This work introduces a novel blind channel estimation (CE) scheme for intelligent reflecting surface (IRS)-assisted networks with heterogeneous communications and sensing devices. The proposed approach operates in two stages: the first stage is dedicated to transmitting the sensing data, and the second to transmitting communications data. By leveraging the fact that sensing information typically has less stringent quality of service (QoS) requirements compared to communication data, the sensing data transmission stage is leveraged for blind CE. The CE is performed using a specific frame structure, where the modulated sensing symbols collaborate to estimate the channel coefficients for all IRS elements blindly and jointly. The performance of sensing data transmission is evaluated in terms of average bit error rate (BER), with exact closed-form expressions derived for the considered modulation schemes. For the communications data, the BER is analyzed, and a tight lower bound is derived. Additionally, an accurate closed-form expression is derived for the mean-squared-error (MSE) of the proposed CE. The MSE results demonstrate that the proposed blind CE outperforms state-of-the-art schemes.

Non-orthogonal multiple access (NOMA) is widely recognized for enhancing the energy and spectral efficiency through effective radio resource sharing. However, uplink NOMA systems face greater challenges than their downlink counterparts, as their bit error rate (BER) performance is hindered by an inherent error floor due to error propagation caused by imperfect successive interference cancellation (SIC). This paper investigates the BER performance improvements enabled by reconfigurable intelligent surfaces (RISs) in multi-user uplink NOMA transmission. Specifically, we propose a novel RIS-assisted uplink NOMA design, where the RIS phase shifts are optimized to enhance the received signal amplitudes while mitigating the phase rotations induced by the channel. To achieve this, we first develop an accurate channel model for the effective user channels, which facilitates our BER analysis. We then introduce a channel alignment scheme for a two-user scenario, enabling efficient SIC-based detection and deriving closed-form BER expressions. We further extend the analysis to a generalized setup with an arbitrary number of users and modulation orders for quadrature amplitude modulation signaling. The analysis is also extended to consider imperfect channel state information (CSI) knowledge and the multi-antenna base station (BS) cases. Using the derived BER expressions, we develop an optimized uplink NOMA power allocation (PA) scheme to minimize the average BER while satisfying the user transmit power constraints. It will be shown that the proposed NOMA detection scheme, in conjunction with the optimized PA strategy, eliminate SIC error floors at the base station. The theoretical BER expressions are validated using simulations, which confirms the effectiveness of the proposed design in eliminating BER floors.

This paper investigates the symbol error rate (SER) performance of the pilot-aided simultaneous communication and localisation (PASCAL) system with a ground-to-air model. A scenario where multiple drones transmit communication signals to a base station (BS), which needs to simultaneously decode the signals and continuously locate the drones’ positions during the communication session, is considered. The BS operates in two stages: first, it estimates the drones’ location parameters using pilot signals; second, it performs data detection by reconstructing the channel response based on the estimated location parameters. The theoretical analysis presented in this paper demonstrates that the distributions of the estimated location parameters follow Gaussian distributions of which the mean values are equal to the actual values of the locations, and their variances are determined by the achievable mean square error of the estimator. Using these distributions, the average SER is derived to quantify the impact of localisation errors on decoding performance. This analysis highlights the synergy between communication and localisation, providing valuable insights into the influence of localisation inaccuracies on the performance of location-aware communication systems. Simulations are conducted to validate the theoretical derivations.

This paper introduces a novel framework aimed at designing integrated waveforms for robust integrated sensing and communication (ISAC) systems. The system model consists of a multiple-input multiple-output (MIMO) base station that simultaneously serves communication user equipments (UEs) and detects multiple targets using a shared-antenna deployment scenario. By leveraging Kullback-Leibler divergence (KLD) to holistically characterise both communication and sensing subsystems, three optimisation problems are formulated: (i) radar waveform KLD maximisation under communication constraints, (ii) communication waveform KLD maximisation subject to radar KLD requirements, and (iii) an integrated waveform KLD-based optimisation for ISAC that jointly balances both subsystems. The first two problems are solved using a projected gradient method with adaptive penalties for the radar waveforms and a gradient-assisted interior point method (IPM) for the communication waveforms. The third, integrated waveform optimisation approach adopts an alternating direction method of multipliers (ADMM) framework to unify radar and communication waveform designs into a single integrated optimisation, thereby synergising sensing and communication objectives and achieving higher overall performance than either radar- or communication-only techniques. Unlike most existing ISAC waveform designs that regard communication signals solely as interference for sensing, the proposed framework utilises the holistic ISAC waveform—that is, the superimposed communication and sensing signals—to boost detection performance in the radar subsystem. Simulation results show significant improvements in both radar detection and communication reliability compared with conventional zero-forcing beamforming, identity-covariance radar baselines, and traditional optimisation approaches, demonstrating the promise of KLD-based waveform designs for next-generation ISAC networks.

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 the 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.

n this paper, we analyze the performance tradeoff between integrated radar and communications (RadCom) systems using the Kullback-Leibler divergence (KLD) measure, also called the relative entropy (RE). Specifically, we derive the KLD measure for both subsystems for a base-station serving a number of communication users while detecting multiple targets simultaneously. We evaluate the trade-off between the radar and the communication systems using a weighted KLD that can enhance the flexibility of the allocations. The results demonstrate that the KLD is an effective mean for achieving the optimal tradeoff between both systems and that it provides a higher degree of controllablity and adaptability for RadCom systems.

This paper proposes a multiple target 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 help of intermediate cluster-heads (CHDs), which employ decode-and-forward relaying (DFR). Upon data collection from sensors, the DCD performs the localization process, which involves estimating the number and positions of the targets. Data transmission from the sensors to 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 of the number and locations of targets. Furthermore, a suboptimal estimator is derived from PMLE that offers comparable performance under certain operating conditions, but 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 can effectively estimate the number and locations of the targets. Furthermore, it is shown that the RMSE of the proposed estimators approaches the CRLB for a large number of sensors and a high signal-to-noise ratio.

This work investigates the performance of intelligent reflective surfaces (IRSs) assisted uplink nonorthogonal multiple access (NOMA) in energy-constrained networks. Specifically, we formulate and solve two optimization problems; the first aims at minimizing the sum of users’ transmit power, while the second targets maximizing the system-level energy efficiency (EE). The two problems are solved by jointly optimizing the users’ transmit powers and the beamforming coefficients at the IRS, subject to the users’ individual uplink rate and transmit power constraints. A novel and low-complexity algorithm is developed to optimize the IRS beamforming coefficients by optimizing the objective function over the complex circle manifold (CCM). To efficiently optimize the IRS phase shifts over the manifold, the optimization problem is reformulated into a feasibility expansion problem which is reduced to a max-min signal-to-interference-plus-noise ratio (SINR). Then, with the aid of a smoothing technique, the exact penalty method is applied to transform the problem from constrained to unconstrained. The proposed solution is compared against three semi-definite programming (SDP)-based benchmarks which are semi-definite relaxation (SDR), SDP-difference of convex (SDP-DC) and sequential rank-one constraint relaxation (SROCR). The results show that the manifold algorithm provides better performance than the SDP-based benchmarks, and at a much lower computational complexity for both the transmit power minimization and EE maximization problems. The results also reveal that IRS-NOMA is only superior to orthogonal multiple access (OMA) when the users’ target achievable rate requirements are relatively high.

In this paper, we provide performance analysis for an integrated radar-communication (RadCom) system based on the relative information (RE), also called the Kullback-Leibler divergence (KLD) theorem. The considered system model consists of a multiple-input-multiple-output (MIMO) base-station (BS) which aims at providing RadCom services to multiple communication user equipments (UEs) and detecting a target. The separated deployment, in which the base-station antennas are distributed among radar and communication subsystems, is considered with Zero forcing (ZF) and maximum ratio transmission (MRT) precoders are applied to precode the communication signal. Results show that the derived formulas in this paper are accurate and imply that MRT suffers from bad performance compared to ZF.

In this paper, the problem of joint direction of arrival (DOA) and Doppler frequency estimation with array model defects is investigated. To this end, a two-dimensional (2D) robust multiple signal classification (MUSIC) algorithm is proposed, which is a generalization of the conventional MUSIC algorithm. The introduced algorithm is based on a colocated multiple-input-multiple-output (MIMO) radar composed of uniform linear arrays, in which a mix of calibrated and uncalibrated antennas exist in its transmitter, while all the receiver antennas are assumed to be uncalibrated. To be specific, the 2D R-MUSIC algorithm adopts a separation scheme to automatically obtain the signals associated with the calibrated subset of transmitter antennas and then eliminates the effect of the signals belonging to the uncalibrated transmitting and receiving antennas. Afterward, the localization is performed by modifying the conventional algorithms in accordance with the separation scheme. In addition, a 2D Genie maximum likelihood (ML) algorithm under the assumption of precisely known errors at the receiver is derived to provide a lower bound benchmark for the introduced method. The simulation results demonstrate the superiority of the proposed algorithms relative to the conventional ones.

Conventional localization algorithms, including maximum likelihood (ML) and multiple signal classification (MUSIC), are significantly influenced by array model imperfections. To address this problem, two generalized algorithms are proposed in this paper by extending the two conventional algorithms. The presented techniques are evaluated within the context of a passive uniform rectangular array (URA) where a rectangular subarray is assumed to be calibrated perfectly, while the remaining antennas incur array model errors. In this case, the performance of the conventional algorithms degrades seriously or even the operations fail. Nevertheless, the proposed algorithms can eliminate this issue by employing a separation technique. In specific, a separation strategy is applied to the introduced algorithms to automatically selects signals belonging to the calibrated subarray from the received signal matrix and eliminate the signals belonging to the uncalibrated antennas prior to conducting the azimuth-elevation-Doppler estimation. In order to reduce the computation complexity, the azimuth-elevation-Doppler estimation is divided into two sub-problems: the estimation process of the Doppler frequency and the estimation process of the azimuth-elevation angles. The simulation results are shown to demonstrate the efficiency and superiority of the proposed algorithms compared to the conventional algorithms.

The need for integrated sensing and communication (ISAC) services has significantly increased in the last few years. This integration imposes serious challenges such as 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 the 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 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 paper presents a novel approach for power-constrained internet of things (IoT) networks that employ non-orthogonal multiple access (NOMA) and are assisted by an intelligent reflecting surface (IRS) for uplink transmissions. The main objective of this work is to maximize the sum rate of power-constrained IoT networks by jointly designing the IRS phase shifts and the users’ transmit power allocation. The proposed solution optimizes the power allocation and phase shifts alternatively. We devise a novel approach to optimize the IRS phase shifts that is based on manifold optimization techniques. Specifically, the IRS phase shifts optimization problem is formulated and solved over the complex circle manifold. Our results show that the proposed method outperforms the widely used semi-definite relaxation (SDR) technique as higher sum rates with less power consumption can be achieved.

This paper presents the design and bit error rate (BER) analysis of a phase-independent non-orthogonal multiple access (NOMA) system. The proposed NOMA system can utilize amplitude-coherent detection (ACD) which requires only the channel amplitude for equalization purposes. In what follows, three different designs for realizing the detection of the proposed NOMA are investigated. One is based on the maximum likelihood (ML) principle, while the other two are based on successive interference cancellation (SIC). Closed-form expressions for the BER of all detectors are derived and compared with the BER of the coherent ML detector. The obtained results, which are corroborated by simulations, demonstrate that, in most scenarios, the BER is dominated by multiuser interference rather than the absence of the channel phase information. Consequently, the BER using ML and ACD are comparable for various cases of interest. The paper also shows that the SIC detector is just an alternative approach to realize the ML detector, and hence, both detectors provide the same BER performance.

This work presents the symbol error rate (SER) and outage probability analysis of multi-layer unmanned aerial vehicles (UAVs) wireless communications assisted by intelligent reflecting surfaces (IRS). In such systems, the UAVs may experience high jitter, making the estimation and compensation of the end-to-end phase for each propagation path prone to errors. Consequently, the imperfect phase knowledge at the IRS should be considered. The phase error is modeled using the von Mises distribution and the analysis is performed using the Sinusoidal Addition Theorem (SAT) to provide accurate results when the number of reflectors$L\leq 3$, and the Central Limit Theorem (CLT) when$L\geq 4$. The achieved results show that accurate phase estimation is critical for IRS based systems, particularly for a small number of reflecting elements. For example, the SER at 10−3degrades by about 5 dB when the von Mises concentration parameter$\kappa =2$and$L=30$, but the degradation for the same$\kappa $surges to 25 dB when$L=2$. 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. 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. For example, using IRS with four elements, N=4, provides a symbol error rate of about 10-5at a signal-to-noise ratio of about 0 dB, even for a large number of hops. Moreover, the obtained analytical results corroborated by Monte Carlo simulation show that the gain obtained by increasing N decreases significantly for N > 5. For example, increasing N from 1 to 2 provides about 8dB of gain, while the increase from 3 to 4 provides about 4dB. Moreover, the degradation caused by the relaying process becomes negligible when the number of IRS elements N= 3.

This article presents a novel decision fusion algorithm for Internet-of-Things-based wireless sensor networks, where multiple sensors transmit their decisions about a certain phenomenon to a remote fusion center (FC) over a wide area network. The proposed algorithm denoted as the individual likelihood approximation (ILA) can significantly reduce the decision fusion error probability performance while maintaining the low computational complexity of other state-of-the-art fusion algorithms. The performance of the ILA rule is evaluated in terms of the global fusion probability of error, and an efficient analytical expression is derived in terms of a single integral. The analytical results corroborated by Monte Carlo simulation show that the ILA significantly outperforms all other considered rules, such as the Chair-Varshney (CV) and MaxLog rules. Moreover, the impact of the link from the cluster head to the FC, which is modeled as a binary symmetric channel with unknown transition probabilities, has been investigated. It is shown that the probability of error over such links should not exceed 10-3to avoid severe performance degradation. Furthermore, we derive a closed-form expression for the system fusion error probability of the CV rule for the most general system parameters.

Amplitude-coherent (AC) detection is an efficient technique that can simplify the receiver design while providing reliable symbol error rate (SER). Therefore, this work considers AC detector design and SER analysis using M-ary amplitude shift keying (MASK) modulation with receiver diversity over Rician fading channels. More specifically, we derive the optimum, near-optimum and a suboptimum AC detectors and compare their SER with the coherent, phase-coherent, noncoherent and the heuristic AC detectors. Moreover, the analytical and asymptotic SER at high signal-to-noise ratios (SNRs) are derived for the heuristic detector using single and multiple receiving antennas. The obtained analytical and simulation results show that the SER of the AC and coherent MASK detectors are comparable, particularly for high values of the Rician K-factor, and small number of receiving antennas. In most of the considered scenarios, the heuristic AC detector outperforms the optimum noncoherent detector significantly, except for the binary ASK case at low SNRs. Moreover, the obtained results show that the heuristic AC detector is immune to phase noise, and thus, it outperforms the coherent detector in scenarios where the system is subject to considerable phase noise.