Recent advancements in 6G technologies have revolutionized vehicle-to-everything communications, accelerating the evolution of autonomous driving (AD) from a conceptual vision to an operational reality. In this paper, we propose an agile architecture integrated with an offloading framework specifically designed to support AD tasks by harnessing the potential of 6G, while adhering to ISO standards for Automotive Safety Integrity Level (ASIL). Unlike existing offloading approaches that rely on edge/cloud, the proposed ASIL-Centric Offloading Framework for Autonomous Driving (ACOFAD) introduces roadside units as additional remote computing nodes and incorporates unmanned aerial vehicles as communication relays to extend coverage in hard-to-reach or underserved areas. Offloading decisions are dynamically made based on node availability, link reliability, latency constraints, and task criticality. Simulation results demonstrate that ACOFAD achieves very low latency, evaluated to 5 ms and 0.5 s for 20 MB and 2GB payloads, respectively, enabling efficient offload of both safety-critical and non-critical tasks.

In Unmanned Aerial System (UAS) Traffic Management (UTM) environment, small UASs generally fly at lower altitudes than large manned aircraft and their transmission power is very limited. They also fly shorter distances and more densely populated in the airspace than their manned counterparts. Therefore, a finer control and more frequent updates are necessary for their traffic control. To cope with the special requirements in UTM, we here explore and compare the potential of three machine learning models to help safe UAS operation, built on the 3GPP ecosystem model, through awareness about weather conditions. The proposed algorithm operates in two steps, wherein the first step it predicts the wind speed that it exploits during the second step to allow or stop a flight depending on the characteristics of the drone. The prediction process also covers two modes: i) premature 24 -hour prediction for an early decision, and ii) One-hour prediction for a refined final decision. Depending on the weather conditions (stable or frequently changing), the proposed algorithm can use either the premature or the hourly mode.

Livestock farming in agriculture has recently wit-nessed the surge of integrating various information and communication technologies for digital farming that improve the efficiency of resource use and increase the added value of agricul-tural products. In this context, this paper proposes a pioneering secured solution for cattle health monitoring. In particular, we focus on secured tracing of animals that have recently been in the vicinity of an infested one. We propose a new message structure that enables real-time detection based on the signal strength. Secured message exchange is ensured through Elliptic curve cryptography. Further, to avoid potential falsification of epidemic history, we develop a mechanism capable of detecting malicious messages wrongly claiming infection. Performance study shows that the proposed model satisfies all privacy requirements in the context of contact-tracing applications.

This letter investigates localization exploiting mm-waves and massive multiple antennas configuration. The particularity of this work is to consider channel estimation errors to evaluate their impact on localization accuracy. The analysis and simulations show that mm-waves provide better performance at high SNR values while cm-waves perform better at low SNR values. Also, the results highlight the resilience of the multistage localization processing to channel estimation errors.

Localization is an important technique in wireless systems as it provides fundamental support for many emerging location-aware applications. This paper proposes a neighbor-assisted localization algorithm to determine the coordinates of a stationary User Equipment (UE) placed in an outdoor environment. The proposed algorithm allows the base station to determine the position of a Target UE (T-UE) with obstructed channel conditions through the assistance of neighboring UEs having Line Of Sight (LOS) components with the base station. Having LOS components, the neighboring UEs can easily be localized with high accuracy at the base station level. A minimum of two previously localized neighbors is required to assist in determining the position of the T-UE through multi-stage processing. The first stage uses one neighbor to calculate the distance separating it from the T-UE based on the signal strength. This distance helps to draw a circle crossed by a line identifying two position candidates using oriented beamforming. The second neighbor is then used to remove the ambiguity on the correct position candidate by the mean of the distance separating it to the T-UE. Our algorithm exploits the capabilities of neighbor discovery, oriented beamforming, and millimeter waves to provide accurate position estimate for UEs experiencing harsh channel conditions that make direct localization at the base station a challenging task. We also show that it is possible to further enhance the localization accuracy by simply increasing the search interval.

Device-to-Device (D2D) communication underlying cellular networks was first introduced in the 3GPP Rel. 12 specifications, and was initially referred to as proximity services (ProSe). Its primary aim was to serve billions of Internet of Things (IoT) devices for 5G and beyond-5G networks. To enable D2D link establishment between various user equipments (UEs), the neighbor discovery process became crucial. This article investigates neighbor discovery for ProSe and Vehicle-to-Everything (V2X) communications through a SideLink interface, which is specifically introduced to support D2D communications over cellular networks. A single-user scenario is first considered to derive the probability of discovery in its closed-form and compare it with simulation results to validate its theoretical analysis. This scenario employs the demodulation reference signal (DMRS), where a power-normalized-correlation (PNC)-based metric is performed to determine the presence of active peers in the vicinity. Moreover, a multiuser scenario is considered to assess the impact of interference on the discovery probability in the cases of low and high vehicular mobility channel models. Then, group discovery is investigated using two strategies: 1) a distributed scheme incorporating out-of-coverage communications (modes 2 and 4) or 2) a network-assisted scheme applicable for supervised communications (modes 1 and 3). In this study, discovery periods are modeled as an Aloha-like protocol in the first case and a Polling-like protocol in the second case with either MAC layer or PHY layer collision models. Simulations are performed to evaluate the time required for group discovery completion as well as the collision rate in both low-mobility and high-mobility channels.

In resource-constrained Wireless Body Area Networks (WBAN), one of the fundamental challenges is to provide sustainable e-health service. We here investigate time resource allocation for e-health applications with energy harvesting enabled sensor nodes to maximize the overall network lifetime. The allocation is processed by a central node that executes an allocation algorithm based on game theory. In particular, first-price sealed-bid auction-based (FPSBA) is formulated as a solution to achieve time allocation while ensuring service sustainability. The proposed solution prioritizes sensor nodes as bidders with a maximum instantaneous energy level to bid while offering the sensor nodes with low instantaneous energy level the opportunity to harvest more energy and increase their possibilities to provide the required energy to continuously submit their data.

Unmanned Aerial Vehicles (UAVs) or drones have found a tremendous range of civilian and commercial applications such as precise agriculture, environmental conservation, and search and rescue missions. In this paper, we re-visit the positioning of a low altitude UAV functioning as a relay node to provide connectivity and communication services to ground users who are out of coverage. A new altitude dependant Rician distribution for the air-to-ground channel - derived from propagation physics - is used and its impact on the optimum altitude for network coverage explored via numerical results.

Localization is a key component of 5G systems because of its obvious potentials for communication performance enhancement and emerging location-based services. In this paper, we propose a new localization technique that reduces the complexity of the problem by minimizing the search space through multi-stage processing. The localization is here based on Angle of Arrival (AoA) whose estimation involves a two-step procedure, where signals detected from different antennas are first processed to obtain a coarse user' s AoA, followed by a fine search performed around the coarse estimate. The user' s position is then calculated based on the estimates of the AoA and the distance to the User Equipment (UE). Our approach exploits the capability of oriented beamforming to provide accurate position estimate especially for very low Signal to Noise Ratio (SNR).

The objective of health monitoring systems is to provide sustainable and high-quality service to subscribers, hence requiring that the system run without interruption for a sufficiently long time. In this paper, we investigate lifetime maximization in Wireless Body Sensor Networks (WBSN) to provide sustainable e-health service with energy harvesting enabled nodes. Maximizing the lifetime is equivalent to minimizing the energy budget within the whole network. To this aim, we optimize the time resource allocation for all involved sensor nodes in order to maintain a sufficient energy level for longer time after each packet transmission. Specifically, we model the energy harvesting process as a discrete-time Markov chain and evaluate the instantaneous energy budget in order to select the node that will occupy a specific time slot. The energy budget is presented as the net energy, which we quantify through the gap between the consumed and harvested energies. Therefore, to achieve sustainability, the idea of the proposed algorithm focuses on prioritizing nodes with a minimum net energy level while offering nodes with high net energy level the opportunity to harvest more energy and increase their capabilities to provide the required energy to continuously submit their data. The proposed algorithm is evaluated in terms of sensor uninterrupted lifetime and packet loss due to energy run out through simulations. The results demonstrate longer uninterrupted lifetime compared to the considered benchmark and low packet loss probability to subscribers.

By introducing the Proximity Services (ProSe) functionalities in its 12threlease, the 3rdGeneration Partnership Project (3GPP) made possible the ability to perform device-to-device (D2D) communication in Long Term Evolution-Advanced (LTE-A) cellular network. Direct communication between nearby User Equipments (UEs) is then enabled through the newly introduced sidelink. This paper investigates neighbor discovery for D2D communication exploiting Demodulation Reference Signals (DMRS), which are associated with physical sidelink channels for coherent demodulation. By simply listening to its sidelink signal, a ProSe-enabled UE can identify its peers and initiate a direct link without any additional overhead. The study examines the properties of DMRS sequences and proposes a robust neighbor discovery scheme tailored to DMRS structure based on a power normalized correlation process. A reduction in the computational load required to establish a link between two neighbors is proposed and evaluated. Simulation results show that nearby users can be discovered with a reasonable complexity while achieving a high discovery accuracy.

Device-to-Device (D2D) communication is one of the key technologies in Long Term Evolution-Advanced (LTE-A) systems to improve network capacity and resource utilization. In this paper, we address the problem of neighbor discovery as an enabler of D2D communications that overlay the LTE-A network and share its resources. The proposed scheme allows distributed neighbor discovery for synchronized D2D users by simply listening to peer UpLink (UL) signals. In particular, the Sounding Reference Signals (SRS) are here exploited as beacons to detect potential users willing to initiate direct communication. This signal, which is built from Zadoff-Chu (ZC) sequences, not only possesses robust detection properties, but also has minimal overhead as no additional signals are required to identify the whole set of users and to achieve the discovery procedure. Moreover, for complexity reduction, we suggest a recursive implementation of the proposed discovery metric to adapt it for practical D2D communication scenarios.

The 3rd Generation Partnership Project (3GPP) has started to investigate feasibility of machine type communication terminal class in Long Term Evolution (LTE) networks in the Release 12 to address the requirements of the Internet of Tings (IoT) targeting low-end device market with improved coverage. Along with that, the 3GPP has defined reference signals to support device discovery, communication and synchronization. These signals are transmitted by either an eNB within the coverage area, or a peer User Equipment (UE) in partial and out of coverage scenarios. The regular LTE synchronization signals, built from Zadoff-Chu (ZC) sequences and M-sequences, are reused in Rel-12. In this paper, we deal with low- complexity synchronization approach based on ZC sequences and tailored to LTE signal. The presented approach carries out Simply-Differential (SD) correlation-based metric that allows the detection of a ZC sequence pattern within the received signal. The SD metric correlates the received signal and compensates the output with an adjustment frequency whose value depends on the training ZC sequence. To reduce the huge computational load resulting from differential correlation operations, a recursive implementation of the SD metric is proposed. The performance is here evaluated in terms of theoretical probability of correct detection and compared to the experimental rate of correct detection to validate the analysis. The obtained results show good match between analytical and experimental probabilities.

In this paper, we propose the use of differential space-time block coding to improve the synchronization for Wireless Local Area Networks (WLAN) without any channel estimation requirement. We study the application of an efficient synchronization technique recently proposed for 2×1 MISO-OFDM systems in the IEEE 802.11n WLAN for 2×1 and 2×2 MIMO configurations. The proposed technique exploits a preamble of two identical parts and splits the synchronization processing into two stages: an auto-correlation based coarse stage and a cross-correlation based fine stage. The coarse synchronization aims to detect a repetitive pattern within the received signal in order to roughly localize the preamble start. Then, to benefit from the spatial diversity, differential decoding must be achieved before carrying the fine synchronization. The cross-correlation based fine metric is calculated over a short interval centered on the coarse time estimate, which provides accurate preamble start estimation with relatively reduced computational load. As a byproduct, the fractional part of the frequency offset is also estimated through the evaluation of the timing metric at the estimated preamble start. Simulation results show that, when applied to the IEEE 802.11n signal, the proposed synchronization scheme provides better detection performance compared to the considered benchmark.

In this paper, we propose a robust timing and frequency synchronization scheme for MIMO-OFDM systems. Exploiting a preamble of two identical parts and proceeding in two stages, the proposed technique achieves synchronization under non- coherent detection. To take advantage from transmit diversity, we use differential Alamouti space time block coding which provides full- spatial diversity without requiring any channel knowledge in neither the transmitter nor the receiver. Two different replicas of the preamble are generated and sent simultaneously from dual- antenna transmitter. At the receiver side, a coarse synchronization is first carried onto the received signal to roughly localize the preamble respecting the famous Cox and Schmidl algorithm. Second, differential Alamouti decoding must be achieved to recover the transmitted signal. Then, a fine synchronization metric is calculated based on cross-correlating the recovered signal with the local known preamble to detect the exact preamble start. The fine metric is calculated over a short interval around the coarse time estimate which relaxes the computational complexity of this stage. As a byproduct, the fractional part of the frequency offset is estimated using the same timing metric. Computer simulations carried to evaluate the proposed technique show its robustness in the Rayleigh fading channel under non-coherent reception.

In 3GPP Long Term Evolution (LTE) system, cell search must be first performed to allow the connection between the user equipment and the serving cell. In this paper, we investigate sector search, which is a part of the cell identifier, as well as timing synchronization based on the standardized Primary Synchronization Signal (PSS) generated from known Zadoff-Chu sequences. To this aim, an initial reduced complexity metric is first calculated to provide a coarse PSS start estimate. Then, to determine the sector identifier and fine tune the PSS start estimate, the received signal is cross-correlated to the known PSS candidates. The fine metric, which exhibits high sharp peak, allows detecting the PSS start and the sector identification. Unlike the conventional methods that perform the cross-correlation over all demodulated OFDM symbols, the proposed one carries it over a short interval centered on the coarse estimate, which greatly reduces the complexity. Simulation results show that the proposed approach provides satisfactory performance in terms of time estimation accuracy and successful sector identification rate. For practical SNR values, it also outperforms the considered benchmarks in the frame of LTE systems primary synchronization.

This paper applies a recently proposed efficient technique that has been conducted regarding timing synchronization in OFDM systems, to the IEEE 802.11a/g standards. The time synchronization is fulfilled using the structure specificity of the short training sequence of IEEE 802.11a/g preamble. Two versions of the applied technique are considered: a single-stage brute force approach, which carries differential correlation exclusively, and a two-stage reduced complexity approach comprising coarse and fine stages. The coarse synchronization is achieved using sliding correlation, characterized by its low computational load, whereas the fine synchronization is realized by differential correlation, characterized by its high computational load and carried around the coarse time estimate. In the two stage approach, the combined use of sliding correlation and differential correlation, carried for short interval, results in an overall reduced complexity approach. Simulation results show that, applied in the IEEE 802.11a/g norm, both of the considered approaches provide accurate time synchronization in the AWGN and multipath channels. Moreover, the two-stage version has a low computational load, which makes it suitable for fast symbol timing synchronization in bursty IEEE 802.11a/g OFDM systems.

A new Genetic Algorithm (GA) based sequence optimization is here proposed. The studied sequence, which we refer to as Encoding Sequence (ES), is used in a recently proposed OFDM preamble-based synchronization technique. The optimal ES is generated offline by differentially correlating the preamble samples to each other respecting a shift. To determine the frame start at the receiver, the ES is correlated to a version of the received signal generated using differential correlation respecting the same shift as in the ES generation. This treatment, carried online, is of high complexity due to the differential correlation operations. To reduce the computational load at the receiver, the idea here is to replace the differential correlation operations, carried during the synchronization process, by simple sign changes. To this aim, we need to derive the best sub-optimal ES, in terms of providing near optimal performance, from a finite QPSK alphabet (#177;1#177;j). Even if this search procedure is carried off-line, considering an exhaustive search over the whole ES candidates within QPSK alphabet results in a prohibitive complexity. As an alternative, we here suggest to use a GA-based approach. Performance comparison of the proposed ES, generated through GA-based search, to the optimal ES, generated from the preamble, and a previously proposed QPSK ES optimized through an iterative algorithm has been conducted. It is demonstrated that compared to the iteratively generated ES, the herein proposed one improves the detection accuracy. The GA-based provides slightly lower detection accuracy with respect to the optimal ES, yet with important reduction in the receiver complexity.

In this paper, we analytically study the performance of a recently proposed efficient reduced complexity time synchronization approach for orthogonal frequency division multiplexing systems. This method uses a preamble of two identical parts and proceeds in two stages. In the first stage, the repetitive structure of the preamble is exploited to provide the coarse time estimate respecting the algorithm of Cox and Schmidl. In the second stage, a fine metric, based on differential correlation, is carried over a reduced time window centered on the coarse estimate. We here study the performance of the fine stage, assuming a successful coarse stage whereby the fine search window is centered on the correct frame start. We approximate the fine metric by a Gaussian distribution to derive a closed form expression of the frame start correct detection probability. To this end, a statistical characterization of the fine metric is achieved by its mean and variance computation. Simulations are used to validate the results of the analysis. Indeed, the evaluated rate of correct detection perfectly concords with the theoretical probability in both additive white Gaussian noise and multipath channels.

In this paper we propose a reduced-complexity two-stage time and frequency synchronization approach for OFDM systems, operating in multipath channels. The proposed approach exploits a single-symbol preamble with a repetitive structure, composed of two identical m-sequences. The first coarse stage, based on a sliding correlation, finds out the reduced uncertainty interval over which the second fine stage, based on a differential correlation, is performed. The combined use of the sliding correlation, characterized by its low complexity, and the differential correlation, which is much more complex, carried for a limited number of times results in an overall reduced complexity approach. For the time synchronization, the performance is evaluated in terms of correct detection rate of the frame start and the estimation variance. For the frequency synchronization, we focus on the fractional part of the frequency offset which is evaluated in terms of mean squared error. The simulation results prove that, compared to the considered benchmarks, the accuracy of the frame start detection and the fractional frequency offset estimation are greatly enhanced, even at very low SNRs. The proposed two-stage reduced-complexity approach is also compared to the single-stage brute-force approach, where differential correlation is exclusively used, to assess the performance degradation occasioned by the complexity reduction.

In this paper, we propose a new scheme for data- aided time and frequency synchronization for OFDM systems, based on a single-symbol preamble. The preamble, of useful length $2^{m}-2$, is composed of two consecutive identical m-sequences (with length $2^{m-1}-1$ each). This preamble is extended by a cyclic prefix of convenient length. This stucture is adequate for a two-stage synchronization scheme, namely a reduced complexity coarse synchronization stage, followed by a finer synchronization one. The first stage, based on Cox and Schmidl-like sliding correlation, determines a reduced uncertainty interval over which the fine stage is carried. The second stage is indeed based on a differential correlation, which is more complex compared to the first stage. The combined use of m-sequences and differential correlation offers an almost perfect peak of the computed metric at the preamble start. To assess the performance degradation occasioned by the reduction of complexity characterizing the proposed two-stage approach, we also consider the brute force single-stage approach, where differential correlation is exclusively used. As a byproduct of our two-stage approach, the fractional frequency offset is estimated and its performance is assessed and compared for both two- stage and one-stage approaches. The brute force approach outperfoms all the considered benchmarks. Compared to the reduced complexity scheme, the brute force one provides similar performance, at the expense of a significant complexity overload. Only for SNR lower than $-5$ dB, the brute force scheme presents a slight enhancement with respect to the reduced complexity one. The simulation results show that the proposed method gives better performance than any other considered estimator. Although our technique is expected to be well suited to multipath channels, thanks to the underlying properties of m-sequences, in this paper we focus on the Additive White Gaussian Noise channel.