-WNB) classifier to identify the Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) for UltraWide Bandwidth (UWB) signals in an Indoor Positioning System (IPS). The FT-WNB classifier assigns each signal feature a specific weight and fine-tunes its probabilities to address the mismatch between the predicted and actual class. The performance of the FT-WNB classifier is compared with the state-of-the-art Machine Learning (ML) classifiers such as minimum Redundancy Maximum Relevance (mRMR)- $k$-Nearest Neighbour (KNN), Support Vector Machine (SVM), Decision Tree (DT), Na\"ive Bayes (NB), and Neural Network (NN). It is demonstrated that the proposed classifier outperforms other algorithms by achieving a high NLoS classification accuracy of $99.7\%$ with imbalanced data and $99.8\%$ with balanced data. The experimental results indicate that our proposed FT-WNB classifier significantly outperforms the existing state-of-the-art ML methods for LoS and NLoS signals in IPS in the considered scenario

Machine Learning Based Approach for Indoor Localization Using Ultra-Wide Bandwidth (UWB) System for Industrial Internet of Things (IIoT)

With the rapid development of wireless communication technology and the emergence of the Industrial Internet of Things (IIoT)s applications, high-precision Indoor Positioning Services (IPS) are urgently required. While the Global Positioning System (GPS) has been a key technology for outdoor localization, its limitation for indoor environments is well known. UltraWideBand (UWB) can help provide a very accurate position or localization for indoor harsh propagation environments. This paper focuses on improving the accuracy of the UWB indoor localization system including the Line-of-Sight (LoS) and NonLine-of-Sight (NLoS) conditions by developing a Machine Learning (ML) algorithm. In this paper, a Naive Bayes (NB) ML algorithm is developed for UWB IPS. The performance of the developed algorithm is evaluated by Receiving Operating Curves (ROC)s. The results indicate that by employing the NB based ML algorithm significantly improves the localization accuracy of the UWB system for both the LoS and NLoS environmen

A joint multi user detection scheme for UWB sensor networks using waveform division multiple access

A joint multiuser detection (MUD) scheme for wireless sensor networks (WSNs) is proposed to suppress multiple access interference (MAI) caused by a large number of sensor nodes. In WSNs, waveform division multiple access ultra-wideband (WDMA-UWB) technology is well-suited for robust communications. Multiple sensor nodes are allowed to transmit modulated signals by sharing the same time periods and frequency bands using orthogonal pulse waveforms. This paper employs a mapping function based on the optimal multiuser detection (OMD) to map the received bits into the mapping space where error bits can be distinguished. In order to revise error bits caused by MAI, the proposed joint MUD scheme combines the mapping function with suboptimal algorithms. Numerical results demonstrate that the proposed MUD scheme provides good performances in terms of suppressing MAI and resisting near-far effect with low computational complexity

Adaptive detection in ultrawide bandwidth wireless communication systems

The main motivation of this thesis is to design low-complexity high-efficiency pulse-based ultrawide bandwidth (UWB) systems with reasonable bit-error-rate (BER) performance. The thesis starts with proposing a new pulse-based UWB system, namely the hybrid direct-sequence time-hopping (DS-TH) UWB system. This novel pulse-based UWB system is capable of inheriting the advantages of both the pure direct-sequence (DS)-UWB and pure time-hopping (TH)-UWB systems, while avoiding their disadvantages. Furthermore, this hybrid DS-TH UWB scheme can be easily converted to the pure DS-UWB or pure TH-UWB scheme. The BER performance of the hybrid DS-TH UWB systems employing either correlation or minimum mean-square error (MMSE) detection is investigated. From our studies it can be found that both the correlation and MMSE detectors have the capability to make use of the multipath diversity. The correlation detector does not have the capability to remove multiuser interference (MUI) and inter-symbol interference (ISI), while the MMSE detector is capable of mitigating efficiently both the ISI and MUI. While for single-user scenario the correlation detector is near-optimum and has low-complexity, it is shown that for multi-user scenarios theMMSE detector must be employed in order to achieve a reasonable BER performance. However, in this case the complexity of the hybrid DS-TH UWB system is found to be extreme. Furthermore, in order to implement MMSE detection, the signature waveforms, delays and complete channel knowledge of all the active users are required to be known by the receiver, which make the MMSE detection impractical. In practical channels obtaining the channel knowledge is highly challenging, since the received UWB signals usually consist of a huge number of resolvable multipaths and the energy conveyed by each resolvable multipath is usually very low.In order to mitigate the above mentioned problems of the MMSE detection, then, in this thesis a range of training-based adaptive detectors are investigated in the context of the hybrid DS-TH UWB systems. In detail, in this thesis a brief introduction to the literature of adaptive detection is first provided, followed by the philosophies of least mean-square (LMS), normalised least-mean squares (NLMS) and recursive least square (RLS) algorithms. In our study decision directed (DD) approaches are also introduced to the adaptive detectors to improve the BER performance and spectral-efficiency of the hybrid DS-TH UWB systems. Our studies show that the complexity of the adaptive LMS and adaptive NLMS detectors may be even lower than that of the conventional correlation detector. For the RLS adaptive detector, our studies show that, if it is initialised properly, it is capable of attaining a faster convergence rate than the LMS and NLMS adaptive detectors. In this case, the RLS adaptive detector requires less number of training bits, and hence provides higher spectral-efficiency than the LMS and NLMS adaptive detectors for the hybrid DS-TH UWB systems. Furthermore, the RLS adaptive detector is more robust and has more degrees of freedom than the LMS and NLMS adaptive detectors. However, the complexity of the RLS adaptive detector is still too high to be implemented in practical UWB systems.In order to further reduce the complexity of the RLS adaptive detector, rank-reduction techniques are introduced. With the aid of reduced-rank techniques, the filter size can be efficiently reduced, which in turn reduces the number of parameters required to be estimated. Consequently, the convergence speed, tracking ability and robustness of the RLS adaptive detector can be improved. In this thesis, three classes of reduced-rank techniques are investigated associated with the RLS adaptive detector, which are derived based on the principles of principal components analysis (PCA), crossspectral metric (CSM) and Taylor polynomial approximation (TPA), respectively. Our study and simulation results show that, given a sufficient rank of the detection subspace on which the RLS adaptive detector is operated, the reduced-rank RLS adaptive detector is capable of achieving a similar BER performance as the corresponding full-rank RLS adaptive detector, while with a detection complexity that is significantly lower than that of the fullrank RLS adaptive detector. Furthermore, our studies shown that the TPA-based reduced-rank RLS adaptive detector constitutes one of the highly efficient detection schemes for the pulse-based UWB systems. The TPA-based reduced-rank RLS adaptive detector is usually capable of attaining the full-rank BER performance with a very low rank, which is typically in the range of 5 ? 8, regardless of the system size in terms of the spreading factor, number of resolvable multipaths and the number of users supported by the UWB systems.Finally, in this thesis we summarise our discoveries and provide discussion on the possible future research issues

Feature-Based Generalized Gaussian Distribution Method for NLoS Detection in Ultra-Wideband (UWB) Indoor Positioning System

Nonline-of-sight (NLoS) propagation condition is a crucial factor affecting the precision of the localization in the ultra-wideband (UWB) indoor positioning system (IPS). Numerous supervised machine learning (ML) approaches have been applied for the NLoS identification to improve the accuracy of the IPS. However, it is difficult for existing ML approaches to maintain a high classification accuracy when the database contains a small number of NLoS signals and a large number of line-of-sight (LoS) signals. The inaccurate localization of the target node caused by this small number of NLoS signals can still be problematic. To solve this issue, we propose feature-based Gaussian distribution (GD) and generalized GD (GGD) NLoS detection algorithms. By employing our detection algorithm for the imbalanced dataset, a classification accuracy of 96.7% and 98.0% can be achieved. We also compared the proposed algorithm with the existing cutting edge, such as support vector machine (SVM), decision tree (DT), naive Bayes (NB), and neural network (NN), which can achieve an accuracy of 92.6%, 92.8%, 93.2%, and 95.5%, respectively. The results demonstrate that the GGD algorithm can achieve high classification accuracy with the imbalanced dataset. Finally, the proposed algorithm can also achieve a higher classification accuracy for different ratios of LoS and NLoS signals, which proves the robustness and effectiveness of the proposed method