This paper investigates the maximization of energy efficiency in an active simultaneously transmitting and reflecting reconfigurable intelligent surface (ASTARS) empowered non-orthogonal multiple access (NOMA) satellite Internet of Things (IoT) with finite blocklength (FBL) transmission. The activated IoT devices (AIoTDs) in the coverage area can be divided into activated reflection-region IoTDs (ARDs) and activated transmission-region IoTDs (ATDs). Drawing on the capability of ASTARS to efficiently establish cascaded links across full-space, thereby enabling more effective adjustment of uplink signal transmission. The ASTARS can properly amplify signal to mitigate its multiplicative fading and long-distance uplink transmission attenuation, which introduce new degrees of freedom (DoF) into the optimization process. Considering the massive-access requirements of the constrained battery-life IoTDs, two device pairing strategies, namely strong-ARD poor-ATD pairing (SPP) and strong-ARD strong-ATD pairing (SSP), are proposed to enable short-packet data transmission. The penalty alternating iterative algorithm (PAIA) is proposed by jointly optimizing the binary matching coefficient, ASTARS coefficient matrix and AIoTDs transmission power. Simulation results illustrate that 1) the ASTARS can achieve the highest energy efficiency than the passive simultaneously transmitting and reflecting surface (PSTARS) and without reconfigurable intelligent surface (RIS); 2) SPP demonstrates superior performance compared to SSP; 3) an optimal number of ASTARS elements exists for each distinct ASTARS maximum amplification coefficient.

Low earth orbit (LEO) satellite-assisted edge computing is a promising paradigm for providing seamless connectivity to remote areas and enabling reliable services in dense urban scenarios. By establishing virtual line-of-sight (LoS) links and coherently combining reflected signals, the reconfigurable intelligent surface (RIS) can significantly enhance the channel conditions of satellite-terrestrial communication links. This paper integrates the novel simultaneously transmitting and reflecting beyond-diagonal RIS (STAR-BD-RIS) technology into the LEO satellite collaborative edge computing (LSCEC) system, where each ground user equipment (GUE) can simultaneously offload task bits to multiple satellites via a hybrid non-orthogonal multiple access (NOMA) scheme with the aid of STAR-BD-RIS. To minimize the weighted sum energy consumption of GUEs and LEO satellites, the transmit power, CPU frequency, offloading strategy, bandwidth allocation, and STAR-BD-RIS beamforming are jointly optimized by an iterative algorithm based on the successive convex approximation (SCA) technique and the penalty dual decomposition (PDD) method. Simulation results illustrate that: 1) the STAR-BD-RIS scheme achieves lower energy consumption than traditional RIS (T-RIS), simultaneously transmitting and reflecting RIS (STAR-RIS) and beyond-diagonal RIS (BD-RIS) schemes in LSCEC systems; 2) the proposed hybrid NOMA demonstrates superior scalability and performance over both NOMA and orthogonal frequency division multiple access (OFDMA); 3) the proposed satellite collaboration scheme significantly reduces the energy consumption compared to the case without collaboration, while exhibiting diminishing energy-saving gains as the number of satellites increases.

With the increasing carrier frequency in next-generation wireless networks, conventional discrete aperture arrays (DAPA) are unable to fully meet the growing demands of sixth-generation wireless networks. To provide a higher degree of freedom, the continuous-aperture array (CAPA) technique emerges as a promising solution for next-generation networks. In this article, a CAPA-aided network is proposed to deliver access services to multiple users. The transmission power of a two-user pair in the downlink is optimized by using the Fourier transformation to derive tractable results. With the aid of Karush-Kuhn-Tucker (KKT) conditions, closed-form expressions for both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) are derived. Time division multiple access (TDMA) and spatial division multiple access (SDMA) are also considered as OMA schemes. Furthermore, DAPA is included as a benchmark for comparison. Our analytical and numerical results demonstrate the following: i) The proposed KKT-based approach achieves optimal performance in transmission power; ii) The minimal required transmission power for NOMA is lower than that for the OMA benchmark schemes; iii) A performance gap is observed between CAPA and DAPA in both NOMA and OMA, emphasizing the advantages of CAPA-aided networks.

Fluid antenna systems (FAS) are among the most promising technologies for the sixth generation (6G) mobile communication networks. Unlike traditional fixed-position multiple-input multiple-output (MIMO) systems, a FAS possesses position reconfigurability to switch on-demand amongNpredefined ports over a prescribed space. This paper explores the performance of a single-input single-output (SISO) model with a fixed-position antenna transmitter and a single-antenna FAS receiver, referred to as the Rx-SISO-FAS model, under spatially-correlated Rician fading channels. Our contributions include exact expressions and closed-form bounds for the outage probability of the Rx-SISO-FAS model, as well as exact and closed-form lower bounds for the ergodic rate. Importantly, we also analyze the performance considering both uniform linear array (ULA) and uniform planar array (UPA) configurations for the ports of the FAS. To gain insights, we evaluate the diversity order of the proposed model and our analytical results indicate that with a fixed overall system size, increasing the number of ports,N, significantly decreases the outage performance of FAS under different Rician fading factors. Our numerical results further demonstrate that:i) the Rx-SISO-FAS model can enhance performance under spatially-correlated Rician fading channels over the fixed-position antenna counterpart;ii) the Rician factor negatively impacts performance in the low signal-to-noise ratio (SNR) regime;iii) FAS can outperform anLbranches maximum ratio combining (MRC) system under Rician fading channels; andiv) when the number of ports is identical, UPA outperforms ULA.

Fluid antenna systems (FAS) are among the most promising technologies for the sixth generation (6G) mobile communication networks. A FAS possesses position reconfigurability to switch on-demand among$N$predefined ports over a prescribed space. This paper explores the performance of a singleinput single-output (SISO) model with a fixed-position antenna transmitter and a single-antenna FAS receiver, referred to as the Rx-SISO-FAS model, under spatially-correlated Rician fading channels. Our contributions include exact expressions and closedform bounds for the outage probability of the Rx-SISO-FAS model with uniform linear array port configuration. To gain insights, we evaluate the diversity order of the proposed model and our analytical results indicate that with a fixed overall system size, increasing the number of ports,$N$, significantly decreases the outage performance of FAS under different Rician fading factors. Our numerical results further demonstrate that:$i$) the Rx-SISO-FAS model can enhance performance under spatiallycorrelated Rician fading channels over the fixed-position antenna counterpart;$i i)$the Rician factor negatively impacts performance in the low signal-to-noise ratio (SNR) regime.

As the landscape of sixth-generation (6G) wireless networks advances, low-Earth-orbit (LEO) satellite communication emerges as a promising solution for offering comprehensive global communication services, though its potential is challenged by severe large-scale path loss that significantly impairs the received channel capacity available to terrestrial users. To address this limitation, this paper investigates a federated learning aided distributed beamforming network for LEO satellite communications, namely the FederSat network. In order to enhance the average achievable rate of the LEO satellite networks, we propose a novel code-book-based distributed beamforming strategy in the FederSat networks. Through numerical analysis, we demonstrate that the proposed FederSat networks substantially outperform in average achievable rate. Additionally, more LEO satellites are encouraged for further enhancing the received signal power, which indicates that a mega-constellation LEO satellite network is preferable.

To address the issues of limited computational capability and constrained battery life faced by users in the Internet of Things, wireless-powered mobile edge computing (MEC) has been proposed as a promising solution. However, the efficiency of its key functions, namely task offloading and energy transfer, can be significantly impaired if the direct links between the access point (AP) and users are obstructed. Inspired by the potentials of active simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) for achieving full-space coverage and mitigating multiplicative fading effects, this paper investigates the incorporation of active STAR-RIS in wireless-powered MEC. To meet the high data rate requirements in future smart environments, we aim to maximize the total number of completed task bits. To address the formulated challenging non-convex problem, a resource allocation and active beamforming algorithm (RAABA) is first proposed for a basic two-user non-orthogonal multiple access (NOMA) scenario, jointly optimizing the energy transfer time, decoding order, transmit power, CPU frequency of users, and beamforming of STAR-RIS. We then extend the RAABA to general multi-user scenarios (RAABAM) by leveraging a matching-theory-based user pairing algorithm. Furthermore, a low-complexity RAABAM (L-RAABAM) is proposed by simplifying the matching process and deriving a closed-form expression for the optimal transmit power of users. Simulation results show that: i) by jointly optimizing multiple highly-coupled variables, our proposed RAABAM and L-RAABAM schemes achieve a higher total number of completed task bits; ii) the active STAR-RIS significantly outperforms passive/active traditional RIS and passive STAR-RIS; iii) the deployment rules for active STAR-RIS differ from those for passive STAR-RIS in wireless-powered MEC, where the optimal deployment location of active STAR-RIS depends on the number of its elements.

Simultaneous transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS) technique has recently received considerable attention due to its omni-directional radiation capability. In this paper, motivated by the interference-mitigation-based (IMB) and signal-enhancement-based (SEB) designs, we introduce an innovative STAR-RIS assisted simultaneous-signal-enhancement-and-interference-mitigation (SSEIM) design in non-orthogonal multiple access (NOMA) multiple-input single-output cellular communication networks. Our objective is to maximize the system spectral efficiency (SE) by jointly optimizing the reflection and transmission phase shifts at the STAR-RIS, the precoding matrix of BSs, and the power allocation factors of NOMA users. We propose a low-complexity simultaneous enhancement and mitigation algorithm. Furthermore, by exploiting the manifold optimization technique, we introduce the Riemannian conjugate gradient algorithm to solve the non-convex subproblems with unit modulus constraint. Our analysis reveals that the proposed SSEIM design exceeds the traditional RIS-aided SEB and IMB designs.

Satellite communication is a crucial component for achieving global communications in sixth generation (6G). Due to the large distance between the satellite and the terrestrial users, the multiplicative fading effects of traditional passive simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) are more severe in satellite communication. Recently, active simultaneous transmitting and reflecting surfaces (ASTARSs) have been proposed to mitigate multiplicative fading by amplifying the incident signal. Motivated by the above, a novel ASTARS-aided non-orthogonal multiple access (NOMA) satellite communication network is proposed in this paper, which includes one low earth orbit (LEO) satellite, one ASTARS and several far-field users (FFUs) and near-field users (NFUs). We propose an adaptive NOMA pairing strategy that allows the satellite to flexibly select pairing schemes. Depending on the users’ quality of service requirements, either the NFU-FFU (NF) pairing scheme or the NFU-NFU (NN) pairing scheme can be used. Then, we formulate an optimization problem to maximize the channel capacity. A two-layer optimization algorithm is proposed, where the outer layer selects the NOMA user pairing schemes, while the inner layer alternately optimizes the NOMA power allocation factors, the satellite precoding matrix and the ASTARS phase shift matrices. To solve the non-convex optimization problem, successive convex approximation and a penalty-based method are employed. The numerical results reveal: 1) The channel capacity of the NF pairing scheme is always higher than that of the NN pairing scheme; 2) Compared to the “Without RIS" and “Passive STARS" schemes, ASTARS can significantly improve the channel capacity of satellite communication.

In this paper, a novel unmanned aerial vehicle (UAV) secure communication system enhanced by the passive coupled phase-shift simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR- RIS) is investigated. Considering the multiple-user and multiple-eavesdropper systems, we aim to maximize the minimum average secrecy rate by the joint design of the UAV base station's (UAV-BS's) beamforming, the UAV's trajectory, and the STAR-RIS's transmission and reflection (T&R) coefficients. Since the formulated optimization problem is highly non-convex with intricately coupled variables, we decompose it into three subproblems, and resolve them alternately by semi-definite relaxation, successive convex approximation, and penalty-based approach. Simulation results show that: 1) the practical coupled phase-shift model of passive STAR-RIS suffers a security performance degradation compared with the ideal independent phase-shift model, but it still outperforms the conventional RIS; 2) the UAV's trajectory tends to be closer to the STAR-RIS to fully exploit the benefits of STAR-RIS; 3) the amplitudes of T&R coefficients highly depend on the UAV's real-time trajectory.

This article investigates the simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted nonorthogonal multiple access (NOMA) systems with one cooperative jammer and dual eavesdroppers. To guarantee the uplink secure transmission, we maximize the sum secrecy rate under both the perfect and imperfect channel state information (CSI) by jointly optimizing the channel allocation, transmit power, and coefficient matrices. For the problem with perfect CSI, a deep reinforcement learning algorithm is proposed based on the deep deterministic policy gradient (DDPG) framework. Then, by introducing the arbitrary distorted noise to the state space, the proposed algorithm is extended to solve the problem under imperfect CSI without causing additional computational complexity. Simulation results illustrate that: 1) the symmetry of STAR-RIS results in severe information leakage and the sum secrecy rate further degrades when the dual eavesdroppers collaborate with each other; 2) the STAR-RIS with independent phase shift can achieve higher sum secrecy rate than that with coupled phase shift, while the performance gap is trivial when there are fewer STAR-RIS elements; and 3) our proposed algorithm can compensate for the impacts of the imperfect CSI, and the sum secrecy rate decreases with the increase of CSI uncertainty.

In this paper, a novel concept called simultaneously transmitting and reflecting RIS (STAR-RIS) is introduced into the wireless-powered mobile edge computing (MEC) systems to improve the efficiency of energy transfer and task offloading. Compared with traditional reflecting-only RIS, STAR-RIS extends the half-space coverage to full-space coverage by simultaneously transmitting and reflecting incident signals, and also provides new degrees-of-freedom (DoFs) for manipulating signal propagation. We aim to maximize the total computation rate of all users, where the energy transfer time, transmit power and CPU frequencies of users, and the configuration design of STAR-RIS are jointly optimized. Considering the characteristics of STAR-RIS, three operating protocols, namely energy splitting (ES), mode switching (MS), and time splitting (TS) are studied, respectively. For the ES protocol, based on the penalty method, successive convex approximation (SCA), and the linear search method, an iterative algorithm is proposed to solve the formulated non-convex problem. Then, the proposed algorithm for ES protocol is extended to solve the MS and TS problems. Simulation results illustrate that the STAR-RIS outperforms traditional reflecting/transmitting-only RIS. More importantly, the TS protocol can achieve the largest computation rate among the three operating protocols of STAR-RIS.

The wireless-powered mobile edge computing (MEC) has been considered as a promising approach to enhance the computation capability and prolong the lifetime of user equipments (DEs). To further improve the efficiency of energy transfer and task offloading, in this paper, a novel concept called simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) is introduced into the wireless-powered MEC. The total computation rate maximization problem is investigated by jointly optimizing the energy transfer time, UEs' resource allocation, and configuration design of STAR-RIS via a linear search algorithm based on the block coordinate decent (BCD) and successive convex approximation (SCA) techniques. Simulation results illustrate that our proposed algorithm can achieve a higher computation rate than the scheme with conventional RIS and that without local computing.

In this paper, we study the total effective capacity (EC) of single-cluster non-orthogonal multiple access (NOMA) networks and demonstrate the performance gain of single-cluster NOMA over user-paired NOMA and orthogonal multiple access (OMA). Specifically, the exact closed-form expression and an approximate closed-form expression at high signal-to-noise ratios (SNRs), in terms of the total EC, are derived for single-cluster NOMA networks. The derivations reveal that the total EC at high SNRs only relies on the statistical delay requirement of the strongest user and is independent of the other users' delay requirements. Further, we theoretically analyze the total EC differences between single-cluster NOMA and user-paired NOMA/OMA communications and explore the impact of transmit SNR. Simulation results verify the accuracy of analytical results and further reveal that the single-cluster NOMA network achieves a greater gain in terms of the total EC, compared to the conventional OMA, when the number of users increases.

The recent advances in low earth orbit (LEO) satellites enable the satellites to provide task processing capability for remote Internet-of-Things (IoT) mobile devices (IMDs) without proximal multiaccess edge computing (MEC) servers. In this article, by leveraging the LEO satellites, a novel MEC framework for terrestrial-satellite IoT is proposed. With the aid of terrestrial-satellite terminal (TST), the computation offloading from IMDs to LEO satellites is divided into two stages in the ground and space segments. In order to minimize the weighted-sum energy consumption of IMDs, we decompose the formulated problem into two layered subproblems: 1) the lower layer subproblem minimizing the latency of space segment, which is solved by sequential fractional programming with attaining the first-order optimality and 2) the upper layer subproblem that is solved by exploiting the convex structure and applying the Lagrangian dual decomposition method. Based on the solutions to the two layered subproblems, an energy-efficient computation offloading and resource allocation algorithm (E-CORA) is proposed. By simulations, it is shown that: 1) there exists a specific amount of offloading bits, which can minimize the energy consumption of IMDs and the proposed E-CORA outperforms full offloading and local computing only; 2) larger transmit power of the TST helps to save the energy of IMDs; and 3) by increasing the number of visible satellites, the ratio of offloading bits increases while the energy consumption of IMDs can be decreased.

Multi-access mobile edge computing (MEC) allows each user to offload partial task bits to multiple MEC servers via multi-access radio transmission, enabling the collaboration of edge computing. By leveraging the advantage of nonorthogonal multiple access (NOMA) in improving the transmission efficiency, it is expected to effectively reduce the energy consumption of users in multi-access MEC with the aid of NOMA. However, considering the co-channel interference of NOMA and computation collaboration among MEC servers, the joint task offloading and resource allocation is a challenging problem. In this article, with the objective to minimize the weighted sum energy of users, we first propose optimal task offloading and resource allocation algorithms for the special single-user (OTORA-SU) and general multi-user (OTORA-MU) cases, by exploiting the convex and layered structure of the formulated problem. Interestingly, it is found that the single user always preferentially offloads task bits to the MEC servers with better channel gains, regardless of the computation capacity of each MEC server. Then, a low-complexity algorithm (LTORA-MU) is proposed for the general multi-user case, which converges fast and achieves near-optimal performances. Considering the channel estimation error, the impacts of imperfect channel state information (CSI) and successive interference cancellation (SIC) are also investigated. Simulation results demonstrate that for both perfect and imperfect CSI and SIC, 1) the proposed OTORA-SU and LTORA-MU outperform the local computing, full offloading, FDMA-based offloading and non-collaborative MEC schemes; 2) as the number of MEC servers grows, the ratio of offloading task bits increases while the energy consumption is decreased.

This paper conceive a large intelligent surface (LIS)aided multiple-input multiple-output network for providing wireless services to randomly roaming users. The network performance is analyzed by utilizing stochastic geometry tools. We aim for serving multiple users by jointly designing the passive beamforming weight at LISs and detection weight vectors at users. As a benefit, the interference imposed by the LISs can be suppressed. In an effort to evaluate the performance of the proposed network, we first derive approximated channel statistics for characterizing the effective channel gains. Then, we derive closed-form expressions for the ergodic rate of users. For gleaning further insights, we investigate the high-signal-to-noise-ratio (SNR) of ergodic rate. Our analytical results demonstrate that the specific fading environments encountered between the LISs and users have almost no impact on the ergodic rate attained.

This paper investigates a massive non-orthogonal multiple access (NOMA) enhanced Internet of Things (IoT) network. In order to provide massive connectivity, a novel cluster strategy is proposed, where massive devices can be served simultaneously. New channel statistics are derived. The exact and the asymptotic expressions in terms of coverage probability are derived. In order to obtain further engineering insights, short-packet communication scenarios are investigated. From our analysis, we show that the performance of NOMA enhanced IoT networks is capable of outperforming orthogonal multiple access (OMA) enhanced IoT networks.

This article investigates a nonorthogonal multiple access (NOMA)-enhanced Internet of Things (IoT) network. In order to provide connectivity, a novel cluster strategy is proposed, where multiple devices can be served simultaneously. Two potential scenarios are investigated: 1) NOMA-enhanced terrestrial IoT networks and 2) NOMA-enhanced aerial IoT networks. We utilize stochastic geometry tools to model the spatial randomness of both terrestrial and aerial devices. New channel statistics are derived for both terrestrial and aerial devices. The exact and the asymptotic expressions in terms of coverage probability are derived. In order to obtain further engineering insights, short-packet communication scenarios are investigated. From our analysis, we show that the performance of NOMA-enhanced IoT networks is capable of outperforming OMA-enhanced IoT networks. Moreover, based on simulation results, there exists an optimal value of the transmit power that maximizes the coverage probability.

Reconfigurable intelligent surfaces (RISs) constitute a promising performance enhancement for next-generation (NG) wireless networks in terms of enhancing both their spectral efficiency (SE) and energy efficiency (EE). We conceive a system for serving paired power-domain non-orthogonal multiple access (NOMA) users by designing the passive beamforming weights at the RISs. In an effort to evaluate the network performance, we first derive the best-case and worst-case of new channel statistics for characterizing the effective channel gains. Then, we derive the best-case and worst-case of our closed-form expressions derived both for the outage probability and for the ergodic rate of the prioritized user. For gleaning further insights, we investigate both the diversity orders of the outage probability and the high-signal-to-noise (SNR) slopes of the ergodic rate. We also derive both the SE and EE of the proposed network. Our analytical results demonstrate that the base station (BS)-user links have almost no impact on the diversity orders attained when the number of RISs is high enough. Numerical results are provided for confirming that: i) the high-SNR slope of the RIS-aided network is one; ii) the proposed RIS-aided NOMA network has superior network performance compared to its orthogonal counterpart.

This paper summarizes the autonomous guidance methods (AGMs) for pinpoint soft landing on celestial surfaces. We first review the development of powered descent guidance methods, focusing on their contributions for dealing with constraints and enhancing computational efficiency. With the increasing demand for reusable launchers and more scientific returns from space exploration, pinpoint soft landing has become a basic requirement. Unlike the kilometer-level precision for previous activities, the position accuracy of future planetary landers is within tens of meters of a target respecting all constraints of velocity and attitude, which is a very difficult task and arouses renewed interest in AGMs. This paper states the generalized three- and six-degree-of-freedom optimization problems in the powered descent phase and compares the features of three typical scenarios, i.e., the lunar, Mars, and Earth landing. On this basis, the paper details the characteristics and adaptability of AGMs by comparing aspects of analytical guidance methods, numerical optimization algorithms, and learning-based methods, and discusses the convexification treatment and solution strategies for non-convex problems. Three key issues related to AGM application, including physical feasibility, model accuracy, and real-time performance, are presented afterward for discussion. Many space organizations, such as those in the United States, China, France, Germany, and Japan, have also developed free-flying demonstrators to carry out related research. The guidance methods which have been tested on these demonstrators are briefly introduced at the end of the paper.

Reconfigurable intelligent surface (RIS) technique stands as a promising signal enhancement or signal cancellation technique for next generation networks. We design a novel passive beamforming weight at RISs in a multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) network for simultaneously serving paired users, where a signal cancellation based (SCB) design is employed. In order to implement the proposed SCB design, we first evaluate the minimal required number of RISs in both the diffuse scattering and anomalous reflector scenarios. Then, new channel statistics are derived for characterizing the effective channel gains. In order to evaluate the network's performance, we derive the closed-form expressions both for the outage probability (OP) and for the ergodic rate (ER). The diversity orders as well as the high-signal-to-noise-ratio (SNR) slopes are derived for engineering insights. Moreover, the network's performance of a finite resolution design has been evaluated. Our analytical results demonstrate that: i) the inter-cluster interference can be eliminated with the aid of large number of RIS elements; ii) the line-of-sight of the BS-RIS and RIS-user links are required for the diffuse scattering scenario, whereas the LoS links are not required for the anomalous reflector scenario.

This paper investigates the non-orthogonal multiple access (NOMA) enhanced Air-to-Everything (A2X) frameworks. A novel 3-Dimension unmanned aerial vehicles (UAVs) framework for providing wireless services to randomly roaming NOMA receivers (Rxs) in the sphere space is proposed by utilizing stochastic geometry tools. In an effort to evaluate the performance of the proposed framework, we first derive closed-form expressions for the outage probability of paired NOMA Rxs. For obtaining more insights, we investigate the diversity order of NOMA enhanced A2X frameworks. Our analytical results demonstrate that the diversity order and the high SNR slope of the proposed framework are m. Numerical results are provided to confirm that for the case of fixed LoS probability, the outage performance of paired NOMA Rxs mainly depends on users with poor channel conditions.

In this paper, the application of non-orthogonal multiple access (NOMA) aided multiple unmanned aerial vehicles (UAVs) wireless networks is investigated in the downlink scenario. A new multiple UAVs framework, user-centric strategy for providing emergency services in the rural area, is proposed by utilizing a stochastic geometry model. In order to provide practical insights for the proposed NOMA assisted UAV framework, an imperfect successive interference cancelation (ipSIC) scenario is taken into account. For the user-centric strategy, we derive new exact expressions for the coverage probability. The derived analytical results explicitly indicate that the ipSIC coefficient is a dominant component in terms of coverage probability.

This paper advocates a pair of strategies in non-orthogonal multiple access (NOMA) in unmanned aerial vehicles (UAVs) communications, where multiple UAVs play as new aerial communications platforms for serving terrestrial NOMA users. A new multiple UAVs framework with invoking stochastic geometry technique is proposed, in which a pair of practical strategies are considered: 1) the UAV-centric strategy for offloading actions and 2) the user-centric strategy for providing emergency communications. In order to provide practical insights for the proposed NOMA assisted UAV framework, an imperfect successive interference cancelation (ipSIC) scenario is taken into account. For both UAV-centric strategy and user-centric strategy, we derive new exact expressions for the coverage probability. We also derive new analytical results for orthogonal multiple access (OMA) for providing a benchmark scheme. The derived analytical results in both user-centric strategy and UAV-centric strategy explicitly indicate that the ipSIC coefficient is a dominant component in terms of coverage probability. Numerical results are provided to confirm that: 1) for both user-centric strategy and UAV-centric strategy, NOMA assisted UAV cellular networks is capable of outperforming OMA by setting power allocation factors and targeted rate properly and 2) the coverage probability of NOMA assisted UAV cellular framework is affected to a large extent by ipSIC coefficient, target rates, and power allocations factors of paired NOMA users.

This paper investigates the multiple-input multiple-output (MIMO) non-orthogonal multiple access (NOMA) assisted unmanned aerial vehicles (UAVs) networks. By utilizing a stochastic geometry model, a new 3-D UAV framework for providing wireless service to randomly roaming NOMA users has been proposed. In an effort to evaluate the performance of the proposed framework, we derive analytical expressions for the outage probability and the ergodic rate of MIMO-NOMA enhanced UAV networks. We examine tractable upper bounds for the whole proposed framework, with deriving asymptotic results for scenarios that transmit power of interference sources being proportional or being fixed to the UAV. For obtaining more insights for the proposed framework, we investigate the diversity order and high signal-to-noise slope of MIMO-NOMA assisted UAV networks. Our results confirm that: 1) Outage probability of NOMA enhanced UAV networks is affected to a large extent by the targeted transmission rates and power allocation factors of NOMA users and 2) For the case that the interference power is proportional to the UAV power, there are error floors for the outage probabilities.

This paper investigates the allocation of resources, including subcarriers and spreading codes, as well as intercell interference (ICI) mitigation for multicell downlink multicarrier direct-sequence code division multiple-access systems, which aim to maximize the system's spectral efficiency (SE). The analytical benchmark scheme for resource allocation and ICI mitigation is derived by solving or closely solving a series of mixed integer non-convex optimization problems. Based on the optimization objectives the same as the benchmark scheme, we propose a novel distributed resource allocation assisted by ICI mitigation scheme referred to as resource allocation assisted by ICI mitigation (RAIM), which requires very low implementation complexity and demands little backhaul resource. Our RAIM algorithm is a fully distributed algorithm, which consists of the subcarrier allocation (SA) algorithm named RAIM-SA, spreading code allocation (CA) algorithm called RAIM-CA and the ICI mitigation algorithm termed RAIM-IM. The advantages of the RAIM are that its CA only requires limited binary ICI information of intracell channels, and it is able to make mitigation decisions without any knowledge of ICI information. Our simulation results show that the proposed RAIM scheme, with very low complexity required, achieves significantly better SE performance than other existing schemes, and its performance is very close to that obtained by the benchmark scheme.

In resource constrained wireless systems, achieving higher spectral efficiency (SE) and energy efficiency (EE), and greater rate fairness are conflicting objectives. Here, a general framework is presented to analyze the tradeoff among these three performance metrics in cooperative OFDMA systems with decode-and-forward relaying, where subcarrier pairing and allocation, relay selection, choice of transmission strategy, and power allocation are jointly considered. In our analytical framework, rate fairness is represented utilizing the α-fairness model, and the resource allocation problem is formulated as a multi-objective optimization problem. We then propose a cross-layer resource allocation algorithm across application and physical layers, and further devise a heuristic algorithm to tackle the computational complexity issue. The SE-EE tradeoff is characterized as a Pareto optimal set, and the efficiency and fairness tradeoff is investigated through the price of fairness. Simulations indicate that higher fairness results in a worse SE-EE tradeoff. It is also shown imposing fairness helps to reduce the outage probability. For a fixed number of relays, by increasing circuit power, the performance of SE-EE tradeoff is degraded. Interestingly, by increasing the number of relays, although the total circuit power is increased, the SE-EE tradeoff is not necessarily degraded. This is thanks to the extra degree of freedom provided in relay selection.

In this paper, the power allocation problem for downlink massive multiple-input multiple-output (MIMO) systems with inter-user interference is investigated considering the tradeoff between energy efficiency (EE) and spectral efficiency (SE). The minimum mean-square-error channel estimate and the rigorous closed-form expression of achievable downlink rate are first derived. Then, the multi-objective optimization problem (MOP) is formulated subject to maximum total transmission power constrain, where conflicting EE and SE are maximized simultaneously. To obtain the solution set characterized as a Pareto set, the MOP is transformed into a single-objective problem (SOP) using weighted sum method. We further convert the SOP into a convex optimization problem by adding an additional interference constraint, and the optimal power allocation algorithm is proposed by using dual method to balance EE and SE efficiently. Simulation results demonstrate the effectiveness of the proposed algorithm and illustrate the fundamental tradeoff between EE and SE for different parameter settings.