Joint terminal-AP association and power allocation for NOMA-enabled space–air–ground integrated networks

This paper considers a space–air–ground integrated network (SAGIN) to provide network access services for aerial and terrestrial terminals. The non-orthogonal multiple access (NOMA) is used for improving spectral efficiency in the uplink transmission between terminals and access points (APs) in SAGIN. A sum rate maximization optimization problem is formulated by optimizing terminal-AP association and power allocation, while simultaneously satisfying the constraints of transmit power, network coverage characteristics, and quality-of-service (QoS) requirements of both aerial and terrestrial terminals. To deal with the formulated mixed integer nonlinear programming (MINLP) optimization problem, we first decouple it into separated terminal-AP association and power allocation problems. Then, we adopt the Q-learning algorithm to solve the terminal-AP association subproblem. Based on the obtained terminal-AP association solution, an iterative power allocation algorithm is developed by exploiting the Lagrange dual method. Moreover, the computational complexity of the proposed algorithm is further analyzed. Simulation results demonstrate that, compared with other schemes, our proposed algorithm can achieves a better performance in terms of the achievable sum rate, average achievable rate, and outage probability.     

Performance analysis of overlay cognitive NOMA network with imperfect SIC and imperfect CSI

In this paper, we investigate a multiple users cooperative overlay cognitive radio non-orthogonal multiple access (CR-NOMA) network in the presence of imperfect successive interference cancellation (SIC) and imperfect channel state information (CSI). In the context of cellular network, cell-center cognitive secondary users act as relays to assist transmission from the primary user (PU) transmitter to the cell-edge PU receiver via NOMA. According to the received signals between the primary transmitter and multiple cognitive secondary center users, the best cell-center cognitive SU with the maximum signal to noise ratio (SNR) is selected to transmit the PU’s signals and its own signal to cell-edge users through NOMA principle. Then, the PU cell-edge user combine the signals received from direct transmission in the first phase and relay transmission from the best cell-center cognitive SU in the second phase by selection combining (SC). To measure the performance of the system quantitatively, we derive the end-to-end outage probability and capacity for the primary and secondary networks by taking the imperfect SIC and CSI into consideration. Finally, the performance analysis is validated by the simulations, and show that serious interference caused by imperfect SIC and (or) imperfect CSI reduce the system performance.     

Secrecy communications of intelligent reflecting surfaces aided NOMA networks

This paper investigates the physical layer security of an intelligent reflecting surface (IRS) aided non-orthogonal multiple access (NOMA) networks, where a remote user is regarded as an eavesdropper to intercept the information of nearby user. To evaluate the security performance of IRS-aided NOMA networks, a problem of maximizing achievable secrecy rate is formulated via jointly optimizing the beamforming and phase shifting. More specifically, we aim to tackle the non-convex problem by optimizing beamforming vector as well as phase shifting matrix with the assistance of block coordinate descent (BCD) and minorization maximization (MM) algorithms. Numerical results illustrate that: 1) The secrecy rates of IRS-aided NOMA with BCD and MM algorithms are superior to that of orthogonal multiple access schemes; 2) With increasing the number of reflecting elements, the secrecy rates of IRS-aided NOMA networks are achieved carefully; and 3) The IRS-aided NOMA networks are capable of relieving the transmission pressure of base station.     

Matching theory based user-grouping for indoor non-orthogonal multiple access visible light communication heterogeneous networks

This Letter proposes a model of indoor visible light communication(VLC) heterogeneous networks entirely based on LEDs with different specifications and applies non-orthogonal multiple access(NOMA) to it because of the narrow modulation bandwidth of LEDs. Moreover, a user-grouping scheme that is based on matching theory is proposed to improve the network achievable sum rate. Simulation results indicate that when each NOMA cluster contains 6 users, the proposed scheme has a 49.54% sum-rate enhancement compared with the traditional user-grouping scheme. As the number of users in each NOMA cluster increases, the proposed scheme performs better at the cost of computational complexity.     

Subchannel and power optimization for sum rate maximization in downlink multicarrier NOMA networks

In a multicarrier NOMA system, the subchannel allocation (SA) and power allocation (PA) are intricately linked and essential for improving system throughput. Also, for the successful execution of successive interference cancellations (SIC) at the receiver, a minimum power gap is required among users. As a result, this research comes up with optimization of the SA and PA to maximize the sum rate of the NOMA system while sticking to the minimum power gap constraint in addition to minimum user rate, maximum number of users in a subchannel and power budget constraints for downlink transmission in multicarrier NOMA networks. To ensure that the formulated problem can be solved in polynomial time, we propose solving it in two stages; SA followed by PA. To obtain SA, we investigate four algorithms: Greedy, WSA, WCA, and WCF. For PA, we propose a low-complexity algorithm. We compare the performance of the proposed method with benchmark method that does not consider the minimum power gap constraint. We conclude that employing WCF algorithm with the PA algorithm gives the best sum rate performance.     

CEAR: A cooperation based energy aware reward scheme for next generation green cognitive radio networks

Cognitive Radio (CR) networks are envisioned as a key empowering technology of the fifth-generation (5G) wireless communication networks, which solves the major issues of 5G, like high-speed data transmission, seamless connectivity, and increased demand for mobile data. Another significant characteristic of the 5G network is green communications, as energy consumption from the communication field is predicted to rise remarkably by the year 2030. In this work, we are concerned about energy-related issues and propose a cooperation-based energy-aware reward scheme (CEAR) for next-generation green CR networks. The proposed CEAR scheme is based on the antenna and temporal diversity of the primary users (PUs). For providing the service to the PUs, the users of another network called cognitive users (CUs) work as a cooperative relay node, and, in return, they get more spectrum access opportunities as a reward from the primary network. The CUs with delay-tolerant data packets take a cooperative decision by recognizing the availability and traffic load of PUs, channel state information, and data transmission requirements. We utilize the optimal stopping protocol for solving the decision-making problem and use the backward induction method to obtain the optimal cooperative solution. The simulation results reveal notable enhancements in energy efficiency (EE) of CUs compared with other cooperative schemes. The proposed CEAR scheme is more energy-efficient for ultra-dense network deployment because results show that the CU’s EE, spectral efficiency (SE), and throughput improved with the increase of PUs.     

Joint user grouping and power allocation in VLC-NOMA system

With the energy consumption of wireless networks increasing, visible light communication (VLC) has been regarded as a promising technology to realize energy conservation. Due to the massive terminals access and increased traffic demand, the implementation of non-orthogonal multiple access (NOMA) technology in VLC networks has become an inevitable trend. In this paper, we aim to maximize the energy efficiency in VLC-NOMA networks. Assuming perfect knowledge of the channel state information of user equipment, the energy efficiency maximization problem is formulated as a mixed integer nonlinear programming problem. To solve this problem, the joint user grouping and power allocation (JUGPA) is proposed including user grouping and power allocation. In user grouping phase, we utilize the average of channel gain among all user equipment and propose a dynamic user grouping algorithm with low complexity. The proposed scheme exploits the channel gain differences among users and divides them into multiple groups. In power allocation phase, we proposed a power allocation algorithm for maximizing the energy efficiency for a given NOMA group. Thanks to the objective function is fraction form and non-convex, we firstly transform it to difference form and convex function. Then, we derive the closed-form optimal power allocation expression that maximizes the energy efficiency by Dinkelbach method and Lagrange dual decomposition method. Simulation results show that the JUGPA can effectively improve energy efficiency of the VLC-NOMA networks.     

A matching-theoretic approach to resource allocation in D2D-enabled downlink NOMA cellular networks

This paper investigates the resource allocation problem in non-orthogonal multiple-access (NOMA) cellular networks underlaid with OMA-based device-to-device (D2D) communication. This network architecture enjoys the intrinsic features of NOMA and D2D communications; namely, spectral efficiency, massive connectivity, and low-latency. Despite these indispensable features, the combination of NOMA and D2D communications exacerbates the resource allocation problem in cellular networks due to the tight coupling among their constraints and conflict over access to shared resources. The aim of our work is to maximize the downlink network sum-rate, while meeting the minimum rate requirements of the cellular tier and underlay D2D communication, and incorporating interference management as well as other practical constraints. To this end, many-to-many matching and difference-of-convex programming are employed to develop a holistic sub-channels and power allocation algorithmic solution. In addition to analyzing the properties of the proposed solution, its performance is benchmarked against an existing solution and the traditional OMA-based algorithm. The proposed solution demonstrates superiority in terms of network sum-rate, users’ connectivity, minimum rate satisfaction, fairness, and interference management, while maintaining acceptable computational complexity.     

A joint resource allocation method for hybrid NOMA MEC offloading

In this article, a joint resource allocation of power, time, and sub-channels that minimizes the total energy consumption of users for Hybrid NOMA MEC Offloading is proposed. By formulating and solving the joint optimization problem, first we propose a novel optimal Hybrid NOMA scheme referred to as Switched Hybrid NOMA (SH-NOMA) for power and time allocation. Subsequently, we address sub-channel allocation as a three-dimensional assignment problem, and propose the Total-Reward Exchange Stable (TES) algorithm to solve it. Analytically, we show that SH-NOMA is more energy efficient than the Hybrid NOMA scheme in the literature and that the TES algorithm converges to a solution with less energy consumption than the widely used two-sided exchange stable algorithm. Finally, via simulations we demonstrate that the proposed methods outperform the results in the literature.     

Contract Theory-Based Incentive Mechanism for Full Duplex Cooperative NOMA with SWIPT Communication Networks

Cooperative Non-Orthogonal Multiple Access (NOMA) with Simultaneous Wireless Information and Power Transfer (SWIPT) communication can not only effectively improve the spectrum efficiency and energy efficiency of wireless networks but also extend their coverage. An important design issue is to incentivize a full duplex (FD) relaying center user to participate in the cooperative process and achieve a win–win situation for both the base station (BS) and the center user. Some private information of the center users are hidden from the BS in the network. A contract theory-based incentive mechanism under this asymmetric information scenario is applied to incentivize the center user to join the cooperative communication to maximize the BS’s benefit utility and to guarantee the center user’s expected payoff. In this work, we propose a matching theory-based Gale–Shapley algorithm to obtain the optimal strategy with low computation complexity in the multi-user pairing scenario. Simulation results indicate that the network performance of the proposed FD cooperative NOMA and SWIPT communication is much better than the conventional NOMA communication, and the benefit utility of the BS with the stable match strategy is nearly close to the multi-user pairing scenario with complete channel state information (CSI), while the center users get the satisfied expected payoffs.     

Deployment and trajectory design of fixed-wing UAVs in NOMA assisted wireless networks

This paper proposes a deployment and trajectory scheme for fixed-wing unmanned aerial vehicles (UAVs) deployed as flying base stations in multi-UAV enabled non-orthogonal multiple access (NOMA) downlink communication. Specifically, the deployment of UAVs and power allocation of users are jointly optimized to maximize the sum-rate. Thereafter, the energy efficiency maximization problem is formulated to optimize the trajectory of UAVs by jointly considering the quality of service (QoS) requirement of users, various flight constraints, limited on-board energy, and users’ mobility. Initially, the existing users are divided into clusters by k-means clustering, where each cluster is served by a single UAV. Then, the clusters are further divided into multiple sub-clusters, each having a pair of near and far users. Orthogonal multiple access (OMA) is applied among sub-clusters and NOMA is applied to intra sub-cluster users. Lastly, the Balanced-grey wolf optimization (B-GWO) algorithm is proposed for solving the non-convex optimization problems. Simulation results prove the superiority of the B-GWO based deployment and trajectory algorithms compared to the benchmarks. In addition, the proposed B-GWO based trajectory algorithm achieves a near-optimal performance with an optimality gap of less than 1.5% compared to the exhaustive search.     

Leakage-based beamforming via game theory for reverse spectrum auction in cellular offloading systems

The explosion of mobile traffic and highly dynamic property often make it increasingly stressful for a cellular service provider to provide sufficient cellular spectrum resources to support the dynamic change of traffic demand in a day. In this paper, considering the dynamic characteristic of the cellular network traffic demand, we not only proposed an optimal, truthful reverse auction incentive framework, but also proposed a valuation function which is based on third-party access points’ capacity. We consider spectrum sharing in a third-party network where several secondary users (SUs) share spectrum with a primary user (PU). A leakage-based beamforming algorithm is proposed via game theory to maximize the sum utility of third-party access points subject to the signal-to-leakage-and-noise (SLNR) constraint of SUs and PU interference constraint. The sum throughput maximization problem is formulated as a non-cooperative game, where the SUs compete with each other over the resources. Nash equilibrium is considered as the solution of this game. Simulation results show that the proposed algorithm can achieve a high sum throughput and converge to a locally optimal beamforming vector.     

Energy-efficient beamforming optimization for MISO communication based on reconfigurable intelligent surface

Reconfigurable intelligent surface (RIS), a planar metasurface consisting of a large number of low-cost reflecting elements, has received much attention due to its ability to improve both the spectrum and energy efficiency (EE) by reconfiguring the wireless propagation environment. In this paper, we propose a base station (BS) beamforming and RIS phase shift optimization technique that maximizes the EE of a RIS-aided multiple-input–single-output system. In particular, considering the system circuits’ energy consumption, an EE maximization problem is formulated by jointly optimizing the active beamforming at the BS and the passive beamforming at the RIS, under the constraints of each user’ rate requirement, the BS’s maximal transmit power budget and unit-modulus constraint of the RIS phase shifts. Due to the coupling of optimization variables, this problem is a complex non-convex optimization problem, and it is challenging to solve it directly. To overcome this obstacle, we divide the problem into active and passive beamforming optimization subproblems. For the first subproblem, the active beamforming is given by the maximum ratio transmission optimal strategy. For the second subproblem, the optimal phase shift matrix at the RIS is obtained by exploiting sine cosine algorithm (SCA). Moreover, for this case where each reflection element’s working state is controlled by a circuit switch, each reflection element’s switch value is optimized with the aid of particle swarm optimization algorithm. Finally, numerical results verify the effectiveness of our proposed algorithm compared to other algorithms.     

Stochastic geometry analysis of coverage probability in energy efficient dense heterogeneous network with sleep control mechanism

The upsurge of data traffic in the macrocell networks has led to the massive deployment of small cells (SCs) for load sharing. Though small cell power consumption is low individually, significant aggregate power consumption, together with the MBS transmission power that is exponentially increasing over its served user load, deteriorates the network energy efficiency (EE). Consequently, EE is seen as a critical requirement for wireless network design in future. The challenge of EE maximization in dense heterogeneous networks is investigated in this research through a strategic small cell sleeping technique. A heuristic method based on distance and load awareness strategy in which the small cells with fewer users near the macrocell will be put into sleep mode. User equipments (UEs) under the service of the sleep small cells will be offloaded to the MBS. A dense heterogeneous network is considered with overlapped small cell coverage regions. All the edge area SCs will be kept ON to avoid QoS degradation. Simulation analysis on the stochastic geometry model indicates that the proposed sleep strategy significantly improves the network EE than the prevailing sleep control strategies while assuring seamless, efficient coverage in the sleep cell areas.     

Physical layer secrecy analysis of HS-UAV NOMA network with spatially random eavesdroppers

This work investigates the physical layer secrecy performance of a hybrid satellite/unmanned aerial vehicle (HS-UAV) terrestrial non-orthogonal multiple access (NOMA) network, where one satellite source intends to make communication with destination users via a UAV relay using NOMA protocol in the existence of spatially random eavesdroppers. All the destination users randomly distributed on the ground comply with a homogeneous Poisson point process in the basis of stochastic geometry. Adopting Shadowed-Rician fading in satellite-to-UAV and satellite-to-eavesdroppers links while Rayleigh fading in both UAV-to-users and UAV-to-eavesdroppers links, the theoretical expressions for the secrecy outage probability (SOP) of the paired NOMA users are obtained based on the distance-determined path-loss. Also, the asymptotic behaviors of SOP expressions at high signal-to-noise ratio (SNR) regime are analyzed and the system throughputs of the paired NOMA users are examined for gaining further realization of the network. Moreover, numerical results are contrasted with simulation to validate the theoretical analysis. Investigation of this work shows the comparison of SOP performance for the far and near user, pointing out the SOP performance of the network depends on the channel fading, UAV coverage airspace, distribution of eavesdroppers and some other key parameters.     

Robust secrecy rate maximization in IRS-assisted cognitive radio networks

In this paper, we focus on the secrecy rate maximization problem in intelligent reflecting surface (IRS)-assisted cognitive radio (CR) networks. In order to improve the security, there is a common scheme to add artificial noise (AN) to the transmitted signal, which is also applied in this paper. Further, in CR networks, the secondary users always cannot obtain accurate channel state information (CSI) about the primary user and eavesdropper. By taking jointly design for the IRS phase shift matrix, the transmitted beamforming of the secondary base station (BS), and the covariance matrix of AN, our objective is to maximize the minimal secrecy rate of all secondary users. Due to the serious coupling among the designed variables, it cannot be solved by conventional methods. We propose an alternating optimization (AO) algorithm. In simulation results, we apply primary users and secondary users randomly distributed in the communication area, which numerically demonstrate the superiority of our proposed scheme.     

Low complexity optimization for user centric cellular networks via large dimensional analysis

Users near cell edges suffer from severe interference in traditional cellular networks. In this paper, we consider the scenario that multiple nearby base stations (BSs) cooperatively serve a group of users which is referred to as the cell free networks. A low complexity optimization method based on the large dimensional analysis is proposed. The advantage of the cell free networks is that the interference caused in the cell edge users can be converted into intended signal. It is not easy to obtain the optimal solution to the network due to coupled relations among the users’ rates. To obtain a suboptimal solution, a precoder that balances signal and interference is adopted to maximize the network capacity. In traditional optimization, it requires instantaneous channel state information. We try to optimize the network sum rate based on the large dimensional analysis. In this way, the optimization can be transformed into another problem that merely depend on the large scale channel statistics. Large dimensional analysis is leveraged to derive the asymptotic signal to interference plus noise ratio that only depends on large scale channel statistics. Based on this result, the power allocation problem does not need to adapt as frequently as the instantaneous channel state information. By this means, signal exchange overhead can be greatly reduced. Numerical results are provided to validate the efficacy of the proposed optimization method.     

Exploring cooperative NOMA assisted hybrid visible light and radio frequency for enhanced vehicular message dissemination at road intersections

Vehicle-to-everything (V2X) communication aims to achieve significantly improved safety and traffic efficiency, more particularly at road intersection where high percentage of accidents usually occur. The existing vehicular radio frequency (V-RF) based V2X utilizes relaying for improving safety message dissemination at road intersections. For a high traffic density scenario, the V-RF communication with relaying solution may suffer from large latency and low packet delivery rates due to channel congestion. In this paper, we explore cooperative non-orthogonal multiple access (NOMA) communication assisted hybrid vehicular visible light communication (V-VLC) and V-RF communication for improving safety message dissemination and enabling massive connectivity among vehicles for road intersection scenarios. We develop a stochastic geometry based analytical framework to model cooperative NOMA (C-NOMA) transmissions subject to interference imposed by other vehicles on roads. We also examine the impact of vehicles headlights radiation pattern viz. Lambertian and empirical path loss models on statistical characterization of the proposed C-NOMA supported hybrid solution. Our numerical findings reveal that C-NOMA assisted hybrid V-VLC/V-RF system leads to considerable improvement in outage performance and average achievable rate as compared to traditional V-RF solution with relaying. Interestingly, Lambertian model offers a lower outage and higher average achievable rate compared to the empirical model for the proposed hybrid solution. Further, we observe the performance improvement using maximal ratio combining (MRC) considering NOMA transmission for the proposed hybrid solution. The presented framework may serve as an alternative for cooperative intelligent transportation system (C-ITS) to meet diverse application needs for beyond 5G (B5G) V2X networks.     

Network capacity of cognitive radio relay network

After successful dynamic spectrum access, cognitive radio (CR) must be able to relay the message/packets to the destination node by utilizing existing primary system(s) (PS) and/or cooperative/cognitive radio nodes in the cognitive radio network. In this paper, we pioneer the exploration of the fundamental behaviors of interference between CRs and PS in such a relay network via network coding. Interference on PS’s network capacity is shown to be unavoidable and unbounded in the one-hop relay network. Extending to the tandem structure, interference is unbounded but avoidable by appropriate constraints. In cooperative relay network, interference is bounded and avoidable. Moreover, parallel cooperative relay network can accommodate more CR transmission pairs. Such an analysis can be generalized to arbitrary networks. We derive that interference is avoidable when at least one route from CR’s source to the sink bypasses the bottlenecks of PS. Then under the constraint of no interference to PS, we derive CR’s maximum network capacity in such a network. Link allocation to achieve the maximum network capacity can be formulated and solved as a linear programming problem. Consequently, given any network topology, we can determine whether CR’s interference is avoidable, and maximize CR’s network capacity without interfering PS’s network capacity. Simulation results on randomly generated network topologies show that CR’s network capacity achieves on average 1.3 times of PS’s network capacity with interference avoidance constraint, and demonstrates spectrum efficiency at networking throughput and high availability.     

On the performance of a virtual user pairing scheme to efficiently utilize the spectrum of unpaired users in NOMA

This paper evaluates the performance of a virtual user pairing scheme that efficiently utilizes the spectrum of unpaired users in non-orthogonal multiple access (NOMA), termed as VP-NOMA. The scheme aims at utilizing the frequency bands of those users which remain unpaired due to the non-uniform distribution of users in a cellular area. We consider a case where the cell edge users are more than the cell center users, so that complete one-to-one correspondence does not exist between all cell center and cell edge users to be accommodated/paired using conventional NOMA (C-NOMA) user pairing. Thus, some cell edge users remain unpaired, and are served using conventional multiple access (OMA) schemes. In such scenario, VP-NOMA pairs a single cell center user with two or more clustered (closely located) cell edge users over non-overlapping frequency bands, thus enabling the cell center user to efficiently use the frequency bands of these previously unpaired cell edge users. Performance of VP-NOMA in terms of ergodic sum capacity (ESC), outage probability (OP), and outage sum capacity (OSC), is analyzed through comprehensive mathematical derivations and simulations for a generalized system model. Moreover, the mathematical analysis is validated through close concordance between analytical and simulation results of ESC, OP, and OSC.