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.

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 hardness of the Unconstrained Binary Quadratic Program (UBQP) problem is due its rugged landscape. Various algorithms have been proposed for UBQP, including the Landscape Smoothing Iterated Local Search (LSILS). Different from other UBQP algorithms, LSILS tries to smooth the rugged landscape by building a convex combination of the original UBQP and a toy UBQP. In this paper, our study further investigates the impact of smoothing rugged landscapes using different toy UBQP problems, including a toy UBQP with matrix$\hat{\boldsymbol{Q}}^{1}$(construct by “$+/-1$‘), a toy UBQP with matrix$\hat{\boldsymbol{Q}}^{2}$(construct by “$+/-\mathrm{i}$’) and a toy UBQP with matrix$\hat{\boldsymbol{Q}}^{3}$(construct randomly). We first assess the landscape flatness of the three toy UBQPs. Subsequently, we test the efficiency of LSILS with different toy UBQPs. Results reveal that the toy UBQP with$\hat{\boldsymbol{Q}}^{1}$(construct by “$+/-1$”) exhibits the flattest landscape among the three, while the toy UBQP with$\hat{Q}^{3}$(construct randomly) presents the most non-flat landscape. Notably, LSILS using the toy UBQP with$\hat{\boldsymbol{Q}}^{2}$(construct by “$+/\cdot \mathbf{i})$emerges as the most effective, while$\hat{\boldsymbol{Q}}^{3}$(construct randomly) has the poorest result. These findings contribute to a detailed understanding of landscape smoothing techniques in optimizing UBQP.

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.

Pareto local search (PLS) is a natural extension of local search for multiobjective combinatorial optimization problems (MCOPs). In our previous work, we improved the anytime performance of PLS using parallel computing techniques and proposed a parallel PLS based on decomposition (PPLS/D). In PPLS/D, the solution space is searched by multiple independent parallel processes simultaneously. This article further improves PPLS/D by introducing two new cooperative process techniques, namely, a cooperative search mechanism and a cooperative subregion-adjusting strategy. In the cooperative search mechanism, the parallel processes share high-quality solutions with each other during the search according to a distributed topology. In the proposed subregion-adjusting strategy, a master process collects useful information from all processes during the search to approximate the Pareto front (PF) and redivide the subregions evenly. In the experimental studies, three well-known NP-hard MCOPs with up to six objectives were selected as test problems. The experimental results on the Tianhe-2 supercomputer verified the effectiveness of the proposed techniques.

As an extension of local search for multiobjective case, the basic version of Pareto Local Search (PLS) suffers from a poor anytime behavior. Researches have been carried out to overcome this drawback from different aspects. In this paper, we focus on the mechanism of neighborhood exploration in bi-objective Travelling Salesman Problems (bTSPs). Inspired by existing fast local search strategies for single objective TSP, we propose two speed-up strategies to help PLS quickly find promising neighboring solutions in bTSPs. In the experimental studies, we investigate the sensitivity of parameters and test the performance of several PLS variants with different combinations of the two strategies. The experimental results verify the effectiveness of the two strategies and their combination.

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.

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.

In this paper, we focus on the physical layer security for a $K$ -user multiple-input-single-output (MISO) wiretap channel in the presence of a malicious eavesdropper, where we propose several interference exploitation (IE) precoding schemes for different types of the eavesdropper. Specifically, in the case where a common eavesdropper decodes the signal directly and Eve's full channel state information (CSI) is available at the transmitter, we show that the required transmit power can be further reduced by re-designing the `destructive region' of the constellations for symbol-level precoding and re-formulating the power minimization problem. We further study the SINR balancing problems with the derived `complete destructive region' with full, statistical and no Eve's CSI, respectively, and show that the SINR balancing problem becomes non-convex with statistical or no Eve's CSI. On the other hand, in the presence of a smart eavesdropper using maximal likelihood (ML) detection, the security cannot be guaranteed with all the existing approaches. To this end, we further propose a random jamming scheme (RJS) and a random precoding scheme (RPS), respectively. To solve the introduced convex/non-convex problems in an efficient manner, we propose an iterative algorithm for the convex ones based on the Karush-Kuhn-Tucker (KKT) conditions, and deal with the non-convex ones by resorting to Taylor expansions. Simulation results show that all proposed schemes outperform the existing works in secrecy performance, and that the proposed algorithm improves the computation efficiency significantly.

In this paper, we study the secrecy performance of multiple-input single-output (MISO) wiretap channel with random beamforming (RB), where the eavesdropper is equipped with multiple antennas. In traditional RB schemes, the transmitter utilizes only one antenna to emit information signal and adopts the rest antennas to produce a random beamforming vector for security. To make full use of the power resource and improve the secrecy performance, we propose a power-minimizing and signal-splitting random beamforming (PM-SSRB) scheme, where the random beamforming vector is generalized with arbitrary number of transmit antennas based on power-minimizing. To evaluate the secrecy performance of the proposed scheme, we analyze the ergodic secrecy rate and derive the closed-form expression of the ergodic rate of the MISO wiretap channel. Simulation results show that, compared with the traditional hybrid artificial fast-fading scheme (AFF) and artificial noise (AN) scheme, the proposed SSRB scheme and PM-SSRB scheme perform much better in terms of the ergodic secrecy rate in all power regimes. More importantly, when the eavesdropper has more antennas than the transmitter, our schemes always outperform the AFF and AN schemes. The PM-SSRB scheme is also shown to be superior to the secret-key AFF scheme.

We investigate the secure degrees of freedom (SDoF) for the two-way 2×2×2 MIMO interference channel (IC) under three wiretap models, i.e., the confidential messages (CM) model, the untrusted relays (UR) model, and the combined CM and UR (CM-UR) model. For the general case of arbitrary antenna configuration under each wiretap model, we derive the upper bound on SDoF with Markov chain and secrecy constraints, and obtain the achievability schemes with designed interference neutralization, cooperative jamming, and interference alignment schemes. To gain insight on these bounds, we further consider the special case where each user node has M antennas and each relay node has N antennas, and highlight the modification process when achieving the maximum SDoF. For such special case, we show that the optimum SDoF of the CM model is achieved in the regimes M ≥ N and M2M, and N = M.

Recently, the demand for indoor positioning has gradually increased. Considering that people walk indoors with a serious restriction, the map information is extremely significant, which can be used as an aid in indoor positioning. In order to exploit map information thoroughly and automatically, and obtain a high- precision positioning result, we propose a map- aided particle filter (PF) algorithm based on WiFi and Pedestrian Dead Reckoning (PDR) in this paper, which exploits WiFi RSS fingerprint, inertial sensors and indoor map information comprehensively. Before the online localization, some specific image processing methods which are Morphological operation, Skeleton extraction and Line detection, are introduced to extract the latent information of indoor map, such as the skeleton of passageway in buildings, the possible forwarding directions, etc. Using the extracted features of the floor plan, the particle filter can adjust the estimated heading direction from the PDR module based on the areas of particle distribution. The real scenario experiments reveal the validity of candidate direction matching. The results also indicate that the proposed algorithm can remarkably alleviate the cumulative error, and effectively solve the problem of trajectory drift and particle deactivation during indoor positioning. Thus, compared with traditional schemes, our proposed algorithm can improve the accuracy, stability and robustness of the indoor positioning system.

We investigate the secure degrees of freedom (SDoF) for the two-user multiple-input multiple-output (MIMO) interference channel with a cooperative jammer, where each transmitter is equipped with M antennas, each receiver is equipped with N antennas, and the jammer is equipped with K antennas. For each one of the four regions of parameters (M, N), i.e., 1 ≤ N/M2M. Moreover, we quantify the SDoF gap of the network and reveal it as a function of the number of the antennas. For large K, the upper and lower bounds coincide in all regimes, and the exact SDoF can be achieved.

This paper investigates the secrecy performance of amplified-and-forward (AF) relay wiretap model by using energy harvesting (EH) and signal alignment technique. All nodes equipped with multi antennas play different roles in terms of source, destination, relay, eavesdropper and jamming node. The jamming node designs artificial noise based on signal alignment principle, and then transmits it to the eavesdropper with harvested energy. Due to different power limitations, we propose power allocation schemes called Local Power Constraints scheme (LPCS) and Global Power Constraints scheme (GPCS), which maximize the secrecy rate of the system and obtain better secrecy performance when compared with the non-EH scheme and the partial nodes power allocation EH scheme. Furthermore, we also analyze the effect of the energy harvesting on the secure degrees of freedom (SDOF) in the high signal-to-noise (SNR) region, and simulate the secrecy performance with convex optimization method by proving the convexity of the optimization problem. Results show that the proposed schemes outperform the compared schemes on the secrecy performance, and achieve more secrecy rate when the destination and the relay have more antennas.

This paper investigates a four-node wiretap network including one source (S), one relay (R), one legitimate receiver (D) and one eavesdropper (E) with cooperative jamming in a two-hop relay transmission. In the first phase, S transmits an useful signal to R while D transmits an interference signal with the purpose of confounding the eavesdropper. In the second phase, R broadcasts the mixture signal together to D. In this paper, based on large-scale fading, we propose two system models named One-link Wiretap Model and Three-link Wiretap Model to distinguish the receiving links at E. Then, a power allocation scheme is proposed to maximize the average secrecy capacity through two-dimension optimization. Meanwhile, we solve the optimization problem by two different methods called IBO and IAO. Simulation results show that the proposed power allocation strategy can achieve a higher average secrecy capacity than the reference scheme RJPA (Rate-optimal jamming power allocation) in the two models. Besides, a larger security range can be obtained by the proposed scheme.