As an emerging flexible antenna technology for wireless communications, pinching-antenna systems offer distinct advantages in terms of cost efficiency and deployment flexibility. This paper investigates joint transmission strategies of the base station (BS) and pinching antennas (PAS), focusing specifically on how to cooperate efficiently between the BS and waveguide-mounted pinching antennas for enhancing the performance of the user equipment (UE). By jointly considering the performance, flexibility, and complexity, we propose three BS-PAS joint transmission schemes along with the best beamforming designs, namely standalone deployment (SD), semi-cooperative deployment (SCD) and full-cooperative deployment (FCD). More specifically, for each BS-PAS joint transmission scheme, we conduct a comprehensive performance analysis in terms of the power allocation strategy, beamforming design, and practical implementation considerations. We also derive closed-form expressions for the average received SNR across the proposed BS-PAS joint transmission schemes, which are verified through Monte Carlo simulations. Finally, numerical results demonstrate that deploying pinching antennas in cellular networks, particularly through cooperation between the BS and PAS, can achieve significant performance gains. We further identify and characterize the key network parameters that influence the performance, providing insights for deploying pinching antennas.

The intelligent transportation system (ITS) was supported by the sixth generation (6G) wireless networks, since it has great potential to realize intelligent transportation with the benefit for the society and economy. In order to overcome the practical problem of spectrum scarcity, ultra-low latency, large-scale connectivity in ITS, we propose a cooperative multiple-input single output non-orthogonal multiple access (MISO-NOMA) for ITS with intelligent reflecting surface (IRS) and simultaneous wireless information and power transfer (SWIPT). An user fairness optimization problem is formulated to maximize the fairness rate of the vehicles, subject to the quality of service requirements of the vehicles and the successive interference cancellation. The optimization problem involves the transmit beamformers design, the IRS reflection matrix design, and the power splitting ratio of the SWIPT, which lead to the problem is difficult to solve. For solving the challenging problem, an iterative successive convex approximation and semi-definite relaxation based algorithm is proposed. Explicitly, we firstly adopt the method of reconstructing epigraph for simplification due to the objective function is non-convex, and then the original problem is decomposed into two sub-problems that are easy to solve. Finally, Experimental results illustrate that the user fairness of the proposed cooperative MISO-NOMA for ITS with IRS and SWIPT is better than that of both the IRS-NOMA for ITS without SWIPT and the IRS-OMA for ITS.

In this paper, we investigate the fairness of integrated sensing and communication (ISAC) systems assisted by simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and non-orthogonal multiple access (NOMA) for eliminating the interference of the sensing signal before decoding the signals of communication users. We formulate the problem of maximizing the fairness between communication users and the sensing target by jointly designing the transmit beamforming vectors of the base station (BS) and the coefficient matrices of the STAR-RIS. For tackling the challenging optimization problem, a low-complexity algorithm based on successive convex approximation (SCA) and semidefinite programming (SDP) techniques is proposed for obtaining the transmit beamforming vectors and the STAR-RIS coefficient matrices. For the ISAC system with a single user, we further derive the closed-form expression of the BS transmit beamforming vector for reducing the complexity of the algorithm. Then, the non-convex optimization problem of the STAR-RIS coefficient matrices can be solved efficiently by transforming it into a convex problem. Simulation results show that the fairness of the proposed STAR-RIS-NOMA assisted ISAC system outperforms the conventional RIS-NOMA assisted ISAC system and the conventional RIS and orthogonal multiple access (RIS-OMA) assisted ISAC system.

This paper investigates the secrecy performance for simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted downlink multi-carrier non-orthogonal multiple access (NOMA) networks, consisting of multiple legitimate users and eavesdroppers. We propose two STAR-RIS-NOMA schemes for maximizing the secrecy performance by jointly optimizing the transmission and reflection beamforming of the STAR-RIS, the transmit beamforming of the base station (BS), the power allocation coefficients and the user pairing vector under the full channel state information (CSI) and the statistical CSI of the eavesdropping channel, respectively. For the full CSI available to the BS, an alternating beamforming algorithm is proposed for maximizing the secrecy sum rate. Specifically, we first propose a user pairing scheme based on the differences of user’s channel gains. Then the beamforming vectors and the power allocation coefficients are optimized based on the techniques of semidefinite programming and surrogate lower bound approximation, respectively. For the statistical CSI available to the BS, the problem of minimizing the maximum secrecy outage probability (SOP) is investigated. By invoking the subroutines of alternating beamforming algorithm, we first derive an exact SOP given the user pairing. Then, we conceive the beamforming vectors and the power allocation coefficients by linear matrix inequality and linear programming, respectively. Simulation results show that: 1) the secrecy performance of the proposed STAR-RIS-NOMA scheme outperforms the existing conventional RIS-NOMA scheme and RIS assisted orthogonal multiple access (RIS-OMA) scheme; 2) the proposed alternating beamforming algorithm is capable of achieving a near-optimal performance with low complexity compared to the exhaustive search.

In this paper, we study an uplink reconfigurable intelligent surfaces-non-orthogonal multiple access (RIS-NOMA) assisted mobile edge computing (MEC) network with simultaneous wireless information and power transfer (SWIPT), where the users want to offload their computing tasks to the BS via a RIS and a relay based on the SWIPT technique. The goal of the paper is to minimize the delay concerning the computing tasks of the users by jointly optimizing the power allocation ratio, the phase shift matrix of the RIS, the offloading task ratio, and the offloading transmit power. For solving the challenging optimization problem, we conceive a low-complexity algorithm by optimizing two subproblems separately, based on the penalty method as well as the successive convex approximation. Simulation results demonstrate that the proposed RIS-NOMA assisted MEC network with SWIPT outperforms both the conventional RIS-NOMA assisted MEC network without SWIPT and the RIS-orthogonal multiple access assisted MEC network.

In this paper, we consider a downlink multiple-input single-output non-orthogonal multiple access (MISO-NOMA) network with several legitimate users and a eavesdropper using successive interference cancellation (SIC). The purpose of this paper is to maximize the secrecy performance of the MISO-NOMA network by designing the transmit power between the legitimate users and the artificial jamming. Explicitly, the secrecy sum rate of the MISO-NOMA network is to be maximized by optimizing the transmit beamforming vectors and the artificial jamming vector, subject to the required quality of service of each legitimate user, the artificial jamming beamforming design constraint and the SIC decoding condition. Due to the non-convexity of the optimization problem, we reformulate the original problem into an equivalent optimization problem and then provide a successive convex approximation based iterative algorithm for solving it. Simulation results demonstrate that the proposed optimization scheme outperforms the existing schemes.

Data packet routing in aeronauticalad hocnetworks (AANETs) is challenging due to their high-dynamic topology. In this article, we invoke deep learning (DL) to assist routing in AANETs. We set out from the single objective of minimizing the end-to-end (E2E) delay. Specifically, a deep neural network (DNN) is conceived for mapping the local geographic information observed by the forwarding node into the information required for determining the optimal next hop. The DNN is trained by exploiting the regular mobility pattern of commercial passenger airplanes from historical flight data. After training, the DNN is stored by each airplane for assisting their routing decisions during flight relying solely on local geographic information. Furthermore, we extend the DL-aided routing algorithm to a multiobjective scenario, where we aim for simultaneously minimizing the delay, maximizing the path capacity, and maximizing the path lifetime. Our simulation results based on real flight data show that the proposed DL-aided routing outperforms existing position-based routing protocols in terms of its E2E delay, path capacity, as well as path lifetime, and it is capable of approaching the Pareto front that is obtained using global link information.

Recent advances in quantum technologies pave the way for noisy intermediate-scale quantum (NISQ) devices, where the quantum approximation optimization algorithm (QAOA) constitutes a promising candidate for demonstrating tangible quantum advantages based on NISQ devices. In this paper, we consider the maximum likelihood (ML) detection problem of binary symbols transmitted over a multiple-input and multiple-output (MIMO) channel, where finding the optimal solution is exponentially hard using classical computers. Here, we apply the QAOA for the ML detection by encoding the problem of interest into a level-$p$QAOA circuit having$2p$variational parameters, which can be optimized by classical optimizers. This level-$p$QAOA circuit is constructed by applying the prepared Hamiltonian to our problem and the initial Hamiltonian alternately in$p$consecutive rounds. More explicitly, we first encode the optimal solution of the ML detection problem into the ground state of a problem Hamiltonian. Using the quantum adiabatic evolution technique, we provide both analytical and numerical results for characterizing the evolution of the eigenvalues of the quantum system used for ML detection. Then, for level-1 QAOA circuits, we derive the analytical expressions of the expectation values of the QAOA and discuss the complexity of the QAOA based ML detector. Explicitly, we evaluate the computational complexity of the classical optimizer used and the storage requirement of simulating the QAOA. Finally, we evaluate the bit error rate (BER) of the QAOA based ML detector and compare it both to the classical ML detector and to the classical minimum mean squared error (MMSE) detector, demonstrating that the QAOA based ML detector is capable of approaching the performance of the classical ML detector.

In this paper, we investigate the resource allocation and vehicle to everything (V2X) offloading in the cognitive vehicular networks. The cognitive radio (CR), mobile edge computing (MEC), and non-orthogonal multiple access (NOMA) schemes are applied aim to solve the combinational problem of resource allocation and V2X offloading. The problem for jointly optimizing power and time allocation in the MEC based CR (CR-MEC) networks is conceived. We decompose the joint optimization problem into two subproblems, which are power allocation and time allocation problems. In order to solve this joint optimization problem, an advanced comprehensive resource allocation (ACRA) algorithm based on three-sided matching theory is employed. More specifically, the proposed algorithm is to realize the most reasonable matching among primary users (PUs), cognitive users (CUs) as well as a cognitive base station (BS), and put forward a V2X offloading strategy, by appropriately allocating power and time aim to minimize the system energy consumption. The simulation results show that, our proposed algorithm converges to stable. Furthermore, the proposed NOMA based CR-MEC networks can achieve lower energy consumption compared to the orthogonal multiple access (OMA) based CR-MEC networks.

Unmanned aerial vehicles (UAVs) are capable of serving as aerial base stations (BSs) for providing both cost-effective and on-demand wireless communications. This article investigates dynamic resource allocation of multiple UAVs enabled communication networks with the goal of maximizing long-term rewards. More particularly, each UAV communicates with a ground user by automatically selecting its communicating user, power level and subchannel without any information exchange among UAVs. To model the dynamics and uncertainty in environments, we formulate the long-term resource allocation problem as a stochastic game for maximizing the expected rewards, where each UAV becomes a learning agent and each resource allocation solution corresponds to an action taken by the UAVs. Afterwards, we develop a multi-agent reinforcement learning (MARL) framework that each agent discovers its best strategy according to its local observations using learning. More specifically, we propose an agent-independent method, for which all agents conduct a decision algorithm independently but share a common structure based on Q-learning. Finally, simulation results reveal that: 1) appropriate parameters for exploitation and exploration are capable of enhancing the performance of the proposed MARL based resource allocation algorithm; 2) the proposed MARL algorithm provides acceptable performance compared to the case with complete information exchanges among UAVs. By doing so, it strikes a good tradeoff between performance gains and information exchange overheads.

In this paper, we consider an unmanned aerial vehicle (UAV) enabled wireless network with a set of ground devices that are randomly distributed in an area and each having a certain amount of data for transmission. The UAV flies over this region from a starting point to a destination. During its flight, the UAV wants to communicate to the ground devices for maximizing the cumulative collected data by optimizing the trajectory of the UAV subject to its flight time constraint. Due to uncertainty in the locations of the ground devices and the communication dynamics, an accurate system model is difficult to acquire and maintain. With the help of stochastic modelling, we present a reinforcement learning based automated trajectory optimization algorithm. By dividing the considered region into small grids with finite state space and action space, we apply the Q-learning based automated trajectory optimization approach for maximizing the cumulative collected data during its flight time. Simulation results demonstrate that the reinforcement learning approach can find an optimal strategy under the flight time constraint.

In this paper, the joint design of user clustering and power allocation is investigated in a downlink non-orthogonal multiple access-based millimeter wave (mm-wave-NOMA) system. To reduce the system overhead, hybrid precoding techniques are adopted at the base station by using the channel state information of cluster heads (CHs) only. In order to maximize the sum rate of the system, Stackelberg game-based optimization problems are formulated for two cases with different quality of some targets: Case 1 focuses on improving the data rates of CHs; and Case 2 aims to increase the rates of cluster members when the CHs' data rates are caped. With the aid of coalitional game theory, a low complexity algorithm is proposed to dynamically allocate users into different clusters. Then, the optimal power allocation coefficients of the users in each cluster are obtained by the derived closed-form expressions. The properties of the proposed joint algorithms are analyzed in terms of Stackelberg equilibrium, the complexity, the convergence and the stability. The simulation results demonstrate that: 1) the proposed algorithms can significantly improve the sum rate and reduce the outage probability of the mm-wave-NOMA system and 2) the application of NOMA in mm-wave systems is capable of achieving promising gains over conventional orthogonal multiple access-based frameworks in both cases.

In this paper, the joint design of user clustering and power allocation is investigated in a downlink non-orthogonal multiple access based millimeter wave (mmWave-NOMA) system. In order to maximize the sum rate of the system, the Stackelberg game based optimization problem is formulated. With the aid of game theory, a low complexity algorithm is proposed to dynamically allocate users into different clusters. By deriving the closed-form expressions, the optimal power allocation coefficients of the users in each cluster are obtained. The properties of the proposed joint algorithm, including complexity, convergence and stability, are analyzed. Simulation results demonstrate that: i) the proposed algorithm can significantly improve the sum rate and reduce the outage probability of the mmWave- NOMA system; ii) the application of NOMA in mmWave systems is capable of achieving promising gains over conventional orthogonal multiple access (OMA) based frameworks in both cases.

Machine learning has been used to develop efficiently optimizing algorithms for practical communication systems. This paper investigates the user clustering and power allocation problem in the millimeter wave non-orthogonal multiple access (mmWave-NOMA) transmission scenario, where we assume that the users' locations of different clusters follows a Poisson cluster process (PCP). Specifically, we develop a machine learning based user clustering algorithm for the application of NOMA. Moreover, to investigate the performance of the proposed mmWave-NOMA system, we derive the optimal power allocation coefficients in closed-form by assuming equal power on each beam. In the simulation results, we firstly investigate the impact of the number of clusters on the system performance. We further show the validation of the proposed machine-learning based user clustering algorithm in the mmWave-NOMA system.

Non-orthogonal multiple access (NOMA) has been recognized as a promising multiple access scheme to be used in fifth-generation wireless networks. In this paper, multiple-input and multiple-output (MIMO) techniques are applied to NOMA systems by considering two types of imperfect channel state information-channel distribution information (CDI) and channel estimation uncertainty. Based on the two considered channel models, the power allocation and beamforming vectors are jointly designed to maximize the system utility of MIMO-NOMA, subjected to probabilistic constraints. Due to the implementation of successive interference cancellation in NOMA, the power allocation coefficients of the users in each cluster become coupled, which complicates the rate outage probability constraints and results in two challenging non-convex problems. For the optimization problem under CDI, we propose an efficient successive convex approximation (SCA) algorithm based on first-order approximation and semidefinite programming (SDP). For the optimization problem under channel estimation uncertainty, a new algorithm for the joint power allocation and receive beamforming design is developed to maximize the system utility based on SCA and an efficient 1-D search. In addition, the convergence and the feasibility are discussed for the two formulated problems. Furthermore, an efficient method to find a feasible initial solution is provided. Finally, the presented simulation results validate that the proposed two algorithms outperform MIMO-orthogonal multiple access and MIMO fixed NOMA (MIMO F-NOMA) with fixed power allocation and beamforming.

Millimeter-wave non-orthogonal multiple access (mm-wave-NOMA) systems exploit the power domain for multiple accesses to further enhance the spectral efficiency. User clustering and power allocation can effectively exploit the potential of NOMA in mm-wave systems. This paper investigates the sum rate maximization problem of mm-wave-NOMA systems under the constraints of the total transmission power and users' predefined rate requirements. The formulated optimization problem is a non-linear programming problem and, thus, is non-convex and challenging to solve, especially when the number of users becomes large. Sparked by the correlation features of the users' channels in mm-wave-NOMA systems, we develop a K-means-based machine learning algorithm for user clustering. Moreover, for a practical dynamic scenario where the new users keep arriving in a continuous fashion, we propose a K-means-based online user clustering algorithm to reduce the computational complexity. Furthermore, to further enhance the performance of the proposed mm-wave-NOMA system, we derive the optimal power allocation policy in a closed form by exploiting the successive decoding feature. Simulation results reveal that: 1) the proposed machine learning framework enhances the performance of mm-wave-NOMA systems compared to the conventional user clustering algorithms and 2) the proposed K-means-based online user clustering algorithm provides a comparable performance to the conventional K-means algorithm and strikes a good balance between performance and computational complexity.

This paper investigates the application of non-orthogonal multiple access (NOMA) in millimeter wave (mm-Wave) communications by exploiting beamforming, user scheduling, and power allocation. Random beamforming is invoked for reducing the feedback overhead of the considered system. A non-convex optimization problem for maximizing the sum rate is formulated, which is proved to be NP-hard. The branch and bound approach is invoked to obtain the ∈-optimal power allocation policy, which is proved to converge to a global optimal solution. To elaborate further, a low-complexity suboptimal approach is developed for striking a good computational complexity-optimality tradeoff, where the matching theory and successive convex approximation techniques are invoked for tackling the user scheduling and power allocation problems, respectively. Simulation results reveal that: 1) the proposed low complexity solution achieves a near-optimal performance and 2) the proposed mm-Wave NOMA system is capable of outperforming conventional mm-Wave orthogonal multiple access systems in terms of sum rate and the number of served users.

Quality of experience (QoE) is an important indicator in the fifth generation (5G) wireless communication systems. For characterizing user-base station (BS) association, subchannel assignment, and power allocation, we investigate the resource allocation problem in multi-cell multicarrier non-orthogonal multiple access (MC-NOMA) networks. An optimization problem is formulated with the objective of maximizing the sum mean opinion scores (MOSs) of users in the networks. To solve the challenging mixed integer programming problem, we first decompose it into two subproblems, which are characterized by combinational variables and continuous variables, respectively. For the combinational subproblem, a 3-D matching problem is proposed for modeling the relation among users, BSs, and subchannels. Then, a two-step approach is proposed to attain a suboptimal solution. For the continuous power allocation subproblem, the branch and bound approach is invoked to obtain the optimal solution. Furthermore, a low complexity suboptimal approach based on successive convex approximation techniques is developed for striking a good computational complexity-optimality tradeoff. Simulation results reveal that: 1) the proposed NOMA networks is capable of outperforming conventional orthogonal multiple access networks in terms of QoE and 2) the proposed algorithms for sum-MOS maximization can achieve significant fairness improvement against the sum-rate maximization scheme.

This paper investigates the application of nonorthogonal multiple access (NOMA) in millimeter wave (mmWave) communication with beamforming, user selection and power allocation. To overcome the burden of feedback, random beamforming to mmWave NOMA systems is considered. We then formulate an optimization problem to maximize the sum rate of the proposed mmWave NOMA systems, which is nonconvex. To solve the challenging problem, we invoke the branch and bound (BB) technique to develop an optimal power allocation algorithm. Then a low complexity suboptimal algorithm based on matching theory is proposed to realize user selection. Simulation results are provided for validating the effectiveness of the proposed algorithms and to show that the sum rate performance of the mmWave NOMA systems can be substantially improved by the proposed algorithms compared to the conventional mmWave orthogonal multiple access (OMA) systems.

This paper studies the power minimization problem for non-orthogonal multiple access (NOMA) downlink systems, in which nodes are equipped with multiple antennas. We develop a joint power allocation and receive beamforming algorithm using the fixed-point update power allocation method under two types of channel state information (CSI). Particularly, we first assume that perfect CSI is available for the base station (BS) and the users, where a closed-form expression for every receive detection vector is derived. Then, we consider only channel distribution information (CDI) is known to the BS and the users, where one-dimension search and semi-definite programming (SDP) relaxation are used to derive the receive detection vectors.

The power allocation for multi-user sparse code multiple access (SCMA) downlink systems with finite- alphabet constraints is investigated. An explicit expression for the achievable rate for downlink SCMA systems with finite-alphabet inputs is derived, which is applicable to arbitrary number of users. Moreover, a novel power allocation scheme that can ensure users' fairness for multi-user SCMA downlink systems is proposed. In an effort to solve the formulated non-convex optimization problem, a low- complexity polynomial algorithm is proposed, which yields an optimal solution. Simulation results demonstrate that the proposed power allocation algorithm is capable of enhancing the performance significantly compared to the equal power allocation scheme.

Non‐orthogonal multiple access (NOMA) is a promising multiple access scheme to enhance the spectrum efficiency of fifth generation networks. In this study, the authors study a downlink multiple‐input single‐output (MISO) system combined with NOMA, where a single beamforming (BF) vector is shared by a group of users. A joint BF and power allocation algorithm is proposed to maximise the sum rate of the users with better channel conditions while guaranteeing the quality of service at the user with poor channel conditions. The formulated problem for sum rate maximisation can be shown as non‐deterministic Polynomial‐time‐hard, and therefore an effective solution based on branch and bound (BB) techniques is proposed. Numerical results are provided to verify that the proposed NOMA‐BF system with the BB algorithm improves the sum capacity significantly compared with the NOMA‐BF system with zero forcing.

In this letter, we study a downlink non-orthogonal multiple access (NOMA) transmission system, where only the average channel state information (CSI) is available at the transmitter. Two criteria in terms of transmit power and user fairness for NOMA systems are used to formulate two optimization problems, subjected to outage probabilistic constraints and the optimal decoding order. We first investigate the optimal decoding order when the transmitter knows only the average CSI, and then, we develop the optimal power allocation schemes in closed form by employing the feature of the NOMA principle for the two problems. Furthermore, the power difference between NOMA systems and OMA systems under outage constraints is obtained.