To efficiently implement Terahertz (THz) communications in the 6G era, ultra-massive multiple-input multiple-output (UM-MIMO) technique is considered essential. However, effective wideband THz UM-MIMO transmissions necessitate low-cost yet accurate channel estimation (CE) methods. In this article, we investigate the wideband THz UM-MIMO CE problem under hybrid near- and far-field propagation, molecular absorption, and multi-path reflection. The CE problem is reformulated into a compressed sensing (CS)-aided counterpart (CSCE), exploiting the inherent sparsity of THz UM-MIMO channels to reduce pilot overhead. Our key contributions are: 1) after analyzing the inefficiency of conventional Bayesian learning (BL)-based CSCE frameworks in solving this CE task, we propose a deep unfolding (DU)-aided BL (DUBL) CE algorithm, in which the unfolded expectation-maximization (EM) iteration is implemented through a carefully tailored deep neural network (DNN) architecture; 2) we design a staged offline training procedure equipped with a dedicated loss function to ensure efficient DUBL training; and 3) we conduct a detailed complexity analysis that explicitly quantifies the computational cost of each unrolled layer, thereby characterizing the online inference overhead of the proposed DUBL method. Simulation results demonstrate that the DUBL solution offers substantial THz UM-MIMO CE gains over representative baselines, while complexity comparison highlights its enhanced real-time inference.

To efficiently implement Terahertz (THz) communications in the 6G era, the ultra-massive multiple-input multipleoutput (UM-MIMO) technique is considered an essential building block. However, effective wideband THz UM-MIMO transmissions can never be achieved without pilot-inexpensive yet accurate channel estimation (CE) methods. In this article, we investigate the wideband THz UM-MIMO CE problem, accounting for the hybrid near- and far-field propagation characteristics, molecular absorption, and multi-path reflection. The CE problem is reformulated into a compressed sensing-aided counterpart, leveraging the inherent sparsity of THz UM-MIMO channels to reduce pilot overhead. We harness the power of model-driven deep learning and propose a deep unfolding (DU)-aided Bayesian learning (DUBL) CE algorithm. We tailor the structure of the deep neural network (DNN)-based unfolded expectation-maximization (EM) iteration, aiming to achieve efficient DUBL training performance. Simulation results demonstrate that the DUBL solution can offer substantial THz UM-MIMO CE gains over the considered representative benchmarks.

In this paper, the problem of energy-efficient uncrewed aerial vehicle (UAV)-assisted multi-access task offloading is investigated. In the studied system, several UAVs are deployed as edge servers to cooperatively aid task executions for several energy-limited computation-scarce terrestrial user equipments (UEs). An expected energy efficiency maximization problem is then formulated to jointly optimize UAV trajectories, UE local central processing unit (CPU) clock speeds, UAV-UE associations, time slot slicing, and UE offloading powers. This optimization is subject to practical constraints, including UAV mobility, local computing capabilities, mixed-integer UAV-UE pairing indicators, time slot division, UE transmit power, UAV computational capacities, and information causality. To tackle the multi-dimensional optimization problem under consideration, the duo-staggered perturbed actor-critic with modular networks (DSPAC-MN) solution in a multi-agent deep reinforcement learning (MADRL) setup, is proposed and tailored, after mapping the original problem into a stochastic (Markov) game. Time complexity and communication overhead are analyzed, while convergence performance is discussed. Compared to representative benchmarks, e.g., multi-agent deep deterministic policy gradient (MADDPG) and multi-agent twin-delayed DDPG (MATD3), the proposed DSPAC-MN is validated to be able to achieve the optimal performance of average energy efficiency, while ensuring 100% safe flights.

Channel estimation (CE) is a critical challenge in harnessing the potential of Terahertz (THz) ultra-massive multiple-input multiple-output (UM-MIMO) systems. Sparsity-exploiting compressed sensing (CS)-aided CE (CSCE) can enhance THz UM-MIMO CE performance with affordable pilot overhead. However, the near-field propagation region becomes significant in THz UM-MIMO networks due to the large array aperture and high carrier frequency, leading to a more profound coexistence of near- and far-field radiation patterns. This hybrid-field propagation characteristic renders existing CSCE frameworks ineffective due to the lack of an appropriate sparsifying matrix. In this work, we investigate the uplink THz UM-MIMO CE problem, by developing a practical THz UM-MIMO channel model that incorporates near- and far-field paths, molecular absorption, and reflection attenuation. We propose a dictionary learning (DL)-aided Bayesian THz CSCE solution to achieve accurate, robust and pilot-efficient CE, even in ill-posed scenarios. Specifically, we tailor a batch-delayed online DL (BD-ODL) algorithm to generate an appropriate dictionary for the hybrid-field THz UM-MIMO channel model. Furthermore, we propose a Bayesian learning (BL)-enabled CSCE framework to leverage THz sparsity and utilize the learnt dictionary. To establish a lower bound for the mean squared error (MSE), we derive the Bayesian Cramér-Rao bound (BCRB). We also conduct a complexity analysis to quantify the required computational resources. Numerical results show a significant improvement in normalized MSE (NMSE) performance compared to conventional CE and CSCE baselines, and demonstrate rapid convergence.

In this paper, the problem of energy-efficient unmanned aerial vehicle (UAV)-assisted computation offloading over the Terahertz (THz) spectrum is investigated. In the studied system, several UAVs are deployed as edge servers to aid task executions for multiple energy-limited computation-scarce terrestrial user equipments (UEs). Then, an expected energy efficiency maximization problem is formulated, aiming to jointly optimize UAVs’ trajectories, UEs’ local central processing unit (CPU) clock speeds, UAV-UE associations, time slot slicing, and UEs’ offloading powers. To tackle the considered multi-dimensional optimization problem, the duo-staggered perturbed actor-critic with modular networks (DSPAC-MN) solution in a multi-agent deep reinforcement learning (MADRL) setup, is proposed and tailored, after mapping the original problem into a stochastic (Markov) game. Compared to representative benchmarks in simulations, e.g., multi-agent deep deterministic policy gradient (MADDPG) and multi-agent twin-delayed DDPG (MATD3), the proposed DSPAC-MN can achieve the optimal performance of average energy efficiency, while ensuring 100% safe flights.

Many organizations have embraced the "Lakehouse" data management paradigm, which involves constructing structured data warehouses on top of open, unstructured data lakes. This approach stands in stark contrast to traditional, closed, relational databases and introduces challenges for performance and stability of distributed query processors. Firstly, in large-scale, open Lakehouses with uncurated data, high ingestion rates, external tables, or deeply nested schemas, it is often costly or wasteful to maintain perfect and up-to-date table and column statistics. Secondly, inherently imperfect cardinality estimates with conjunctive predicates, joins and user-defined functions can lead to bad query plans. Thirdly, for the sheer magnitude of data involved, strictly relying on static query plan decisions can result in performance and stability issues such as excessive data movement, substantial disk spillage, or high memory pressure. To address these challenges, this paper presents our design, implementation, evaluation and practice of the Adaptive Query Execution (AQE) framework, which exploits natural execution pipeline breakers in query plans to collect accurate statistics and re-optimize them at runtime for both performance and robustness. In the TPC-DS benchmark, the technique demonstrates up to 25× per query speedup. At Databricks, AQE has been successfully deployed in production for multiple years. It powers billions of queries and ETL jobs to process exabytes of data per day, through key enterprise products such as Databricks Runtime, Databricks SQL, and Delta Live Tables.

In this paper, we investigate a cellular-connected unmanned aerial vehicle (UAV) network, where multiple UAVs receive messages from base stations (BSs) in the down-link, and in the meantime, BSs serve their paired ground user equipments (UEs). To effectively manage inter-cell interferences (ICIs) among UEs due to intense reuse of time-frequency resource block (RB) resource, a first p-tier based RB coordination criterion is adopted. Then, to enhance wireless transmission quality for UAVs while protecting terrestrial UEs from being interfered by ground-to-air (G2A) transmissions, a radio resource management (RRM) problem of joint dynamic RB coordination and time-varying beamforming design is formulated to minimize UAV's ergodic outage duration (EOD). To cope with conventional optimization techniques' inefficiency in solving the formulated RRM problem, a deep reinforcement learning (DRL)-aided solution is proposed, where deep double duelling Q network (D3QN) and twin delayed deep deterministic policy gradient (TD3) are invoked to deal with RB coordination in the discrete action domain and beamforming design in the continuous action regime, respectively. Numerical results illustrate the effectiveness of the proposed hybrid D3QNTD3 algorithm, compared to representative baselines.

In this paper, physical layer security issue for a down-link wireless communication system is examined, composed of an unmanned aerial vehicle (UAV), a legitimate receiver and a passive eavesdropper. The destination is equipped with two antennas and applies the full-duplex (FD) Bob-based jamming (FD-BBJ) strategy to achieve secure transmission. Considering that practical air-to-ground (A2G) channels experience Nakagami-m fading and the FD legitimate receiver is affected by self-interference (SI), closed-form expressions of approximate ergodic achievable secrecy rate (EASR) with help of Gauss-Laguerre Quadrature (GLQ) and compact secrecy outage probability (SOP) expression are derived, respectively. To gain more insights, asymptotic secrecy performance is analysed in the case of extreme total system transmit power, via deriving closed-form expression for asymptotic EASR and compact expression for asymptotic SOP. Numerical results have verified the correctness of our theoretical analysis and proved that the FD-BBJ strategy applied in the UAV-aided wireless communication system can help achieve considerable secrecy performance gain.

Integrating unmanned aerial vehicles (UAVs) into existing cellular networks encounters lots of challenges, among which one of the most striking concerns is how to achieve harmonious coexistence of aerial transceivers, inter alia, UAVs, and terrestrial user equipments (UEs). In this paper, a cellular-connected UAV network is focused, where multiple UAVs receive messages from base stations (BSs) in the down-link, while BSs are serving ground UEs in their cells. For effectively managing inter-cell interferences (ICIs) among UEs due to intense reuse of time-frequency resource block (RB) resource, a first$p$-tier based RB coordination criterion is proposed and adopted. Then, to enhance wireless transmission quality for UAVs while protecting terrestrial UEs from being interfered by ground-to-air (G2A) transmissions, a radio resource management (RRM) problem of joint dynamic RB coordination and time-varying beamforming design minimizing UAV’s ergodic outage duration (EOD) is investigated. To cope with conventional optimization techniques’ inefficiency in solving the formulated RRM problem, a deep reinforcement learning (DRL)-aided solution is initiated, where deep double duelling Q network (D3QN) and twin delayed deep deterministic policy gradient (TD3) are invoked to deal with RB coordination in discrete action domain and beamforming design in continuous action regime, respectively. The hybrid D3QN-TD3 solution is trained via interacting with the considered outer and inner environments in an online centralized manner so that it can then help achieve the suboptimal EOD minimization performance during its offline decentralized exploitation. Simulation results have illustrated the effectiveness of the proposed hybrid D3QN-TD3 algorithm, compared to several representative baselines.

In the presence of Warden’s detection, a maximization problem on transmission throughput from unmanned aerial vehicle (UAV) to legitimate nodes is considered and solved via UAV trajectory design, subject to covert, velocity and mobility constraints. With the building-distribution-based pathloss model and the Warden’s uncertain location model, the formulated optimization problem is challenging to be tackled through standard offline optimization methods. Alternatively, a twin delayed deep deterministic policy gradient (TD3) approach enhanced by multi-step learning and prioritized experience replay (PER) techniques, termed as multi-step TD3-PER, is proposed to help the UAV adaptively select velocity from continuous action space. Numerical results demonstrate the effectiveness of the proposed multi-step TD3-PER solution and showcase the corresponding superiorities against provided baselines.

In cellular-connected unmanned aerial vehicle (UAV) network, a minimization problem on the weighted sum of time cost and expected outage duration is considered. Taking advantage of UAV’s adjustable mobility, an intelligent UAV navigation approach is formulated to achieve the aforementioned optimization goal. Specifically, after mapping the navigation task into a Markov decision process (MDP), a deep reinforcement learning (DRL) solution with novel quantum-inspired experience replay (QiER) framework is proposed to help the UAV find the optimal flying direction within each time slot. Via relating experienced transition’s importance to its associated quantum bit (qubit) and applying Grover-iteration-based amplitude amplification technique, the proposed DRL-QiER solution commits a better trade-off between sampling priority and diversity. Compared to several representative baselines, the effectiveness and supremacy of the proposed DRL-QiER solution are demonstrated and validated in numerical results.

In cellular-connected unmanned aerial vehicle (UAV) network, a minimization problem on the weighted sum of time cost and expected outage duration is considered. Taking advantage of UAV’s adjustable mobility, a UAV navigation approach is formulated to achieve the aforementioned optimization goal. Conventional offline optimization techniques suffer from inefficiency in accomplishing the formulated UAV navigation task due to the practical consideration of local building distribution and directional antenna radiation pattern. Alternatively, after mapping the navigation task into a Markov decision process (MDP), a deep reinforcement learning (DRL)-aided solution is proposed to help the UAV find the optimal flying direction within each time slot, and thus the designed trajectory towards the destination can be generated. To help the DRL agent commit a better trade-off between sampling priority and diversity, a novel quantum-inspired experience replay (QiER) framework is proposed, via relating experienced transition’s importance to its associated quantum bit (qubit) and applying Grover iteration based amplitude amplification technique. Compared to several representative DRL-related and non-learning baselines, the effectiveness and supremacy of the proposed DRL-QiER solution are demonstrated and validated in numerical results.

To enhance transmission performance, privacy level, and energy manipulating efficiency of wireless networks, this article initiates a novel simultaneous wireless information and power transfer (SWIPT) full-duplex (FD) relaying protocol, named harvest-and-opportunistically-relay (HOR). Due to the FD characteristics, the dynamic fluctuation of relay's residual energy is difficult to quantify and track. To solve this problem, the Markov Chain (MC) theory is invoked. Furthermore, to improve the privacy level of the proposed HOR relaying system, covert transmission performance analysis is performed, where closed-form expressions of the optimal detection threshold and minimum detection error probability are derived. Last but not least, with the aid of stationary distribution of the MC, closed-form expression of transmission outage probability is calculated, based on which transmission outage performance is analyzed. Numerical results have validated the correctness of analyses on transmission outage and covertness. The impacts of key system parameters on the performance of transmission outage and covertness are given and discussed. Based on mathematical analysis and numerical results, we showcase that the proposed HOR model can not only reliably enhance the transmission performance via smartly managing residual energy but also efficiently improve the privacy level of the legitimate transmission party via dynamically adjusting the optimal detection threshold.

In Rayleigh fading channels, a novel full-duplex destination jamming with optimal antenna selection (FDJ-OAS) scheme is proposed to improve the secrecy performance of the untrusted relay system with multiple-antenna destination. The traditional half-duplex destination jamming scheme and the nonjamming scheme both combined with OAS are presented to compare with FDJ-OAS. The approximate closed-form expressions of ergodic achievable secrecy rate and optimal power allocation factor for FDJ-OAS are significantly derived in the large-scale antennas analysis. Furthermore, simulation results show that, the analytical curves match well with the simulation curves, and the FDJ-OAS is superior to the other two schemes.

In Rayleigh fading channels, we investigate the secrecy performance of a full-duplex relay secure transmission system. To improve the secrecy capacity of the system and efficiently interfere the interception of the eavesdropper, the multiple-antenna source applies the artificial noise aided precoding (ANP) scheme to broadcast the intended signal and the artificial noise simultaneously, and the decode-and-forward relay operates in full- duplex mode. To improve the received signal-to-noise ratio (SNR), the multiple-antenna destination applies maximum ratio combining (MRC) strategy. In the presence of self-interference at the relay, the approximate closed-form expression of ergodic achievable secrecy rate (EASR) for any values of antenna number and the exact closed-form expression of EASR for large-scale antennas array were derived respectively. Both the theoretical analysis and numerical simulations show that the ANP combined with full-duplex scheme can achieve considerable secrecy performance gain.