This paper addresses trajectory optimization, resource allocation, and access management in a multi-unmanned aerial vehicle (UAV) assisted collaborative mobile edge computing network for low-altitude economy. In the network, UAVs collaborate to compute offloaded tasks and improve fairness among time-varying UAV battery levels. The objective of this paper is to maximize the network utility defined by the size of successful offloaded tasks, the fairness among the user equipments, and the processing time and the energy consumption of the UAVs. In particular, we consider the time-varying UAV battery model, which affects the energy cost weights of the UAVs. Therefore, we propose a heuristic optimization framework which integrates utility partitioning two stage matching (UPTSM) algorithm and variables constrained whale optimization algorithm (VC-WOA). The UPTSM algorithm decomposes the original optimization problem into two sub-problems and models them as the bipartite graph matching problems. The VC-WOA achieves the search for legal solutions by limiting the variables which violate the task processing time constraints. Simulation results demonstrate the effectiveness of the proposed heuristic optimization framework in speeding up the convergence and improving the fairness among the UAV battery levels.

With the growing demand for computation-intensive applications, multi-access edge computing (MEC) has emerged as a critical paradigm that decentralizes computation and storage by bringing resources closer to users. As distributed computing undergoes ongoing development propelled by the advancements in the Internet of Things (IoT) and mobile communication technologies, the issue of edge node cooperation and resource sharing needs to be investigated. In this paper, the issue of edge node cooperation and resource sharing is modeled as a two-layer framework. More specifically, in the lower layer, a heuristic matching algorithm between users and edge nodes is developed, and a resource sharing algorithm among edge nodes in the same coalition is proposed. In the upper layer, a centralized coalition formation algorithm is designed based on the Hungarian method, and then we further define the coalition rules among edge nodes and propose a distributed coalition formation algorithm. Simulation results demonstrate that the proposed algorithms reduce the network cost effectively compared with non-cooperative schemes. Moreover, we analyze the impact of various network parameters on the network cost, thereby providing insights for future optimization and development in MEC networks.

This paper proposes a channel decomposition method and a novel beamforming architecture for line-of-sight (LoS) multi-input multi-output (MIMO) systems with dual continuous apertures (CAPs). Specifically, we develop a three-dimensional (3D) dual-CAP geometric model through orthogonal basis projection, establishing a unified electromagnetic analysis framework for arbitrarily oriented non-coplanar CAPs and enabling consistent 3D wave propagation modeling. Building on this model, we derive the closed-form expression for the effective degrees of freedom (EDoF) of the dual-CAP LoS MIMO system by analyzing spherical wave phase differences via sampling theory. This explicitly connects EDoF to wavelength, propagation distance, and angular rotation. Our proposed channel decomposition method decouples dual-CAP LoS MIMO channels into multiple independent SISO subchannels, which enables a novel beamforming architecture. We implement this architecture through a wavefront degrees of freedom orthogonal subchannel decomposition (WDOSD) algorithm to maximize the achievable rate of the dual-CAP LoS MIMO system. Numerical results demonstrate that: i) both component-wise EDoF and total EDoF can be accurately obtained using our proposed closed-form expressions; ii) under varying transmitter CAP sizes with fixed power, our WDOSD algorithm achieves up to three times performance improvement over the minimum mean-squared error (MMSE) algorithm; and iii) under varying communication distance with fixed angle, the WDOSD algorithm achieves up to two times performance improvement over the MMSE algorithm.

This paper addresses the coalition formation and the resource allocation in a device-to-device assisted mobile edge computing network, where the user equipments (UEs) collaborate to share the communication bandwidth and the computation resources for the task offloading. Our goal is to minimize the task execution cost, which is defined as the weighted sum of energy consumption and processing delay. In particular, we model waiting time of UEs in a coalition for the task offloading and incorporate it in the task execution cost. Therefore, we propose a three-layer hierarchical optimization framework which integrates the convex optimization, the heuristic algorithm, and the coalition game theory. In particular, we propose a double weighted mutation genetic algorithm to enhance the convergence of the algorithm, which applies weighted mutations to the offloading leader and the offloading order in the coalition. Furthermore, the task execution costs in both middle and upper layers are analytically evaluated. Simulation results validate the effectiveness of our proposed algorithms in reducing the task execution costs and speeding up the convergence.

The sixth-generation (6G) mobile communication systems are expected to provide seamless connectivity for unmanned aerial vehicles (UAVs) to support them in fulfilling various tasks. However, the line-of-sight (LoS)-dominated channels of cellular-connected UAVs expose them to severe co-channel interference from nearby base stations (BSs), which significantly degrades communication reliability. To address this challenge, this paper investigates a movable antenna (MA)-enhanced cellular-connected UAV communication system, where the additional spatial degrees of freedom (DoFs) offered by MAs are exploited for the interference-aware UAV trajectory planning. Specifically, we formulate an optimization problem to minimize the UAV mission completion time by jointly optimizing the UAV beamforming matrix, antenna position vector (APV), UAV trajectory, and UAV–BS association, subject to constraints on signal-to-interference-plus-noise ratio (SINR) requirements, UAV mobility, and MA mobility. To overcome the inherent challenges of the continuous-time formulation, we discretize both the flight region and trajectory of the UAV, thereby reformulating the problem into a tractable discrete optimization problem. A selective uniform cost search (SUCS) algorithm is then developed for UAV trajectory planning, where the feasibility of candidate grid points is evaluated by jointly optimizing beamforming, APV, and UAV–BS association to maximize the expected SINR. Simulation results show that, compared with benchmark schemes, the proposed MA-enhanced design significantly improves the expected SINR of cellular-connected UAVs along the optimized trajectory, thereby reducing UAV mission completion time while ensuring reliable communication links.

Integrated satellite-terrestrial networks (ISTNs) have played a crucial role in next-generation wireless systems. In this paper, we introduce partial non-orthogonal multiple access (p-NOMA) into ISTNs for downlink transmission, focusing on the system performance of satellite user equipments (UEs) and terrestrial UEs with co-channel interference and imperfect successive interference cancellation. We employ the Poisson point process to model the spatial distribution of the ground base stations, with Shadowed-Rician fading for satellite-terrestrial links and Rayleigh fading for terrestrial links. The closed-form expressions for the outage probability and average achievable rate of both satellite UEs and terrestrial UEs are derived. Additionally, we analyze the performance of the p-NOMA scheme and present the comparisons with non-orthogonal multiple access (NOMA) and orthogonal multiple access schemes. We validate the analytical results through simulations, confirming that p-NOMA outperforms NOMA in terms of both outage probability and average achievable rate. Moreover, we investigate the impact of p-NOMA parameters on the average achievable rate in the terrestrial network, providing insights for optimizing system performance.

We devise a cutting-edge near-field terahertz (THz) covert communication strategy with noise uncertainty where the beam is focused at a specific location, defined by both distance and direction. Leveraging unique near-field properties, the strategy combats eavesdroppers by increasing the capacity gap between legitimate and eavesdropping channels. We first develop a novel performance analytical framework for the near-field THz covert communication system, based on which we derive closed-form expressions for key performance metrics and thresholds, including the average covert probability, covert outage probability, covert rate, optimal detection threshold, and received power threshold of the eavesdropper. To further improve the secrecy performance, we design a new true-time-delay (TTD)-based piecewise approximation hybrid beamforming scheme for maximizing the covert rate, while effectively mitigating the negative impact caused by the beam split effect. Our numerical results demonstrate that the near-field THz covert communication strategy effectively combats eavesdroppers at all distances in the sector covering the main beam, demonstrating its practical significance for future wireless networks.

In this article, we develop a dynamic weighting strategy which considers the residual energy of different devices in aerial edge computing networks, and formulate a weighted energy consumption optimization problem aimed at extending device operating duration. To solve the formulated problem, we develop a clustering algorithm using K-means++ to establish optimal user-to-unmanned-aerial-vehicle access relationships, and the optimization problem is decomposed into trajectory, transmit power, and bandwidth subproblems. Each subproblem is sequentially solved by using the successive convex approximation algorithm, and the entire optimization problem is resolved by using the block coordinate descent algorithm. Simulation results demonstrate the effectiveness of our proposed weighting strategy in managing the energy levels of users, which prolongs the operational duration of the devices.

Integrating feeder- and user-links in multi-beam satellite communications significantly enhances system flexibility but requires effective resource allocation to fully realize its potential. Multi-agent deep reinforcement learning (MADRL) has emerged as a scalable solution for beam hopping, by allowing each agent to optimize the transmission parameters for one beam. However, integrating feeder- and user-links introduces complicated dependencies, including resource competition between feeder- and user-links and data-flow coupling between uplinks and downlinks, dramatically deteriorating agent cooperation. To approach the performance limit, this paper introduces a dependency-elimination MADRL framework incorporating model decomposition, link decoupling, and novel agent-level collaboration mechanisms to allocate beams, power, and bandwidth with reduced complexity. Specifically, to facilitate beam-level agent reuse for complexity reduction under the heterogeneity of feeder- and user-links, characterized by data-flow aggregation and division, we decouple bandwidth allocation from the learning model. The uplink-downlink dependencies in the bandwidth allocation is then resolved using a generalized water-filling strategy based on the performance upper bounds. Furthermore, we improve agent cooperation efficiency through state and reward decomposition and a novel non-cooperation penalty. Evaluations show that our method improves the system performance by up to 57.7% compared to sota MADRL methods while reducing training complexity by more than 50%.

Terahertz (THz) hybrid analog-digital beamforming systems face significant performance degradation due to group delay mismatch in phase shifters, which distorts beam patterns and significantly reduces the array gain. This paper presents a comprehensive framework to model, analyze, and mitigate group delay mismatch in THz hybrid analog-digital beamforming systems, addressing a critical oversight in existing research. We first establish a theoretical model for group delay mismatch in THz hybrid analog-digital beamforming systems, revealing its frequency-dependent Gaussian distribution and its cascading impact on beam squint and capacity degradation. Our analysis explicitly incorporates spatial group delay gradients, ultra-wideband channel characteristics, and hardware non-idealities, deriving closed-form expressions for beam misalignment and capacity loss. These results demonstrate that group delay-induced degradation scales quadratically with center frequency and is insensitive to bandwidth or array size, redefining THz system design priorities. We then propose a robust frequency selective hybrid analog-digital beamforming algorithm that jointly optimizes analog and digital beamformers to counteract group delay distortions. The analog beamformer, designed via Riemannian manifold optimization, minimizes spatial group delay gradients under the constant modulus constraints, while the digital precoders employs minimax optimization to compensate residual phase errors across subcarriers. Simulations under realistic THz channel conditions validate the accuracy of our analysis and the effective of our proposed design. Specially, our algorithm achieves 33.91 bps/Hz spectral efficiency at 10 dB SNR, outperforming conventional methods by 31.5% without requiring additional expensive analog lines.

With the diversification of wireless applications, the traffic patterns in the evolving wireless networks are becoming more dynamic and heterogeneous. Although numerous methods have been developed for spatio-temporal analysis, the impact of traffic patterns on spatial capacity has yet to be fully addressed. To this end, this paper studies the spatial outage capacity (SOC) in Poisson networks with Bernoulli traffic, which answers the question: “What is the maximum density of concurrently active links that satisfy a certain outage constraint?” We perform the analysis by integrating stochastic geometry with queueing theory and derive the meta distribution (MD) of signal-to-interference ratio (SIR). Unlike the conventional approaches that approximate the MDs by beta distributions, we consider the spatio-temporal correlations of the dominant interference exactly while treating the remaining interference in an average sense. Our analysis maintains tractability and achieves a highly accurate characterization of the SIR, especially for dense networks in the high-reliability regime that is particularly significant for network design. Moreover, we prove that the SOC in the high-reliability regime is achieved when all transmitters are always active. Simulations validate the accuracy of the theoretical results and show that the packet arrival rate has a marginal effect on the SOC as well as the corresponding SIR MD. We also show that the optimal density that maximizes the SOC is approximately inversely proportional to the packet arrival rate.

Distinguishing the near-field and far-field regions in line-of-sight (LoS) multiple-input multiple-output (MIMO) systems is crucial, as their distinct characteristics significantly impact performance and system design. In this paper, we propose a novel criterion for identifying the near-field and far-field regions based on the number of independent spatial streams. We introduce and derive a closed-form expression of effective degree of freedom (EDoF) for LoS MIMO systems by taking into account both the phase differences and path attenuation. Then, the spatial multiplexing distance (SMD) and resolvable distance (RD) are derived to define the boundary of the near field. Based on these boundaries, we propose a hybrid search-gradient descent (HSGD) algorithm to estimate the near-field channel information, which combines a coarse search through non-uniform step sizes with a precise estimation based on the gradient descent. Our numerical results unveil that i) the EDoF can be accurately calculated using the closed-form expression, ii) the HSGD algorithm achieves at least 28.72% improvement over the polar-domain simultaneous iterative gridless weighted (PSIGW) algorithm and 22.91% improvement over the orthogonal matching pursuit (OMP) algorithm across the different signal-to-noise ratio (SNR), and iii) the HSGD algorithm achieves at least 95.84% improvement over the PSIGW algorithm and 79.83% improvement over the OMP algorithm across varying communication distances.

This paper investigates an unmanned aerial vehicle (UAV)-base station (BS) integrated network, where a UAV transmits downlink secrecy data to multiple ground cognitive users while a ground BS utilizes jamming beamforming to help the UAV counter the eavesdropping attack of a ground eavesdropper. In particular, we consider the imperfect eavesdropping and jamming channel state information (CSI) related to the eavesdropper. To maximize the minimum sum secrecy rate of the cognitive users, a robust secure transmission scheme is proposed. The UAV trajectory, UAV transmit power, BS beamforming, and user scheduling are jointly optimized with the constraints of the communication quality of the primary users served by the BS and the UAV available propulsion energy. We formulate a non-convex optimization problem which is challenging to be solved mathematically, and we utilize an alternating optimization technique to divide the original problem into three sub-problems, i.e., UAV trajectory sub-problem, transmit power sub-problem, and user scheduling sub-problem. Besides, they can be solved by the successive convex approximation, semi-definite relaxation and S-procedure, and bivariate relaxation methods, respectively. Moreover, we explore the impact of different parameters of the proposed transmission scheme on the minimum sum secrecy rate of the cognitive users, and verify the superiority of the proposed robust secure transmission scheme design.

Embodied Artificial Intelligence (AI) bridges the cyberspace and the physical space, driving advancements in autonomous systems like theVehicularEmbodiedAINETwork (VEANET). VEANET integrates advanced AI capabilities into vehicular systems to enhance autonomous operations and decision-making. Embodied agents, such as Autonomous Vehicles (AVs), are autonomous entities that can perceive their environment and take actions to achieve specific goals, actively interacting with the physical world. Embodied Agent Twins (EATs) are digital models of these embodied agents, with various Embodied Agent AI Twins (EAATs) for intelligent applications in cyberspace. In VEANETs, EAATs act as in-vehicle AI assistants to perform diverse tasks supporting autonomous driving using generative AI models. Due to limited onboard computational resources, AVs offload EAATs to nearby RoadSide Units (RSUs). However, the mobility of AVs and limited RSU coverage necessitates dynamic migrations of EAATs, posing challenges in selecting suitable RSUs under information asymmetry. To address this, we construct a multi-dimensional contract theoretical model between AVs and alternative RSUs. Considering that AVs may exhibit irrational behavior, we utilize prospect theory instead of expected utility theory to model the actual utilities of AVs. Finally, we employ a Generative Diffusion Model (GDM)-based algorithm to identify the optimal contract designs, thus enhancing the efficiency of EAAT migrations. Numerical results demonstrate the superior efficiency of the proposed GDM-based scheme in facilitating EAAT migrations compared with traditional deep reinforcement learning methods.

A millimeter wave (mmWave) line-of-sight (LOS) multi-input-multioutput (MIMO) system is studied, where spatial multiplexing is achieved through LOS transmissions in near-field areas. A comprehensive analysis of the achievable rate in mmWave LOS MIMO system with respect to the rotation of uniform linear arrays (ULAs) is provided for both full-rank orthogonal and rank-deficient LOS MIMO channels. 1) For full-rank orthogonal scenarios, the necessary and sufficient condition for the LOS MIMO system to have the maximum achievable rate is established. 2) For rank-deficient scenarios, a closed-form expression for the Degrees of Freedom (DoF) of mmWave LOS MIMO channels is derived. Subsequently, the alternating phase approximation-based (APA-based) hybrid precoding algorithm is proposed, where the number of radio frequency (RF) chains is determined by the DoF of mmWave LOS MIMO channels and the initial phase of iterative optimizations is determined according to the optimal digital precoder. Our numerical results confirm the effectiveness of our analysis and proposed algorithm. It is also unveiled that 1) the DoF can be maximized through a specific angle design and 2) compared to existing hybrid precoding algorithms, our proposed APA-based hybrid precoding algorithm exhibits superior robustness against the angular rotation of ULAs.

In the coming sixth-generation era, space-air–ground integrated network (SAGIN) is a technology with the potential for seamless coverage and high-data rate transmission. However, the inherent broadcast nature of wireless communication forces us to consider physical-layer security. This article explores secure communications with the aid of hybrid free space optical/radio frequency (FSO/RF) links in a two-phase uplink transmission. Specifically, in the first-phase transmission, a ground device transmits secrecy data to an unmanned aerial vehicle (UAV) via an radio frequency (RF) link, while the UAV emits artificial noise to confuse an eavesdropper. In the second-phase transmission, the UAV sends the secrecy data to a satellite via an FSO link to defend against RF eavesdropping. More specifically, we design two transmission schemes, i.e., slot-based scheme and period-based scheme, which are suitable for transmitting delay-sensitive data and delay-insensitive data, respectively. In order to maximize the average secrecy rate of the system, the trajectory and power allocation of the UAV are jointly optimized. The objective functions of these two schemes are both nonconvex, which are mathematically intractable to tackle by the interior-point method. Therefore, we use block coordinate descent and successive convex approximation techniques to obtain approximate solutions. Numerical results reveal the impact of the UAV trajectory and power allocation optimization on the average secrecy rate during different flight periods in different schemes. In addition, other benchmark schemes are considered for comparison, and the results indicate that our proposed schemes can achieve higher average secrecy rates.

In this study, a joint blind frame synchronization and decoding method is proposed based on the normalized syndrome satisfaction probability (NSSP) provided by the soft Low-Density Parity-Check (LDPC) codes decoder. To reduce complexity, a stopping criterion is introduced into the iterative process through the convergence of NSSP evolution patterns. In addition to achieving an excellent synchronization performance, this method also eliminates the redundancy caused by the pilot sequence. Furthermore, it is compared with the optimal pilot-based and other code-aided frame synchronization algorithms. According to analytical and simulation results, the proposed technique outperforms other advanced code-aided frame synchronization solutions in synchronization, which makes it applicable to achieve comparable performance to the pilot-based methods in specific coding gains. Due to the introduction of a novel stopping criterion, the average number of joint detection & decoding is reduced by up to 90%. Analog probability processing technology integrates sub-threshold region circuits with probability domain-based iterative message-passing algorithms inspired by high energy efficiency and low complexity. Hence, hardware implementation is presented based on the analog LDPC decoder chip as fabricated in a 0.35-μm CMOS technology for our proposed algorithms. The experimental results demonstrate the effectiveness of the proposed method and the realization loss is within 0.2 dB compared with the theoretical circuit simulation results.

In this paper, we investigate the mmWave beam tracking for vehicle-to-infrastructure (V2I) communications to find the optimal beam via sub-6 GHz channel state information (CSI). We consider two scenarios: 1) sub-6 GHz and mmWave transceivers are co-located on the same base station (BS), and 2) sub-6 GHz and mmWave BSs are separated in different places constituting heterogeneous networks (HetNets) where one sub-6 GHz BS controls multiple mmWave BSs. Considering the mobility of the vehicle and time-varying channels, we propose a predictive beam tracking method based on long short-term memory (LSTM) to construct the maps from historical sequential sub-6 GHz CSI to the future optimal mmWave beam. A single LSTM model can handle the beam tracking in the co-located scenario, since there is a one-to-one correspondence between the sub-6 GHz and mmWave transceivers, and the propagation of sub-6 GHz and mmWave signals is similar. However, in the HetNet scenario, it is difficult to select the best one among the beams of multiple mmWave BSs only via the CSI of one sub-6 GHz BS. To address this challenge, we design an LSTM fusion model, which exploits not only the historical sequential sub-6 GHz CSI but also a number of mmWave wide beam measurements, to obtain the optimal mmWave BS and beam in the HetNet. In this case, the collected sub-6 GHz CSI and mmWave wide beam measurements are analyzed by the LSTM and fully connected network (FCN) modules, respectively, providing two beam prediction results. Then the results are fused by an attention-based FCN module to accomplish the final prediction. Simulation results verify the effectiveness and superiority of our LSTM-based beam tracking models compared with other state-of-the-art deep learning beam tracking models that also leverage sub-6 GHz channels. Besides, the robustness and generalization of our proposed LSTM models are illustrated through simulations.

Remote first-aid treatment on ambulances is a promising application of 5G. However, there still exist gaps between the capabilities of current 5G networks and the stringent requirements of remote emergency on ambulances. Dual connectivity (DC) is an efficient technology to fill these gaps by integrating 5G millimeter wave (mmWave) with Sub-6GHz networks. In this paper, we investigate a dual-connectivity handover scheme to enhance the transmission rate of the wireless links for a 5G-enabled ambulance. Due to the long delay caused by signal transmission and processing, the conventional handover schemes based on reference signal received power (RSRP) measured by users are not sufficiently sensitive to the rapidly changing propagation environments surrounding the 5G-enabled ambulance. Instead, considering the randomness of environments and the delay caused by the handover process, we employ a deep Q network (DQN)-based algorithm to find a far-sighted policy for solving the handover problem. However, due to the drawbacks of single-step bootstrapping, value overestimation, and low-efficiency exploration, the vanilla DQN is performance-limited. To this end, we adopt effective techniques including multi-step learning, double DQN, and NoisyNet to improve learning performances, and propose a noisy double DQN (NDDQN)-based dual-connectivity handover scheme. Simulation results verify the effectiveness and superiority of our NDDQN-based handover scheme compared with the vanilla DQN and upper confidence bound (UCB)-based handover schemes, and then show that our handover scheme can adapt to various handover models.

This paper presents a unicast beamforming network for covert ambient backscatter communications (AmBC). Different from the prior covert backscatter works using artificial noise or a power-variable RF source, our work achieves covertness by arming the backscatter tag with multiple antennas. The tag uses a subset of its antennas to modulate covert information on backscattered RF signals while using the other antennas to modulate overt message as a cover to hide the covert transmission from the warden. We first derive the warden’s optimum detection threshold to minimize the detection error probability. To fight against the optimum warden, the tag changes impedance matching on each antenna to vary the reflected power and make the reflected overt and covert signals beamforming at space. We derive the optimum beamforming vectors by solving the problems of maximizing the covert rate at the receiver and the detection error probability at the warden, respectively. To address the non-convex constraints, we use semi-definite relaxation (SDR) and obtain near optimal parameters by designing binarySearch algorithms. Numerical results confirm the superiority of our work to the state-of-the-art one, and the existence of tradeoffs between the covert rate and the detection error probability.

In wireless-powered device-to-device (D2D) networks with backscatter-aided passive relays, the cooperation among D2D pairs is of special necessity. With the cooperation among D2D pairs, the devices can act as passive relays to enhance the signal reception, and share their active transmission time to improve the resource utilization. Therefore, how the D2D pairs form coalitions and how to reallocate the active transmission time to the D2D pairs after forming coalitions should be studied. In this article, we develop a hierarchical cooperative game framework for the network to investigate how the D2D pairs cooperate with each other. Specifically, in the upper layer of the game, we employ the coalition game to formulate how the D2D pairs form coalitions and analyze the stability of the coalition structure and the complexity of the proposed strategy. In the lower layer of the game, we employ the bargaining game to investigate how the D2D pairs share the active transmission time, and analytically evaluate the complexity of deriving the Nash bargaining solution. Simulation results demonstrate the effectiveness of our proposed scheme. The results also show that the computational complexity of our proposed scheme is only a small fraction of that of the exclusive search-based scheme. Nevertheless, compared with the exclusive search-based scheme, the total effective amount of the transmitted data in the proposed cooperative scheme reduces no more than 2%, which is very minor.

Unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) system will play an essential role in future wireless networks, owing to the flexible deployment of the UAVs and the Line-of-Sight (LoS) dominant channels. In this article, we take the limited onboard energy into account and investigate the three-dimensional (3-D) trajectory of the UAV and the transmission scheduling of the ground devices (GDs) for the UAV-enabled IoT system, where multiple GDs transmit data to the UAV in a time-division multiple access fashion. Specifically, taking the angle-dependent probabilistic LoS channel into account, we derive the mathematical expression of the amount of the transmitted data of the GDs and model the energy consumption of the UAV. Next, considering the fairness among the GDs, we aim to maximize the minimum expected amount of the transmitted data of the GDs, and formulate it as an optimization problem, which is further discretized to a problem with a finite number of variables. Due to the nonconvexity of the discretized problem, we transform the problem into a tractable form, and develop an effective iterative algorithm to solve it by using the block coordinate descent and successive convex approximation methods. The convergence and the complexity of the developed algorithm are analytically evaluated. Simulation results demonstrate that our designed 3-D UAV trajectory can effectively improve the minimum expected amount of the transmitted data of the GDs.

In order to satisfy the requirement of extremely high data rate in traffic hotspot regions, millimeter wave (mmWave) has attracted significant attention in wireless communication networks. While the coverage performance of mmWave networks based on the distribution of signal-to-interference-plus-noise ratio (SINR) has been widely studied, it provides only very limited information on the link reliability. In this paper, we provide a fine-grained performance analysis of the mmWave networks with hotspots. Specifically, we first establish a general and tractable framework to investigate the performance of mmWave networks using the Poisson cluster process integrated with several features of the mmWave band. Both open and closed association strategies are considered. To show what fraction of users in the networks achieves target reliability when the SINR is given, we derive the tier association probability and the moments of the conditional SINR distribution, based on which the exact meta distributions of SINR are given. Interestingly, in clustered mmWave networks, the widely used standard and generalized beta approximations do not work well when the blockage effect is severe. To resolve this issue, we provide a modified approximation by scaling the standard beta distribution, which is shown to be closer to the exact results. We conduct extensive simulations to study the impact of mmWave and deployment features on the performance of clustered mmWave networks. Numerical results reveal that the optimal scattering variance of mmWave base stations scales with the cluster size to maximize the number of concurrent reliable links, and increasing the antenna directivity results in more reliable communications than elevating the transmit power of mmWave base stations.

With the rapid development of the backscatter technology, backscatter-aided passive relays can be employed to improve the performance of wireless-powered device-to-device (D2D) networks. In this paper, we investigate the strategy of how the D2D pairs cooperate with each other in the network, and formulate the problem as a coalition game. In the coalition game, the players are the D2D pairs, the payoff of a D2D pair is the amount of its transmitted data, and the action is that each D2D pair can choose to stay or switch its coalition. We then develop the preference relation and the switch rule, and further design the switch algorithm for the D2D pairs to form coalitions. Moreover, we analyse the stability of the coalition game and the complexity of the proposed strategy. Simulation results demonstrate that compared with the non-cooperative strategy, our developed cooperative strategy can efficiently improve the amount of the transmitted data.

This paper investigates the joint base station (BS) association and beamforming for energy efficiency maximization in coordinated multi-cell multiuser downlink systems. In particular, we assume that only the channel distribution information is known to the BSs. The considered problem is difficult to be solved optimally due to the non-smooth and non-convex functions in the formulation. Therefore, we propose an iterative suboptimal algorithm to solve the problem efficiently based on the successive convex approximation (SCA). More specifically, the convex approximation of the original problem at each iteration can be solved efficiently by the second-order cone programming and the solution obtained by the proposed algorithm satisfies the generalized Karush-Kuhn-Tucker (KKT) conditions. To facilitate the implementation of decentralized beamforming, we transform the convex approximation problem at each iteration of the SCA into an equivalent form, which is amenable to applying the alternating direction method of multipliers (ADMM). By combining the SCA and the ADMM, a decentralized energy-efficient beamforming algorithm is proposed. Numerical results are presented to show the performance of the proposed algorithms.

In this paper, we investigate the dynamic access point and service selection in a backscatter-assisted radio-frequency-powered cognitive network, where many secondary transmitters (STs) can choose different transmission services provided by multiple access points. To analyze the access point and service selection of the STs, we formulate the problem as an evolutionary game. The STs act as the players and adjust their selections of the access points and services based on their utilities. Specifically, we model the access point and service adaptation of the STs by the replicator dynamics, and analytically prove the existence and uniqueness, and the stability of the evolutionary equilibrium. We also consider the delay of information used by the STs to adapt their selection and perform the analysis by using delayed replicator dynamics. In particular, the stability region of the delayed replicator dynamics in a special case is derived. Furthermore, we develop a low-complexity algorithm for the access point and service selection in the network based on evolutionary game. Extensive simulations have been conducted to demonstrate the effectiveness of the proposed access point and service selection strategy in the network.

This paper investigates the time scheduling for a backscatter-aided radio-frequency-powered cognitive radio network, where multiple secondary transmitters transmit data to the same secondary gateway in the backscatter mode and the harvest-then-transmit mode. With many secondary transmitters connected to the network, the total transmission demand of the secondary transmitters may frequently exceed the transmission capacity of the secondary network. As such, the secondary gateway is more likely to assign the time resource, i.e., the backscattering time in the backscatter mode and the transmission time in the harvest-then-transmit mode, to the secondary transmitters with higher transmission valuations. Therefore, according to a variety of demand requirements from secondary transmitters, we design two auction-based time scheduling mechanisms for the time resource assignment. In the auctions, the secondary gateway acts as the seller as well as the auctioneer, and the secondary transmitters act as the buyers to bid for the time resource. We design the winner determination, the time scheduling, and the pricing schemes for both the proposed auction-based mechanisms. Furthermore, the economic properties, such as individual rationality and truthfulness, and the computational efficiency of our proposed mechanisms are analytically evaluated. The simulation results demonstrate the effectiveness of our proposed mechanisms.

In this paper, we propose an energy-efficient joint resource allocation and power control scheme for downlink multi-cell orthogonal frequency division multiple access (OFDMA) networks with imperfect channel state information (CSI). Considering the maximum allowed transmit power, the tolerable outage probability, and the fact that each resource block is allocated to at most one user in each cell, we formulate the energy-efficient joint resource allocation and power control problem as a probabilistic mixed non-convex fractional programming problem, which is hard to be tackled. In order to solve the problem efficiently, we first substitute the probabilistic constraints into the objective function and then introduce new auxiliary variables to transform the original problem into a difference of two convex functions (D.C.) programming problem. As a result, we resort to D.C. algorithm to obtain a solution satisfying Karush-Kuhn-Tucker conditions of the D.C. problem. Simulation results are presented to demonstrate the effectiveness of the proposed scheme.

In this paper, we investigate the energy-efficient power control for device-to-device (D2D) communications underlaying cellular networks with max-min fairness, where uplink resource blocks allocated to one cellular user equipment are reused by multiple D2D pairs to improve the frequency reuse factor, and the minimum individual energy efficiency (EE) is maximized. This is a generalized fractional programming (GFP) problem, and is hard to tackle due to its non-concave nature, which means the complexity of global optimal solution is unaffordable. In order to give sub-optimal solution with reasonable complexity, we first transform the GFP problem into equivalent optimization problem in a parametric subtractive form, and then add constraints on the co-channel interferences to convert the non-concave GFP problem into concave one. The sub-optimal solution, which can be obtained through solving the deduced concave problem based on sophisticated convex optimization methods, gives a tight lower bound on the optimal EE. Simulation results are presented to demonstrate the effectiveness of the proposed scheme.