High-Order Hybrid Stratified Sampling: Fast Uniform-Convergence Fourier Transform Estimation

This paper considers the problem of estimating the Fourier transform of continuous-time signals from N nonuniformly collected observations. Here, we introduce a new class of Hybrid Stratified sampling scheme in conjunction with a suitable estimator, which can provide the rate of convergence of order \u1d7cf/\u1d475(\u1d7d0\u1d472+\u1d7d1) in the mean-square sense, for signals with \u1d472+\u1d7cf continuous derivatives. Most importantly, it is shown that this rate is not only faster, but also uniform and independent of the analysed frequency (unlike) compared with other existing random-sampling-based techniques. In this paper, we establish the statistical properties of the proposed approach and illustrate its performance analytically as well as numerically

Matrix Decomposition Methods for Efficient Hardware Implementation of DOA Estimation Algorithms: A Performance Comparison

Matrix operations form the core of array signal processing algorithms such as those required for direction of arrival (DOA) angle estimation of radio frequency signals incident on an antenna array. In this paper, we present a performance comparison of matrix decomposition methods for efficient FPGA hardware implementation of DOA estimation algorithms. These methods are very important in subspace-based DOA estimation algorithms as they are used for signal space extraction. DOA estimation algorithms employing LU, LDL, Cholesky, and QR decomposition methods are implemented on a Xilinx Virtex-5 FPGA. These DOA estimation algorithms are simulated in LabVIEW as well as experimentally validated in real-time on a prototype testbed constructed using Universal Software Radio Peripheral (USRP) Software Defined Radio (SDR) platform from National Instruments. Performance comparison of these algorithms is made in terms of resources consumption, computation speed, and estimation accuracy

Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent

Addressing the interpretability problem of NMF on Boolean data, Boolean Matrix Factorization (BMF) uses Boolean algebra to decompose the input into low-rank Boolean factor matrices. These matrices are highly interpretable and very useful in practice, but they come at the high computational cost of solving an NP-hard combinatorial optimization problem. To reduce the computational burden, we propose to relax BMF continuously using a novel elastic-binary regularizer, from which we derive a proximal gradient algorithm. Through an extensive set of experiments, we demonstrate that our method works well in practice: On synthetic data, we show that it converges quickly, recovers the ground truth precisely, and estimates the simulated rank exactly. On real-world data, we improve upon the state of the art in recall, loss, and runtime, and a case study from the medical domain confirms that our results are easily interpretable and semantically meaningful

Towards joint communication and sensing (Chapter 4)

Localization of user equipment (UE) in mobile communication networks has been supported from the early stages of 3rd generation partnership project (3GPP). With 5th Generation (5G) and its target use cases, localization is increasingly gaining importance. Integrated sensing and localization in 6th Generation (6G) networks promise the introduction of more efficient networks and compelling applications to be developed

Low Complexity DOA Estimation of Multiple Coherent Sources Using a Single Direct Data Snapshot

The direction of arrival (DOA) estimation of multiple radio frequency (RF) coherent signals using conventional algorithms such as Multiple Signal Classification (MUSIC), Estimation of the Signal Parameters via the Rotational Invariance Technique (ESPRIT), and their variants is computationally complex and usually requires a large number of data snapshots for accurate estimation. As the number of antenna elements grows, particularly in massive MIMO systems, the complexity of real-time DOA estimation algorithms significantly rises, placing higher demands on computational power and memory resources. In this paper, we present an efficient approach that operates effectively with just a single snapshot for DOA estimation of multiple coherent and non-coherent signals. The proposed method has the following advantages over existing methods: 1) constructs a Toeplitz structure data matrix from a single data snapshot, 2) applies forward-backward averaging operation to the data matrix instead of the covariance matrix constructed using hundreds of snapshots, 3) resolves the differences in the noise elements of the data matrix, preserving the conjugate symmetry property of the Toeplitz matrix, 4) converts the complex Toeplitz data matrix to a real-valued matrix in an efficient way without unitary transformations, and 5) employs QR decomposition to extract the signal and noise subspaces, eliminating the need for computationally complex eigenvalue (EVD) or singular value decomposition (SVD). Finally, we establish the effectiveness of our proposed method through both MATLAB simulations and real-time experiments. Compared to existing methods like Unitary root-MUSIC, the proposed approach demonstrates significantly reduced complexity and faster estimation times

Decentralized Ultra-Reliable Low-Latency Communications through Concurrent Cooperative Transmission

Emerging cyber-physical systems demand for communication technologies that enable seamless interactions between humans and physical objects in a shared environment. This thesis proposes decentralized URLLC (dURLLC) as a new communication paradigm that allows the nodes in a wireless multi-hop network (WMN) to disseminate data quickly, reliably and without using a centralized infrastructure. To enable the dURLLC paradigm, this thesis explores the practical feasibility of concurrent cooperative transmission (CCT) with orthogonal frequency-division multiplexing (OFDM). CCT allows for an efficient utilization of the medium by leveraging interference instead of trying to avoid collisions. CCT-based network flooding disseminates data in a WMN through a reception-triggered low-level medium access control (MAC). OFDM provides high data rates by using a large bandwidth, resulting in a short transmission duration for a given amount of data. This thesis explores CCT-based network flooding with the OFDM-based IEEE 802.11 Non-HT and HT physical layers (PHYs) to enable interactions with commercial devices. An analysis of CCT with the IEEE 802.11 Non-HT PHY investigates the combined effects of the phase offset (PO), the carrier frequency offset (CFO) and the time offset (TO) between concurrent transmitters, as well as the elapsed time. The analytical results of the decodability of a CCT are validated in simulations and in testbed experiments with Wireless Open Access Research Platform (WARP) v3 software-defined radios (SDRs). CCT with coherent interference (CI) is the primary approach of this thesis. Two prototypes for CCT with CI are presented that feature mechanisms for precise synchronization in time and frequency. One prototype is based on the WARP v3 and its IEEE 802.11 reference design, whereas the other prototype is created through firmware modifications of the Asus RT-AC86U wireless router. Both prototypes are employed in testbed experiments in which two groups of nodes generate successive CCTs in a ping-pong fashion to emulate flooding processes with a very large number of hops. The nodes stay synchronized in experiments with 10 000 successive CCTs for various modulation and coding scheme (MCS) indices and MAC service data unit (MSDU) sizes. The URLLC requirement of delivering a 32-byte MSDU with a reliability of 99.999 % and with a latency of 1 ms is assessed in experiments with 1 000 000 CCTs, while the reliability is approximated by means of the frame reception rate (FRR). An FRR of at least 99.999 % is achieved at PHY data rates of up to 48 Mbit/s under line-of-sight (LOS) conditions and at PHY data rates of up to 12 Mbit/s under non-line-of-sight (NLOS) conditions on a 20 MHz wide channel, while the latency per hop is 48.2 µs and 80.2 µs, respectively. With four multiple input multiple output (MIMO) spatial streams on a 40 MHz wide channel, a LOS receiver achieves an FRR of 99.5 % at a PHY data rate of 324 Mbit/s. For CCT with incoherent interference, this thesis proposes equalization with time-variant zero-forcing (TVZF) and presents a TVZF receiver for the IEEE 802.11 Non-HT PHY, achieving an FRR of up to 92 % for CCTs from three unsyntonized commercial devices. As CCT-based network flooding allows for an implicit time synchronization of all nodes, a reception-triggered low-level MAC and a reservation-based high-level MAC may in combination support various applications and scenarios under the dURLLC paradigm