Vehicular edge computing (VEC) is an emerging computing paradigm that alleviates the limitations of local computing resources for the Internet of Vehicles. However, the lack of trust among distributed nodes and ineffective incentive mechanisms discourage Roadside Units (RSUs) from providing resources. In addition, information asymmetry can lead to the clearing prices of resources failing to accurately reflect the actual value of resources. To address these issues, we propose a trustbased double auction framework in VEC networks. In this framework, to incentivize the RSUs with high trust to participate in resource trading and ensure the clearing prices accurately reflect the actual value of resources, we first design a trustbased resource pricing mechanism. In this mechanism, RSUs with higher trust can set higher resource prices and the clearing prices of resources are closer to the buyers’ bids. Then, we develop a trust-based double auction mechanism that incorporates the Edmonds-Karp algorithm for efficient buyer-seller matching. Considering the time-varying nature of the VEC networks, we employ deep reinforcement learning to optimize decision-making to maximize the social welfare. Finally, we demonstrate that the proposed double auction model satisfies key economic properties such as individual rationality and incentive compatibility. Simulation results validate that our proposed approach outperforms benchmark schemes in terms of social welfare.

In this work, we introduce an MLP-Mixer-based unrolling UAMPSBL approach for near/far-field structured channel estimation in terahertz extremely large-scale antenna arrays (ELAA) systems. The MLP-Mixer-based unrolling UAMPSBL approach can effectively alleviate the diverge problem of the original UAMP-SBL algorithm. Moreover, the MLP-Mixer-based unrolling UAMPSBL approach has a more simplified structure and suite for block-sparse structures compared to the CNN model. Simulation results show that the proposed MLP-Mixer-based unrolling UAMPSBL approach significantly outperforms the benchmark algorithms for near/far-field channel estimation problem.

Blockchain is considered as a promising technology to ensure the security of resource trading between the IoT user equipment (UEs) and the edge service providers (ESPs) in mobile edge computing (MEC) networks. However, blockchain cannot guarantee the trustworthiness of ESPs and cannot effectively incentivize ESPs to participate in resource trading and blockchain consensus. In addition, the high computational resource demands, energy consumption, and the limited transaction throughput of traditional blockchain pose challenges to IoT applications that require frequent micro transactions. To address these issues, we develop an integrated blockchain and MEC framework based on a directed acyclic graph (DAG) ledger to meet the demands of IoT applications. In this framework, we first model the resource trading between the UEs and the ESP as a multi-follower Stackelberg game. To incentivize the ESP to participate in resource trading and blockchain consensus, we design a trust based resource pricing mechanism, wherein the trust of ESP is evaluated by UEs and the ESP with higher trust can set a higher resource price. Additionally, to enable UEs to better assess the security of transactions on the DAG blockchain, we design a metric called transaction security satisfaction and adopt it as the revenue of UEs. Second, we verify the existence and uniqueness of the Stackelberg equilibrium. Furthermore, we propose a backward induction based iterative algorithm to optimize the resource pricing strategy for ESP and the resource demand strategy for UEs, while maximizing the utilities of both ESP and UEs. Numerical simulations demonstrate the effectiveness of our proposed scheme, showing its superiority over benchmark scheme in terms of transaction security satisfaction and the utilities of ESP and UEs.

Sparsity-based joint active user detection and channel estimation (JADCE) algorithms are crucial in grant-free massive machine-type communication (mMTC) systems. The conventional compressed sensing algorithms are tailored for noncoherent communication systems, where the correlation between any two measurements is as minimal as possible. However, existing sparsity-based JADCE approaches may not achieve optimal performance in strongly coherent systems, especially with a small number of pilot subcarriers. To tackle this challenge, we formulate JADCE as a joint sparse signal recovery problem, leveraging the block-type row-sparse structure of millimeter-wave (mmWave) channels in massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. Then, we propose an efficient difference-of-convex function algorithm (DCA) based JADCE algorithm with multiple measurement vector (MMV) frameworks, promoting the row-sparsity of the channel matrix. To mitigate the computational complexity further, we introduce a fast DCA-based JADCE algorithm via a proximal operator, which allows a low-complexity alternating direction multiplier method (ADMM) to resolve the optimization problem directly. Finally, simulation results demonstrate that the two proposed difference-of-convex (DC) algorithms achieve effective active user detection and accurate channel estimation compared with state-of-the-art compressed sensing based JADCE techniques.

Abstract Over the past 70 years, the semiconductor industry has undergone transformative changes, largely driven by the miniaturization of devices and the integration of innovative structures and materials. Two-dimensional (2D) materials like transition metal dichalcogenides (TMDs) and graphene are pivotal in overcoming the limitations of silicon-based technologies, offering innovative approaches in transistor design and functionality, enabling atomic-thin channel transistors and monolithic 3D integration. We review the important progress in the application of 2D materials in future information technology, focusing in particular on microelectronics and optoelectronics. We comprehensively summarize the key advancements across material production, characterization metrology, electronic devices, optoelectronic devices, and heterogeneous integration on silicon. A strategic roadmap and key challenges for the transition of 2D materials from basic research to industrial development are outlined. To facilitate such a transition, key technologies and tools dedicated to 2D materials must be developed to meet industrial standards, and the employment of AI in material growth, characterizations, and circuit design will be essential. It is time for academia to actively engage with industry to drive the next 10 years of 2D material research.

Compressed sensing-based joint active user detection and channel estimation (JADCE) algorithms play a key role in massive grant-free access systems. However, current compressed sensing-based JADCE approaches cannot perform well when the sampling matrix has strong coherence, which results in expensive implementations of JADCE as the number of pilot decreases. To address this, we formulate the JADCE as a joint sparse signal recovery problem and then propose an efficient solver from difference of convex programming (DCP) perspective. The proposed DCP-based algorithm leverages the weighted$\mathcal{L}_{2,1-F}$minimization to solve the multiple measurement vector of JADCE problem. Simulation results demonstrate that the proposed DCP-based$\mathcal{L}_{2,1-F}$minimization algorithm achieves effective active user detection and accurate channel estimation compared to conventional compressed sensing-based JADCE approaches.

This paper considers a joint active user detection and channel estimation (JADCE) problem in the grant-free massive machine-type communications (mMTC) circumstances. Specifically, we exploit the millimeter-wave (mmWave) channel in the uplink with the continuous angular domains based on massive multi-input multi-output systems. The sporadic communication nature of the mMTC scenario and the inherent angular domain sparsity of the mmWave channel make the space-angle domain sparsity of the mmWave channel even more serious. Hence, the JADCE problem is formulated as a convex optimization problem under the gridless reweighted atomic norm minimization (RAM) framework in a multiple measurement vector settings (MMV), which can enhance the sparsity in the continuous anular domains. Moreover, RAM has nature of Semidefinite programming (SDP) which can be computed by CVX solver. Meanwhile, to reduce the computational complexity of CVX, we design a fast alternating direction method of multipliers approach to settle the SDP formulation. The simulation results demonstrate that our proposed method has achieved excellent estimation performance and a substantial reduction in computational complexity compared with conventional JADCE methods.

The essence of connection in vehicle network is the social relationship between people, and thus Vehicular Social Networks (VSNs), characterized by social aspects and features, can be formed. The information collected by VSNs can be used for context prediction of autonomous vehicles. Multivariate relations are common in square connected relations caused by geographic characteristics in VSNs. They can effectively reflect the high-order structural features of the network dataset. It is necessary to exploit the multivariate relations of VSNs to improve the performance of context prediction. However, The representation of entity-relationes in the network often adopts a binary form, and the existing graph learning methods rely on the neighborhood information of nodes to achieve the aggregation or diffusion of information. Using this to represent multivariate relations will result in partial omissions or even complete loss of valuable information, which ultimately affects the learning effect of learning methods. In order to better understand the social behavior of the VSNs, this paper uses the network motif to implement the representation of the multivariate relations in the network, and proposes the graPh learnIng with moTif mAtrix (PITA) method. This method can be used as a preprocessing step for the measurement strategy of the relations in VSNs and the graph learning, which can mine the information in VSNs and improve the accuracy of the original graph learning method by the multivariate relation information. We performed experiments on 6 network datasets. The experimental results show that in the node classification task, the baseline method modified by the PITA method has a higher classification accuracy than the original method.

In this paper, a fast optimization-based off-grid channel estimation method is proposed for millimeter wave (mmWave) or sub-terahertz(THz) cellular systems. Different from most existing works concerned with compressed sensing (CS) or off-grid methods, we proposed a novel first-order convex off-grid channel estimation approach to relieve the resolution loss from the angle quantization. Moreover, our proposed method is approximate inverse-free without costly matrix inversion. The numerical results demonstrate the effectiveness of the proposed FOG method, which achieves superior performance compared to CS-based approach and existing off-grid algorithm in terms of computational complexity and estimation accuracy.

We study the question of phase retrieval aided Millimeter wave (mmWave) channel estimation and propose a sparse phase retrieval-aided mmWave channel estimation technique which can estimate the sparse mmWave channel parameters from quadratic measurements. The proposed scheme has low-cost hardware implementation compared with traditional compressed sensing-based methods and robust to carrier frequency offset caused by high-frequency hardware imperfections. Based on the proposed sparse phase retrieval-aided model, we introduce a two-stage algorithm to estimate the mmWave channel parameters (up to a global phase) and then compute the exact solution via the anchor measurements. Simulation results are provided to illustrate the effectiveness of the proposed method.

Centrality is an effective method to identify important nodes in complex networks, but it is still a challenge to find influential nodes by making full use of multiple relationships and global network topological features in complex networks. To address these problems, this article proposes an importance identification method for multilayer heterogeneous network node by incorporating multirelational information (MLC). This method studies the relational characteristics of heterogeneous nodes in detail and divides the heterogeneous nodes into different layers according to the node types, which can be further divided into core and auxiliary layers. The importance of the auxiliary layer is quantified by designing the interlayer influence and determining the interlayer influence weights of different connectivity influences; the centrality score of heterogeneous nodes under multiconnectivity relationships is fused using the transmission characteristics of internode relationships in the auxiliary layer, which in turn measures the importance of nodes in the core layer. To evaluate the proposed algorithm, we conduct experiments on five real multilayer heterogeneous networks of different sizes. The results show that MLC can make full use of different types of internode association relationship information, effectively fuse network structure information such as the neighbor weights of core and auxiliary layer nodes, and outperform the existing techniques in identifying important nodes.

It is challenging to acquire accurate channel knowledge for sufficient beamforming gain because of the large number of antennas. In this paper, a deep learning aided channel estimation algorithm is proposed for mmWave cellular systems, in which both the base station (BS) and mobile station (MS) gain directional beamforming by employing large antenna arrays. We consider the off-grid (OG) mmWave channel estimation and propose a deep network architecture for solving this problem, which is different from most existing works concerning the compressed sensing (CS)-based channel estimation. Firstly, the off-grid channel model is used to relieve the basis mismatch issue by exploiting first-order Taylor-series approximation of the array manifold taken on a fixed grid of both BS and MS. Secondly, a new formulation of this off-grid channel estimation problem is solved by using a low computational complexity alternating direction method of multipliers (ADMM)-based algorithm, dubbed ADMM-OG algorithm. Thirdly, the idea of algorithm unrolling guides us to design a deep network architecture ADMM-OGChannelNet corresponding to the ADMM-OG algorithm for the mmWave channel estimation. Based on the interpretability of this deep network architecture, the optimal parameters of this network can be learned without tuning in a hand-crafted way. The Cramér-Rao bound (CRB) of the off-grid channel model is derived for performance comparison as well. Finally, from the simulation results, we can verify that the ADMM-OGChannelNet has better estimation accuracy and relatively low computational complexity compared with the state-of-the-art algorithms.

The traffic congestion and accidents can be relieved by deploying the software defined internet of vehicles (SDN-IoV). However, the traffic of pedestrians and vehicles is particularly heavy near commercial streets and campuses. In particular scenarios, the SDN-IoV may not ensure the quality of service (QoS) for pedestrians and vehicles. In this paper, we construct a novel system architecture consisting of multiple non-cooperative unmanned aerial vehicles (UAVs) and a SDN-IoV. The non-cooperative UAV is equipped with an antenna array to receive the signals from the vehicles and pedestrians of SDN-IoV. In order to locate the positions of vehicles and pedestrians, two source enumeration methods are proposed in a complex SDN-IoV environment with color noise. The projection matrix of the low dimensional signal subspace is constructed by the proposed criterion based on signal subspace projection (SSP). The sequence of the projected difference values of the local covariance matrix is applied to estimate the number of vehicles and pedestrians. The eigenvalues can be grouped to construct different subspaces by the proposed eigen-subspace projection (ESP). By projecting a new covariance matrix into the eigen-subspaces, the variance of values represents the projection difference can be exploited to estimate the number of vehicles and pedestrians. Simulation results and real system test verify the validity of the two proposed methods by comparing them with the state-of-the-art methods. Both of the methods have excellent estimation performance especially in color noise.

In this article, a multiple signal classification (MUSIC) based algorithm is proposed for two-dimensional (2-D) direction-of-arrival (DOA) and polarization estimation of non-circular signals in three-dimensional (3-D) millimeter wave polarized massive multiple-input-multiple-output (MIMO) systems. The traditional MUSIC-based algorithms can estimate either the DOA and polarization for circular signals or the DOA for non-circular signals by using spectrum search. By contrast, based on the quaternion theory, a novel algorithm named quaternion non-circular MUSIC (QNC-MUSIC) is proposed for parameter estimation of non-circular signals with high estimation accuracy. Moreover, only the DOA estimation needs spectrum search, and the polarization estimation has a closed-form expression. First, the DOA estimation can be achieved based on the derivation principle. Then the closed-form expression of the polarization estimation can be obtained based on the chain rule of the derivation w.r.t. the polarization parameters. In addition, the computational complexity analysis shows that compared with the conventional DOA and polarization estimation algorithms, our proposed QNC-MUSIC has much lower computational complexity, especially when the source number is large. The stochastic Cramér-Rao Bound (CRB) for the estimation of the 2-D DOA and polarization parameters of the non-circular signals is derived as well. Finally, numerical examples are provided to demonstrate that the proposed algorithms can improve the parameter estimation performance when large-scale/massive MIMO systems are employed.

Phase retrieval problem is an nonlinear inverse problem of recovering real- or complex-valued signal from quadratic measurements, which arises in various applications. The best-known algorithms for solving this problem are non-convex methods starting with spectral initializers that provide an initial point within a local basin sufficiently close to the target signal. This paper introduces a simple method, called weighted null vector initialization (WNI), which can be used to compute accurate initialization vectors for solving non-convex phase retrieval. The introduced WNI method is more robust against measurement noise and outperforms the best spectral initializer, and null initializer. Simulation results are provided to illustrate the effectiveness of the proposed method.

The recently developed technique of wireless power transfer (WPT) provides a promising way to charge the wireless sensor networks (WSNs) of cognitive industrial Internet of Things (IoT) deployed in areas that are difficult for humans to access. Previous work has focused on the power allocation strategy at the wireless node level. However, the priority among different modes in an identical wireless node has not been taken into consideration, and different modes equipped with different types of batteries accomplish different tasks in an identical wireless node. One challenging scenario is rechargeable WSNs with a large number of wireless nodes. In this article, we aim to optimize the power allocation strategy in priority constraint WPT systems with a large number of wireless nodes. Traditional WPT systems consist of a rechargeable WSN and a mobile charger, which are deployed for charging wireless nodes in a wireless manner. However, the constructed WPT system consists of a rechargeable WSN and multiple mobile chargers with adequate power, which can charge wireless nodes simultaneously. Each solution of the power allocation strategy can be represented as one disjunctive graph, and the critical path (CP) in the disjunctive graph is the core factor in determining the final maximum cost. Thus, we propose a decomposition strategy that can identify the interacting variables based on the CP by exploiting the perturbation technique. Then, the decomposed subcomponents are cooperatively evolved by adopting a cooperative evolutionary algorithm (CEA). The proposed CP-based grouping strategy combined with CEA is named CPCEA. Three state-of-the-art methods are tested and compared with CPCEA, and three scales of datasets are considered. The experimental results demonstrate the validity of CPCEA.

In this paper, a novel off-grid channel model is considered for millimeter-wave (mmWave) cellular systems, where both the BS and MS employ large antenna arrays for directional beamforming. Accurate acquisition of channel knowledge for sufficient beamforming gain is challenging due to the large number of antennas and low coherence time. Different from most existing studies that are concerned with compressed sensing (CS)-based channel estimators, a novel convex formulation without semidefinite programming (SDP) relaxation is proposed for the off-grid/super-resolution channel estimation and then a low complexity solver based on alternating direction method of multipliers (ADMM) is developed. The simulation results demonstrate that the proposed off-grid method has better estimation accuracy and lower computational complexity compared with other state- of-the-art algorithms.

In this letter, we introduce a new sparsity-promoting prior, namely, the “normal product” prior, and develop an efficient algorithm for sparse signal recovery under the Bayesian framework. The normal product distribution is the distribution of a product of two normally distributed variables with zero means and possibly different variances. Like other sparsity-encouraging distributions such as the Student’s$t$-distribution, the normal product distribution has a sharp peak at the origin, which makes it a suitable prior to encourage sparse solutions. A two-stage normal product-based hierarchical model is proposed. We resort to the variational Bayesian (VB) method to perform the inference. Simulations are conducted to illustrate the effectiveness of our proposed algorithm as compared with other state-of-the-art compressed sensing algorithms.

MOTIVATION: In the developing nervous system, the expression of proneural genes, i.e. Hes1, Neurogenin-2 (Ngn2) and Deltalike-1 (Dll1), oscillates in neural progenitors with a period of 2-3 h, but is persistent in post-mitotic neurons. Unlike the synchronization of segmentation clocks, oscillations in neural progenitors are asynchronous between cells. It is known that Notch signaling, in which Notch in a cell can be activated by Dll1 in neighboring cells (trans-activation) and can also be inhibited by Dll1 within the same cell (cis-inhibition), is important for neural fate decisions. There have been extensive studies of trans-activation, but the operating mechanisms and potential implications of cis-inhibition are less clear and need to be further investigated. RESULTS: In this article, we present a computational model for neural fate decisions based on intertwined dynamics with trans-activation and cis-inhibition involving the Hes1, Notch and Dll1 proteins. In agreement with experimental observations, the model predicts that both trans-activation and cis-inhibition play critical roles in regulating the choice between remaining as a progenitor and embarking on neural differentiation. In particular, trans-activation is essential for generation of oscillations in neural progenitors, and cis-inhibition is important for the asynchrony between adjacent cells, indicating that the asynchronous oscillations in neural progenitors depend on cooperation between trans-activation and cis-inhibition. In contrast, cis-inhibition plays more critical roles in embarking on neural differentiation by inactivating intercellular Notch signaling. The model presented here might be a good candidate for providing the first qualitative mechanism of neural fate decisions mediated by both trans-activation and cis-inhibition.