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This paper investigates a symbiotic unmanned aerial vehicle (UAV)-assisted intelligent reflecting surface (IRS) radio system, where the UAV is leveraged to help the IRS reflect its own signals to the base station, and meanwhile enhance the UAV transmission by passive beamforming at the IRS. First, we consider the weighted sum bit error rate (BER) minimization problem among all IRSs by jointly optimizing the UAV trajectory, IRS phase shift matrix, and IRS scheduling, subject to the minimum primary rate requirements. To tackle this complicated problem, a relaxation-based algorithm is proposed. We prove that the converged relaxation scheduling variables are binary, which means that no reconstruct strategy is needed, and thus the UAV rate constraints are automatically satisfied. Second, we consider the fairness BER optimization problem. We find that the relaxation-based method cannot solve this fairness BER problem since the minimum primary rate requirements may not be satisfied by the binary reconstruction operation. To address this issue, we first transform the binary constraints into a series of equivalent equality constraints. Then, a penalty-based algorithm is proposed to obtain a suboptimal solution. Numerical results are provided to evaluate the performance of the proposed designs under different setups, as compared with benchmarks.

This paper investigates unmanned aerial vehicle (UAV)-aided backscatter communication (BackCom) networks, where the UAV is leveraged to help the backscatter device (BD) forward signals to the receiver. Based on the presence or absence of a direct link between BD and receiver, two protocols, namely transmit-backscatter (TB) protocol and transmit-backscatter-relay (TBR) protocol, are proposed to utilize the UAV to assist the BD. In particular, we formulate the system throughput maximization problems for the two protocols by jointly optimizing the time allocation, reflection coefficient and UAV trajectory. Different static/dynamic circuit power consumption models for the two protocols are analyzed. The resulting optimization problems are shown to be non-convex, which are challenging to solve. We first consider the dynamic circuit power consumption model, and decompose the original problems into three sub-problems, namely time allocation optimization with fixed UAV trajectory and reflection coefficient, reflection coefficient optimization with fixed UAV trajectory and time allocation, and UAV trajectory optimization with fixed reflection coefficient and time allocation. Then, an efficient iterative algorithm is proposed for both protocols by leveraging the block coordinate descent method and successive convex approximation (SCA) techniques. In addition, for the static circuit power consumption model, we obtain the optimal time allocation with a given reflection coefficient and UAV trajectory and the optimal reflection coefficient with low computational complexity by using the Lagrangian dual method. Simulation results show that the proposed protocols are able to achieve significant throughput gains over the compared benchmarks.

In this paper, in order to reduce the energy consumption and time of data transmission, the non-orthogonal multiple access (NOMA) and mobile edge caching technologies are jointly considered in mobile edge computing (MEC) networks. As for the cache-assisted vehicular NOMA–MEC networks, a problem of minimizing the energy consumed by vehicles (mobile devices, MDs) is formulated under time and resource constraints, which jointly optimize the computing resource allocation, subchannel selection, device association, offloading and caching decisions. To solve the formulated problem, we develop an effective joint computation offloading and task-caching algorithm based on the twin-delayed deep deterministic policy gradient (TD3) algorithm. Such a TD3-based offloading (TD3O) algorithm includes a designed action transformation (AT) algorithm used for transforming continuous action space into a discrete one. In addition, to solve the formulated problem in a non-iterative manner, an effective heuristic algorithm (HA) is also designed. As for the designed algorithms, we provide some detailed analyses of computation complexity and convergence, and give some meaningful insights through simulation. Simulation results show that the TD3O algorithm could achieve lower local energy consumption than several benchmark algorithms, and HA could achieve lower consumption than the completely offloading algorithm and local execution algorithm. [ABSTRACT FROM AUTHOR]

Cell-free massive multiple input multiple outputs (CF-mMIMO) is considered a promising technology for sixth-generation (6G) telecommunication systems. In the CF-mMIMO system, an extensive array of distributed small base stations (BSs) is deployed across the network, which enables us to facilitate seamless collaboration among BSs. To achieve this goal, the baseband signal from these BSs needs to be transmitted to a central server via fronthaul networks. Due to the large number of BSs, the data that needs to be transmitted is usually huge, which brings severe requirements on fronthaul networks. Time and wavelength division multiplexed passive optical networks (TWDM-PON) can be a potential solution for CF-mMIMO fronthaul due to their large capacity and high flexibility. However, how to efficiently allocate both optical and wireless resources in a TWDM-PON-enabled CF-mMIMO system is still a problem to be addressed. This paper proposes a joint scheduling method of wavelength, antenna, radio unit (RU), and radio resource block (RB) resources in the TWDM-PON-enabled CF-mMIMO system. Furthermore, an integer linear programming (ILP) model for joint resource allocation is proposed to minimize the fronthaul resource occupancy, thereby increasing network scalability. Considering the complexity of the ILP model, two heuristic algorithms are also presented to solve this model. We compare the ILP with heuristic algorithms under different scenarios. Simulation results show that the proposed algorithm can reduce the fronthaul resource occupancy to improve the network scalability of the CF-mMIMO system. [ABSTRACT FROM AUTHOR]

The optimization objective function of sensor management for target identification is commonly established based on information theory indicators such as information gain, discrimination, discrimination gain, and quadratic entropy, which can control the sensors to reduce the overall uncertainty of all targets to be identified but ignores the speed of target being confirmed as identified. Therefore, inspired by the maximum posterior criterion of target identification and the target identification confirmation mechanism, we study a sensor management method that preferentially allocates resources to identifiable targets. Firstly, in the distributed target identification framework based on Bayesian theory, an improved identification probability prediction method that provides feedback the global identification results to local classifiers is proposed, which can improve the accuracy of identification probability prediction. Secondly, an effective sensor management function based on information entropy and expected confidence level is proposed to optimize the identification uncertainty itself rather than its variation, which can increase the priority of targets that satisfy the desired confidence level. In the end, the sensor management for target identification is modeled as a sensor allocation problem, and the optimization objective function based on the effective function is constructed, which can improve the target identification speed. The experimental results show that the correct identification rate of the proposed method is comparable to the methods based on information gain, discrimination, discrimination gain, and quadratic entropy in different scenarios, but the average time to confirm the identification is the shortest. [ABSTRACT FROM AUTHOR]

In this letter, we investigate user-centric access point (AP) selection for cell-free massive multiple-input multiple-output (MIMO) systems. In contrast to existing works, we study this problem from the perspective of game theory and model user-centric AP service cluster formation as a local altruistic game, which is proven to be an exact potential game (EPG) and to have at least one pure Nash equilibrium (NE). Then, an improved maximum non-neighbor-set-based concurrent spatial adaptive play (MNSB-C-SAP) algorithm is proposed to achieve the NE. Simulation results show that the proposed algorithm outperforms existing algorithms in terms of convergence speed and verify the superiority of the game-theoretic AP selection method compared with some existing approaches. [ABSTRACT FROM AUTHOR]

This paper investigates a novel wireless information surveillance scheme assisted by reconfigurable intelligent surface (RIS) beamforming and artificial noise jamming cooperation, aiming at monitoring the information sent by an access point (AP) to a suspicious illegal user (SIU). It is assumed that the AP adopt the fixed maximum ratio transmission (MRT) precoding scheme, which is not affected by the information monitoring party. The goal of this paper is to maximize the effective information monitoring rate by jointly optimizing the RIS phase shifts, the receive beamforming vector of the legitimate receiver (LR), and the transmit beamforming vector along with jamming power of the jamming antenna (JA). The resultant optimization problem is non‐convex, and its optimization variables are highly coupled in the objective function and constraints. To tackle this difficulty, the optimization variables are optimized under the alternate optimization (AO) framework. Especially, the intractable RIS phase shifts are optimized by using Riemannian manifold optimization (RMO) algorithm under the penalty dual decomposition (PDD) framework and the semidefinite relaxation (SDR) technique, respectively. Numerical results verify the effectiveness of the proposed algorithms, and also demonstrate the superiority of the designed wireless information surveillance scheme over other benchmark schemes. [ABSTRACT FROM AUTHOR]

Random interleavers are widely employed in digital communication systems to combat channel fading and burst errors. In applications such as grant-free access by Internet of Things (IoT) devices, accurately identifying a specific irregular interleaving pattern within an interleaver period is vital to both terminal recognition and data recovery. In this work, we investigate effective approaches for random interleaving pattern identification in Reed-Solomon (RS) coded data streams. We first propose an algorithm of low computational complexity to detect positions of code symbols belonging to the same RS codeword group (RSCG) under modest bit error rate. We further develop another low-complexity algorithm to successfully identify random interleaving patterns for RS code symbols within each RS codeword under moderate to high error rate applications. Our theoretical analysis and simulation results corroborate to demonstrate the effectiveness of our algorithms. [ABSTRACT FROM AUTHOR]

It is crucial for legitimate wireless surveillance to enhance the continuity of the monitor. In this letter, we investigate a non-outage probability maximization problem for a jamming-assisted eavesdropping scenario, where MRC and MRT schemes are applied to receive and transmit antennas at a full-duplex monitor, respectively. We first take QoS to a suspicious receiver into non-outage probability to formulate continuous eavesdropping. A closed-form expression of continuous eavesdropping non-outage probability is derived subject to the desired minimum SINR at a suspicious receiver. Then we prove the quasiconcavity of this expression and show the optimal solution to maximize non-outage probability by a simple bisection search. Numerical results demonstrate the continuous eavesdropping performance is optimal when the jamming signals are sent with the optimal power. The number of receive and transmit antennas at the legitimate monitor is positively correlated with the continuity of eavesdropping. [ABSTRACT FROM AUTHOR]

The incorporation of intelligent reflecting surface (IRS) into wireless communication systems can extend the coverage and enhance the data transmission rate. This paper studies the joint transceiver and IRS designs in IRS-assisted multi-input multi-output (MIMO) systems under both perfect channel state information (CSI) and imperfect CSI. Specifically, the transmit precoder, reflection coefficients at the IRS, and receive equalizer are jointly optimized to minimize the data detection mean square error (MSE), subject to the transmission power constraint and the modulus constraints for IRS reflection coefficients. The design problems, non-convex and challenging, are tackled under the framework of alternating optimization. For the design with perfect CSI, we successively optimize the IRS reflection coefficients given the precoder and present the closed-form optimal angle of one reflection coefficient given the others. For the robust design with imperfect CSI, we first average the detection MSE over channel uncertainties by using a generalized statistical CSI error model. Then, the averaged MSE is approximated by a more tractable upper bound. Subsequently, the robust design problem is elaborately transformed into a form similar to the problem with perfect CSI. Numerical results demonstrate the effectiveness of the proposed designs as compared to various benchmark schemes. [ABSTRACT FROM AUTHOR]

The effects of the slot hot blowing of the hollow stator blades on the size of secondary water droplets and the thickness of the water film were experimentally investigated in this paper. The experiment was carried out on the turbine blade cascades in a wet air tunnel with an inlet air wetness fraction of 7.9%, an outlet air velocity of 170 m/s, a slot width of 1.0 mm, and a slot angle of 45 deg to blade surface. The Malvern droplet antiparticle size analyzer was utilized to measure the secondary water droplet size and distribution downstream of the hollow stator blades in the experimental tests. The experimental results show that the maximum diameter and Sauter mean diameter of the secondary water droplets were reduced greatly, and the water droplet size distribution became narrower. The larger blowing pressure difference resulted in the smaller secondary water droplets and the narrower water droplet size distributions. In addition, the efficiency of water separation from the hollow stator blade surfaces was higher for slot on the suction side than that of the pressure side case. Another simplified experimental test was also carried out on the flat plate to investigate the effect of slot hot blowing on the thickness of the water film downstream of the slot. The conductivity probes were used to measure the thickness of the water film downstream and upstream of the blowing slot. The results show that the slot hot blowing reduced the thickness of the water film downstream of the slot, which was affected by the blowing pressure difference and temperature difference between the hot blowing air and the main airflow. In conclusion, the slot hot blowing of the hollow stator blades has reduced the size of the secondary water droplets and secondarily has evaporated a little water film on the blade surfaces. Both effects are beneficial to reduce the erosion damage to the rotor blades. [DOI: 10.1115/1.3030877] Keywords: slot hot blowing, secondary water droplet, hollow stator blade

Power control in massive multiple input multiple output (MIMO) systems is an appealing technique to improve network performance and reliability. The traditional methods to solve such problems are based on the convex optimization theory, which incurs high computational complexity. In contrast, this work leverages deep neural networks to maximize the minimum data rate of the downlink users in the massive MIMO network. Based on the different setups in the practical systems, namely in-distribution case, weakly out-of-distribution case, and out-of-distribution case, we propose three approaches to solve them. Specifically, first, we establish a densely connected neural network for solving the power control problem. Then, for the case where the test data and training data come from the same distributions (the in-distribution case), we use the established neural network to obtain an approximately optimal power control strategy, and propose a self-training-based algorithm to use the unlabeled samples collected in the actual system to further improve the system performance. For the case that the test data and training data come from different distributions and a small amount of labeled target domain data can be obtained (the weakly out-of-distribution case), we propose a pre-training and fine-tuning algorithm to solve the problem. For the issue that we can not obtain the labels of the target domain data (the out-of-distribution case), we transform the problem into a covariate shift problem and propose an algorithm by weighting the loss function. Numerical results demonstrate that the proposed schemes not only match the optimal solution well but also have low online inference complexity. [ABSTRACT FROM AUTHOR]

With increasing traffic congestion and accidents, the Internet of Vehicles (IoV) has become a focus of fifth generation (5G) heterogeneous networks (HetNets) and intelligent transportation systems (ITS). IoV has been studied extensively to improve vehicles, road safety, and efficiency. However, the limited spectrum efficiency and interference caused in the 5G HetNets may require robust technology and architecture. The enabling requirements of IoV in 5G HetNets are elaborated and conceptualized in the article. The deep learning algorithms proposed and used in IoV in 5G HetNets can be achieved to reduce delay and improve transmission reliability. The research progress, advantages, and challenges of deep learning in 5G HetNets IoV are illustrated and explored in detail. Moreover, the overall framework of IoV in 5G HetNets is outlined and compared with the related research, which can become the Foundation for future development of intelligent IoV. [ABSTRACT FROM AUTHOR]