The explosive growth of mobile data traffic has prompted operators to deploy a large number of base stations (BSs). However, due to the uneven traffic distribution, many BSs remain underutilized or idle during off-peak periods while still consuming substantial amounts of energy. To tackle this issue, we propose a Proactive Optimization-based (PO-based) BS sleeping strategy for large scale scenarios with hundreds of BSs. Specifically, by analyzing the Autocorrelation Function (ACF) of BS traffic in real-world scenarios, we identify multiple potential periods. Guided by this insight, we introduce multi-time-window mechanism and Graph Convolutional Network (GCN), designing Multi-Time-Window Spatio-Temporal Graph Convolutional Network (MTSGCN) to effectively capture the complex spatio-temporal dependencies present large-scale settings. The forecasted results acquired by MTSGCN serve as inputs to a multiple-BSs cooperative sleeping problem with the objective to minimize the total energy consumption. To tackle this huge problem efficiently, we first use K-means++ to divide the large region into several small cooperative clusters and then adopt the Integral Linear Programming (ILP) algorithm to solve each subproblem. Experimental results demonstrate that MTSGCN reduce the forecasting error by 10.9% compared with the state-of-the-art methods. Furthermore, the proposed MTSGCN-ILP algorithm achieves over 20% energy savings gains compared to the other typical strategies.

As the sixth generation wireless technology evolves, applications such as, holography, autonomous driving, and telemedicine require enhanced data rates, reliability, and spectral efficiency. Unmanned Aerial Vehicles (UAVs) have gained attention due to their flexible deployment, line-of-sight transmission, and dynamic adaptability. However, UAV-assisted communication encounters challenges stemming from UAV position deviations caused by environmental factors such as wind and turbulence, which degrade transmission reliability. To address these problems, we propose a Non-Orthogonal Multiple Access-based full-duplex UAV relay protocol to improve the system transmission rate. The protocol utilizes successive interference cancellation for signal separation and maximal ratio combining for signal enhancement. Considering UAV position uncertainty, we formulate a robust optimization problem for joint UAV position optimization and power allocation. By employing the Bernstein-type inequality, we transform the probabilistic constraints into the deterministic constraints and solve the problem using a block coordinate descent-based algorithm. Simulation results demonstrate that, compared to the benchmark schemes, the proposed strategy improves system throughput and exhibits enhanced robustness, particularly under significant UAV position deviations.

As edge applications demand real-time processing with limited bandwidth and energy, traditional communication systems face challenges to meet performance requirements due to the centralized architecture and redundant data transmission. To address these challenges, we propose a UAV-assisted semantic edge computing network that leverages UAV mobility and semantic communication. We formulate a joint optimization problem involving UAV trajectory, data allocation, and semantic extraction to maximize the semantic processing rate. To solve this problem, we develop a hybrid deep deterministic policy gradient (H-DDPG) algorithm that integrates deep reinforcement learning (DRL) with convex optimization via block coordinate descent (BCD), thereby enabling efficient joint decision-making across tightly coupled variables. Furthermore, we propose a hybrid diffusion deep deterministic policy gradient (H-D3PG) algorithm, which incorporates denoising diffusion models into the DRL framework. By addressing the limited adaptability of deterministic strategies, this design enhances policy expressiveness and stability. As a result, the algorithm enables adaptive trajectory control under time-varying semantic tasks and wireless channel conditions in UAV-assisted edge networks. Simulations show that H-D3PG improves the semantic processing rate by up to 38.8% while reducing energy consumption compared to Raw Data Transmission.

In this paper, we propose a task-oriented semantic communication system based on the cloud-network-end (C-N-E) architecture to improve the energy efficiency of image transmission. Within the system, a cloud server provides storage and computation resources for image data collected by multiple cameras. The semantic information of an image is modeled as a scene graph, enabling the analysis of end-user interests. To reduce communication overhead, only useful semantic information relevant to user interests is transmitted. Considering the delay constraint, we formulate an optimization problem to minimize the total energy consumption by jointly selecting semantic information and allocating computation and communication resources. To solve this problem efficiently, an iterative algorithm based on optimal matching and sequential convex approximation is developed. Comparative simulations validate the efficacy of our algorithm.

Cell-free massive MIMO (CF mMIMO) systems overcome inter-cell interference, enhancing overall communication rates for next-generation networks. However, the pilot contamination exacerbates channel estimation errors and the complex connectivity makes it difficult to deal with resource allocation optimization problem. In this paper, we investigate the robust beamforming problem under channel uncertainty with the goal of improving the minimum quantile rate. Specifically, we introduce hypergraph neural network (HGNN) into the wireless resource allocation of CF mMIMO ststems for the first time, leveraging hypergraph modeling to capture the many-to-many relationships between Access Points (APs) and User Equipments (UEs). Furthermore, we significantly reduce the search space of the optimization problem by applying optimal interference suppression beamforming theory. In order to soften the sorting process, we adopt the Monte Carlo sampling strategy for data augmentation. Simulation results demonstrate that the proposed algorithm outperforms conventional schemes, achieving 14.1% performance gain and converging more than twice as fast as the state-of-the-art machine learning models.

Federated Learning (FL) offers promising solutions for deploying AI in wireless networks, allowing resourceconstrained devices to collaboratively train machine learning models, and reducing deployment costs. However, FL faces challenges due to device heterogeneity and unreliable communication links, which extend training time. Unmanned Aerial Vehicles (UAVs), with their flexibility and deployment advantages, have emerged as valuable assets in addressing these limitations by enhancing line-of-sight communication and providing proximal computational resources. This paper proposes a UAV-assisted FL framework that jointly optimizes resource allocation, task loads, and UAV trajectories to minimize FL completion time. Through a block coordinate descent (BCD) approach, our framework addresses the formulated joint optimization problem. Simulation results demonstrate that our proposed framework effectively balances resource allocation and significantly reduces FL completion time compared to benchmark schemes.

The rapid development of 5G technology leads to increasing energy consumption in base stations (BSs). For the vision of green and sustainable communications, we propose a scheme aimed at reducing BS energy consumption through symbol shutdown. This approach reduces BS energy consumption while ensuring the quality of service (QoS) for user equipments (UEs). Our scheme considers highly dynamic channel conditions and formulates a joint optimization problem, including BS shutdown and power allocation, constrained to the long-term average rate demand of all UEs. To address this problem, we design a Lyapunov method-based algorithm (LMBA) that transforms the mixed integer and dynamic optimization problem into a more tractable form and solves it using the Karush-Kuhn-Tucker conditions. Simulations indicate that our proposed strategy significantly reduces the BS energy consumption and the LMBA outperforms several benchmark algorithms.

The development of the next generation ubiquitous network puts forward higher requirements for the connection density in the communication network, e.g., massive IoT and UAV swarm, which has led to a lot of research on link management. With the expansion of network scale, the weaknesses of existing algorithms in computing efficiency, performance, and realizability have become prominent. The emerging graph neural network (GNN) provides another way to solve this problem. In this paper, we design a cross-receptive distributed GNN structure from the perspective of communication system, combining measurable index of the actual scene with message passing frame. This new GNN structure and the additional input feature dimension work together to provide richer and more comprehensive information for network training. After the initial deployment of the power decision from GNN, we select some links to shut down and others to reduce their transmit power to further improve system performance and save energy. Simulation results show that our proposed method reaches 83.1% performance of the centralized mechanism. In addition, the discussion on scalability suggests that in order to save training cost, small-scale scenes with the same density can be selected for training in the application of large-scale scenes.

The emergence of new application scenarios has enabled integrated sensing and communication (ISAC) as one of the potential technologies of 6-th generation mobile communication (6G). To meet both communication efficiency and sensing efficiency, a satisfactory ISAC waveform is essential. In this paper, we primarily investigate the impact of different modulation schemes on sensing performance for UAV-to-ground scenarios. Firstly, we analyze the sensing performance differences of modulation schemes in the sensing algorithm based on orthogonal frequency division multiplexing (OFDM) systems. Secondly, we examine the periodic auto-correlation functions (PACFs) with different modulation schemes and modulation orders. We observe that the waveforms modulated by phase shift keying (PSK) exhibit the lower sidelobes compared to waveforms modulated by quadrature amplitude modulation (QAM). Finally, we simulate the probability of detection (Pd) with different modulation schemes for UAV-to-ground scenarios. Numerical results demonstrate that the modulated waveform with constant modulus exhibit superior sensing performance. For the modulated waveform with non-constant modulus, the higher modulation orders result in the poorer sensing performance. This inspires us to change the sensing performance by designing the power spectrum of the modulated waveform with non-constant modulus. This work is helpful for the waveform design and performance analysis of ISAC.

Studying the characteristics of the satellite-toground (S2G) channel model is essential for the development and assessment of satellite communication systems. In this study, we introduce a unique approach by combining a low-earth-orbit (LEO) random geometric satellite channel model with line-of-sight (LoS) and single-bounced (SB) non-line-of-sight (NLoS) components for the S2G multiple-input multiple-output (MIMO) channel. By utilizing the coaxial cylinders reference model in a lightly shadow environment occluded by terrain features, we compute the space correlation function (SCF) and time correlation function (TCF). To simplify the simulation process, we present a deterministic simulation model using the finite number of scatterers and analyze the various factors that influence channel characteristics. The findings from the simulation indicate that the orientation of both the satellite and terrestrial receiver antennas, as well as their respective movement directions, distances, and the density of scatterers’ azimuth angles, all play a significant role in shaping the statistical properties of the channel model.

In blockchain systems characterized by computation competition, allocating computation resources is of paramount significance for the economic benefits of nodes. Besides, uncer-tainties of computation resources also affect the node's profits. In this paper, we address the computation resource allocation issue within a proof-of-work (PoW) blockchain system without exact information on the available resources, which impedes the direct investigation of the maximum mining profit. Correspondingly, we establish the chance-constrained threshold for maximum achievable profit through the blockchain in an uncertain environment and maximize this threshold under a given outage probability. Particularly, the uncertain computation resource is modeled only with its first and second statistics, which lack the exact distribution information. In this respect, we propose the distributionally robust approach to tackle the chance-constrained resource allocation strategy, which guarantees the intended profit threshold regardless of the actual distribution. We show that the considered problem admits a conditional value-at-risk (CVaR) approximation reformulation, which can be handled by alternately optimizing the resource allocation strategy and the profit threshold. Simulation results demonstrate that the proposed design is robust against the uncertainty distribution, and effectively guarantees the profits of miners.

Satellite communication is considered as one of the key technologies to achieve global seamless coverage and has attracted wide attention. It is crucial to establish an accurate clutter loss model for satellite-ground communication. Clutter loss refers to the extra path loss caused by the obstruction of the terrain and objects on the ground, especially when a satellite has low elevation angle. The clutter loss model proposed by ITU-R P.2108-0 only considers the influence of the elevation angle, and the traditional prediction model for the clutter loss is limited in accuracy and stability. In this work, we used a satellite ground station to carry out the channel measurement at 8.25 GHz on the clutter loss of the X-band satellite-to-ground (S2G) links in the suburban campus environment, and extracted the clutter loss data set involved in the process of satellite inbound and outbound from the received signal strength. We utilize the multi-layer perceptron (MLP), long short-term memory (LSTM), and bidirectional-long short-term memory (Bi-LSTM) neural networks to build channel models to predict the clutter loss based on the measurement data. The model prediction results show that the Bi-LSTM-based model has higher prediction accuracy than the MLP-based and LSTM-based models.

Integrating the unmanned aerial vehicles (UAVs) assisted mobile edge computing (MEC) network with the blockchain technology emerges its superiority in the network utilization, differentiated service, and security, which has been regarded as a promising technique for time-critical applications. In this paper, we propose a UAV-assisted MEC network architecture and a comprehensive data processing flow, where the UAVs cooperate with the base station in computation as edge servers and act as blockchain nodes. We formulate an optimization problem that jointly considers UAVs’ position, data offloading, and resource allocation for minimizing the total time consumption of data processing. To address this problem, we decouple it as three tractable subproblems and propose a Block Coordinate Descent (BCD)-based iterative algorithm. In addition, we analyze the task migration and resource allocation problem in computation, and obtain analytical solutions by the Karush-Kuhn-Tucker (KKT) conditions. The simulated results indicate that the proposed algorithm leads to substantial performance gains.

How to improve the coverage capability of network including connectivity and throughput is vital for enabling the Internet of Everything (IoE) in B5G/6G. However, the traditional terrestrial networks are confronted with the high-cost and inflexible challenges especially in the remote area and emergency applications. In order to solve these challenges, we consider a heterogeneous aerial network (HetAN), where some low-altitude base stations (LBSs) are deployed as access points for wireless coverage and a high-altitude base station (HBS) hovers as the hub for backhaul of LBSs. Furthermore, we apply the non-orthogonal multiple access (NOMA) to uplink transmission for the terrestrial users, which enable massive connectivity in the IoE. To maximize connectivity and throughput, we jointly optimize the LBSs’ deployment, power control, channel allocation, and rate control by fully exploiting the potential of the HetAN in wide-area coverage. For solving the formulated problem efficiently, we propose an iterative algorithm based on the methods of graph theory, bionic algorithm, and theoretical analysis. Simulation results are provided to reveal the influence of the control variables on network performance and indicate that our algorithm can greatly improve the connectivity and throughput with the other schemes.

Integrated sensing and communication (ISAC) is considered one of the key technologies for next-generation wireless communication. To achieve satisfactory communication and sensing performance simultaneously, it is necessary to maximize compatibility with the existing communication waveform. In this paper, we mainly investigate the ISAC constellation design based on communication waveforms (random waveforms). Firstly, we derive the modulated waveform with constant modulus has a smaller side lobe of periodic auto-correlation function (PACF), i.e., the modulated waveform with constant modulus is more suitable for sensing. To improve the sensing performance of the modulated waveform with non-constant modulus, we propose a ISAC constellation design method based on PCS, which designs the power spectrum of the modulated waveform by adjusting the probabilities of constellation points, thereby reducing the side lobe of PACF. In addition, we design a joint optimization problem between the weighted variance of normalized energy and the communication information entropy (CIE) to obtain the tradeoff between communication and sensing. Finally, we simulate the communication performance and sensing performance of the reshaped waveform, and obtain some interesting conclusions. The simulation results show that, for the modulated waveform with non-constant modulus, the proposed method can reduce the side lobe of PACF, and increase the probability of detection (Pd), along with a minimal loss for CIE and a tiny growth for bit error rate (BER). This work is helpful for the theoretical exploration and system design for ISAC.

The emerging artificial intelligence (AI) puts forward high requirement for big data acquisition, which is difficult to be met with the existing communication technologies in real time. In this paper, we investigate new graph learning based access management scheme for supporting the real-time big data acquisition in the sixth-generation mobile communication system (6G). We model the network scene with a mass of communication links as a fully connected graph which takes into account the accumulative interference of all links. Then, the joint admission and power control problem is formulated as a combinatorial optimization problem. We propose a graph attention network (GAT) based algorithm which can learn the system features by weighted aggregation of neighbor nodes. In addition, we construct a differentiable loss function that can accurately express the optimization objective and train the network by the change of loss. Based on the output of the GAT, we iteratively optimize the link admission and power to active more links. Simulation results demonstrate that the proposed algorithm is superior to the traditional convex optimization based algorithms and the nonmodified GAT based algorithms in the number of activated links. Moreover, the training of the constructed network is unsupervised with high computational efficiency, which makes them suitable for the big data access management.

In 5G Radio Access Networks (RANs), the energy consumption of the ground base station (GBS) accounts for more than 80%. Therefore, reducing the energy consumption of GBSs has been an important research direction for building green and environment-friendly communication networks. In view of this, we formulate an unmanned aerial vehicle (UAV)-assisted GBS sleep strategy for energy saving, which utilizes the mobility of UAVs to fill the wireless coverage holes caused by the sleeping of GBSs. To further enhance the effect of the formulated strategy, we propose a joint GBS sleeping, UAV trajectory planning, and UAV transmission power allocation problem to minimize the energy consumption of the entire system. To address the intractable problem, we first devise an iterative algorithm to optimize the trajectory and power of the UAV based on the block coordinate descent (BCD), and then nest it into the branch and bound (BaB) to obtain the GBSs operation status. Simulations demonstrate that the formulated strategy efficiently reduces the energy consumption of the network compared with other schemes.

Unmanned aerial vehicles (UAVs) combining with mobile edge computing (MEC) networks have promoted the application of Internet of Things (IoT) devices, providing enhanced coverage with flexible computing services. But the energy consumption of data processing is still a shortage in the UAV-assisted MEC architecture. Motivated by that, we propose a dynamic UAV-assisted MEC network and formulate a problem and jointly optimize association strategies, UAV trajectory, data offloading, and resource distribution for minimizing total energy consumption. To deal with this tricky problem, we devise a dichotomy-based joint iterative optimization algorithm. Specifically, we divide the problem into three sub-problems, solving by the integer programming, successive convex optimization, and dichotomy method. Finally, the simulation consequences prove that the devised network and algorithm significantly reduce total energy consuming.

The unmanned aerial vehicle (UAV) communication with the multiple-input–multiple-output (MIMO) system has attracted lots of attention to improve spectral efficiency and channel capacity. The large antenna spacing is often required to reduce the spatial correlation between subchannels. However, this requirement cannot be well satisfied in practical scenarios due to the limited dimension of UAV platforms. Therefore, polarization diversity is a promising approach for UAV MIMO systems. In this work, we propose a three-dimensional (3-D) geometrical-based polarized model for UAV-correlated MIMO channels. By utilizing the geometrical theory of polarization, we describe the channel depolarization caused by the terrestrial scattering environment based on the multicylinder geometrical model and then acquire the channel polarization function and polarized channel impulse response (CIR). Furthermore, we investigate the impact of the key factors, such as the UAV tilt rotation, cross-polarization, and limited antenna spacing on the UAV MIMO channel capacity. To validate the proposed UAV channel model, we compare the spatial correlations of the numerical simulation and UAV field measurement results for the co- and cross-polarized channels, and the close agreements between them are observed. This work can provide useful guidance and support for the design and performance evaluation of UAV communication networks.

Air-to-ground (A2G) communication based on Unmanned aerial vehicle (UAV) is an important part of the future communication system. In this paper, an A2G channel model with UAV three-dimensional (3D) wobbles (pitch, roll, and yaw) based on the geometry-based stochastic model (GBSM) is proposed. On this basis, the UAV’s internal vibration is modeled as a sinusoidal random process, and the UAV wobble caused by the atmospheric flow is modeled as the uniform distribution random process. We derive the channel temporal correlation function (CF) with UAV 3D wobbles, analyze the variation of the temporal CF with different carrier frequencies, and amplitudes of the wobble angles. It is found that, even if the UAV wobbles slightly, the channel temporal correlation will be significantly affected. Numerical results show that the channel CF will decrease rapidly with the increase of the amplitudes of wobble angles and the carrier frequency. Therefore, the coherence time of millimeter wave (mmWave) band is significantly less than that of sub-6 GHz band. The consistency of simulation results and measurement results in published papers ensures the availability of the proposed model. For the MUAVs scenario, when the distance between different UAVs is much greater than the wavelength, the A2G channels between different UAVs and user equipment (UE) on the ground are not correlated to each other, and the temporal auto-correlation function (ACF) of each UAV is the same as that of the SUAV scenario. This work contributes to the theoretical exploration and system design of A2G communication based on UAV.

Wireless communication based on Unmanned aerial vehicle (UAV) is one of the important technologies in the future communication system. It is necessary to establish an accurate air-to-ground (A2G) wireless channel model. In this paper, a A2G channel model with UAV three-dimensional (3D) wobbles (pitch, roll, and yaw) is proposed. The internal vibration of the UAV is modeled as a sinusoidal random process, and the UAV wobble caused by the random air fluctuations is modeled as the uniform distribution random process. We derive the A2G channel temporal auto-correlation function (ACF) with UAV 3D wobbles, analyze the variation of the temporal ACF with different time instants, carrier frequencies, and amplitudes of the wobble angles. It is found that, even if the UAV wobbles slightly, the channel temporal correlation will be significantly affected. Numerical results show that the channel ACF will decrease rapidly with the increase of the amplitudes of the wobble angles and the carrier frequency. This work contributes to the establishment of the next generation wireless channel model and the design of communication system.

In this paper, we study the coverage maximization problem in the aerial networks. Specifically, we propose a heterogeneous aerial network (HetAN) consisting of a high-altitude base station (HBS) acting as a hub to provide wireless backhaul and multiple low-altitude BSs (LBSs) acting as access points to provide on-demand wireless coverage. Besides, we adopt the non-orthogonal multiple access (NOMA) technique for the uplink transmissions of the terrestrial users so as to support massive connections. Then, we formulate a joint power control, channel assignment, and rate control problem with the objective to maximize user connectivity and network throughput. Based on the graph methods and theoretical analysis, we propose an efficient iterative algorithm to solve the formulated problem. Simulation results demonstrate that our algorithm outperforms the other schemes in terms of connectivity and throughput.

The Internet of Things (IoT) has gained rapid development, but due to the limited battery capacity and access capacity, there are many complex problems in the application. In this paper, we consider an air-and-ground cooperative wireless network, which can provide dynamic coverage for sensor equipments (SEs). Jointly considering the dynamic deployment of the aerial base stations (ABSs) and the admission-and-power control of the SEs, we formulate a two time-scale network control problem to minimize the long-term power consumption of all SEs under their individual rate requirement. On large time scales, we propose a particle swarm optimization algorithm (PSOA) to adjust the positions of the ABSs. On small time scales, we devise a joint admission-and-power control algorithm (JACA). Simulation results indicate that the air-and-ground network incorporated with the proposed algorithms can significantly reduce the total power consumption of the SEs compared with the other schemes.

Internet of Things (IoT) can be conveniently de-ployed while empowering various applications, where the IoT nodes can form clusters to finish certain missions collectively. In this paper, we propose to employ unmanned aerial vehicles (UAVs) to assist the IoT data collection with blockchain-based security provisioning, towards efficient and safeguarded IoT operations. In particular, a blockchain with proof-of-stake (PoS) consensus mechanism is constructed among the UAVs with the collected IoT data. Correspondingly, we optimize the IoT communication and the UAV deployment for the maximum blockchain throughput considering the PoS procedure. The problem is solved with a deep deterministic policy gradient-based approach, where the power allocation is obtained with closed-form solutions and the UAV deployment is learned with actor-critic networks. Simulation results are provided to show the deployment and performance, corroborating the effectiveness of our proposal.

The unbalanced traffic distribution is a severe problem in cellular networks, which leads to congestion and reduces spectrum efficiency. To tackle this problem, we propose an unmanned aerial vehicle (UAV)-assisted wireless network architecture in which UAV acts as relay to divert the traffic from the overloaded cell to its neighbor underloaded cell. Considering that UAV communications are easily eavesdropped, we use the secrecy capacity to evaluate the performance of the network. To fully exploit the advantages of the proposed architecture, we formulate a joint UAV position optimization, user association, and time allocation problem to maximize the sum-log-rate of all users in two adjacent cells. To tackle the complicated joint optimization problem, we first design a genetic-based algorithm to optimize the UAV position, and then use the branch-and-bound method to devise a low-complexity algorithm to get the optimal user association and time allocation schemes. The simulation results indicate that the proposed UAV-assisted wireless network architecture is superior to the terrestrial network, and the proposed algorithms can further improve the network performance in comparison with the other schemes.

In this paper, we focus on the energy saving problem for the wireless sensor networks (WSNs). Specifically, we propose a UAV-assisted wireless network architecture, where the cell-edge sensor devices (SDs) can access the aerial access points (AAPs) instead of the terrestrial access point (TAP). Since the transmitter-to-receiver distance is shortened and the ground-to-air channel is usually line-of-sight, the SDs can use lower power to transmit data and thereby prolong their lifetime. To fully exploit the potential of the network architecture, we jointly optimize the AAPs' position, channel allocation, and power control to minimize the total transmission power of all SDs. In order to solve the complex joint optimization problem, we reformulate it as three tractable subproblems and use the methods in graph theory to design low-complex algorithms. Simulation results indicate that the proposed network architecture greatly outperforms the traditional WSNs, and the proposed algorithms can further reduce the total power consumption.

Buffer-aided relay technique can improve the diversity order and offer secrecy provision. To further improve secrecy performance, this paper proposes a secure link selection for relay networks where a new link selection policy is first designed under the constraint on the buffers and channel states using a Markov chain. The stationary state and the corresponding state transition matrix can be derived, and they are used to analyze the secrecy performance. Through the derivation of secrecy outage probability, we can get its closed-form expressions. Numerical results demonstrate that the proposed secure transmission scheme has a better performance than the conventional buffer-aided secure transmission schemes in terms of secrecy outage probability.

In this paper, we consider an unmanned aerial vehicle (UAV)-assisted relay network for B5G, where the UAV and the base station (BS) cooperated with each other to serve the cell-edge users. Aiming at this network, we propose a new decode-and-forward (DF) relay protocol incorporated with Non-orthogonal multiple access (NOMA) and maximum ratio combining (MRC), by which the coverage for the cell-edge users is enhanced. Theoretical analysis demonstrates that the proposed NOMA based relay protocol is superior to the traditional orthogonal multiple access (OMA) based relay protocol in terms of channel capacity. In order to make full use of the advantages of the proposed protocol, we formulate the 3D spatial position optimization problem of the UAV relay with the objective to maximize the sum-rate of all the cell-edge users. Based on the genetic algorithm, we propose an effective algorithm to solve the formulated problem. simulation results indicate that the proposed relay protocol greatly outperforms the traditional protocols, and the proposed algorithm achieves orders of magnitude speedup in computational time with only slight loss of performance.1

Mobile-edge computing (MEC) is considered as a promising technology to enable low latency applications while consuming less energy, and nonorthogonal multiple access (NOMA) is regarded as a hopeful method of increasing spectrum efficiency and the wireless network capacity. In this article, we consider a NOMA-MEC-based Internet-of-Things (IoT) network, and propose a joint optimization framework to maximize the effective system capacity, i.e., the number of IoT devices whose tasks are processed successfully, and meanwhile to maximize the total energy saving. First, we concentrate on improving the effective system capacity from the wireless side by introducing NOMA, and from the IoT device side by task offloading decision optimization, where distributed optimization is conducted and closed-form solution is obtained. Then, we maximize the total energy saving also from two aspects, i.e., the device-side computation resource allocation, and the wireless side joint admission control, user clustering, orthogonal subcarrier assignment, and transmit power control, where we resort to graph theory and propose a low-complexity heuristic algorithm to solve it. Abundant simulation results demonstrate our proposed joint optimization algorithm performs well in both effective system capacity optimization and energy saving maximization.

Unmanned aerial vehicles (UAVs) have played an important role in wireless communications due to the advantages, such as highly controllable mobility in 3-D space, swift deployment, Line-of-Sight (LoS) aerial–ground links, and so on. In this article, we consider a UAV-enabled two-way relaying system, where the UAV relay assists the information exchange between two ground users (GUs) under three different schemes, i.e., four-slot multihopping (FSMH) without network coding (NC), three-slot NC, and two-slot physical NC (PNC). First, the capacity region of each scheme in this relaying system is analyzed. Then, we maximize the system average sum rate by jointly optimizing the time resource allocation, transmission powers of the transceivers, and the UAV trajectory subject to the constraints on UAV mobility and information causality under each scheme. To solve those problems, we propose an iterative algorithm by applying the successive convex approximation and block coordinate descent techniques. Specifically, the time resource allocation, transmission powers, and the UAV trajectory are alternatively optimized in each iteration. In addition, the nonconvex trajectory optimization problem is solved by successively solving an approximate convex optimization problem. To gain more insights, we also investigate the performance of those three schemes with symmetric and asymmetric traffic, respectively, by introducing a new traffic pattern constraint. Numerical results show that the proposed relaying schemes with moving relay can achieve great throughput gains as compared to the conventional scheme with static relay. The three relay schemes also show great performance heterogeneity under different traffic patterns.

In this paper, we investigate the application of the non-orthogonal multiple access (NOMA) technique into the unmanned aerial vehicle (UAV) aided relay networks. Specifically, we first incorporate the NOMA protocol with the decode-and-forward (DF) relay protocol to enhance the performance of the cell edge users in a macrocell network. Theoretical analysis indicates that the NOMA-DF-relay protocol outperforms the conventional orthogonal multiple access (OMA) based DF-relay protocol in terms of data rate. To fully exploit the advantages of the proposed protocol, we formulate a joint UAV height optimization, channel allocation, and power allocation problem with the objective to maximize the total data rate of the cell edge users under the coverage of the UAV. For solving the formulated problem effectively, we first analyze its property and employ the golden section method to propose a general framework to obtain the optimal height of the UAV. Then, we design a low-complexity iterative algorithm to solve the joint channel-and-power allocation problem based on the matching theory and the Lagrangian dual decomposition technique. Finally, simulation results demonstrate that the NOMA-DF-relay protocol is superior to the OMA-DF-relay protocol even when the system parameters are not optimized, and the proposed algorithms can further significantly improve the network performance in comparison with the other schemes.

To prolong the time duration of smart mobile devices (SMDs) or enable low‐latency tasks, mobile edge computing (MEC) has emerged as a promising paradigm by offloading tasks to nearby MEC servers (MECSs). In this study the authors propose an optimisation problem to minimise the weighted sum of the total delay and energy consumption of all SMDs in a multi‐MECS‐multi‐SMD network via multi‐dimensional optimisation on offloading strategy making, load balancing, computation resource allocation and transmit power control. Since the problem is NP‐hard, the authors decompose it into three subproblems to solve. First, they propose a low complexity heuristic algorithm to obtain the offloading strategies while guaranteeing load balancing between the multiple MECSs. Then they solve computation resource allocation subproblem using Lagrange dual decomposition. Finally, employing fractional programming, the authors transform the transmit power control subproblem into a convex programming problem where the closed‐form solution is obtained. The proposed simulation results verify the convergence of the proposed iterative algorithms, and demonstrate that the proposed joint optimisation could achieve good performance in both delay and energy reduction.

In wireless mobile sensor networks, mobile sensors are usually composed of some mobile carriers equipped with sensors. In daily life, wireless mobile sensors need to be monitored, reconnaissance, and maintenance in hazardous areas. Because there is no specific infrastructure for centralized control in this network, in order to meet coverage requirements in a particular environment, wireless mobile sensors are often required to be moved to a specific location in a decentralized manner. How to design a mobile control coverage algorithm that controls the moving direction and moving position of each mobile sensor becomes a very important research direction. In this paper, based on the existing K-nearest neighbor rules, we propose a coverage algorithm of K-nearest neighbor based on communication beacon, which can be applied to wireless sensor networks to solve the coverage problem. We propose K neighbor node determination rules, establish a neighbor model, and give the determination principle of the neighbor node connection matrix. The simulation results show that the coverage algorithm is more efficient than the traditional K-nearest neighbor algorithm, and we find that improving the transmit power and reducing the transmission bit length can improve the coverage efficiency.

Non-Orthogonal Multiple Access (NOMA) has recently been conceived as a promising technology for the fifth-generation mobile communication system. In this paper, we apply NOMA into the device-to-device (D2D) and cellular hybrid networks to improve the data rate of the D2D links. Specifically, we formulate a D2D transmission rate maximization problem by jointly considering user pairing and power control under the constraints of the decoding threshold of cellular users. To solve the formulated problem, we first analyze the optimal transmission power of the D2D users. According to the obtained power control strategy, the user pairing problem is transformed into a bipartite graph matching problem, which can be solved optimally by the Hungarian algorithm. Simulation results demonstrate that our algorithm outperforms the existing schemes in terms of data rate.

In this paper, we performed a measurement campaign at 3.5 GHz using two ±45° polarized antenna arrays in two typical industrial scenarios, the manufacturing district and workshop corridor. We obtain averaged power delay profiles (APDP) at a number of positions and fit the APDPs by power-decaying curves. We have found that the dense metallic facilities cause large excess delay of the multipath component arrival. On the other hand, we model the small-scale fading by Lognormal distribution. We also obtain root-mean-square delay spread (RMS-DS) and model it by Nakagami distribution. Futhermore, the positive correlation between RMS-DS and spatial separation distance between the transmitter and receiver is modeled by a linear function. Dense metallic facilities in workshops leads to complicated propagation environments. Channel coherent bandwidth and energy dispersion should be considered carefully in the design of wireless communication systems in industrial environments.

Cooperative communication in wireless networks has received much attention in both academia and industry. How to effectively allocate and schedule radio resources to improve system performance becomes an important issue of cooperative communication. This paper mainly studies the ultra-low complexity wireless channel allocation algorithm for interference-limited networks. Firstly, we use the traditional sequential convex approximation (SCA) technique to design the channel allocation algorithm. Then, we utilize the characteristics of deep neural network (DNN) that can approximate a complex function with multiple layers of mapping to approximate the SCA-based algorithm. Based on DNN, we design an ultra-low complexity algorithm. Simulation results indicate that the DNN-based algorithm can achieve good performance with ultra-low computation time, which is a feature for practical application.

Non-orthogonal multiple access (NOMA) and device-to-device (D2D) are two key technologies of the fifth-generation wireless networks. In this paper, we propose a new D2D-and-NOMA integrated framework, where the D2D users (DUEs) can reuse the spectrum of the cellular users (CUEs) in four NOMA-aided spectrum-sharing modes. In order to fully exploit the potential of the proposed framework, we jointly optimize user pairing and power control to maximize the number of accessed D2D links and meanwhile reduce the total power consumption under the constraints of the decoding thresholds of the DUEs and CUEs. We first analytically obtain the optimal transmission power for each DUE-CUE pair. Then, based on the power control policy, we reformulate the user pairing problem as a min-cost max-flow problem in graph theory and solve it efficiently. Specifically, our proposed algorithm can solve the formulated problem optimally with low complexity. Finally, simulation results indicate that our algorithm can significantly improve the number of accessed D2D links and reduce the power consumption in comparison with the other schemes.

Nonorthogonal multiple access (NOMA) and device-to-device (D2D) are two promising technologies that have great potential in improving user connectivity. In this paper, we incorporate NOMA into the D2D-capable cellular networks and propose a new NOMA-aided D2D access scheme. In the proposed scheme, the D2D users (DUEs) can operate in four spectrum-sharing modes, which are the extension of the traditional underlay mode. To fully exploit the advantages of the NOMA-and-D2D integrated framework, we formulate a connectivity-maximization problem by jointly considering user pairing, mode selection, and power control under the constraints of the decoding thresholds of cellular users and DUEs. Based on the graph theory, we devise an efficient algorithm with polynomial complexity to solve the formulated problem optimally. We first analytically obtain the optimal transmission power and spectrum-sharing mode for every possible user pair through a graphical method. Based on the power control and mode selection policies, we transform the user pairing problem into a min-cost max-flow problem which can be tackled by the Ford-Fulkerson algorithm. Finally, simulation results indicate that the NOMA-aided D2D access scheme outperforms the traditional underlay mode, and the proposed algorithm yields a large performance gain in comparison with other schemes in terms of user connectivity and power consumption.

Simultaneous wireless information and power transfer (SWIPT) technique offers a potential solution to ease the contradiction between high data rate and long standby time in the fifth generation (5G) mobile communication systems. In this paper, we focus on the SWIPT network design and optimization with 5G new frequencies. To design an efficient SWIPT network, we first investigate the propagation properties of 5G low-frequency (LF) and high-frequency (HF) channels. Specifically, a measurement campaign focusing on 3.5 GHz and 28 GHz is conducted in both outdoor and outdoor-to-indoor scenarios. Motivated by the measurement results, we design a dual-band SWIPT network, where the HF band is used for short-distance information delivery, while the LF band is used for short-distance energy transfer and long-distance information delivery. The designed network has a win-win architecture which can enhance the throughput of cell-edge users and improve the energy-harvesting efficiency of cell-center users. To further boost the network performance, we devise a joint power-and-channel allocation algorithm, which has the advantages of low complexity and fast convergence. Finally, simulation results demonstrate that the designed dual-band network outperforms the conventional single-band network in terms of energy-harvesting efficiency and user fairness, and the proposed algorithm can further upgrade the network performance significantly.

Nonorthogonal multiple access (NOMA) exhibits superiority in spectrum efficiency and device connections in comparison with the traditional orthogonal multiple access technologies. However, the nonorthogonality of NOMA also introduces intracell interference that has become the bottleneck limiting the performance to be further improved. To coordinate the intracell interference, we investigate the dynamic user scheduling and power allocation problem in this paper. Specifically, we formulate this problem as a stochastic optimization problem with the objective to minimize the total power consumption of the whole network under the constraint of all users' long-term rate requirements. To tackle this challenging problem, we first transform it into a series of static optimization problems based on the stochastic optimization theory. Afterward, we exploit the special structure of the reformulated problem and adopt the branchand-bound technique to devise an efficient algorithm, which can obtain the optimal control policies with a low complexity. As a good feature, the proposed algorithm can make decisions only according to the instantaneous system state and can guarantee the long-term network performance. Simulation results demonstrate that the proposed algorithm has good performance in convergence and outperforms other schemes in terms of power consumption and user satisfaction.

In this paper, we investigate the energy-saving resource management problem for a new device-to-device (D2D) and cellular coexisting network, where D2D users employ orthogonal frequency division multiple access (OFDMA) and cellular users employ sparse code multiple access (SCMA). This hybrid network can support massive connectivity by exploiting the degrees of freedom in code and space domains, however, the complicated spectrum sharing pattern also leads to serious interference, which further boosts the power consumption of mobile devices (MDs). To tackle this problem, we propose a unified resource management scheme to minimize the total transmit power of all MDs by jointly optimizing mode selection, resource allocation, and power control. First, we analytically get the optimal resource-sharing mode (dedicated mode or reuse mode) for cellular users and D2D users based on the mapping rule between SCMA codebooks and OFDMA resource blocks. For each resource-sharing mode, we reformulate the resource management problems as classical problems in graph theory, and then devise efficient algorithms leveraging the special structure of the constructed graphs. Finally, simulation studies indicate that the network capacity is upgraded with the hybrid multiple access technologies, and the energy efficiency performance is also enhanced through the unified resource management.

In this paper, we investigate the energy-efficient transmission problem by resource allocation in SCMA networks. We formulate it as an optimization problem to maximize the network energy efficiency (EE) subject to quality-of-service (QoS) requirements, codebook assignment, power allocation, and subcarrier reuse constraints. Due to its mixed combinatory, we separate codebook assignment and power allocation to devise suboptimal but cost- efficient algorithms. With power equally distributed, we first propose a novel scheme to assign codebooks. We then develop a derivative- bisection based algorithm to optimally solve the resultant power allocation problem by exploiting its quasiconcave structure. Simulation results exhibit the superiority of the proposed algorithms against the existing classical schemes and of SCMA over OFDMA in terms of the network EE.

To improve the spectrum efficiency (SE), sparse code multiple access (SCMA) has been proposed as an candidate for 5G wireless networks. Although SCMA has good SE performance, it suffers from high detection complexity, which may degrade its energy efficiency (EE) performance. To make up for this deficiency, we in this paper jointly consider codebook design (i.e., mapping matrix and constellation graph design) and codebook assignment to investigate the detection complexity minimization problem for uplink SCMA networks. To tackle this hard problem effectively, we first borrow the idea of dual coordinate search to devise a cost-efficient algorithm to determine the mapping matrix and codebook assignment. Based on the mapping matrix, we exploit the multi-dimensional modulation characteristic of SCMA to carefully design the constellations for each codebook to further reduce the detection complexity. Finally, we present some simulations to illustrate the performance gain of our proposed algorithm as compared with other schemes.

Energy harvesting is a promising technique to reduce conventional grid energy consumption, which caters for 5G visions on the green evolution of current cellular networks. To fully exploit the harvested energy, an inefficient factor caused by the battery leakage must be taken into account to tackle the energy dissipation problem, which triggers a new dimensional optimization related to the transmission time. However, most approaches are studied for perfect battery models and neglect the optimization for the transmission time. In this paper, we formulate the battery leakage process into our model to explore the grid energy conservation problem by jointly considering admission control, power allocation, subcarrier assignment, and transmission time determination in cellular networks powered by grid and renewable energy. To tackle this problem, we exploit the Lyapunov optimization technique to develop an online algorithm, referred to as leakage-aware dynamic resource allocation policy (LADRA). Specifically, the LADRA only needs to track the current system states (e.g., channel and energy conditions) but without requiring their prior-knowledge. Furthermore, we prove that the minimum grid energy consumption value can be achieved by our proposed algorithm asymptotically. Simulation results verify the correctness of the theoretical analysis, as well as exhibit the performance improvement against other algorithms in terms of grid energy consumption and queue backlog.

We investigate the local connectivity of cognitive radio ad hoc networks (CRAHNs), i.e., node degree and probability of node isolation. The local connectivity of CRAHNs depends on not only its own network parameters but also the primary networks. To analyze the local connectivity, we use stochastic geometry and probability theory to derive the distribution of node degree, probability of available spectrum and probability of node isolation of the Secondary Users (SUs). The relation between the local connectivity of CRAHNs and the parameters of both primary and secondary networks is given. Theoretical analysis and simulation results indicate that the average node degree of SUs scales linearly for increases in the density of SUs with the slope determined by the density of Primary Users (PUs). It also indicates that the SUs' node isolation probability is largely determined by the density of PUs.

In cognitive radio networks (CRNs), secondary users (SUs) must vacate the spectrum when it is reclaimed by the primary users (PUs). As such, multiple SUs that operate on the same channel requested by the PUs will be affected, resulting in a possible network partition. Therefore, how to maintain the connectivity of CRNs when PU appears is a critical problem. In this paper, we propose a topology control algorithm to address this problem. Particularly, we combine power control and channel assignment to construct a bi-channel-connected and conflict-free topology using minimum number of channels. Theoretical analysis shows that the CRN can maintain connectivity upon any single channel interruption by PUs. The simulation results demonstrate that the proposed algorithm can reduce the number of required channels efficiently and preserve energy spanner property.

In cognitive radio networks (CRNs), secondary users (SUs) must vacate the spectrum when it is reclaimed by the primary users (PUs). As such, multiple SUs transmitting on the same channel will be affected when the channel is requested by the PUs, thereby resulting in a possible network partition of CRNs. Therefore, how to maintain the connectivity of CRNs considering the activity of PUs is a critical problem. In this paper, we propose a centralized and a distributed topology control algorithm respectively to address this problem. Particularly, we combine power control and channel assignment to construct a bi-channel-connected and conflict-free topology using the minimum number of channels. In the power control phase, we tailor the topology for the channel assignment in the second phase. In the channel assignment phase, we utilize the graph coloring algorithm to achieve conflict-free transmission by assigning a channel to each SU. Theoretical analysis and simulation study show that the derived topology can maintain connectivity in the event of any single channel interruption by PUs. Simulation results also demonstrate that the proposed algorithms can efficiently reduce the average number of required channels for achieving bi-channel-connectivity and conflict-free transmission and ensure that the minimum power paths in the original network preserved in the final topology.

Most of previous topology control algorithms that aim to extend the network lifetime focus only on the energy consumption of transmissions, and thus construct a static topology without adaptation to the varying energy consumption rates at different nodes. As a result, the network lifetime has not been prolonged to the most extent as expected. However, other topology control algorithms that consider the residual energy levels of nodes have not addressed the problem of fault tolerance. In this paper, we propose an adaptive topology control algorithm, Residual Energy-aware Shortest Path (RESP), which not only balances the energy consumption of different nodes but also provides fault tolerance. Particularly, RESP is able to ensure k-edge connectivity and preserve the minimum-weight path. Simulation results show that RESP extends the network lifetime and is superior to other existing localized fault-tolerant algorithms.