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This letter presents the power of deep learning for signal detection in orthogonal frequency division multiplexing with index modulation (OFDM-IM) systems. In this letter, we propose the Y-shaped net-based scheme with fully connected layers (Y-FC) and with bidirectional long short-term memory units (Y-BLSTM) to further optimize the data reception. In the Y-shaped scheme, the active indices and the constellation demapping are learned by two parallel sub-neural networks respectively, the output features are then concatenated and flattened to obtain binary bits. The performance is evaluated under various system parameters, such as modulation methods, number of hidden neural nodes and channel states. Simulation results show that compared with the data-driven signal detection with direct input-output, the proposed scheme achieves several dB of bit-error-rate performance improvement with almost the same running time. [ABSTRACT FROM AUTHOR]

The impulsive noise and the time-varying channel are two major detrimental factors which greatly constrain the performance of underwater acoustic communications (UACs). Utilizing the joint sparsity of the impulsive noise and the channel impulse response, the paper proposes the generalized approximate message passing (GAMP)-based sparse Bayesian learning (SBL) frameworks for joint impulsive noise mitigation and channel estimation and tracking for orthogonal frequency division multiplexing (OFDM) UACs. Firstly, the SBL framework for the joint estimation is employed. To reduce the computational complexity, the GAMP is introduced into the expectation-maximization (EM) algorithm, and a low-complexity GAMP based SBL framework is formulated without performance degradation. To further estimate and track the impulsive noise and channel state information in the slow time-varying scenarios, we propose a novel GAMP-based temporal SBL framework. The factor graph and GAMP are used to achieve the approximated estimation for the posterior statistics of both the channel state information and the impulsive noise. The algorithm formulates the message passing scheduling for the EM algorithm to solve the joint multiple sparse signal recovery problem. Simulations and sea-trial results demonstrate that the proposed algorithms significantly improve the performance in terms of the mean square error of channel estimation, impulsive noise estimation, bit error rate and computational complexity compared with their corresponding SBL-based counterparts. [ABSTRACT FROM AUTHOR]

The ocean has been investigated for centuries across the world, and planning the travel path for vessels in the ocean has become a hot topic in recent decades as the increasing development of worldwide business trading. Planning such suitable paths is often based on big data processing in cybernetics, while not many investigations have been done. We attempt to find the optimal path for vessels in the ocean by proposing an online learning dispatch approach on studying the mission–executing–feedback (MEF) model. The proposed approach explores the ocean subdomain (OS) to achieve the largest average traveling feedback for different vessels. It balances the ocean path by a deep and wide search, and considers adaptation for these vessels. Further, we propose a contextual multiarmed bandit-based algorithm, which provides accurate exploration results with sublinear regret and significantly improves the learning speed. The experimental results show that the proposed MEF approach possesses 90% accuracy gain over random exploration and achieves about 25% accuracy improvement over other contextual bandit models on supporting big data online learning pre-eminently. [ABSTRACT FROM AUTHOR]

Noise fitting plays a key role in underwater acoustic communications. Traditional approximate models can fit global heavy-tail distribution of the impulsive noise with fixed parameters. These models are unable to cover local distributions with arbitrary lengths. In this paper, we propose a generative-adversarial-network-based underwater noise simulator (GUNS), which constructs a deep-neural-network-based generator and a convolutional-neural-network-based discriminator are constructed to learn the heavy-tail distribution of the impulsive noise. Based on the noise collected in the Wuyuanwan Bay, Xiamen, probability distribution function of the underwater acoustic noise simulated by the proposed GUNS performs lower Kullback-Leibler divergence, Jensen-Shannon divergence and mean square error than that employed traditional approximate models. [ABSTRACT FROM AUTHOR]

For classification of underwater acoustic signals, we propose a novel sparse anisotropic chirplet transform (ACT) to reveal fine time–frequency structures. The signal features in the form of a time–frequency map are fed into a deep convolutional neural network, referred to as a time–frequency feature network (TFFNet), which brings flexibility to signal classification. The TFFNet is based on a novel efficient feature pyramid enhancing feature (EFP) maps by aggregating the context information at different scales. To remove the gridding artifacts on enhanced feature maps, a form of aggregating transformation, a forward feature fusion, is utilized to merge the forward feature maps. Main contributions of this work are a novel sparse ACT, a TFFNet classifier, and an EFP with forward feature fusion. Experimental results demonstrate that the sparse ACT provides a high-resolution time–frequency representation of underwater signals and the TFFNet improves the classification performance compared to known networks and two machine learning methods (random forest and support vector machine with radial basis function kernel) on two real data sets, an underwater acoustic communication signal data set and whale sounds data set. [ABSTRACT FROM AUTHOR]

Multiple features can be extracted from time–frequency representation of signals for the purpose of acoustic event detection. However, many underwater acoustic signals are formed by multiple events (impulsive and tonal), which generates difficulty on the high-resolution TFR for each component. For the characterization of such different events, we propose an anisotropic chirplet transform to achieve the TFR with high energy concentration. Such transform applies a time–frequency varying Gaussian window to compensate the energy of each component while suppressing unwanted noise. Using a set of directional chirplet ridges from the obtained TFR, a structure-split-merge algorithm is designed to reconstruct a multimodal sparse representation, which provides instantaneous frequency and time features. Specifically, a pulsed-to-tonal ratio, based on these features, is computed to distinguish two acoustic signals. The presented method is validated using shallow water experimental underwater acoustic communication signals and large sequences of harmonics and pulsed bursts from common whales. [ABSTRACT FROM AUTHOR]

In this letter, we propose a new orthogonal coding-based modulation (OCBM) scheme, in which all available transmitting subcarriers and antennas are activated at a time instant to transmit the data constellation symbols, and the indices of the activated subcarriers and antennas are also harnessed to carry information bits. Our OCBM especially addresses a novel scheme in spatial modulation (SM) domain via employing virtual subarray, and improves the spectral efficiency compared with the existing orthogonal frequency division multiplexing (OFDM)-spread spectrum (SS), index modulation (IM)-OFDM-SS and IM-OFDM based SM (the number of transmit antennas is more than 2). In addition, our investigated detection scheme of the OCBM reduces the complexity of the receiver design by using the unique behavior of the orthogonal code, especially for the detection of the transmitted antenna index bits. Our simulated experiments have been carried out to test the performance of the proposed scheme, and demonstrate the benefits of our suggested approach. [ABSTRACT FROM AUTHOR]

This paper presents an efficient double-layer ant colony optimization algorithm, called DL-ACO, for autonomous robot navigation. This DL-ACO consists of two ant colony algorithms that run independently and successively. First, a parallel elite ant colony optimization method is proposed to generate an initial collision-free path in a complex map, and then, we apply a path improvement algorithm called turning point optimization algorithm, in which the initial path is optimized in terms of length, smoothness, and safety. Besides, a piecewise B-spline path smoother is presented for easier tracking control of the mobile robot. Our method is tested by simulations and compared with other path planning algorithms. The results show that our method can generate better collision-free path efficiently and consistently, which demonstrates the effectiveness of the proposed algorithm. Furthermore, its performance is validated by experiments in indoor and outdoor environments. [ABSTRACT FROM AUTHOR]

In underwater acoustic (UWA) communications, Doppler estimation is one of the major stages in a receiver. Two Doppler estimation methods are often used: the cross-ambiguity function (CAF) method and the single-branch autocorrelation (SBA) method. The former results in accurate estimation but with a high complexity, whereas the latter is less complicated but also less accurate. In this paper, we propose and investigate a multibranch autocorrelation (MBA) Doppler estimation method. The proposed method can be used in communication systems with periodically transmitted pilot signals or repetitive data transmission. For comparison of the Doppler estimation methods, we investigate an orthogonal frequency-division multiplexing (OFDM) communication system in multiple dynamic scenarios using the Waymark simulator, allowing virtual UWA signal transmission between moving transmitter and receiver. For the comparison, we also use the OFDM signals recorded in a sea trial. The comparison shows that the receiver with the proposed MBA Doppler estimation method outperforms the receiver with the SBA method and its detection performance is close to that of the receiver with the CAF method, but with a significantly lower complexity. [ABSTRACT FROM AUTHOR]

Underwater acoustical communication channels are characterized by the spreading of received signals in space (direction of arrival) and in time (delay). The spread is often limited to a small number of space-time clusters. In this paper, the space-time clustering is exploited in a proposed receiver designed for guard-free orthogonal frequency-division multiplexing with superimposed data and pilot signals. For separation of space clusters, the receiver utilizes a vertical linear array (VLA) of hydrophones, whereas for combining delay-spread signals within a space cluster, a time-domain equalizer is used. We compare a number of space-time processing techniques, including a proposed reduced-complexity spatial filter, and show that techniques exploiting the space-time clustering demonstrate an improved detection performance. The comparison is done using signals transmitted by a moving transducer, and recorded on a 14-element nonuniform VLA in sea trials at distances of 46 and 105 km. [ABSTRACT FROM PUBLISHER]