Mobile edge computing (MEC), integrated with directed acyclic graph (DAG) blockchain technology, has emerged as a promising paradigm for ensuring secure and efficient resource trading between IoT user equipment (UEs) and edge service providers (ESPs). However, due to the open and heterogeneous nature of MEC networks, ESPs are susceptible to malicious attacks, rendering resource trading information potentially unreliable. While DAG blockchains ensure the reliability of on-chain data, they fail to guarantee the trustworthiness of ESPs and cannot effectively incentivize their participation in resource trading and blockchain consensus. To address these challenges, we develop a trust-driven resource trading framework for DAG blockchain-aided MEC networks. In this framework, we first design an off-chain trust-driven resource pricing mechanism, in which the resource price set by each ESP is positively correlated with its trust value. In addition, we design an on-chain trust-driven consensus mechanism, wherein the on-chain security of transactions published by each ESP is positively associated with its trust level. To enable UEs to better evaluate the on-chain transaction security, we design a novel metric termed transaction security satisfaction and incorporate it into the utility function of UEs. Furthermore, we model the resource trading between UEs and ESPs as a multi-leader multi-follower Stackelberg game, and verify the existence and uniqueness of its equilibrium. To maximize the utilities of both ESPs and UEs, we propose a backward induction-based iterative algorithm to jointly optimize resource pricing, resource demand, and offloading strategy. Numerical simulations validate the effectiveness of our proposed scheme, demonstrating its superior performance compared with baseline schemes.

Massive multi-input multi-output (MIMO) combined with orthogonal time frequency space (OTFS) modulation has emerged as a promising technique for high-mobility scenarios. However, its performance could be severely degraded due to channel aging caused by user mobility and high processing latency. In this paper, an integrated scheme of uplink (UL) channel estimation and downlink (DL) channel prediction is proposed to alleviate channel aging in time division duplex (TDD) massive MIMO-OTFS systems. Specifically, first, an iterative basis expansion model (BEM) based UL channel estimation scheme is proposed to accurately estimate UL channels with the aid of carefully designed OTFS frame pattern. Then a set of Slepian sequences are used to model the estimated UL channels, and the dynamic Slepian coefficients are fitted by a set of orthogonal polynomials. A channel predictor is derived to predict DL channels by iteratively extrapolating the Slepian coefficients. Simulation results verify that the proposed UL channel estimation and DL channel prediction schemes outperform the existing schemes in terms of normalized mean square error of channel estimation/prediction and DL spectral efficiency, with less pilot overhead.

Time synchronization is critical for time-sensitive cell-free massive MIMO systems, as packet loss from mobility or poor channels degrades performance. In this paper, we propose robust coordinated time synchronization (Co-TS) schemes for the cell-free massive MIMO to combat the impact of packet loss. Specifically, a multi-criteria based AP association mechanism is first proposed where in addition to the large-scale fading factor, the effects of received signal strength and packet loss probability are also considered during the AP-device association process. In addition, the fuzzy logic theory is utilized to balance among the three criteria to achieve higher robustness against packet loss over the conventional single-criterion based AP association approaches. After that, a Co-TS model considering both the coordination among APs and the effect of packet loss is developed, where the unsynchronized node can receive a set of timestamps from its associated APs during each synchronization cycle. We use maximum likelihood estimation (MLE) to jointly estimate the clock offset and skew under Gaussian-distributed random delays, and derive their closed-form Cramér-Rao Lower Bounds. Simulation results demonstrate that our proposed schemes are robust against packet loss and can significantly enhance time synchronization performance over the existing methods.

Sparse vector coding (SVC) is a promising short-packet transmission method for ultra reliable low latency communication (URLLC) in next generation communication systems. In this paper, a dual-mapping SVC (DM-SVC) based short packet transmission scheme is proposed to further enhance the transmission performance of SVC. The core idea behind the proposed scheme lies in mapping the transmitted information bits onto sparse vectors via block and single-element sparse mappings. The block sparse mapping pattern is able to concentrate the transmit power in a small number of non-zero blocks thus improving the decoding accuracy, while the single-element sparse mapping pattern ensures that the code length does not increase dramatically with the number of transmitted information bits. At the receiver, a two-stage decoding algorithm is proposed to sequentially identify non-zero block indexes and single-element non-zero indexes. Extensive simulation results verify that proposed DM-SVC scheme outperforms the existing SVC schemes in terms of block error rate and spectral efficiency.

In this paper, a comprehensive and detailed uplink performance analysis is provided for cell-free massive multipleinput multiple-output orthogonal frequency division multiplexing (CF m-MIMO OFDM) systems, which consider the impact of multiple user equipment (UE) heterogeneous factors. This is the first performance analysis work on CF m-MIMO OFDM systems that simultaneously accounts for the heterogeneous mobility speed, activation probability and serving priority. Considering that UE's serving priority determines the amount of its allocated time-frequency resources, a novel closed-form expression of uplink spectral efficiency (SE) is derived by weighting each UE's SE based on its allocated time-frequency resources. The derived SE expression can quantify the impact of multiple UE heterogeneous factors on the uplink performance. Additionally, the SE performance analysis of local processing and fully centralized processing is also included for comparison. Simulation results show that CF m-MIMO OFDM systems under multiple UE heterogeneous factors outperform existing CF m-MIMO systems in terms of the 90 %-likely uplink SE, and allow a trade-off among fronthaul overhead, complexity and SE performance.

Vehicular digital twins (DTs) create virtual representations of physical vehicles, enabling real-time data exchange to enhance intelligence and ensure safe driving. Reducing DT content delivery latency in infrastructure-deficient, sparsely populated areas is crucial. This paper develops a novel Satellite-UAV multi-path content delivery framework for data synchronization in vehicular DT applications. Satellites offer wide coverage but suffer from high latency, while UAVs provide rapid deployment and low-latency communication. The framework leverages these unique characteristics to facilitate simultaneous content downloading through multiple paths, thereby reducing latency. A Stackelberg game model is used to motivate effective resource allocation by UAVs. Given the typically private utility model of DTs, a learning-based algorithm is developed to determine optimal pricing strategies for UAVs. Simulation results demonstrate significant enhancements in UAV utility and reduced DT costs, meeting diverse service requirements.

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.

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.

Sparse vector coding (SVC) is a promising short-packet transmission method for ultra reliable low latency communication (URLLC) in next generation mobile communication systems. However, the storage burden of codebook and high decoding complexity limit its application in Internet of Things (loT) devices with constrained storage space and computational capabilities. To tackle this challenge, a semi-tensor SVC (ST-SVC)-based short-packet transmission scheme is proposed in this paper. The core idea behind ST-SVC is that it utilizes the semi-tensor product (STP) model in random spreading process, replacing the matrix multiplication model used in traditional SVC schemes. At the transmitter, a low-dimensional codebook is utilized to perform random spreading on a high-dimensional sparse vector carrying information bits. At the receiver, by exploiting the Kronecker structure induced by the STP model, a low-complexity parallel support identification algorithm is proposed for ST-SVC decoding. The proposed scheme breaks through the dimension matching condition required between the codebook matrix and high-dimensional sparse vector in traditional SVC schemes, allowing the loT devices to store an ultra-low-dimensional codebook, which significantly reduces storage overhead. Simulation results demonstrate that the proposed ST-SVC scheme can achieve a substantial reduction in both storage overhead and decoding latency compared to state-of-the-art SVC schemes, with only a slight performance loss in block error rate.

In this paper, a novel three-stage data-aided dual-space (DADS) (i.e., beamspace and signal subspace) channel estimation scheme is proposed for massive multiple-input multiple-output hybrid beamforming (m-MIMO-HBF) systems subject to pilot contamination. By exploiting the orthogonality of signal subspace, the non-overlapping interference caused by pilot contamination is identified and mitigated in the coarse channel estimate via subspace projection. Thanks to the independence of transmitted data between users, the overlapping interference is suppressed through alternating iterative refinement of the channel estimate and detected data. Additionally, to initially address the under-determined estimation problem arisen from hybrid beamforming (HBF) structures, an improved matching pursuit algorithm is proposed for coarse sparse beamspace channel estimation by appropriately selecting the scaling factor and adjusting the step size in a piecewise manner, followed by enhancement via subspace projection. Furthermore, by accurately detecting the overlap level of interference in the beamspace, the proposed channel estimation scheme selects an appropriate channel enhancement or refinement strategy to address both non-overlapping and overlapping interference subject to several typical channels without significantly increasing computational complexity. Simulation results demonstrate that the proposed channel estimation scheme achieves higher channel estimation accuracy and exhibits stronger resilience to both interference intensity and the number of interference compared to existing channel estimation schemes.

Low Earth Orbit (LEO) satellite networks offer a promising approach for achieving global coverage and high-speed Internet access. However, the existing communication frameworks within these networks may result in low robustness and reliability. This paper introduces QTER, a novel and lightweight routing framework specifically designed for LEO networks, aimed at addressing these challenges. Our contributions are threefold. First, QTER establishes a cost-effective and reliable routing framework that accommodates a variety of applications with distinct Quality of Service (QoS) requirements. By enabling multi-path routing, the framework minimizes costs while ensuring end-to-end reliability, thus enhancing adaptability to diverse service demands. Second, we leverage the unique structural characteristics of LEO satellite networks by modeling the satellite constellation as a three-dimensional network, wherein higher-shell satellites serve as management satellite nodes (MSNs) to coordinate routing strategies among lower-shell satellites. This architecture significantly improves system efficiency and adaptability. Third, we propose a failure recovery mechanism that allows MSNs to relay packets when lower-orbit satellites are rendered unavailable due to environmental factors, thereby enhancing system robustness. Extensive simulations demonstrate that QTER exhibits resilience to node failures and dynamic network conditions, achieving reductions in average cost and delay by 59.4% and 38.9%, respectively, compared to baselines.

Sparse vector coding (SVC) is a promising short-packet transmission method for ultra reliable low latency communication (URLLC) in next-generation communication systems. In this paper, a block SVC (BSVC) based short-packet transmission scheme is proposed to further enhance the transmission performance of SVC. The core idea behind the proposed scheme is to transmit short-packet data information after block sparse transformation. At the transmitter, the information bits are divided into two parts: one part is mapped into the non-zero indexes of block sparse vectors, and the other part is mapped into non-zero values through phase or amplitude modulation. After pseudo-random spreading, the block sparse vectors are mapped to time-frequency resources for transmission. At the receiver, the decoding problem is transformed into a block sparse signal recovery problem. A cyclic block orthogonal matching pursuit (CBOMP) algorithm is proposed for decoding by leveraging block-structured sparse prior information. The upper bound of block error rate (BLER) performance over Rayleigh channels is derived to verify the decoding performance of the proposed CBOMP algorithm. Extensive simulation results verify that the proposed BSVC scheme outperforms the existing SVC schemes in terms of BLER, transmission latency and spectral efficiency over fading channels.

In this paper, a novel dual-space (DS) two-stage channel estimation scheme is proposed for massive multiple-input multiple-output hybrid beamforming (m-MIMO-HBF) systems with pilot contamination. This is the first work in m-MIMO which takes into account both HBF and pilot contamination. The proposed channel estimation scheme consists of two stages. In Stage I, a coarse sparse channel estimation is conducted in the beamspace based on the proposed variable-stepsize adaptive matching pursuit (VS-AMP) algorithm, which enhances the channel recovery accuracy by carefully selecting and setting the scaling factor and step size. In Stage II, the impact of pilot contamination from interfering users is initially mitigated through the beamspace separability, while coarse channel estimate is further refined into a quasi-sparse format by subspace projection. Simulation results show that the proposed channel estimation scheme outperforms the state-of-the-art schemes in terms of normalized mean square error (NMSE) of channel estimation and bit error rate. The NMSE of the proposed channel estimation scheme also exhibits higher resilience to interference intensity while maintaining comparable computational complexity.

Sparse vector coding (SVC) is a promising short-packet transmission method for ultra reliable low latency communication (URLLC) in next-generation communication networks. In this paper, a block SVC (BSVC) based short-packet transmission scheme is proposed to further enhance the transmission performance of SVC. The core idea behind the proposed scheme is to transmit short-packet data information after block sparse transformation. At the transmitter, the transmitted information bits are divided into two parts: one part is mapped into the non-zero indexes of block sparse vectors, and the other part is mapped into non-zero values through quadrature amplitude modulation. After pseudo-random spreading, the block sparse vectors are mapped to time-frequency resources for transmission. At the receiver, the decoding problem is transformed into a block sparse signal recovery problem. A cyclic block matching pursuit algorithm is proposed for accurate decoding by leveraging block-structured sparse prior information. Simulation results verify that the proposed BSVC scheme outperforms the existing SVC schemes in terms of packet error rate and spectral efficiency.

In industrial Internet of Things (IIoT), short-packet communication requires higher reliability and lower latency, enabling Grant-free (GF) access in massive machine-type communication (mMTC) to gain a tremendous research interest. However, in practical applications, the accurate channel state information (CSI) required by coherent GF (C-GF) is challenging to acquire. Concurrently, the spectral efficiency and reliability of the non-coherent (NC-GF) scheme are areas necessitating enhancement. Inspired by sparse vector coding (SVC), this paper introduces an innovative SVC-GF scheme. This scheme redefines joint user activity detection (UAD) and data decoding as a bifurcated sparse recovery issue. A novel block refinement orthogonal matching pursuit and multipath matching pursuit (BROMP-MMP) algorithm is designed to resolve this intricate problem. Simulation results demonstrate that the proposed SVC-GF scheme outperforms traditional C-GF and NC-GF schemes in terms of probability of successful detection and average block error rate (BLER) performance, heralding a paradigm shift for massive GF access.

Orthogonal time frequency space (OTFS) modulation has become an effective technique for high-mobility scenarios. However, its performance could be severely degraded due to channel aging caused by user mobility and high processing latency. In this paper, an integrated scheme of uplink (UL) channel estimation and downlink (DL) channel prediction is proposed to alleviate channel aging in time division duplex (TDD) multi-input multi-output (MIMO) OTFS systems. Specifically, first, an iterative data-aided channel estimation scheme is proposed to accurately acquire UL channels with the aid of specifically designed frame pattern. Then the discrete prolate spheroidal basis expansion model (DPS-BEM) is used to model the time-varying UL channel estimates, and the dynamic DPS-BEM coefficients are fitted by a set of orthogonal polynomials. A channel predictor is derived to predict DL channels for all antenna pairs and paths by iteratively extrapolating the fitting coefficients. Simulation results verify that the proposed scheme outperforms the existing schemes in terms of normalized mean square error of channel prediction and DL sum-rate.

Pilot contamination incurred by strong inter-cell interference seriously degrades the performance of channel estimation in massive multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We propose an independent component analysis (ICA) and sparse recovery algorithm based semi-blind channel estimation scheme, referred to as sparse ICA (SICA), for multi-cell massive MIMO-OFDM systems, which does not require any prior information of intercell interference and therefore is more practical. The proposed SICA scheme enables accurate channel estimation by exploiting both the high-order statistics of the received signal and channel sparsity in angle domain. The SICA scheme performs in a semi-blind manner as it is much more robust against pilot overhead than the previous approaches, and requires only one OFDM symbol as pilot to achieve a superior normalized mean square error of channel estimation. Furthermore, the complexity required by SICA is much lower than that required by the previous work, thanks to the negligible complexity of interference sources number estimation based on sparse recovery algorithm.

In this paper, by utilizing the temporal correlation of wireless channels, a hierarchical basis expansion model (HBEM) based estimation scheme is proposed for orthogonal frequency division multiplexing systems over doubly selective channel, where the complex exponential basis expansion model (CE-BEM) is used to extract the channel impulse response and the discrete Legendre polynomials BEM is used to refine the CE-BEM coefficients to improve the performance of channel estimation. We design a non-periodic sparse pilot pattern, and hence only scarce subcarriers of a small number of pilots are required for channel estimation, resulting a training overhead reduction of around 50% over the previous CE-BEM based schemes. A block-based signal space matching pursuit algorithm is proposed to enhance the estimation accuracy of CE-BEM coefficients. Furthermore, the proposed HBEM scheme enables a reduction in the number of estimated CE-BEM coefficients by more than 50%, compared to the previous work. A lower bound on the mean square error (MSE) of the proposed HBEM scheme is derived. Simulation results show that the proposed HBEM scheme significantly outperforms the previous CE-BEM based schemes in terms of MSE of channel estimation and bit error rate.