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.

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.

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.

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.

Pilot contamination has been identified as one of the main limitations for massive MIMO. In this work, we consider to reduce pilot contamination by having time-shifted non-overlapping uplink pilot transmissions in neighboring cells, which are time-multiplexed with the uplink data transmission. As simply shifting the pilots will not avoid the pilot contamination, we propose an unequal power allocation between the pilot and the uplink data transmission. We show that the proposed approach with an optimum power allocation between the pilot and uplink data transmission is able to mitigate pilot contamination and achieve significant gains compared to related schemes. Furthermore, lower bounds of the uplink and downlink sum rate with the proposed scheme are provided, which can be employed for obtaining the optimum power allocation.

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.

In this paper, we propose a sequential hybrid beamforming design for multi-link transmission over mmwave frequency bands. As a starting point, a baseline data communication link is established via traditional analog beamforming at both the BS and UE. If an extra RF chain is available at the UE, it can continue to probe the propagation environment at the same frequencies. In case the environment is favorable and system resources allow, a secondary data communication link is established to enable multi-stream transmission. In principle, the secondary link could be served by the same BS and/or one or several other BS(s). To initialize the secondary data communication link, a parallel beam search scheme is proposed, which helps the UE/BS to find a suit-able beam pair with given optimization criteria without interrupting the baseline data communication. By applying the proposed two-step approach, hybrid beamforming becomes an add-on feature that can be easily switched on over an analog beamforming enabled system without interrupting its operation whenever system requires. Meanwhile, the information obtained by deploying the proposed parallel beam search scheme can also be used for deciding a back-up beam pair if signal blockage occurs to the baseline data communication link.

Millimeter wave (mm-wave) communication is essential for the next generation cellular networks. To exploit mm-wave frequencies, directional transmissions have to be applied to compensate the high propagation loss. Due to directional transmissions, initial access procedure of mm-wave communication systems needs specific design compared to conventional networks operating at sub-6 GHz. This paper focuses on an important step in the initial access procedure, namely broadcast signaling design for cell discovery. An analysis of such design is conducted based on an information theoretical approach, where four fundamental beam patterns, which cover most of the design options, are compared. Their performances in terms of cell discovery latency and signaling overhead are analyzed. The analysis reveals three key findings: (i) the average cell discovery latency depends only on beam duration and frame length, if the entire beacon interval can be accommodated in one frame; (ii) for low latency, single beam exhaustive scanning provides the best performance, but results in high signaling overhead; (iii) simultaneous multi-beam scanning can significantly reduce the overhead, and provide the flexibility to achieve trade-off between latency and overhead. The analytical results are verified by extensive simulations.

Given a set of observations, the estimation of covariance matrices is required in the analysis of many applications. To this end, any know structure of the covariance matrix can be taken into account. For instance, in case of separable processes, the covariance matrix is given by the Kronecker product of two factor matrices. Assuming the covariance matrix is full rank, the maximum likelihood (ML) estimate in this case leads to an iterative algorithm known as the flip-flop algorithm in the literature. In this work, we first generalize the flip-flop algorithm to the case when the covariance matrix is rank deficient, which happens to be the case in several situations. In addition, we propose a non-iterative estimation approach which incurs in a performance loss compared to the ML estimate, but at the expense of less complexity.

The problem of parameter estimation from large noisy data is considered. If the observation size N is large, the calculation of efficient estimators is computationally expensive. Further, memory can be a limiting factor in technical systems where data is stored for later processing. Here we follow the idea of reducing the size of the observation by projecting the data onto a subspace of smaller dimension M ≪ N, but with the highest possible informative value regarding the estimation problem. Under the assumption that a prior distribution of the parameter is available and the output size is fixed to M, we derive a characterization of the Pareto-optimal set of linear transformations by using a weighted form of the Bayesian Cramér-Rao lower bound (BCRLB) which stands in relation to the expected value of the Fisher information measure. Satellite-based positioning is discussed as a possible application. Here N must be chosen large in order to compensate for low signal-to-noise ratios (SNR). For different values of M, we visualize the information-loss and show by simulation of the MAP estimator the potential accuracy when operating on the reduced data.

The available channel state information (CSI) in a limited feedback system like the frequency division duplex (FDD) downlink is not perfect since it is subject to estimation, quantization and feedback errors, and in addition, can be outdated. Despite the fact that the capacity of limited feedback systems is unknown in general, we derive a novel lower bound on the capacity of single user limited feedback systems with imperfect CSI. Based on this bound, we propose the design of an FDD system by finding the optimum training and number of feedback bits. To this end we also take the FDD uplink into account, since practical FDD systems represent two-way systems. We also provide closed-form approximations for the optimum downlink training, uplink training and number of feedback bits which basically maximize lower bounds on the FDD downlink and uplink capacity with imperfect CSI.

It is well known that the availability of M transmit antennas at the base station enables to serve K single-antenna users (K ≤ M), under the assumption of perfect channel state information (CSI) at the base station. However, in practical frequency division duplex (FDD) systems, the channel knowledge available at the base station is not perfect since it is obtained through a limited feedback in the uplink consisting of B bits per user. Part of the B feedback bits can be employed to quantize the channel direction information (CDI), while the rest can be used to quantize the channel magnitude information (CMI) of each user. In this work we address the transceiver design of a multiuser MISO system based on the minimum mean square error criterion and with transmit CSI obtained through a limited feedback composed of quantized CDI and CMI. We treat the case of K ≤ M users with different average channel gains and based on our findings we conclude that for uncorrelated channels it is optimum to employ all the B bits for quantizing the CDI under the assumption that the base station knows the statistics of the CMI of the users.

We comment on the famous work GtOpportunistic beamforming using dumb antennasLt by P. Viswanath, D.N.C. Tse, and R. Laroia, from 2002. In that paper, it is argued that in an independent and fast fading environment, the opportunistic beamforming technique provides no performance gain. This assessment is based on the argument that the fading distribution of the equivalent channel, which is obtained through opportunistic beamforming, does not depend on the number of transmit antennas in independent and fast fading scenarios. In this paper, we show that this argument of the original work is based on a non-physical model of Rayleigh fading, and as a consequence, results in a wrong conclusion. In contrast, we show that there is beamforming gain to be obtained by the opportunistic beamforming technique even for independent and fast fading environments. For the case of uncoupled antennas, the amount of obtainable beamforming gain approaches - with probability one - the number of transmit antennas from below as the number of users approaches infinity.

The major difference between a non-cellular system, i.e. a single isolated cell, and a cellular system is the intercell interference (ICI). In the uplink, the base station can measure the users' signal to noise and interference ratio (SINR) and through a feedforward channel it can inform the users which coding and modulation scheme to apply in order to perform link adaptation (LA). However, a user's SINR observed in the uplink by the base station when determining the link adaptation decision might no longer be the same when the link adaptation is effected by the user due to fluctuations of the intercell interference. By then, the intercell interference could have greatly changed even if the users are static, due to changes in the scheduling decisions in the interfering cells. Hence, the uplink transmission would no longer have the correct link adaptation for the current SINR, since we are to some extent blind with respect to the ICI. In this work, we quantify this degree of ICI blindness by the correlation between the measured ICI and the actual experienced ICI. Furthermore, we analyze the degradation in throughput for different degrees of ICI correlation which depends on the scheduling in the interfering cells. Additionally, we show how much benefit correct link adaptation provides in a cellular environment over outdated link adaptation. In this work, we assume that there is no intracell interference as a consequence of an orthogonal multiple access scheme.

The main difference between a non-cellular and a cellular communication system is the intercell interference. Therefore, modelling the intercell interference and analyzing its effects is of particular interest for cellular communication systems. On the one hand, the intercell interference can be modeled by system level simulations. On the other hand, it is also meaningful to assess the intercell interference without performing exhaustive simulations which nonetheless at the same time still capture the major effects that determine the interference. To this end, we consider the intercell interference as a random variable composed of many other random variables. In this paper, we present a semi-analytical method to analyze the downlink intercell interference in a cellular system. Through such an approach a quick and reliable assessment of the downlink inter-cell interference can be obtained. Our focus is on the methodology and we consider basic access schemes: WCDMA, TDMA, FDMA, and random OFDMA. However, our model can be extended to include other access schemes and other features.

A new semi-analytical method to analyze intercell interference in a cellular system is introduced. A sound comprehension of the properties of intercell interference is the key to a meaningful assessment of communication systems without exhaustive simulation campaigns. We consider the basic access schemes TDMA, WCDMA and random OFDMA for uplink transmission. We will also give hints, how the methods can be extended to capture other access schemes, and/or additional features. The focus of this work is more on the introduction of the new methodology. In principle, we describe the intercell interference as a random variable which is composed of many other random variables. The distributions, and in particular the mutual dependencies of those have to be carefully studied. Applications and extensions of the ideas will be addressed in future work, only first examples are given here.