This paper presents a non-orthogonal retransmission framework for integrated satellite-terrestrial networks (ISTNs). This framework integrates hybrid automatic repeat request (HARQ) with incremental redundancy (HARQ-IR) and rate splitting multiple access (RSMA). HARQ-IR and RSMA are utilized for downlink retransmission and multi-user interference management, respectively, to address the requirements for extensive and highly reliable concurrent connections. Employing the inclusion-exclusion principle, we establish precise upper and lower bounds for the exact outage probability (OP), which function as approximations. We also examine the asymptotic OP, which yields significant insights and informs a partial HARQ-IR-RSMA scheme. We propose a joint common-private power allocation (JCPPA) algorithm based on alternating optimization (AO) to enhance the energy efficiency (EE) of the partial retransmission scheme while adhering to power and outage probability (OP) constraints. The non-convex problem is addressed effectively via asymptotic OP, which allows for the decomposition into common and private power optimization subproblems utilizing the Dinkelbach method. The common power optimization subproblem is convex and can be solved using the CVX toolbox. The private power optimization subproblem is addressed through variable substitution and the application of the Lagrangian dual algorithm. The numerical results indicate the accuracy and superiority of the proposed special scheme regarding outage performance and energy efficiency when compared to benchmarks.

Mega-constellation networks (MCNs) of low Earth orbit (LEO) satellites are poised to serve as critical enablers for next-generation 6G wireless systems. These satellite infrastructures not only provide ubiquitous Internet access to terrestrial users but also facilitate relay-assisted data transmission for space-based remote sensing and positioning services. However, the inherent challenges of ubiquitous coverage and overlapping service regions in dense LEO constellations necessitate rigorous optimization of user-satellite association strategies, especially when the serving satellites are from multiple constellations. This paper provides insights into user access selection and resource optimization for mega-constellations that cover extensive terrestrial areas. The selection of multiple satellites from various constellations is predicated on the calculation of their coverage areas and the geolocation of urban areas. By explicitly modeling the transmission traffic requests and data collection process, the access strategy is investigated to maximize network throughput while maintaining a balance in quality-of-service (QoS). Thus, an optimization algorithm is proposed for user access selection that synergistically combines graph convolutional attention networks (GCAN) and deep reinforcement learning (DRL). Simulation results based on the Starlink Phase I and Phase IV models show that the proposed algorithm achieves performance improvements in both throughput and access quality compared to other benchmark algorithms.

This research investigates the application of Integrated Sensing and Communication (ISAC) systems in maritime secure communication networks. Considering the severe path loss caused by complex maritime environments, we employ an active Reconfigurable Intelligent Surface (RIS) to establish supplementary communication links and enhance system performance. The primary objective is to maximize the sum secrecy rate (SSR) through the joint optimization of the hybrid precoding at the Dual-Function Base Station (DFBS) and the phase shift matrix of the active RIS, subject to both transmit power constraints and sensing performance requirements. We propose an alternating optimization algorithm based on fractional programming (FP) and successive convex approximation (SCA) techniques to solve this joint optimization problem. Numerical results demonstrate that the proposed algorithm achieves up to 10% SSR gain compared to passive RIS schemes, while maintaining excellent convergence performance.

Mega-constellation networks of low Earth orbit (LEO) satellites are poised to become an integral part of future 6G networks. Satellite infrastructures can provide Internet access services to terrestrial users and act as relay satellites for on-board remote sensing and positioning services. Given the extensive coverage of LEO satellite constellations and the overlapping coverage areas between satellites, the selection of appropriate access satellites for terrestrial users is critical. This paper provides insights into user access selection and resource optimization for large LEO constellations covering large terrestrial areas. By explicitly modeling the transmission traffic requests and data collection process, the access strategy is investigated to maximize network throughput while maintaining a balance on quality of service. Thus, an optimization algorithm is proposed for user access selection that synergistically combines graph convolutional attention networks and deep reinforcement learning (DRL). Simulation results based on the Starlink Phase I model show that our algorithm achieves performance improvements in both throughput and access quality compared to random access and standalone DRL frameworks.

Unmanned aerial vehicle (UAV) assisted communication is becoming a promising technology for future networks. Leveraging this benefit, the ambient backscatter communication can utilize the UAV’s emitted signal as the radio frequency carrier to transmit its own information. However, this transmission behavior is easily to be detected by the UAV due to the high possibility of line-of-sight (LoS) air-ground channel. Thus, in this paper, we propose a covert ambient backscatter communication scheme by exploiting the UAV relay as the radio frequency source. Specifically, the UAV relays the information for two legitimate ground nodes, and monitors the potential ambient backscatter communication. Our goal is to maximize the covert ambient backscatter communication rate under the worst case that the UAV performs with the optimal detection threshold, transmit power and hovering location. First, the UAV’s optimal detection threshold is analyzed, and the corresponding closed-form expression of error detection probability is derived. Then, we propose an iterative algorithm to achieve the minimum error detection probability by optimizing the transmit power and hovering location of UAV. To fight against the detection of UAV, we formulate a convex optimization problem to maximize the worst-case covert ambient backscatter communication rate by adjusting the reflection coefficient. Simulation results show that the proposed scheme can effectively improve the covert ambient backscatter communication rate.

Unmanned aerial vehicle (UAV) assisted relaying has become the focus of the next generation network owing to its superiority of low cost and swift deployment. In addition, the orthogonal frequency division multiplexing (OFDM) based simultaneous wireless information and power transfer (SWIPT) is known to have significant advantages in system complexity compared to the traditional time-switching (TS) and power-splitting (PS) techniques. Since the UAV is known to have limited size, weight and power, we investigate the OFDM-based SWIPT for a multi-source-destination UAV relaying system in this paper. With the purpose of maximizing the average transmission rate (ATR) under the constraint of the harvested energy, we jointly optimize user scheduling, resource allocation and UAV trajectory. To tackle this issue, we first divide it into three subproblems of user scheduling, power and subcarrier allocation, and UAV trajectory optimization. Subsequently, the user scheduling subproblem is optimally solved. The power and subcarrier subproblem is approximately solved by addressing its dual problem and the UAV trajectory optimization subproblem is addressed by successive convex approximation technique. Then, we put forward an iterative algorithm based on block coordinate descent method by solving the three subproblems alternately. Numerical results demonstrate that our proposed algorithm is preferable than the two benchmark schemes.

In dynamic and complex environments, the positioning accuracy of global navigation satellite system (GNSS) will be seriously reduced. Deep reinforcement learning (DRL) has been found to give effective dynamic policy learning for complex GNSS positioning correction tasks. However, catastrophic interference in DRL models caused by the high correlation between successive positioning states, together with instability in gradient backpropagation in deep neural networks (DNNs), produces inaccurate DRL value approximation thereby degrades GNSS positioning performance. In this article, we develop a dictionary learning-based reinforcement learning (RL) algorithm with the nonconvex log regularizer for GNSS positioning correction. To avoid DNN instability problems, a dictionary learning-structured RL model is proposed. It has a feed-forward learning architecture obviating the need for gradient backpropagation. The nonconvex log regularizer for dictionary learning reduces the correlation between states and thereby alleviates interference in RL. This provides sparse representations, which can more effectively capture features and produce representations with lower biases than convex regularizers. Furthermore, the nonconvex optimization is made efficient through a decomposition scheme that generates an explicit closed-form solution using the proximal operator. Finally, based on the proposed dictionary learning-structured RL model, a novel positioning correction method is developed to enhance GNSS positioning accuracy. The experimental results indicate that the proposed method outperforms state-of-the-art sparse coding-based RL methods in benchmark environments. Moreover, the proposed method effectively improves GNSS positioning accuracy relative to the glsms Kalman filter acrlong KF method and the glsms weighted least squares acrlong WLS method.

Mega-constellation networks (MCNs), comprising an expansive array of orbits and satellites, will be a pivotal component of the prospective nonterrestrial network (NTN). Laser intersatellite links (LISLs) represent a promising technology for the establishment of satellite networks, offering high capacity and highly reliable communication links. Nevertheless, LISL still has some technical challenges, such as link establishment instability, satellite payload capacity, and topology design. For a considerable number of satellites and laser communication constraints, LISLs have elevated the complexity and difficulty of routing and topology construction. In this article, we focus on the LISL connecting stability and propose a novel method to evaluate the intersatellite link (ISL) selection based on the acquisition probability of the laser terminal system. Subsequently, a nonlinear optimization model is formulated for the laser link selection problem, where the terminal acquisition probability is maximized. Finally, an MCN topology design algorithm (MTDA) is proposed to establish greater stability and higher channel gain within the different access distances of the laser terminal. Using the satellite constellation for Phase I of Starlink, three different laser MCN topologies were constructed by MTDA, adapting to the maximum access distance of the laser terminal within 5000 km. The impact of these differing topologies on network latency and hop count was then analyzed in a full satellite period. Compared with the baseline, MTDA has a 15.7% latency advantage and 14.0% hop advantage under the same access distance of laser terminal. The numerical results show that the proposed algorithm has a positive impact on the average network latency, hop count and their respective variances.

This research delves into an integrated sensing and communication (ISAC) system, which leverages a ship-based station to simultaneously offer maritime communication services and perform target detection. Through the establishment of a channel model that captures the complexities of the maritime environment and the analysis of the dynamic behavior of the ship-based station, this article introduces a unified signal processing framework for this ISAC system. This framework is tailored for various ship movement patterns through the integration of alternating optimization coupled with trust-region-based sequential convex approximation (SCA) techniques, with the goal of maximize the communication data rate without compromising the sensing performance. Simulation results corroborate the validity of the proposed model, laying a theoretical foundation for the advancement of maritime communication and perception systems, thereby anticipating enhanced support for future generations of integrated maritime applications.

The incompleteness of knowledge graphs (KGs) negatively impacts the performance of KGs in downstream applications (e.g., recommendation systems and information retrieval). This phenomenon has brought an increasing rise in research related to knowledge graph reasoning. Recently, emerged reinforcement learning (RL)-based multi-hop reasoning methods can infer missing information through multi-hop reasoning according to the existing information in KGs, which has better reasoning performance and interpretability. However, these methods always use relation-entity pairs that have been pre-cropped as the action space of agents for path reasoning, which leads to two problems: 1) insufficient learning and reasoning ability of reasoning models and 2) the hard convergence of the training process of agents. To address these problems, we propose aHierarchicalSequenceMultiHop (HSMH) reasoning framework, which consists of the interactive search reasoning model, local-global knowledge fusion mechanism, and action optimization mechanism. We use interactive search reasoning models to select relations and entities independently, thus fully mining the semantic information of relations and entities and improving the learning and reasoning ability of reasoning models. In the HSMH framework, we design the local-global knowledge fusion and action optimization mechanisms for path reasoning, which can enhance agents' state information and action space. Specifically, the local-global knowledge fusion mechanism is designed to acquire the local knowledge of entities and neighboring relations and the global knowledge about KG structure. This local-global knowledge can improve the learning ability of reasoning models. In addition, the action optimization mechanism can combine the filtered action space and the additional action space for efficient path reasoning for agents. Experimental results on five benchmark datasets show that our proposed HSMH framework comprehensively outperforms the state-of-the-art multi-hop reasoning model.

The integration of mobile edge computing (MEC) and unmanned aerial vehicles (UAVs) has created new opportunities for efficient data processing and calculating services within the Internet of Things. However, the presence of the active eavesdropper brings serious vulnerabilities to the security calculation of terminal users (TUs), which can eavesdrop on TUs’ confidential content and compromise the quality of offloading calculation. In this paper, we propose an efficient energy consumption minimization scheme for the considered secure UAV-enabled MEC network including an active UAV eavesdropper. While ensuring security calculation for all TUs’ data, the network’s weighted energy consumption is achieved through trajectory and resource optimization, including time, local calculation and offloading calculation allocation. Due to the coupling of multi-variables and the non-convexity of the constraints, the problem is highly challenging to solve directly. To address this, an auxiliary variable is introduced to transform the problem into a more tractable form. The optimizing solution is then obtained through iterative updates, allowing for the convergence towards an optimizing solution. Simulation results show that the proposed scheme exhibits superior performance of reducing the network’s energy consumption compared to the benchmark scheme.

Integrated satellite-terrestrial networks can enable seamless global communication coverage while providing rapid responses to areas requiring emergency communications. However, existing satellite-terrestrial links are severely degraded, making it difficult to guarantee communication reliability and resulting in extremely poor throughput. What’s worse, satellite-based relay solutions for satellite-terrestrial information interaction are scarce which has limited support for emergency communications between multiple terminals in different areas. In this paper, a comprehensive satellite-terrestrial information interaction architecture is proposed integrating hybrid automatic repeat request (HARQ), cooperative communication, and network coding to address the poor throughput and low reliability of existing networks. Then, we present the relevant mechanism and process of this framework for multi-user information interaction in different areas. To evaluate the performance of this scheme, the outage probability of satellite-terrestrial links and the throughput of this system are derived and analyzed, respectively. The simulation results demonstrate that this approach can effectively reduce the link outage probability and significantly improve the system throughput compared to traditional methods.

Nowadays, emerging sixth-generation (6G) mobile networks, the Internet of Things (IoT), and mobile-edge computing (MEC) technologies have played significant roles in developing a sustainable computing network. In sustainable computing networks, with the increasing scale of data-driven applications, massive privacy-sensitive data are generated. How to effectively process such data on resource-limited IoT devices is challenging. Although edge intelligence (EI) is designed to maintain an appropriate level of ultradelay reliability, low-latency communication (URLLC), real-time data processing, and security and privacy are concerning. In this article, we propose a novel blockchain-supported hierarchical digital twin IoT (HDTIoT) framework, which combines the digital twin to edge network and adopts blockchain technology to achieve secure and reliable real-time computation. We first propose a data and knowledge dual-driven learning solution to ensure real-time interaction and efficient optimization between the physical and the digital worlds. To improve communication and computation efficiency with data and knowledge dual-driven learning, the optimization goal is to minimize the system delay and energy consumption and ensure system reliability and the learning accuracy of IoT devices. Moreover, we propose a proximal policy optimization (PPO)-based multiagent reinforcement learning (MARL) algorithm to solve the resource allocation (RA) problem. Experimental results show that the proposed RA scheme can improve the efficiency of the HDTIoT system, guarantee learning accuracy, reliability, and security, and make a balance between system delay and energy consumption.

This paper studies optimal day-ahead scheduling of a battery swapping and charging system (BSCS) for electric vehicles (EVs) from a new perspective of multiple decision makers. It is considered that the BSCS locally incorporates the battery swapping and charging processes, and the two processes are managed by two operators, called a battery swapping operator (BSO) and a battery charging operator (BCO), respectively. Our main contribution is to propose a bilevel model where the BSO acts as the leader to receive and serve the battery swapping requests from EV users, and the BCO acts as the follower to interact with the grid and control battery charging and discharging power. We reformulate the bilevel optimization problem into an equivalent single-level problem that is a nonconvex mixed-integer nonlinear program (MINLP), and its size can easily become very large. To solve the problem efficiently, we develop a new heuristic composed of two parts, i.e., an estimation of the integer solution and an algorithm based on the alternating direction method (ADM). The results show that the proposed heuristic performs well in solving large-scale problems, providing close-to-optimal solutions quickly. In addition, compared to a social welfare maximization model that follows most existing related works, the proposed bilevel model can increase the number of swapped-out batteries by 35% and the batteries’ average energy state by 6%, improving the quality of battery swapping services.

Estimating the location parameters of moving target is an important part of intelligent surveillance for the Internet of Vehicles (IoV). Satellite has the potential to play a key role in many applications of space–air–ground-integrated networks (SAGINs). In this article, a novel passive location parameter estimator using multiple satellites for the moving aerial target is proposed. In this estimator, the direct wave signals in reference channels are first filtered by a band-pass filter, followed by a sequence cancelation algorithm to suppress the direct-path interference and multipath interference. Then, the fourth-order cyclic cumulant cross ambiguity function (FOCCCAF) of the signals in the reference channels and the four-weighted fractional Fourier transform FOCCCAF (FWFRFT-FOCCCAF) of signals in the surveillance channels are derived. Using them, the time difference of arrival (TDOA) and the frequency difference of arrival (FDOA) are estimated and the distance between the target and the receiver and the velocity of the moving aerial target are estimated by using multiple satellites. Finally, the Cramer–Rao lower bounds of the proposed location parameter estimators are derived to benchmark the estimator. The simulation results show that the proposed method can effectively and precisely estimate the location parameters of the moving aerial target.

The integration of device-to-device (D2D) communications with cooperative non-orthogonal multiple access (NOMA) can achieve superior spectral efficiency. However, the mutual interference caused by D2D communications may prevent NOMA from diverging its high spectral efficiency advantage. Meanwhile, the low adaptability of the fixed transmission strategy can decrease the reliability of the cell-edge user (CEU). To further improve the spectral efficiency, we investigate a device-to-multi-device (D2MD) assisted cooperative NOMA system, where two cell-center users (CCUs) and one CEU are paired as a D2MD cluster. Specifically, the base station directly serves the two CCUs while communicating with the CEU via one CCU. Moreover, we propose an adaptive aggregate transmission scheme using dynamic superposition coding, pre-designing the decoding orders and prior information cancellation for the D2MD assisted cooperative NOMA system to enhance the reliability of the CEU. We provide the closed-form expressions for the outage probability, diversity order, outage throughput, ergodic sum capacity, average spectral efficiency, and spectral efficiency scaling over Nakagami-$m$fading channels under perfect and imperfect successive interference cancellation. The numerical results validate the correctness of the analytical derivations and the effectiveness of the proposed scheme.

Aiming at solving the problem that the unambiguous Doppler range of slow-time MIMO is narrower than that of conventional MIMO signals, a new signal processing method based on tensor decomposition is proposed. In the proposed method, a novel tensor model is designed to describe the echo signals of slow-time MIMO radar and retains more useful information which is lost in conventional methods based on Doppler spectrum separation. The proposed algorithm applies PARAFAC to separate target parameters into three matrices, then minimum mean squared error estimation and least squares method are used to estimate the target parameters respectively. Simulation experiments have demonstrated that the proposed method can exploit unambiguous Doppler range, and simultaneously estimate target’s direction of departure, direction of arrival and Doppler frequency without further parameter pairing.

In this paper, we study a full-duplex (FD) relaying network operating with finite blocklength (FBL) codes. Based on Polyanskiy’s FBL model, we characterize the FBL reliability of the relaying network under both decode-and-forward (DF) and amplify-and-forward (AF) relaying schemes. Based on the characterisation, we provide reliability-optimal designs via optimal power allocation for both schemes in a single-carrier scenario. In particular, we prove that under the FD DF relaying scheme the (tightly approximated) overall error probability is convex in the transmit power at the relay. In addition, we show that minimizing the overall error probability of the FD AF relaying is equivalent to maximizing the overall signal to interference plus noise ratio (SINR), which is further proved to be pseudo-concave. Then, the designs for a single-carrier scenario are further extended to a multi-carrier scenario with a joint power and data allocation among carriers. In particular, for either the FD DF or FD AF relaying scheme, a joint optimization problem is reformulated to a single problem maximizing the reliability via finding and achieving the optimal SINRs, while auxiliary variables are introduced in FD AF relaying to facilitate the reformulation. Based on mathematical analysis, we respectively construct convex approximations and subsequently propose iterative algorithms, with which the error probability is reduced iteratively until an eventual convergence to an efficient suboptimal value. Hence, a corresponding suboptimal data and power allocation solution can be constructed for the multi-carrier scenario. Via numerical analysis, we validate our analytical model and the proposed allocation algorithms. The FD DF and FD AF relaying schemes are compared with direct transmission in both single-carrier and multi-carrier scenarios, and the benefits of applying FD relaying schemes and joint optimization among multiple carriers are observed.

Coupled neural P (CNP) systems are a recently developed Turing-universal, distributed and parallel computing model, combining the spiking and coupled mechanisms of neurons. This paper focuses on how to apply CNP systems to handle the fusion of multi-modality medical images and proposes a novel image fusion method. Based on two CNP systems with local topology, an image fusion framework in nonsubsampled shearlet transform (NSST) domain is designed, where the two CNP systems are used to control the fusion of low-frequency NSST coefficients. The proposed fusion method is evaluated on 20 pairs of multi-modality medical images and compared with seven previous fusion methods and two deep-learning-based fusion methods. Quantitative and qualitative experimental results demonstrate the advantage of the proposed fusion method in terms of visual quality and fusion performance.

In sixth-generation (6G) communication networks, ultrahigh-data rate and reliability are greatly vital for massive user connections and network sensors, such as Internet of Things (IoT) devices. Simultaneous wireless information and power transfer (SWIPT) has been evolved as an efficient strategy to enhance the reliability of wireless communication systems through prolonging the battery lifetime by harvesting energy from the received radio-frequency (RF) signals. Furthermore, cooperative relay sensors in IoT networks can extend the network coverage. In this article, we consider a massive multiple-input–multiple-output (MIMO) two-way relaying system, where the relay node splits the received RF signals into two power streams, one for information decoding (ID) and the other for energy harvesting (EH). Two classical and linear relay precodings, i.e., zero-forcing reception/zero-forcing transmission (ZFR/ZFT) and maximum-ratio combining/maximum-ratio transmission (MRC/MRT), are adopted to satisfy the requirements of high rate in this relay system. Different from prior work, the SWIPT technique and large-scale fading effects of MIMO channels are taken into account for deriving the asymptotic sum-rates of four prevalent power scaling cases when the number of relay antennas grows to infinity. Finally, the analytical results are evaluated by the presented simulation and numerical results.

Although the use of coordinated direct and relay transmission (CDRT) in non-orthogonal multiple access (NOMA) can extend the coverage, its duplicated transmission reduces the spectrum efficiency (SE) of NOMA. To improve the SE, we propose a spectrum-efficient scheme for NOMA-based CDRT over Nakagami-m fading channels. In this scheme, the base station (BS) connects with a cell-center user (CCU) directly while communicating with a cell-edge user (CEU) via a relay and the CCU. Then, the relay and the CCU use network coding to process and retransmit the signals sent by the BS first and the CEU later. Finally, the BS and the relay simultaneously broadcast downlink signals. We derive the closed-form expressions for the average SE, the user fairness index and the energy efficiency (EE) as well as the asymptotic average SE using both perfect and imperfect successive interference cancellation (SIC). Simulations verify the correctness of our theoretical analysis and the superiority of the proposed scheme in SE and EE.

Smart agriculture is able to optimize the information resources of agriculture, which can improve the quality and productivity of agricultural products. Wireless sensor networks (WSNs) provide smart agriculture with effective solutions for collecting, transmitting, and processing of information. However, the large number of sensor networks consume too much energy that violates the principle of green communication. Simultaneous wireless information and power transfer (SWIPT) technology utilizes radio-frequency signals to transmit information and provide energy to WSNs, which can extend the lifetime of WSNs effectively. In this article, an architecture design of smart agriculture is first proposed by exploiting the SWIPT. Then, an energy efficiency optimization scheme is studied to achieve green communication, in which the subcarriers' pairing and power allocation are jointly optimized. The process of communication is divided into two phases. Specifically, in the first phase, source sensor sends information to relay sensor and destination sensor. Relay sensor utilizes a part of the subcarriers to receive the information, and utilizes the remaining subcarriers to collect energy. Destination sensor uses all the subcarriers to receive the information. In the second phase, relay sensor utilizes the energy collected in the first phase to forward the information to destination sensor. An effective iterative optimization algorithm is proposed to resolve the proposed optimization problem through Lagrangian dual function. Simulation results validate that the performance of the algorithm can improve energy efficiency of the system effectively.

Wireless sensor networks (WSNs) have the disadvantages of short lifetime due to the limited energy of the energy storage batteries of the sensor nodes and scarcity of spectrum resources as the number of sensor nodes increasing. Simultaneous wireless information and power transfer (SWIPT) can make WSNs solve the problem of short lifetime through sensor nodes harvest energy from radio-frequency (RF) signals. Cognitive radio(CR) can make WSNs solve the problem of the scarcity of spectrum resources through sensor nodes sense and access free licensed spectrum. This paper mainly investigates the performance of an underlay cognitive sensor network (CSN). The sensor nodes in the underlay CSN can communicate with each other through the help of energy harvesting (EH) relay sensor node (RSN) by using amplify-and-forward (AF) relaying protocol. To maximize the throughput of CSN, we propose a algorithm through optimizing the transmit power of sensor nodes. Simulation results show the algorithm is correct and has good performance.

Wireless power transfer (WPT) and mobile edge computing (MEC) are two prospective technologies to enhance the computing power and endurance of mobile devices. Integrating unmanned aerial vehicle (UAV) into wireless powered MEC system, the energy collection efficiency can be effectively improved with the short‐distance line‐of‐sight path power transfer. However, WPT is susceptible to the ‘double near‐far’ effect. Therefore, in this study, the authors study power optimisation in UAV‐assisted wireless powered cooperative MEC system, which utilises the user cooperation to make the mobile device which is closer to the UAV acting as a relay for offloading. They aim to minimise the total transmission energy of the UAV through the joint power optimisation while satisfying the delay and size of the computational task. Simulation results demonstrate the performance of the proposed scheme.

This paper proposes a new variant of spiking neural P systems (in short, SNP systems), nonlinear spiking neural P systems (in short, NSNP systems). In NSNP systems, the state of each neuron is denoted by a real number, and a real configuration vector is used to characterize the state of the whole system. A new type of spiking rules, nonlinear spiking rules, is introduced to handle the neuron's firing, where the consumed and generated amounts of spikes are often expressed by the nonlinear functions of the state of the neuron. NSNP systems are a class of distributed parallel and nondeterministic computing systems. The computational power of NSNP systems is discussed. Specifically, it is proved that NSNP systems as number-generating/accepting devices are Turing-universal. Moreover, we establish two small universal NSNP systems for function computing and number generator, containing 117 neurons and 164 neurons, respectively.

To fully explore the benefits of developing device- to-device (D2D) communications in cellular systems, enabling relay-assisted (RA) D2D transmissions is a promising way. However, involving RA D2D mode will make the design of scheduling scheme at the base station (BS) side even more challenging. This work focuses on design of a scheduling scheme involving RA D2D mode for BS, which jointly considers power coordination, relay selection, mode selection, and resource allocation. Aiming to maximize the cell- wise throughput, we formulate such a scheduling issue into a mathematical optimization problem. We show how to decompose the formulated problem into two subproblems and solve them separately by using exiting algorithm and corresponding mathematical optimization theories. Particularly, the integer programming problem on mode and channel assignments is transformed into a linear programming problem to improve the solving efficiency. Simulation results validate the performance of the joint scheduling scheme in terms of cell-wise system capacity.

Network coding, which works in the network layer, is an effective technology to improve the throughput of two-way relay networks. In this paper, we compare the existing network coding schemes: traditional network coding (NC) scheme, physical-layer network coding (PNC) scheme and soft network coding (SNC) scheme, and propose a new physical layer scheme which can be seen as the implementation of network coding in two-way relay networks. Relay nodes combine the received signals from two source nodes before demodulation and channel decoding. The computational complexity and BER performance of the proposed scheme are analyzed compared with the previous schemes. Theoretical analysis show that the proposed scheme can save 50% computational cost of demodulation and channel decoding processes compared with NC scheme, meanwhile increase the power efficiency of the relay nodes in two-way relay networks significantly. Future works with the proposed scheme are also discussed.