This paper investigates the secrecy performance of a hardware-impaired secure rate-splitting multiple access (HIS-RSMA) network in multi-user multiple-input single-output (MISO) Internet of Things (IoT) scenarios over Nakagami-mfading channels. Closed-form expressions for the secrecy capacity (SC) and secrecy outage probability (SOP) are derived and validated through Monte-Carlo simulations. The proposed analytical framework explicitly accounts for the joint effects of non-ideal hardware (NIH), imperfect successive interference cancellation (SIC), and imperfect channel state information (CSI), thereby providing a realistic evaluation of secrecy in IoT networks. Numerical results reveal that NIH has a negligible impact on the secrecy of the common message but significantly degrades the secrecy of private messages at high transmit powers. Moreover, both NIH and imperfect SIC introduce non-vanishing SOP error floors, while imperfect CSI at the eavesdropper can substantially enhance secrecy. The number of transmit antennas and the operating frequency are also shown to critically influence secrecy performance. Overall, the findings highlight that HIS-RSMA is a promising solution for secure and efficient transmission in IoT networks, underscoring the need for joint optimization of transceiver design, antenna configuration, and physical-layer security strategies in beyond fifth-generation (B5G) wireless systems.

In this article, we describe a novel active reconfigurable repeater-aided nonorthogonal multiple access networks within the context of the Internet of Things. The study focuses on a scenario, where a source simultaneously transmits public information to an untrusted user and a covert signal to a legitimate user in the surveillance of an external warden or eavesdropper. We develop comprehensive analytical and optimization frameworks to evaluate the reliability, security, and covertness of the proposed system’s performance, measuring three respective key metrics: 1) outage probability (OP); 2) secrecy OP (SOP); and 3) detection error probability (DEP). First, we derive exact closed-form and asymptotic expressions for OP, SOP in internal and external eavesdropping scenarios, and DEP in external monitoring situations. Based on an asymptotic analysis of the OP, we propose two optimization methods for power allocation (PA) to achieve fairness in outage among users: 1) a convex approximation method and 2) an approximate closed-form solution. We then introduce an alternative method for optimizing PA to improve SOP in both eavesdropping scenarios while maintaining minimal OP requirements. In addition, we propose an effective approach for determining the warden’s detection threshold to minimize the DEP, with low complexity and fast convergence, thereby improving communications covertness. Finally, we validate the theoretical and optimization frameworks through extensive Monte Carlo simulations, exploring the impact of key system parameters on each performance metric.

In this study, we investigate the trade-off between reliability and security in unmanned aerial vehicle (UAV) communications systems, considering a UAV-terrestrial network aided by a relay powered by a dedicated power beacon. For this system, we derive the outage probability (OP) under both exact and approximate frameworks and compute the approximations in the closed-form expressions. For the security aspect, we also derive the intercept probability (IP) under exact and approximated frameworks. To minimize the IP, a friendly jamming technique is employed whereby the power beacon constantly broadcasts artificial noise (AN) toward an eavesdropper. Based on the derived mathematical framework, we then formulate a bi-objective optimization problem by jointly minimizing the OP and IP with respect to the UAV’s position and the time-switching (TS) ratio. A suitable algorithm, named non-dominated sorting genetic algorithm version II (NSGA-II), is deployed to obtain the sub-optimal solution. Finally, numerical results are presented to verify the accuracy of the proposed mathematical framework and the superiority of jointly minimizing both the OP and IP using only the channel statistics.

Reconfigurable intelligent surface (RIS) has recently gained significant interest as an emerging technology for future wireless networks thanks to its potential for improving the coverage in challenging propagation environments. This paper studies an RIS-assisted communication system, where a source transmits data to a destination in the presence of a weak direct link. We analyze and compare RIS designs based on long-term and short-term channel statistics in terms of coverage probability and ergodic rate. For the considered optimization designs, we derive closed-form expressions for the coverage probability and ergodic rate, which explicitly unveil the impact of the propagation environment and the RIS on the system performance. Besides the optimization of the RIS phase profile, we formulate an RIS placement optimization problem with the aim of maximizing the coverage probability by relying only on partial channel state information. An efficient algorithm is proposed based on the gradient ascent method. Simulation results are illustrated in order to corroborate the analytical framework and findings. The proposed RIS phase profile is shown to outperform several heuristic benchmark schemes in terms of outage probability and ergodic rate. In addition, the proposed RIS placement strategy provides an extra degree of freedom that remarkably improves the system performance.

The optimal transmit power that maximizes energy efficiency (EE) in Longe Range (LoRa) networks is investigated by using the deep learning (DL) approach. Particularly, the proposed artificial neural network (ANN) is trained two times; in the first phase, the ANN is trained by the model-based data which are generated from the simplified system model while in the second phase, the pre-trained ANN is re-trained by the practical data. Numerical results show that the proposed approach outperforms the conventional one which directly trains with the practical data. Moreover, the performance of the proposed ANN under both partial and full optimum architecture are studied. The results depict that the gap between these architectures is negligible. Finally, our findings also illustrate that instead of fully re-trained the ANN in the second training phase, freezing some layers is also feasible since it does not significantly decrease the performance of the ANN.

Reconfigurable intelligent surfaces (RISs) have recently gained significant interest as an emerging technology for future wireless networks. This paper studies an RIS-assisted propagation environment, where a single-antenna source transmits data to a single-antenna destination in the presence of a weak direct link. We analyze and compare RIS designs based on long-term and short-term channel statistics in terms of coverage probability and ergodic rate. For the considered optimization designs, closed-form expressions for the coverage probability and ergodic rate are derived. We use numerical simulations to validate the obtained analytical framework. Also, we show that the considered optimal phase shift designs outperform several heuristic benchmarks.

The system-level performance of multi-gateway downlink long-range (LoRa) networks is investigated in the present paper. Specifically, we first compute the active probability of a channel and the selection probability of an active end-device (ED) in the closed-form expressions. We then derive the coverage probability (Pcov) and the area spectral efficiency (ASE) under the impact of the capture effects and different spreading factor (SF) allocation schemes. Our findings show that both the Pcov and the ASE of the considered networks can be enhanced significantly by increasing both the duty cycle and the transmit power. Finally, Monte-Carlo simulations are provided to verify the accuracy of the proposed mathematical frameworks.

In the present paper a closed-form framework is derived for the analysis and optimization of the coverage probability (Pcov) and of the area spectral efficiency (ASE) in long-range (LoRa) networks. The proposed framework exploits stochastic geometry tools to associate the Pcov and the ASE to the end device (ED) transmit power and to the ED density. The analysis reveals the trends of the Pcov and of the ASE curves, with respect to both of the two parameters, while the robustness of the framework holds even at the asymptotic cases. Building upon the derived framework, the analysis demonstrates that no joint global optimum exists that jointly maximizes the Pcov over both parameters, suggesting that the optimization of the Pcov must be performed separately, for the two key network parameters considered. As opposed to that, the analysis demonstrates that a set of global optima exists that jointly maximize the ASE over both parameters, and these global maxima are subsequently derived in closed form. Thus, the derived framework fully characterizes the performance of LoRa networks, while defining in closed form the optimal points of operation that can be proven of significant value, for the transceiver and network design, of practical LoRa networks.

In this work, coverage probability (Pcov) and area spectral efficiency (ASE) of LoRa networks with multiple classes of quality-of-service (QoS) of end-devices (EDs) are investigated. In particular, an approximated but tractable mathematical framework is proposed to compute a recent definition of the Pcov. Based on the proposed framework, the closed-form expressions of both Pcov and ASE are provided. Moreover, the trends of both metrics are unveiled that respect to some key parameters such as the density of the EDs, the transmit power, and the spreading factor (SF). Our findings show that depending on networks parameters, the ASE is either a unimodal function or monotonically increasing as a function of EDs' density. In addition, the optimal value of EDs' density that maximizes the ASE are computed in closed-form expression too. Finally, Monte Carlo simulations are provided to verify the correctness of our framework.

In this paper, we introduce an analytical approach for modeling and analyzing the performance of multi-tier cellular networks that are powered by the power grid and by renewable energy sources. The proposed approach relies on modeling the locations of the base stations, either powered by the power grid or by renewable energy sources, by using Poisson point processes. The availability of renewable energy is modeled by using a Poisson point process in the time domain. In addition, the temporal dynamics of the batteries of the base stations powered by renewable energy sources are modeled by using a discrete Markov chain with a number of states that is equal to the finite storage capacity of the batteries. In particular, the coverage probability, the spectral efficiency, and the energy efficiency of the considered network model are formulated in an analytical, closed-form, expression, which depends on the probability that the typical base station is available, i.e., it has sufficient power to serve at least one mobile terminal in its cell. This latter probability is shown to be the solution of the steady state equation of the Markov chain that models the temporal dynamics of the batteries of the base stations. Under the assumption that the batteries of the base stations can be either empty or fully charged, we formulate an optimization problem in order to maximize the energy efficiency as a function of the transmit power and the deployment density of the base stations, and identify sufficient conditions for which the problem admits a unique solution. The accuracy of the proposed approach and the performance trends inferred from it are substantiated with the aid of extensive Monte Carlo simulations.

In this paper, we analyze and optimize the energy efficiency of downlink cellular networks. With the aid of tools from stochastic geometry, we introduce a new closed-form analytical expression of the potential spectral efficiency (bit/sec/m2). In the interference-limited regime for data transmission, unlike currently available mathematical frameworks, the proposed analytical formulation depends on the transmit power and deployment density of the base stations. This is obtained by generalizing the definition of coverage probability and by accounting for the sensitivity of the receiver not only during the decoding of information data, but during the cell association phase as well. Based on the new formulation of the potential spectral efficiency, the energy efficiency (bit/Joule) is given in a tractable closed-form formula. An optimization problem is formulated and is comprehensively studied. It is mathematically proved, in particular, that the energy efficiency is a unimodal and strictly pseudo-concave function in the transmit power, given the density of the base stations, and in the density of the base stations, given the transmit power. Under these assumptions, therefore, a unique transmit power and density of the base stations exist, which maximize the energy efficiency. Numerical results are illustrated in order to confirm the obtained findings and to prove the usefulness of the proposed framework for optimizing the network planning and deployment of cellular networks from the energy efficiency standpoint.

In this paper, we propose a high-speed wireless signal transmission over a simple analog intensity modulation/direct detection (IM/DD) fiber-millimeter-wave (MMW) system at 92.5 GHz. The system employs a frequency and phase stabilized optical MMW signal generation at transmitter and a simple direct detection at receiver. We confirm a successful transmission of 10-Gb/s 16-QAM OFDM-based wireless signal over a seamless 8-km SMF and 27-cm 92.5-GHz wireless system. To further increase the fiber transmission range, we use a simple adaptive modulation approach. Successful transmission over 10-km single mode fiber (SMF) system is confirmed for a 8.1 Gb/s wireless signal using both 16-QAM and QPSK modulation. We also estimate the maximum transmission range for the MMW link, and it is proved that a transmission over a 1-km MMW link can be achieved. Both the confirmed fiber length and estimated MMW range are sufficiently long for future mobile front-haul systems in next-generation wireless networks. The system therefore can provide a high-speed, low-latency, and flexible solution for front-hauling in new emerging wireless systems such as 5G mobile networks.

In this study, the authors evaluate secrecy performance of cooperative protocols with relay selection methods under impact of co‐channel interference. In particular, the authors propose an optimal selection scheme to maximise the secrecy capacity obtained at the cooperative phase. In addition, the authors also consider suboptimal and random selection schemes which reduce the complexity of implementation, when compared with the optimal one. For performance evaluation, the authors derive exact and asymptotic closed‐form expressions of secrecy outage probability and probability of non‐zero secrecy capacity for the considered schemes over Rayleigh fading channels. Monte Carlo simulations are performed to validate the derivations.