The rapid advancement of autonomous driving technologies has imposed stringent requirements on low-latency and high-reliability computation, which often exceed the capabilities of onboard processors. Vehicular edge computing (VEC) provides a promising solution by offloading computation to external servers; however, terrestrial infrastructure suffers from fragmented coverage and limited scalability, particularly in highway and rural scenarios. To address these limitations, this paper considers a multi-layer aerial VEC network integrating a high-altitude platform and multiple unmanned aerial vehicles (UAVs) to jointly provide wide-area coverage and proximity services. Different from existing works that primarily focus on latency minimization under homogeneous resources, this paper explicitly models the heterogeneous leasing pricing of aerial platforms and investigates its impact on task offloading decisions. A joint task offloading and resource allocation problem is formulated to minimize the total system cost, defined as a weighted combination of latency and economic expenditure. To ensure the feasibility of UAV-assisted offloading under high mobility, a dwell-time constraint is incorporated to restrict task execution within the effective service duration. The resulting problem is formulated as a mixed-integer nonlinear programming problem, which is solved via a low-complexity iterative algorithm based on Lagrangian duality, linear relaxation, and the alternating direction method of multipliers. Simulation results demonstrate that the proposed scheme achieves significant cost reduction compared with benchmark strategies, especially under high-mobility conditions.

As an emerging communication technology, fluid antenna (FA) offers remarkable diversity and multiplexing gains due to its port mobility, which significantly reduces transmission delays in communication processes. This capability makes FA a promising solution for enhancing mobile edge computing (MEC) by optimizing communication delay. This paper establishes an FA-aided MEC offloading architecture and proposes a game theory-assisted multi-agent deep reinforcement learning (DRL) scheme to minimize the system delay of MEC. We aim to address the joint optimization problem of FA port selection, beamforming, user transmit power design, and MEC server computation resource allocation. However, the dynamic nature of FA ports and the variability of the associated large number of parameters introduce significant challenges, such as non-convexity and high dimension, in the optimization problem. In this paper, we employ game theory to reduce the dimension of the optimization variables by modeling the power control problem among multiple users as a non-cooperative game. Therefore, we propose a multi-agent deep deterministic policy gradient (MADDPG) algorithm, featuring two types of agents that collaboratively solve the problem. Simulation results validate the effectiveness of the proposed scheme, achieving 19.1-65.8% lower delays than benchmarks in MEC efficiency across all scenarios.

Due to some transmission delays and channel estimation errors, it is always difficult to acquire perfect channel state information (CSI) in practical communications. Therefore, in this study, taking the practical scenario of imperfect CSI into consideration, a simultaneously transmitting and reflecting-reconfigurable intelligent surface (STAR-RIS)-assisted satellite-autonomous aerial vehicle (AAV)-terrestrial non-orthogonal multiple access (NOMA) network is investigated, in which the AAV is assumed to be randomly located in a spherical cap space while the users are randomly located in an inner circular and an outer annular plane on the ground. Specifically, a satellite source first sends NOMA signals to a AAV relay, which then forwards them to both ground users via an STAR-RIS through reflection as well as transmission. In addition, considering that the satellite-AAV link is subject to the shadowed-Rician distribution while the other links are subject to the Nakagami-m distributions, the cumulative distribution functions and probability density functions of channel gains in the presence of imperfect CSI are derived. Furthermore, in practical application scenarios, the positions of the AAV and both users may be randomly located in some regions. Therefore, three randomly distributed scenarios are considered: 1) The users are randomly located while the AAV’s position is fixed; 2) The AAV is randomly located while both users are in fixed locations; and 3) Both are randomly located. Under those three scenarios, both analytical and asymptotic expressions of outage probability (OP) for two users as well as the system OP are derived using the stochastic geometry approach, and their accuracy is confirmed with Monte-Carlo simulations.

In remote or high-demand wireless cellular networks, efficient data collection from ground users (GUs) with fixed infrastructure poses a significant challenge. Unmanned aerial vehicles (UAVs) have emerged as a promising solution due to their flexible deployment and cost-effectiveness. This paper focuses on a UAV-aided wireless cellular communication system comprising a UAV and multiple adjacent GUs, where the mission of the UAV is to collect data from these GUs. The objective is to minimize UAV propulsion energy consumption while ensuring fair data uploading among all GUs. Due to the non-convex and intractable nature of the above problem, we propose a novel real-time waypoint localization method based on the parallel projection method from a geometric perspective. By enhancing the projection process, this approach achieves energy-efficient and fair data collection, along with an efficient trajectory design algorithm. Further, considering the scenario of densely distributed GUs in large-scale areas, a GU-clustering algorithm is proposed based on Mean Shift. Additionally, this paper categorizes GUs into homogeneous and heterogeneous scenarios and designs distinct trajectory designing algorithms to accommodate diverse real-world situations. Simulations and comparisons validate the effectiveness and efficiency of the proposed algorithms in tackling the UAV trajectory design challenges.

Since uncrewed aerial vehicles (UAVs) possess inherent characteristics such as exceptional maneuverability and versatile deployment, they can offer integrated sensing and communication (ISAC) services to vehicles in mobile environment. This paper designs a UAV-assisted ISAC system model, wherein the UAV is employed to provide sensing and communication services to mobile vehicles during its flight. In order to evaluate the radar detection performance of the ISAC system, we introduce radar mutual information (MI) from the information theory perspective. A resource optimization problem for the system model is formulated, which seeks to maximize the system communication rate under the constraints of signal-to-noise ratio (SNR) and MI of the radar detection link by jointly optimizing ISAC task scheduling, UAV transmit power allocation and UAV flight trajectory. The simulation results indicate that the proposed scheme significantly improves both the communication rate and radar MI.

Owing to the advantages of high mobility, low cost, and on-demand deployment, uncrewed aerial vehicles (UAVs) can serve as triple-function aerial service platforms, providing sensing, calculation, and communication services for ground users in remote areas or emergencies. In this paper, a UAV-assisted integrated sensing, calculation, and communication (ISCC) system is proposed, where the UAV detects and processes the status information of the sensing target, and then sends the calculation results to the data collection center. In order to evaluate the performance of ISCC system, the age of information (AoI) and the radar estimation rate are introduced to define the freshness and amount of sensing data, respectively. Taking into account the UAV energy limitations, the amount of sensing data is maximized while the AoI is minimized through jointly optimizing the sensing scheduling, sensing times, transmit power, operating frequency, and motion parameters of the UAV under the constraint of radar signal-to-noise ratio (SNR). The formulated mixed-integer nonlinear programming problem is decomposed into five subproblems, and the optimal solutions can be achieved by proposing an alternating optimization (AO)-based five-stages optimization algorithm to optimize these subproblems iteratively. Simulation results show that both the sensing performance and information freshness of the system can be effectively improved by optimizing the UAV parameters.

Unmanned aerial vehicle (UAV) has been widely used as an aerial base station (BS) to assist the ground communications for Internet of Things (IoT) due to its wide-area coverage, high mobility and line-of-sight (LoS) communication link. In this paper, a multi-UAV enabled IoT is considered, where the UAVs provide integrated sensing and communication (ISAC) services to the IoT nodes. The radar mutual information (MI) is introduced to measure the sensing performance of ISAC from the information theory perspective. To achieve fair communications, we seek to maximize the minimum communication rate per IoT node by jointly optimizing node scheduling, transmit power and 3D trajectory of the UAV under the constraint of radar MI for each IoT node. The formulated non-convex multi-variable optimization problem is divided into three subproblems, including UAV scheduling optimization, UAV transmit power optimization, and UAV 3D trajectory optimization. The near-optimal solutions of the original optimization problem can be achieved by proposing a three-layer iterative optimization algorithm to optimize the three subproblems iteratively. The simulation results demonstrate that the proposed optimization scheme can obviously improve communication rate under the constraint of radar MI as well as achieve fair communication for each node.

As an aerial service platform for Internet of Things (IoT), unmanned aerial vehicle (UAV) can provide integrated sensing, computing and communication (ISCAC) services for the IoT nodes. In this paper, a UAV-enabled ISCAC system is proposed for IoT to meet the evolving requirements of emerging services in 6G networks. This system has three functions: sensing user equipments (UEs) for acquiring radar sensing information, executing computing tasks, and offloading incomplete tasks to the access point (AP) for further processing. Through jointly optimizing UAV CPU frequency, UAV radar sensing power, transmit power of UEs, and UAV trajectory, the weighted total energy consumption of both the UAV and the UEs can be minimized. We present a three-layer iterative optimization algorithm to tackle the original non-convex optimization problem. Finally, the effectiveness of the algorithm and its superiority in energy consumption compared to other benchmark schemes are verified through simulation results.

As a symbol-level precoding scheme, constructive interference precoding (CIP) has been demonstrated its superiority in multi-antenna orthogonal multiple access (OMA). By utilizing both the channel state information (CSI) and data symbols, harmful multi-user interference can be converted into useful reception power via the well-designed CIP. When CIP meets non-orthogonal multiple access (NOMA) whose bottle-neck is usually at the weaker user, this paper is the first to propose CIP to enhance the downlink MISO-NOMA networks, by making the desired signal of the stronger user in a typical NOMA pair constructive to the weaker user. In our CIP-NOMA scheme, we properly design the CIP precoder for transmit power minimization at the base station (BS), subject to signal-to-interference-plus-noise ratio (SINR) requirements of NOMA users. We further derive its closed-form solutions with Karush-Kuhn-Tucker (KKT) conditions, and optimally obtain the desired CIP precoders. Moreover, as compared to conventional NOMA schemes, we theoretically prove that once two NOMA users possess distinct channel gains, our optimized CIP-NOMA scheme always uses lower transmit power to reach the SINR thresholds. To be robust against the channel estimation errors, we extend our CIP-NOMA scheme to the scenario of imperfect CSI, by further addressing the hidden CSI errors. Specifically, we first introduce some auxiliary variables to separate the coupled vectors, and then use S-Procedure and semi-definite relaxation (SDR) to further transform them into convex ones. Extensive simulations verify that our CIP-NOMA scheme greatly outperforms the benchmarks with both perfect and imperfect CSI.

Cognitive Internet of Vehicles (CIoV) is an intelligent vehicle network envisioned to opportunistically access spectrum licensed to primary users (PUs) on the premise of not interrupting their normal communications. Dynamic spectrum access enables the CIoV to choose the best possible spectrum for communications based on the outcomes of spectrum data sensing, which can improve the spectrum access performance effectively. In this paper, we enable the CIoV to adapt to various spectrum states through: a) a collaborative big spectrum data sensing scheme to sense a massive amount of spectrum data; and b) a reinforcement learning (RL) based dynamic spectrum access scheme to optimize spectrum selection strategies. Q-learning, which is a popular RL approach, is proposed for underlay, overlay, and collaborative spectrum access modes to allocate spectrum resources to the CIoV intelligently. The Q-learning models, which include the spectrum state vector, the action vector of CIoV, and the spectrum access reward received in different spectrum situations, are defined for the spectrum access modes. A Q-learning based spectrum access algorithm is proposed to improve the communication performance of the CIoV in different spectrum access modes. Simulation results indicate that the collaborative spectrum access mode can achieve higher average throughput, lower interference power and lower communication outage compared with the underlay and overlay spectrum access modes.

Effective beam alignment is essential for vehicle-to-infrastructure (V2I) millimeter wave (mmWave) communication systems, particularly in high-mobility vehicle scenarios. This paper explores a three-dimensional (3D) vehicle environment and introduces a novel deep learning (DL)-based beam search method that incorporates an image-based coding (IBC) technique. The mmWave beam search is approached as an image processing problem based on situational awareness. We propose IBC to leverage the locations, sizes, and information of vehicles, and utilize convolutional neural network (CNN) to train the image dataset. Consequently, the optimal beam pair index(BPI)can be determined. Simulation results demonstrate that the proposed beam search method achieves satisfactory performance in terms of accuracy and robustness compared to conventional methods.

High-mobility unmanned aerial vehicles (UAVs) can serve as dual-function aerial service platforms for the Internet of Things (IoT), providing both sensing and communication services for IoT nodes without a base station (BS), particularly in emergency situations. In this paper, a UAV-assisted integrated sensing and communications (ISAC) system is proposed for IoT, which simultaneously senses the status information around the IoT and sends the sensing information to both the IoT nodes and a data collection center. In order to assess the sensing performance of ISAC, the radar estimation rate is introduced as a significant metric from the perspective of information theory. Considering the mutual interference between sensing and communications, the radar estimation rate is maximized through the coordinated optimization of UAV task scheduling, transmit power allocation, and 3D flight parameters under the constraint of communication rate. The formulated non-convex mixed-integer programming problem is divided into three subproblems, including UAV task scheduling optimization, UAV sensing and communication power optimization, and UAV 3D flight parameters optimization. The optimal solutions can be achieved by proposing a three-layer iterative optimization algorithm to optimize the three subproblems iteratively. The simulation results show that the radar estimation rate can well measure the sensing performance of the ISAC, which can be effectively improved by optimizing the 3D UAV flight parameters.

As a symbol-level precoding scheme, constructive interference precoding (CIP) has been demonstrated its superiority in multi-antenna orthogonal multiple access (OMA) systems. By utilizing both the channel state information (CSI) and data symbols, harmful multi-user interference can convert to useful reception power via the well-designed CIP precoder. When CIP meets non-orthogonal multiple access (NOMA) whose bottleneck is usually at the weaker user side, this paper is the first to propose CIP to enhance the downlink MISO-NOMA networks, by making the desired signal of the stronger user in a typical NOMA pair constructive to the weaker user. In our CIP-NOMA scheme, we properly design the CIP precoder for transmit power minimization at the base station (BS), subject to signal-to-interference-plus-noise ratio (SINR) requirements of NOMA users. By replacing the non-convex constraints with convex linear matrix inequalities, we optimally obtain the precoding solutions for our CIP-NOMA scheme by semi-definite relaxation (SDR) method. Moreover, as compared to conventional NOMA schemes, we theoretically prove that once two NOMA users possess distinct channel gains, our optimized CIP-NOMA scheme always uses smaller transmit power to reach the target SINR thresholds. We further extend our CIP-NOMA scheme to the scenario of imperfect CSI, by further addressing the hidden CSI errors. Finally, we run extensive simulations to verify that our proposed CIP-NOMA scheme greatly outperforms zero-forcing (ZF), conventional NOMA and conventional CIP schemes.

This paper proposes a general unmanned aerial vehicle (UAV)-to-ground (U2G) channel model. The proposed model is consistent with real scenarios by considering the impacts of flight altitude and UAV posture on channel gain. Machine learning and ray tracing (RT) techniques are employed to improve the generation method of altitude-dependent parameters, i.e., path loss (PL) and shadow fading (SF). In addition, posture-related fuselage shadowing coefficient (FSC) is introduced to modify the channel gain, and three-dimensional (3D) geometry modeling of the fuselage is conducted to calculate the FSC. Numerical simulation results show that the flight altitude and UAV posture have obvious effects on channel gain. The proposed model with modified channel gain can effectively describe the PL, SF, and received power under fuselage shadowing. The validity and advantage of the improved channel gain are verified by comparing the simulation results with the measured ones.

Industrial Internet of Things (IIoT) usually utilizes 2.4-GHz unlicensed frequency band, which is also heavily used by many other communication systems, such as ZigBee, WiFi, Bluetooth, etc. Therefore, the lack of spectrum resources has become a key technical bottleneck to restrict the development of IIoT. Integrating cognitive radio (CR) into IIoT, Cognitive IIoT (CIIoT) can cope with the spectrum resource shortage by accessing the frequency bands licensed to primary user (PU). However, spectrum sensing and access control must be performed to avoid bringing severe interference to the PU. In this article, an integrated cooperative spectrum sensing (CSS) and access control model is proposed to improve the transmission performance of the CIIoT while guaranteeing the CSS’s detection probability and controlling the interference to the PU. This model is optimized to maximize the total throughput of IIoT in each frame by jointly optimizing sensing time, the number of sensing nodes and the transmit power for each node under the constraints of the minimum detection probability, the total power control, the interference control, and the minimum rate for each node. The optimization problem is solved by the joint optimization of spectrum sensing and access control. A simultaneous CSS and access control model is also proposed to increase the communication time by using one time slot to perform CSS and access control simultaneously. The simulation results show that there exist optimal sensing and control parameters to maximize the total throughput of CIIoT.

The cognitive industrial Internet of Things (CIIoT) can improve transmission performance by utilizing the spectrum licensed to a primary user (PU), providing that the normal communication of the PU is not disturbed. However, the traditional spectrum access schemes for the CIIoT are difficult to adapt to the various communication environments. In this article,$Q$-learning-based dynamic spectrum access is proposed for the CIIoT to intelligently utilize the spectrum resources in three access scenarios: orthogonal multiple access (OMA), underlay spectrum access, and nonorthogonal multiple access (NOMA). In the OMA scheme, the CIIoT learns to access the idle channels to avoid distributing the PUs, but its communication continuity cannot be guaranteed when most of the channels are occupied by the PUs. In the underlay scheme, the CIIoT learns to utilize the busy channels to ensure the communication continuity by limiting its transmit power within the tolerance of the PU. However, the interference to the PU cannot be eliminated, which will decrease the PU’s throughput. In the NOMA scheme, however, the CIIoT can utilize the busy channels by canceling the interference to the PU with successive interference cancellation, which will guarantee the transmission performance of both the CIIoT and the PU. A$Q$-learning-based spectrum access algorithm is proposed to improve the transmission performance of the CIIoT in the three schemes. The simulation results have shown the advantages of the$Q$-learning-based NOMA scheme in terms of guaranteeing the throughput of the CIIoT nodes and decreasing the interference to the PUs.

Due to its flexibility and high maneuverability, Unmanned Aerial Vehicle (UAV) is able to quickly provide wireless connections to the ground vehicles in mobile environment. In this paper, a multi-UAV enabled mobile Internet of Vehicles (IoV) model is proposed, where the UAVs track to serve the mobile vehicles and send downlink information to the vehicles during the flight time. Considering the constraints of anti-collision and communication interference between the UAVs, the system throughput is maximized by jointly optimizing vehicle communication scheduling, UAV power allocation and UAV trajectory. The formulated non-convex optimization problem is separated into three subproblems, including communication scheduling optimization, power allocation optimization and UAV trajectory optimization, which can be solved by successive convex approximation (SCA). A joint iterative optimization algorithm of the three subproblems is put forward to get the optimal solution. Then, a fairness optimization problem is proposed to guarantee the fair communications for each vehicle. The numerical results reveal the excellent performance of the multi-UAV enabled mobile IoV by joint communication and trajectory optimization.

In this paper, we consider a reconfigurable intelligent surface (RIS) assisted unmanned aerial vehicle (UAV) relaying communication system, where the RIS is mounted on the UAV and can move at a high speed. Compared with the conventional static RIS, better performance and more flexibility can be achieved with the assistance of the mobile UAV. We maximize the average downlink throughput by jointly optimizing the UAV trajectory, RIS passive beamforming and source power allocation for each time slot. The formulated non-convex optimization problem is decomposed into three subproblems: passive beamforming optimization, trajectory optimization and power allocation optimization. An alternating iterative optimization algorithm of the three subproblems is proposed to achieve the suboptimal solutions. The numerical results indicate that the RIS-UAV relaying communication system with trajectory optimization can get higher throughput.

The integration of intelligent reflecting surface (IRS) and multiple access provides a promising solution to improved coverage and massive connections at low cost. However, securing IRS-aided networks remains a challenge since the potential eavesdropper also has access to an additional IRS reflection link, especially when the eavesdropping channel state information is unknown. In this paper, we propose an IRS-assisted non-orthogonal multiple access (NOMA) scheme to achieve secure communication via artificial jamming, where the multi-antenna base station sends the NOMA and jamming signals together to the legitimate users with the assistance of IRS, in the presence of a passive eavesdropper. The sum rate of legitimate users is maximized by optimizing the transmit beamforming, the jamming vector and the IRS reflecting vector, satisfying the quality of service requirement, the IRS reflecting constraint and the successive interference cancellation (SIC) decoding condition. In addition, the received jamming power is adapted at the highest level at all legitimate users for successful cancellation via SIC. To tackle this non-convex optimization problem, we first decompose it into two subproblems, and then each subproblem is converted into a convex one using successive convex approximation. An alternate optimization algorithm is proposed to solve them iteratively. Numerical results show that the secure transmission in the proposed IRS-NOMA scheme can be effectively guaranteed with the assistance of artificial jamming.

Due to the flexible deployment and low cost of unmanned aerial vehicle (UAV), the integration of UAV and wireless cellular networks is widely regarded as a promising technology to enhance the performance of wireless cellular communications. This paper considers a UAV-aided wireless cellular communication system with multiple adjacent ground users (GUs), where the primary mission of UAV is to collect data from all of the GUs. We take the GUs as the topological nodes and combine their communication ranges to construct a ground topology structure (GTS), with the purpose of designing a reasonable trajectory for the UAV to execute the data collection tasks, while ensuring the fairness of transmission among all of GUs. In order to solve these problems, we utilize parallel projection algorithm onto homogeneous and heterogeneous GTS respectively to obtain a group of waypoints which construct the UAV trajectory, we formulate the fairness of data collection as a min-max problem. Finally, simulation experiments show the trajectory design results of the homogeneous and heterogeneous GTS respectively. Numerical results further vaildate the effectiveness of our proposed algorithms.

In this paper, we propose a time-efficient data collection scheme, in which multiple ground devices upload their data to the unmanned aerial vehicle (UAV) via uplink nonorthogonal multiple access (NOMA). The total flight time of the UAV is equally divided into N time slots. The duration of each time slot is minimized by jointly optimizing the straight-line trajectory, device scheduling, and transmit power. To solve this mixed integer non-convex optimization problem, we decompose it into two steps. In the first step, we study the device scheduling strategy based on the UAV trajectory and the channel gains between the UAV and ground devices, through which the original problem can be greatly simplified. In the second step, the duration of each time slot is minimized by optimizing the transmit power and the UAV trajectory. An iterative algorithm based on alternating optimization is proposed, where each subproblem can be alternatively solved by applying successive convex approximation with the device scheduling updated at the end of each iteration. Numerical results are presented to evaluate the effectiveness of the proposed scheme.

Sixth generation (6G)-enabled Internet of Things (IoT) needs sufficient spectrum resources to provide spectrum access for massive IoT’s terminals. However, traditional orthogonal multiple access restricts the full use of limited spectrum resources. In this article, a nonorthogonal multiple access (NOMA)-based hybrid spectrum access scheme is proposed for 6G-enabled cognitive IoT (CIoT), where the CIoT may access both the idle and busy spectrum via NOMA regardless of the primary user’s (PU) state. The uplink resource allocations for the CIoT are optimized in the decoding-PU-last and decoding-PU-first schemes, respectively, which seek to maximize the average total transmission rate of CIoT while ensuring the minimum transmission rates for PU and each CIoT node. Then, a clustering NOMA-based CIoT is presented to decrease the interuser interference, where the nodes in each cluster use NOMA to transmit in the allocated subchannel. The simulation results have shown the performance advantages for the NOMA-based hybrid spectrum access and better rate guarantee for the clustering NOMA-based CIoT.

The 6th generation (6G) communication envisions a highly integrated network where aerial vehicles connect satellites and terrestrial systems. As low altitude vehicle, unmanned aerial vehicle (UAV) is able to quickly establish wireless networks without resorting to terrestrial infrastructure. Due to strong invulnerability, synergistic cooperation and flexible scheduling, the multi-UAV communication system can significantly improve system performance through collaboratively designing multi-UAV trajectory and radio resource scheduling. This article proposes a multi-UAV wireless powered communication (WPC) system for 6G-enabled Internet of Things (IoT). Specifically, each time slot is split into uplink and downlink subslot. In the downlink subslot, multiple UAVs dispatched as aerial communication platforms transfer energy to multiple IoT users. In the uplink subslot, the association between UAVs and users is designed, and then the scheduled user uploads data to the specific UAV by using the harvested energy. According to the proposed system, we propose a collaborative scheme of multi-UAV trajectory optimization and resource scheduling. By synergistically optimizing UAV-user association, subslot duration, user transmit power, and multi-UAV trajectory, we maximize the minimum average achievable rate among all users. Particularly, the nonconvex optimization problem can be efficiently figured out by an alternative iteration algorithm proposed in this article. Finally, numerical results show that our design can not only optimize multi-UAV flight path, but also achieve higher objective value than benchmark schemes.

Unmanned aerial vehicle (UAV) swarms have shown great potential for Internet of Things (IoT). Meantime, its malicious use may cause huge threat to the national security. UAV swarms show the characteristic of high density which poses formidable challenges to radar resolution in the defense of critical areas. In this article, we consider a radar equipped with the coprime array, and then, use the coherent long-time integration (LTI) technique and gridless sparse technique to detect and localize UAVs in a swarm. This strategy takes full account of advantages of the coprime array, coherent LTI technique, and gridless sparse technique, i.e.: 1) the coprime array can provide a larger array aperture than the uniform linear array with the same number of array elements to relieve the stress of the gridless sparse technique and 2) the combination of coherent LTI technique and gridless sparse technique can maximize their advantages and make up for their shortcomings. By mathematical analyses and extensive numerical examples, we show the superiority of the proposed strategy in terms of accurate detection and localization of UAV swarms.

Due to the mobility and line-of-sight conditions, unmanned aerial vehicle (UAV) is deemed as a promising solution to sensor data collection. On the other hand, it is vital to guarantee the timeliness of information for UAV-assisted data collection. In this paper, we propose a time-efficient data collection scheme, in which multiple ground devices upload their data to the UAV via uplink non-orthogonal multiple access (NOMA). The total flight time of the UAV is equally divided into$N$time slots. The duration of each time slot is minimized by jointly optimizing the straight-line trajectory, device scheduling, and transmit power. To solve this mixed integer non-convex optimization problem, we decompose it into two steps. In the first step, we study the device scheduling strategy based on the UAV trajectory and the channel gains between the UAV and ground devices, through which the original problem can be greatly simplified. In the second step, the duration of each time slot is minimized by optimizing the transmit power and the UAV trajectory. An iterative algorithm based on alternating optimization is proposed, where each subproblem can be alternatively solved by applying successive convex approximation with the device scheduling updated at the end of each iteration. Numerical results are presented to evaluate the effectiveness of the proposed scheme.

Cognitive industrial Internet of Things (CIIoT) can extend available spectrum resources by accessing the spectrum licensed to primary user (PU) on the premise of not disturbing the PU's communications. However, additional spectrum sensing and long-time working may consume much energy of CIIoT. In this article, a CIIoT with wireless energy harvesting (WEH) is proposed to harvest the radio frequency energy of PU's signal, and energy-efficient resource allocations in different spectrum access modes are presented to maximize the average transmission rate of CIIoT while guaranteeing its energy saving requirements. The underlay and overlay spectrum access modes for CIIoT with WEH are described, respectively, in which the energy-efficient resource allocations are formulated as joint optimization problems that can be solved using the alternating direction optimization and water-filling algorithm. By combining underlay and overlay modes, a hybrid spectrum access mode is proposed to enable the CIIoT to access both idle and busy spectrum without limiting its transmission power at the absence of PU. Simulation results show that the CIIoT with WEH can consume less power to achieve larger transmission rate, and the hybrid mode outperforms the underlay and overlay modes in the aspects of transmission rate and energy saving.

With the rapid increase of industrial systems, industrial spectrum is stepping into the era of big data, and at the same time spectrum resources are facing serious shortage. Cognitive industrial system (CIS) based on cognitive radio can improve spectrum utilization by accessing the idle spectrum licensed to primary user. However, the CIS must find enough idle channels by performing spectrum sensing. In this article, a reinforcement learning-based multislot double-threshold spectrum sensing with Bayesian fusion is proposed to sense industrial big spectrum data, which can find required idle channels faster while guaranteeing spectrum sensing performance. Double thresholds are set to guarantee both high detection probability and spectrum access probability, and weighed energy detection is proposed to maximize detection probability when the energy statistic falls into the confusion area between the double thresholds. Bayesian fusion is proposed to get a final decision on the channel availability by combining the local sensing decisions of all the time slots. A prediction and selection algorithm for idle channels is proposed to predict the idle probability of each channel and find required idle channels from the sorted channel set. From simulation results, the proposed spectrum sensing scheme outperforms cooperative spectrum sensing and energy detection, which can predict idle channels accurately and get needed idle channels with fewer sensing operations.

The traditional ground industrial Internet of Things (IIoT) cannot supply wireless interconnections anywhere due to its small-scale communication coverage. In this article, a multibeam satellite IIoT in Ka-band is proposed to realize wide-area coverage and long-distance transmissions, which uses nonorthogonal multiple access (NOMA) for each beam to improve transmission rate. To guarantee Quality of Service (QoS) for the satellite IIoT, the beam power is optimized to match the theoretical transmission rate with the service rate. The NOMA transmission rate for each beam is maximized by optimizing the power allocation proportion of each node subject to the constraints of the total power for the beam and the minimal transmission rate for each node within the beam. Satellite-ground integrated IIoT is proposed to use the ground cellular network to supplement the satellite coverage in the blocked areas. The power allocation and network selection for the integrated IIoT are proposed to decrease the transmission cost. Simulation results are provided to validate the superiority of employing NOMA in the satellite IIoT and show higher transmission performance for the QoS-guarantee resource allocation.

Along with the exponentially increasing quantity of intelligent terminals connected to the Internet, the spectrum competition among users becomes more and more severe in wireless networks. The network have not the ability to satisfy all communication requirements due to the significantly increasing users and demanded rates. Energy-aware admission control has been proved to be an efficient way to tackle the infeasibility caused by the severe spectrum competition among users. However, the traditional admission control is limited by gradually removing chosen users, and pays less attention to the fairness. In this article, we elaborate the concept of the fairness in a max-min optimization problem with respect to the transmission rates, by leveraging the model of bit error rates with Q-function for general fading communications. Then, we make use of the max-min rate fairness to smartly determine the subset of users to be admitted in wireless networks. Meanwhile, the overall energy consumption is minimized and the network fairness is guaranteed. In particular, the algorithms can tackle more than one user at each iteration. Numerical evaluations show the effectiveness of the algorithms.

Combination of unmanned aerial vehicle (UAV) and non-orthogonal multiple access (NOMA) is deemed as an promising solution to achieving massive connectivity in future wireless networks. In this paper, a UAV-aided NOMA scheme is proposed to achieve simultaneous wireless information and power transfer (SWIPT) and guarantee the secure transmission for ground passive receivers (PRs), in which the nonlinear energy harvesting model is applied. Each time frame is divided into two phases. In the first phase, the received power at each PR is maximized to achieve rapid charging. In the second phase, SWIPT is performed via NOMA with the remaining energy at each PR, and artificial jamming is generated at UAV together with the NOMA information to guarantee the security. The throughput of PRs is maximized, with the highest received jamming power cancelled at each PR via successive interference cancellation (SIC). This disrupts the eavesdropping effectively by jamming without affecting the legitimate transmission. Due to the non-convexity of these two optimization problems, we first convert them to convex ones and then propose iterative algorithms to solve them. Simulation results are presented to show the effectiveness of the proposed scheme.

The development of Industrial Internet of Things (IIoT) has been limited due to the shortage of spectrum resources. Based on cognitive radio, the cognitive IIoT (CIIoT) has been proposed to improve spectrum utilization via sensing and accessing the idle spectrum. To improve sensing and transmission performance of the CIIoT, a cluster-based CIIoT is proposed, in this article, wherein the cluster heads perform cooperative spectrum sensing to get available spectrum, and the nodes transmit via nonorthogonal multiple access (NOMA). The frame structure of the CIIoT is designed, and the spectrum access probability and average total throughput of the CIIoT are deduced. A joint resource optimization for sensing time, node powers, and the number of clusters is formulated to maximize the average total throughput. The optimal solution is obtained via sensing and power optimization. The clustering algorithm and cluster head alternation are proposed to improve transmission performance and ensure energy balance, respectively. The simulations have indicated that the NOMA for the cluster-based CIIoT can better guarantee the transmission performance of each node, especially the node decoded first, than the traditional NOMA and orthogonal multiple access.

The shortage of spectrum resources has limited the development of Internet of Things (IoT). Fifth generation (5G) network can flexibly support a variety of devices and services, which makes it possible to combine 5G with IoT. In this paper, a novel multichannel IoT is proposed to dynamically share the spectrum with 5G communication, where an IoT node including transmitter and receiver is designed to perform 5G communication and IoT communication simultaneously. The subchannel sets allocated for 5G communication and IoT communication are defined by two complementary spectrum marker vectors, respectively. Two independent spectrum sequences are generated by calculating the inner products of spectrum marker vectors, presudo-random phases and power scaling vectors. Two time-domain fundamental modulation waveforms generated by the inverse fast Fourier transform of the spectrum sequences are used to modulate 5G data and IoT data, respectively. The receiver can detect the data using the same spectrum marker vectors as the transmitter. The BER performances of the system using binary modulation and cyclic code shift keying modulation in the cases of spectrum marker error and multiple access are analyzed, respectively. A subchannel and power optimization unit is formulated as a joint optimization problem, which seeks to maximize the 5G throughput under the constraints of minimal IoT throughput, maximal power, and maximal interference. An alternative optimization problem is proposed to maximize the IoT throughput while guaranteeing the minimal 5G throughput. A joint optimization algorithm based on Lagrange dual decomposition is proposed to achieve the optimal solution. Simulation results indicate that the proposed IoT can improve the 5G throughput significantly while the IoT throughput is guaranteed.

Internet of Things (IoT) is facing the shortage of spectrum resources due to the rapid growth of IoT terminals and big data services. Fifth generation (5G) network owns sufficient spectrum resources and supplies large data volume business, which can help to expand the communication resources of the IoT by combing IoT with 5G network. In this paper, a 5G-based IoT is designed to transfer both 5G and IoT information simultaneously. Two simultaneous transfer models including time switching model and power splitting model are proposed to carry out 5G and IoT communications using different time slots and power streams, respectively. For these two models, we have formulated joint optimization problem of allocation factors and node powers to maximize the 5G transmission rate while the IoT transmission rate and the total power are constrained. An alternative optimization problem is also proposed to maximize the IoT transmission rate while guaranteeing the minimal 5G transmission rate. A joint optimization algorithm based on the Lagrange dual optimization is proposed to obtain the solution to the optimization problems. An energy efficiency model is proposed to minimize the consumed total power of the IoT while keeping the minimal 5G and IoT transmission rates. Simulation results are given to evaluate the performance of our proposed models from diverse perspectives.

Adapting the data rate is an important performance in mobile cloud networking, especially for the fast growth of intelligent terminals. We study a max-min fairness problem for the mobile cloud networking to guarantee the minimal transmit data rate, by leveraging the bit error rate (BER) with Q-function for modeling achievable data rates. We propose a distributed power control algorithm to obtain the optimal solution. Then, we address a total power minimization problem with the given rate requirement constraints. When there are plenty of users and excessive interferences, its feasibility issue is solved by making use of the max-min fairness of the networks. We propose a dynamic algorithm that adapts the rate requirements to minimize the total energy consumption and to simultaneously provide fairness guarantees. Numerical simulations show the efficient performance of the proposed algorithms.

With the rapid development of modern satellite communications, broadband satellite services are experiencing a period of remarkable growth in both the number of users and the available bandwidth. More efficient spectrum management schemes require deeper investigation in order to meet the ever-increasing demand for broadband spectrum. In this paper, we propose a band allocation method for multibeam satellite systems by introducing a market-driven pricing mechanism. Instead of adopting static and fixed band selling, we consider a satellite network operator that utilizes the mode of price bargaining to trade the unused band with terrestrial network operators. By applying market-based mechanism to support satellite spectrum allocation, higher spectrum efficiency can be attained in order for satellite systems to meet the increasing demands for satellite bandwidth at an affordable cost. Besides, for the one-to-many bargaining case without terrestrial operator involved in, a differential spectrum pricing solution is devised to address heterogeneous users' spectrum preferences. In a typical price bargaining model, market participants (i.e., terrestrial network operators) are assumed to know exactly their needs dynamically, which is hard to achieve in near real-time; thus, our approach approximates it with a sub-optimal estimation on the network operators' benefit threshold. To be specific, we obtain the optimal pricing at every round of bargaining by predicting the overall benefits of terrestrial network operators and reaching the Nash equilibrium. Essential discussions and proofs for the pricing rationality are provided. Numerical results are given to evaluate the impact of the pricing scheme on the profits of satellite systems.

Smart city is able to make the city source and infrastructure more efficiently utilized, which improves the quality of life for citizens. In this framework, wireless sensor networks (WSNs) play an important role to collect, process, and analyze the corresponding information. However, the massive deployment of WSNs consumes a significant energy consumption, which has raised the growing demand for green WSNs for smart cities. Exploiting the recent advance in collaborative energy and information transfer to power the WSNs and transmit the data has been considered a promising approach to realize the green WSNs for smart cities. We propose an architecture design of the green WSNs for smart cities, by exploiting the collaborative energy and information transfer protocol, and illustrate the challenging issues in this design. To achieve a green system design, the sensor nodes in WSNs harvest the energy simultaneously with the information decoding (ID) from the received radio frequency signals. Specifically, the energy-constrained sensor nodes partition the received signals into two independent groups to perform energy harvesting (EH) and ID. The sensor nodes then use the harvested energy to amplify and forward the information signals. We study the joint optimization of subcarrier grouping, subcarrier pairing, and power allocation such that the transmission rate performance is maximized with the EH constraint. The joint optimization problem is solved via dual decomposition after transforming it into an equivalent convex optimization problem. Simulation results tested with the real WSNs system data indicate that the performance of our proposed protocol can be significantly improved.

Content caching is a promising approach to enhancing bandwidth utilization and minimizing delivery delay for new‐generation Internet applications. The design of content caching is based on the principles that popular contents are cached at appropriate network edges in order to reduce transmission delay and avoid backhaul bottleneck. In this paper, we propose a cooperative caching replacement and efficiency optimization scheme for IP‐based wireless networks. Wireless edges are designed to establish a one‐hop scope of caching information table for caching replacement in cases when there is not enough cache resource available within its own space. During the course, after receiving the caching request, every caching node should determine the weight of the required contents and provide a response according to the availability of its own caching space. Furthermore, to increase the caching efficiency from a practical perspective, we introduce the concept of quality of user experience (QoE) and try to properly allocate the cache resource of the whole networks to better satisfy user demands. Different caching allocation strategies are devised to be adopted to enhance user QoE in various circumstances. Numerical results are further provided to justify the performance improvement of our proposal from various aspects.

This paper studies the impacts of antenna selection algorithms in decode‐and‐forward (DF) cooperative nonorthogonal multiple access (NOMA) networks, where the secure information from the relay can be overheard by an eavesdropper in the networks. In order to ensure the secure transmission, an optimal antenna selection algorithm is proposed to choose one best relay’s antenna to assist the secure transmission. We study the impact of antenna selection on the system secure communication through deriving the analytical expression of the secrecy outage probability along with the asymptotic expression in the high regime of signal‐to‐noise ratio (SNR) and main‐to‐eavesdropper ratio (MER). From the analytical and asymptotic expressions, we find that the system secure performance is highly dependent on the system parameters such as the number of antennas at the relay, SNR, and MER. In particular, the secrecy diversity order of the system is equal to the antenna number, when the interference from the second user is limited.

Heading estimation using inertial sensors built‐in smartphones has been considered as a central problem for indoor pedestrian navigation. For practical daily lives, it is necessary for heading estimation to allow an unconstrained use of smartphones, which means the varying device carrying positions and orientations. As a result, three special human body motion states, namely, random hand movements, carrying position transitions, and user turns, are introduced. However, most existing heading estimation approaches neglect the three motion states, which may render large estimation errors. We propose a robust heading estimation system adapting to the unconstrained use of smartphones. A novel detection and classification method is developed to detect the three motion states timely and discriminate them accurately. For normal working, the user heading is estimated by a PCA‐based approach. If a user turn occurs, it is estimated by adding horizontal heading change to previous user heading directly. If one of the other two motion states occurs, it is obtained by averaging estimation results of the adjacent normal walking steps. Finally, an outlier filtering algorithm is developed to smooth the estimation results. Experimental results show that our approach is capable of handling the unconstrained situation of smartphones and outperforms previous approaches in terms of accuracy and applicability.

As the indispensable supplement of terrestrial communications, Low Earth Orbit (LEO) satellite network is the crucial part in future space‐terrestrial integrated networks because of its unique advantages. However, the effective and reliable routing for LEO satellite network is an intractable task due to time‐varying topology, frequent link handover, and imbalanced communication load. An Extreme Learning Machine (ELM) based distributed routing (ELMDR) strategy was put forward in this paper. Considering the traffic distribution density on the surface of the earth, ELMDR strategy makes routing decision based on traffic prediction. For traffic prediction, ELM, which is a fast and efficient machine learning algorithm, is adopted to forecast the traffic at satellite node. For the routing decision, mobile agents (MAs) are introduced to simultaneously and independently search for LEO satellite network and determine routing information. Simulation results demonstrate that, in comparison to the conventional Ant Colony Optimization (ACO) algorithm, ELMDR not only sufficiently uses underutilized link, but also reduces delay.

The wireless caching has attracted a lot of attention in recent years, since it can reduce the backhaul cost significantly and improve the user‐perceived experience. The existing works on the wireless caching and transmission mainly focus on the communication scenarios without eavesdroppers. When the eavesdroppers appear, it is of vital importance to investigate the physical‐layer security for the wireless caching aided networks. In this paper, a caching network is studied in the presence of multiple eavesdroppers, which can overhear the secure information transmission. We model the locations of eavesdroppers by a homogeneous Poisson Point Process (PPP), and the eavesdroppers jointly receive and decode contents through the maximum ratio combining (MRC) reception which yields the worst case of wiretap. Moreover, the main performance metric is measured by the average probability of successful transmission, which is the probability of finding and successfully transmitting all the requested files within a radius R. We study the system secure transmission performance by deriving a single integral result, which is significantly affected by the probability of caching each file. Therefore, we extend to build the optimization problem of the probability of caching each file, in order to optimize the system secure transmission performance. This optimization problem is nonconvex, and we turn to use the genetic algorithm (GA) to solve the problem. Finally, simulation and numerical results are provided to validate the proposed studies.

We investigate cache technique from the spatial view and study its impact on the relaying networks. In particular, we consider a dual‐hop relaying network, where decode‐and‐forward (DF) relays can assist the data transmission from the source to the destination. In addition to the traditional dual‐hop relaying, we also consider the cache from the spatial view, where the source can prestore the data among the memories of the nodes around the destination. For the DF relaying networks without and with cache, we study the system performance by deriving the analytical expressions of outage probability and symbol error rate (SER). We also derive the asymptotic outage probability and SER in the high regime of transmit power, from which we find the system diversity order can be rapidly increased by using cache and the system performance can be significantly improved. Simulation and numerical results are demonstrated to verify the proposed studies and find that the system power resources can be efficiently saved by using cache technique.

In this paper, we propose a cooperative spectrum sharing protocol based on OFDM two-way relaying with joint optimal subcarrier and power allocation. Specifically, the secondary system helps the primary system achieve their target rates through OFDM two-way relaying, where the secondary system forwards the primary signal by using a fraction of subcarriers and power. In return, the secondary system can gain spectrum access by using the remaining subcarriers and power to transmit its own signal. Joint optimal subcarrier and power allocation is derived aiming to maximize secondary transmission rate with primary transmission rate constraint. Simulation results demonstrate a significant enhancement in spectrum efficiency compared with several benchmark schemes.

In this paper, we proposed a cooperative spectrum sharing strategy based on contract theory with optimal power and bandwidth allocation. Specifically, primary user (PU) and secondary users (SUs) act as the employer and employees like labor consumption market, respectively. SUs provide labor, i.e. the relay power used for forwarding the PU's signal, in exchange for the reward, i.e. the spectrum accessing bandwidth for transmitting their own signals. PU needs to overcome a challenge how to balance the relationship between contributions and incentives for SUs. We designed an optimal contract with joint power and bandwidth optimization. We study how to allocate the power and bandwidth to maximize primary user's utility. Simulation results confirm that the utility of the primary user is significantly enhanced with our proposed cooperative spectrum sharing strategy.

Non-orthogonal multiple access (NOMA) has been considered as a highly efficient communication technology in the fifth generation (5G) networks by serving multiple users concurrently through non-orthogonal sharing communication resources. NOMA can be combined with both massive multiple input multiple output (MIMO) and relaying technologies to further improve 5G system efficiency at the cost of increased complexity. These combinations rely on the efficient utilization of 3-D communication resources. In the first part of this paper, we investigate highly efficient 3-D resource allocation for massive MIMO-NOMA systems. Due to hardware complexity constraints and channel variation in the massive MIMO-NOMA system, efficient antenna selection and user scheduling algorithms are proposed for sum rate maximization. In the second part of this paper, a collaborative NOMA-assisted relaying (CNAR) system is proposed to serve multiple cell-edge users by 3-D resource utilization. To reduce the relaying complexity in CNAR system, a simplified-CNAR (S-CNAR) system is proposed as an alternative NOMA-enabled relaying strategy. Numerical results show that our antenna selection and user scheduling algorithms achieve similar performance to existing methods with reduced complexity. Under high target rate, CNAR obtains better performance over other transmission strategies and S-CNAR reaches similar performance by simplified relaying scheme.

Anti-jamming interference alignment (IA) is an effective method for battling adversarial jammers for IA networks. Nevertheless, the number of antennas may not be enough to make it feasible in anti-jamming IA. Besides, the abundant power from the jammers and interferences, which used to be deemed as a harmful factor, can be exploited for energy harvesting (EH) by the legitimate users as a power supply. Thus, in this paper, we propose an anti-jamming opportunistic IA (OIA) scheme with wireless EH, which optimizes the transmission rate and EH together. In the proposed scheme, to make the anti-jamming IA network feasible, we select some of the users to transmit information at each time slot, and EH is performed by the other unselected users. Furthermore, to improve the performance of the proposed scheme, EH is also performed by the selected users, and the transmit power and power partition coefficient are jointly optimized to minimize the total transmit power of the OIA network. To reduce the computational complexity of the joint optimization, a suboptimal algorithm is also developed with much lower complexity. Extensive simulation results are presented to show the effectiveness of the proposed anti-jamming OIA scheme with wireless EH.

In cognitive radio (CR), the spectrum sensing of the primary user (PU) may consume some electrical power from the battery capacity of the secondary user (SU), yielding to decrease the transmission power of the SU. In this paper, a multislot simultaneous spectrum sensing and energy harvesting model is proposed, which uses the harvested radio frequency (RF) energy of the PU signal to supply the spectrum sensing. In the proposed model, the sensing duration is divided into multiple sensing slots consisted of one local-sensing subslot and one energy-harvesting subslot. If the presence of the PU is detected in the local-sensing subslot, the SU will harvest RF energy of the PU signal in the energy-harvesting slot, otherwise, the SU will continue spectrum sensing. The global decision is obtained through combining local sensing results from all the sensing slots by adopting "OR Rule". A joint optimization problem of sensing time and time splitter factor is proposed to maximize the throughput of the SU under the constraints of probabilities of false alarm and detection and energy harvesting. The simulation results have shown that the proposed model can improve the maximal throughput of the SU obviously compared to the traditional sensing-throughput tradeoff model.

In this paper, we propose an anti-interference cooperative spectrum sharing strategy based on fairness secondary users selection where the secondary system can gain spectrum access to the primary system. Specifically, secondary user STband STqare selected to transmit the primary and secondary signal by using different bandwidth in the second transmission slot which occupies a part of the whole transmission time. The primary and secondary systems will not interfere with each other as they use orthogonal bandwidth to transmit their signals. We study the secondary users selection to guarantee the fairness among the secondary users, and the joint optimization of time and bandwidth allocation such that the transmission rate of the secondary system is maximized, while guaranteeing the primary system achieve its target rate. Simulation results confirm efficiency of the proposed spectrum sharing strategy, and the significant performance improvement of the cognitive system.

In this paper, a cooperative spectrum sharing protocol with quality-of-service (QoS) support for both of the primary and secondary systems is proposed. Specifically, the secondary system gains primary spectrum access by allocating a fraction of its power to forward the primary signal helping the primary system achieve the target rate, and meanwhile exploits the remaining power to transmit its own signal. We analyze the achievable rates for the primary and secondary systems, and determine the optimal power allocation such that the sum transmission rate of primary and secondary systems is maximized, while the QoS of both primary and secondary systems can be guaranteed. Simulation results demonstrate the efficiency of the proposed spectrum sharing protocol and its benefit to both primary and secondary systems.

In this paper we proposed a cooperative spectrum sharing protocol with two-way decode-and-forward (DF) relaying. Specifically, two primary users A and B communicate with each other with the assistant of the secondary user S. The secondary user uses a fraction of power to forward the primary signals by acting as a DF relay. As a reward, the secondary user can gain spectrum access by using the remaining power to transmit its own signal. We study the optimization of power allocation such that the secondary transmission rate is maximized, while both of the primary users can achieve their target rates. Numerical simulation and comparisons are presented to illustrate the performance of the proposed spectrum sharing protocol, and both primary and secondary users can benefit from the proposed spectrum sharing protocol.

Energy harvesting (EH) is a prominent method to prolong the operation time of energy-constrained wireless networks. Integrating EH into wireless communications to support simultaneous wireless information and power transfer (SWIPT) allows the spectrum to be used for both purposes without compromising the quality of service (QoS). In this paper, we propose a subcarrier allocation based SWIPT scheme in orthogonal frequency division multiplexing (OFDM) systems. Specifically, the received OFDM subcarriers are partitioned into two groups. A part of the received subcarriers are allocated to form one group which are used for information decoding (ID), and the remained subcarriers form another group, which are used for energy harvesting. Thus, no splitter is needed at the receiver. We study the optimal subcarrier allocation such that the harvested energy is maximized with the ID constraint. By using the Lagrangian method, we develop efficient algorithm to solve the optimization problem.

To achieve extremely high spectral efficiency in the 5- th generation (5G) communication network, the combination of massive multiple input multiple output (MIMO) and non-orthogonal multiple access (NOMA) technologies becomes a promising solution. However, due to limited radio frequency (RF) chains and channel condition variation, it is important to develop efficient antenna selection and user scheduling algorithms in complex MIMO-NOMA system. In this paper, efficient antenna selection and user scheduling algorithms are investigated to maximize the sum rate in two MIMO-NOMA scenarios. In the first simple single- band two-user scenario, the proposed antenna selection algorithm achieves higher search efficiency by limiting the candidate antennas to those are beneficial to the relevant users. In the multi-band multi-user scenario, the proposed joint antenna and user (AU) contribution algorithm considers the contribution of each antenna's and user's channel gain to total channel gain jointly. Numerical results show that proposed antenna selection algorithm achieves near-optimal performance, and joint AU contribution algorithm achieves similar performance to existing methods with reduced complexity.

Transform domain communication system (TDCS) has been proposed to establish a communication link with a low probability of interception by synthesising an adaptive waveform containing energy only in the unused frequency bands. However, this communication system suffers from a low spectral efficiency. To improve its spectral efficiency, a unified modulation framework for a TDCS is proposed in this study to embrace the previously reported modulation schemes under one framework. The resulting spectral efficiency is higher compared to these previous schemes. Also, to combat the slow channel fading, a TDCS system using two transmit antennas and one receive antenna with the proposed modulation scheme is presented. Simulation results show that the multiple‐antenna system improves the performance dramatically compared to the single antenna system under the slow fading environment.