The Kohn-Luttinger (KL) mechanism of pairing, which describes superconductivity emergent from repulsive interactions, typically yields Cooper pairs at high angular-momentum (ℓ > 0) and extremely low transition temperatures (T_c). Here, we reveal an inter-layer s-wave (ℓ=0) KL superconductivity with greatly elevated T_c in a multi-layer Hubbard model, which prototypes stacked two-dimensional (2D) electrons in layered van der Waals materials. By employing determinant quantum Monte Carlo and dynamical mean-field theory simulations, we show that a strong pairing attraction V^*, without the mediation of collective modes, can emerge between inter-layer electrons in the system. As inter-layer repulsion U increases, V^* evolves from a conventional KL relation of V^*∝ -U^2, to a linear strong-coupling scaling of V^*∝ -U, resulting in enhanced superconductivity at large U. This strong-coupling KL pairing is robust against changes in lattice geometries and dimensionalities, and it can persist, in the presence of a large remnant Coulomb repulsion U^* between pairing electrons. Using ab initio calculations, we propose a few 2D layered van der Waals materials that can potentially realize and control this unconventional superconductivity.

This paper investigates secure hybrid beamforming with subarray architectures in a millimeter-wave (mmWave) integrated sensing and communication (ISAC) system. To achieve secure communication against a potential eavesdropper, we design the sensing signal in a dual-functional form, serving both to detect the target and to generate artificial noise (AN) that disrupts the eavesdropper. With fixed subarray architectures, the security and sensing performance are analyzed, including the impact of imperfect channel state information (CSI). Moreover, the design of analog and digital beamformers is formulated as the problem of minimizing transmit power while satisfying the constraints of sensing beampattern and secrecy rate. To solve the non-convex problem, semi-definite relaxation (SDR) and successive convex approximation (SCA) techniques are employed to transform the original problem and obtain the solution. For the scenario with imperfect CSI, the robust hybrid beamforming is designed based on S-procedure. Additionally, to optimally utilize spatial domain resources provided by multiple antennas, a dynamic subarray architecture is introduced, along with two dynamic subarray partitioning algorithms. This flexible architecture can be dynamically adjusted according to the CSI, ensuring a bidirectional alignment with the actual channels and the proposed scheme. Specifically, the greedy algorithm and the secrecy rate increment maximization (SRIM) algorithm are designed to partition the dynamic subarray, and the alternating optimization is utilized to further improve the quality of the solution. Numerical results demonstrate the effectiveness of the proposed schemes.

At present, most of China adopts Train to Ground (T2G) communication, which requires a large number of trackside equipment to maintain and has the disadvantage of high transmission delay. Train to Train (T2T) communication can achieve lower delay and autonomous safety control of trains. This article proposes a vehicle communication related algorithm based on Non Orthogonal Multiple Access (NOMA) technology, using Convolutional Neural Network (CNN) Long Short Term Memory (LSTM). The CNN is used to simulate the channel state, extract channel features and power allocation factors, simulate channels that are closer to the actual situation, and quickly calculate the optimal power allocation scheme; Use LSTM algorithm to minimize the error between actual output and ideal output. The research results indicate that the proposed scheme has better spectral efficiency than traditional Zero Forcing (ZF) algorithm and Minimum Mean Square Error (MMSE) algorithm. The more training times the CNN-NOMA-LSTM algorithm has, the better the fitting performance, and the smaller the error between the ideal output and the actual output. Moreover, it improves spectral efficiency under the lowest signal-to-noise ratio (SNR) T2T communication requirement. The research results can serve as a reference for future T2T communication.

Private-Internet of Things (P-IoT) is IoT network with for private users using dedicated frequencies. Ad hoc mode is the main operation mode of P-IoT. Since Ad hoc mode of P-IoT is a narrowband network, the congestion is the fatal problem on the intermediate nodes. Topology control is an effective way to reduce congestion. A topology control algorithm based on node degree for P-IoT is proposed in this paper. The topology control algorithm minimizes the node degree of the communicating nodes under the premise of guaranteeing network connectivity and constructs a network topology with less sparsity. The simulation results show that our algorithm can markedly reduce the average node degree and the sparsity of topology without decline of the network connectivity.

The integration of communication technologies is regarded as a potential direction of future network. Combining cell-free massive multiple-input multiple-output (CF-mMIMO) with nonorthogonal multiple access (NOMA) can achieve substantial access and uniform coverage, but it also poses challenges on channel state information (CSI) acquisition. Although systems operating in time division duplex can approximate uplink CSI for downlink precoding design, the performance degradation resulting from nonreciprocity is still nonnegligible. This article proposes a calibration method to improve system sum rate of CF-mMIMO-NOMA with nonreciprocal channels. The proposed method exploits the impact of all access points (APs) on achievable rate to provide a prioritization and calibrates the selected APs orderly, finding an optimal compromise between pilot overhead and performance gain. The order and number of calibrated APs can be adjusted according to the parameters including coherence interval, number of users, etc. Based on the calibration method, a power allocation algorithm focusing on maximizing the minimum rate is presented. The nonconvex initial model is transformed into a standard second-order cone programming problem through slack variables for bisection solution. Simulation results validate the importance of calibration and indicate that the proposed method provides enhanced achievable rate with flexibility and adaptability.

In ultra-dense networks (UDN), multiple association can be regarded as a user-centric pattern in which a user can be served by multiple base stations (BSs). The data rate and quality of service can be improved. However, BSs in user-centric paradigm are required to serve more users due to this multiple association scheme. The improvement of system performance may be limited by the improving load of BSs. In this letter, we develope an analytical framework for the load distribution of BSs in heterogeneous user-centric UDN. Based on open loop power control (OLPC), a user-centric scheme is considered in which the clustered serving BSs can provide given signal to interference plus noise ratio (SINR) for any typical user. As for any BS in different tiers, by leveraging stochastic geometry, we derive the Probability Mass Function (PMF) of the number of the served users, the Cumulative Distribution Function (CDF) of total power consumption, and the CDF bounds of downlink sum data rate. The accuracy of the theoretical analysis is validated by numerical simulations, and the effect the system parameters on the load of BSs is also presented.

In this letter, a hybrid beam combining algorithm for arbitrary-shaped beam patterns in phase-only controllable massive MIMO is proposed. It enables precise control of beam combining gain in all directions based on the derived expression of non-stationary complex exponential signal, allowing for the generation of beam patterns with arbitrary geometric shapes. Both analytical and simulation results demonstrate the proposed algorithm can accurately approximate arbitrary-shaped beam patterns without compromising SNR performance compared with traditional methods. It becomes possible to achieve flexible beam training and sparse channel estimation algorithms for massive MIMO by these precise geometric shapes.

The Private Internet of Things (P-IoT) is a network that provides IoT services to users of private networks. Effective routing protocols are essential for P-IoT to ensure optimal network performance. This paper proposes a routing algorithm called Priority Experience Replay Double Deep-Q-network (PER-DDQN), which considers both congestion and traffic load as reward functions. PER-DDQN combines deep learning and reinforcement learning to address the challenges of traditional routing algorithms in terms of learning and environmental perception. PER-DDQN enhances the learning speed of reinforcement learning and capacity for processing high-dimensional data. The simulation results indicate that the PER-DDQN algorithm outperforms in terms of convergence speed, congestion reduction, and load balancing, compared with conventional shortest path algorithms, Q-learning algorithms, and deep Q-learning algorithms.

In the vehicular networks integrated with mobile edge computing (MEC), vehicle users are permitted to offload latency-sensitive and computation-intensive tasks to nearby MEC servers, which can extend battery life of the vehicle while improving the experience of users. In this paper, we consider a multi-user computation offloading scenario in vehicular networks with MEC server, in which tasks are executed at vehicle and MEC server parallelly through partial offloading. However, the finite communication and computation resource limit the flexibility of offloading. We propose a joint offloading and resource allocation algorithm based on improved hybrid particle swarm and simulated annealing to reduce the system energy consumption as much as possible. The simulation results demonstrate that our algorithm performs well in convergence and energy consumption under strict time constraint.

In order to adapt to the rising traffic in 5G even 6G systems, operators have turned their attention to the dense deployment of base stations, which makes the problem of user association more complicated. At the same time, in order to adapt to the differences of traffic in different regions and the asymmetry of uplink and downlink requirement, dynamic TDD technology has also been proposed in some papers. Hence, dynamic TDD based Ultra-dense networks require more complex association algorithms, and these algorithms should consider not only the channel characteristics, but also the load of the base stations and the uplink and downlink resource configuration of the base stations. Although some researches have been done on user association and dynamic TDD, most papers consider them separately. In this paper, we consider user association and dynamic TDD together and add constraints on cross-slot interference and load limits. We propose an algorithm which can decompose the problem into independent subproblems using the generalized Benders method. The subproblems can be solved by the gradient descent algorithm and two-sided matching game algorithm, respectively. Simulation results show that compared with traditional algorithms, our proposed algorithm can significantly improve the performance of user throughput and network load balancing.

Edge computing assisted autonomous driving technology has become a promising method to satisfy exacting computation requirements of achieving high or even full automation. However, the computation and spectrum resources of multi-access vehicular edge computing (VEC) system are also limited, which may not guarantee the best experience for all users, so we make a tradeoff between resource consumption and user experience. First, according to the characteristics of task scalability in driving assistance applications, we model the problem as maximizing system utility under the deadline constraint and the total resource constraint. Then, we formulate a collaborative computation offloading and resource allocation optimization scheme (JORA). Since the problem is NP-Hard, the JORA scheme eventually solves the problem by the mutual iteration of the two sub-algorithms, which includes offloading strategy and resource allocation. Simulation results prove that the proposed algorithm can effectively improve the system utility.

Ultra dense network (UDN) is one of the key technologies to improve the network capacity by densely de-ployed small cells. As for the asymmetry and variations of future traffic, dynamic time division duplex (D-TDD) has been proposed as a promising solution to dynamically adjust the uplink (UL)/downlink (DL) configuration. In this paper, we propose a general and tractable analytical framework for the theoretical performance under D-TDD two-tier heterogeneous networks. We firstly model user equipment (UE) and base station (BS) as homogenous Poisson point process (HPPP). Then, by leveraging stochastic geometry and geometry relationship, we propose an analysis method based on geometry approximation and derive analytical expressions for the UL and DL coverage probability. To increase the accuracy of analysis, we consider the coupling between UEs and BSs, so that more accurate interference can be obtained. Our results show that the derived coverage probabilities match the simulation results quite well and suitable for heterogeneous UDN scenarios. In addition, a comparison of the coverage probabilities between D-TDD system and static TDD (S-TDD) system is also provided to show that compared with S-TDD system, D-TDD system sacrifices uplink coverage performance to obtain better downlink performance.

The increasing demand for the internet of vehicle (IoV) services has prompted the prosperity of cellular-based vehicle-to-everything (C-V2X). This paper investigates the resource allocation in an uplink SCMA-enabled C-V2X network. At the aim to maximize the sum rate of cellular links in condition of guaranteeing the reliability of V2X links, we propose a Two-Stage space code multiple access (SCMA) codebook allocation algorithm. The complicated resource matching problem is transformed into a 2-D matching problem by using V2X grouping algorithm at first. Then a Matching-Auction-based codebook allocation algorithm is adopted to address the issue of matching conflict. Finally, numerical simulation results indicate that the proposed scheme outperforms in terms of the sum data transmission rate of the cellular links and the reliability of V2X links.

There is a risk of information leakage in the process of wireless communication. The introduction of key generation in the physical layer can effectively improve the security of wireless communication. This paper proposes a new physical layer key generation method in the TDD (time division duplex) communication system. Assume that the uplink and downlink channels are reciprocal. Through LDPC (Low Density Parity Check)channel coding and negotiation algorithm, the inconsistency rate of key generation between Alice and Bob is reduced. Simulation and analysis results show that the method improves the key generation rate and reduces the key inconsistency rate.

With the increasing number of vehicles, many road accidents have occurred. To solve this problem, this paper investigates a security application in vehicle communications where all links require high reliability. We consider that each vehicle-to-infrastructure (V2I) communication shares spectrum resource with multiple vehicle-to-vehicle (V2V) communications. Firstly, we aim to maximize the successful transmission probability (STP) of V2V communications while guaranteeing the reliability of all V2I communications. Then, we formulate the above resource allocation problem as a combinatorial double resource auction (CDRA) problem. In the auction, radio resources occupied by V2I communications are considered as bidders competing for V2V packages. We propose an algorithm to solve the resource allocation algorithm. Finally, simulation results indicate that the proposed scheme outperforms the traditional resource allocations in terms of the reliability.

Ultra-dense network (UDN) can effectively improve the network throughput by increasing the deployment density of base stations. However, due to the randomness of the large number of base station deployments, UDN will lead to huge computational complexity and signaling overhead. The existing clustering algorithms cannot obtain effective clustering results in extremely dense scenarios. In this paper, we propose a minimum separation clustering (MSC) algorithm, which selects the split base stations (SBSs) to connect the multiple dense cluster base stations (CBSs). SBSs can reduce the interference between CBS clusters by using different radio resources from CBSs, because it has higher priority in the resource allocation procedure. Furthermore, the traditional clustering evaluation indexes such as the sum of square error are not applicable to UDN where the base stations are deployed randomly, and hence we design the separation degree function, which evaluates the clustering effect from the compactness within a cluster and the dispersion between different clusters. Simulation results show that the proposed algorithm can not only reduce the proportion of SBSs so as to improve the spectral efficiency, but also reduce the inter-cluster interference and network scale.

Sparse Code Multiple Access (SCMA) is expected to accommodate massive machine-type communications (mMTC) in 5G wireless networks. Since the overloading system creates enormous signaling overheads, massive connections with grant-free transmission methodology have received significant attention. In this paper, we study active user detection (AUD) and channel estimation (CE) based on compressed sensing technology in the uplink of a grant-free system. We firstly propose a pilot design scheme considering the optimization of sensing matrix, and then a dynamic sensing matrix-based Group Orthogonal Matching Pursuit (DSM-based GOMP) algorithm is proposed for block sparse channel estimation, and hence pilot overhead in the cellular network can realize self-adaptation with the number of potential users or communication channel states. In low SNR scenarios, the sensing matrix composed of Zadoff-Chu (ZC) sequence is considered. When the SNR exceeds the threshold, the sensing matrix is constructed by optimizing Gram matrix to reduce inter-cell interference. Simulation results prove that the proposed algorithm is capable of achieving multiple access with low detection error, and adjust pilot resource overhead adaptively.

Ultradense network (UDN) is an effective solution to providing high capacity and massive connections in hotspots. However, a large number of randomly deployed small base stations (SBSs) cause severe interference. In order to solve the strong co-channel interference among user equipment (UE), we proposed an efficient resource allocation algorithm based on simulated annealing (SA). First, we established a system-level architecture of UDN, which is more realistic and suitable for dense SBSs scenario. In addition, considering the properties of UDN, we formulated the resource allocation problem into a 0–1 problem. Moreover, we transform this problem into a traveling salesman problem (TSP) and propose a resource allocation algorithm to solve it. Unlike the previous works, we study different ratios between UEs and SBSs. Simulation results show that the proposed algorithm can efficiently avoid co-channel interference and improve system throughput with lower signaling overhead. Moreover, SBSs multiple association is excellent in improving system throughput.

D2D multicast communications can reduce communication cost and decrease interference in Internet of Vehicles (IoV). In this paper, the resource allocation algorithm for Device-to-Device (D2D) multicast communication reusing LTE network uplink resource is studied to improve the reliability of networks. The optimization objective of the algorithm is to minimize the outage probability of D2D multicast group links on the basis of guaranteeing the constraint of cellular link quality. To solve this optimization problem, a low-complexity heuristic algorithm is designed for spectrum resource allocation and power control. By comparing performance through simulation, it can be found that the outage probability of the heuristic algorithm designed in this paper can be very close to that of the Hungarian algorithm, but the computational complexity is greatly reduced compared with the latter.

An interference graph based adaptive interference coordination method was proposed for indoor scenario of ultra dense network (UDN).The algorithm aimed at maximizing system throughput.Firstly,the interference relationship in the system was modeled as an interference graph,and the iterative coloring algorithm was used to determine the available resources of each small cell base station (SBS).Thereafter,the SBS allocated resources to each user by using a throughput optimizing resource allocation algorithm.The method could adaptively select a resource allocation strategy according to the network topology and channel conditions,thereby mitigating interference in the system.The simulation results show that compared with the existing methods,the proposed method effectively reduces the system outage probability while significantly improving the throughput performance through a small additional signaling overhead.