Mobile Edge Computing (MEC) is considered as a promising paradigm to overcome the computational constraints of mobile devices by offloading intensive tasks to nearby edge servers. As the number of users in the MEC network increases, users inevitably compete for limited wireless and computing resources, leading to a substantial escalation in the complexity of network resource allocation. Motivated by this challenge, this paper focuses on a multi-user binary computation offloading system in an MEC environment over quasi-static competitive wireless channels. We propose a non-cooperative game model, where user devices strategically optimize their offloading decisions to minimize total costs in terms of latency and energy consumption. Building upon this, the existence and feasibility of a Nash equilibrium is rigorously proved, thereby ensuring stability within the system. Furthermore, a distributed computation offloading algorithm is proposed based on game optimization, which enables user devices to adaptively attain balanced offloading strategies with minimal computational overhead. Extensive simulations validate the effectiveness of the proposed algorithm, demonstrating that it achieves near-optimal performance compared with the centralized optimization methods while avoiding additional server load or the need for user-specific configuration.

The rapid development of ultra large-scale MIMO (Multiple Input Multiple Output) systems has posed challenges to traditional channel state information (CSI) feedback methods. The increase in the number of antennas significantly increases the amount of data required for feedback, resulting in higher feedback overhead and affecting system performance. In response to this issue, this article used compressive sensing technology to reduce the amount of CSI feedback data, thereby reducing feedback overhead and optimizing system performance. To this end, this article constructed a super large-scale MIMO system model and studies channel characteristics. Gaussian random measurement matrix is selected for channel sampling, and sparse reconstruction is achieved by combining orthogonal matching pursuit (OMP) algorithm. Through simulation experiments, it was found that under different channel conditions, the OMP algorithm reduced the amount of data fed back by 25% -50% compared to the Least Square (LS) algorithm. When processing large-scale data, the OMP algorithm not only improves efficiency, but also significantly reduces computational complexity and resource consumption. Under ideal channel conditions, the system exhibits extremely high reliability, with almost zero error rate and packet loss rate. This study provides an effective solution for CSI feedback in ultra large-scale MIMO systems.

The channel matrix dimension of the near-field ultra-large-scale MIMO (Multiple-Input Multiple-Output) system is extremely high due to the significant increase in the number of antennas, thus aggravating the computational and storage burdens, and posing challenges to real-time processing and system resource management. Furthermore, traditional methods for obtaining Channel State Information (CSI) may perform poorly in near-field extremely large-scale MIMO systems, making it difficult to accurately capture the channel characteristics, which in turn affect the overall performance of the system. This study utilized the CsiNet-LSTM (Long Short-Term Memory) model to realize the channel capacity prediction. This method combined the efficient CSI compression technique of CsiNet model with the temporal prediction capability of LSTM network, which could more accurately capture the dynamic characteristics of near-field extremely large-scale MIMO channels, thereby improving the accuracy of channel capacity prediction. During the research process, this article utilized communication simulation tools to generate CSI data under multiple propagation environments and normalize and segment them, then built encoders and decoders for the CsiNet model for extracting and reconstructing CSI features, and finally combined them with the LSTM model for time series modeling. The experimental results showed that the signal strength of the normalized signal strength of CsiNet-LSTM in a multipath propagation environment reaches 0.6, and the signal quality under noise conditions reached 0.7, which was superior to other models and demonstrated stability in complex environments. In terms of real-time performance, CsiNet-LSTM had an average prediction time of 0.35 s and a processing speed of 2857 samples per second, demonstrating excellent real-time processing capabilities compared to other models.

To guarantee the low-congestion performance and quality of service (QoS) requirements of multi-services in Mega Low Earth Orbit Constellation Networks (MLEOCN), this paper focuses on the comprehensive communication link model in MLEOCN, commencing from users to access satellites, relayed by relay satellites, and finally delivered to the gateway by feeder satellites. Aiming at the problems of high congestion and low throughput in traditional path planning algorithms, we innovatively propose a multi-objective optimization service-correlated path optimization algorithm based on stochastic hill climbing strategy (MSCPO-SHCS). The algorithm initially achieves the joint optimization of three metrics through regular mapping and judicious weighting. Subsequently, it assesses the interplane hop via geometric parameter theory analysis (GPTA), then decouples the large-scale mixed integer optimization problem into the integer optimization problem superimposed linear programming problem, and ultimately employs the stochastic hill climbing strategy (SHCS) for path intelligent optimization. Based on the path Gaussianity assumption, we theoretically prove and numerically verify the convergence of the proposed algorithm. The simulation results indicate that the proposed algorithm boosts the throughput and load balancing coefficient compared with the greedy strategy, service-uncorrelated, minimum hop count, and resource allocation optimization. Additionally, it decreases the hop count compared with the maximum throughput and maximum balancing coefficient and maintains the optimal overall performance.

The unique fingerprints of radio frequency (RF) devices play a critical role in enhancing wireless security, optimizing spectrum management, and facilitating device authentication through accurate identification. However, high‐accuracy identification models for radio frequency fingerprint (RFF) often come with a significant number of parameters and complexity, making them less practical for real‐world deployment. To address this challenge, our research presents a deep convolutional neural network (CNN)–based architecture known as the separation and fusion convolutional neural network (SFCNN). This architecture focuses on enhancing the identification accuracy of RF devices with limited complexity. The SFCNN incorporates two customizable modules: the separation layer, which is responsible for partitioning the data group size adapted to the channel dimension to keep the low complexity, and the fusion layer which is designed to perform deep channel fusion to enhance feature representation. The proposed SFCNN demonstrates improved accuracy and enhanced robustness with fewer parameters compared to the state‐of‐the‐art techniques, including the baseline CNN, Inception, ResNet, TCN, MSCNN, STFT‐CNN, and the ResNet‐50‐1D. The experimental results based on the public datasets demonstrate an average identification accuracy of 97.78% among 21 USRP transmitters. The number of parameters is reduced by at least 8% compared with all the other models, and the identification accuracy is improved among all the models under any considered scenarios. The trade‐off performance between the complexity and accuracy of the proposed SFCNN suggests that it is an effective architecture with remarkable development potential.

Abstract In remote and inaccessible areas, the traffic request for Internet‐of‐Things (IoT) terminals is growing. This paper proposes a cognitive satellite‐aerial network (CSAN) to provide sufficient access services. The proposed CSAN consists of the primary beam‐hopping (BH) satellite and secondary aerial‐based station (ABS) systems. Since the two systems share spectrums, co‐channel interference (CCI) between the two systems is complicated, and the quality of service (QoS) is seriously degraded. To improve the QoS, the dynamic BH (DBH) pattern, ABS access in the time domain, and ABS power control in the power domain are studied. First, based on the sparsity of the DBH pattern, the greedy quick tracking (GAT) algorithm is proposed to design the DBH pattern quickly. Then, subject to the DBH pattern, a greedy access monitor (GAM) algorithm is determined for timely ABS access and power control. Since each ABS only serves terminals within a suitable distance, the placement and terminal cluster of multi‐ABSs in the space domain are required to ensure full terminal coverage. Thus, the mutual selection K algorithm is proposed to save required ABS numbers and improve service fairness among terminal clusters. Simulation results demonstrate the efficacy of time‐space‐power allocation for enhanced IoT‐terminal services in the proposed CSAN.

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.

A multiple-input multiple-output wireless communication system is analytically studied, which operates with the aid of a large-scale reconfigurable intelligent surface (LRIS). LRIS is equipped with multiple passive elements with discrete phase adjustment capabilities, and independent Rician fading conditions are assumed for both the transmitter-to-LRIS and LRIS-to-receiver links. A direct transceiver link is also considered which is modeled by Rayleigh fading distribution. The system performance is analytically studied when the linear yet efficient zero-forcing detection is implemented at the receiver. In particular, the outage performance is derived in closed-form expression for different system configuration setups with regards to the available channel state information (CSI) at the receiver. In fact, the case of both perfect and imperfect CSI is analyzed. Also, an efficient phase shift design approach at LRIS is introduced, which is linear on the number of passive elements and receive antennas. The proposed phase shift design can be applied on two different modes of operation; namely, when the system strives to adapt either on the instantaneous or statistical CSI. Finally, some impactful engineering insights are provided, such as how the channel fading conditions, CSI, discrete phase shift resolution, and volume of antenna/LRIS element arrays impact on the overall system performance.

Constructive Interference (CI) has shown great advantages in improving security and reliability of communication systems, which utilizes channel state information (CSI) and knowledge of the instantaneous information data on symbol level. In this paper, we explore the CI-based secure precoding problem under a dual-hop and half-duplex downlink transmission relay system in the presence of an eavesdropper. We propose to jointly optimize the precoding strategy at the source and at the relay through an alternating optimization process. To alleviate the high computational costs and circumvent the difficulty of practical implementation, we propose a low-complexity iterative algorithm for the optimization, where we use Karush-Kuhn-Tucker (KKT) conditions to analyze and simplify the previous optimization problem at the relay. Numerical results show that the proposed algorithm can achieve an improved performance compared with traditional zero-forcing/regularized zero-forcing (ZF/RZF) methods and significantly degrade the eavesdropper’s performance.

Abstract In high‐speed mobile scenarios, due to the high‐speed relative motion between transmitter and receiver, the high Doppler frequency shift interferes with the inter‐subcarrier orthogonality in orthogonal frequency‐division multiplexing (OFDM) systems. Therefore its performance is significantly degraded. Recently, orthogonal time–frequency space (OTFS) is considered as an effective alternative scheme to OFDM for time‐varying channels. As with OFDM‐massive multiple input multiple output (MIMO) systems, downlink channel estimation is necessary for OTFS‐massive MIMO systems to improve the spectral efficiency in frequency‐division duplex (FDD) mode without channel reciprocity. Here, first the cyclic prefix ‐OFDM‐based massive MIMO OTFS system channel with antenna directivity pattern is analyzed, and transform the burst sparsity in the angle domain into block sparsity by using non‐uniform Fourier transform (NUFT). Furthermore, to solve the problem that the pilot overhead grows linearly with the number of antennas, we propose a three‐dimensional (3D) dynamic support detect (DSD) algorithm. Compared with the traditional OMP algorithm, and the 3D‐ structured orthogonal matching pursuit algorithm, simulation results demonstrate the proposed DSD algorithm has higher channel estimation accuracy, and lower pilot overhead.

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.

In wireless communication systems, relay can improve the communication quality and increase communication distance. However, most of the current researches treat the relay node as a selfless node, and seldom pay attention to the individual needs and fairness. To address this issue, in this paper, we study power allocation scheme based on Game theory in untrusted relay networks. The model price incentive mechanism based on Stackelberg Game is used to solve the power allocation problem, which aims to achieve active participation assistance of relay and reduce the system signaling overhead. Meanwhile, the convergence of the algorithm is also analyzed. The simulation results show that compared with the existing fixed allocation methods, the power allocation based on this scheme has better destination node utility and global maximum secrecy rate. Moreover, we find that the dynamic power allocation strategy based on Stackelberg Game scheme is more suitable for dynamic scenes, which only rely on the imperfect channel state information.

This paper investigates the security problem of a multi-eavesdrop multiple-input-single-output (MISO) wiretap channel, where an N-antenna transmitter communicates with a single-antenna legitimate user in the presence of multiple single-antenna smart eavesdroppers. To overcome the security risk of the traditional secure constructive interference-based (CI-based) scheme when facing the smart eavesdroppers, we propose a novel deception scheme (DS) via a random transmission strategy, where the eavesdroppers are expected to decode the deception symbols correctly but unable to distinguish the authenticity of the decoded symbol. Then, an efficient algorithm is proposed for the deception signal-to interference-plus-noise (SINR)-balancing problem when perfect channel state information (CSI) is assumed. Furthermore, we consider a practical scenario where only imperfect CSI is available, and explore two different methods for the deception optimization problem, i.e., convexification relaxation approach (CRA) and Lagrangian relaxation approach (LRA), respectively. For both CSI cases, a closed-form solution to the considered CI-based deception scheme is obtained. Simulation results validate the superiority of the proposed approach over traditional secure precoding schemes, and also demonstrate the significant computation efficiency improvements for the proposed algorithms.

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 joint beamforming alignment and suboptimal power allocation (Sub-OPA) for a two-way untrusted relay network is presented. Considering the link between users is established via only an untrusted relay because of either the shadowing fading or long distance between users, we utilize a destination-assisted-jamming (DAJ) technique to improve the security performance. In each time slot, a user transmits confidential signals and the other emits jamming signals simultaneously to prevent the untrusted relay from intercepting the confidential signals. First, we design a novel beamforming to align the confidential signal to the subspace corresponding to the confidential transmission channel and direct the cooperative jamming signal toward untrusted relay. Then, an iterative algorithm is presented to generate the Sub-OPA of each user for the transmission of confidential and cooperative jamming signal. The correctness and efficiency of the proposed beamforming scheme and the Sub-OPA are validated by numerical simulations and the results are compared via MatLab optimization toolbox. Moreover, the simulations show the iterative algorithm is converged after the fourth iteration at lower signal-to-noise ratios (SNRs) and rapidly converged with allocating more power to the confidential signals at higher SNRs.

In this paper, a cooperative secure transmission in a two-way relay network is investigated. Due to untrusted relay, physical layer security is adopted to secure the user''s messages from relay interruption. Considering the secure sum rate and power consumption for the network, we formulate an optimal power allocation (OPA) problem for the transmission to maximize the secure energy efficiency (EE). To lower the complexity of the optimization solution, an approximation based on Taylor expansion is introduced to achieve near optimal secure EE with acceptable errors. Finally, the numerical results are presented to validate the correctness and efficiency of the proposed algorithm and solution. The relative error of secure EE is not greater than 6% in the worst case, and about 1% in most cases, which is acceptable.

In this paper, we study cooperative precoder design in two-tier networks, consisting of a macro-cell (MC) and several small-cells (SCs). By exploiting multiuser Vandermonde-subspace frequency division multiplexing (VFDM) transmission, an MC downlink can co-exist with cognitive SCs. In this paper, we first propose a cooperative cross-tier precoder (CTP) among the transmitters in the SCs to increase the transmitted dimension. The cooperative CTP allows us to use more efficient intra-tier precoder (ITP) in SCs to handle intracell interference and improve the throughput of the cognitive system. And then, three ITPs, a block-diagonal zero-forcing (BD-ZF) ITP, a capacity-achieving (CA) ITP, and a generalized MMSE channel inversion (GMI) ITP, are developed. Complexities of all CTPs and ITPs are discussed and compared. The overhead of channel state information (CSI) exchange is analyzed. Numerical results are presented to demonstrate the throughput improvement of the proposed schemes and to discover the impact of the imperfect CSI. From the complexity comparison and the numerical results, the GMI ITP offers a good tradeoff between complexity and throughput.

Classical Turbo code suffers from high error floor due to its small minimum Hamming distance (MHD). Newly-proposed 3D-Turbo code can effectively increase the MHD and achieve a lower error floor by adding a rate-1 post encoder, through which part of the parity bits from the classical Turbo encoder are further encoded. In this paper, a novel bit-interleaved coded modulation (BICM) system is proposed by combining rotated mapping QAM and 3D-Turbo code to effectively improve the performance of 3D-Turbo code over Raleigh fading channels. A 2D iterative soft demodulating-decoding algorithm is developed for the proposed BICM system. Simulation results show that the proposed system can obtain about 0.8-1.0 dB gain at bit error rate (BER) of 10-6, compared with the existing BICM system with Gray mapping QAM.

This study deals with channel parameter based channel estimation in time‐varying channels for orthogonal frequency division multiplexing in an uplink transmission. By modelling the uplink channel properly, the time‐varying ‘channel response’ can be determined by estimating the corresponding ‘channel parameters’. A novel algorithm is proposed in this study to estimate the channel parameters by exploiting the time‐frequency‐representation of the time‐varying channel response. Moreover, a decision‐directed receiver structure is adopted to cancel the impact of residual Doppler shifts. Theoretical analysis on the performance is given as well. The optimum power allocation ratio of training signal power to data signal power is also discussed. Simulations are performed, demonstrating the effectiveness of the proposed algorithm.

A novel simulation model is proposed by investigating the characteristic of Ricean fading channel. We deduce the second-order and high-order statistical properties, the probability density functions (PDF) of envelope and phase, as well as the level crossing rate (LCR) and average fading duration (AFD) for this model. From the deduction, the correct statistical properties of the proposed model are stationary in the wide sense. Moreover, the fading phases are uniformly distributed and mutually independent with the distribution of envelope. The simulation results further illuminate that the statistical properties of the proposed model are consistent with theoretical ones. Compared with all the existing models, the proposed model is more effective and efficient in simulation for its much fewer random variables and less computation complexity