Integrated sensing and communication (ISAC) and the Frequency Range 3 (FR3) (upper mid-band) spectrum are among the key enablers of future wireless systems. ISAC promises new sensing functionalities for networks historically designed for communications, while the FR3 spectrum, approximately from 7 to 24GHz, offers large bandwidths and diverse propagation characteristics that significantly extend deployment possibilities. Motivated by the potential synergy between these two paradigms, this work presents an experimental investigation of a multiband ISAC channel in the FR3 range under realistic conditions. Using the Pi-Radio software-defined radio (SDR) platform and superresolution parameter estimation methods, we design a multiband testbed that measures sensing metrics such as the probability of detection (PD), probability of false alarm (PFA), and localization root mean-squared error (RMSE) across sub-bands at 6.5, 8.75, 10, 15, and 21.7 GHz. To analyze how communication performance reacts to environmental dynamics, we introduce the channel update rate gain (CURG), a new metric that quantifies achievable data-rate gains induced by target-dependent channel variations.

Joint detection and localization of users and scatterers in multipath-rich channels on multiple bands is critical for integrated sensing and communication (ISAC) in 6G. Existing multiband sensing methods are limited by classical beamforming or computationally expensive approaches. This paper introduces alternating direction method of multipliers (ADMM)-assisted compressed multiband sensing (CMS), hereafter referred to as ADMM-CMS, which is a novel framework for multiband sensing using uplink quadrature amplitude modulation-modulated pilot symbols. To solve the CMS problem, we develop an adaptive ADMM algorithm that adjusts to noise and ensures automatic stopping if converged. ADMM combines the decomposability of dual ascent with the robustness of augmented Lagrangian methods, making it suitable for large-scale structured optimization. Simulations show that ADMM-CMS achieves higher spatial resolution and improved denoising compared to Bartlett-type beamforming, yielding a 34 dB gain in per-antenna transmit power for achieving a 0.9 successful recovery probability (SRP). Moreover, compared to performing compressed sensing separately on the constituent 7 GHz and 10 GHz sub-bands, ADMM-CMS achieves reductions in delay root mean squared error of 34.46% and 40.76%, respectively, at -41 dBm per-antenna transmit power, while also yielding improved SRP. Our findings demonstrate ADMM-CMS as an efficient enabler of ISAC in frequency range 3 (FR3, 7-24 GHz) for 6G systems.

This paper introduces two novel algorithms designed to address the challenge of super-resolution sensing parameter estimation in bistatic configurations within communication-centric integrated sensing and communication (ISAC) systems. Our approach leverages the estimated channel state information derived from reference symbols originally intended for communication to achieve super-resolution sensing parameter estimation. The first algorithm, IFFT-C2VNN, employs complex-valued convolutional neural networks to estimate the parameters of different targets, achieving significant reductions in computational complexity compared to traditional methods. The second algorithm, PARAMING, utilizes a parametric method that capitalizes on the knowledge of the system model, including the transmit and receive array geometries, to extract the sensing parameters accurately. Through a comprehensive performance analysis, we demonstrate the effectiveness and robustness of both algorithms across a range of signal-to-noise ratios, underscoring their applicability in realistic ISAC scenarios.

In this study, we perform statistical radar cross section (RCS) analysis for various test targets in an indoor factory at 25-28 GHz, with the goal of determining the best-fit parametric distributions that characterize the target scattering properties to be used in integrated sensing and communication channel modeling standardization. The analysis is conducted based on measurements in quasi-monostatic and bistatic configurations with bistatic angles of 20°, 40°, and 60°. The test targets include unmanned aerial vehicles, an autonomous mobile robot, and a robotic arm. Goodness-of-fit tests validate that the RCS of these targets is best modeled by lognormal and gamma distributions with high statistical confidence. Additionally, we provide a framework for evaluating the near-field (NF), specular-dominant effective bistatic RCS of a rectangular sheet under controlled bistatic geometries. Novel deterministic RCS models are evaluated, incorporating dependencies on the bistatic angle, transmitter-target separation ( 2 m to 10 m ). The results demonstrate that some proposed deterministic RCS models accurately fit the measured data, highlighting their applicability in deterministic RCS characterization in NF bistatic configurations.

This paper proposes a comprehensive framework for a geometry-based statistical model for integrated sensing and communication (ISAC) tailored for bistatic systems. Our dualcomponent model decomposes the ISAC channel into a target channel encompassing all multipath components produced by a sensing target parameterized by the target’s radar cross-section and scattering points, and a background channel comprising all other propagation paths that do not interact with the sensing target. The framework extends TR38.901 via a hybrid clustering approach, integrating spatiotemporally consistent deterministic clusters with stochastic clusters to preserve channel reciprocity and absolute delay alignment for sensing parameter estimation. Extensive simulations across urban macro, urban micro, and indoor factory scenarios demonstrate that the model maintains communication performance parity with the standard TR38.901, validated through bit-error rate analysis obtained via simulated and measured ISAC channels and channel capacity assessment, while enabling sensing performance evaluation, such as target ranging error for localization and receiver operating characteristic curves for detection probability.

Integrated sensing and communication (ISAC) has emerged as a key paradigm for next-generation wireless systems, which allows wireless resources to be used for data transmission and target sensing simultaneously. In this paper, multi-user collaborative target detection in the uplink ISAC system is investigated. To incorporate the target sensing functionality, the system relies on the reuse of uplink signals from the communication users. Specifically, we analyze an uplink multi-user single-input multiple-output (MU-SIMO) communication system with bistatic sensing. Using the channel statistics, we formulate the problem of joint optimal pilot and data power allocation to maximize the uplink ergodic sum rate while meeting communication and sensing quality-of-service (QoS) requirements. To address this non-convex problem, we propose an alternating optimization (AO)-based iterative framework, where the joint power allocation problem is decomposed into two sub-problems. Specifically, the pilot power allocation is optimized using a penalty dual decomposition (PDD)-based gradient ascent algorithm, while the data power allocation is solved via successive convex approximation (SCA). Once the long-term power allocation is determined, the base station (BS) estimates the instantaneous channels using a minimum mean-squared error (MMSE) estimator. Subsequently, based on the estimated instantaneous channel state information (CSI), the receive beamforming for communication users is optimized via another SCA-based method to maximize the sum rate. Meanwhile, the optimal receive beamforming for the target is obtained in closed-form through eigenvalue decomposition (EVD). We provide comprehensive simulation results to analyze the performance of the proposed iterative algorithm and to demonstrate its dependence on different design parameters. Our results also confirm the superiority of the proposed resource allocation approach over conventional benchmark schemes.

Affine frequency division multiplexing (AFDM) and orthogonal time frequency space (OTFS) are two promising advanced waveforms proposed for reliable communications in high-mobility scenarios. In this paper, we introduce a simple transmit diversity technique, termed cyclic delay-Doppler shift (CDDS), for these two advanced waveforms to achieve ultra-reliable communications in doubly selective channels (DSCs). Two simple CDDS schemes, named modulation-domain CDDS (MD-CDDS) and time-domain CDDS (TD-CDDS), are proposed, which perform CDDS in advance at the transmitter before and after the modulation, respectively. We demonstrate that both of the two proposed CDDS schemes can be implemented efficiently and flexibly by multiplying the transmit vector with a well-designed precoding matrix, which is nothing but a sparse phase-compensated permutation matrix. Moreover, we theoretically and numerically prove that CDDS can provide MIMO-AFDM and MIMO-OTFS with optimal transmit diversity gain when a proper CDDS step is adopted. Compared to the conventional transmit diversity techniques, the proposed CDDS scheme enjoys the advantages of lower channel estimation overhead, implementation complexity, and signal processing latency, making it particularly suitable for ultra-reliable communications in high-mobility scenarios.

The following paper presents a systematic 3rd Generation Partnership Project (3GPP)-compliant characterization of radar cross section (RCS) for indoor factory (InF) objects, including small and mid-sized unmanned aerial vehicles (UAVs), robotic arms, and automated guided vehicles (AGVs). Through measurements in the 25GHz to 28GHz range, we validate the 3GPP standardized log-normal distribution model for RCS for above-mentioned target objects. The 3GPP-complaint RCS parameters obtained for the small-sized UAV are in close agreement (< 1 dB deviation) with 3GPP agreed values. The mid-sized UAVs exhibit higher reflectivity compared to the small-sized UAV due to enhanced specular components attributed to material and lithium-ion battery packs. On the other hand, the robotic arm exhibits dynamic RCS behavior due to mechanical articulation, whereas AGVs show height and motion-dependent reflectivity patterns. Our findings provide empirical validation for RCS characterization for integrated sensing and communication channel modeling in InF environments.

Integrated Sensing and Communication (ISAC) systems vulnerability to information interception has become more dramatic with the usage of unified signaling techniques. This paper introduces a secure full-duplex (FD) multi-eavesdroppers (Eve) ISAC system, where malicious users, termed as Eve, aim at intercepting the downlink (DL) as well as the uplink (UL) information interchanged between legitimate DL/UL users and the dual functional radar and communication (DFRC) base station (BS). Following the model, we design a suitable secure ISAC method that invests minimal power in the FD system in order to satisfy desired UL/DL secrecy rates, while illuminating radar beams at Eves. The motivation being that the FD system should expose minimal useful information towards the Eves, while simultaneously sensing their physical parameters. To this end, a suitable optimization problem is formed taking into account the aforementioned requirements, and a successive convex approximation approach is taken to iteratively solve the problem. Simulation results also reveal secrecy-sensing trade-offs and demonstrate the effectiveness of the method for different system parameters and requirements.

The following paper presents a multi-band sensing channel quality analysis in the upper mid-band, also known as frequency range 3 (FR3). Measurements were conducted at 6.5 GHz, 8.75 GHz, 10 GHz, and 15 GHz, using a setup designed for integrated sensing and communication (ISAC). The sensing channel quality is evaluated using the estimation reliability metric, based on the iterative Levenberg–Marquardt (LM) algorithm. Given the static environment, we also validate a method to handle time-invariant dense multipath components (DMCs). Results show that lower bands enable the detection of more specular components due to lower path loss, but stronger DMC leads to lower estimation SNR. Higher bands provide cleaner estimates despite detecting fewer components. The trade-offs inherent to upper and lower FR3 bands highlight the potential of multi-band ISAC in the FR3 spectrum.

Recent advancements have underscored the relevance of low-resolution analog-to-digital converters (ADCs) in integrated sensing and communication (ISAC) systems. Nevertheless, their specific impact on hybrid radar fusion (HRF) remains largely unexplored. In HRF systems, where uplink (UL) paths carry direct and reflected signals in the same frequency band, the reflected signal is often significantly weaker, making HRF performance particularly sensitive to ADC resolution. To study this effect, we use the quantized Cramér-Rao bound (CRB) to measure sensing accuracy. This work derives an upper bound on the quantized CRB for angle of arrival (AoA) estimation and explores CRB-rate trade-offs through two formulated optimization problems. Simulation results indicate that HRF becomes infeasible when the dynamic range of the received signal exceeds the dynamic range supported by the ADC, which is inherently limited by its resolution. Furthermore, the UL communication rate does not increase significantly when the ADC resolution is raised beyond a certain threshold. These observations highlight a fundamental trade-off between sensing and communication performance: while HRF performance benefits from higher ADC resolutions, the corresponding gains in communication rate plateau. This trade-off is effectively characterized using CRB-rate boundaries derived through simulation.

Incorporating integrated sensing and communication capabilities into forthcoming 6G wireless networks is crucial for achieving seamless synchronization between the digital and physical worlds. The following paper focuses on a scenario where a passive radar (PR) is subject to weak line-of-sight signals of opportunity, emanating from an access point and subsequently reflecting off targets, ultimately reaching the PR. Furthermore, a normalized least mean squares method is presented for jointly detecting the number of targets and estimating target angles of arrival (AoAs). The algorithm iteratively adjusts the steering vector estimates to minimize a suitable error cost function, while the target AoAs are identified via a peak-finding search conducted on the resulted power spectrum. Simulation results show the capabilities of the proposed localization method, as well as a 14 dB dynamic range reduction that can be achieved at the PR.

This paper presents a new optimization framework dedicated for integrated sensing and communication (ISAC) waveform design. In particular, the problem aims at maximizing the total achievable sum-rate, through multi-user interference minimization, while preserving a certain level of similarity to a given desired radar waveform. Aiming towards feasible and practical PHY architectures, we also offer the flexibility of tuning the peak-to-average power ratio to a desired level. Towards this design, a non-convex optimization problem is formulated, and an alternating direction method of multipliers based solution is derived to converge towards the superiority of the final ISAC waveform. Finally, simulation results validate the proposed ISAC waveform design, as compared to state-of-the-art solutions.

The upcoming sixth-generation (6G) wireless standard is anticipated to support a variety of applications that will require a seamless integration of sensing and communication services in a single network infrastructure. At the same time, 6 G is also expected to support millions of low-powered Internet-of-Things (IoT) devices. Previous studies have demonstrated that the power requirements of these IoT devices can be met through wireless power transfer facilitated by intelligent metasurfaces. Therefore, in this paper, we try to formulate and answer a fundamental question: How much transmit power is required for an integrated sensing, communication, and power transfer (ISCPT) system? More specifically, we consider the problem of optimal active, passive, and receive beamforming design for a simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-enabled ISCPT system with multiple sensing targets, multiple information receivers, and multiple energy receivers, to minimize the required transmit power from the base station, while guaranteeing predefined sensing, communication, and energy harvesting requirements. To tackle the challenging non-convex optimization problem, we use an alternating optimization (AO)-based approach, where the receive beamforming is obtained in closed form while the active and passive beamforming are obtained using a second-order cone program (SOCP) approach. Our numerical results show the benefit of using STAR-RIS to reduce the transmit power requirement for the ISCPT system compared to its corresponding conventional RIS (cRIS)-enabled and non-RIS ISCPT systems.

Global allocations in the upper mid-band spectrum (4-24 GHz) necessitate a comprehensive exploration of the propagation behavior to meet the promise of coverage and capacity. This paper presents an extensive Urban Microcell (UMi) outdoor propagation measurement campaign at 6.75 GHz and 16.95 GHz conducted in Downtown Brooklyn, USA, using a 1 GHz bandwidth sliding correlation channel sounder over 40-880 m propagation distance, encompassing seven Line of Sight (LOS) and 13 Non-Line of Sight (NLOS) locations. Analysis of the path loss (PL) reveals lower directional and omnidirectional PL exponents compared to mmWave and sub-THz frequencies in the UMi environment, using the close-in (CI) free space PL (FSPL) model with a 1 m reference distance. Additionally, a decreasing trend in root mean square (RMS) delay spread (DS) and angular spread (AS) with increasing frequency was observed. The measured NLOS RMS DS and RMS AS mean values (as computed by 3GPP methods) are found to be consistently lower compared to 3GPP model predictions. Point-data tables with corresponding site-specific environmental information for all measured statistics at each TX-RX location are provided to support the models and results. The spatio-temporal statistics presented here offer valuable insights for the design of nextgeneration wireless systems and networks.

In this paper, we investigate the information piggyback capability of the unmanned aerial vehicle (UAV) by encoding observed motion states. Specifically, at specific moments throughout the holistic navigation process, the distance between the UAV and the starting point, the position expressed by the three-dimensional (3D) Cartesian coordinates, the linear velocities, and the attitude angles are encoded into 16-bit digital symbols through a proposed codebook. In this way, covert data communications can be enabled, complementing conventional radio frequency (RF) communications in harsh electromagnetic environments. To achieve a well-designed flight controller that is necessary to enable fluent movement against external disturbances and accurate motion state encoding, we introduce the whole flight control structure and perform system dynamic analysis. The proposed motion control mechanism can mitigate the jitter and oscillation during the journey while ensuring the required motion status for information encoding purposes.

The following paper presents a framework for DC-biased optical OFDM (DCO-OFDM) in visible light communication (VLC) systems, addressing critical implementation challenges in timing synchronization and channel estimation. We propose a novel preamble design based on modified Zadoff-Chu (ZC) sequences that preserve constant amplitude zero autocorrelation (CAZAC) properties in the frequency-domain while satisfying intensity modulation/direct detection (IM/DD) constraints. The frequency-domain cross-correlation of the modified ZC sequence enables robust timing offset estimation, particularly in low-SNR scenarios. We evaluate the probability of correct timing offset estimation and the normalized mean square error (NMSE) for the proposed channel estimation scheme. Furthermore, we characterize the system-level trade-offs between clipping distortion, quantization resolution, and DC biasing, deriving optimal operating points for power efficiency under the proposed framework. Simulation results demonstrate significant improvements in synchronization accuracy compared to other alternatives, with the proposed method achieving a correct timing estimation probability of ≥ 0.99 even at low SNR values. The channel estimation NMSE performance confirms the scheme’s efficiency, outperforming conventional approaches. The approach presented hereby establishes practical design guidelines for robust timing synchronization and channel estimation for IM/DD-based DCO-OFDM implementations in VLC systems.

The following paper presents a reconfigurable intelligent surface (RIS)-aided integrated sensing and communication (ISAC) system model scenario, where a base station communicates with a user, and a bi-static sensing unit, i.e. the passive radar (PR), senses targets using downlink signals. Given that the RIS aids with communication and sensing tasks, this paper introduces new interfering paths that can overwhelm the PR with unnecessarily high power, namely the path interference (PI), which is itself a combination of two interfering paths, the direct path interference (DPI) and the reflected path interference (RPI). For this, we formulate an optimization framework that allows the system to carry on with its ISAC tasks, through analog space-time beamforming at the sensing unit, in collaboration with RIS phase shift and statistical transmit covariance matrix optimization, while minimizing the PI power. As the proposed optimization problem is non-convex, we tailor a block-cyclic coordinate descent (BCCD) method to decouple the non-convex sub-problem from the convex one. A Riemannian conjugate gradient method is devised to generate the RIS and PR space-time beamforming phase shifts per BCCD iteration, while the convex sub-problem is solved via off-the-shelf solvers. Simulation results demonstrate the effectiveness of the proposed solver when compared with benchmarking ones.

The following paper presents a novel orthogonal pilot design dedicated for integrated sensing and communications (ISAC) systems performing multi-user communications and target detection. After careful characterization of both sensing and communication metrics based on mutual information (MI), we propose a multi-objective optimization problem (MOOP) tailored for pilot design, dedicated for simultaneously maximizing both sensing and communication MIs. Moreover, the MOOP is further simplified to a single-objective optimization problem, which characterizes trade-offs between sensing and communication performances. Due to the non-convex nature of the optimization problem, we propose to solve it via the projected gradient descent method on the Stiefel manifold. Closed-form gradient expressions are derived, which enable execution of the projected gradient descent algorithm. Furthermore, we prove convergence to a fixed orthogonal pilot matrix. Finally, we demonstrate the capabilities and superiority of the proposed pilot design, and corroborate relevant trade-offs between sensing MI and communication MI. In particular, significant signal-to-noise ratio (SNR) gains for communication are reported, while re-using the same pilots for target detection with significant gains in terms of probability of detection for fixed false-alarm probability. Other interesting findings are reported through simulations, such as an information overlap phenomenon, whereby the fruitful ISAC integration can be fully exploited.

This paper considers an integrated sensing and communication (ISAC) system with monostatic radar functionality using a zero-padding orthogonal frequency division multiplexing (ZP-OFDM) downlink transmission. We focus on ISAC’s sensing aspect, employing an energy-detection (ED) method. The ZP-OFDM transmission is motivated by the fact that sensing can be performed during the silent periods of the transmitter, thereby avoiding self-interference (SI) cancellation processing of the in-band full duplex operation, which is needed for the cyclic prefix (CP)-OFDM. Additionally, we also show that ZP-OFDM can reject nearby clutter interference. We derive the probability of detection (PD) for the ZP and CP-OFDM systems, allowing useful performance analyses. In particular, we show that the PD expressions lead to an upper bound for the ZP-OFDM transmission, which is useful for selecting the best ZP size for a given system configuration. We also provide an expression that allows range comparison between ZP and CP-OFDM, where we consider a general case of imperfect SI cancellation for the CP-OFDM system. The results show that when the ZP size is 25% of the fast Fourier transform size, the range loss of the ZP system range is only 17% larger than the CP transmission.

The forthcoming sixth-generation (6G) communications standard is anticipated to provide integrated sensing and communication (ISAC) as a fundamental service. These ISAC systems present unique security challenges because of the exposure of information-bearing signals to sensing targets, enabling them to potentially eavesdrop on sensitive communication information with the assistance of sophisticated receivers. Recently, reconfigurable intelligent surfaces (RISs) have shown promising results in enhancing the physical layer security of various wireless communication systems, including ISAC. However, the performance of conventional passive RIS (pRIS)-enabled systems are often limited due to multiplicative fading, which can be alleviated using active RIS (aIRS). In this paper, we consider the problem of beampattern gain maximization in a secure pRIS/aRIS-enabled ISAC system, subject to signal-to-interference-plus-noise ratio constraints at communication receivers, and information leakage constraints at an eavesdropping target. For the challenging non-convex problem of joint beamforming design at the base station and the pRIS/aRIS, we propose a novel successive convex approximation (SCA)-based method. Unlike the conventional alternating optimization (AO)-based methods, in the proposed SCA-based approach, all of the optimization variables are updated simultaneously in each iteration. The proposed method shows significant performance superiority for pRIS-aided ISAC system compared to a benchmark scheme using penalty-based AO method. Moreover, our simulation results also confirm that aRIS-aided system has a notably higher beampattern gain at the target compared to that offered by the pRIS-aided system for the same power budget. We also present a detailed complexity analysis and proof of convergence for the proposed SCA-based method.

Integrated sensing and communication (ISAC) aims at enhancing the network functionalities and enabling new applications in the upcoming communications networks. In this paper, we propose two unique word (UW)-based frame designs for bistatic ISAC. The approach consists of replacing the cyclic prefix (CP) with a Zadoff-Chu (ZC)-based sequence. With this approach, the radar receiver does not need to know the data symbols to perform sensing and the data rate is not compromised by the addition of extra pilots. We derive the Cramér-Rao bound (CRB) considering a band-limited system with raised-cosine filtering. Furthermore, we evaluate the performance of a low-complexity fast Fourier transform (FFT)-based radar receiver that performs integer and fine grid delay-Doppler (DD) estimation, using the CRB as a benchmark.

To reuse the scarce spectrum efficiently, a large full-duplex cellular network with integrated sensing and communication (ISAC) is studied. Monostatic detection at the base station (BS) is considered. At the BS, we receive two signals: the communication-mode uplink signal to be decoded and the radar-mode signal to be detected. After self-interference cancellation (SIC), inspired by NOMA, successive interference cancellation (SuIC) is a natural strategy at the BS to retrieve both signals. However, the ordering of SuIC, usually based on some measure of channel strength, is not clear as the radar-mode target is unknown. The detection signal suffers a double path-loss making it vulnerable, but the uplink signal to be decoded originates at a user which has much lower power than the BS making it weak as well. Further, the intercell interference from a large network reduces the channel disparity between the two signals. We investigate the impact of both SuIC orders at the BS, i.e., decoding 1stor detecting 1stand highlight the importance of careful order selection. We find the existence of a threshold target distance before which detecting 1stis superior and decoding 2nddoes not suffer much. After this distance, both decoding 1stand detecting 2ndis superior. Similarly, a threshold UE power exists after which the optimum SuIC order changes. We consider imperfections in SIC; this helps highlight the vulnerability of the decoding and detection in the setup.

In this work, we provide a system level analysis of integrated sensing and communication (ISAC) systems, where a setup with a mono-static dual-functional radar communication base station is assumed. We derive the ISAC signal-to-noise ratio (SNR) equation that relates communication and radar SNRs for different distances. We also derive the ISAC range equation, which can be used for sensing-assisted beamforming applications. Specifically, we show that increasing the frequency and bandwidth is more favorable to the radar application in terms of relative SNR and range while increasing the transmit power is more favorable to communications. Numerical examples reveal that if the range for communication and radar is desired to be in the same order, the ISAC system should operate in mmWave or sub-THz bands, whereas sub-6 GHz allows scenarios where the communication range is of orders of magnitude higher than that of radar.

Driven by the compelling advantages of agility and cost-efficiency inherent in unmanned aerial vehicles (UAV s), this study introduces TANAGERS (emergenT communication for uA vs as flyinG passivE RadarS), an innovative communication-augmented multi-agent reinforcement learning algorithm (MARL) designed for the movement control of UAVs operating as flying passive radars in bistatic integrated sensing and communication scenarios. In this research, we employ the proposed MARL framework to address the sensing signal-to-noise ratio (SNR) maximization problem for targets within a given environment by leveraging signals from base stations, all while taking into account realistic communication channels between pairs of UAV s. Simulation results underscore the significant enhancement brought by our proposed algorithm in radar performance, as measured by the total achievable sensing SNR of the UAV s during their trajectory. The key strength lies in the algorithm's ability to learn a resilient communication protocol that effectively mitigates the stochastic and unreliable nature of channel links between UAV s.

The next generation of wireless communication is expected to harness the potential of the sub- THz bands to achieve exceptional performance and ubiquitous connectivity. However, network simulators such as ns-3 currently lack support for channel models above 100 GHz. This limits the ability of researchers to study, design, and evaluate systems operating above 100 GHz. Here, we use the drop-based NYUSIM channel model to simulate channels above 100 GHz in all 3GPP scenarios including urban microcell (UMi), urban macrocell (UMa), rural macrocell (RMa), indoor hotspot (InH), and indoor factory (InF). We evaluate the full stack downlink end-to-end performance (throughput, latency, and packet drop) experienced by a single user equipment (UE) connected to a Next Generation Node B (gNB) operating in the sub- THz bands for three gNB-UE antenna configurations: 8x8-4x4, 16xI6-4x4, and 64x64-8x8 by using the NYUSIM channel model at 140 GHz in the ns-3 mmWave module. Our simulations demonstrate that sub-THz bands can enable high-fidelity applications that require data rates exceeding 1 Gbps and latency below 15 milliseconds (ms) using the current mmWave protocol stack, and large antenna arrays. In addition, we show the variation in throughput vs number of realizations and find the optimal number of realizations required to obtain statistically significant results. We strongly encourage researchers worldwide to adopt a similar approach, as it enables the readers to assess the accuracy and reliability of the reported results and enhance the findings” overall interpretability.

On behalf of the entire Organizing Committee, we welcome you to IEEE WCNC 2024 in Dubai, UAE! We are delighted to gather in one of the most vibrant cities and attractive destinations in the Middle East for the 25th edition of one of the most successful conferences of IEEE Communication Society (ComSoc).

The following paper proposes a new target localization system design using an architecture based on reconfigurable intelligent surfaces (RISs) and passive radars (PRs) for integrated sensing and communications systems. The preamble of the communication signal is exploited in order to perform target sensing tasks, which involve detection and localization. The RIS in this case can aid the PR in sensing targets that are otherwise not seen by the PR itself, due to the many obstacles encountered within the propagation channel. Therefore, this work proposes a localization algorithm tailored for the integrated sensing and communications RIS-aided architecture, which is capable of uniquely positioning targets within the scene. The algorithm is capable of detecting the number of targets along with estimating the position of targets via angles and times of arrival. Our simulation results demonstrate the performance of the localization method in terms of different localization and detection metrics and for increasing RIS sizes.

This article examines two chirp spread spectrum techniques specifically devised for low-power wide-area networks (LPWANs) to optimize energy and spectral efficiency (SE). These methods referred to as layered CSS (LCSS) and layered dual-mode CSS (LDMCSS), involves utilizing multiple layers for multiplexing symbols with varying chirp rates. These waveform designs exemplify a high degree of SE compared to existing schemes. Additionally, LDMCSS necessitates a lesser number of layers than LCSS to attain comparable SE, thereby reducing computational complexity. These proposed techniques can employ coherent and non-coherent detection and can be adjusted to achieve various spectral efficiencies by altering the number of multiplexed layers. Unlike our proposed LCSS and LDMCSS, other CSS alternatives for LPWANs cannot provide the same level of flexibility and SE. The performance of these techniques is evaluated in terms of bit error rate under different channel conditions, as well as with phase and frequency offsets.

The following paper models a secure full duplex (FD) integrated sensing and communication (ISAC) scenario, where malicious eavesdroppers aim at intercepting the downlink (DL) as well as the uplink (UL) information exchanged between the dual functional radar and communication (DFRC) base station (BS) and a set of communication users. The DFRC BS, on the other hand, aims at illuminating radar beams at the eavesdroppers in order to sense their physical parameters, while maintaining high UL/DL secrecy rates. Based on the proposed model, we formulate a power efficient secure ISAC optimization framework design, which is intended to guarantee both UL and DL secrecy rates requirements, while illuminating radar beams towards eavesdroppers. The framework exploits artificial noise (AN) generation at the DFRC BS, along with UL/DL beamforming design and UL power allocation. We propose a beamforming design solution to the secure ISAC optimization problem. Finally, we corroborate our findings via simulation results and demonstrate the feasibility, as well as the superiority of the proposed algorithm, under different situations. We also reveal insightful trade-offs achieved by our approach.

Security is an issue in non-orthogonal multiple access (NOMA) and partial-NOMA because a user may decode the message of its paired-user with which it shares a resource element (RE). Three scenarios are studied where, of the paired-users, the eavesdropper is: 1) an actively malicious strong-user, 2) a passive strong-user, 3) an actively malicious weak-user. We define the event of secure-communication in each scenario and derive the corresponding secrecy probabilities for partial-NOMA and NOMA. Our results highlight that with careful selection of the RE’s overlap α, partial-NOMA can significantly outperform NOMA in terms of secrecy probability. Further, careless selection of α can cause partial-NOMA to perform worse than NOMA. We show the non-trivial impact of incorporating the impact of intercell interference on secrecy. Our results shed light on parameter-selection if knowledge of the eavesdropper type is available highlighting that security can be improved without traditional techniques such as jamming that increase power consumption and interference. While NOMA decoding uses successive-interference-cancellation (SIC), partial-NOMA decoding employs receive-filtering followed by flexible-SIC (FSIC). We show that not employing receive-filtering or using SIC instead of FSIC can have a drastic negative impact on secrecy, highlighting the role of the partial-NOMA decoding approach in enhancing secure-communication.

Various precoders have been recently studied by the wireless community to combat the channel fading effects. Two prominent precoders are implemented with the discrete Fourier transform (DFT) and Walsh-Hadamard transform (WHT). The WHT precoder is implemented with less complexity since it does not need complex multiplications. Also, spreading can be applied sparsely to decrease the transceiver complexity, leading to sparse DFT (SDFT) and sparse Walsh-Hadamard (SWH). Another relevant topic is the design of iterative receivers that deal with inter-symbol-interference (ISI). In particular, many detectors based on expectation propagation (EP) have been proposed recently for channels with high levels of ISI. An alternative is the maximum a-posterior (MAP) detector, although it leads to unfeasible high complexity in many cases. In this paper, we provide a relatively low-complexity computation of the MAP detector for the SWH. We also propose two feasible methods based on the Log-MAP and Max-Log-MAP. Additionally, the DFT, SDFT, and SWH precoders are compared using an EP-based receiver with one-tap FD equalization. Lastly, SWH-Max-Log-MAP is compared to the (S)DFT with EP-based receiver in terms of performance and complexity. The results show that the proposed SWH-Max-Log-MAP has a better performance and complexity trade-off for QPSK and 16-QAM under highly selective channels, but has unfeasible complexity for higher QAM orders.

Integrated sensing and communication (ISAC) aims at enhancing the network functionalities and enabling new applications in the upcoming communications networks. In this paper, we propose two unique word (UW)-based frame designs for bistatic ISAC. The approach consists of replacing the cyclic prefix (CP) with a Zadoff-Chu (ZC)-based sequence. With this approach, the radar receiver does not need to know the data symbols to perform sensing and the data rate is not compromised by the addition of extra pilots. The sensing performance of the UW-based frames is compared with that of orthogonal frequency division multiplexing (OFDM) as well as the pilot-symbol (PS) based radar processing. We derive the Cramér-Rao bound (CRB) considering a band-limited system with raised-cosine filtering. Furthermore, we provide low-complexity fast Fourier transform (FFT)-based radar receivers that perform integer and fine grid multi-target delay-Doppler (DD) estimations. For the integer FFT-based receiver, an upper bound for the outlier probability is derived when the true DD falls outside the integer grid. The results demonstrate that the UW frames exhibit competitive radar performance with PS while having a 16.67% higher data rate for the cases investigated.

The following paper introduces a novel integrated sensing and communication (ISAC) scenario termed hybrid radar fusion. In this setting, the dual-functional radar and communications (DFRC) base station (BS) acts as a mono-static radar in the downlink (DL), for sensing purposes, while performing its DL communication tasks. Meanwhile, the communication users act as distributed bi-static radar nodes in the uplink (UL) following a frequency-division duplex protocol. The DFRC BS fuses the information available at different DL and UL resource bands to estimate the angles-of-arrival (AoAs) of the multiple targets existing in the scene. In this work, we derive the maximum likelihood (ML) criterion for the hybrid radar fusion problem at hand. Additionally, we design efficient estimators; the first algorithm is based on an alternating optimization approach to solve the ML criterion, while the second one designs an optimization framework that leads to an alternating subspace approach to estimate AoAs for both the target and users. Finally, we demonstrate the superior performance of both algorithms in different scenarios, and the gains offered by these proposed methods through numerical simulations.

A large network employing integrated sensing and communication (ISAC) where a single transmit signal by the base station serves both the radar and communication modes is studied. The radar-mode uses bistatic detection at a passive radar. Radar-mode performance, in general, is significantly more vulnerable than the communication-mode due to the double path loss in the signal component while interferers have direct links. To combat this, we propose a novel dynamic transmission strategy (DTS) where the quantity of radar-mode detection is traded for quality. We analyze the performance both the radar and communication modes with and without DTS and benchmark it with a traditional radar-only network. Our results highlight that with DTS we are able to significantly improve quality of radar detection at the cost of quantity. Further, DTS causes some performance deterioration to the communication-mode due to increased interference; however, the radar-mode gains attained are much higher. We show that dense deployment of low-cost passive radars offers superior detection for farther off targets, particularly compared to a radar-only network. With DTS, ISAC can further improve radar gains from its traditional counterpart.

Integrated sensing and communication (ISAC) is expected to be offered as a fundamental service in the upcoming sixth-generation (6G) communications standard. However, due to the exposure of information-bearing signals to the sensing targets, ISAC poses unique security challenges. In recent years, intelligent reflecting surfaces (IRSs) have emerged as a novel hardware technology capable of enhancing the physical layer security of wireless communication systems. Therefore, in this paper, we consider the problem of transmit and reflective beamforming design in a secure IRS-enabled ISAC system to maximize the beampattern gain at the target. The formulated non-convex optimization problem is challenging to solve due to the intricate coupling between the design variables. Moreover, alternating optimization (AO) based methods are inefficient in finding a solution in such scenarios, and convergence to a stationary point is not theoretically guaranteed. Therefore, we propose a novel successive convex approximation (SCA)-based second-order cone programming (SOCP) scheme in which all of the design variables are updated simultaneously in each iteration. The proposed SCA-based method significantly outperforms a penalty-based benchmark scheme previously proposed in this context. Moreover, we also present a detailed complexity analysis of the proposed scheme, and show that despite having slightly higher per-iteration complexity than the benchmark approach the average problem-solving time of the proposed method is notably lower than that of the benchmark scheme.

Doubly-selective channel estimation represents a key element in ensuring communication reliability in wireless systems. Due to the impact of multi-path propagation and Doppler interference in dynamic environments, doubly-selective channel estimation becomes challenging. Conventional channel estimation schemes encounter performance degradation in high mobility scenarios due to the usage of limited training pilots. Recently, deep learning (DL) has been utilized for doubly-selective channel estimation, where convolutional neural network (CNN) networks are employed in the frame-by-frame (FBF) channel estimation. However, CNN-based estimators require high complexity, making them impractical in real-case scenarios. For this purpose, we overcome this issue by proposing an optimized and robust bi-directional recurrent neural network (Bi-RNN) based channel estimator to accurately estimate the doubly-selective channel, especially in high mobility scenarios. The proposed estimator is based on performing end-to-end interpolation using gated recurrent unit (GRU) unit. Extensive numerical experiments demonstrate that the developed Bi-GRU estimator significantly outperforms the recently proposed CNN-based estimators in different mobility scenarios, while substantially reducing the overall computational complexity.

Target detection and recognition is a very challenging task in a wireless environment where a multitude of objects are located, whether to effectively determine their positions or to identify them and predict their moves. In this work, we propose a new method based on a convolutional neural network (CNN) to estimate the range and velocity of moving targets directly from the range-Doppler map of the detected signals. We compare the obtained results to the two dimensional (2D) periodogram, and to the similar state of the art methods, 2DResFreq and VGG-19 network and show that the estimation process performed with our model provides better estimation accuracy of range and velocity index in different signal to noise ratio (SNR) regimes along with a reduced prediction time. Afterwards, we assess the performance of our proposed algorithm using the peak signal to noise ratio (PSNR) which is a relevant metric to analyse the quality of an output image obtained from compression or noise reduction. Compared to the 2D-periodogram, 2DResFreq and VGG-19, we gain 33 dB, 21 dB and 10 dB, respectively, in terms of PSNR when SNR = 30 dB.

This work presents an investigation on the scalability of a deep leaning (DL)-based blind transmitter positioning system for addressing the multi transmitter localization (MLT) problem. The proposed approach is able to estimate relative coordinates of non-cooperative active transmitters based solely on received signal strength measurements collected by a wireless sensor network. A performance comparison with two other solutions of the MLT problem are presented for demonstrating the benefits with respect to scalability of the DL approach. Our investigation aims at highlighting the potential of DL to be a key technique that is able to provide a low complexity, accurate and reliable transmitter positioning service for improving future wireless communications systems.

Reliable and fast channel estimation is crucial for next-generation wireless networks supporting a wide range of vehicular and low-latency services. Recently, deep learning (DL)-based channel estimation has been explored as an efficient alternative to conventional least-square (LS) and linear minimum mean square error (LMMSE) approaches. Most of these DL approaches have not been realized on system on chip (SoC), and preliminary study shows that their complexity exceeds the complexity of the entire physical layer (PHY). The high latency of DL is another concern. This article considers the design and implementation of deep neural network (DNN) augmented LS (LSDNN)-based channel estimation for preamble-based orthogonal frequency-division multiplexing (OFDM) PHY on SoC. We demonstrate the gain in performance compared with the conventional LS and LMMSE approaches. Via software–hardware codesign, word-length optimization, and reconfigurable architectures, we demonstrate the superiority of the LSDNN over LS and LMMSE for a wide range of signal-to-noise ratio (SNR), number of pilots, preamble types, and wireless channels. Furthermore, we evaluate the performance, power, and area (PPA) of the LS and LSDNN application-specific integrated circuit (ASIC) implementations in 45-nm technology. We demonstrate that word-length optimization can substantially improve PPA for the proposed architecture in ASIC implementations.

This article studies and derives beamforming design in a dual-functional radar-communication (DFRC) multiple-input-multiple-output system. We focus on a scenario, where the DFRC base station communicates with downlink communication users, with imperfect channel state information knowledge, and performs target detection, all via the same transmit signal. Through careful relaxation procedures, we arrive at a suitable and novel optimization problem, which maximizes the radar output power in the Bartlett sense, under probabilistic outage signal-to-interference-and-noise ratio constraints. Theoretical analysis proves optimality of the solution given by the relaxed version of the problem, as well as closed-form solutions in certain scenarios. Finally, the achieved performances and trade-offs of the proposed beamforming design are demonstrated through numerical simulations.

We present a novel approach to the problem of dual-functional radar and communication (DFRC) waveform design with adjustable peak-to-average power ratio (PAPR), while minimizing the multi-user communication interference and maintaining a similarity constraint towards a radar chirp signal. The approach is applicable to generic radar chirp signals and for different constellation sizes. We formulate the waveform design problem as a non convex optimization problem. As a solution, we adopt the alternating direction method of multipliers (ADMM), hence iterating towards a stable waveform for both radar and communication purposes. Additionally, we prove convergence of the proposed method and analyze its computational complexity. Moreover, we offer an extended version of the method to cope with imperfect channel state information (CSI). Finally, we demonstrate its superior performance through simulations, in comparison to state-of-the-art radar-communication waveform designs.

Accurate indoor positioning for wireless communication systems represents an important step towards enhanced reliability and security, which are crucial aspects for realizing Industry 4.0. In this context, this paper presents an investigation on the real-world indoor positioning performance that can be obtained using a deep learning (DL)-based technique. For obtaining experimental data, we collect power measurements associated with reference positions using a wireless sensor network in an indoor scenario. The DL-based positioning scheme is modeled as a supervised learning problem, where the function that describes the relation between measured signal power values and their corresponding transmitter coordinates is approximated. We compare the DL approach to two different schemes with varying degrees of online computational complexity. Namely, maximum likelihood estimation and proximity. Furthermore, we provide a performance comparison of DL positioning trained with data generated exclusively based on a statistical path loss model and tested with experimental data.

Several waveforms have been recently proposed in the literature as alternatives to orthogonal frequency division multiplexing (OFDM) for frequency selective channels. However, in order to achieve a superior performance, it is necessary to employ iterative equalization. In this paper, we consider the sparse Walsh-Hadamard (SWH) waveform with maximum a-Posterior (MAP) equalization. We show that the inherent structure of the SWH matrix allows a significant reduction in the number of multiplications required for the MAP equalizer implementation. The proposed solutions is compared with the zero padding single carrier (ZP-SC) with MAP equalization. We show that SWH with MAP equalization achieves a good trade-off performance vs complexity compared with ZP-SC. In particular, for 16-QAM under the Proakis C channel, ZP-SC is not even feasible while SWH with MAP equalization has manageable complexity.

Power-domain asynchronous non-orthogonal multiple access (ANOMA) is a novel radio access technique with non-orthogonal resource allocation that enables asynchronous transmissions and has an enhanced spectrum efficiency compared to orthogonal multiple access. In this work, an iterative receiver is derived for linearly modulated waveforms. Orthogonal frequency division multiplexing (OFDM), single-carrier (SC) and orthogonal chirp division multiplexing (OCDM) are investigated. The receiver is based on triangular successive interference cancellation (T-SIC) in combination with a minimum mean square error parallel interference cancellation (MMSE-PIC) detector. It is advantageous to utilize a waveform which spreads the data symbols in the frequency domain as OCDM or SC in order to exploit the multipath diversity in frequency-selective channels. However, it is numerically shown that OCDM performs the best due to its additional time-spreading property, which is desirable for the time-dependent interference that occurs in an ANOMA system. Furthermore, for the considered scenario of two users and four blocks, we show that all the studied waveforms achieve the best performance in terms of block error rate with the derived receiver when the blocks overlap halfway.

In this paper, we evaluate and make a comparison of the channel estimation performance for three different frame structures of IEEE 802.11p, IEEE 802.11bd-draft and a unique-word (UW)-based physical layer (PHY). As in vehicle-to-everything communication the wireless channel conditions may vary significantly depending on the environment and vehicle velocity, severe fading in both time and frequency domains may occur. Through simulation results, we show that the UW-based PHY achieves an interference-free performance of channel estimation via a low complexity technique, whereas the 802.11bd would need to employ a high complexity approach in order to achieve a comparable estimation performance.

Power-domain non-orthogonal multiple access (NOMA) is a promising radio access technique with non-orthogonal resource allocation that provides a greater spectrum efficiency than the conventional orthogonal multiple access (OMA). In this paper, an iterative receiver is derived for NOMA. It is based on soft-information successive interference cancellation (SIC) combined with a minimum mean square error parallel interference cancellation (MMSE-PIC) detector. Orthogonal frequency division multiplexing (OFDM) is usually the typical waveform employed. However, with the proposed receiver design, any linear modulation can be used. In addition to OFDM, single-carrier (SC) and the recently proposed sparse Walsh-Hadamard (SWH) are investigated. The NOMA scheme is analysed in a multi-path fading channel, where two users have different power ratios and waveforms. Simulation results show that mixing OFDM and SWH for a two-user NOMA gives the best performance with low receiver complexity.

In vehicular communications, reliable channel estimation is critical for the system performance due to the doubly-dispersive nature of vehicular channels. IEEE 802.11p standard allocates insufficient pilots for accurate channel tracking. Consequently, conventional IEEE 802.11p estimators suffer from a considerable performance degradation, especially in high mobility scenarios. Recently, deep learning (DL) techniques have been employed for IEEE 802.11p channel estimation. Neverthe-less, these methods suffer either from performance degradation in very high mobility scenarios or from large computational complexity. In this paper, these limitations are solved using a long short term memory (LSTM)-based estimation. The proposed estimator employs an LSTM unit to estimate the channel, followed by temporal averaging (TA) processing as a noise alleviation technique. Moreover, the noise mitigation ratio is determined analytically, thus validating the TA processing ability in improving the overall performance. Simulation results reveal the performance superiority of the proposed schemes compared to the recently proposed DL-based estimators, while recording a significant reduction in the computational complexity.

This paper describes a blind transmitter localization technique based on the deep neural network (DNN) framework. Blind localization assumes no previous knowledge on the transmit signal. It is shown that DNN based location approaches the maximum likelihood solution with reduced computational complexity. Moreover, the maximum likelihood, least squares and radio environment map localization estimators are presented in order to compare the design and performance of the proposed DNN algorithm. The system model is built based on a wireless sensor network that collects received signal strength measurements assuming disturbances of distance dependent correlated shadowing noise. Performance evaluation using numerical simulations shows that the proposed DNN scheme achieves location accuracy similar to the optimum maximum likelihood estimator while presenting computational complexity reduction of more than 90%.

Communication using orbital angular momentum (OAM) modes has recently received a considerable interest in free space optical (FSO) communications. Propagating OAM modes through free space may be subject to atmospheric turbulence (AT) distortions that cause signal attenuation and crosstalk which degrades the system capacity and increases the error probability. In this paper, we propose to enhance the OAM FSO communications in terms of bit error rate and spectral efficiency, for different levels of AT regimes. The performance gain is achieved by introducing orthogonal frequency division multiplexing (OFDM) with index modulation technique to the OAM FSO system.

Autocorrelation (AC) is one of the most frequently used methods for initial acquisition. Usually, algorithms that employ a metric based on AC for this purpose, focus on the estimation of synchronization parameters, ignoring the detection performance. This paper analyses the relationship between the structure of the reference sequence that serves to compute the AC and the corresponding attainable detection performance. The distributions of test statistics based on AC for frame detection are derived and validated through simulations in a frequency-flat fading channel. It is shown that the use of AC can outperform, in terms of detection performance, the more complex matched filtering in a certain signal-to-noise ratio region, at the cost of significantly longer required sequences. Besides, we show that a set of sequences, with different periodic structures, can provide the same target detection probability, allowing a flexible sequence design.

Indoor localization can be based on a matrix of pairwise distances between nodes to localize and reference nodes. This matrix is usually not complete, and its completion is subject to distance estimation errors as well as to the noise resulting from received signal strength indicator measurements. In this paper, we propose to use convolutional neural networks in order to denoise the completed matrix. A trilateration process is then applied on the recovered euclidean distance matrix (EDM) to locate an unknown node. This proposed approach is tested on a simulated environment, using a real propagation model based on measurements, and compared with the classical matrix completion approach, based on the adaptive moment estimation method, combined with trilateration. The simulation results show that our system outperforms the classical schemes in terms of EDM recovery and localization accuracy.

In this paper, we investigate three different concepts for robust link-level performance under doubly-dispersive wireless channels, namely, i) channel estimation, ii) cyclic prefix (CP)-free transmission, and iii) waveform design. We employ a unique word-based channel estimation, where we decouple the channel related errors into channel estimation error (CEE) and Doppler error (DE). Then, we show that a trade-off between CEE and DE emerges in the frame design, where the system can be optimized to achieve the minimum composite channel error. Another strategy to improve the link-level performance is to suppress the CP of the sub-blocks. This allows for better channel estimation due to the reduced transmission time, with the penalty of requiring the CP-restoration processing at the receiver. Furthermore, we propose the waveform design based on the equal-reliability criterion (ERC), leading to the block multiplexing-orthogonal chirp division multiplexing (BM-OCDM). This waveform is advantageous in the CP-free transmission mode, where the data symbols have equally distributed interference from adjacent sub-blocks. Our framework is a generalization of the recently proposed orthogonal time frequency space (OTFS), which fails to achieve the ERC. The link-level simulations show that at high modulation and coding scheme, the proposed BM-OCDM provides superior link-level performance than OTFS.

This paper describes a coherent chirp spread spectrum (CSS) technique based on the Long-Range (LoRa) physical layer (PHY) framework. LoRa PHY employs CSS on top of a variant of frequency shift keying (FSK), and non-coherent detection is employed at the receiver for obtaining the transmitted data symbols. In this paper, we propose a scheme that encodes information bits on both in-phase and quadrature components of the chirp signal, and rather employs a coherent detector at the receiver. Hence, channel equalization is required for compensating the channel induced phase rotation on the transmit signal. Moreover, a simple channel estimation technique exploits the LoRa reference sequences used for synchronization to obtain the complex channel coefficient used in the equalizer. Performance evaluation using numerical simulation shows that the proposed scheme achieves approximately 1 dB gain in terms of energy efficiency, and it doubles the spectral efficiency when compared to the conventional LoRa PHY scheme. This is due to the fact that the coherent receiver is able to exploit the orthogonality between in-phase and quadrature components of the transmit signal.

Least square (LS) channel estimation employed in various communications systems suffers from performance degradation especially in low signal-to-noise ratio (SNR) regions. This is due to the noise enhancement in the LS estimation process. Minimum mean square error (MMSE) takes into consideration the noise effect and achieves better performance than LS with higher complexity. This paper proposes to correct the LS estimation error using deep learning (DL). Simulation results show that the proposed DL-based schemes perform better than both LS and MMSE channel estimation scheme, with less complexity than accurate MMSE.

Channel state information is very critical in various applications such as physical layer security, indoor localization, and channel equalization. In this paper, we propose an adaptive channel estimation based on deep learning that assumes the signal-to-noise power ratio (SNR) knowledge at the receiver, and we show that the proposed scheme highly outperforms linear minimum mean square error based channel estimation in terms of normalized minimum square error, with similar order of online computational complexity. The proposed channel estimation scheme is also evaluated for an imperfect estimation of the SNR and showed to be robust for a high SNR estimation error.

In analog receivers, the use of matched filtering with linear frequency modulated signals constitutes an effective means for detecting the presence of an incoming frame as well as for estimating the symbol timing offset (STO). However, in digital receivers, the discrete nature of the corresponding polyphase codes leads, under certain conditions of carrier frequency offsets, to a significant degradation at the output of the matched filter, which, consequently, can result in erroneous STO estimations. Exploiting the symmetry of a reference sequence consisting of a polyphase code and its complex conjugate, this work proposes a new algorithm based on reversed autocorrelation (RC) to improve the accuracy of the STO estimation obtained by matched filtering. On the one hand, the theoretical analysis demonstrates the superiority of matched filtering over RC in terms of detection performance in the low signal-to-noise ratio regime. On the other hand, it is shown by means of numerical evaluation that the RC can efficiently resolve the STO estimation ambiguity.

Matched filtering based on linear frequency modulated (LFM) or chirp signals constitutes a reliable approach for frame detection even in the presence of high carrier frequency offsets (CFOs). However, the use of polyphase codes as discrete versions of LFM signals can lead to strong degradation at the output of the matched filter for certain CFO conditions and sequence lengths, yielding missed detection. This work proposes a new approach to improve the probability of detection achievable with matched filtering for these unfavorable situations. The proposed approach exploits the symmetry of a reference sequence that consists of a polyphase code followed by its complex conjugate with an algorithm based on reversed autocorrelation (RC) and successfully utilizes the robustness of RC against CFO for detection. It is shown that joint detection based on matched filtering and RC can enhance the detection performance significantly compared to conventional matched filtering.

In conventional multicarrier systems, a cyclic prefix (CP) is added to the transmission block in order to protect it from multi-path propagation of the wireless channel. Nonetheless, due to the random nature of the CP, it is usually discarded at the receiver side, and from a synchronization perspective, this energy is wasted. Unique Word (UW) is a promising concept for CP replacement, because, in addition to protecting the signal from multi-path propagation, it allows per-block synchronization. Considering a multiple-input-multiple-output (MIMO) system, the state-of-the-art (SoA) data-aided synchronization approaches are mainly preamble based, while, on the other hand, the synchronization techniques for UW sequences are being applied to single-input-single-output systems in low mobility scenarios. In this paper, we investigate time and frequency synchronization of UW-based MIMO systems in high mobility conditions where the wireless channel is both frequency selective and fast fading. Through theoretical derivations as well as extensive simulations, we show that the proposed UW-based synchronization approach for MIMO outperforms the SoA MIMO synchronization techniques.

In this paper, we design a frame structure for unique word (UW) based transmission of multiple-input-multiple-output (MIMO) systems under doubly-dispersive wireless channel conditions. We elaborate an energy and spectral efficiency analysis of a MIMO UW-based system vs. a conventional MIMO cyclic prefix (CP)-based system. Considering the UW-based transmission for a MIMO multi-carrier, we derive its signal processing algorithms for channel estimation and joint channel-equalization-and-demodulation. Through theoretical derivations as well as extensive simulations, we show that the proposed MIMO UW-based system significantly outperforms the state-of-the-art approaches.

We consider the channel estimation (CE) of a non-orthogonal multi-carrier system where the wireless channel is both frequency-selective and time-variant. In non-orthogonal multi-carriers e.g. generalized frequency division multiplexing (GFDM), the reference signals for channel estimation become contaminated by the data symbols, which consequently, limits the transceiver performance. On the other hand, the well time-localization of the pilot symbols in GFDM, allows a more efficient use of cyclic prefix (CP). Particularly, by localizing the energy of the pilot symbols to the end of block, it is possible to use the pilot's information also from CP for channel estimation. Moreover, since in a non-orthogonal waveform, the energy concentration of the pilots might not be uniform over the transmit block duration, the CE algorithm that relies solely on block-fading assumptions might have its best performance at a specific time sample within the block duration. The knowledge of such time sample is specifically important for deriving the channel autocorrelation for adaptive filtering in time-variant situations. In this paper, we first propose an approach to efficiently use the whole transmission block for channel estimation including its CP, and then, we derive the well-known adaptive Wiener-Hopf filters for CE of the non-orthogonal interference-limited GDFM system. From the simulation results, we observe that using CP information for channel estimation and applying the Wiener-Hopf filters achieves up to 1.45 dB smaller frame error rate in comparison to an orthogonal frequency division multiplexing system.

Lack of coordination between network layers limits the performance of most proposed solution for new challenges posed by wireless networks. To overcome such limitations, cross-layer physical and medium access (PHY-MAC) design for multi-input-multi-output orthogonal frequency division multiple access system in heterogeneous networks (HetNETs) is proposed. In this paper, we formulate an optimization problem for hybrid beamforming, in a multi-user HetNET scenario aiming to maximize the total system throughput. Furthermore, analog beamforming is selected from a codebook containing a limited number of candidates for steering vectors. The proposed problem is non-convex and hard to solve. Thus it is relaxed by transforming it into a subtraction form of two convex funcions. Afterward we apply a group of well-known metaheuristic algorithms to calculate the normalized hybrid beamforming vectors. The optimal solution is obtained using an exhaustive search (ES) algorithm that provides an ideal solution, but with high complexity. In addition, zero-forcing-based approach (ZFA), matched filter (MF), and QR-based approach (QR) are applied to get quick sub-optimal solutions. Hence, we analyze the performance of our systems using the throughput metric. The simulation results show that QR algorithm outperforms ZFA and MF in low and middle signal-tonoise ratio (SNR) regime, while ZFA outperforms QR and MF at higher SNRs. Moreover, QR is close to the optimal solution ES.

This paper addresses the energy consumption concern of LPWAN by proposing an extension for the LoRa modulation. Conventional LoRa encodes data in the frequency shift of a chirp, our extension consists in encoding additional data in the phase- shift using the PSK modulation, giving rise to the PSK-LoRa. Our motivation is to encode more data per unit of time without performance degradation, such that we have a more energy efficient system. In order to assess the performance of PSK-LoRa, we derive approximate bit error rate and packet error rate expressions, and then we compare against simulation. For instance, both analytical and numerical outcomes demonstrate that QPSK-LoRa has no performance loss in comparison to LoRa, indicating the feasibility of the new scheme.

To meet the requirements of new 5G use cases, two methodologies have been proposed. The first considers additional processing on orthogonal frequency division multiplexing (OFDM), e.g. windowing, filtering, and precoding. The other aims at the design of new modulation schemes. Essentially, any block-based linear modulation, such as generalized frequency division multiplexing (GFDM), can be seen as precoded-OFDM, where the precoded data result from the frequency domain modulation. This representation has a significant benefit for the implementation of new waveforms. Namely, the available OFDM transceiver techniques can be reused, whereas the required signal features can be fulfilled by the configuration of the GFDM modem. In this paper, we propose flexible OFDM precoding based on the GFDM framework. As a particular case, we focus on orthogonal precoding to enhance the performance in fading channels. By means of closed-form expressions, we show that all the sybsymbols within the same GFDM subcarrier attain the same signal-to-interference-plus-noise ratio (SINR) in frequency selective channel. In special precoding cases, all symbols achieve equal SINR. This feature is important to enhance the performance without a need for power allocation.

The low complexity, low cost of implementation, as well as the spectral and energy efficiency, are key features for the development of the internet of things (IoT) networks. In this context, the introduction of index modulation on single carrier-frequency division multiple access (SC-FDMA-IM) has reported significant gains in terms of energy efficiency. In this paper, we evaluate an SC-FDMA-IM scheme tailored for IoT devices taking into account its complexity and performance. For that end, computational simulations and a complexity analysis for different detectors are carried out. Our results show that a significant bit error rate gain is obtained in comparison to a conventional SC-FDMA scheme, while maintaining a reduced computational complexity and power consumption.

The conventional receiver designs of generalized frequency division multiplexing (GFDM) system assume full subcarrier allocation. In this case, the optimal linear receivers can be implemented with low-complexity channel equalization followed by zero-forcing (ZF) demodulation. In some use cases, e.g. multiuser, only a subset of the subcarriers is active for data transmission. Therefore, the optimal receiver design needs to consider the effective joint channel and modulation matrix, which complicates the practical implementation. To maintain low-complexity realization in these cases, full allocation can still be assumed, however, the performance loss is remarkable. In this paper, we propose an efficient transceiver design for non-fully allocated GFDM system. In the proposed approach, the frequency-domain (FD) sparsity of GFDM is exploited to represent the transmitted signal by means of an effective small-size GFDM model with one non-active subcarrier. Therefore, the assumption of full allocation becomes more realistic. Moreover, the received signal can be further reformulated with fully allocated system, but at the cost of altering the effective channel gains. The proposed design significantly reduces the computation cost of the practical GFDM receiver, whereas the performance still approaches the counterpart optimal linear receiver.

Motivated by 5G application requirements that challenge the use of orthogonal frequency division multiplexing (OFDM), non-orthogonal multi-carriers are being investigated. Unlike OFDM that takes advantage of orthogonal pilot observation, in non-orthogonal waveforms, pilots are contaminated by interference from multiple dimensions, i.e., inter-subsymbol-, inter-carrier-, and inter-antenna-interference, when multiple-input-multiple-output (MIMO) is also part of the transmission. Employing cyclic-prefix (CP) in multi-carrier systems not only protects the signal from inter-symbol-interference but also allows circular interpretations of the channel, which simplifies the estimation and equalization techniques. Nevertheless, the CP information is usually discarded at the receiver side. In this paper, by considering the fact that non-orthogonal waveforms suffer from multiple dimensions of interference, we derive a MIMO linear-minimum-mean-squared-error (LMMSE)-based parallel-interference-cancellation (PIC) method for joint channel estimation and equalization of non-orthogonal waveforms. Unlike the common practice, by properly localizing the pilots in time domain, we also use the pilots' information from CP. We apply our proposed algorithm to a flexible non-orthogonal waveform known as generalized frequency division multiplexing (GFDM). Taking advantage of block-circularity of GFDM, we investigate the complexity aspects for such CP-aided LMMSE-PIC channel estimation. Through simulation results, we show that using CP information of pilots for GFDM gains up to 2.4-dB better frame error rate performance than an OFDM signal.

Orthogonal time frequency space modulation (OTFS) has been recently proposed to achieve time and frequency diversity, especially in linear time-variant (LTV) channels with large Doppler frequencies. The idea is based on the precoding of the data symbols using symplectic finite Fourier transform (SFFT) then transmitting them by mean of orthogonal frequency division multiplexing (OFDM) waveform. Consequently, the demodulation and channel equalization can be coupled in one processing step. As a distinguished feature, the demodulated data symbols have roughly equal gain independent of the channel selectivity. On the other hand, generalized frequency division multiplexing (GFDM) modulation also employs the spreading over the time and frequency domains using circular filtering. Accordingly, the data symbols are implicitly precoded in a similar way as applying SFFT in OTFS. In this paper, we present an extended representation of GFDM which shows that OTFS can be processed as a GFDM signal with simple permutation. Nevertheless, this permutation is the key factor behind the outstanding performance of OTFS in LTV channels, as demonstrated in this work. Furthermore, the representation of OTFS in the GFDM framework provides an efficient implementation, that has been intensively investigated for GFDM, and facilitates the understanding of the OTFS distinct features.

One of the most challenging issues of Internet of Things (IoT) devices is energy consumption. A massive deployment of connected devices is power hungry. Maximizing the energy efficiency of their batteries reduces the maintenance cost and extends their autonomy and then their utility. To address this issue, this paper proposes the introduction of index modulation on single carrier-frequency division multiple access (SC-FDMA) uplink transmissions for cellular IoT and machine-to-machine (M2M) networks. It is shown that the proposed SC-FDMA with index modulation (SC-FDMA-IM) scheme achieves 50% of energy efficiency increase, while improving the bit error rate performance by up to 3.5 dB of Eb/N0for the same spectral efficiency performance. However peak-to average power ratio (PAPR) performance are decreased, and PAPR reduction techniques have to be used for this scheme.

Narrow Band-Internet of Thing (NB-IoT) is a recently proposed technology by 3GPP in Release-13. It provides low energy consumption and wide coverage in order to meet the requirements of its diverse applications that span social, industrial and environmental aspects. Increasing the number of repetitions of the transmission has been selected as a promising approach to enhance the coverage in NB-IoT up to 164 dB in terms of maximum coupling loss for uplink transmissions, which is a significant improvement compared with legacy LTE technologies, especially to serve users in deep coverage. However, a large number of repetitions reduces the system throughput and increases the energy consumption of the IoT devices, which reduces their battery lifetime and increases their maintenance cost. In this work, we propose a new method for enhancing the NB-IoT coverage based on machine learning algorithms. Instead of employing a random spectrum access procedure, dynamic spectrum access can reduce the number of required repetitions, increase the coverage, and reduce the energy consumption.

In this paper, we propose a new adaptive modulation based on the wavelet packet transform, which targets a good resistance against frequency selective channels while avoiding a large PAPR. Classical multi-carrier modulation schemes divide the channel bandwidth into narrowband sub-channels to improve its robustness against frequency selective fading. However, they suffer from the peak-to-average power ratio (PAPR) problem, which occurs due to a random constructive addition of sub-carriers. By contrast, single carrier modulation schemes, where each transmitted symbol fully occupies the bandwidth, are more sensitive to frequency selective environments and less affected by the PAPR problem. In this paper, we show how the bandwidth division can be reconfigurable and adapted to the channel properties, and we provide several examples to prove that the proposed adaptive modulation represents an alternative modulation which is adjustable between two extreme cases: single carrier modulation and classical multi-carrier modulation.

Multicarrier modulation systems suffer from large peak-to-average power ratio (PAPR). Tone reservation is a popular technique for PAPR reduction. It consists in reserving a number of carriers to produce a redundant additive signal which reduces the peak power. There are several schemes to select the reserved carriers. In this paper, we propose a new selection method for adaptive tone reservation. It is showed through simulations that this new proposed selection technique allows 5 dB gain in terms of signal-to- noise ratio for a bit error rate of 10-3, for different constellations, in frequency-selective Rayleigh fading channel.

This paper establishes a necessary condition that must be satisfied by the modulation waveforms of any generalized waveforms for multicarrier (GWMC) system with better peak-to-average power ratio (PAPR) than conventional orthogonal frequency division multiplexing (OFDM). GWMC systems include in particular all classical multicarrier modulation systems. As a consequence, we show that OFDM has the best PAPR performance over all GWMC systems that do not satisfy this necessary condition. We also identify an infinite family of GWMC systems with the same PAPR performance as OFDM. To illustrate our results, we present the simulations of the PAPR behavior for different GWMC systems, including some with better PAPR performance than OFDM.