In this article, we investigate the performance of a holographic active reconfigurable intelligent surface (HARIS)-aided near-field (NF) uplink non-orthogonal multiple access (NOMA) secure communication system with an imperfect channel state information (iCSI) in the presence of an eavesdropper (Eve). In order to provide efficient resource utilization, a sum secrecy rate (SSR) maximization problem is formulated, where the combining vector at base station (BS), power allocation at each uplink user, and the HARIS phase profile are jointly optimized under the strict constraints of quality of service (QoS) requirement and limited power budget at each uplink user and HARIS considering norm-bounded CSI uncertainty. In order to tackle the non-convex nature of the formulated problem, we propose an alternating optimization (AO)-based algorithm that adopts an iterative approach and uses optimization techniques such as semidefinite programming (SDP), convex upper bound approximation, and semidefinite relaxation (SDR) to optimize all three design variables simultaneously. Then, extensive simulations are performed to validate the efficacy and convergence of the proposed algorithm. Furthermore, we also demonstrate the impact of key system parameters, such as HARIS elements, minimum QoS corresponding to each user, the total power budget at uplink users, and maximum amplification factor at the HARIS. It is shown that the use of NOMA can achieve up to 45% higher performance compared to SDMA, OMA, and TDMA. It is also highlighted that, under the proposed algorithm with NF assumptions, the achieved average SSR is around 65% higher compared to the hybrid and far-field (FF) assumptions.

This work investigates the performance of rate splitting multiple access (RSMA) in a holographic reconfigurable intelligent surface (HRIS)-aided near-field (NF) driven downlink secure communication system under imperfect channel state information (iCSI) in the presence of an eavesdropper (Eve). We formulate a weighted sum secrecy rate (WSSR) while ensuring a minimum quality of service (QoS) at each node under the available resource constraints, such as the total power budget at the base station (BS). Since the optimization problem is nonconvex due to the coupling of the variables, we propose an iterative algorithm based on alternating optimization (AO) that jointly optimizes transmit beamforming at BS and phase shift at HRIS. Numerical results are shown to validate the effectiveness and convergence of the proposed algorithm. Furthermore, we also discuss the impact of the key system parameters, such as HRIS reflecting elements, minimum QoS constraint, transmit power budget, and number of users. The dominance of RSMA over conventional multiple-access technologies such as non-orthogonal multiple access (NOMA) and space division multiple access (SDMA) is also demonstrated.

This article proposes a holographic active reconfigurable intelligent surface (HARIS)-aided near-field (NF)-driven uplink non-orthogonal multiple access (NOMA) secure communication system under imperfect channel state information (iCSI) in the presence of an eavesdropper (Eve). To ensure efficient resource utilization, a sum secrecy rate (SSR) maximization problem is formulated by jointly optimizing the combined vector at the base station (BS), the power allocation for each uplink user, and the HARIS phase profile under strict quality of service (QoS) requirements and limited power budgets at both the uplink users and HARIS. Due to the non-convex nature of the problem, an alternating optimization (AO)-based algorithm is proposed, which uses semidefinite programming (SDP), convex upper bound approximation, and semidefinite relaxation (SDR) techniques to optimize all variables iteratively. Extensive simulations validate the performance and convergence of the proposed algorithm. In addition, the effects of system parameters, such as the number of HARIS elements, minimum QoS per user, maximum BS receive power, and the HARIS amplification factor, are investigated.

This work proposes the integration of the holographic active reconfigurable intelligent surface (HARIS) into a multi-user uplink near-field-driven wireless communication system. In order to provide efficient resource utilization, a sumrate maximization problem is formulated, where the equalizer design, the power allocation at each user, and the HARIS phase profile are jointly optimized under the strict constraint of QoS requirement and limited power budget at each user and HARIS. In order to tackle the non-convex nature of the formulated problem, we propose an alternating optimization (AO)-based algorithm that adopts an iterative approach and uses optimization techniques such as minimum mean square error (MMSE), convex upper bound approximation, and semidefinite relaxation (SDR) to simultaneously optimize the equalizer at the BS, beamforming at the HARIS, and power allocation at each user. Then, extensive simulations are performed to validate the efficacy and convergence of the proposed algorithm. Furthermore, we also demonstrate the impact of key system parameters, such as HARIS elements, minimum quality of service (QoS) constraint corresponding to each user, maximum receive power at the base station (BS), and maximum amplification factor.

In this work, we investigate the performance of a multi-active reconfigurable intelligent surface (ARIS)-aided full-duplex (FD) secure Internet of Things (IoT) network with imperfect Channel State Information (iCSI) in the presence of an eavesdropper (Eve). We formulate a power minimization problem while ensuring the minimum Quality of Service (QoS) of all the nodes within available resource constraints considering the norm-bounded iCSI. To tackle the nonconvex nature of the formulated problem, we adopt analytical methods, such as semidefinite programming, S-procedure, and general sign-definiteness, and propose an alternating optimization (AO)-based algorithm that jointly optimizes the receive and transmit beamforming at Alice, power allocation at each uplink user, and active beamforming at ARIS. The efficacy and convergence of the proposed algorithm are validated via extensive numerical simulation. The potential of ARISs, compared to its passive RIS (PRIS) counterpart, toward achieving a robust and secure FD system is demonstrated. Finally, we discuss the impact of key parameters, such as maximum amplification factor, RIS, and CSI error on the performance of the considered system.

Holographically driven active reconfigurable intelligent surface (HARIS), leveraging densely packed subwavelength elements, overcomes the limitations of conventional RIS in signal processing, unlocking advanced capabilities for next-generation networks. Thus, to exploit its full potential, this work proposes the integration of HARIS into an Internet of Things (IoT) multiuser uplink near-field-driven wireless communication system. A sum-rate maximization problem is formulated to provide efficient resource utilization by jointly optimizing the equalizer design, power allocation at each IoT user, and the HARIS phase shift, while satisfying strict constraints of Quality-of-Service (QoS) requirement and limited power budget at each IoT user and HARIS. Due to the nonconvex nature of the problem, we propose an alternating optimization (AO)-based algorithm, incorporating techniques, such as minimum-mean-square error (MMSE), convex upper bound approximation, and semidefinite relaxation (SDR). Then, extensive simulations validate the algorithm’s efficacy and convergence, demonstrating up to 63% higher performance with HARIS than passive RIS. Additionally, we highlight that near-field communication yields up to 90% higher sum-rate than hybrid 76% and far-field model 73%. Moreover, we demonstrate the impact of imperfect channel state information (iCSI) on the system performance.

This work investigates the performance of rate splitting multiple access (RSMA) in a holographic reconfigurable intelligent surface (HRIS)-aided downlink network for efficient near-field communication. We formulate a sum-rate maximization problem that jointly optimizes the transmit beamforming at the base station (BS), the common rate of each receiving internet of things (IoT) node, and beamforming at the HRIS transmission design to ensure a minimum quality of service (QoS) at each node under the available resource constraints, such as the total power budget at the BS. Since the optimization problem is non-convex due to the coupling of the variables, we propose an iterative algorithm based on alternating optimization (AO) that efficiently solves the joint optimization problem utilizing analytical tools such as the successive convex approximation (SCA). Various numerical results are shown to validate the effectiveness and convergence of the proposed algorithm. Furthermore, we also discuss the impact of the key system parameters, such as reflecting elements, minimum QoS constraint, transmit power budget, and number of IoT nodes. The dominance of RSMA over the counterpart, non-orthogonal multiple access (NOMA), is also demonstrated. It is shown that the use of RSMA can achieve up to 96% higher performance compared to NOMA. It is also highlighted that with near-field assumptions, the average sum rate increases around 69% compared to hybrid 66% and far-field model 64%.

This paper investigates a cooperative Internet of Things (IoT) non-orthogonal multiple access (NOMA) network comprising of a multi-antenna base station (BS), a near IoT user (NU) with full-duplex capabilities, and multiple far IoT users (FU). The NU utilizes the power-splitting (PS) energy harvesting (EH) protocol and augments the harvested energy with a little energy from its battery to assist the FU. Considering the novel cooperative NOMA/non-cooperative (C-NM/NC) switching, the impact of successive interference cancellation (SIC), channel state information (CSI) errors, and nonlinear EH, closed-form expressions are derived for average blocklength error rates (BLER) of both users in finite blocklength regime. Utilizing the derived BLER expressions, the Goodput, Reliability, Latency, and battery power efficiency are expressed in closed form. We then demonstrate that the CSI errors severely degrades the performance of both the users. Monte Carlo simulations validate the accuracy of the derived analytical expressions. We also establish that a significantly better maximum performance is attained at the FU, given a target NU Goodput by the careful choice of NOMA and EH parameters. C-NM/NC switching offers a higher EE and better self-interference immunity. Results indicate that the choice of blocklength is crucial for efficient system performance.

In this work, we consider a cooperative communication network wherein a base station (BS) utilizes a simultaneous wireless power and information transfer (SWIPT) energized full duplex-decode and forward relay to communicate with a downlink user. The relay augments the harvested energy with a bit of energy from its battery. Assuming a practical nonlinear energy harvesting (EH) model, we derive approximated closed-form expressions for the end-to-end blocklength error rate (BLER) under the finite blocklength (FBL) regime for both power splitting (PS) and time switching (TS) protocols. We then derive a high-SNR approximated expression for end-to-end BLER to demonstrate the interplay of the battery energy, blocklength, and EH parameters on the system’s performance. We also analytically establish the convexity of the BLER with respect to the relay’s battery energy. Using the expression for the BLER, we derive expressions for the goodput and battery energy efficiency in approximated closed-form. Moreover, for a desired target BLER requirement, we show that, by carefully choosing the TS/PS parameter, the required relay’s battery energy can be minimized. The accuracy of the derived analytical expressions is validated using Monte Carlo simulations. Finally, we discuss the impact of key parameters such as transmit power, total available blocklength, nonlinear EH, TS, and PS parameters, and battery energy on the network’s performance.

In this work, we investigate the performance of an active reconfigurable intelligent surface (RIS)-aided multi-user uplink secure private industrial network. With an aim to provide a more sophisticated and consolidated framework towards the robust transmission design, we formulate a sum secrecy rate maximization while ensuring a minimum performance at each user within available resource constraints considering the norm-bounded imperfect channel state information (CSI) at Eavesdropper (Eve). To tackle the non-convex nature of the formulated problem, we propose an alternating optimization (AO)-based algorithm that jointly optimizes the equalizer, beamforming at the RIS, and power allocation at each user. The efficacy and convergence of the proposed algorithm are validated via extensive numerical simulation. The potential of active RIS, compared to passive RIS, towards a robust uplink secure private network is demonstrated. Finally, we discuss the impact of key parameters such as maximum power budget at each user and RIS, and CSI error on the secrecy performance of the considered network.

This work investigates the performance of simultaneous wireless information and power transfer (SWIPT) in a reconfigurable intelligent surface (RIS)-aided internet of things (IoT) communications under imperfect channel state information (CSI). We formulate a multi-objective optimization problem (MOOP) to design transmit precoding vector (TPV) at the base station (BS) and phase shift matrix (PSM) at the RIS that jointly maximizes energy efficiency (EE) and harvested power (HP) under the norm bounded CSI error model. Due to the conflicting objective functions and non-convex nature of the above optimization problem, the MOOP is simplified using the.-constraint method and subsequently adopting advanced optimization tools, such as Dinkelbach method, S-procedure, general sign-definiteness, semidefinite programming and convex-concave procedure. Thereafter, we propose an alternating optimization-based algorithm which determines optimal TPV and PSM iteratively that jointly maximizes the EE and HP of the considered system. Through numerical simulations, we validate the robustness, optimality, convergence, accuracy and effectiveness of our proposed algorithm. Furthermore, we assess the impact of several key parameters such as the number of RIS elements, available transmit power at BS and the minimum HP on the performance of the considered system.

In this paper, we propose a novel approach for optimal resource management in ultra-reliable low-latency communication (URLLC)-enabled Internet of Vehicles (IoV) networks. The framework includes mobile edge computing (MEC) servers integrated into roadside units (RSUs), unmanned aerial vehicles (UAVs), and base stations (BSs) for hybrid vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication. We utilize asynchronous federated learning (AFL) approach to enhance the accuracy of the global model by considering the mobility characteristics of vehicles. The problem of optimal resource allocation is formulated to achieve the best allocation of frequency, computation, and caching resources while complying with the delay restrictions. To solve the non-convex problem, a multi-agent actor-critic type deep reinforcement learning algorithm called D-MAAC algorithm is introduced. Extensive simulations show the effectiveness of the proposed framework and algorithms compared to existing schemes.

This work investigates the performance of simultaneous wireless information and power transfer (SWIPT) in a reconfigurable intelligent surface (RIS)-aided green multiple input single output (MISO) communications under imperfect channel state information (CSI). With an aim to provide green and robust transmission, we formulate a multi-objective optimization problem (MOOP) to design transmit precoding vector (TPV) at the base station (BS) and phase shift matrix (PSM) at the RIS that maximizes the energy efficiency at the information receivers and harvested power at the energy receivers under the norm bounded CSI error model. Due to the conflicting objective functions and non-convex nature, the MOOP is simplified using the$\epsilon$-constraint method. We then propose an alternating optimization-based iterative algorithm that utilizes semidefinite programming to determine the optimal TPV and PSM one by one until convergence is achieved. Via exhaustive simulations, we graphically demonstrate the effectiveness and robustness of the proposed algorithm. Furthermore, we assess the impact of several key parameters such as the number of RIS elements, available transmit power at BS and the minimum harvested power at each energy receiver on the performance of the considered system.

In order to support the need for higher spectral and energy efficiencies with a wider coverage area, simultaneous refracting/transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and rate splitting multiple access (RSMA) have emerged as the potential technologies required for architectural advancement in the next-generation wireless communication networks. In this work, we propose a novel analytical framework of an RSMA-enhanced STAR-RIS-aided downlink multi-user communication system. First, we discuss the statistical characteristics of the different channels involved in the transmission and derive their probability density function (PDF). Using the derived PDF, we analyze the performance of the system and derive the analytical closed-form expressions of the outage probability at each reflecting and refracting downlink user for two different STAR-RIS operational protocols namely i) energy splitting (ES) and ii) mode switching (MS). Furthermore, we validate the accurateness of the all analytical expressions through Monte-Carlo (MC) simulations. We also highlight the impact of some important parameters of the system such as transmit power at the BS, elements in the STAR-RIS, imperfect channel state information (CSI) on the outage probability of each user. Finally, we demonstrate the dominance of RSMA over non-orthogonal multiple access (NOMA) on the system performance.

Simultaneous refracting/transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) has emerged as a potential technology for future-generation wireless networks to support extremely high data rates with a broader coverage area. In this work, with an aim to provide a novel analytical framework, we investigate the performance of a STAR-RIS assisted full duplex (FD) wireless communication system under finite block length (FBL) transmission. In particular, we first derive the probability density function and cumulative distribution function of the signal-to-interference-plus-noise ratio (SINR) for the uplink and downlink users. We then analyze the system performance by deriving closed form expressions for their block error rate (BLER) and goodput. Finally, we validate the accuracy of the derived analytical expressions using Monte-Carlo simulations and show that as the number of elements in the STAR-RIS is increased the system performance also improves. Furthermore, we graphically demonstrate the impact of imperfect channel state information and compare the performance of STAR-RIS in mode switching (MS) and energy splitting (ES) protocol.

In this work, we consider a novel reconfigurable intelligent surface (RIS)-assisted full duplex (FD) sixth generation (6G)-vehicle-to-everything (V2X) communication network having a FD base station (BS) simultaneously communicating with an uplink (UL) and a downlink (DL) mobile vehicles with the aide of two RISs, one for each link. We provide an analytical framework to investigate the performance of this network and, consequently, formulate an optimization problem to jointly optimize the phase-shift matrices at both the RISs that maximizes the achievable sum-rate. Thereafter, we propose a successive refinement algorithm which uses an iterative approach to solve the problem and provide optimum values of phase-shift matrix at each RIS. We validate the accuracy of the proposed algorithm by exhaustive simulation based graphical results. Accordingly, we demonstrate the dominance of the considered FD system over its half-duplex (HD) counterpart. Moreover, we also highlight the impact of imperfect self interference cancellation and discuss the trade-off between the UL and DL performances due to this imperfection.

In this work, we investigate the performance of a federated edge learning (FEEL)-enhanced edge computing in unmanned aerial vehicles (UAV)-aided internet of things (IoT) system under the consideration of limited energy at each UAV. It consists of multiple UAVs which apply FEEL for local training and, then, transmit the required parameters to a centralized IoT-server. We use a new cost metric obtained by a linear combination of latency and energy consumption, and formulate an optimization problem to jointly optimize the central processing unit (CPU)-frequency during FEEL and allotted bandwidth under the consideration of the limited overall system bandwidth and energy available at each UAV. Due to the non-convex nature of the formulated problem, we propose a twin delayed deep deterministic policy gradient (TD3)-based algorithm that solves the problem and provides the optimum CPU frequency and allotted bandwidth to each user. We validate the accuracy and convergence of the proposed algorithm via exhaustive simulations and highlight its effectiveness by comparing its performance with that of deep deterministic policy gradient (DDPG) and deep Qnetwork (DQN)-based solutions.

With the advancement of wireless communication technologies, reconfigurable intelligent surfaces (RISs) have recently paved the way to augmenting the performance of wireless networks with the aid of multiple reflecting surfaces by efficiently attuning the signal reflection through a large number of low-cost passive elements. In this paper, we consider an RIS-aided full-duplex (FD) communication network consisting of a FD access point (AP) that communicates with an uplink and a downlink user simultaneously with the aid of an RIS as well as through the direct link between the AP and users. To evaluate the system performance under infinite blocklength (IBL) and finite blocklength (FBL) codes, we derive the analytical expressions for the outage probability and throughput in case of IBL, and for block-error rate (BLER) and goodput in the case of FBL, for both uplink and downlink transmission. Furthermore, the expressions for the maximum achievable rate under FBL and IBL transmission are derived. Next, we also extend the analysis of the single-user framework to a more practical scenario with multiple users utilizing non-orthogonal multiple access (NOMA) and derive analytical expressions for the outage probability and BLER at each downlink user and at the AP. The accuracy of the derived expressions is validated via simulation results, and insights are provided regarding the impact of the number of reflecting elements and imperfect channel state information (CSI) on the performance of the considered system. Finally, from the comparative analysis, it is shown that the RIS-aided system outperforms the system without RIS in both IBL and FBL scenarios, providing remarkable improvement in the outage probability and BLER.

In this work, we investigate rate-splitting multiple access (RSMA) for a multiuser downlink wireless network consisting of an unmanned aerial vehicle (UAV)-assisted base station (BS) that serves multiple ground users (GUs) simultaneously. Considering two different transmission regimes, namely infinite blocklength (IBL), and finite blocklength (FBL), we analyze the performance of the considered network under the effect of imperfections in channel state information (CSI) estimation and successive interference cancellation (SIC) with the probabilistic line of sight fading channels. For IBL transmission, we derive the closed-form expressions of the outage probability, throughput, and achievable ergodic rate at each GU. Furthermore, for short packet communication, the closed-form expressions of block error rate (BLER), goodput, and achievable ergodic rate are determined with FBL transmission. Moreover, for the FBL regime we also formulate an optimization problem that jointly optimizes the 3D-position of the UAV, power allocated to each user, and common rate distribution at each user to maximize the ergodic sum rate subject to the practical constraints such as maximum tolerable BLER, minimum private and total rate at each user. We then propose an alternating optimization-based iterative algorithm to solve the problem. Monte-Carlo simulations are used to verify the accuracy of derived analytical results and demonstrate the trade-off between transmit power and achievable BLER. In addition to this, the effectiveness of RSMA in UAV-assisted communication under IBL and FBL transmission regimes is also observed compared to non-orthogonal multiple access.

In this work, we consider a hybrid aerial full-duplex (FD) relaying consisting of a reconfigurable intelligent surface (RIS) mounted over an FD unmanned aerial vehicle (UAV) relay operating in decode and forward mode to assist the information transfer between the base station and multiple users. For better spectral efficiency, we investigate the use of rate splitting multiple access (RSMA) in such networks and focus on joint optimization of RSMA parameters, 3D-coordinates of the UAV/RIS, and phase shift matrix at the RIS along with analyzing the outage probability, block error rate (BLER) and achievable weighted sum rate for finite blocklength (FBL) and infinite blocklength (IBL) codes under imperfect successive interference cancellation (SIC) at each user and residual-self interference (RSI) at the UAV. We first formulate the weighted sum rate maximization problem and adopt the block coordinate descent (BCD) method to deal with the non-convex nature of the problem. Thereafter, we propose a BCD-based algorithm that jointly optimizes these parameters using a heuristic approach for optimum power allocation, a Riemannian conjugate gradient-based algorithm to get the optimal phase shift at the RIS, and an iterative algorithm to obtain the optimal UAV/RIS position. It also distributes the common rate among the users optimally. Next, with obtained optimal parameters, we further analyze the performance of the network and derive the closed-form expressions of BLER, outage probability, and average weighted sum rate. We present Monte Carlo simulation-based results to validate the accuracy of the proposed algorithms and derived expressions, and demonstrate the superiority of RSMA over non-orthogonal multiple access (NOMA) and conventional orthogonal multiple access (OMA) schemes.

Reconfigurable intelligent surface (RIS) is an emerging next-generation technology that can improve the energy efficiency using multiple low-power passive metasurfaces which reflect the desired signal to the users. In this work, we consider a RIS-assisted underlay multiuser multiple-input multiple-output cognitive radio network and formulate a weighted energy efficiency maximization problem in order to jointly optimize the active precoding matrix (APM) at the secondary transmitter and passive precoding matrix (PPM) at the RIS subject to the constraints of available transmission power at the secondary transmitter and maximum allowable interference towards the primary user/receiver. However, due to the coupling of APM and PPM variables, the problem becomes non-convex, and conventional optimization methods cannot be used to solve it. Therefore, by adopting the weighted minimum mean-square error method we first transform the non-convex objective function into a convex one. Next, based on the block coordinate descent method, we propose an iterative algorithm that determines the optimal APM and PPM using Lagrange dual decomposition method and inner approximation method, respectively. Finally, the optimality and efficacy of the proposed algorithm are validated using numerical simulations. The impact of the channel state information (CSI) error has been studied via numerical simulations on the proposed network and it shows that the proposed design is relatively robust against the CSI imperfections, especially at low SNR conditions.

The recent advent of simultaneous refracting and re-flecting reconfigurable intelligent surface (STAR-RIS) has paved the way for next generation wireless technology by enhancing the quality of the signal with wider coverage area connectivity. In this work, we propose a novel STAR-RIS assisted full duplex (FD) wireless communication system where a FD base station (BS) communicates with an uplink user and a downlink user simultaneously with the aid of a STAR-RIS. First, we derive the probability density function (PDF) of the uplink and downlink signal to interference plus noise ratio (SINR). Using the derived PDF, we analyze the system performance and derive analytical closed-form expressions for the outage probability and achievable throughput for both the uplink and downlink communication. Finally, we validate the accuracy of the derived analytical expressions using Monte-Carlo simulations and show that the use of the STAR- RIS provides a significantly improved performance compared to the conventional RIS.

This paper investigates a power-efficient simultaneous wireless information and power transfer (SWIPT)-enabled reconfigurable intelligent surface (RIS)-assisted multi-input multi-output (MIMO) communication network, where a multiple antenna base station (BS) communicates with multiple information and energy receivers simultaneously with the aid of a RIS. To jointly optimize the active and passive beamforming, a transmit power minimization problem is formulated subject to minimum rate and harvested energy requirements at respective users. Due to non-convex nature of the formulated problem, we adopt the mean squared error (MSE)-based approach to convert it into a convex one. Next, we propose an iterative algorithm to determine the optimal active and passive beamforming matrices. Finally, simulation results are presented to validate the effectiveness of the proposed algorithm and highlights the impact of important parameters such as reflecting elements, minimum rate constraint, etc. It is established that the use of RIS along with the proposed algorithm requires approximately 10%–15% less power compared to the algorithms with random phase shifts and no-RIS.

Reconfigurable Intelligent Surface (RIS) is a transformative technology which can enhance the performance of the ubiquitous wireless networks and achieve better signal quality with the aide of multiple reflecting surfaces. In this work, an analytical framework of a RIS-aided full duplex (FD) communication network consisting of a FD-access point (AP) that communicates with an uplink and a down-link users simultaneously is provided. In particular, we analyze the performance of the considered system by deriving analytical expressions of outage probability for both uplink and downlink transmissions. Further, the accuracy of the derived expressions is validated using simulation results. Finally, from the comparative analysis, it is shown that the RIS outperforms the system without RIS providing remarkable improvement in the outage probability.

Owing to the factors such as controllable mobility, ready-to-use technology, low cost, easy implementation, and so on, unmanned aerial vehicle (UAV) possesses tremendous potential to be one of the primary candidates for next-generation (6G) wireless networks. This paper presents a UAV-assisted multiuser communication network where a multiple antenna UAV base station (BS) serves multiple single antenna ground users (GUs). UAV-BS uses a laser source-based charging mechanism to fulfill its power requirement and applies simultaneous wireless information and power transfer (SWIPT) in the downlink in order to provide desired power to energy-constrained GUs. Also, a clustering-based hybrid multiple access technique is used that combines both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) signaling to transmit the information to all GUs, simultaneously. Due to the involved analytical complexity corresponding to the multiple antennas and users, we use a hybrid beamforming method for efficient communication. Next, we analyze the performance of the proposed framework in terms of user outage probabilities, their respective throughput, and average power harvested considering non-linear energy harvesting and derive expressions of these performance metrics. Moreover, we formulate an optimization problem where the throughput of one GU is maximized by optimally choosing the power allocation parameter while ensuring the desired target throughput at other GU in each cluster. We also illustrate how crucial is the optimal selection of the target rates to maximize the network performance. Simulation results are provided to validate the accuracy of derived expressions and to highlight the dominance of hybrid beamforming and hybrid NOMA compared to conventional methods on the performance of the considered network.

In this work, we consider a hybrid aerial full-duplex (FD) relaying protocol consisting of a reconfigurable intelligent surface (RIS) mounted over an FD unmanned aerial vehicle (UAV) relay operating in the decode and forward mode to assist the information transfer between the base station and multiple users. For better spectral efficiency, we investigate the use of non-orthogonal multiple access (NOMA) in such networks and focus on both the performance analysis and design optimization of the considered RIS-NOMA network under imperfect channel state information (CSI) and successive interference cancellation (SIC) at each user, and residual-self interference (RSI) at UAV. We first formulate the sum rate maximization problem and adopt the block coordinate descent method to deal with the non-convex nature of the problem. Thereafter, we propose an algorithm based on the Riemannian conjugate gradient method to get the optimal phase shifts at the RIS, an iterative algorithm to obtain the optimal UAV/RIS position and the exhaustive method to obtain the optimum power allocation coefficients. Next, with obtained optimal position, phase shift and power coefficients, we further analyze the performance of the network and derive the closed-form expressions of outage probability, achievable throughput and ergodic capacity. We present Monte Carlo simulation-based results to validate the accuracy of the proposed algorithms and derived expressions and demonstrate the superiority of NOMA over OMA.

In this work, we investigate an unmanned aerial vehicle (UAV)-aided novel hybrid wireless communication network consisting of a cellular user and a small internet of things (IoT) network having one low-power IoT hub which serves a sensor node. During the first signalling phase, the UAV-assisted base station (BS) uses non-orthogonal multiple access signalling to serve the cellular user and the IoT-hub, simultaneously. In the second phase, the cellular user uplinks the control signal to UAV-BS, whereas, at the same time the IoT-hub communicates with the sensor node using the power harvested by applying simultaneous wireless information and power transfer (SWIPT) and nonlinear energy harvesting. We derive the closed-form expressions of the achievable ergodic capacity of the cellular user and the IoT-hub during the first signalling phase and the sensor node and the UAV-BS during the second phase considering nonlinear energy harvesting at the IoT hub. Further, we demonstrate the trade-off between available transmit power at UAV-BS, IoT-hub (harvested power), and cellular user to achieve desired capacity at the sensor node and UAV-BS. We validate the accuracy of derived expressions by using numerical simulations.