Advanced optimization algorithms for sensor arrays and multi-antenna communications

Optimization problems arise frequently in sensor array and multi-channel signal processing applications. Often, optimization needs to be performed subject to a matrix constraint. In particular, unitary matrices play a crucial role in communications and sensor array signal processing. They are involved in almost all modern multi-antenna transceiver techniques, as well as sensor array applications in biomedicine, machine learning and vision, astronomy and radars. In this thesis, algorithms for optimization under unitary matrix constraint stemming from Riemannian geometry are developed. Steepest descent (SD) and conjugate gradient (CG) algorithms operating on the Lie group of unitary matrices are derived. They have the ability to find the optimal solution in a numerically efficient manner and satisfy the constraint accurately. Novel line search methods specially tailored for this type of optimization are also introduced. The proposed approaches exploit the geometrical properties of the constraint space in order to reduce the computational complexity. Array and multi-channel signal processing techniques are key technologies in wireless communication systems. High capacity and link reliability may be achieved by using multiple transmit and receive antennas. Combining multi-antenna techniques with multicarrier transmission leads to high the spectral efficiency and helps to cope with severe multipath propagation. The problem of channel equalization in MIMO-OFDM systems is also addressed in this thesis. A blind algorithm that optimizes of a combined criterion in order to be cancel both inter-symbol and co-channel interference is proposed. The algorithm local converge properties are established as well

Low-complexity hardware and algorithm for joint communication and sensing

Joint Communication and Sensing (JCAS) is foreseen as one very distinctive feature of the emerging 6G systems providing, in addition to fast end reliable communication, the ability to obtain an accurate perception of the physical environment. In this paper, we propose a JCAS algorithm that exploits a novel beamforming architecture, which features a combination of wideband analog and narrowband digital beamforming. This allows accurate estimation of Time of Arrival (ToA), exploiting the large bandwidth and Angle of Arrival (AoA), exploiting the high-rank digital beamforming. In our proposal, we separately estimate the ToA and AoA. The association between ToA and AoA is solved by acquiring multiple non-coherent frames and adding up the signal from each frame such that a specific component is combined coherently before the AoA estimation. Consequently, this removes the need to use 2D and 3D joint estimation methods, thus significantly lowering complexity. The resolution performance of the method is compared with that of 2D MUltiple SIgnal Classification (2D-MUSIC) algorithm, using a fully-digital wideband beamforming architecture. The results show that the proposed method can achieve performance similar to a fully-digital high-bandwidth system, while requiring a fraction of the total aggregate sampling rate and having much lower complexity.Comment: 13 pages, 9 figures. Submitted to IEEE Transactions on Wireless Communication

Positioning and Sensing in 6G: Gaps, Challenges, and Opportunities

Among the key differentiators of 6G compared to 5G will be the increased emphasis on radio-based positioning and sensing. These will be utilized not only for conventional location-aware services and for enhancing communication performance but also to support new use case families with extreme performance requirements. This article presents a unified vision from stakeholders across the value chain in terms of both opportunities and challenges for 6G positioning and sensing as well as use cases, performance requirements, and gap analysis. Combined, this motivates the technical advances in 6G and guides system design

Unitary checkerboard precoded OFDM for low-PAPR optical wireless communications

Future 6G wireless networks will once again have to raise the performance in most technology domains by a factor of 10-100. Depending on the application, future requirements include peak data rates of 1 Tb/s per user, 0.1 ms latency, less than one out of a million outage, centimeter accurate positioning, near zero energy consumption at the device, and operation in different environments including factories, vehicles, and more. Optical wireless communications (OWCs) have the potential to provide ultrahigh data rates in a cost effective way, due to the vast and freely available light spectrum, and the availability of devices for transmitters and receivers. 5G New Radio architecture permits the integration of stand-alone OWC nodes on network layers. Current 6G research investigates advanced physical layer designs including OWC-compatible waveforms. In this context, in this paper, a new precoded orthogonal frequency division multiplexing (OFDM) waveform is proposed that is tailored to OWCs' specific needs. Its prime advantage compared to OFDM is the ultralow peak-to-average power ratio, while preserving other benefits, such as high spectral efficiency, flexible subcarrier nulling, and low computational complexity

Construction of Minimum Euclidean Distance MIMO Precoders and Their Lattice Classifications

This correspondence deals with the construction of minimum Euclidean distance precoders for multiple-input multiple-output (MIMO) systems with up to four transmit antennas. By making use of a state-of-the-art technique for optimization over the unitary group, we can numerically optimize the MIMO precoders. The correspondence then proceeds by identifying the obtained precoders as well-known lattices (square, Z(2), Schlafli D-4, D-6, Gosset E-8). With three transmit antennas, the results are slightly different compared with other numbers of transmit antennas since the obtained precoder is not an instance of the densest 6-dimensional lattice. The overall conclusions of the correspondence are that the found precoders for MIMO transmission are highly structured and that, even with small constellations, lattice theory can be used for the design of MIMO precoders

Fusing Time-of-Flight and Received Signal Strength for adaptive radio-frequency ranging

Abstract—Teams of mobile cooperative robots are ideal candi-dates for applications where the presence of humans is impossible or should be avoided. Knowing the positions of the robots in crucial in such scenarios. A possible solution is to derive relative positions from local communication. In this work, we propose an anchor-free online channel estimation method aimed at small multi-robot teams. By combining both the Time-of-Flight (ToF) and Received Signal Strength Indicator (RSSI) ranging, provided by the nanoLoc devices, we perform an online estimation of the indoor log-distance path loss model. This model will then be used together with an Extended Kalman Filter to track distance between every pair of units. The advantages compared to previous work are: 1) we do not use any extra sensors, since all the data is captured from the transceiver module; 2) we do not use any a priori knowledge, the channel model is estimated online, without the need of fixed anchor nodes; 3) we support the high dynamics of RSSI with the improved accuracy of ToF. I

6G Radio Requirements to Support Integrated Communication, Localization, and Sensing

| openaire: EC/H2020/101015956/EU//Hexa-X Funding Information: ACKNOWLEDGMENT This work was supported, in part, by the European Commission through the H2020 project Hexa-X (Grant Agreement no. 101015956) and the MSCA-IF grant 888913 (OTFS-RADCOM).6G will be characterized by extreme use cases, not only for communication, but also for localization, and sensing. The use cases can be directly mapped to requirements in terms of standard key performance indicators (KPIs), such as data rate, latency, or localization accuracy. The goal of this paper is to go one step further and map these standard KPIs to requirements on signals, on hardware architectures, and on deployments. Based on this, system solutions can be identified that can support several use cases simultaneously. Since there are several ways to meet the KPIs, there is no unique solution and preferable configurations will be discussed.Peer reviewe