Joint localization and environmental mapping in multipath environments aims to jointly estimate the states of user equipment (UE) and the positions of landmarks using radio signals in multipath environments. Most existing approaches for joint localization and environmental mapping in multipath environments handle the data association (DA) problem using random finite sets (RFS) or factor graphs (FG). This paper introduces a novel clustering-based landmark optimization approach (CLOM) for multipath joint localization and environmental mapping, designed for global optimization in multipath environments. CLOM handles DA by iteratively clustering the landmark positions observed at different time steps and optimizing the corresponding UE positions. Unlike traditional methods based on RFS or FG, CLOM offers an alternative approach focused on optimizing UE positions while maintaining the consistency of landmarks across different time steps. It is demonstrated that the proposed method achieves comparable performance to the Rao-Blackwellized particle filter (RBPF)-based method, with relatively low complexity.

The Terahertz (THz) and sub-THz frequency bands hold great promise for next-generation wireless systems, particularly the sixth generation (6G), due to their wide bandwidth, support for high data rates, and enhanced sensing precision in integrated sensing and communication (ISAC) applications. This paper presents a multi-block discrete Fourier transform spread orthogonal frequency division multiplexing (MB DFT-s-OFDM) waveform, suitable for THz-ISAC systems. We evaluate the performance of the proposed waveform using two types of receivers: least squares (LS) and matched filter (MF). Our results demonstrate that MB DFT-s-OFDM achieves a lower peak-to-average power ratio (PAPR) compared to OFDM and improves sensing accuracy over DFT-s-OFDM for detecting multiple targets, while maintaining robust communication performance in typical THz channels. Additionally, the MF receiver demonstrates high accuracy in detecting strong targets across all waveforms. However, the LS receiver proves more effective for detecting a weak target in the presence of a strong target where in this case the MB DFT-s-OFDM waveform outperforms the DFT-s-OFDM waveform.

Utilizing deep learning (DL) techniques for radio-based positioning of user equipment (UE) through channel state information (CSI) fingerprints has demonstrated significant potential. DL models can extract complex characteristics from the CSI fingerprints of a particular environment and accurately predict the position of a UE. Nonetheless, the effectiveness of the DL model trained on CSI fingerprints is highly dependent on the particular training environment, limiting the trained model’s applicability across different environments. This paper proposes a novel DL model structure consisting of two parts, where the first part aims at identifying features that are independent from any specific environment, while the second part combines those features in an environment specific way with the goal of positioning. To train such a two-part model, we propose the multi-environment meta-learning (MEML) approach for the first part to facilitate training across various environments, while the second part of the model is trained solely on data from a specific environment. Our findings indicate that employing the MEML approach for initializing the weights of the DL model for a new unseen environment significantly boosts the accuracy of UE positioning in the new target environment as well the reliability of its uncertainty estimation. This method outperforms traditional transfer learning methods, whether direct transfer learning (DTL) between environments or completely training from scratch with data from a new environment. The proposed approach is verified with real measurements for both line-of-sight (LOS) and non-LOS (NLOS) environments.

In the fifth generation (5G) Vehicle-to-Everything (V2X) autonomous mode, vehicles may select radio resources for their packet transmissions without the support of the base station (BS). For periodic traffic, the same resources can be accessed at consecutive reservation intervals to improve the selection procedure. However, potential collisions occurring during this period may remain undetected and persist over time. Previous studies introduced algorithms leveraging in-band full-duplex (FD) transceivers to detect and react to collisions by performing resource reselection. However, as demonstrated in this paper, in more realistic scenarios where part of the data traffic is not periodic, performing reselections after collisions, regardless of the traffic type, may not be effective and could even be counterproductive. To address this issue, this work introduces two novel alternative schemes, each with its own advantages and drawbacks, both based on FD transceivers. Simulation results demonstrate that both solutions effectively handle scenarios with mixed traffic. Without penalising the aperiodic traffic, the range of periodic traffic is improved by up to 38% over the legacy Mode 2 under heavy traffic conditions.

The Third Generation Partnership Project (3GPP) recently introduced the fifth-generation (5G) new radio (NR) sidelink to enable vehicle-to-everything (V2X) communications supporting advanced safety services. Nevertheless, improvements over the previous generation still pose challenges to meet the reliability and latency requirements of V2X communications, particularly in the allocation of distributed resources, i.e., Mode 2. In Mode 2, vehicles autonomously select radio resources for their message transmissions and can maintain the selected resources for a given reservation period to efficiently handle periodic data traffic. However, potential collisions during this period may remain undetected due to half-duplex communications and unacknowledged broadcast transmissions, resulting in persistent message losses and posing a threat to road safety. This paper aims to improve the 5G NR-V2X sidelink for systems beyond 5G-Advanced by exploiting full-duplex transceivers. We propose a novel medium access control (MAC) scheme where vehicles can detect collisions while transmitting, dynamically adapt the collision detection threshold according to the measured channel load, and react to detected collisions through appropriate resource reselection and retransmission procedures. Extensive simulations conducted under various settings show that this MAC scheme brings substantial performance gains in terms of reliability and latency, compared to the current legacy Mode 2 procedure and a benchmark full-duplex scheme from the literature.

The array design of multiple-input multiple-output (MIMO) systems in a line-of-sight (LoS) environment is investigated. Properly designed uniform array configurations at the transmitter (Tx) and receiver (Rx) can extract maximum spatial multiplexing gain only for a fixed transmit distance between the Tx and Rx arrays and for a fixed orientation of the arrays. However, such designs suffer from significant capacity variations when the position and/or orientation of the arrays is modified. To alleviate this, we examine robust, joint design of non-uniform Tx and Rx arrays, where the minimum capacity over a range of varying array positions and orientations is maximized. First, we show that, by leveraging convex relaxation, the joint Tx and Rx array design problem can be solved with convex optimization techniques in an iterative manner. Moreover, an alternative design method based on dynamic programming (DP) is proposed, which is shown to outperform the convex optimization approach. As the DP algorithm is quite demanding in terms of computational complexity, a modified DP-based algorithm is also proposed, where the Tx and Rx arrays are designed to have the same configuration. It is shown that the resulting non-uniform array configurations with the proposed designs outperform both uniform and non-uniform array designs of the literature in terms of the system robustness.

User positioning with deep learning (DL) models based on channel state information (CSI) fingerprints, e.g., obtained at a base station (BS), has emerged as a promising technology. Related prior works generally assume a CSI fingerprint with multiple spatial dimensions (i.e antennas or beams) at the BS but only a single spatial dimension at the user equipment (UE). However, a UE may be equipped with multiple antennas or may need to perform beamforming, e.g., to support transmissions at higher frequencies. In this work we consider user positioning with DL models based on uplink beamformed CSI fingerprints considering multiple spatial dimensions at both the BS and the UE. By considering a single or multiple beams at the BS and UE, the use of different CSI fingerprints is proposed. The positioning accuracy achieved with the different beamformed CSI fingerprints is evaluated and compared. The different orientation during training and UE deployment is also considered. In addition, we also consider the positioning of UEs with different spatial capabilities, i.e. with different number of beams. This work provides valuable insights into the design of wireless positioning with CSI fingerprints considering multiple spatial dimensions at both the BS and UE.

The array design problem of multiple-input multiple-output (MIMO) systems in a line-of-sight (LoS) transmit environment is examined. As uniform array configurations at the transmitter (Tx) and receiver (Rx) are optimal at specific transmit distances only, they lead to reduced spectral efficiency over a range of transmit distances. To that end, the joint design of non-uniform Tx and Rx arrays towards maximizing the minimum capacity of a LoS MIMO system across a range of transmit distances is investigated in this paper. By introducing convex relaxation, the joint Tx and Rx array design is cast as a convex optimization problem, which is solved in a iterative manner. In addition, we also implement a local search to obtain a refined solution that achieves an improved performance. It is shown that the non-uniform configurations designed with our proposed approach outperform uniform and non-uniform array designs of the literature in terms of capacity and/or complexity.

Applications envisioned to enable connected and autonomous vehicles entail the exchange of data through reliable and low-latency direct communications between vehicles. The current specifications for the cellular-vehicle-to-everything (C-V2X) technology can still barely match such requirements. To tackle this issue, we investigate here the joint application to the C-V2X sidelink with autonomous resource allocation of two techniques that are being considered for the evolution of cellular systems, i.e., in-band full-duplex (FD) and non-orthogonal multiple access (NOMA). In particular, we consider FD capabilities to detect ongoing collisions while transmitting and thus resolve persistent resource allocations, which are causing repeated errors at the neighboring receivers. Regarding NOMA, we consider the use of successive-interference cancellation (SIC) for the decoding of multiple signals when different vehicles are transmitting simultaneously using the same resources, thus converting packet losses into improved spectral efficiency. The investigation is based on the latest fifth generation (5G) New Radio (NR)-V2X specifications and focuses on broadcast transmissions, which are expected to cover the majority of the exchanged traffic in V2X contexts. As shown through simulations in large-scale scenarios, the two techniques, both relying on signal processing at the receiver to cancel the (self or successive) interference, provide complementary advantages under different conditions. FD is in fact very effective to improve the delivery performance at short distances when periodic data traffic is assumed, while SIC-based NOMA improves the reception probability at medium distances both under periodic and aperiodic data traffic conditions.

Radio based positioning of a user equipment (UE) based on deep learning (DL) methods using channel state information (CSI) fingerprints have shown promising results. DL models are able to capture complex properties embedded in the CSI about a particular environment and map UE’s CSI to the UE’s position. However, the CSI fingerprints and the DL models trained on such fingerprints are highly dependent on a particular propagation environment, which generally limits the transfer of knowledge of the DL models from one environment to another. In this paper, we propose a DL model consisting of two parts: the first part aims to learn environment independent features while the second part combines those features depending on the particular environment. To improve transfer learning, we propose a meta learning scheme for training the first part over multiple environments. We show that for positioning in a new environment, initializing a DL model with the meta learned environment independent function achieves higher UE positioning accuracy compared to regular transfer learning from one environment to the new environment, or compared to training the DL model from scratch with only fingerprints from the new environment. Our proposed scheme is able to create an environment independent function which can embed knowledge from multiple environments and more effectively learn from a new environment.

Deep learning (DL) methods have been recently proposed for user equipment (UE) localization in wireless communication networks, based on the channel state information (CSI) between a UE and multiple base stations (BSs) in the uplink. With the CSI from the available BSs, UE localization can be performed in different ways. On the one hand, a single neural network (NN) can be trained for the UE localization by considering the CSI from all the available BSs as one overall fingerprint of the user’s location. On the other hand, the CSI at each BS can be used to obtain an estimate of the UE’s position with a separate NN at each BS, and then the position estimates of all BSs are combined to obtain an overall estimate of the UE position. In this work, we show that UE localization with the latter approach can achieve a higher positioning accuracy. We propose to consider the uncertainty in the UE localization at each BS, such that overall UE’s position is determined by combining the position estimates of the different BSs based on the uncertainty at each BS. With this approach, a more reliable position estimate can be obtained in case of variations in the channel.

Due to the increasing interest for wireless communications at higher frequencies, line-of-sight multiple-input multiple-output (LoS MIMO) systems are expected to be deployed in many future applications. In such systems, maximum capacity is achieved with optimal antenna placement at the transmit (Tx) and receive (Rx) arrays. Regarding uniform circular arrays (UCAs), alignment of the Tx and Rx arrays is also necessary to extract full capacity. However, there are scenarios where the arrays are tilted and hence, alignment may not always be guaranteed. Recently, it was shown that the capacity performance of a tilted array is determined by its projection onto the plane perpendicular to the line between the Tx and Rx. So, an elliptical array can be designed so that when tilted, such projection results in an aligned UCA. We first propose an array design consisting of several concentric elliptical subarrays, where each subarray is selected to compensate a range of tilts, enabling large capacity over a range of tilting angles. Moreover, we propose the use of a more flexible uniform planar array with antenna selection to approximate the elliptical subarrays in order to compensate different tilts.

Efforts are underway to finalize the fifth generation (5G) vehicle-to-everything (V2X) communication technology. Direct communication among vehicles over the sidelink interface through the autonomous selection of radio resources is among the main and more challenging capabilities of such a connectivity solution. Nonetheless the crucial enhancements conceived by the Third Generation Partnership Project (3GPP) in Release 16, the autonomous resource allocation (a.k. a. Mode 2) still suffers from collisions due to the wrong estimation of the resource occupancy status enforced by half-duplex (HD) transceivers. In this paper, we argue in favour of the usage in vehicles of in-band full-duplex (FD) transceivers, gaining momentum in the research towards sixth generation (6G) systems thanks to their simultaneous transmission and reception capability. Exploiting FD, we propose to enhance the autonomous mode by detecting collisions and triggering resource reselection procedures in a more conscious manner. Simulation results show that the conceived proposal allows sidelink V2X to achieve significant improvements in terms of reliability and timeliness when compared against the legacy Mode 2.

Fingerprint-based indoor positioning has attracted a lot of interest due to its potential to meet a positional accuracy that enables many location-based 5G indoor services. However, the accuracy of fingerprinting decreases with changes in the environment which prevents positioning in new scenarios. On the other hand, naively acquiring up-to-date training data from the changed environment to retrain the model is often time-consuming. It is unclear whether after a change in the environment, a fingerprint model can be (data-)efficiently updated.This paper examines the generalizability (with respect to accuracy, robustness, and effort in recording data) of state-of-the-art fingerprint models based on a convolutional neural network (CNN) in realistic setups with changes in the environment. We propose a transfer learning (TL) method that exploits realistic synthetic Channel State Information (CSI) obtained with the Quasi Deterministic Radio channel Generator (QuaDRiGa), used to pre-train the CNN-based fingerprint model so that it can be adapted to any real (NLoS) propagation scenario with a low number of real training samples. Our experiments show that the positioning accuracy using fine-tuning improves by 37% in changed and by 19% in new environments.

As millimeter wave wireless communications are expected to be widely deployed in future applications, line-of-sight multiple-input multiple-output (LoS MIMO) systems are likely to become more important. The performance of LoS MIMO systems relies heavily on the placement of the antennas at the transmit (Tx) and receive (Rx) arrays, where the optimal antenna placement depends on the distance between the arrays. However, as a LoS MIMO system may be required to operate over a range of distances, the variation of the capacity and the spatial multiplexing gain are key design factors. Considering uniform circular arrays (UCA), by assuming that more than one UCAs are available at the Rx, we propose three array selection schemes for robust design of LoS MIMO UCA systems over a range of distances between the arrays. Their performance is evaluated in terms of their practical implications and their robustness with regard to a suitably defined measure.

In this paper we study the use of the Channel State Information (CSI) as fingerprint inputs of a Convolutional Neural Network (CNN) for localization. We examine whether the CSI can be used as a distinct fingerprint corresponding to a single position by considering the inconsistencies with its raw phase that cause the CSI to be unreliable. We propose two methods to produce reliable fingerprints including the phase information. Furthermore, we examine the structure of the CNN and more specifically the impact of pooling on the positioning performance, and show that pooling over the subcarriers can be more beneficial than over the antennas.

In this work, we study optimal transmit strategies for minimizing the positioning error bound in a line-of-sight scenario, under different levels of prior knowledge of the channel parameters. For the case of perfect prior knowledge, we prove that two beams are optimal, and determine their beam directions and optimal power allocation. For the imperfect prior knowledge case, we compute the optimal power allocation among the beams of a codebook for two different robustness-related objectives, namely average or maximum squared position error bound minimization. Our numerical results show that our low-complexity approach can outperform existing methods that entail higher signaling and computational overhead.

The fundamental limits of single-anchor multi-antenna positioning are investigated. Exploiting the structure of the multiple input-multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) channel at millimeter-wave frequencies, we present geometrically intuitive asymptotic expressions for the Fisher information on position, orientation and velocity for large bandwidth and number of antennas. The effects of synchronization errors and mobility are studied and it is shown that non-line-of-sight (NLOS) paths can be used to estimate the synchronization error and drastically improve the positioning performance. We also show that, in the presence of line-of-sight (LOS), mobility has a small impact on the achievable positioning accuracy, but in the NLOS-only scenario it can significantly improve the achievable performance, depending on the variance of the synchronization error. Finally, considering a communication system with device-specific transmission and reception constraints, we compare the positioning accuracy between the downlink and the uplink and show that they are equivalent under the same received signal-to-noise ratio (SNR).

We study the performance bounds of vehicle-to-vehicle (V2V) relative positioning for vehicles with multiple antenna arrays. The Cramér-Rao bound for the estimtion of the relative position and the orientation of the Tx vehicle is derived, when angle of arrival (AOA) measurements with or without time-difference of arrival (TDOA) measurements are used. In addition, geometrically intuitive expressions for the corresponding Fisher information are provided. The derived bounds are numerically evaluated for different carrier frequencies, bandwidths and array configurations under different V2V scenarios, i.e. overtaking and platooning. The significance of the AOA and TDOA measurements for position estimation is investigated. The achievable positioning accuracy is then compared with the present requirements of the 3rd Generation Partnership Project (3GPP) 5G New Radio (NR) vehicle-to-everything (V2X) standardization.

The use of multiple antennas in a transmit and receive antenna array for MIMO wireless communication allows the spatial degrees of freedom in rich scattering environments to be exploited. However, for line-of-sight (LOS) MIMO channels with uniform linear arrays (ULAs) at the transmitter and receiver, the antenna separations at the transmit and receive array need to be optimized to maximize the spatial degrees of freedom and the channel capacity. In this paper, we first revisit the derivation of the optimum antenna separation at the transmit and receive ULAs in a LOS MIMO system, and provide the general expression for the optimum antenna separation product, which consists of multiple solutions. Although only the solution corresponding to the smallest antenna separation product is usually considered in the literature, we exploit the multiple solutions for a LOS MIMO design over a range of distances between the transmitter and receiver. In particular, we consider the LOS MIMO design in a vehicle-to-vehicle (V2V) communication scenario, over a range of distances between the transmit and receive vehicle.

The availability of abundant spectrum makes millimeter wave (mm-wave) a prominent candidate technology for the next generation of cellular networks. Highly directional transmission is essential for the exploitation of mm-wave bands to compensate for high propagation loss. The directional transmission, nevertheless, necessitates a specific design for mm-wave initial cell discovery, as conventional omni-directional broadcasting may fail in delivering cell discovery information. To address this issue, this paper provides an analytical framework for mm-wave beamformed cell discovery based on an information-theoretic approach. Design options are compared considering four fundamental and representative broadcasting schemes to evaluate discovery latency and overhead. The schemes are then simulated under realistic system parameters. Analytical and simulation results reveal four key findings: 1) analog/hybrid beamforming performs as well as digital beamforming in terms of cell discovery latency; 2) single-beam exhaustive scan optimizes the latency and, however, leads to the overhead penalty; 3) multi-beam simultaneous scan can significantly reduce the overhead and provide the flexibility to achieve tradeoff between the latency and the overhead; and 4) the latency and the overhead are relatively insensitive to extreme low block error rates.

This paper presents the theoretical capacity performance of different downlink multi-user MIMO (MU-MIMO) beam-forming strategies for a millimeter wave communication system using hybrid beam-forming. Hybrid beam-forming uses both RF and digital beam-forming and is a practical way to achieve the antenna array gains required when the large bandwidths available at millimeter wave frequencies are utilized. Since both RF and digital beam-forming are used, there are various possible beam-forming approaches. We investigate a system which uses code book based RF beam-forming with non-code book based digital beam-forming which utilizes sounding reference symbol (SRS) based uplink measurements for TDD operation. For this system we examine different strategies for selecting the RF beams in conjunction with different digital pre-coding schemes to support MU-MIMO. The performances of the different strategies are simulated under the assumption of perfect channel knowledge at the base station and at the mobile station.

This paper presents the theoretical capacity performance of different linear multi-user MIMO (MU-MIMO) pre-coding algorithms for a millimeter wave communication system using hybrid beam-forming. Hybrid beam-forming uses both RF and digital beam-forming and is a practical way to achieve the required array gains when the high bandwidths available at millimeter wave frequencies are utilized. The performances of the different linear pre-coding schemes are simulated using a millimeter wave channel model in different interference scenarios under the assumption of perfect channel knowledge at the transmitter and at the receiver. Furthermore, enhancements to the linear pre-coding algorithms are described and the resulting performance is presented.

It has been shown that the extrinsic information produced by the soft output Viterbi algorithm (SOVA) is too optimistic. To compensate for this, the extrinsic information must be normalised. Standard normalisation techniques involve calculating the mean and variance of all of the bits in the SOVA output frame. Since this adds complexity to every iteration when the SOVA is used in a turbo decoder, there is merit in investigating the performance of reduced complexity techniques. We present the performance of a parallel concatenated SOVA based turbo decoder using the suggested reduced complexity normalisation techniques. BER results are presented for the case of an AWGN channel.