Rate Maximization Research Articles

Weighted Sum Rate Maximization for RIS Backscatter Aided NOMA Networks

Weighted Sum Rate Maximization for RIS Backscatter Aided NOMA Networks

Downlink Rate Maximization with Reconfigurable Intelligent Surface Assisted Full-Duplex Transmissions

Downlink Rate Maximization with Reconfigurable Intelligent Surface Assisted Full-Duplex Transmissions

Inventory planning for self-serve pharmacy kiosks: A fill rate maximization approach

In recent years, self-serve kiosks have become increasingly popular due to their 24/7 availability and lower setup and operational costs compared to traditional brick-and-mortar stores. However, the inventory planning for pharmacy kiosks is a challenge due to its limited capacity to store thousands of drugs ordered in various quantities, each with low and sporadic drug demand. In this work, we model the pharmacy kiosk inventory planning problem as a capacitated multiproduct newsvendor problem under fill rate maximization objective. We present a data-driven robust optimization framework where product demand lies within an uncertainty set generated from product clustering. A column-and-constraint generation based solution approach is proposed to solve industry scale instances. The proposed robust framework is tested on actual pharmacy sales data and randomly generated instances with 2000 products. The robust solutions outperform scenario-based stochastic solutions with an increase in out-of-sample fill rate of 5.8%, on average, and of up to 17%. Comparative analysis with profit objective reveals that the fill rate objective results in 17% higher out-of-sample fill rate by compromising 20% in profits, on average.

Effort Foraging Task reveals positive correlation between individual differences in the cost of cognitive and physical effort in humans

Effort-based decisions, in which people weigh potential future rewards against effort costs required to achieve those rewards involve both cognitive and physical effort, though the mechanistic relationship between them is not yet understood. Here, we use an individual differences approach to isolate and measure the computational processes underlying effort-based decisions and test the association between cognitive and physical domains. Patch foraging is an ecologically valid reward rate maximization problem with well-developed theoretical tools. We developed the Effort Foraging Task, which embedded cognitive or physical effort into patch foraging, to quantify the cost of both cognitive and physical effort indirectly, by their effects on foraging choices. Participants chose between harvesting a depleting patch, or traveling to a new patch that was costly in time and effort. Participants’ exit thresholds (reflecting the reward they expected to receive by harvesting when they chose to travel to a new patch) were sensitive to cognitive and physical effort demands, allowing us to quantify the perceived effort cost in monetary terms. The indirect sequential choice style revealed effort-seeking behavior in a minority of participants (preferring high over low effort) that has apparently been missed by many previous approaches. Individual differences in cognitive and physical effort costs were positively correlated, suggesting that these are perceived and processed in common. We used canonical correlation analysis to probe the relationship of task measures to self-reported affect and motivation, and found correlations of cognitive effort with anxiety, cognitive function, behavioral activation, and self-efficacy, but no similar correlations with physical effort.

Joint Power Control and Resource Allocation for Optimizing the D2D User Performance of Full-Duplex D2D Underlying Cellular Networks

D2D communication is a promising technology for enhancing spectral efficiency (SE) in cellular networks, and full-duplex (FD) has the potential to double SE. Due to D2D’s short-distance communication and low transmittance power, it is natural to integrate FD into D2D, creating FD-D2D to underlay a cellular network to further improve SE. However, the residual self-interference (RSI) resulting from FD-D2D and interference arising from spectrum sharing between D2D users (DUs) and cellular users (CUs) can restrict D2D link performance. Therefore, we propose an FD-D2D underlying cellular system in which DUs jointly share uplink and downlink spectral resources with CUs. Moreover, we present two algorithms to enhance the performance experience of DUs while improving the system’s SE. For the first algorithm, we tackle an optimization problem aimed at maximizing the sum rate of FD-DUs in the system while adhering to transmittance power constraints. This problem is formulated as a mixed-integer nonlinear programming problem (MINLP), known for its mathematical complexity and NP-hard nature. In order to address this MINLP, our first algorithm decomposes it into two subproblems: power control and spectral resource allocation. The power control aspect is treated as a nonlinear problem, which we solve through one-dimensional searching. Meanwhile, spectral resource allocation is achieved using the Kuhn–Munkres algorithm, determining the pairing of CUs and DUs sharing the same spectrum. As for the second algorithm, our objective is to enhance the individual performance of FD-DUs by maximizing the minimum rate among them, ensuring more uniform rate performance across all FD-DUs. In order to solve this optimization problem, we propose a novel spectral resource allocation algorithm based on bisection searching and the Kuhn–Munkres algorithm, whereas the power control aspect remains the same as in the first algorithm. The numerical results demonstrate that our proposed algorithm effectively enhances the performance of DUs in an FD-D2D underlying cellular network when compared to the sum rate maximization design.

IRS-Aided Secure Communications Over an Untrusted AF Relay System

In this paper, an intelligent reflecting surface (IRS) assisted wireless secure communication system is studied. A multi-antenna access point (AP) intends to send confidential information to a single-antenna user in the presence of an amplify-and-forward (AF) untrusted relay, which may eavesdrop the message when helping relay the signal. It is assumed that the direct link between the AP and the user is blocked by the obstacles. The achievable secrecy rate maximization problem is then formulated. To overcome the non-convexity of the formulated problem, an alternating iteration algorithm is proposed to jointly optimize the active and passive beamforming. Specifically, the transmit beamforming vector at the AP, the phase shift matrix at the IRS, and the relay beamforming matrix are jointly optimized to maximize the secrecy rate. Moreover, the asymptotic expressions for the maximum secrecy rates of the proposed scheme in the high and low transmitted signal-to-noise ratio (SNR) regimes are derived as well. Finally, numerical evaluations demonstrate the superiority of the proposed scheme compared with other benchmark schemes, highlight the importance of properly designed phase shifts at the IRS, and validate the theoretical analysis. It is also demonstrated that the number of antennas at the AP should be strictly larger than the number of antennas at the relay to harvest the benefits of increasing the transmit power at the AP in increasing the secrecy rate.

Understanding behavioral intention of landowners to promote wildlife richness and biodiversity in the Southern Great Plains

Ranchers have been conducting traditional management practices such as mono-species grazing and limited burning that focus on stocking rate maximization. This has resulted in negative environmental consequences such as woody plant encroachment, land use change, and species diversity loss. Recently, there has been a growing appreciation for alternative range management that simultaneously promotes biodiversity and agricultural productivity through fire and grazing interactions. The purpose of the study was to investigate the influence of landowner values, attitudes, and norms on their behavioral intention to adopt best management practices (BMPs) that enhance wildlife abundance in the southern Great Plains. The required data was generated through a mail survey following the Tailored Design method. We utilized the structural equation path model to construct two latent variables for norms and attitudes and analyzed the relationship of the latent variables with value orientations and behavioral intentions. The study results indicated that the attitudes had a significant positive relationship with biocentric (β = 0.15, p-value<0.05) and a negative relationship with anthropocentric (β = −0.259, p-value<0.05) value orientations, however, the relationship of value orientations was statistically insignificant with norms. Further, the results indicated that both norms (β = 0.404, p-value<0.05) and attitudes (β = 0.508, p-value<0.05) had a positive and statistically significant relation with ranchers’ intentions to increase wildlife abundance on their land. Study results emphasize the need for innovative communication and non-traditional outreach methods to educate rangeland owners on the importance of patch-burn grazing and other best management practices.

Hydropower station scheduling with ship arrival prediction and energy storage

Effectiveness improvement in power generation and navigation for grid-connected hydropower stations have emerged as a significant concern due to the challenges such as discrepancies between declared and actual ship arrival times, as well as unstable power generation. To address these issues, this paper proposes a multi-objective real-time scheduling model. The proposed model incorporates energy storage and ship arrival prediction. An energy storage mechanism is introduced to stabilize power generation by charging the power storage equipment during surplus generation and discharging it during periods of insufficient generation at the hydropower stations. To facilitate the scheduling with the eneragy storage mechanism, the arrival time of ships to the stations are predicted. We use the maximization of generation minus grid load demand and the maximization of navigability assurance rate as two objective functions in the scheduling process. The model uses the Non-Dominated Sorting Beluga Whale Optimization (NSBWO) algorithm to optimize and solve the real-time discharge flow scheduling of the hydropower stations in different time periods. The NSBWO algorithm combines the Elitist Non-Dominated Sorting Genetic Algorithm (NSGA-II) and the Beluga Whale Optimization (BWO). The experimental results show that the proposed method has advantages in predicting the expected arrival time of ships and scheduling the discharge flow. The prediction using XGBoost model reaches accuracy with more than 0.9, and the discharged flow obtained from scheduling meets the demand of hydropower stations grid load while also improves the navigation benefits. This study provides theoretical analysis with its practical applications in a real hyropower station as a case study for solving hydropower scheduling problems.

Reputation-Aware Rate Maximization for Cross-Media Cooperative Transmission in Smart Ocean IoT

In smart ocean Internet of Things (IoT) systems, autonomous underwater vehicles (AUVs) are responsible for underwater information collection. Due to the nature of the medium of water, the acoustic communications for AUVs are of low bandwidth and adverse environmental conditions causing severe transmission problems. In order to realize cross-media transmission from AUVs to the offshore platform, unmanned surface vehicles (USVs) have been suggested to forward the collected information in a coordinated manner. Against this backdrop, this contribution develops a cooperative USV-to-USV (U2U) cross-media cooperative communications scheme. Then, we formulate a rate maximization problem with the objective of optimizing the reputation-aware USV selection strategy. Furthermore, to characterize the impact of the mobility of AUVs/USVs, a long-term dynamic process is constructed. Meanwhile, we also develop an efficient algorithm which transforms the reputation-aided dynamic USVs selection problem into the infinite time horizon average one restricted by time average rate constraints in the collection of penalty processes with the help of the Lyapunov optimization framework and drift-plus-penalty method. Finally, numerical results are presented to validate the convergence behavior and the performance for the designed dynamic USVs selection algorithm.

Secure Transmission for STAR-RIS Aided NOMA Against Internal Eavesdropping

Simultaneous transmitting and reflecting reconfig- urable intelligent surface (STAR-RIS) has attracted extensive attentions due to its prominent advantages to future wireless networks. This correspondence considers a downlink multi-input single-output non-orthogonal multiple access system, in which the STAR-RIS is utilized to improve the secrecy performance. To restrain the internal eavesdropping, we formulate a secrecy rate maximization problem and propose a secure transmission design via the joint active and passive beamforming optimization with optimum power allocation. To decouple optimization variables, the original non-convex problem is divided into two subproblems, which are transformed into convex forms via successive convex approximation and solved by alternating optimization. Simula- tion results demonstrate the superiority of the proposed scheme in the security enhancement.

Rate Splitting Multiple Access for Next Generation Cognitive Radio Enabled LEO Satellite Networks

Low Earth Orbit (LEO) satellite communication (SatCom) has drawn particular attention recently due to its high data rate services and low round-trip latency. It has low launching and manufacturing costs than Medium Earth Orbit (MEO) and Geostationary Earth Orbit (GEO) satellites. Moreover, LEO SatCom has the potential to provide global coverage with a high-speed data rate and low transmission latency. However, the spectrum scarcity might be one of the challenges in the growth of LEO satellites, impacting severe restrictions on developing ground-space integrated networks. To address this issue, cognitive radio and rate splitting multiple access (RSMA) are the two emerging technologies for high spectral efficiency and massive connectivity. This paper proposes a cognitive radio enabled LEO SatCom using RSMA radio access technique with the coexistence of GEO SatCom network. In particular, this work aims to maximize the sum rate of LEO SatCom by simultaneously optimizing the power budget over different beams, RSMA power allocation for users over each beam, and subcarrier user assignment while restricting the interference temperature to GEO SatCom. The problem of sum rate maximization is formulated as non-convex, where the global optimal solution is challenging to obtain. Thus, an efficient solution can be obtained in three steps: first we employ a successive convex approximation technique to reduce the complexity and make the problem more tractable. Second, for any given resource block user assignment, we adopt KarushKuhnTucker (KKT) conditions to calculate the transmit power over different beams and RSMA power allocation of users over each beam. Third, using the allocated power, we design an efficient algorithm based on the greedy approach for resource block user assignment. For comparison, we propose two suboptimal schemes with fixed power allocation over different beams and random resource block user assignment as the benchmark. Numerical results provided in this work are obtained based on the Monte Carlo simulations, which demonstrate the benefits of the proposed optimization scheme compared to the benchmark schemes.

The hierarchy of sugar catabolization in Lactococcus cremoris.

Bacteria adapt to nutrient availability by regulating the synthesis of enzymes. Transcriptome- and multi-sugar growth studies suggest that Lactococcus cremoris represses genes involved in the catabolization of lower growth rate-supporting (lower quality) sugars in a hierarchical order. Furthermore, L. cremoris appears to always express genes involved in the catabolization of higher growth rate-supporting sugars (higher quality) relative to the sugar it is growing on. Here, we unraveled the sugar catabolization hierarchy by determining the sugar catabolizing capacity and the proteome of cells exponentially growing on glucose (µmax = 0.72 h-1), lactose (µmax = 0.6 h-1), galactose (µmax = 0.44 h-1), or maltose (µmax = 0.43 h-1). We found that L. cremoris can grow on 14 of the 96 sugars in a Biolog plate, with µmax ranging from 0.32 to 0.72 h-1. Proteome and catabolization rate measurements show that L. cremoris consistently prepares for the catabolization of higher-quality sugars, except trehalose. While cells were not prepared for the catabolization of most lower-quality sugars, some proteins related to fructose and lactose consumption were always present. Moreover, reducing the growth rate of glucose through salt stress had only a minor influence on the sugars that L. cremoris could catabolize. These findings demonstrate that the catabolization hierarchy is not strictly linked to absolute growth rate or sugar quality. Cells instantly catabolizing a higher-quality sugar require enhanced expression of ribosomal and nucleotide metabolism functions for growth rate maximization, whereas transitioning to lower-quality sugars requires enhanced synthesis of proteins related to arginine catabolism and mixed acid fermentation, besides sugar-specific catabolic proteins.IMPORTANCEThe availability of nutrients to microorganisms varies considerably between different environments, and changes can occur rapidly. As a general rule, a fast growth rate-typically growth on glucose-is associated with the repression of other carbohydrate utilization genes, but it is not clear to what extent catabolite repression is exerted by other sugars. We investigated the hierarchy of sugar utilization after substrate transitions in Lactococcus cremoris. For this, we determined the proteome and carbohydrate utilization capacity after growth on different sugars. The results show that the preparedness of cells for the utilization of "slower" sugars is not strictly determined by the growth rate. The data point to individual proteins relevant for various sugar transitions and suggest that the evolutionary history of the organism might be responsible for deviations from a strictly growth rate-related sugar catabolization hierarchy.

On Joint Optimization of Trajectory and Phase Shift for IRS-UAV Assisted Covert Communication Systems

In this paper, we propose an unmanned aerial vehicle carrying intelligent reflecting surface (IRS-UAV) as a relaying node to assist covert communication system (CCS). To ensure the security of the covert messages, we derive the minimum error detection probability for the eavesdropper when the locations of the UAV and BS are known. Under the assumption that the eavesdropper can minimize the detection error probability, we then formulate the maximization of the average rate of the proposed IRS-UAV assisted CCS as an optimization problem with respect to the UAVs trajectory and the IRSs phase shift. Furthermore, we use the Taylor's extension to approximate the optimization problem to be convex, and propose an iterative algorithm to solve the convex problem. Numerical results demonstrate that the developed optimization method significantly improves the data rate for IRS-UAV assisted covert communication in comparison with existing algorithms.

Optimization of Machining Parameters for Mild Steel in Dry Turning Using Taguchi Method

In present days every manufacturing industry thrives hard to produce quality products at an economical price while fulfilling the requirements of both manufacturer and the customer who happens to the end user of the product. Machining of sophisticated parts has posed a greater challenge to manufacturer. Optimization techniques like Taguchi method has been introduced and widely applied in industries in order to achieve the desiredgoals. In this analysis, an attempt is made to study the effects of machining parameters like speed, feed and depth of cut in a turning operation of mild steel by employing one of the widely used Optimization techniques which is Taguchi Method. The main input machining parameters namely Speed, Feed and Depth of cut are taken Page 7 into consideration, while the output parameter Material Removal Rate (MRR) is considered as the response variable. Taguchi method is applied and signal-to-noise ratio is calculated using L9 Orthogonal Array (OA) with the help of Minitab software. Based on the graphs from the Minitab software optimum levels of machining parameters were obtained and maximization of material removal rate (MRR) is found out and the same is checked experimentally in a machining workshop Keywords: Optimization technique, Taguchi Method, Turning Operation, Material Removal Rate, Minitab software.

A Deep Reinforcement Learning Scheme for Sum Rate and Fairness Maximization Among D2D Pairs Underlaying Cellular Network With NOMA

Device-to-device (D2D) communication is an emerging technology in 5G and upcoming 6G networks due to its properties to enhanced spectral efficiency (SE), energy-efficiency (EE), and sum rate. Despite these advantages, co-channel and cross-channel interference, and ultra-massive connectivity are major issues which can deteriorate performance of any implemented solution in this environment. To address these issues, in this paper, we integrated the power domain non-orthogonal multiple access techniques (PD-NOMA) on the base station (BS). NOMA serves more than one user using the same resource block (RB) and reduces the effect of interference at CUs due to the presence of successive interference cancellation (SIC). The problem is formulated as a mixed-integer non-linear programming (MINLP) with associated resources and power constraints of the BS and DDPs with an aim to maximize the sum rate and fairness among the NOMA-enabled CUs and D2D pairs (DDPs). We firstly used the centralized deep deterministic policy gradient (DDPG) and arithmetic-geometric mean approximation (AGMA) technique to reduce cross-channel interference (CR-CI) and control the power. Then, to provide fairness to all the users, we transformed the proposed solution into distributed deep deterministic policy gradient (D3PG). Also, the successive convex approximation technique is then integrated into the D3PG to mitigate the effect of co-channel (CO-CI) interference among DDPs. The experimental results show that the proposed scheme has superior performance with respect to sum rate and fairness. Also, the results reveal that the proposed scheme has 21.05%, 34.21%, and 49.8% higher sum rate in comparison to DDPG, Deep dueling, and DQN scheduling.

Joint Optimization of 3D Placement and Radio Resource Allocation for Per-UAV Sum Rate Maximization

Unmanned aerial vehicles (UAV) have emerged as a practical solution that provides on-demand services to users in areas where the terrestrial network is non-existent or temporarily unavailable, e.g., due to natural disasters or network congestion. In general, UAVs' user-serving capacity is typically constrained by their limited battery life and the finite communication resources that highly impact their performance. This work considers the orthogonal frequency division multiple access (OFDMA) enabled multiple unmanned aerial vehicles (multi-UAV) communication systems to provide on-demand services. The main aim of this work is to derive an efficient technique for the allocation of radio resources, 3D placement of UAVs, and user association matrices. To achieve the desired objectives, we decoupled the original joint optimization problem into two sub-problems: (i) 3D placement and user association and (ii) sum-rate maximization for optimal radio resource allocation, which are solved iteratively. The proposed iterative algorithm is shown via numerical results to achieve fast convergence speed after fewer than 10 iterations. The benefits of the proposed design are demonstrated via superior sum-rate performance compared to existing reference designs. Moreover, results showed that the optimal power and sub-carrier allocation help to mitigate the inter-cell interference that directly impacts the system's performance.

Secrecy Rate Maximization in THz-Aided Heterogeneous Networks: A Deep Reinforcement Learning Approach

Densely deployed femtocells in heterogeneous networks (HetNets) improve the quality-of-service (QoS) and quality-of-experience (QoE) for both next-generation networks and end users, respectively. However, users near the femtocell edge, i.e., femto edge users (FEUs), which are far away from the Femto base station (FBS) may receive a low signal-to-interference-plus-noise ratio (SINR). Moreover, due to the distributed and dynamic nature of HetNets, femtocells may not resist various eavesdropping attacks from different attackers. Thus, it is essential to ensure the wireless channel's secrecy through which FEUs communicate with FBS since they may face eavesdropping attacks and may have low SINR and achievable data rates. Aiming to enhance the secrecy rate of femtocells, in this article, we formulate a joint optimization problem of beamforming vectors, artificial noise (AN), and power control for terahertz (THz)-enabled femtocells. Addressing this non-convex optimization problem is challenging due to the system's complexity and dynamic nature of FEUs. Thus, we translate the optimization problem into a multi-agent reinforcement learning (MARL) problem using the Markov decision process (MDP). Then, to solve the MDP with continuous action space, two deep reinforcement learning (DRL) based schemes, i.e., <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Deep Deterministic Policy Gradient-based Secrecy Maximization (DDPG-SM)</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Asynchronous-Advantage-Actor-Critic based Secrecy Maximization (A3C-SM)</i> are proposed to maximize the secrecy rate of femtocells. The secrecy rate performance of the proposed schemes is compared and analyzed with Advantage-Actor-Critic (A2C) and state-of-the-art Perfect Secrecy Rate Maximization (PSRM) schemes. Simulation results show that the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A3C-SM</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DDPG-SM</i> schemes achieve 37.73% and 22.64% better average secrecy rate in comparison to the PSRM scheme. Also, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A3C-SM</i> scheme outperforms all other <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DDPG-SM</i> , A2C, and PSRM schemes in terms of average secrecy rate, received SINR, and energy-efficiency of femtocells.

Multi-Objective Robust Beamforming for Integrated Satellite and Aerial Networks Supporting Heterogeneous Services

An integrated satellite and aerial network (ISAN) is considered a promising candidate to provide seamless connectivity for future wireless communication systems. In this paper, we propose a multi-objective based robust beamforming (BF) scheme for an ISAN to support heterogeneous services with high flexibility, where the satellite network serves various heterogeneous satellite terminals through multicast non-orthogonal multiple access (MC-NOMA), while the aerial network offers services to many internet of things devices using layered division multiplexing (LDM). Specifically, we first formulate a multi-objective optimization problem (MOOP) to achieve a good trade-off between sum rate maximization and total transmit power minimization. To tackle this mathematically intractable problem, we exploit the weighted Tchebycheff approach to transform the MOOP into a single-objective problem. Since only the angular information based channel state information is available, we exploit the angular discretization method and sequential convex approximation to design a robust BF algorithm to obtain the Pareto optimal solutions. Finally, simulation results demonstrated that our proposed scheme can achieve a optimal trade-off between multiple performance metrics with high spectrum and energy efficiency, so as to support heterogeneous services in the ISAN and fill the gap of only single type of serivce in the existing ISAN works.

UAV-Assisted Maritime Legitimate Surveillance: Joint Trajectory Design and Power Allocation

This paper investigates a novel maritime wireless surveillance scenario, where a legitimate monitor vessel moves around to eavesdrop the suspicious communication from a suspicious unmanned aerial vehicle (UAV) to a suspicious vessel with the help of a cooperative UAV. Specifically, the cooperative UAV can adjust its jamming power and trajectory to exactly control the transmission rate of the suspicious link, thus improving the monitor vessel's surveillance performance. Furthermore, since the cooperative UAV cannot land or replenish energy on the sea surface, its jamming power allocation on the ocean should be carefully designed by the energy thresholds. Under such setup, we formulate a sum eavesdropping rate maximization problem, which jointly optimizes the jamming power and three-dimensional (3D) trajectory of the cooperative UAV, as well as the two-dimensional (2D) trajectory of the monitor vessel. To address this non-convex optimization problem, we decompose the design problem into three subproblems and propose an iterative algorithm to find its suboptimal solution. Numerical results show that the proposed jamming-assisted 3D joint design can significantly improve the eavesdropping rate and save the jamming power compared to the benchmark schemes.

Broadcast Secrecy Rate Maximization in UAV-Empowered IRS Backscatter Communications

The backscatter communications (BackCom) and physical layer security are respected to realize extremely low-power secure communications in the imminent sixth generation (6G). In a BackCom system, the backscatter device without radio frequency components sends messages to users by reflecting the external signals. However, the double-fading effect limits BackCom’s performance and the commonly used broadcast mode is vulnerable to eavesdropping. Two promising technologies, intelligent reflecting surface (IRS) and unmanned aerial vehicle (UAV), show excellent potential in handling these problems. In this paper, we propose a UAV-empowered IRS-BackCom network, where an IRS acts as the backscatter device and uses the received signals from a UAV for BackCom. We aim to guarantee secure transmission and maximize the broadcast secrecy rate by jointly optimizing the UAV’s beamformer and trajectory and the IRS’s reflection coefficient. To tackle the non-convex problem, we leverage the block coordinate descent method to decompose it into three subproblems. Specifically, the UAV’s beamformer and trajectory and the IRS’s reflection matrix are optimized alternatively. Further, we adopt reinforcement learning to facilitate the intractable UAV’s trajectory optimization. Simulation results verify the feasibility and effectiveness of the proposed system model and the optimization scheme.