One of the major research topics in unmanned aerial vehicle (UAV) collaborative control systems is the problem of multi-UAV target assignment and path planning (MUTAPP). It is a complicated optimization problem in which target assignment and path planning are solved separately. However, recalculation of the optimal results is too slow for real-time operations in dynamic environments because of the large number of calculations required. In this paper, we propose an artificial intelligence method named simultaneous target assignment and path planning (STAPP) based on a multi-agent deep deterministic policy gradient (MADDPG) algorithm, which is a type of multi-agent reinforcement learning algorithm. In STAPP, the MUTAPP problem is first constructed as a multi-agent system. Then, the MADDPG framework is used to train the system to solve target assignment and path planning simultaneously according to a corresponding reward structure. The proposed system can deal with dynamic environments effectively as its execution only requires the locations of the UAVs, targets, and threat areas. Real-time performance can be guaranteed as the neural network used in the system is simple. In addition, we develop a technique to improve the training effect and use experiments to demonstrate the effectiveness of our method.

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Joint Optimization of Multi-UAV Target Assignment and Path Planning Based on Multi-Agent Reinforcement Learning

Orthogonal frequency division multiplexing (OFDM) is a superior technology for the high-speed data rate of wire-line and wireless communication systems. The OFDM has many advantages over other techniques such as its high capacity and immunity against multipath fading channels. However, one of the main drawbacks of the OFDM system is the high-peak-to-average power ratio (PAPR) that leads the system to produce in-band distortion and out-of-band radiation because of the non-linearity of the high-power amplifiers. Therefore, numerous techniques have been proposed to overcome the PAPR problem such as selective mapping, partial transmit sequence (PTS), clipping, and nonlinear companding. In this paper, the PTS technique was analytically reviewed as one of the important methods to reduce the high PAPR problem. The PAPR performance and the computational complexity level are discussed in terms of modifying the PTS technique in the frequency domain, time domain and modulation stage (inverse fast Fourier transform block). Moreover, the numerical statistic comparison of the current modified-PTS methods is introduced, and the criteria for selecting the suitable modified-PTS method in the OFDM system are also given. The simulation and the numerical calculations results show that the rows exchange-interleaving PTS scheme is the best method for reducing the PAPR value with low complexly in the frequency domain, and the cooperative PTS method is the best among the modulation stage methods, while the cyclic shift sequence PTS method achieves the superior performance in PAPR reduction and computational complexity for the time domain methods.

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A Review of Partial Transmit Sequence for PAPR Reduction in the OFDM Systems

Orthogonal frequency division multiplexing (OFDM) is a modulation technique which is now used in most new and emerging broadband wired and wireless communication systems because it is an effective solution to intersymbol interference caused by a dispersive channel. Very recently a number of researchers have shown that OFDM is also a promising technology for optical communications. This paper gives a tutorial overview of OFDM highlighting the aspects that are likely to be important in optical applications. To achieve good performance in optical systems OFDM must be adapted in various ways. The constraints imposed by single mode optical fiber, multimode optical fiber and optical wireless are discussed and the new forms of optical OFDM which have been developed are outlined. The main drawbacks of OFDM are its high peak to average power ratio and its sensitivity to phase noise and frequency offset. The impairments that these cause are described and their implications for optical systems discussed.

OFDM for Optical Communications

Deploying unmanned aerial vehicle (UAV) swarms in delivery systems are still in its infancy with regard to the technology, safety, and aviation rules and regulations. Optimal use of UAVs in dynamic environments is important in many aspects, e.g., increasing efficacy and reducing the air traffic, resulting in a safer environment, and it requires new techniques and robust approaches based on the capabilities of UAVs and constraints. This paper analyzes several delivery schemes within a platform, such as delivery with and without using air highways and delivery using a hybrid scheme along with several delivery methods (i.e., optimal, premium, and first-in first-out) to explore the use of UAV swarms as part of the logistics operations. In this platform, a dimension reduction technique, “dynamic multiple assignments in multi-dimensional space,” and several other new techniques along with Hungarian and cross-entropy Monte Carlo techniques are forged together to assign tasks and plan 3D routes dynamically. This particular approach is performed in such a way that UAV swarms in several warehouses are deployed optimally given the delivery scheme, method, and constraints. Several scenarios are tested on the simulator using small and big data sets. The results show that the distribution and the characteristics of data sets and constraints affect the decision on choosing the optimal delivery scheme and the method. The findings are expected to guide the aviation authorities in their decisions before dictating rules and regulations regarding effective, efficient, and safe use of UAVs. Furthermore, the companies that produce UAVs are going to take the demonstrated results into account for their functional design of UAVs along with other companies that aim to deliver their products using UAVs. Additionally, private industries, logistics operators, and municipalities are expected to benefit from the potential adoption of the simulator in strategic decisions before embarking on the practical implementation of UAV delivery systems.

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Analysis and Optimization of Unmanned Aerial Vehicle Swarms in Logistics: An Intelligent Delivery Platform

Radio-over-free-space-optics (Ro-FSO) technology may pave the way towards a ubiquitous platform for seamless integration of radio and optical networks without expensive optical fiber cabling. In this paper, to increase the capacity of Ro-FSO, mode division multiplexing (MDM) of two modes has been capitalized in a three-channel WDM system spaced by 1 nm over a FSO link of 80 km, resulting in a 120 Gbps six-channel Ro-FSO system. The SNR and received power of MDM of two Laguerre-Gaussian modes LG00 and LG01 is compared with respect to MDM of two transverse donut modes. The performance of four-level quadrature amplitude modulation (QAM) for orthogonal frequency division multiplexing (OFDM) of radio subcarriers in the WDM-MDMs system is investigated for mitigation of frequency-selective fading under strong atmospheric turbulence.

Comparison of Laguerre-Gaussian and Donut modes for MDM-WDM in OFDM-Ro-FSO transmission system

In this paper, we propose a novel method to address the nighttime single image dehazing problem. Estimation of the ambient illumination map and transmission map are the key steps of modern dehazing approaches. For hazy scenes at night, ambient illumination is usually not globally isotropic as a nighttime scene typically contains multiple light sources. Frequently, Light source regions and non-light source regions exhibit distinct color features. However, existing nighttime dehazing methods have been attempting to process these two regions based on identical prior assumptions. Moreover, the commonly-used local maximum pixel method tends to over-estimate the ambient illumination. These two drawbacks result in color distortions and halo artifacts around the light source regions in the output images. In this work, we present a pixel-wise alpha blending method for estimating the transmission map, where the transmissions estimated from dark channel prior (non-light source region) and the proposed bright channel prior (light source region) are effectively blended into one transmission map guided by a brightness-aware weights map. Based on the Retinex theory, a channel difference guided filtering method is proposed to estimate the ambient illumination, which produces a spatially variant low-frequency passband that selectively retains the high-frequency edge details. Extensive experiments on the benchmarks demonstrate that our method outperforms the state-of-the-art methods for nighttime image dehazing, especially in terms of color consistency and halo artifacts reduction in the dehazed images.

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Nighttime Single Image Dehazing via Pixel-Wise Alpha Blending

The nonlinearity of light emitting diodes (LED) has restricted the bit error rate (BER) performance of visible light communications (VLC). In this paper, we propose model-driven deep learning (DL) approach using an autoencoder (AE) network to mitigate the LED nonlinearity for orthogonal frequency division multiplexing (OFDM)-based VLC systems. Different from the conventional fully data-driven AE, the communication domain knowledge is well incorporated in the proposed scheme for the design of network architecture and training cost function. First, a deep neural network (DNN) combined with discrete Fourier transform spreading (DFT-S) is adopted at the transmitter to map the binary data into complex I-Q symbols for each OFDM subcarrier. Then, at the receiver, we divide the symbol demapping module into two subnets in terms of nonlinearity compensation and signal detection, where each subnet is comprised of a DNN. Finally, both the autocorrelation of the learned mapping symbols and the mean square error of demapping symbols are taken into account simultaneously by the cost function for network training. With this approach, the LED nonlinearity and the interference introduced by the multipath channel can be effectively mitigated. The simulation results show that the proposed scheme exhibits better BER performance than some existing methods and further accelerates the training speed, which demonstrates the prospective and validity of DL in the VLC system.

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A Model-Driven Deep Learning Method for LED Nonlinearity Mitigation in OFDM-Based Optical Communications

In this paper, we propose a low-complexity peak-to-average power ratio (PAPR) reduction scheme that combines both multi-band (MB)-Hadamard precoding and clipping for the optical orthogonal frequency division multiplexing (OFDM) systems. Approximations of PAPR distribution for baseband OFDM signals are analyzed and the effective signal-to-noise ratio (SNR) of the whole transmission link considering both the clipping and quantization noise are presented. After that, the MB-Hadamard precoding is adopted to compresses the peak signals, minimizing the contaminating influence of signal distortions in subsequent clipping operations. In addition, the received SNRs and bit error rate (BER) are calculated theoretically for each split sub-band. The 50-m step-index polymer optical fiber (POF) transmission is adopted as a special case to both evaluate the system performance and then compare the proposed scheme with other well-known PAPR reduction techniques. With this scheme, the PAPR is reduced effectively and the system’s BER performance is improved significantly. The results show that the proposed scheme with appropriate number of sub-bands precoding provides favorable trade-offs among PAPR reduction, power spectral density, transmission rate, BER, and computational complexity, which demonstrates its feasibility and validity.

https://www.mdpi.com/2079-9292/7/2/11/pdf

Low-Complexity PAPR Reduction Scheme Combining Multi-Band Hadamard Precoding and Clipping in OFDM-Based Optical Communications

Nonlinear post equalization (NPE) based on Volterra series (VS) is considered as an effective way to mitigate the severe light emitting diode (LED) nonlinearity and multipath effect in a visible light communication (VLC) system. However, it is restricted by kernel complexity in practical applications. In this paper, we formulate the kernel extraction of VS-based NPE to be a sparse recovery problem, and propose an efficient sparsity-aware approach, using combined sparse Bayesian learning (SBL) and Kalman filtering (KF) to extract the active VS kernels and thus to reduce the redundancy of NPE. First, from the view of probability, a Bayesian strategy is applied to select the dominant regressors from the original measurement matrices by exploiting the learning of hyperparameters, which encourages the sparseness of VS kernels with an imposed prior. Then, based on the specified regression matrix, the improved KF iteration is used in the estimation of the kernel coefficients to overcome the system instability in a dynamic noise environment. With this methodology, the active VS kernels can be effectively extracted and the corresponding kernel quantity is significantly reduced at least by 65%. Moreover, the system can still work effectively in the case of a lower size of training samples. The simulation results show that the proposed scheme is beneficial to both the nonlinearity compensation and multipath interference mitigation, and exhibits better overall performance than some existing methods, which demonstrates the potential and validity of kernel extraction in VS-based NPE.

Adaptive Nonlinear Equalization Combining Sparse Bayesian Learning and Kalman Filtering for Visible Light Communications

Existing algorithms on path planning with obstacles for unmanned aerial vehicles (UAVs) suffer from high computational complexity and unpredictability when the considered scenario is complicated. In this paper, we propose a novel path-planning algorithm for UAVs, which relies on continuously updating virtual regional field and its local gradients. The information of target regions and obstacles is incorporated in a virtual regional field. The algorithm circumvents the large number of variables to be optimized, and does not rely on any black boxes with unpredictable outputs. Real data show that the proposed algorithm can design a path with high coverage rate of the target region in a certain time duration, and guides the UAV to bypass the obstacles. The approach based on the regional field provides an option for low-cost hardwares, and reveals insights into the problem of path planning.

Pu Miao

Papers

Nighttime Single Image Dehazing via Pixel-Wise Alpha Blending

A Model-Driven Deep Learning Method for LED Nonlinearity Mitigation in OFDM-Based Optical Communications

Low-Complexity PAPR Reduction Scheme Combining Multi-Band Hadamard Precoding and Clipping in OFDM-Based Optical Communications

Adaptive Nonlinear Equalization Combining Sparse Bayesian Learning and Kalman Filtering for Visible Light Communications

Low-Complexity Path Planning Algorithm for Unmanned Aerial Vehicles in Complicated Scenarios