Unmanned swarms can effectively improve the utility of tasks by collaboratively executing them in dynamic environments, but the heterogeneity of tasks and scarcity of spectrum resources lead to the dynamic matching of task resources becoming the core problem of improving utility. To address the interaction characteristics between the mission layer and the spectrum layer of unmanned swarm, this paper constructs a partially overlapping coalition formation game (POCFG) model, which is proposed to be an exact potential game with at least one Nash Equilibrium (NE). Inspired by the idea of parallel search of quantum superposition states and cooperative decision making of entangled states, a dual rationality guided quantum inspired partial overlapping coalition formation game (DRGQI-POCFG) algorithm is designed. The quantum entanglement state mechanism is utilized to correlate task allocation with the decision variables of spectrum resources. The simulation results show that the efficiency of joint task spectrum allocation is improved and the computational complexity is reduced. Compared with the selfish criterion algorithm, the Pareto algorithm and the non-joint allocation algorithm, the utility has increased by 12.2%, 26.4% and 34.6% respectively.

The expansion of the low-altitude economy has underscored the significance of Low-Altitude Network Coverage (LANC) prediction for designing aerial corridors. While accurate LANC forecasting hinges on the antenna beam patterns of Base Stations (BSs), these patterns are typically proprietary and not readily accessible. Operational parameters of BSs, which inherently contain beam information, offer an opportunity for data-driven low-altitude coverage prediction. However, collecting extensive low-altitude road test data is cost-prohibitive, often yielding only sparse samples per BS. This scarcity results in two primary challenges: imbalanced feature sampling due to limited variability in high-dimensional operational parameters against the backdrop of substantial changes in low-dimensional sampling locations, and diminished generalizability stemming from insufficient data samples. To overcome these obstacles, we introduce a dual strategy comprising expert knowledge-based feature compression and disentangled representation learning. The former reduces feature space complexity by leveraging communications expertise, while the latter enhances model generalizability through the integration of propagation models and distinct subnetworks that capture and aggregate the semantic representations of latent features. Experimental evaluation con firms the efficacy of our framework, yielding a 7% reduction in error compared to the best baseline algorithm. Real-network validations further attest to its reliability, achieving practical prediction accuracy with MAE errors at the 5 dB level.

A mega hybrid constellation comprising low Earth orbit (LEO) and geostationary orbit (GEO) satellites represents a prevalent architecture for future space-based networks. However, the emergence of mega constellations has exacerbated the shortage of spectrum resources. To address this issue, this paper investigates a method for LEO satellites within such constellations to expand their available spectrum by sharing the downlink spectrum of GEO satellites. Firstly, to avoid interference with GEO satellites and optimize the beam coverage for LEO user (LU), a coalition formation game model for LU based on cooperation criteria is constructed, and the existence of a stable coalition structure is proven. Secondly, to determine this stable coalition structure, a coalition formation game algorithm based on the best response (BR) is proposed, and its convergence is theoretically validated. Additionally, to more efficiently determine the beam radius and center covering the LU in the coalition, an improved algorithm for solving the outer circle of LU in the coalition using a K-dimensional tree is presented. Simulation results demonstrate that the proposed method effectively balances convergence time and accuracy. Without affecting GEO satellite communications, LEO satellites can share the downlink spectrum of GEO satellites, thereby enhancing the utilization of spectrum resources within the hybrid constellation.

By exploring spacial freedom, unmanned aerial vehicle (UAV) equipped with simultaneous transmission-reflection reconfigurable intelligent surfaces (STAR-RIS) can be used as airborne relay platforms to significantly improve the coverage and transmission performance of wireless networks. In this work, considering a UAV-STAR-RIS assisted multi-user network, we investigate the throughput maximization problem by jointly optimizing the beamforming (BF) vector, transmission-reflection coefficients (TRCs), and UAV trajectory. Since the throughput maximization problem is inherently NP-hard, we apply a two-step alternating optimization method (AOM) based algorithm using the penalty dual decomposition (PDD) and the successive convex approximation (SCA) to effectively solve the problem. Numerical results verify that the algorithms have better convergence and can improve the overall throughput.

Reconstructing accurate radio maps is crucial for optimizing wireless network performance and managing spectrum efficiently. In real-world scenarios, radio map data, often sparse and incompletely labelled, poses significant challenges to traditional learning techniques. Graph Neural Networks (GNNs) have become instrumental in efficiently reconstructing radio maps (RMR) in such environments by effectively encoding correlations in unstructured data. Existing GNN-based methods, however, are limited as they typically consider only single factors like location, environment, or transmitter characteristics during correlation encoding. To overcome this limitation, we introduce RadioGAT, a propagation model-based approach that comprehensively integrates these factors. We further utilize Graph Attention Networks to enable semi-supervised learning, enhancing the accuracy of radio map reconstruction. Our experimental results demonstrate the superiority and robustness of RadioGAT, particularly at low sampling rates, and highlight the importance of selecting appropriate correlation encoding methods based on the data availability for RMR.

The downlink channel estimation is currently a critical bottleneck for IRS-assisted cell-free multiple input multiple output communication. Conventionally, most studies have employed deep learning methods to estimate the high-dimensional, complex cascaded channels generated by IRS, necessitating data collection from all users for centralized model training, which results in excessively large overheads, and data privacy problems. To tackle this challenge, a federated learning (FL)-based channel estimation framework incorporates coalition formation to guide the formation of FL user groups. We propose a coalition formation-enabled federated learning framework for channel estimation, utilizing a deep reinforcement learning (DRL) approach to intelligently group users into multiple coalitions, thereby improving channel estimation accuracy. Moreover, considering that nodes with similar distances to the base station and similar received signal power have a strong likelihood that they experience similar channel fading, we designed a transfer learning method that incorporates both received reference signal power and distance similarity metrics. The transfer learning technique is designed to accelerate the convergence of DRL-federated learning process. Simulations reveal that the proposed algorithms significantly reduce communication overhead for local users and improve data privacy while maintaining commendable channel estimation accuracy.

A mega hybrid constellation of low-Earth orbit (LEO) and geosynchronous orbit (GEO) satellites represents a typical architecture for future space networks. However, the emergence of such large constellations exacerbates the shortage of spectrum resources. To address this issue, this article investigates a hierarchical optimization method for beam scheduling and power control of LEO satellites, aiming to extend the available spectrum by sharing the downlink spectrum of GEO satellites. In the upper layer optimization, a many-to-one matching game model is constructed to achieve optimal matching of LEO satellite beams and LEO users (LUs). A distributed beam matching learning algorithm (DBMLA) is designed to find a stable matching solution for the model, with the convergence of the algorithm theoretically proven. In the lower layer optimization, an LEO beam power level optimization game model is developed for discrete LEO beam power levels. This model is demonstrated to be an exact potential game with at least one Nash equilibrium (NE). To solve this NE, a dynamic power allocation logarithmic learning algorithm (DPALLA) is proposed. Simulation results verify that the proposed DBMLA-DPALLA hierarchical optimization scheme effectively balances convergence time and accuracy compared to traditional independent optimization strategies. By leveraging the combined effects of the two optimization strategies, it better mitigates the co-channel interference experienced by GEO satellites and improves the average network satisfaction of the LUs.

In agricultural and food production, sensors are widely used for real-time monitoring of the production process. These sensors transmit data to access points (APs) in wireless sensor networks (WSNs), forming an Internet of Things-empowered advanced production paradigm. Due to limited power, sensors have constrained transmission ranges, necessitating unmanned ground vehicles (UGVs) to assist in timely sensor data collection. A critical problem is the intelligent coordination among multiple UGVs to realize safe path planning, as well as fair and timely data collection. However, it encounters the following challenges: 1) real-time monitoring introduces the dynamics in the volume of sensor data; 2) unknown obstacles, such as mobile packaging containers and vehicles, complicate safe path planning and fair data collection in WSNs; and 3) inefficient action explorations deteriorate action selection. To address these challenges, we propose a multiagent path planning algorithm based on coalition formation game and Bayesian optimization (BO) (MAPP-CFGBO) to optimize UGVs paths and sensor association in industrial WSNs. First, we construct a dynamic data caching model and design a fairness index. Second, a cooperative communication coalition formation (C3F) algorithm is proposed to facilitate cooperation among UGVs. Next, the safe path planning problem is solved with our proposed BO algorithm, which addresses challenges 2 and 3. Extensive simulations are performed. Compared with the benchmark algorithms, the proposed algorithm improves the fairness of communication services by$\rm 39.20{\,}\% $and increases the amount of collected data by$\rm 142.07{\,}\%$.

This paper presents an American sign language (ASL) semantic communications scheme. The scheme consists of a semantic encoder that leverages a convolutional neural network to effectively utilize the ASL alphabet. The encoded information is transmitted with the 24-QAM quadrature amplitude modulation (QAM). Additionally, this paper introduces a dataset that involves the overlaying of red-green-blue landmarks and key-points onto the acquired images, thereby augmenting the depiction of hand posture. The quantification of the proposed system’s training, testing, and communication performance is accomplished through numerical results, which serve to emphasize the attainable benefits and stimulate meaningful discussions.

Abstract Intelligent reflective surface (IRS) provides an effective solution for reconfiguring air‐to‐ground wireless channels, and intelligent agents based on reinforcement learning can dynamically adjust the reflection coefficient of IRS to adapt to changing channels. However, most exiting IRS configuration schemes based on reinforcement learning require long training time and are difficult to be industrially deployed. This paper, proposes a model‐free IRS control scheme based on reinforcement learning and adopts transfer learning to accelerate the training process. A knowledge base of the source tasks has been constructed for transfer learning, allowing accumulation of experience from different source tasks. To mitigate potential negative effects of transfer learning, quantitative analysis of task similarity through unmanned aerial vehicle (UAV) flight path is conducted. After identifying the most similar source task to the target task, parameters of the source task model are used as the initial values for the target task model to accelerate the convergence process of reinforcement learning. Simulation results demonstrate that the proposed method can increase the convergence speed of the traditional DDQN algorithm by up to 60%.

With the rapid growth in the number of high-mobility vehicles and booming enhanced applications with restricted latency requirements, downlink communication in Internet of Vehicles (IoV) systems has become increasingly vulnerable to active eavesdropping attacks. This paper proposes a federated learning-enabled secure communication framework for IoV against active eavesdropping, in which the roadside units (RSUs) apply reinforcement learning (RL) model to optimize their downlink transmit power levels, and the server helps update the RL models of the RSUs. First, we design a multi-agent deep RL algorithm for each RSU, which designs a punishment and a blacklist mechanism to mitigate risky explorations related to severe data leakage or communication outages. Second, this framework designs a risk-aware RL for the server, which uses a two-level hierarchical structure to choose the number of participated RSUs and the corresponding local training data size for higher optimization speed. This framework considers both the reward and risk in the selection of policies to reduce the probability of exploring the risky training policies that cause defense failure of the RSUs against active eavesdropping. Third, we analyze the convergence performance, computational complexity, and reward upper bound, which reveals how the power constraint, radio bandwidth and data size affect the secure communication performance. Simulation and experimental results validate the effectiveness of our schemes, such as the reductions of the eavesdropping rate, training latency, and the loss of local models compared to the benchmarks.

Multi-band radiomap reconstruction (MB-RMR) is a key component in wireless communications for tasks such as spectrum management and network planning. However, traditional machine-learning-based MB-RMR methods, which rely heavily on simulated data or complete structured ground truth, face significant deployment challenges. These challenges stem from the differences between simulated and actual data, as well as the scarcity of real-world measurements. To address these challenges, our study presents RadioGAT, a novel framework based on Graph Attention Network (GAT) tailored for MB-RMR within a single area, eliminating the need for multi-region datasets. RadioGAT innovatively merges model-based spatial-spectral correlation encoding with data-driven radiomap generalization, thus minimizing the reliance on extensive data sources. The framework begins by transforming sparse multi-band data into a graph structure through an innovative encoding strategy that leverages radio propagation models to capture the spatial-spectral correlation inherent in the data. This graph-based representation not only simplifies data handling but also enables tailored label sampling during training, significantly enhancing the framework’s adaptability for deployment. Subsequently, The GAT is employed to generalize the radiomap information across various frequency bands. Extensive experiments using raytracing datasets based on real-world environments have demonstrated RadioGAT’s enhanced accuracy in supervised learning settings and its robustness in semi-supervised scenarios. These results underscore RadioGAT’s effectiveness and practicality for MB-RMR in environments with limited data availability.

Abstract In view of the diverse jamming environment, the single‐level anti‐jamming method faces some challenges such as poor timeliness, high cost and poor effect. In this paper, routing delay, cost overhead and diversified jamming threats are comprehensively considered. In addition, a cross‐layer anti‐jamming method is proposed in the scenario of busy satellite Internet communication. The proposed cross‐layer anti‐jamming method involves two levels: link‐layer anti‐jamming based on path repair and network‐layer anti‐jamming based on path reconstruction. On the one hand, the channel is selected based on the improved Q‐learning anti‐jamming algorithm to confront common jamming. On the other hand, the route from the source to the destination node is selected based on the cross‐layer anti‐jamming algorithm to confront high‐intensity jamming. Finally, the simulation results show that, compared with other anti‐jamming algorithms, the proposed algorithm can achieve higher efficiency, lower cost, and more robust anti‐jamming routing.

Abstract In this paper, a two‐way relay‐aided cognitive satellite terrestrial network (TR‐CSTN) model is proposed, where primary users are located at the edge of the base station. In the TR‐CSTN, one of satellite terminal users (STUs) is selected by the fusion center as the TR to forward information between two edge primary users with power of the TR. Meanwhile, these edge primary users share the licensed frequency band with the selected TR to send information to the satellite. Then, given the limited spectrum utilization and energy efficiency (EE) of the communication system, the cooperative spectrum sensing is employed to realize green communication. Specifically, the fusion center threshold, energy detection threshold, sensing duration and number of STUs are jointly optimized to enhance EE. Furthermore, considering that the node's energy shortage results in a short network lifetime, absolute EE gets improved. In detail, a power allocation scheme named normalized power aided Lévy flight trajectory‐based whale optimization algorithm (NP‐LWOA) is provided, which fulfills effective energy compensation among STUs to prolong the network lifetime notably. Finally, numerical results confirm the theoretical analysis and show the effectiveness of the TR‐CSTN and the NP‐LWOA in efficiently achieving the concept of green communication compared with other methods.

Unmanned aerial vehicles (UAVs) enable promising solutions in assisting data collection in wide-area distributed sensor networks, leveraging their advanced properties of high mobility and line-of-sight communication links. However, existing UAV-assisted data collection methods mainly focus on unilaterally maximizing the utility of UAVs or sensors. Unfortunately, the problem driven by the market economy is ignored, namely the game between buyer and seller, in the process of sensors competing for UAV services. To address this problem, we propose a group-buying coalition auction method that encourages sensors to form coalitions to bid for UAV data collection services. Then, a parallel variable neighborhood ascent search algorithm is designed to quickly search the approximately optimal group-buying coalition structure. We further propose a novel group-buying coalition auction method, named TRUST, which can ensure the economical properties, i.e., truthfulness, individual rationality, and maximization of social welfare. Numerical results show that the sensors' average age of information (AoI) under the proposed method is reduced by 16.7% and 44.5% compared with the coalition formation game (CFG) and joint trajectory design-task scheduling (TDTS) UAV-to-community methods. To our best knowledge, this is the first effort on truthful coalition formation-based group-buying auction.

A heterogeneous unmanned aerial vehicle (UAV) network where UAVs carrying different resources form coalition and cooperatively carry out tasks is of crucial importance for fulfilling diverse tasks. However, the existing coalition formation (CF) game model only optimizes the composition of UAVs in a single coalition, which results in disjoined coalitions. In order to tackle this issue, a sequential overlapping coalition formation (OCF) game is proposed by considering the overlapping and complementary relations of resource properties and the task execution order. Moreover, different from the traditional Pareto and Selfish orders, a bilateral mutual benefit transfer (BMBT) order is proposed to optimize the cooperative task resource allocation through partial cooperation among overlapping coalition members. Furthermore, using the preference relation between UAVs carrying resources and tasks requiring the same type of resource, a preference gravity-guided Tabu Search (PGG-TS) algorithm is developed to obtain a stable coalitional structure. Numerical results verify that the utility of the proposed OCF game scheme based on the PGG-TS algorithm increases by 18% against that of the non-overlapping CF game scheme, and the utility of the proposed BMBT order increases by 25%, compared with other orders.

With their high mobility, unmanned aerial vehicles (UAVs) become appealing data collectors in hard-to-reach wide-area distributed sensor networks. Different from existing works focusing on the perspective of UAVs for service order optimization and UAV utility maximization, we consider the utilities of both sensors and UAVs, and innovatively model the competition among sensors (buyers) for the service of UAVs (sellers) as an auction game. A “unity makes strength” strategy is exploited. That is, to strengthen the bidding competitiveness, a group-buying coalition auction method that encourages sensors to form coalitions to bid for UAV service is proposed. Besides, we propose a parallel variable neighborhood ascent search algorithm, we can quickly determine the approximately optimal group-buying coalition structure. Numerical results show that the proposed method outperforms the joint trajectory design-task scheduling (TDTS) UAV-to-community method and the single coalition formation game (CFG) method.

Unmanned aerial vehicle (UAV) carrying intelligent reflecting surface (IRS) can serve as an aerial platform to improve the coverage area and transmission performance of traditional wireless network. In this paper, we investigate the weighted sum rate maximization problem for a UAV-assisted and IRS-based multi-user communication system. Specifically, by applying the alternating optimization approach, we propose a two-phase approach to effectively optimize the number of activated reflective elements, the precoding matrix, the phase shift, and the UAV location. Numerical results validate that the proposed approach can converge at a faster rate and improve the weighted sum rate performance.

In this paper, we investigate the secrecy outage probability (SOP) for a multi-tier unmanned aerial vehicular (UAV) assisted cognitive communication network. Specifically, the low-altitude rotary-wing (LARW) UAV relays harvest radio frequency (RF) energy from the transmission of a high-altitude fixed-wing (HAFW) UAV and forward the information to a ground destination under a decode-and-forward protocol while a ground eavesdropper tries to capture the relay signal. The multi-tier UAV system transmits in an underlay fashion over a licensed spectrum of a primary user. We study the exact statistics of SOP assuming that the number of UAV relays follows a Poisson point process (PPP). The simulation results are presented to illustrate and verify the analytical results.

Abstract Although unmanned aerial vehicle (UAV) relay can provide auxiliary communication due to its flexible mobility, it is vulnerable to jamming attacks. This paper considers the UAV relay anti‐jamming communication issue under the threat of a malicious jammer with beam‐forming jamming capability. To prevent the relay link from deteriorating, UAV trajectory adjustment and transmission mode switching between half‐duplex and full‐duplex are two available schemes, while they will incur the additional flying costs and continuous mode switching, respectively. To balance the trade‐off between trajectory optimization and mode selection, this paper investigates the joint trajectory optimization and mode selection anti‐jamming approach. First, an anti‐jamming utility considering the cost‐efficient and end‐to‐end capacity gains is designed. Second, to model the bounded rationality of both the UAV relay and the jammer due to the adversarial context, a biased Stackelberg game to analyse the competitive system interactions is proposed. Moreover, the existence of Stackelberg equilibrium (SE) in the problem is proved. Finally, a joint mode selection and trajectory optimization (JMSTO) algorithm based on the multi‐armed bandit is proposed to obtain the SE. It is further demonstrated that the JMSTO algorithm has a logarithmic regret. The results show that our proposed JMSTO algorithm is superior to non‐joint optimization methods.

Abstract In this paper, the dynamic spectrum access is investigated in satellite internet. Firstly, to describe the confrontation characteristics of the electromagnetic environment, two opposing teams denoted as the blue team (BT) and red team (RT), are designed. In addition, an “electromagnetic barrier” jamming strategy is proposed to achieve the effect of promoting approach defence with the attack. Precisely, the “electromagnetic barrier” can significantly weaken the communication ability of enemies (i.e. RT) while protecting the (i.e. BT) communication quality. Secondly, internal interference, external interference and malicious jamming of BT and RT are considered, and expected weighted aggregate interference and jamming (EWAIJ) are introduced to integrate these interference. Moreover, minimising interference is taken as the optimisation goal. Thirdly, Game theory is introduced to describe the combinatorial optimisation problem. The process of each team finding the optimal spectrum access strategy is proved to be an exact potential energy game. These two sub‐games together constitute the Stackelberg game framework. Finally, a distributed hierarchical confrontation channel selection algorithm (DHCCSA) is proposed to find the Stackelberg equilibrium solution. The simulation results show that the proposed algorithm can converge to a better effect compared with other algorithms. In addition, with the assistance of the “electromagnetic barrier,” BT is more robust than RT in terms of convergence rate, total utility, and total network throughput.

Recently, the development of unmanned aerial vehicle (UAV) mobile-edge computing (MEC) networks has brought unprecedented gains and opportunities. In this article, the joint computation offloading, UAV role, and location selection problem in hierarchical multicoalition UAV MEC network is investigated. To capture the hierarchical feature and discrete optimization, the discrete Stackelberg game with multiple leaders and followers is formulated. We prove that both the leader-level and member-level subgames are ordinal potential games (OPGs) with Nash equilibrium (NE). Thus, the Stackelberg equilibrium (SE) is guaranteed. To achieve the SE, the log-linear-based hierarchical learning algorithm (LHLA) is proposed and analyzed. The simulation results show that the LHLA can converge fast and achieve better performance compared with the existing schemes.

This paper is the first to investigate both communication and control in traffic channel (TCH) and control channel (CCH) respectively when considering leader-follower formation keeping in UAV communication networks. In this paper, we analyze the relationship between the mutual interference and information exchange cost, and then formulate the joint channel and link selection problem as a two-way consensus game between CCH and TCH. To characterize the two-way choice of link selection, we creatively propose the generalized two-way consensus equilibrium (GTCE) to capture the stable state. Then, we prove that the formulated game has at least one pure-strategy GTCE which can maximize the UAV communication network utility. A distributed better reply based joint channel and link selection (BRJCLS) algorithm as well as two-dimensional minimum spanning tree (MST) based initialization (TMSTI) algorithm is proposed to achieve the GTCE. Simulation results are presented to show the convergence and effectiveness of the formulated two-way consensus game and proposed algorithms.

In various practical orthogonal frequency-division multiplexing (OFDM) systems, the estimation accuracy at the receiver is challenging, and, specifically when operate over time-varying channels. This occurs mostly due to the presence of multipath Doppler shifts. Meanwhile, deep learning has quite recently demonstrated its superiority in extracting features information from big data. To this end, in this paper, a deep learning-assisted approach for channel estimation refinement is proposed in OFDM systems, under uplink time-varying channels. By exploitingfully-connected deep neural network (FC-DNN) properly, we successfully design a channel parameter refine network (CPR-Net) which combines deep learning with existing channel estimation algorithms. Simulation results demonstrate that, compared with conventional channel estimation algorithms, the proposed CPR-Net can significantly improve the estimation accuracy of channel parameters and provide more accurate and robust signal recovery performance.

Abstract This paper investigates the problem of multi‐user anti‐jamming channel access with partially overlapping channels (POC). Compared with traditional anti‐jamming systems that use non‐overlapping channels, POC improve the spectral efficiency. However, the partial overlap of channels also brings more serious interference. For this, physical distance and channel separation on the interference intensity under partially overlapping channels are first considered and the malicious jamming and interference among users are formulated as a hierarchical binary model. Secondly, to cope with multi‐user decisions under dynamic jamming conditions, the Markov game framework is adopted to analyse the problem. Thirdly, a multi‐user collaborative anti‐jamming channel selection algorithm based on reinforcement learning is proposed as well as the optimal anti‐jamming strategy can be obtained. Finally, the simulation results validate that the proposed algorithm helps users cope with jamming and eliminate mutual interference. Compared with the non‐overlapping channel access scheme, the POC access scheme achieves higher network throughput.

In adversarial scenarios, it is crucial to timely monitor what tactical messages that opponent transmitters are sending to intended receiver(s), and disrupt the transmissions immediately if in need. The issue becomes more challenging in face of an intelligent transmitter. To address the above-stated issue, a full-duplex (FD) technique is utilized to enable simultaneous jamming and eavesdropping (SJE) at a friendly jammer node. In particular, the “Two Birds with One Stone” strategy is utilized at the jammer node to realize effective rate degradation and information eavesdropping. A confrontation game between an intelligence-empowered FD jammer and its opponent is investigated. Specifically, to capture their adversarial relationship in an environment with incomplete information, a power-domain Bayesian-Stackelberg game is proposed. The existence of a Stackelberg equilibrium (SE) power solution is proved. The semi-closed-form solutions of SE are derived, which are proved to be asymptotically optimal (have a gap of less than 1% with the exact utility), and improves the jammer node 10% utility compared with the Nash equilibrium. Additionally, the SJE strategy outperforms the half-duplex (HD) and other benchmark schemes.

Coalition structure is an efficient networking architecture for task implementation in unmanned aerial vehicle (UAV) networks. However, both the formation of coalition and the spectrum resource for intra-coalition communication affect the reconnaissance performance. In this paper, we investigate a cooperative reconnaissance and spectrum access (CRSA) scheme for task-driven heterogeneous coalition-based UAV networks by jointly optimizing task layer and resource layer. Specifically, coalition formation game (CFG) is formulated to jointly optimize task selection and bandwidth allocation. In addition to the traditional Pareto order and selfish order, coalition expected altruistic order maximizing coalitions' utility is proposed. The CFG under the proposed order is proved to be an exact potential game (EPG). Then the existence of stable coalition partition is guaranteed with the help of Nash equilibrium (NE). We propose a joint bandwidth allocation and coalition formation (JBACF) algorithm to achieve stable coalition partition wherein an efficient gradient projection (GP) based method is applied to solve bandwidth allocation. The effectiveness of the proposed scheme and algorithms are demonstrated through in-depth numerical simulations. The results show that our proposed CRSA scheme is superior to non-joint optimization scheme. Also, the proposed order is superior to traditional Pareto order and selfish order.

An energy-efficient two-way (TW) full-duplex (FD) network with the assistance of an unmanned aerial vehicle (UAV) is proposed, where the UAV acts as a mobile relay to assist the information exchange between two terrestrial transceivers. In particular, the self-interference (SI) channel gains follow complex Gaussian distribution and the perfect channel state information (CSI) of SI channels is unavailable at the receiver. To maximize the energy efficiency (EE), UAV flight speeds are controlled and power adaptation at the UAV relay is performed. The genetic algorithm (GA) is applied to efficiently obtain the optimal solution. Numerical results show that our scheme performs better than the one-way (OW) FDR scheme, fixed power (FP) and fixed flight speed (FS) policy. In addition, the SI cancellation factor on the EE is also demonstrated.

In an energy harvesting untrusted relay network, power allocation influences the cooperative jamming, the energy harvesting and thus the achievable secrecy rate. In our previous work, theoretical computation of power allocation is derived with high computation. To tackle this issue, in this paper, we propose a deep learning aided power allocation. We here utilize fully-connected deep neural network (FC-DNN) to predict the optimal power allocation factor, where the feature vector and the model structure are carefully designed. Simulation results show the deep learning aided power allocation achieves almost the same power allocation factor and the maximum secrecy rate as the theoretical one, which validates the correctness and accuracy of the proposed scheme. Special case with small optimal power allocation factor is simulated and analyzed in detail. Furthermore, the convergence with different learning rate and batch size is also discussed.

To boost energy saving for the general delay-tolerant IoT networks, a two-stage, and single-relay queueing communication scheme is investigated. Concretely, a traffic-aware N-threshold and gated-service policy are applied at the relay. As two fundamental and significant performance metrics, the mean waiting time and long-term expected power consumption are explicitly derived and related with the queueing and service parameters, such as packet arrival rate, service threshold and channel statistics. Besides, we take into account the electrical circuit energy consumptions when the relay server and access point (AP) are in different modes and energy costs for mode transitions, whereby the power consumption model is more practical. The expected power minimization problem under the mean waiting time constraint is formulated. Tight closed-form bounds are adopted to obtain tractable analytical formulae with less computational complexity. The optimal energy-saving service threshold that can flexibly adjust to packet arrival rate is determined. In addition, numerical results reveal that: 1) sacrificing the mean waiting time not necessarily facilitates power savings; 2) a higher arrival rate leads to a greater optimal service threshold; and 3) our policy performs better than the current state-of-the-art.

In this paper, a multi-user multi-relay network with wireless energy harvesting (EH) and transferring (ET) is studied. In our system, a simultaneous two-level cooperation, i.e., information-level and energy-level cooperation is conducted for uplink data transmissions (from the users to a destination). Specifically, network coding is employed at the relays to facilitate the information-level cooperation; meanwhile, ET is adopted to share the harvested energy among the users for the energy-level cooperation. The energy minimization problem that takes into account the energy causality and outage probability constraints is formulated. However, the optimization problem is non-convex and hard to be solved directly. Alternatively, an approximation technique is adopted to convert it into a convex one. By solving the convex problem, efficient power allocation and ET policies are designed. Numerical results show that the proposed algorithm is able to achieve a near-optimal performance and outperforms the state of arts.

In this paper, a multi-user multi-relay network with integrated energy harvesting and transferring (IEHT) strategy is studied. In our system, a simultaneous two-level cooperation, i.e., information- and energy-level cooperation is conducted for uplink data transmissions (from the users to a destination). Specifically, network coding is employed at the relays to facilitate the information-level cooperation; meanwhile, ET is adopted to share the harvested energy among the users for the energy-level cooperation. For generality purposes, the Nakagami-m fading channels that are independent but not necessarily identically distributed (i.n.i.d.) are considered. The problem of energy efficiency maximization under constraints of the energy causality and a predefined outage probability threshold is formulated and shown to be non-convex. By exploiting fractional and geometric programming, a convex form-based iterative algorithm is developed to solve the problem efficiently. Close-to-optimal power allocation and energy cooperation policies across consecutive transmissions are found. Moreover, the effects of relay locations, wireless energy transmission efficiency, battery capacity as well as the existence of direct links are investigated. The performance comparison with the current state of solutions demonstrates that the proposed policies can manage the harvested energy more efficiently.

The energy efficiency (EE) of a multi-user multi-relay system with the maximum diversity network coding (MDNC) is studied. We explicitly find the connection among the outage probability, energy consumption, and EE, and formulate the maximizing EE problem under the outage probability constraint. Relay scheduling (RS) and power allocation (PA) are applied to schedule the relay states (transmitting, sleeping, and so on) and optimize the transmitting power under the practical channel and power consumption models. Since the optimization problem is NP hard, to reduce computational complexity, the outage probability is first tightly approximated to a log-convex form. Furthermore, the EE is converted into a subtractive form based on the fractional programming. Then, a convex mixed-integer nonlinear problem is eventually obtained. With a generalized outer approximation algorithm, RS and PA are solved in an iterative manner. The Pareto-optimal curves between the EE and the target outage probability show the EE gains from PA and RS. Moreover, by comparing with the no network coding (NoNC) scenario, we conclude that with the same number of relays, MDNC can lead to EE gains. However, if RS is implemented, NoNC can outperform MDNC in terms of the EE when more relays are needed in the MDNC scheme.