Next-generation wireless networks aim to deliver transformative improvements in data rates, latency, and reliability. Among critical enabling technologies, rate-splitting multiple access (RSMA) combined with intelligent reflecting surfaces (IRS) mounted on unmanned aerial vehicles (UAV) has gained considerable attention. In this paper, we address the secrecy energy efficiency (SEE) maximization problem for short-packet communications in UAV-mounted IRS-assisted RSMA networks operating under finite block length (FBL) conditions. Unlike traditional scenarios optimized for long packets, our approach explicitly incorporates the significant impact of decoding errors and strict latency constraints inherent to short-packet transmission. We propose a comprehensive framework that jointly optimizes UAV deployment, IRS phase shifts, transmit precoding vectors, and common rate allocation. The formulated non-convex optimization problem is effectively solved via iterative decomposition and efficient approximation methods. Numerical evaluations demonstrate notable improvements in SEE compared to existing benchmark methods, underscoring the efficacy of UAV-mounted IRS and RSMA integration in addressing practical constraints of short-packet communication.

This paper investigates a multi-user downlink communication system where an unmanned aerial vehicle (UAV)-mounted intelligent reflecting surface (IRS) assists a base station employing rate-splitting multiple access (RSMA) under hardware impairments. A sum-rate maximization problem is formulated, jointly optimizing the common rate allocation, beamforming vectors, and IRS phase shifts. To tackle the non-convexity, an alternating optimization framework is developed in which each subproblem is efficiently solved using successive convex approximation (SCA). Simulation results demonstrate that the proposed design outperforms conventional benchmarks.

This paper presents a user cooperation scheme for wireless-powered communication network (WPCN). Two energy constraints users first harvest RF energy from a dedicated power station (PS) and cooperatively transmit their representative information to an information receiver (IR). We aim to investigate the energy-efficiency (EE) maximization problem while considering the quality of service (QoS) requirements. In order to achieve that, we formulate the maximization problem by jointly optimizing time allocation, power allocation, and energy beamforming vectors. Since the proposed problem is non-convex, variable substitutions, fractional programming, and semidefinite relaxation (SDR) are used to convert it into a convex problem. Finally, an algorithm is proposed for determining the optimal solution. Simulation results show that the proposed user cooperation scheme improves system efficiency by comparing with the non-cooperation scheme as the benchmark.

This paper considers a backscatter communication (BC) system, which is based on the non-orthogonal multiple access (NOMA) protocol and assisted by a full-duplex unmanned aerial vehicle (UAV). To improve the communication quality of this NOMA-based system, we increase the number of backscatter devices (BDs) and maximize the sum rate by optimizing the reflection coefficient (RC) of BDs and the location of the UAV. As the sum rate problem is a non-convex problem, we propose an iterative algorithm to solve the problem by using the block coordinated descent (BCD) technique and quadratic transform algorithm. The RC problem is solved by monotonicity. Then, the location problem is solved by the quadratic transform algorithm. Finally, simulation results demonstrate that the proposed algorithm achieves higher sum rate than the other schemes.

Unmanned aerial vehicle (UAV) combined with non-orthogonal multiple access (NOMA) can overcome the high-capacity demand in hotspots. This paper studies the resource allocation for UAV downlink NOMA network with the user’s location uncertainty, where UAV is deployed in the air as an aerial base station and sends information to ground users. Under the condition of user’s location uncertainty, we investigate sum-rate maximization problem, which is a mixed binary non-convex optimization issue. Therefore, to resolve the issue, we trans-form paired variables into successive variables by processing inequality constraints and then introduce an iterative robust placement and power control optimization algorithm based on first-order taylor technology, penalty function approach, and variable substitution. Our proposed algorithm can effectively improve the sum rate compared with the traditional orthogonal multiple access (OMA) algorithm.

This paper investigates a reconfigurable intelligent surface (RIS)-assisted unmanned aerial vehicle (UAV)-enabled wireless sensor network (WSN) under the probabilistic line-of-sight (LoS) channel in urban areas, where a UAV is dispatched to collect data from spatially distributed sensor nodes (SNs) with the aid of an RIS to enhance the communication quality. With the objective of maximizing the minimum average data collection rate from all the SNs for the UAV, we jointly design the communication scheduling, the phase shift of the RIS, and the UAV trajectory. However, due to its non-convexity, the formulated problem is difficult to solve. Therefore, we propose an efficient algorithm to attain its suboptimal solution by leveraging alternating optimization (AO), successive convex approximation (SCA), and semidefinite relaxation (SDR). Simulation results reveal new insights on the elevation angle-distance trade-off for the RIS-assisted UAV-enabled WSN, and the data collection rate of our proposed algorithm is significantly improved compared with other benchmark schemes.

In this paper, we investigate energy-efficient resource allocation for downlink cooperative non-orthogonal multiple access (C-NOMA) systems, where a source node communicates with two users via an amplify-and-forward (AF) relay with hardware impairment (HI) conditions. We first derive the energy efficiency (EE) of the system with HI under the power constraint of the source, relay and minimum signal to interference plus noise ratio (SINR) requirement of each user. Then, fractional programming is utilized to convert the energy efficient optimization problem into a subtractive form. Next, a joint power and amplification gain allocation algorithm is proposed to maximize the system energy efficiency (EE) based on Lagrangian dual and Dinkelbach method. Simulation results show that the proposed algorithm can improve the system EE compared with the orthogonal multiple access (OMA) scheme.

In this paper, we studied the joint subchannel and power allocation problem to maximize energy efficiency (EE) for the multi-carrier non-orthogonal multiple access (MC-NOMA) systems. First, a matching algorithm is proposed to allocate the user in the subchannel. Then, the EE maximization problem is converted into a series of subproblems, which can be solved by the penalty function method. Next, the joint channel allocation and power allocation is proposed based on the Dinkelbach algorithm. Simulation results show that the proposed algorithm is superior to the traditional fractional power allocation (FTPA) scheme and the OFDMA scheme in terms of the EE.

In this paper, the interference-efficient based resource allocation for uplink transmission in an OFDMA-based cognitive radio network is studied. The interference efficiency (IE) is defined as the total data rate of secondary users (SUs) over sum interference power imposed on primary users (PUs). The objective is to maximize the total IE of SUs subject to transmit power constraints of SUs, subcarrier assignment constraint and the interference power threshold constraints of SUs. The original mixed integer fractional programming problem is converted into the convex one which is solved by using the convex optimization technique. Simulation results show the effectiveness of the proposed algorithm in terms of the interference to PUs and transmission efficiency.

This paper investigates the price-based power control problem in the spectrum sharing cognitive radio networks. The primary user (PU) can admit secondary users (SUs) to access by pricing their interference power under the interference power constraint. We model the interaction between the PU and the SUs as a Stackelberg game. The revenue function of the PU is expressed as a nonconcave function of SU's transmit power by backward induction. Variable substitution is used to transform the nonconcave maximization problem into a concave maximization problem. Based on the equivalent concave maximization problem, a novel algorithm is proposed to find the optimal price for the PU to maximize its revenue. The optimal price of each SU is given as a closed-form expression with one parameter, which can be determined with the complexity of$O(K)$, where$K$is the number of the SUs. Furthermore, asymptotic analysis is exploited to derive the number of the admitted SUs at low and high interference-to-noise ratio. Simulation results show the effectiveness of the proposed algorithm in comparison with the nonuniform pricing algorithm.

In the spectrum underlay model, cognitive radios can access the spectrum owned by primary users if they can limit their interference power below a certain threshold, called interference temperature limit (ITL). In this paper, we study the power control problem under ITL. By considering both efficiency and fairness, the power control problem is modeled as a cooperative game. A power control scheme based on Nash bargaining solution (NBS) is proposed. We present a distributed power control (DPC) algorithm based on NBS. The convergence of DPC algorithm is proven by theoretic analysis. Simulation results show that proposed DPC algorithm achieves an effective tradeoff between fairness and efficiency.