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Supporting high mobility is an important feature of the 6G mobile communication systems, which puts forward a great challenge for conventional orthogonal frequency division multiplexing (OFDM) systems due to the severe Doppler effects. Recently, orthogonal time frequency space (OTFS) has been proposed as a promising waveform for high-mobility scenarios, due to its excellent capability of exploiting delay-Doppler (DD) diversity to significantly improve the system performance. However, in multicell OTFS systems, intercell interference (ICI) is one of the major challenges, especially for cell-edge users (CEUs). In this article, the impacts of different types of interference on OTFS systems are first studied. Then, interference coordination and cooperative communications-based multicell OTFS systems are studied to overcome ICI, respectively. Next, simulation results are presented for the performance evaluation of these schemes. Finally, the main challenges are discussed and future research directions for multicell OTFS systems are highlighted. The performance evaluations demonstrate the effectiveness of interference coordination and cooperative communications on multicell OTFS systems. [ABSTRACT FROM AUTHOR]

This paper proposes an off-grid channel estimation scheme for orthogonal time-frequency space (OTFS) systems adopting the sparse Bayesian learning (SBL) framework. To avoid channel spreading caused by the fractional delay and Doppler shifts and to fully exploit the channel sparsity in the delay-Doppler (DD) domain, we estimate the original DD domain channel response rather than the effective DD domain channel response as commonly adopted in the literature. OTFS channel estimation is firstly formulated as a one-dimensional (1D) off-grid sparse signal recovery (SSR) problem based on a virtual sampling grid defined in the DD space, where the on-grid and off-grid components of the delay and Doppler shifts are separated for estimation. In particular, the on-grid components of the delay and Doppler shifts are jointly determined by the entry indices with significant values in the recovered sparse vector. Then, the corresponding off-grid components are modeled as hyper-parameters in the proposed SBL framework, which can be estimated via the expectation-maximization method. To strike a balance between channel estimation performance and computational complexity, we further propose a two-dimensional (2D) off-grid SSR problem via decoupling the delay and Doppler shift estimations. In our developed 1D and 2D off-grid SBL-based channel estimation algorithms, the hyper-parameters are updated alternatively for computing the conditional posterior distribution of channels, which can be exploited to reconstruct the effective DD domain channel. Compared with the 1D method, the proposed 2D method enjoys a much lower computational complexity while only suffers a slight performance degradation. Simulation results verify the superior performance of the proposed channel estimation schemes over state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

To distinguish underwater images from natural images is one of the challenge of collecting and generation of underwater image data. Common image classification and recognition models classify the objects in an image depending on the saliency while suppressing the background. In this article, an inception–attention network (IA-Net), a convolutional neural network (CNN)-based model to classify the underwater images from natural images is reported, in which an inception–attention (I-A) module is constructed to simulate the visual correlation mechanism of classifying images taken from special environments such as fog, nighttime and under water. It is illustrated that the context background is as important as the salient object when understanding the underwater images. We executed experiments on a data set, which consists of 4000 underwater images and 5000 nonunderwater images, and demonstrate that the proposed IA-Net achieves an accuracy of 99.3 $\%$ on underwater image classification, which is significantly better than classical image classification networks, such as AlexNet, InceptionV3, and ResNet. In addition, the comparative experiments prove that the IA-Net is superior to other networks when distinguishing underwater images from foggy, nighttime images and fish images taken in nonunderwater environments, although these images have indistinguishable characteristics with underwater images. Moreover, we demonstrate the I-A structure we proposed can be used to boost the performance of the existing object recognition networks. By substituting the inception module with the I-A module, the Inception-ResNetV2 network achieves a 10.7 $\%$ top-1 error rate on the subset of ILSVRC-2012, which further illustrates the effectiveness of the correlation between the image background and subjective perception in improving the performance of the visual analysis tasks. [ABSTRACT FROM AUTHOR]

Wireless powered mobile edge computing (WP-MEC) has been widely studied as a promising technology to liberate wireless terminals from the computation-intensive and energy-consuming tasks. This article considers a WP-MEC system consisting of multiple base stations (BSs) and mobile devices (MDs), where the MDs offload tasks to the BSs for computational resources and the BSs charge the MDs using wireless power transfer (WPT). In practice, each BS and MD are equipped with a task buffer with limited size and a battery with limited capacity. First, we develop a time slotted WP-MEC system with task and energy queuing dynamics to study long-term system performance under time-varying fading channels and stochastic task and energy arrivals. Second, we propose a dynamic throughput maximum (DTM) algorithm based on perturbed Lyapunov optimization to maximize the system throughput under task and energy queue stability constraints, by optimizing the allocation of communication, computation, and energy resources. For the DTM algorithm, we characterize a throughput-backlog trade-off of [ $\mathcal {O}(1/V)$ O (1 / V) , $\mathcal {O}(V)$ O (V) ] to indicate that the system throughput goes up as the queue backlog increases, where $V$ V is a control parameter between the system throughput and the queue backlog. However, we find that, as $V$ V goes large, the system throughput can be pushed arbitrarily close to the optimum at the cost of linearly increasing queue backlog (i.e., $\mathcal {O}(V)$ O (V) ). To reduce the cost, we further develop an improved dynamic throughput maximum (IDTM) algorithm, and verify that the IDTM algorithm can achieve a trade-off of [ $\mathcal {O}(1/V)$ O (1 / V) , $\mathcal {O}((\log (V))^2)$ O ((log (V)) 2) ] between the system throughput and the queue backlog. The simulation results demonstrate that IDTM retains close system throughput to DTM with only $\mathcal {O}((\log (V))^2)$ O ((log (V)) 2) queue backlog. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel integrated sensing and communication (ISAC) transmission framework based on the spatially spread orthogonal time frequency space (SS-OTFS) modulation by considering the fact that communication channel strengths cannot be directly obtained from radar sensing. We first propose the concept of SS-OTFS modulation, where the key novelty is the angular domain discretization enabled by the spatial spreading/de-spreading. This discretization gives rise to simple and insightful effective models for both radar sensing and communication, which results in simplified designs for the related estimation and detection problems. In particular, we design simple beam tracking, angle estimation, and power allocation schemes for radar sensing, by utilizing the special structure of the effective radar sensing matrix. Meanwhile, we provide a detailed analysis on the pair-wise error probability (PEP) for communication, which unveils the key conditions for both precoding and power allocation designs for communication. Based on those conditions, we design a symbol-wise precoding scheme for communication based only on the delay, Doppler, and angle estimates from radar sensing, without the a priori knowledge of the communication channel fading coefficients, and also propose a suitable power allocation. Furthermore, we notice that radar sensing and communication requires different power allocations. Therefore, we discuss the performances of both the radar sensing and communication with different power allocations and show that the power allocation should be designed leaning towards radar sensing in practical scenarios. The effectiveness of the proposed ISAC transmission framework is verified by our numerical results, which also agree with our analysis and discussions. [ABSTRACT FROM AUTHOR]

Recently proposed orthogonal time frequency space (OTFS) modulation has been considered as a promising candidate for accommodating various emerging communication and sensing applications in high-mobility environments. In this paper, we propose a novel cross domain iterative detection algorithm to enhance the error performance of OTFS modulation. Different from conventional OTFS detection methods, the proposed algorithm applies basic estimation/detection approaches to both the time domain and delay-Doppler (DD) domain and iteratively updates the extrinsic information from two domains with the unitary transformation. In doing so, the proposed algorithm exploits the time domain channel sparsity and the DD domain symbol constellation constraints. We evaluate the estimation/detection error variance in each domain for each iteration and derive the state evolution to investigate the detection error performance. We show that the performance gain due to iterations comes from the non-Gaussian constellation constraint in the DD domain. More importantly, we prove that the proposed algorithm can indeed converge and, in the convergence, the proposed algorithm can achieve almost the same error performance as the maximum-likelihood sequence detection even in the presence of fractional Doppler shifts. Furthermore, the computational complexity associated with the domain transformation is low, thanks to the structure of the discrete Fourier transform (DFT) kernel. Simulation results are consistent with our analysis and demonstrate a significant performance improvement compared to conventional OTFS detection methods. [ABSTRACT FROM AUTHOR]

Faster-than-Nyquist (FTN) signaling aided non-orthogonal multiple access (NOMA) is conceived and its achievable rate is quantified in the presence of random link delays of the different users. We reveal that exploiting the link delays may potentially lead to a signal-to-interference-plus-noise ratio (SINR) gain, while transmitting the data symbols at FTN rates has the potential of increasing the degree-of-freedom (DoF). We then unveil the fundamental trade-off between the SINR and DoF. In particular, at a sufficiently high symbol rate, the SINR gain vanishes while the DoF gain achieves its maximum, where the achievable rate is almost $(1+\beta)$ times higher than that of the conventional synchronous NOMA transmission in the high signal-to-noise ratio (SNR) regime, with $\beta $ being the roll-off factor of the signaling pulse. Our simulation results verify our analysis and demonstrate considerable rate improvements over the conventional power-domain NOMA scheme. [ABSTRACT FROM AUTHOR]

In recent years, convolutional regression trackers have shown increasing attention for visual object tracking due to their favorable performance and easy implementation. However, most of them are restricted to features from a certain layer and hardly benefit from temporal spatial information, which limits the potential to significant appearance changes. In this work, we go beyond the traditional deep regression trackers and build a novel twofold tracking network, which exploits rich hierarchical features and incorporates both temporal and spatial information to boost the tracking performance. The proposed network is composed of two streams, i.e., an appearance stream and a semantic stream, each stream is independently learned from different convolutional layers. Specially, we propose temporal and spatial mechanism for robust target representation by considering historical information in previous frames as well as spatial information. By design, the proposed twofold convolutional regression tracking network with spatial and temporal mechanism can better tolerate the target appearance changes and improve the tracking accuracy. Extensive experimental results on the benchmarks OTB-2015, Temple-Color, UAV123, and VOT-2018 demonstrate the effectiveness of our method, as compared with a number of state-of-the-art trackers. [ABSTRACT FROM AUTHOR]

Over the last decade, simultaneous wireless information and power transfer (SWIPT) has become a practical and promising solution for connecting and recharging battery-limited devices due to significant advances in low-power electronics technology and wireless communications techniques. To realize the promised potentials, advanced resource allocation design plays a decisive role in revealing, understanding, and exploiting the intrinsic rate–energy tradeoff capitalizing on the dual use of radio frequency (RF) signals for wireless charging and communication. In this article, we provide a comprehensive tutorial overview of SWIPT from the perspective of resource allocation design. The fundamental concepts, system architectures, and RF energy harvesting (EH) models are introduced. In particular, three commonly adopted EH models, namely, the linear EH model, the nonlinear saturation EH model, and the nonlinear circuit-based EH model, are characterized and discussed. Then, for a typical wireless system setup, we establish a generalized resource allocation design framework that subsumes conventional resource allocation design problems as special cases. Subsequently, we elaborate on relevant tools from optimization theory and exploit them for solving representative resource allocation design problems for SWIPT systems with and without perfect channel state information (CSI) available at the transmitter, respectively. The associated technical challenges and insights are also highlighted. Furthermore, we discuss several promising and exciting future research directions for resource allocation design for SWIPT systems intertwined with cutting-edge communication technologies, such as intelligent reflecting surfaces, unmanned aerial vehicles, mobile edge computing, federated learning, and machine learning. [ABSTRACT FROM AUTHOR]

Orthogonal time frequency space (OTFS) modulation is a promising candidate for supporting reliable information transmission in high-mobility vehicular networks. In this paper, we consider the employment of the integrated (radar) sensing and communication (ISAC) technique for assisting OTFS transmission in both uplink and downlink vehicular communication systems. Benefiting from the OTFS-ISAC signals, the roadside unit (RSU) is capable of simultaneously transmitting downlink information to the vehicles and estimating the sensing parameters of vehicles, e.g., locations and speeds, based on the reflected echoes. Then, relying on the estimated kinematic parameters of vehicles, the RSU can construct the topology of the vehicular network that enables the prediction of the vehicle states in the following time instant. Consequently, the RSU can effectively formulate the transmit downlink beamformers according to the predicted parameters to counteract the channel adversity such that the vehicles can directly detect the information without the need of performing channel estimation. As for the uplink transmission, the RSU can infer the delays and Dopplers associated with different channel paths based on the aforementioned dynamic topology of the vehicular network. Thus, inserting guard space as in conventional methods are not needed for uplink channel estimation which removes the required training overhead. Finally, an efficient uplink detector is proposed by taking into account the channel estimation uncertainty. Through numerical simulations, we demonstrate the benefits of the proposed ISAC-assisted OTFS transmission scheme. [ABSTRACT FROM AUTHOR]