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Abstract To support communication channels with non‐stationary properties, the pilots are usually configured for the channel worst case in order to ensure the accuracy of tracking the channel, which inevitably causes a waste of resources. To solve this issue, first, a flexible two‐dimensional pilot muting mechanism is proposed by exploiting the time and frequency diversity of the pilot sequence of the resource grid for non‐stationary channel fading environment. Then a super‐resolution based non‐stationary channel recovery scheme for data channel recovery is further proposed for two‐dimensional pilot muting, and the energy efficiency of a resource block is also maximized. Numerical results show that the proposed mechanism can bring about as much as 8 dB performance gain in system energy efficiency.

Dual connectivity (DC) is a key technology to improve the throughput of the fifth generation communication networks (5G) system. Due to the limitation of the splitting strategy and transmission delay of different transmission nodes, the out-of-order phenomenon of the packet data convergence protocol (PDCP) layer is bound to occur in the actual DC system. Packet out-of-order in PDCP layer will affect the end-to-end throughput of PDCP layer and there is no effective solution at present. Existing schemes either neglected to consider the transmission rate difference between New Radio (NR) Base Station (BS) and Long Term Evolution-Advanced (LTE-A) BS, or performed unified modeling for queuing dynamics, or did not consider the existence of Xn delay, thus failing to model the out-of-order problem of PDPC layer. In this paper, we modeling PDCP out-of-order into a hierarchical model and continuously reduce the maximum out-of-order depth through recursion. Two traffic splitting mechanisms are proposed based on user equipment (UE) feedback and BS observation. These two low complexity traffic splitting mechanisms both show efficient link resource utilization ratio of more than 90% in static and mobile user scenarios, and are superior to conventional algorithms.

The emergence of Mobile Edge Computing (MEC) provides a near-field tasking platform to meet the latency requirements of the growing number of compute-intensive mobile applications. However, physical memory constraints limit the number of application services that can be loaded on the edge server (ES) simultaneously, and the non-uniform distribution of traffic in the mobile network makes it difficult to fully utilize the resources in the edge network. In addition, for MEC platform operators, how to achieve the expected profit of application service providers (ASP) to attract more ASPs is also important. To address these issues, in this paper we propose an ASP profit-aware solution for jointly optimizing application loading, task allocation, and compute resource allocation across multi-ES, minimizing system latency while maintaining ASP profitability. We first formulate the problem as a long-term stochastic optimization problem with ASP profit constraints, transform it into a single time slot optimization problem using the Lyapunov optimization framework, and then, using the power of genetic algorithms (GA), we propose an online heuristic algorithm to obtain a near-optimal strategy for each time slot. Simulation results show that our algorithm is effective in reducing system latency in the long term, while demonstrating performance that ensures more ASPs with desired profits.

The commercial deployment of 5G communication networks makes the industry and academia to seriously consider the possible solutions for the next generation. Although the conventional approach indicates that 6G vision and requirements can be figured out by simply multiplying a certain amount of magnitude on top of the previous generations, we argue in this article that 1000 times price reduction from the customer's view point is the key to success. Guided by this vision, we categorize the current candidate technologies in a well organized manner and select AI-assisted intelligent communications as an example to elaborate the drive-force behind. Although it is impossible to identify every detail of 6G during the current time frame, we believe this article will help to eliminate the technical uncertainties and aggregate the efforts towards key breakthrough innovations for 6G.

Text detection/localization, as an important task in computer vision, has witnessed substantial advancements in methodology and performance with convolutional neural networks. However, the vast majority of popular methods use rectangles or quadrangles to describe text regions. These representations have inherent drawbacks, especially relating to dense adjacent text and loose regional text boundaries, which usually cause difficulty detecting arbitrarily shaped text. In this paper, we propose a novel text region representation method, with a robust pipeline, which can precisely detect dense adjacent text instances with arbitrary shapes. We consider a text instance to be composed of an adaptive central text region mask and a corresponding expanding ratio between the central text region and the full text region. More specifically, our pipeline generates adaptive central text regions and corresponding expanding ratios with a proposed training strategy, followed by a new proposed post-processing algorithm which expands central text regions to the complete text instance with the corresponding expanding ratios. We demonstrated that our new text region representation is effective, and that the pipeline can precisely detect closely adjacent text instances of arbitrary shapes. Experimental results on common datasets demonstrate superior performance of our work.

In this paper, targeting at improving the energy efficiency (EE) for Quality-of-Service (QoS)-guaranteed wireless communications, we develop new adaptive modulation and data scheduling algorithms for delay-sensitive bursty data. Assuming a-priori knowledge on data arrivals and latency requirements, the problem is formulated as a mix-integer programming that minimizes the total energy consumption at the transmitter with a non-linear Doherty power amplifier (PA) and non-negligible circuit power. According to the different properties of the PA in different output power regions, we decouple the formulated problem and solve it in two stages. In the first stage, assuming the PA has a linear efficiency, we develop an optimal modulation and data scheduling scheme (MDS) relying on convex relaxation and the resultant optimality conditions. The MDS is able to reveal the specific structure of the optimal policy in a computationally efficient and graphical manner. On top of that, a heuristic MDS scheme (HMDS) is proposed to adjust the MDS when the PA works in the non-linear region in the second stage, where a quadratic function is obtained to approximate the non-linear PA model. The offline HMDS algorithm is further extended to practical online scenarios in a well-structured way, where the modulation and data scheduling policy is produced on-the-fly. Simulation corroborates that the proposed offline algorithm can achieve the exactly same performance as the standard CVX solver, while requiring only 0.69% of its computational time.

As only a few parts of wireless resources can be utilized for pilot transmission, channel estimation, especially the interpolation process, has often been recognized as a challenging ill-posed reconstruction problem. To deal with this task, we formulate it as a typical image super resolution problem, and propose a recurrent residual learning framework named LSRN. Our proposed scheme jointly utilizes the advantages of recurrent and residual structure in the machine learning area to approximate the non-linear interpolation relations between the reference signal and surrounding resource elements. In addition, we propose a low complexity implementation scheme called LSRN-L to address the stringent processing delay requirement in the channel estimation tasks. Through numerical examples as well as prototype verification, the proposed LSRN/LSRN-L can easily outperform the convolutional GI plus DFT based interpolation scheme by 10dB in terms of normalized mean square error. Meanwhile, the low complexity LSRN-L can maintain the processing delay within one millisecond.

Ultra-dense networks (UDN) have been considered as one of the best ways to improve the network throughput and energy efficiency (EE). However, massive and unplanned deployment of small cells will cause severe intra-tier interference among small cells and then deteriorate the network EE. For this, a Cluster-based Energy-Efficient Resource Management (CEERM) scheme is proposed to mitigate the interference while guaranteeing the quality of service (QoS) of user equipments (UEs) in this paper. Firstly, we propose a cell clustering algorithm to divide small cells into disjoint cell clusters according to the neighboring relationship. Then, the UEs in each cell cluster are further grouped into UE groups with the target of minimizing intra-cluster interference. Finally, a two-step subchannel allocation and non-cooperative game based power allocation scheme is proposed to maximize the network EE. The simulation results show that the proposed scheme CEERM can effectively boost the network EE with low computation complexity.

Human–robot collaboration (HRC) has been widely utilized in industrial manufacturing and requires a human to cooperate with a robot at the same workspace. However, as HRC focuses on workspace sharing along with independent work, it is not a real collaboration between a human and a robot and, thus, cannot guarantee a smooth cooperation and synchronous operation. To this end, a cooperative shared control scheme based on intention recognition is proposed in this study by sharing workspace and time. In the proposed method, a classification algorithm based on three-dimensional (3D) point cloud is utilized to recognize the human operation intention. Then, the robot can select a suitable tool to match the human's operation. A robot motion control algorithm is developed to detect the obstacles in the HRC process. A cooperative control strategy is introduced to achieve synchronous operation. A simple assembly task is also performed to demonstrate the proposed scheme's effectiveness. The proposed HRC method with shared control can be extended to more complicated and delicate flexible tasks in assembly manufacturing.

Precise indoor localization is an increasingly demanding requirement for various emerging applications, like Virtual/Augmented reality and personalized advertising. Current indoor environments are equipped with pluralities of WiFi access points (APs), whose deployment is expected to be massive in the future enabling highly precise localization approaches. Though the conventional model-based localization schemes have achieved sub-meter level accuracy by fusing multiple channel state information (CSI) observations, the corresponding computational overhead is usually significant, especially in the current multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems. In order to address this issue, model-free localization techniques using deep learning frameworks have been lately proposed, where mainly classification methods were applied. In this paper, instead of classification based mechanism, we propose a logistic regression based scheme with the deep learning framework, combined with Cramér-Rao lower bound (CRLB) assisted robust training, which achieves more robust sub-meter level accuracy (0.97m median distance error) in the standard laboratory environment and maintains reasonable online prediction overhead under the single WiFi AP settings.

In the 5G communication systems, polar code has been adapted as the control channel coding solution in the enhanced mobile broadband (eMBB) scenario. Although different decoding schemes, including belief propagation (BP) and successive cancellation (SC) based algorithms, have been proposed, the decoding complexity as well as the latency are still significant. To address this critical issue, several low-complexity schemes, e.g., the use of simplified decoding operation and stop the decoding operation in earlier stage, have been proposed recently. However, conventional early stopping strategies have to check a pre-defined metric in each iteration, and the associated decoding delay is significant. In this paper, to address this challenge, we proposed a low-complexity BP based decoding scheme, which contains the decodability detection stage and the early stopping prediction stage. The decodability detection stage can identify the codewords in the deep channel fading environment and eliminate the unnecessary decoding operations to reduce the decoding complexity, while the early stopping prediction stage can directly predict the required number of iterations rather than checking the metric in each iteration to avoid the associated decoding delay. Through the above two approaches, our proposed scheme is shown to achieve 71% decoding delay reduction and maintain the same decoding performance as traditional BP, G-matrix, MinLLR schemes.

Environmental sound classification (ESC) is a challenging problem due to the complex temporal structure and diverse energy modulation patterns of environmental sounds. In order to deal with the former, temporal attention mechanism is originally adopted to focus on the informative frames. However, no existing works pay attention to the latter problem. In this paper, we consider the role of convolution filters in detecting energy modulation patterns and propose a channel attention mechanism to focus on the semantically relevant channels generated by corresponding filters. Furthermore, we incorporate the temporal attention and channel attention to enhance the representative power of CNN via generating complementary information. In addition, to avoid possible overfitting caused by limited training data, we explore a data augmentation scheme that is other contribution in this paper. We evaluate our proposed method on three benchmark ESC datasets: ESC-10 and ESC-50 and DCASE2016. Experimental results show the effectiveness of proposed method and achieve the state-of-the-art or competitive results in terms of classification accuracy. Finally, we visualize our attention results and observe that the proposed attention mechanism is able to lead the network to focus on the semantically relevant parts of environmental sounds.

High-precision indoor localization is growing extremely quickly, especially for multi-floor scenarios. The data on existing indoor positioning schemes, mainly, come from wireless, visual, or lidar means, which are limited to a single sensor. With the massive deployment of WiFi access points and low-cost cameras, it is possible to combine the above three methods to achieve more accurate, complete, and reliable location results. However, the existing SLAM rapidly advances, so hybrid visual and wireless approaches take advantage of this, in a straightforward manner, without exploring their interactions. In this paper, a high-precision multi-floor indoor positioning method, based on vision, wireless signal characteristics, and lidar is proposed. In the joint scheme, we, first, use the positioning data output in lidar SLAM as the theoretical reference position for visual images; then, use a WiFi signal to estimate the rough area, with likelihood probability; and, finally, use the visual image to fine-tune the floor-estimation and location results. Based on the numerical results, we show that the proposed joint localization scheme can achieve 0.62 m of 3D localization accuracy, on average, and a 1.24-m MSE for two-dimensional tracking trajectories, with an estimation accuracy for the floor equal to 89.22%. Meanwhile, the localization process takes less than 0.25 s, which is of great importance for practical implementation.

The transmission of a large amount of video and picture content brings more challenges to wireless communication networks. Unmanned aerial vehicle (UAV)-aided small cells with active content caching deployed on cellular networks are recognized as a promising way to alleviate wireless backhaul and support flexible coverage. However, a UAV cannot operate for a long time due to limited battery life, and its caching capacity is also limited. For this, a multi-UAV content-caching strategy and cooperative, complementary content transmission among UAVs are jointly studied in this paper. Firstly, a user-clustering-based caching strategy is designed, where user clustering is based on user similarity, concurrently taking into consideration similarities in content preference and location. Then, cooperative, complementary content transmission between multiple UAVs is modeled as a coalition formation game (CFG) to maximize the utility of the whole network. Finally, the effectiveness of the proposed algorithms is demonstrated through numerical simulations.

In the important and challenging field of environmental sound classification (ESC), a crucial and even decisive factor is the feature representation ability, which can directly affect the accuracy of classification. Therefore, the classification performance often depends to a large extent on whether the effective representative features can be extracted from the environmental sound. In this paper, we firstly propose a sub-spectrogram segmentation with score level fusion based ESC classification framework, and we adopt the proposed convolutional recurrent neural network (CRNN) for improving the classification accuracy. By evaluating numerous truncation schemes, we numerically figure out the optimal number of sub-spectrograms and the corresponding band ranges, and, on this basis, we propose a joint attention mechanism with temporal and frequency attention mechanisms and use the global attention mechanism when generating the attention map. Finally, the numerical results show that the two frameworks we proposed can achieve 82.1% and 86.4% classification accuracy on the public environmental sound dataset ESC-50, respectively, which is equivalent to more than 13.5% improvement over the traditional baseline scheme.

Heterogeneous networks (HetNets), consisting of macro-cells and overlaying pico-cells, have been recognized as a promising paradigm to support the exponential growth of data traffic demands and high network energy efficiency (EE). However, for two-tier heterogeneous architecture deployment of HetNets, the inter-tier interference will be challenging. Time domain further-enhanced inter-cell interference coordination (FeICIC) proposed in 3GPP Release-11 becomes necessary to mitigate the inter-tier interference by applying low power almost blank subframe (ABS) scheme. Therefore, for HetNets deployment in reality, the pico-cell range expansion (CRE) bias, the power of ABS and the density of pico base stations (PBSs) are three important factors for the network EE improvement. Aiming to improve the network EE, the above three factors are jointly considered in this paper. In particular, we first derive the closed-form expression of the network EE as a function of pico CRE bias, power reduction factor of low power ABS and PBS density based on stochastic geometry model. Then, the approximate relationship between pico CRE bias and power reduction factor is deduced, followed by a linear search algorithm to get the near-optimal pico CRE bias and power reduction factor together at a given PBS density. Next, a linear search algorithm is further proposed to optimize PBS density based on fixed pico CRE bias and power reduction factor. Due to the fact that the above pico CRE bias and power reduction factor optimization and PBS density optimization are optimized separately, a heuristic algorithm is further proposed to optimize pico CRE bias, power reduction factor and PBS density jointly to achieve global network EE maximization. Numerical simulation results show that our proposed heuristic algorithm can significantly enhance the network EE while incurring low computational complexity.

In this paper, we present a WiFi-based intrusion detection system called Wi-Alarm. Motivated by our observations and analysis that raw channel state information (CSI) of WiFi is sensitive enough to monitor human motion, Wi-Alarm omits data preprocessing. The mean and variance of the amplitudes of raw CSI data are used for feature extraction. Then, a support vector machine (SVM) algorithm is applied to determine detection results. We prototype Wi-Alarm on commercial WiFi devices and evaluate it in a typical indoor scenario. Results show that Wi-Alarm reduces much computational expense without losing accuracy and robustness. Moreover, different influence factors are also discussed in this paper.

Heterogeneous networks, constituted by conventional macro cells and overlaying pico cells, have been deemed a promising paradigm to support the deluge of data traffic with higher spectral efficiency and Energy Efficiency (EE). In order to deploy pico cells in reality, the density of Pico Base Stations (PBSs) and the pico Cell Range Expansion (CRE) are two important factors for the network spectral efficiency as well as EE improvement. However, associated with the range and density evolution, the inter-tier interference within the heterogeneous architecture will be challenging, and the time domain Enhanced Inter-cell Interference Coordination (eICIC) technique becomes necessary. Aiming to improve the network EE, the above factors are jointly considered in this paper. More specifically, we first derive the closed-form expression of the network EE as a function of the density of PBSs and pico CRE bias based on stochastic geometry theory, followed by a linear search algorithm to optimize the pico CRE bias and PBS density, respectively. Moreover, in order to realize the pico CRE bias and PBS density joint optimization, a heuristic algorithm is proposed to achieve the network EE maximization. Numerical simulations show that our proposed pico CRE bias and PBS density joint optimization algorithm can improve the network EE significantly with low computational complexity.

Objective. To study risk factors related to ischemic stroke (IS) in youth and the influence of sleep quality on youth ischemic stroke incidence. Methods. 223 patients aged 18 to 45 years who were admitted to Puyang People’s Hospital from June 2011 to February 2013 with a first-ever ischemic stroke were selected as the research cases. 158 young people with a normal physical examination were selected as the control group. The Pittsburgh Sleep Quality Index (PSQI) questionnaire was used to analyse the correlation between sleep quality and youth IS incidence. The US National Institutes of Health Stroke Scale (NIHSS) and modified Rankin Scale (MRS) scores were used to assess cases’ state of illness and prognosis three months after IS. Results. Univariate and multivariate logistic regression analysis showed that the association of these risk factors with youth IS incidence, from highest to lowest, was hypertension, hyperlipidaemia, smoking history, high homocysteine, the quality of sleep, family history of stroke, and alcoholism. Poor sleep quality ranked fifth among all risk factors and was positively correlated with poor prognosis for youth IS patients. Conclusion. The results of this study showed that sleep quality is an important factor in the pathogenesis and prognosis of youth IS.

This paper considers the resource allocation problem in the wireless OFDMA system with a relay node. We consider orthogonal relay in which the relay node cannot transmit and receive simultaneously on the same frequency. As a result, the use of relay node may enhance or degrade the achievable transmission rate depending on the instantaneous channel states between the source and the relay, the relay and the destination as well as the source and the destination. Hence, it is very important to dynamically adjust the resources (subbands) allocated to the relay node so that the relay is used only at the right time according to the instantaneous channel states. Conventional approaches dynamically schedule the usage of the relay node in a centralized manner in which full knowledge of the channel states between any two nodes in the network is required. However, perfect knowledge of the channel states at various nodes is very difficult to obtain. In this paper, we shall propose a resource allocation algorithm, which iteratively allocates resource to the source and relay, and converge to the close-to-optimal allocation. Asymptotic achievable rate of the proposed algorithm is derived. We show that the system achieves significant improvement of the achievable rate compared to the point-to-point baseline system without relay as well as the baseline system with random subband allocation. [ABSTRACT FROM PUBLISHER]

Traditional mobile wireless network mainly design focuses on ubiquitous access and large capacity. However, as energy saving and environmental protection become global demands and inevitable trends, wireless researchers and engineers need to shift their focus to energy-efficiency- oriented design, that is, green radio. In this article, we propose a framework for green radio research and integrate the fundamental issues that are currently scattered. The skeleton of the framework consists of four fundamental tradeoffs: deployment efficiency?energy efficiency, spectrum efficiency?energy efficiency, bandwidth? power, and delay?power. With the help of the four fundamental trade-offs, we demonstrate that key network performance/cost indicators are all strung together. [ABSTRACT FROM PUBLISHER]