Reconfigurable antennas (RAs) utilize the electromagnetic (EM) domain to provide dynamic control over antenna radiation patterns, which offers an effective way to enhance power efficiency in wireless links. Unlike conventional arrays with fixed element patterns, RAs enable on-demand beampattern synthesis by directly controlling each antenna's EM characteristics. While existing research on RAs has primarily focused on improving spectral efficiency, this paper explores their application for downlink localization. Moreover, the majority of existing works focus on far-field scenarios with little attention on near-field (NF). Motivated by these gaps, we consider a synthesis model in which each antenna generates desired beampatterns from a finite set of EM basis functions. We then formulate a joint optimization problem for the baseband (BB) and EM precoders with the objective of minimizing the user equipment (UE) position error bound (PEB) in NF conditions. Our analytical derivations and extensive simulation results demonstrate that the proposed hybrid precoder design for RAs significantly improves UE positioning accuracy compared to traditional non-reconfigurable arrays.

High-mobility communications, which are crucial for next-generation wireless systems, cause the orthogonal frequency division multiplexing (OFDM) waveform to suffer from strong intercarrier interference (ICI) due to the Doppler effect. In this work, we propose a novel receiver architecture for OFDM that leverages the angular domain to separate multipaths. A block-type pilot is sent to estimate direction-of-arrivals (DoAs), propagation delays, and channel gains of the multipaths. Subsequently, a decision-directed (DD) approach is employed to estimate and iteratively refine the Dopplers. Two different approaches are investigated to provide initial Doppler estimates: an error vector magnitude (EVM)-based method and a deep learning (DL)-based method. Simulation results reveal that the DL-based approach allows for constant bit error rate (BER) performance up to the maximum 6G speed of 1000 km/h.

The sixth-generation (6G) wireless networks promises the integration of radar-like sensing capabilities into communication infrastructure. In this paper, we investigate a multi-static sensing framework where half-duplex base stations (BSs) are assigned as either transmitter or sensing receiver nodes. We propose a randomized sparse resource allocation scheme based on orthogonal frequency division multiplexing (OFDM) waveform design tailored for the multi-static scenario to simultaneously mitigate inter-BS interference (IBI) and sensing ambiguities. The waveform design also ensures robustness against inter-symbol interference (ISI) and intercarrier interference (ICI) via a judicious choice of subcarrier spacing according to the deployment of BSs. The potential ambiguity caused by sparse signaling is addressed through controlled irregularity in both time and frequency domains, with a negligible noise floor elevation. Simulation results demonstrate the effectiveness and resilience of the proposed design in the presence of multiple targets and clutter.

We study uplink orthogonal frequency-division multiplexing (OFDM) pilot distortion to deliberately obfuscate time-of-arrival (ToA) estimation at a single base station while preserving communication performance. We design a complex persubcarrier distortion vector that increases sidelobes of the mismatched ambiguity function (MAF) relative to its mainlobe, using two objectives: the sidelobe-to-peak level ratio and the integrated sidelobe level. The design is subject to a transmit-power budget and a proximity (dissimilarity) constraint around the communication-optimal pilot. Communication impact is quantified by a capacity-motivated lower bound obtained from the linear minimum mean-squared error error covariance with a mismatched channel estimate. The resulting generalized fractional program is solved with Dinkelbach's transform and a difference-of-convex update that yields a closed-form Karush-Kuhn-Tucker step. Simulations on a single-input single-output OFDM link show that the optimized distortions raise MAF sidelobes and degrade delay estimation, as validated by a mismatched maximumlikelihood ToA estimator, while incurring only marginal capacity loss over a broad signal-to-noise ratio range. The method requires no protocol changes or artificial path injection and provides a signal-level mechanism to control ToA observability under communication constraints.

Integrated sensing and communications (ISAC) is considered an innovative technology in sixth-generation (6G) wireless networks, where utilizing orthogonal frequency division multiplexing (OFDM) communication signals for sensing provides a cost-effective solution for implementing ISAC. However, the sensing performance of matched and mismatched filtering schemes can be significantly deteriorated due to the signaling randomness induced by finite-alphabet modulations with non-constant modulus, such as quadrature amplitude modulation (QAM) constellations. Therefore, improving sensing performance without significantly compromising communication capability (i.e., maintaining randomness), remains a challenging task. To that end, we propose a unified probabilistic constellation shaping (PCS) framework that is compatible with both matched and mismatched filtering schemes, by maximizing the communication rate while imposing constraints on mean square error (MSE) of sensing channel state information (CSI), power, and probability distribution. Specifically, the MSE of sensing CSI is leveraged to optimize sensing capability, which is illustrated to be a more comprehensive metric compared to the output SNR after filtering (SNRout) and integrated sidelobes ratio (ISLR). Additionally, the internal relationships among these three sensing metrics are explicitly analyzed. Building upon this, we further reveal that the normalized MSE can be interpreted as a penalty function version of the dynamic range, which is usually exploited to evaluate the behavior of delay-Doppler profiles. Finally, both simulations and field measurements validate the efficiency of proposed PCS approach in achieving a flexible S&C trade-off, as well as its credibility in enhancing 6G wireless transmission in real-world scenarios.

Cell-free massive multiple-input multiple-output (mMIMO) networks enhance coverage and spectral efficiency (SE) by distributing antennas across access points (APs) with phase coherence between APs. However, the use of cost-efficient local oscillators (LOs) introduces phase noise (PN) that compromises phase coherence, even with centralized processing. Sharing an LO across APs can reduce costs in specific configurations but cause correlated PN between APs, leading to correlated interference that affects centralized combining. This can be improved by exploiting the PN correlation in channel estimation. This paper presents an uplink orthogonal frequency division multiplexing (OFDM) signal model for PN-impaired cell-free mMIMO, addressing gaps in single-carrier signal models. We evaluate mismatches from applying single-carrier methods to OFDM systems, showing how they underestimate the impact of PN and produce over-optimistic achievable SE predictions. Based on our OFDM signal model, we propose two PN-aware channel and common phase error estimators: a distributed estimator for uncorrelated PN with separate LOs and a centralized estimator with shared LOs. We introduce a deep learning-based channel estimator to enhance the performance and reduce the number of iterations of the centralized estimator. The simulation results show that the distributed estimator outperforms mismatched estimators with separate LOs, whereas the centralized estimator enhances distributed estimators with shared LOs.

Radio-based simultaneous localization and mapping (SLAM) has the potential to provide precise user equipment (UE) localization and environmental sensing capabilities by exploiting radio signals. Most existing approaches leverage line-of-sight (LoS) and single-bounce non-line-of-sight (NLoS) paths solely, while higher-order NLoS paths are treated as disturbance. In this paper, we investigate the benefits of leveraging double-bounce NLoS paths for solving the bistatic snapshot radio SLAM problem.We derive the Cramér-Rao bound (CRB) for joint estimation of the UE state and landmark positions when double-bounce NLoS paths are present. In addition, we propose an algorithm to identify double-bounce NLoS paths and leverage them into joint UE and landmarks estimation. The derived bounds are validated through simulated data, and the proposed algorithms are evaluated using experimental millimeter wave (mmWave) measurements harnessing beamformed 5G cellular reference signals. The numerical and experimental results demonstrate that the double-bounce NLoS paths which share at least one incidence point (IP) with the single-bounce NLoS paths improve the estimation accuracy of the UE state and existing IPs of single-bounce NLoS paths. Importantly, exploiting double-bounce NLoS paths enhances environmental mapping capabilities by revealing landmarks that are unobservable with single-bounce NLoS paths alone.

Integrated sensing and communication enables simultaneous communication and sensing tasks, including precise radio positioning and mapping, essential for future 6G networks. Current methods typically model environmental landmarks as isolated incidence points or small reflection areas, lacking detailed attributes essential for advanced environmental interpretation. This paper addresses these limitations by developing an end-to-end cooperative uplink framework involving multiple base stations and users. Our method uniquely estimates extended landmark objects and incorporates obstruction-based outlier removal to mitigate multi-bounce signal effects. Validation using realistic ray-tracing data demonstrates substantial improvements in the richness of the estimated environmental map.

Accurate mobile device localization is critical for emerging 5G/6G applications such as autonomous vehicles and augmented reality. In this paper, we propose a unified localization method that integrates model-based and machine learning (ML)-based methods to reap their respective advantages by exploiting available map information. In order to avoid supervised learning, we generate training labels automatically via optimal transport (OT) by fusing geometric estimates with building layouts. Ray-tracing based simulations are carried out to demonstrate that the proposed method significantly improves positioning accuracy for both line-of-sight (LoS) users (compared to ML-based methods) and non-line-of-sight (NLoS) users (compared to model-based methods). Remarkably, the unified method is able to achieve competitive overall performance with the fully-supervised fingerprinting, while eliminating the need for cumbersome labeled data measurement and collection.

Gain-phase impairments (GPIs) affect both communication and sensing in 6G integrated sensing and communication (ISAC). We study the effect of GPIs in a single-input, multiple-output orthogonal frequency-division multiplexing ISAC system and develop a model-based unsupervised learning approach to simultaneously (i) estimate the gain-phase errors and (ii) localize sensing targets. The proposed method is based on the optimal maximum a-posteriori ratio test for a single target. Results show that the proposed approach can effectively estimate the gain-phase errors and yield similar position estimation performance as the case when the impairments are fully known.

Distributed multi-antenna systems are an important enabling technology for future intelligent transportation systems (ITS), showing promising performance in vehicular communications and near-field (NF) localization applications. This work investigates optimal deployments of phase-coherent sub-arrays on a vehicle for NF localization in terms of a Cramér-Rao lower bound (CRLB)-based metric. Sub-array placements consider practical geometrical constraints on a three-dimensional vehicle model accounting for self-occlusions. Results show that, for coherent NF localization of the vehicle, the aperture spanned by the sub-arrays should be maximized and a larger number of subarrays results in more even coverage over the vehicle orientations under a fixed total number of antenna elements, contrasting with the outcomes of incoherent localization. Moreover, while coherent NF processing significantly enhances accuracy, it also leads to more intricate cost functions, necessitating computationally more complex algorithms than incoherent processing.

Reconfigurable intelligent surfaces (RISs) have emerged as a transformative technology for sixth-generation (6 G) communication networks, offering the ability to dynamically shape wireless propagation environments and thus efficiently enhance received signal quality. However, practical implementation of RIS faces challenges, including potential failures of individual elements (pixels), which can degrade the performance significantly. This paper leverages autoencoders and end-to-end (E2E) learning in RIS-aided systems to jointly optimize the RIS phase profiles and receiver angle-of-departure (AoD) estimation in the presence of pixel failures. The proposed E2E approach demonstrates resilience against practical pixel errors while is shown to achieve performance close to the fundamental bounds, thereby advancing the state-of-the-art in RIS-aided systems towards the 6 G era.

This study reveals the vulnerabilities of Wireless Local Area Networks (WLAN) sensing, under the scope of joint communication and sensing (JCAS), focusing on target spoofing and deceptive jamming techniques. We use orthogonal frequency-division multiplexing (OFDM) to explore how adversaries can exploit WLAN’s sensing capabilities to inject false targets and disrupt normal operations. Unlike traditional methods that require sophisticated digital radio-frequency memory hardware, we demonstrate that much simpler software-defined radios can effectively serve as deceptive jammers in WLAN settings. Through comprehensive modeling and practical experiments, we show how deceptive jammers can manipulate the range-Doppler map (RDM) by altering signal integrity, thereby posing significant security threats to OFDM-based JCAS systems. Our findings comprehensively evaluate jammer impact on RDMs and propose several jamming strategies that vary in complexity and detectability.

Hybrid analog-digital beamforming stands out as a key enabler for future communication systems with a massive number of antennas. In this paper, we investigate the hybrid precoder design problem for angle-of-departure (AoD) estimation, where we take into account the practical constraint on the limited resolution of phase shifters. Our goal is to design a radio-frequency (RF) precoder and a base-band (BB) precoder to estimate AoD of the user with high accuracy. To this end, we propose a two-step strategy where we first obtain the fully digital precoder that minimizes the angle error bound, and then the resulting digital precoder is decomposed into an RF precoder and a BB precoder, based on the alternating optimization and the alternating direction method of multipliers. Furthermore, we derive the quantization error upper bound and provide convergence conditions for the proposed algorithm. Numerical results demonstrate the superior performance of the proposed method over state-of-the-art baselines.

Integrated sensing and communication (ISAC) has been considered a key feature of next-generation wireless networks. This paper investigates the joint design of the radar receive filter and dual-functional transmit waveform for the multiple-input multiple-output (MIMO) ISAC system. While optimizing the mean square error (MSE) of the radar receive spatial response and maximizing the achievable rate at the communication receiver, besides the constraints of full-power radar receiving filter and unimodular transmit sequence, we control the maximum range sidelobe level, which is often overlooked in existing ISAC waveform design literature, for better radar imaging performance. To solve the formulated optimization problem with convex and nonconvex constraints, we propose an inexact augmented Lagrangian method (ALM) algorithm. For each subproblem in the proposed inexact ALM algorithm, we custom-design a block successive upper-bound minimization (BSUM) scheme with closed-form solutions for all blocks of the variable to enhance the computational efficiency. Convergence analysis shows that the proposed algorithm is guaranteed to provide a stationary and feasible solution. Extensive simulations are performed to investigate the impact of different system parameters on communication and radar imaging performance. Comparison with the existing works shows the superiority of the proposed algorithm.

This paper undertakes a holistic investigation of two fundamental trade-offs in monostatic OFDM integrated sensing and communication (ISAC) systems, namely, the time-frequency trade-off and the spatial trade-off, originating from the choice of modulation order for random data and the design of beamforming strategies, respectively. To counteract the elevated side-lobe levels induced by varying-amplitude data in high-order QAM signaling, we introduce a novel linear minimum mean-squared-error (LMMSE) estimator. We also provide a rigorous theoretical characterization of side-lobe levels achieved by the proposed LMMSE estimator and two benchmark schemes, proving its superiority for any modulation scheme and SNR level. Moreover, we explore spatial domain trade-offs through two ISAC transmission strategies: concurrent, employing joint beams, and time-sharing, using separate beams for sensing and communications not overlapping in time. Simulations demonstrate improved performance of the LMMSE estimator, especially in detecting weak targets in the presence of strong ones with high-order QAM, consistently yielding more favorable ISAC trade-offs than existing baselines under various modulation schemes, SNR conditions, RCS levels and transmission strategies. Additionally, we present experimental results to validate the effectiveness of the LMMSE estimator in reducing side-lobe levels, based on real-world measurements

We study model-based end-to-end learning in the context of integrated sensing and communication (ISAC) under hardware impairments. Hardware impairments are usually addressed by means of array calibration with a focus on communication performance. However, residual impairments may exist that affect sensing performance. This paper proposes a data-driven framework for mitigating such impairments. A monostatic orthogonal frequency-division multiplexing (OFDM) sensing and multiple-input single-output (MISO) communication scenario is considered, incorporating hardware imperfections at the ISAC transceiver antenna array. Since conventional ISAC signal processing algorithms rely on mathematical models of the wireless channel, a mismatch occurs between the assumed mathematical models and the underlying reality in the presence of hardware impairments. We first study the detrimental effects of such impairments at the transmitter and receiver side of the proposed scenario, showcasing different levels of degradation on communication and sensing performances. As the core contribution of this work, we propose a novel differentiable version of the orthogonal matching pursuit (OMP) algorithm that is suitable for multi-target sensing and allows for efficient end-to-end learning of the hardware impairments. Based on the differentiable OMP, we devise two model-based parameterization strategies of the ISAC beamformer and sensing receiver to account for hardware impairments: (i) learning a dictionary of steering vectors for different angles and (ii) learning the parameterized hardware impairments. We carry out a comprehensive performance analysis of the proposed model-based learning approaches and a strong baseline consisting of least-squares beamforming, conventional OMP, and maximum-likelihood symbol detection for communication. Results show that by parameterizing the hardware impairments, learning approaches offer gains in terms of higher detection probability, position estimation accuracy, and lower symbol error rate (SER) compared to the baseline. We demonstrate that learning the parameterized hardware impairments outperforms learning a dictionary of steering vectors, also exhibiting the lowest complexity.

Sidelink positioning research predominantly focuses on the snapshot positioning problem, often within the mmWave band. Only a limited number of studies have delved into vehicle-to-anything (V2X) tracking within sub-6 GHz bands. In this paper, we investigate the V2X sidelink tracking challenges over sub-6 GHz frequencies. We propose a Kalman-filter-based tracking approach that leverages the estimated error covariance lower bounds (EECLBs) as measurement covariance, alongside a gating method to augment tracking performance. Through simulations employing ray-tracing data and super-resolution channel parameter estimation, we validate the feasibility of sidelink tracking using our proposed tracking filter with two novel EECLBs. Additionally, we demonstrate the efficacy of the gating method in identifying line-of-sight paths and enhancing tracking performance.

This paper investigates subspace-based target detection in OFDM integrated sensing and communications (ISAC) systems, considering the impact of various constellations. To meet diverse communication demands, different constellation schemes with varying modulation orders (e.g., PSK, QAM) can be employed, which in turn leads to variations in peak sidelobe levels (PSLs) within the radar functionality. These PSL fluctuations pose a significant challenge in the context of multi-target detection, particularly in scenarios where strong sidelobe masking effects manifest. To tackle this challenge, we have devised a subspace-based approach for a step-by-step target detection process, systematically eliminating interference stemming from detected targets. Simulation results corroborate the effectiveness of the proposed method in achieving consistently high target detection performance under a wide range of constellation options in OFDM ISAC systems.

We investigate the problem of reconfigurable intelligent surface (RIS)-aided monostatic sensing of a mobile target under line-of-sight (LoS) blockage considering a single-antenna, full-duplex, and dual-functional radar-communications base station (BS). For the purpose of target detection and delay/Doppler/angle estimation, we derive a detector based on the generalized likelihood ratio test (GLRT), which entails a high-dimensional parameter search and leads to angle-Doppler coupling. To tackle these challenges, we propose a two-step algorithm for solving the GLRT detector/estimator in a low-complexity manner, accompanied by a RIS phase profile design tailored to circumvent the angle-Doppler coupling effect. Simulation results verify the effectiveness of the proposed algorithm, demonstrating its convergence to theoretical bounds and its superiority over state-of-the-art mobility-agnostic benchmarks.

Integrated sensing and communications (ISAC) is poised to be a native technology for the forthcoming Sixth Generation (6G) era, with an emphasis on its potential to enhance communications performance through the integration of sensing information, i.e., sensing-assisted communications (SAC). Nevertheless, existing research on SAC has predominantly confined its focus to scenarios characterized by minimal clutter and obstructions, largely neglecting indoor environments, particularly those in industrial settings, where propagation channels involve high clutter density. To address this research gap, background subtraction is proposed on the monostatic sensing echoes, which effectively addresses clutter removal and facilitates detection and tracking of user equipments (UEs) in cluttered indoor environments with SAC. A realistic evaluation of the introduced SAC strategy is provided, using ray tracing (RT) data with the scenario layout following Third Generation Partnership Project (3GPP) indoor factory (InF) channel models. Simulation results show that the proposed approach enables precise predictive beamforming largely unaffected by clutter echoes, leading to significant improvements in effective data rate over the existing SAC benchmarks and exhibiting performance very close to the ideal case where perfect knowledge of UE location is available.

In this paper, we investigate sub-6 GHz V2X sidelink positioning scenarios in 5G vehicular networks through a comprehensive end-to-end methodology encompassing ray-tracing-based channel modeling, novel theoretical performance bounds, high-resolution channel parameter estimation, and geometric positioning using a round-trip-time (RTT) protocol. We first derive a novel, approximate Cramér-Rao bound (CRB) on the connected road user (CRU) position, explicitly taking into account multipath interference, path merging, and the RTT protocol. Capitalizing on tensor decomposition and ESPRIT methods, we propose high-resolution channel parameter estimation algorithms specifically tailored to dense multipath V2X sidelink environments, designed to detect multipath components (MPCs) and extract line-of-sight (LoS) parameters. Finally, using realistic ray-tracing data and antenna patterns, comprehensive simulations are conducted to evaluate channel estimation and positioning performance, indicating that sub-meter accuracy can be achieved in sub-6 GHz V2X with the proposed algorithms.

In this article, we address the timely topic of cellular bistatic simultaneous localization and mapping (SLAM) with specific focus on end-to-end processing solutions, from raw I/Q samples, via channel parameter estimation to user equipment (UE) and landmark location information in millimeter-wave (mmWave) networks, with minimal prior knowledge. Firstly, we propose a new multipath channel parameter estimation solution that operates directly with beam reference signal received power (BRSRP) measurements, alleviating the need to know the true antenna beampatterns or the underlying beamforming weights. Additionally, the method has built-in robustness against unavoidable antenna sidelobes. Secondly, we propose new snapshot SLAM algorithms that have increased robustness and identifiability compared to prior art, in practical built environments with complex clutter and multi-bounce propagation scenarios, and do not rely on any a priori motion model. The performance of the proposed methods is assessed at the 60GHz mmWave band, via both realistic ray-tracing evaluations as well as true experimental measurements, in an indoor environment. A wide set of offered results demonstrate the improved performance, compared to the relevant prior art, in terms of the channel parameter estimation as well as the end-to-end SLAM performance. Finally, the article provides the measured 60GHz data openly available for the research community, facilitating results reproducibility as well as further algorithm development.

Future wireless networks will integrate sensing, learning, and communication to provide new services beyond communication and to become more resilient. Sensors at the network infrastructure, sensors on the user equipment (UE), and the sensing capability of the communication signal itself provide a new source of data that connects the physical and radio frequency (RF) environments. A wireless network that harnesses all these sensing data can not only enable additional sensing services but also become more resilient to channel-dependent effects such as blockage and better support adaptation in dynamic environments as networks reconfigure. In this article, we provide a vision for integrated sensing and communication (ISAC) networks and an overview of how signal processing, optimization, and machine learning (ML) techniques can be leveraged to make them a reality in the context of 6G. We also include some examples of the performance of several of these strategies when evaluated using a simulation framework based on a combination of ray-tracing measurements and mathematical models that mix the digital and physical worlds.

Localization is envisioned as a key enabler to satisfy the requirements of communications and context-aware services in the fifth/sixth generation (5G/6G) communication systems. User localization can be achieved based on delay and angle estimation using uplink/downlink pilot signals. However, hardware impairments (HWIs) (such as phase noise and mutual coupling) distort the signals at both the transmitter and receiver sides and thus affect the localization performance. While this impact can be ignored at lower frequencies with less severe HWIs, and less stringent localization requirements, modeling and analysis efforts are needed for high-frequency bands to assess degradation in localization accuracy due to HWIs. In this work, we model various types of impairments for a mmWave multiple-input-multiple-output communication system and conduct a misspecified Cramér-Rao bound analysis to evaluate HWI-induced performance losses in terms of angle/delay estimation and the resulting 3D position/orientation estimation error. We also investigate the effect of individual and overall HWIs on communications in terms of symbol error rate (SER). Our extensive simulation results demonstrate that each type of HWI leads to a different level of degradation in angle and delay estimation performance, and the prominent impairment factors on delay estimation will have a dominant negative effect on SER.

Positioning is expected to support communication and location-based services in the fifth/sixth generation (5G/6G) networks. With the advent of reflective reconfigurable intelligent surfaces (RISs), radio propagation channels can be controlled, making high-accuracy positioning and extended service coverage possible. However, the passive nature of the RIS requires a signal source such as a base station (BS), which limits the positioning service in extreme situations, such as tunnels, dense urban areas, or complicated indoor scenarios where 5G/6G BSs are not accessible. In this work, we show that with the assistance of (at least) two RISs and sidelink communication between two user equipments (UEs), the absolute positions of these UEs can be estimated in the absence of BSs. A two-stage 3D sidelink positioning algorithm is proposed, benchmarked by the derived Cramér-Rao bounds. The effects of multipath and RIS profile designs on positioning performance are evaluated, and localization analyses are performed for various scenarios. Simulation results demonstrate the promising positioning accuracy of the proposed BS-free sidelink communication system. Additionally, we propose and evaluate several solutions to eliminate potential blind areas where positioning performance is poor, such as removing clock offset via round-trip communication, adding geometrical prior or constraints, as well as introducing more RISs.

Orthogonal time frequency space (OTFS) is a promising alternative to orthogonal frequency-division multiplexing (OFDM) for high-mobility communications. We propose a novel multiple-input multiple-output (MIMO) integrated sensing and communication (ISAC) system based on OTFS modulation. We begin by deriving new sensing and communication signal models for the proposed MIMO-OTFS ISAC system that explicitly capture inter-symbol interference (ISI) and inter-carrier interference (ICI) effects. We then develop a generalized likelihood ratio test (GLRT) based multi-target detection and delay-Doppler-angle estimation algorithm for MIMO-OTFS radar sensing that can simultaneously mitigate and exploit ISI/ICI effects, to prevent target masking and surpass standard unambiguous detection limits in range/velocity. Moreover, considering two operational modes (discovery/track), we propose an adaptive MIMO-OTFS ISAC transmission strategy. For the discovery mode, we introduce the concept of delay-Doppler (DD) multiplexing, enabling omnidirectional probing of the environment and large virtual array at the OTFS radar receiver. For the track mode, we pursue a directional transmission approach and design an OTFS ISAC optimization algorithm in spatial and DD domains, seeking the optimal trade-off between radar signal-to-noise ratio (SNR) and achievable rate. Simulation results verify the effectiveness of the proposed sensing algorithm and reveal valuable insights into OTFS ISAC trade-offs under varying communication channel characteristics.

In the upcoming sixth generation (6G) of wireless communication systems, reconfigurable intelligent surfaces (RISs) are regarded as one of the promising technological enablers, which can provide programmable signal propagation. Therefore, simultaneous radio localization and mapping (SLAM) with RISs appears as an emerging research direction within the 6G ecosystem. In this paper, we propose a novel framework of RIS-enabled radio SLAM for wireless operation without the intervention of access points (APs). We first design the RIS phase profiles leveraging prior information for the user equipment (UE), such that they uniformly illuminate the angular sector where the UE is probabilistically located. Second, we modify the marginal Poisson multi-Bernoulli SLAM filter and estimate the UE state and landmarks, which enables efficient mapping of the radio propagation environment. Third, we derive the theoretical Cramér-Rao lower bounds on the estimators for the channel parameters and the UE state. We finally evaluate the performance of the proposed method under scenarios with a limited number of transmissions, taking into account the channel coherence time. Our results demonstrate that the RIS enables solving the radio SLAM problem with zero APs, and that the consideration of the Doppler shift contributes to improving the UE speed estimates.

Reconfigurable intelligent surfaces (RISs) hold great potential as one of the key technological enablers for beyond-5G wireless networks, improving localization and communication performance under line-of-sight (LoS) blockage conditions. However, hardware imperfections might cause RIS elements to become faulty, a problem referred to aspixel failures, which can constitute a major showstopper especially for localization. In this paper, we investigate the problem of RIS-aided localization of a user equipment (UE) under LoS blockage in the presence of RIS pixel failures, considering the challenging single-input single-output (SISO) scenario. We first explore the impact of such failures on accuracy through misspecified Cramér-Rao bound (MCRB) analysis, which reveals severe performance loss with even a small percentage of pixel failures. To remedy this issue, we develop two strategies for joint localization and failure diagnosis (JLFD) to detect failing pixels while simultaneously locating the UE with high accuracy. The first strategy relies on ℓ1-regularization through exploitation of failure sparsity. The second strategy detects the failures one-by-one by solving a multiple hypothesis testing problem at each iteration, successively enhancing localization and diagnosis accuracy. Simulation results show significant performance improvements of the proposed JLFD algorithms over the conventional failure-agnostic benchmark, enabling successful recovery of failure-induced performance degradations.

Integrated sensing and communication (ISAC) is envisioned to be one of the pillars of 6G. However, 6G is also expected to be severely affected by hardware impairments. Under such impairments, standard model-based approaches might fail if they do not capture the underlying reality. To this end, data-driven methods are an alternative to deal with cases where imperfections cannot be easily modeled. In this paper, we propose a model-driven learning architecture for joint single-target multi-input multi-output (MIMO) sensing and multi-input single-output (MISO) communication. We compare it with a standard neural network approach under complexity constraints. Results show that under hardware impairments, both learning methods yield better results than the model-based standard baseline. If complexity constraints are further introduced, model-driven learning outperforms the neural-network-based approach. Model-driven learning also shows better generalization performance for new unseen testing scenarios.

We investigate the problem of reconfigurable intelligent surface (RIS)-aided near-field localization of a user equipment (UE) served by a base station (BS) under phase-dependent amplitude variations at each RIS element. Through a misspecified Cramér-Rao bound (MCRB) analysis and a resulting lower bound (LB) on localization, we show that when the UE is unaware of amplitude variations (i.e., assumes unit-amplitude responses), severe performance penalties can arise, especially at high signal-to-noise ratios (SNRs). Leveraging Jacobi-Anger expansion to decouple range-azimuth-elevation dimensions, we develop a low-complexity approximated mismatched maximum likelihood (AMML) estimator, which is asymptotically tight to the LB. To mitigate performance loss due to model mismatch, we propose to jointly estimate the UE location and the RIS amplitude model parameters. The corresponding Cramér-Rao bound (CRB) is derived, as well as an iterative refinement algorithm, which employs the AMML method as a subroutine and alternatingly updates individual parameters of the RIS amplitude model. Simulation results indicate fast convergence and performance close to the CRB. The proposed method can successfully recover the performance loss of the AMML under a wide range of RIS parameters and effectively calibrate the RIS amplitude model online with the help of a user that has an a-priori unknown location.

Radio localization is applied in high-frequency (e.g., mmWave and THz) systems to support communication and to provide location-based services without extra infrastructure. For solving localization problems, a simplified, stationary, narrowband far-field channel model is widely used due to its compact formulation. However, with increased array size in extra-large multiple-input-multiple-output (XL-MIMO) systems and increased bandwidth at upper mmWave bands, the effect of channel spatial non-stationarity (SNS), spherical wave model (SWM), and beam squint effect (BSE) cannot be ignored. In this case, localization performance will be affected when an inaccurate channel model deviating from the true model is adopted. In this work, we employ the misspecified Cramer-Rao lower bound to lower bound the localization error using a simplified mismatched model while the observed data is governed by a more complex true model. The simulation results show that among all the model impairments, the SNS has the least contribution, the SWM dominates when the distance is small compared to the array size, and the BSE has a more significant effect when the distance is much larger than the array size.

Integrated sensing and communication (ISAC) aims to unify radar and communication systems through a combination of joint hardware, joint waveforms, joint signal design, and joint signal processing. At high carrier frequencies, where ISAC is expected to play a major role, joint designs are challenging due to several hardware limitations. Model-based approaches, while powerful and flexible, are inherently limited by how well the models represent reality. Under model deficit, data-driven methods can provide robust ISAC performance. We present a novel approach for data-driven ISAC using an auto-encoder (AE) structure. The approach includes the proposal of the AE architecture, a novel ISAC loss function, and the training procedure. Numerical results demonstrate the power of the proposed AE, in particular under hardware impairments.

We investigate a reconfigurable intelligent surface (RIS)-aided near-field localization system with single-antenna user equipment (UE) and base station (BS) under hardware impairments by considering a practical phase-dependent RIS amplitude variations model. To analyze the localization performance under the mismatch between the practical model and the ideal model with unit-amplitude RIS elements, we employ the misspecified Cramér-Rao bound (MCRB). Based on the MCRB derivation, the lower bound (LB) on the mean-squared error for estimation of UE position is evaluated and shown to converge to the MCRB at low signal-to-noise ratios (SNRs). Simulation results indicate more severe performance degradation due to the model misspecification with increasing SNR. In addition, the mismatched maximum likelihood (MML) estimator is derived and found to be tight to the LB in the high SNR regime. Finally, we observe that the model mismatch can lead to an order-of-magnitude localization performance loss at high SNRs.

The rising concept of reconfigurable intelligent surface (RIS) has promising potential for Beyond 5G localization applications. We herein investigate different phase profile designs at a reflective RIS, which enable non-line-of-sight positioning in nearfield from downlink single antenna transmissions. We first derive the closed-form expressions of the corresponding Fisher information matrix (FIM) and position error bound (PEB). Accordingly, we then propose a new localization-optimal phase profile design, assuming prior knowledge of the user equipment location. Numerical simulations in a canonical scenario show that our proposal outperforms conventional RIS random and directional beam codebook designs in terms of PEB. We also illustrate the four beams allocated at the RIS (i.e., one directional beam, along with its derivatives with respect to space dimensions) and show how their relative weights according to the optimal solution can be practically implemented through time sharing (i.e., considering feasible beams sequentially).

We propose a novel three-stage delay-Doppler-angle estimation algorithm for a MIMO-OFDM radar in the presence of inter-carrier interference (ICI). First, leveraging the observation that spatial co-variance matrix is independent of target delays and Dopplers, we perform angle estimation via the MUSIC algorithm. For each estimated angle, we next formulate the radar delay-Doppler estimation as a joint carrier frequency offset (CFO) and channel estimation problem via an APES (amplitude and phase estimation) spatial filtering approach by transforming the delay-Doppler parameterized radar channel into an unstructured form. In the final stage, delay and Doppler of each target can be recovered from target-specific channel estimates over time and frequency. Simulation results illustrate the superior performance of the proposed algorithm in high-mobility scenarios.

Exploiting wavefront curvature enables localization with limited infrastructure and hardware complexity. With the introduction of reconfigurable intelligent surfaces (RISs), new opportunities arise, in particular when the RIS is functioning as a lens receiver. We investigate the localization of a transmitter using a RIS-based lens in close proximity to a single receive antenna element attached to reception radio frequency chain. We perform a Fisher information analysis, evaluate the impact of different lens configurations, and propose a two-stage localization algorithm. Our results indicate that positional beamforming can lead to better performance when a priori location information is available, while random beamforming is preferred when a priori information is lacking. Our simulation results for a moderate size lens operating at 28 GHz showcased that decimeter-level accuracy can be attained within 3 meters to the lens.

We consider the problem of joint three-dimensional localization and synchronization for a single-input single-output (SISO) multi-carrier system in the presence of a reconfigurable intelligent surface (RIS), equipped with a uniform planar array. First, we derive the Cramér-Rao bounds (CRBs) on the estimation error of the channel parameters, namely, the angle-of-departure (AOD), composed of azimuth and elevation, from RIS to the user equipment (UE) and times-of-arrival (TOAs) for the path from the base station (BS) to UE and BS-RISUE reflection. In order to avoid high-dimensional search over the parameter space, we devise a low-complexity estimation algorithm that performs two 1D searches over the TOAs and one 2D search over the AODs. Simulation results demonstrate that the considered RIS-aided wireless system can provide submeter-level positioning and synchronization accuracy, materializing the positioning capability of Beyond 5G networks even with single-antenna BS and UE. Furthermore, the proposed estimator is shown to attain the CRB at a wide interval of distances between UE and RIS. Finally, we also investigate the scaling of the position error bound with the number of RIS elements.

Integrated communications, localization and sensing is one of the most addressed technologies considered for future mobile communications systems. In this context, a user equipment (UE)-centric mobile radar has been proposed to introduce improved situational awareness, and consequently potential improvement in network performance. In this paper, we derive an extended Kalman probability hypothesis density (EK-PHD) filter with a novel feature model, for a mobile radar based environment mapping, where range-angle detections are used to track map objects over time for dynamic map construction. In order to evaluate the performance of the proposed filtering approach, we employ a realistic ray-tracing-based simulation setup, which models the full transmission chain from the transmitted IQ-samples to mapping results. Besides this, a simplified measurement model considering solely single-bounce specular reflections is exploited for providing further insight into the filter performance. The obtained results show that the proposed EK-PHD filter is able to provide high-quality mapping results, reaching around 10 cm landmark estimation accuracy in the considered millimeter wave simulation setup.

6G will likely be the first generation of mobile communication that will feature tight integration of localization and sensing with communication functionalities. Among several worldwide initiatives, the Hexa-X flagship project stands out as it brings together 25 key players from adjacent industries and academia, and has among its explicit goals to research fundamentally new radio access technologies and high-resolution localization and sensing. Such features will not only enable novel use cases requiring extreme localization performance, but also provide a means to support and improve communication functionalities. This paper provides an overview of the Hexa-X vision alongside the envisioned use cases. To close the required performance gap of these use cases with respect to 5G, several technical enablers will be discussed, together with the associated research challenges for the coming years.

In the automotive sector, both radars and wireless communication are susceptible to interference. However, combining the radar and communication systems, i.e., radio frequency (RF) communications and sensing convergence, has the potential to mitigate interference in both systems. This article analyses the mutual interference of spectrally coexistent frequency modulated continuous wave (FMCW) radar and communication systems in terms of occurrence probability and impact, and introduces RadChat, a distributed networking protocol for mitigation of interference among FMCW based automotive radars, including self-interference, using radar and communication cooperation. The results show that RadChat can significantly reduce radar mutual interference in single-hop vehicular networks in less than 80 ms.

The problem of optimal power allocation among light emitting diode (LED) transmitters in a visible light positioning system is considered for the purpose of improving localization performance of visible light communication (VLC) receivers. Specifically, the aim is to minimize the Cramér-Rao lower bound (CRLB) on the localization error of a VLC receiver by optimizing LED transmission powers in the presence of practical constraints, such as individual and total power limitations and illuminance constraints. The formulated optimization problem is shown to be convex and thus can efficiently be solved via standard tools. We also investigate the case of imperfect knowledge of localization parameters and develop robust power allocation algorithms by taking into account both overall system uncertainty and individual parameter uncertainties related to the location and orientation of the VLC receiver. In addition, we address the total power minimization problem under predefined accuracy requirements to obtain the most energy-efficient power allocation vector for a given CRLB level. Numerical results illustrate the improvements in localization performance achieved by employing the proposed optimal and robust power allocation strategies over the conventional uniform and non-robust approaches.

Visible light communication (VLC) is an emerging paradigm that enables multiple functionalities to be accomplished concurrently, including illumination, high-speed data communications, and localization. Based on the VLC technology, visible light positioning (VLP) systems aim to estimate locations of VLC receivers by utilizing light-emitting diode (LED) transmitters at known locations. VLP presents a viable alternative to radio frequency (RF)-based positioning systems by providing inexpensive and accurate localization services. In this paper, we consider the problem of localization in visible light systems and provide an extensive survey of various location estimation techniques, accompanied by discussions of their relative merits and demerits within the context of accuracy and computational complexity. In addition, we investigate a cooperative VLP system architecture in which VLC receiver units are able to communicate with each other for the purpose of cooperation, and present a low-complexity, iterative localization algorithm to demonstrate the benefits of cooperation in VLP systems. Finally, we investigate optimal strategies for power allocation among LED transmitters to maximize the localization accuracy subject to power and illumination constraints.

Visible light positioning (VLP) systems based on light emitting diodes can facilitate high accuracy localization services for indoor scenarios. In this paper, direct and two-step positioning approaches are investigated for both synchronous and asynchronous VLP systems. First, the Cramér-Rao lower bound (CRLB) and the direct positioning-based maximum likelihood estimator are derived for 3-D localization of a visible light communication receiver in a synchronous scenario by utilizing information from both time delay parameters and channel attenuation factors. Then, a two-step position estimator is designed for synchronous VLP systems by exploiting the asymptotic properties of time-of-arrival and received signal strength estimates. The proposed two-step estimator is shown to be asymptotically optimal, i.e., converges to the direct estimator at high signal-to-noise ratios. In addition, the CRLB and the direct and two-step estimators are obtained for positioning in asynchronous VLP systems. It is proved that the two-step position estimation is optimal in asynchronous VLP systems for practical pulse shapes. Various numerical examples are provided to illustrate the improved performance of the proposed estimators with respect to the current state-of-the-art and to investigate their robustness against model uncertainties in VLP systems.

In this study, optimal channel switching (time sharing) strategies are investigated under average power and cost constraints in order to maximize the average number of correctly received symbols between a transmitter and a receiver that are connected via multiple additive Gaussian noise channels. The optimal strategy is shown to perform channel switching either among at most three channels with full channel utilization (i.e., no idle periods), or between at most two channels with partial channel utilization. In addition, it is stated that the optimal solution must operate at the maximum average power and the maximum average cost, which facilitates low-complexity approaches for calculating the optimal strategy. For two-channel strategies, an upper bound in terms of the noise standard deviations of the employed channels is provided for the ratio between the optimal power levels. Furthermore, a simple condition depending solely on the systems parameters is derived, under which partial channel utilization cannot be optimal. Numerical examples are presented to demonstrate the validity of the theoretical results.

A new framework for signal processing is introduced based on a novel vector product definition that permits a multiplier-free implementation. First a new product of two real numbers is defined as the sum of their absolute values, with the sign determined by product of the hard-limited numbers. This new product of real numbers is used to define a similar product of vectors in RN. The new vector product of two identical vectors reduces to a scaled version of the l1norm of the vector. The main advantage of this framework is that it yields multiplication-free computationally efficient algorithms for performing some important tasks in signal processing. An application to the problem of cancer cell line image classification is presented that uses the notion of a co-difference matrix that is analogous to a covariance matrix except that the vector products are based on our new proposed framework. Results show the effectiveness of this approach when the proposed co-difference matrix is compared with a covariance matrix.

In this paper, we present a novel method for classification of cancer cell line images using complex wavelet-based region covariance matrix descriptors. Microscopic images containing irregular carcinoma cell patterns are represented by randomly selected subwindows which possibly correspond to foreground pixels. For each subwindow, a new region descriptor utilizing the dual-tree complex wavelet transform coefficients as pixel features is computed. ℂWT as a feature extraction tool is preferred primarily because of its ability to characterize singularities at multiple orientations, which often arise in carcinoma cell lines, and approximate shift invariance property. We propose new dissimilarity measures between covariance matrices based on Kullback-Leibler (KL) divergence and L2-norm, which turn out to be as successful as the classical KL divergence, but with much less computational complexity. Experimental results demonstrate the effectiveness of the proposed image classification framework. The proposed algorithm outperforms the recently published eigenvalue-based Bayesian classification method.