This work proposes an efficient two-stage jamming detection and channel estimation algorithm for orthogonal frequency division multiplexing (OFDM)-based unmanned aerial vehicles (UAVs) communications. The proposed scheme is designed based on the unique time and frequency domain statistical characteristics of OFDM signals. In the time domain (TD), a likelihood ratio test (LRT)-based decision rule is derived as a function of the inherent correlation between the cyclic prefix (CP) samples and their counterparts in the OFDM symbol. In addition, in the frequency domain (FD), a closed-form joint jamming detection and channel estimation scheme is derived using the maximum a posteriori probability (MAP) principle as a function of the statistics of the received pilots and virtual subcarriers (VSCs) signals, which is then re-expressed using generalized MAP ratio test (GMAPRT). The system’s complexity is reduced by applying the two stages sequentially, where the possible implementation of the second stage is conditioned on the outcome of the first stage. The performance of the proposed algorithm is evaluated using Monte Carlo simulations, where the results demonstrate its effectiveness compared to the TD-only and FD-only approaches. The results confirm the superior performance of the proposed scheme compared to the cyclostationary feature (CF)-based technique under various operating scenarios.

This paper introduces a system model called pilot-aided simultaneous communication and localisation (PASCAL) and illustrates its performance in the presence of practical gain and phase imperfections. Specifically, we consider the scenario where multiple single-antenna unmanned aerial vehicles (UAVs) transmit data packets to a multi-antenna base station (BS) that has the dual responsibility of detecting communication signals and localising UAVs using their pilot symbols. Two forms of receiver signal processing approaches are adopted, including disjoint localisation and communication by using maximum likelihood estimation and multiple signal classification (MUSIC), as well as joint localisation and data detection achieved by the newly proposed algorithms. To evaluate the asymptotic localisation performance in the presence of gain-phase imperfections, the Cramér-Rao lower bound (CRLB) is derived, while for evaluating the communication's performance, the average sum data rate (SDR) for all the UAVs is derived in closed-form. It is shown that these derived expressions concur with simulations. The results reveal that while the proposed PASCAL system can be sensitive to gain-phase imperfections, it remains to be a powerful and efficient means to achieve reliable simultaneous localisation and communications.

In this paper, we introduce a novel resource allocation approach for integrated sensing-communication (ISAC) using the Kullbackâ\euro“Leibler divergence (KLD) metric. Specifically, we consider a base-station with limited power and antenna resources serving a number of communication users and detecting multiple targets simultaneously. First, we analyze the KLD for two possible antenna deployments, which are the separated and shared deployments, then use the results to optimize the resources of the base-station through minimising the average KLD for the network while satisfying a minimum predefined KLD requirement for each user equipment (UE) and target. To this end, the optimisation is formulated and presented as a mixed integer nonlinear programming (MINLP) problem and then solved using two approaches. In the first approach, we employ a genetic algorithm, which offers remarkable performance but demands substantial computational resources; and in the second approach, we propose a rounding-based interior-point method (RIPM) that provides a more computationally-efficient alternative solution at a negligible performance loss. The results demonstrate that the KLD metric can be an effective means for optimising ISAC networks, and that both optimisation solutions presented offer superior performance compared to uniform power and antenna allocation.Â

This paper proposes a multiple targets localization scheme using a clustered wireless sensor network (WSN) for terrestrial and underwater environments. In the considered system, sensors measure the total energy emitted by the targets and transmit quantized versions of their measurements to a data central device (DCD) with the assistance of intermediate cluster-heads (CHDs), which employ decode-and-forward relaying (DFR). Upon data collection from the sensors, the DCD performs the localization process, which involves estimating the number and positions of the targets. The data transmission from the sensors to the CHDs takes place through an imperfect medium, which is characterized by a Rician fading model. The penalized maximum likelihood estimator (PMLE), also known as regularized maximum likelihood estimation (MLE), is applied at the DCD to provide optimal estimates for the number and locations of targets. Furthermore, a suboptimal estimator is derived from PMLE, which can offer comparable performance under certain operating conditions with significantly reduced computational complexity. Cramer-Rao lower bound (CRLB) is derived to serve as an asymptotic benchmark for the root mean square error (RMSE) of the estimators in addition to the centroid-based localization benchmark. Monte Carlo simulation is used to evaluate the performance of the proposed estimation techniques under various system conditions. The results show that PMLE is an effective tool for estimating the number and locations of the targets. Additionally, it is demonstrated that the RMSE of the proposed estimation approaches the CRLB for a large number of sensors and high signal-to-noise ratio.

The need for integrated sensing and communication (ISAC) services has significantly increased in the last few years. This integration imposes serious challenges such as the joint system design, resource allocation, optimization and analysis. Since sensing and telecommunication systems have different approaches for performance evaluation, introducing a unified performance measure which provides a perception about the quality of sensing and telecommunication is very beneficial. To this end, this paper provides performance analysis for ISAC systems based on the information theoretical framework of Kullback-Leibler divergence (KLD). The considered system model consists of a multiple-input-multiple-output (MIMO) base-station (BS) providing ISAC services to multiple communication user equipments (CUEs) and a number of targets (or sensing-served users). The KLD framework allows for a unified evaluation of the error rate performance of CUEs, and the detection performance of the targets. The relation between the detection capability for the targets and error rate of CUEs on one hand, and the proposed KLD on the other hand is illustrated analytically. Theoretical results corroborated by simulations show that the derived KLD is very accurate and can perfectly characterize both subsystems, namely the communication and radar subsystems.

This work considers optimizing the IRS elements' selection process to reduce the average BER. In addition to BER reduction, reducing the number of elements reduces the overhead associated with phase information feedback to the IRS panel, and allows serving other users in the network. Moreover, the phase error distribution is derived for the case of LoS communications in closed-form.

This work considers the problem of joint IRS elements and power allocation for UAV networks in the presence of imperfect phase compensation and energy constraints. The allocation problem is formulated to maximize the network sum-rate with minimum individual rate and energy constraints.

This letter considers minimizing the bit error rate (BER) of unmanned aerial vehicle (UAV) communications assisted by intelligent reflecting surfaces (IRSs). By noting that increasing the number of IRS elements in the presence of phase errors does not necessarily improve the BER, it is crucial to use only the elements that contribute to reducing the BER. Consequently, we propose an efficient algorithm to activate only the elements that improve the BER. The proposed algorithm has lower complexity and comparable BER to the optimum selection process, which is an NP-hard problem. The accuracy of the estimated phase is evaluated by deriving the probability distribution function (PDF) of the least-square (LS) channel estimator, and showing that the PDF can be closely approximated by the von Mises distribution at high signal-to-noise ratios (SNRs). The obtained analytical and simulation results show that using all the available reflectors can significantly deteriorate the BER, and thus, elements’ selection is necessary. In particular scenarios, using about 26% of the reflectors provides more than 10 fold BER reduction.

This paper presents the capacity analysis of unmanned aerial vehicles (UAVs) communications supported by flying intelligent reflecting surfaces (IRSs). In the considered system, some of the UAVs are equipped with an IRS panel that applies certain phase-shifts to the incident waves before being reflected to the receiving UAV. In contrast to existing work, this letter considers the effect of imperfect phase knowledge on the system capacity, where the phase error is modeled as a von Mises random variable with parameter k. Analytical results, corroborated by Monte Carlo simulations, show that the achievable capacity is dependent on the phase error, however, the capacity loss becomes negligible at high signal-to-noise ratio (SNR) and when k>6.

This work presents a performance analysis on unmanned aerial vehicles (UAVs) assisted wireless communications systems, where one of the UAVs supports intelligent reflecting surfaces (IRS). As the estimation and compensation of the end-to-end phase for each propagation path is prone to errors, imperfect phase compensation at the IRS is taken into consideration. The performance is derived in terms of symbol error rate (SER) and outage probability, where the phase error is modeled using the von Mises distribution. The air-to-air (A2A) channel for each propagation path is modeled as a single dominant line-of-sight (LoS) component, and the results are compared to the Rician channel model. The obtained results reveal that the considered A2A model can be used to accurately represent the A2A channel with Rician fading.

This paper considers the deployment of intelligent reflecting surfaces (IRSs) technology for wireless multi-hop backhauling of multiple basestations (BSs) connected in a mesh topology. The performance of the proposed architecture is evaluated in terms of outage and symbol error probability in Rician fading channels, where closed-form expressions are derived and demonstrated to be accurate for several cases of interest. The analytical results corroborated by simulation, show that the IRS-mesh backhauling architecture has several desired features that can be exploited to overcome some of the backhauling challenges, particularly the severe attenuation at high frequencies.

This work considers the exact bit error rate (BER) analysis of a two-user non-orthogonal multiple access (NOMA) system using quadrature amplitude modulation (QAM). Unlike existing work, no constraints on the modulation order of the QAM symbols for any user. Closed-form expressions are derived for the BER of joint multiuser detector (JMuD), which is demonstrated that it is equivalent to the successive interference cancellation (SIC) receiver. Moreover, a general expression is derived for the relation between the power allocation factors for the two users, which depends on the modulation order for each user.