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 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.

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.

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.

The next generation Internet of Things (IoT) is expected to support extremely diverse applications and terminals, which requires the massive connectivity. Unfortunately, current mobile communication systems with orthogonal multiple access technique can hardly satisfy this demand. On the other hand, the narrow band property has become the trend of the next generation IoT, however, the problem of interference alleviation in the narrow band system is difficult to be resolved. Recently, the sparse code multiple access (SCMA), a non- orthogonal multiple access technique for 5G, shows the obvious improvement in user connectivity and system capacity. After carrier aggregation brought into the future network, The IoT could offer the frequency hopping mechanism larger bandwidth resources. In this paper, by introducing frequency hopping mechanism to the SCMA, a frequency-hopping based sparse code multiple access (FH-SCMA) multiple access technique is proposed to meet the demand of both massive connectivity and the improvement in the interference alleviation. Simulation results show that the FH-SCMA obtains a spectrum efficiency gain of almost 300% compared with the frequency division multiple access (FDMA) based IoT system with frequency-hopping, and the performance of interference alleviation can also be significantly improved by about 2 to 5 times over the FDMA with frequency hopping.

Due to the dramatic growth of human demands for wireless business, wireless local area network (WLAN) achieves rapid popularization and world-wide development recently. In order to improve the efficiency of the next generation WLAN, especially in the dense deployment scenarios, IEEE sets up a new task group named TGax to draft out the amendment 802.11ax in 2014. The motion of TGax confirms that Enhanced Distributed Channel Access (EDCA) is still one important method of channel access in 802.11ax. In this paper, we show that the traditional EDCA mechanism faces the fairness issues, including intra-node problem and inter-node problem, in the next generation WLAN. Thus, we propose an enhanced virtual collision management mechanism named EVCM by introducing access weight factor and rate of successful access to improve fairness of EDCA. Simulation results show that the EVCM significantly improves both the intra-node fairness and inter-node fairness in the dense deployment scenarios. Especially, it obtains ten-fold intra-node fairness compared with EDCA, and three times inter-node fairness than that in EDCA.

The ever-increasing data demands and the density of wireless nodes require higher data rate in wireless networks. To satisfy the requirement, it is necessary to propose novel medium access control (MAC) protocols to enhance multiuser channel access and multiuser data transmission in wireless network. The existing studies prove that full-duplex (FD) is able to improve the network capability without any extra bandwidth. However, there are few studies focusing on multiuser FD MAC protocol design for the next generation wireless networks. In this paper, a multiuser FD MAC protocol named as MU-FuPlex is introduced based on our proposed system model, and the interference information collection, FD transmit opportunity (FD-TXOP) mechanism, and frame format design for MU-FuPlex are discussed. To the best of our knowledge, this is the first work focusing on the combination of multiuser MAC and FD technology. The simulation results confirm that MU-FuPlex significantly improves the saturation throughput up to 200% compared with IEEE 802.11 DCF.

The increase in collision and interference induced by the network densification poses the intractable challenge of improving network throughput. Channel reservation and cooperative transmission, two schemes improving the medium access control (MAC) efficiency and transmission reliability respectively, have drawn considerable attention. Joint optimization of reservation and cooperation is theoretically proved to be promising in improving the network throughput in our recent study. However, up to now no practical MAC protocol is proposed to evaluate its effectiveness. In this paper, we propose an ALOHA-based joint reservation and cooperation MAC (AJRC-MAC) protocol which adopts the reservation-based channel access and enables the cooperative transmission simultaneously for the dense wireless network. Simulation results evaluate the effectiveness of the joint reservation and cooperation, and show that a throughput gain of 330%, 250%, 130% can be achieved respectively for AJRC-MAC compared with the basic MAC, cooperation- only MAC and reservation-only MAC.

As the most widely used standards for wireless local area network (WLAN), IEEE 802.11 standards are continuously amended by introducing new techniques so as to meet the increasing demands. In order to verify the performance of amended protocols, network simulation is considered as a significant method. However, as far as we know, current simulation tools are only for either media access control layer (MAC) or physical layer (PHY). The separate simulation of MAC and PHY can hardly evaluate the performance of IEEE 802.11ax in whole system level for authenticity and objectivity. Hence, the next generation WLAN (IEEE 802.11ax) requires integrated system simulation to take impacts of both MAC and PHY techniques into account. Moreover, IEEE 802.11ax introduces some new techniques, such as orthogonal frequency division multiple access (OFDMA), multi-user multiple input multiple output (MU- MIMO) and non-continuous channel bonding. In this paper, we design and further implement the integrated link-system level simulation platform, which makes it possible to evaluate the new technologies for IEEE 802.11ax. Moreover, we propose a MAC protocol combining OFDMA, MU-MIMO, non-continuous channel bonding and link adaptation and further evaluate its performance. Finally, we validate performance gains of IEEE 802.11ax through simulation, and the simulation results show that IEEE 802.11ax has obviously higher throughput, better quality of service (QoS) and higher multi- channel efficiency. To the best of our knowledge, this is the first work to design and implement simulation platform for IEEE 802.11ax with an integrated link- system level framework.

In this paper, a heuristic clique based spatial time division multiplexing access (STDMA) scheduling algorithm is proposed for concurrent transmission in millimeter wave networks. Firstly, based on the physical interference model an interference level caused by one transmission request to another is defined, which transforms the SINR condition to a summation form. Then, an un-directional conflict graph is constructed, a feasible clique of which is proved corresponding to a feasible concurrent transmission requests group in one timeslot. Finally, a heuristic clique based STDMA scheduling algorithm is proposed to find the maximum feasible concurrent scheduled transmission requests in one timeslot. Extensive simulations are conducted, and the simulation results show that compared to the existing blind scheduling algorithm, the spatial sharing gain is improved by 11%-36%.