We investigate in this article joint trajectory design and resource allocation for secure air–ground integrated Internet of Things (IoT) networks with unmanned aerial vehicle (UAV) jamming and device-to-device (D2D) enhancement. By jointly optimizing ground user (GU) scheduling, UAV flight trajectory, and transmit power, we are able to maximize the minimum system secrecy rate of UAV and D2D communications. The formulated optimization problems of the two typical network scenarios, i.e., with and without UAV jamming, are challenging to be solved due to the corresponding nonsmooth and nonconcave objective functions. Therefore, we propose alternating iterative algorithms to solve the problems by employing the successive convex approximation and block coordinate descent methods. Extensive results indicate that the proposed joint optimization schemes can effectively improve the secrecy communication performance under different spatial distributions of GUs.

With the fast development of emerging Internet of Everything applications, human-type communications (HTC) and machine-type communications (MTC) will inevitably coexist in future 6G cellular networks. To support massive connectivity while fulfilling diverse requirements of both HTC and MTC, we present a device-to-device aided rate splitting multiple access (RSMA) scheme by encoding both the MTC devices’ messages and HTC users’ common messages into a general common data stream in each cell (or group). Nevertheless, such deploying strategy may bring challenges in complex resource allocation and transmission modes selection. In view of this, we introduce a simple received signal strength (RSS) based transmission modes selection scheme, through which the RSS-threshold selection problem is formulated to maximize the HTC and MTC sum rates. Considering the computational complexity and scalability, we employ a multi-agent reinforcement learning based algorithm for each small base station to choose the optimal RSS threshold thus to achieve maximum sum rate while maintaining massive connectivity. Simulation results reveal our proposed RSMA deploying strategy outperforms non-orthogonal multiple access (NOMA) in system coverage while maintaining high-level system rate. Besides, the proposed MARL based scheme can further improve the system sum rate and coverage for both the HTC and MTC.

To fulfill the stringent requirements brought by human-type communication (HTC) along with massive machine-type communication (MTC), device-to-device (D2D) and non-orthogonal multiple access (NOMA) techniques will inevitably be incorporated into dense cellular networks to cater massive connectivity and maintain high spectral efficiency. However, such combination may lead to very complex network topologies and bring challenge in resource allocation, interference management and transmission mode selection. Note the received signal strength (RSS) is an important factor for cellular and D2D mode selection, it can affect multi-access mode determination in D2D-aided HTC/MTC dense NOMA systems. Therefore, the RSS threshold of each cell has great impact on system performance and should be carefully tuned. To this end, we formulate the RSS-threshold selection problem as a decentralized partially observable Markov decision process to maximize the performance for downlink and uplink communications. Accordingly, we employ a multi-agent reinforcement learning based scheme wherein each small base station acts as an agent and chooses the optimal RSS threshold to achieve maximum sum rate by interacting with the environment continuously. Extensive simulation results reveal our proposed scheme can improve the system sum rate and coverage by enhancing the connectivity of massive HTC and MTC devices via D2D and NOMA techniques.

The development of the fifth-generation (5G) communication technology and various emerging Internet of Things (IoT) applications have brought in great challenge of seamless connectivity for massive IoT devices. Recently, unmanned aerial vehicle (UAV) has been regarded as a promising solution to improve network coverage and energy efficiency for IoT devices in disaster or remote areas. Under this circumstance, how to minimize the data transmission delay with respect to the limited UAV energy is crucial. Thus we in this paper try to solve the problem by optimizing the UAV trajectory. Specifically, we first formulate the device grouping issue as a maximum clique problem, which is solved by an ant colony algorithm. Then, a Q-learning algorithm is proposed to solve the UAV trajectory planning problem. Simulation results show that the proposed algorithms can yield a globally optimal solution, and outperform the existing K-means and NTE based algorithms.

Multiple unmanned aerial vehicles (UAVs) can function as aerial base stations to provide flexible and reliable communication services for massive ground devices (GDs). It is quite a challenging task to analyze such multi-UAV networks when considering practical mutually exclusive relationships among UAVs. Based on the tools of stochastic geometry, we develop in this paper a theoretical framework for modeling and analyzing the coverage probability and average rate in a 3D air-ground network with UAVs following Matérn Hard-Core Point Process (MHCPP) distribution. As the tractable probability generating functional (PGFL) for such repulsive point processes is unavailable, we employ the approximate signal to interference ratio (SIR) analysis based on the Poisson point process (ASPPP) to obtain the Laplace transform approximation expression of an arbitrary GD’s cumulative interference in the network by considering both line-of-sight (LoS) and none-line-of-sight (NLoS) communications. Finally, extensive simulation results are presented to validate the effective and accuracy of our proposed framework.

With the rapid development of ultra-dense networks (UDNs) and random access technologies, device-to-device (D2D) and non-orthogonal multiple access (NOMA) techniques will incorporated into future UDNs supporting both human-type communications (HTC) and machine-type communications (MTC) to fulfill the stringent requirements brought by various potential Internet of Everything (IoE) applications. Nevertheless, the combination of D2D and NOMA will make the network management more complicated. In view of this, we optimize the received signal strength (RSS) threshold value of each small base stations (SBSs) in the UDN where HTC and MTC coexist. Considering the computational complexity, we employ a multi-agent reinforcement learning based RSS threshold value selection scheme, in which each SBS acts as an agent and chose the optimal RSS threshold value to achieve maximum system throughput performance by interacting with the environment. Extensive numerical results show our proposed scheme can greatly improve the system throughput by enhancing the connectivity of massive HTC users and MTC devices via D2D and NOMA techniques.

Multiple unmanned aerial vehicles (UAVs) can function as aerial base stations to provide flexible and reliable communication services for massive ground devices (GDs). It is quite a challenging task to analyze such multi-UAV networks when considering practical mutually exclusive relationships among UAVs. Based on the tools of stochastic geometry, we in this paper develop a theoretical framework for modeling and analyzing aerial networks with UAVs following Matérn hard-core point process (MHCPP). As the tractable probability generating functional (PGFL) of repulsive point processes is unavailable, we employ an approximate approach based on the Poisson point process to analyze the cumulative interference and the signal-to-interference ratio (SIR) of a typical GD. By considering both line-of-sight (LOS) and none-line-of-sight (NLOS) communications, we obtain the approximation expressions of the network coverage probability and average rate. Finally, extensive simulation results are presented to validate the efficiency and accuracy of our proposed framework.

With the rapid development of machine-type communications (MTC), the future communication architecture needs to provide services for both human-type communications (HTC) and MTC with unique characteristics. The huge connections from MTC bring serious challenges to the existing wireless network. Ultra-dense network (UDN), a promising candidate technology, can support massive device access through dense deployment of small base stations (SBSs). Different from the resource management in traditional wireless network with single base station (BS), the resource allocation problem at BS level is more prominent in UDN, and the diversity of devices will make this problem more complicated. In view of this, we investigate the joint optimization of massive access and resource management in the UDN where HTC and MTC coexist. Considering the computational complexity and scalability, we propose a multi-agent deep reinforcement learning based SBS state selection scheme, in which each SBS acts as an agent and selects the optimal state between active and idle by continuously interacting with the environment. In addition, we adopt the power-domain non-orthogonal multiple access to further improve system throughput, and use grant-based and grant-free access manners for HTC and MTC respectively, so as to meet their unique characteristics. Extensive numerical results demonstrate the superior performances of proposed scheme in multiple perspectives.

The utilization of UAV may suffer severe security problems due to the inherent characteristics of wireless air-to-ground (A2G) channels. To this end, researchers have drawn much attention on utilizing physical layer security (PLS) techniques to maintain secrecy data transmission in UAV enabled networks. Different from previous works, we jointly optimize user scheduling strategy, signal transmission power and 3D flying trajectory of the UAV to maximize the minimum system secrecy rate by considering UAV position and an eavesdropper with partial location information simultaneously. Because the formulated problem is intractable and non-convex, we in this paper develop an iteration approach to solve the problem based on the successive convex approximation (SCA) method. Finally, experimental results are further derived to validate the performance gains of our scheme.

The explosive growth of machine-type communications (MTC) devices poses critical challenges to the existing cellular networks. Therefore, how to support massive MTC devices with limited resources is an urgent problem to be solved. Bursty traffic is an important characteristic of MTC devices, which makes it difficult for agents to learn useful experience and has a negative impact on model convergence. However, most existing reinforcement learning-based literatures assume that devices have saturate data. Towards this end, we propose two distributed Q-learning aided uplink grant-free non-orthogonal multiple access (NOMA) schemes (including all-devices distributed Q-learning (ADDQ) scheme and portion-devices distributed Q-learning (PDDQ) scheme) to maximize the number of accessible devices, where the bursty traffic of massive MTC devices is carefully considered. In order to reduce the dimension of scheduling space and mitigate the impact of bursty traffic, the idea of grouping devices as well as transmission resources and the intermittent learning mode are adopted in our schemes. Extensive numerical results demonstrate the advantages of proposed schemes from multiple perspectives.

In this paper, multiple machine learning-enabled solutions are adopted to tackle the challenges of complex sensing model in cooperative spectrum sensing for non-orthogonal multiple access transmission mechanism, including unsupervised learning algorithms (K-Means clustering and Gaussian mixture model) as well as supervised learning algorithms (directed acyclic graph-support vector machine, K-nearest-neighbor and back-propagation neural network). In these solutions, multiple secondary users (SUs) collaborate to perceive the presence of primary users (PUs), and the state of each PU need to be detected precisely. Furthermore, the sensing accuracy is analyzed in detail from the aspects of the number of SUs, the training data volume, the average signal-to-noise ratio of receivers, the ratio of PUs' power coefficients, as well as the training time and test time. Numerical results illustrate the effectiveness of our proposed solutions.

Due to the advantages of large area coverage, low capital cost and fast deployment, unmanned aerial vehicles (UAVs) are believed to play a key role in the emerging Internet of Things (IoT). In this paper, we first develop an effective analytical approach to characterize the properties of UAV-assisted communications over a large number of IoT devices by introducing average channel access delay for packets that can be successfully transmitted. Specifically, an IoT device is said to establish a full transmission to a UAV, only if its time duration covered by the UAV is greater than the specified average channel access delay. Then, we present a stochastic geometry based mathematical framework to analyze the coverage probability and average achievable rate for a multi-UAV assisted downlink network. Different from previous works: 1) we consider a flexible multi-UAV deployment strategy connecting IoT devices to the Internet via sky-haul links to the satellite, where the altitudes of the UAVs can be adjusted to fulfill the requirements of various IoT applications and 2) we derive analytical expressions, in particular integral form, for the coverage probability and average achievable rate. Our results indicate that the developed framework is very helpful for network designers to efficiently determine the optimal network parameters at which the optimum IoT system performances can be achieved.

In this paper, we present a model for average rate analysis in a D2D communication overlaying two-tier downlink cellular network. Each mobile UE is able to establish D2D link with adjacent UEs or connect to a nearby macro or pico base station. Stochastic geometry analysis is adopted to characterize the medium contentions within macro and pico cells, as well as the D2D pair distributions, based on which closed-form per user average rate is derived with a careful consideration of the important issues such as frequency allocation, UE density, content hit rate, and cell coverage radius. Our results show that even for the overlaying case, D2D communication can significantly improve the per user average rate. Another finding is that the frequency allocation for D2D pairs should be carefully tuned according to network settings, which may result in totally different varying behaviors for the per user average rate.

Based on the tool of stochastic geometry, we present in this paper a framework for analyzing the coverage probability and ergodic rate in a D2D overlaying multi-channel downlink cellular network. Different from previous works, 1) we consider a flexible new scheme for mobile UEs to select operation mode individually, under which a mobile UE decides to establish a cellular link (with a BS) or a D2D link (with a neighboring UE) based on the pilot signal strength received from its nearest BS; 2) we allow a mobile UE which is located far from BSs to connect to a nearby BS via another intermediate UE in a two-hop manner. Our results indicate that the developed framework is very helpful for network designers to efficiently determine the optimal network parameters at which the optimum system performance can be achieved. Furthermore, as corroborated by extensive numerical results, enabling the D2D link based two-hop connection can significantly improve the network coverage performance, especially for the low SIR regime.