The theory and experiment of quantum information have been studied extensively in recent years, and the feasibility of quantum communication has been proved. Although the fundamental technology is not yet mature, research on quantum internet should be conducted. To implement quantum internet, an architecture that describes how quantum nodes are linked to form networks and how protocol functions are vertically composed need to be developed urgently. In this paper, we present a novel design of a cluster-based structure to describe how quantum nodes are interconnected, and how the structure can improve the performance of qubit transmission and reduce the network complexity. The idea of the quantum local area network (QLAN) is proposed as an essential component of the quantum internet. Besides, each quantum repeater links to neighboring repeaters to form a core network, and multiple QLANs are connected through the core network. The core network can be grouped into different hierarchical quantum repeater networks according to needed service requirements. For the sake of interoperability and fast prototyping, we adopt the idea of OSI layering model of the current Internet in the design of quantum internet. Finally, we elaborate on the composition of quantum nodes and the realization of end-to-end communication.

This paper exploits coding to speed up computation offloading in a multi-server mobile edge computing (MEC) network with straggling servers and channel fading. The specific task we consider is to compute the product between a user-generated input data matrix and a large-scale model matrix that is stored distributively across the multiple edge nodes. The key idea of coding is to introduce computation redundancy to improve robustness against straggling servers and to create communication redundancy to improve reliability against channel fading. We utilize the hybrid design of maximum distance separable (MDS) coding and repetition coding. Based on the hybrid coding scheme, we conduct theoretical analysis on the average task uploading time, average edge computing time, and average output downloading time, respectively and then obtain the end-to-end task execution time. Numerical results demonstrate that when the task uploading phase or the edge computing phase is the performance bottleneck, the hybrid coding reduces to MDS coding; when the downlink transmission is the bottleneck, the hybrid coding reduces to repetition coding. The hybrid coding also outperforms the entangled polynomial coding that causes higher uplink and downlink communication loads.

The exponentially increasing number of heterogeneous Internet of Things (IoT) devices (e.g., WiFi and ZigBee) crowed in the same ISM band (2.4G) and recent advances in Cross-Technology Communications (CTC) motivate us to explore more efficient data collection and maximize network throughput. CTC enables WiFi and ZigBee devices to communicate directly without any hardware changes or gateway equipment, which sheds light on a more efficient data collection design. In this work, we propose a distributed algorithm, named MaxBee, to compute the maximum network throughput, which is formulated as a linear programming problem. Considering that the problem turns out to be non-convex and hard to solve exactly, we propose a distributed algorithm to solve nonlinear programming by using the dual decomposition method and gradient/subgradient algorithms. Through extensive simulations on different sets of deployed ZigBee and WiFi devices, we observe that the proposed algorithm significantly increases the network throughput based on CTC for Sensor Networks.

Recently, the evolution of Generative Adversarial Networks (GANs) has embarked on a journey of revolutionizing the field of artificial and computational intelligence. To improve the generating ability of GANs, various loss functions are introduced to measure the degree of similarity between the samples generated by the generator and the real data samples, and the effectiveness of the loss functions in improving the generating ability of GANs. In this paper, we present a detailed survey for the loss functions used in GANs, and provide a critical analysis on the pros and cons of these loss functions. First, the basic theory of GANs along with the training mechanism are introduced. Then, the most commonly used loss functions in GANs are introduced and analyzed. Third, the experimental analyses and comparison of these loss functions are presented in different GAN architectures. Finally, several suggestions on choosing suitable loss functions for image synthesis tasks are given.

Efficient estimation of line spectral from quantized samples is of significant importance in information theory and signal processing, e.g., channel estimation in energy efficient massive MIMO systems and direction of arrival estimation. The goal of this paper is to recover the line spectral as well as its corresponding parameters including the model order, frequencies and amplitudes from heavily quantized samples. To this end, we propose an efficient gridless Bayesian algorithm named VALSE-EP, which is a combination of the high resolution and low complexity gridless variational line spectral estimation (VALSE) and expectation propagation (EP). The basic idea of VALSE-EP is to iteratively approximate the challenging quantized model of line spectral estimation as a sequence of simple pseudo unquantized models, where VALSE is applied. Moreover, to obtain a benchmark of the performance of the proposed algorithm, the Cramér Rao bound (CRB) is derived. Finally, numerical experiments on both synthetic and real data are performed, demonstrating the near CRB performance of the proposed VALSE-EP for line spectral estimation from quantized samples.

Due to the different signal-to-noise ratio (SNR) of each subchannel, the bit error rate (BER) of hybrid precoding based on singular value decomposition (SVD) decreases. In this paper, we propose a multi-task learning based precoding network (PN) model to solve the BER loss problem caused by SVD based hybrid precoding under imperfect channel state information (CSI). Specifically, we firstly generate a dataset including imcomplete CSI input channel matrix and corresponding output labels to train the PN model. The output labels are designed based on uniform channel decomposition (UCD) which decomposes the channel into multiple subchannels with same gain, while the vertical-bell layered space-time structure (V-BLAST) signal processing technology is combined to eliminate the inner interference of the subchannels. Then, the PN model is trained to design the analog and digital precoding/combining matrix simultaneous. Simulation results show that the proposed scheme has only negligible gap in spectrum efficiency compared with the fully digital precoding, while achieves better BER performance than SVD based hybrid precoding.

This paper focuses on boosting the performance of small cell networks (SCNs) by integrating multiple-input multiple-output (MIMO) and non-orthogonal multiple access (NOMA) in consideration of imperfect channel-state information (CSI). The estimation error and the spatial randomness of base stations (BSs) are characterized by using Kronecker model and Poisson point process (PPP), respectively. The outage probabilities of MIMO-NOMA enhanced SCNs are first derived in closed-form by taking into account two grouping policies, including random grouping and distance-based grouping. It is revealed that the average outage probabilities are irrelevant to the intensity of BSs in the interference-limited regime, while the outage performance deteriorates if the intensity is sufficiently low. Besides, as the channel uncertainty lessens, the asymptotic analyses manifest that the target rates must be restricted up to a bound to achieve an arbitrarily low outage probability in the absence of the inter-cell interference. Moreover, highly correlated estimation error ameliorates the outage performance under a low quality of CSI, otherwise it behaves oppositely. Afterwards, the goodput is maximized by choosing appropriate precoding matrix, receiver filters and transmission rates. In the end, the numerical results verify our analysis and corroborate the superiority of our proposed algorithm.

This paper proposes a flexible eight-mode high parallel Galois SIMD ASIP(Application Specific Instruction Set Processor). It supports parallel executions of Gold,Scrambling, CRC, CC, Turbo, RM, PSS, SSS encoding LFSR (linear feedback shift registers) algorithms with high performance and flexibility. It can perform also general bit processing and m-sequence. Our design is based on proposed table conversion and a datapath for unified eight-mode encoding. Based on 28 nm digital CMOS technology, the total area is 0.177mm² and the clock frequency can be up to 1 GHz. The throughputs of Gold, Scrambling, CRC32, CRC24, CRC16, CRC8, CC, Turbo are 64Gb/s, 64Gb/s, 128Gb/s, 168Gb/s, 256Gb/s, 512Gb/s, 3×80Gb/s, and 72Gb /s, respectively.

Massive machine-type communication (mMTC) is a typical application scenario of the fifth generation (5G) mobile communications. To keep the mMTC reliable and minimize the energy consumption of the mMTC devices, this paper proposes an enhanced power choice barring (EPCB) scheme based on the distributed layered grant-free non-orthogonal multiple access (NOMA) framework, where the cell is divided into different layers according to a predetermined power levels. The proposed EPCB scheme not only combines the grant-free strategy with the sleep mode to reduce the energy consumption, but also designs a power level choosing strategy to increase the access success probability. Simulation results show that when compared with existing schemes, the proposed EPCB scheme has better performance in the aspects of the access success probability and energy efficiency.

In this paper, the concept of computation diversity is proposed by employing generalized hybrid carrier (GHC) system. The signal is backed up by transform-domain computation to enhance the anti-fading capability. Theoretical analysis is presented to demonstrate the superiority of the proposed method. Numerical simulations in doubly-selective fading channels are provided to verify the results. The bit error rate (BER) performance is guaranteed with a small computational complexity increment. The proposed method not only extends the new diversity dimension, but also improves the diversity gain of the system without occupying additional wireless resources. Moreover, the proposed method shows good adaptability with single carrier modulation and improves diversity performance. As the simulation results shown, time-domain dual component computation diversity (TDC-CD) has 1dB BER gain and 0.1 diversity gain at high signal-to-noise ratio, and single carrier system with TDC-CD achieves 2dB BER gain at 10^-4.

The in-band full-duplex (IBFD) mechanism is of interest in beyond 5G systems due to its potential to enhance spectral efficiency and reduce delay. To achieve the maximum gain of IBFD systems, the significant self-interference (SI) must be efficiently suppressed. The challenges of wideband self-interference cancellation (SIC) lie in the radio frequency (RF) domain, where the performance will be limited by the hardware. This paper reviews current RF cancellation mechanisms and investigates an efficient mechanism for future wideband systems with minimum complexity. The working principle and implementation details of multi-tap cancellers are first introduced, then an optical domain-based RF canceller is reviewed, and a novel low-cost design is proposed. To minimize the cost and complexity of the canceller, the minimum required number of taps are analyzed. Simulation results show that with the commonly used 12-bits analog-to-digital converter (ADC) at the receiver, the novel optical domain-based canceller can enable efficient SIC in the 3GPP LTE specifications compatible system within 400 MHz bandwidth.

On accomplishing an efficacious object tracking, the activity of an object concerned becomes notified in a forthright manner. An accurate form of object tracking task necessitates a robust object tracking procedures irrespective of hardware assistance. Such approaches inferred a vast computational complexity to track an object with high accuracy in a stipulated amount of processing time. On the other hand, the tracking gets affected owing to the existence of varied quality diminishing factors such as occlusion, illumination changes, shadows etc., In order to rectify all these inadequacies in tracking an object, a novel background normalization procedure articulated on the basis of a textural pattern is proposed in this paper. After preprocessing an acquired image, employment of an Environmental Succession Prediction algorithm for discriminating disparate background environment by background clustering approach have been accomplished. Afterward, abstract textural characterizations through utilization of a Probability based Gradient Pattern (PGP) approach for recognizing the similarity between patterns obtained so far. Comparison between standardized frame obtained in prior and those processed patterns detects the motion exposed by an object and the object concerned gets identified within a blob. Hence, the system is resistant towards illumination variations.These illumination variation was interpreted in object tracking residing within a dynamic background. Devised approach certainly outperforms other object tracking methodologies like Group Target Tracking (GTT), ViPER-GT, grabcut, snakes in terms of accuracy and average time. Proposed PGP-based pattern texture analysis is compared with Gamifying Video Object (GVO) approach and hence, it evidently outperforms in terms of precision, recall and F1 measure.

Exploiting random access for the underlying connectivity provisioning has great potential to incorporate massive machine-type communication (MTC) devices in an Internet of Things (IoT) network. However, massive access attempts from versatile MTC devices may bring congestion to the IIoT network, thereby hindering service increasing of IIoT applications. In this paper, an intelligence enabled physical (PHY-)layer user signature code acquisition (USCA) algorithm is proposed to overcome the random access congestion problem with reduced signaling and control overhead. In the proposed scheme, the detector aims at approximating the optimal observation on both active user detection and user data reception by iteratively learning and predicting the convergence of the user signature codes that are in active. The cross-entropy based low-complexity iterative updating rule is present to guarantee that the proposed USCA algorithm is computational feasible. A closed-form bit error rate (BER) performance analysis is carried out to show the efficiency of the proposed intelligence USCA algorithm. Simulation results confirm that the proposed USCA algorithm provides an inherent trade-off between performance and complexity and allows the detector achieves an approximate optimal performance with a reasonable computational complexity.

The European organization for nuclear research (CERN) is planning a high performance particle collider by 2050, which will update the currently used Large Hadron Collider (LHC). The design of the new experiment facility includes the definition of a suitable communication infrastructure to support the future needs of scientists. The huge amount of data collected by the measurement devices call for a data rate of at least 1Gb/s per node, while the need of timely control of instruments requires a low latency of the order of 0.01$\mu$s. Moreover, the main tunnel will be 100 km long, and will need appropriate coverage for voice and data traffic, in a special underground environment subject also to strong radiations.Reliable voice, data and video transmission in a tunnel of this length is necessary to ensure timely and localized intervention, reducing access time. In addition, using wireless communication for voice, control and data acquisition of accelerator technical systems could lead to a significant reduction in cabling costs, installation times and maintenance efforts. The communication infrastructure of the Future Circular Collider (FCC) tunnel must be able to circumvent the problems of radioactivity, omnipresent in the tunnel. Current technologies transceivers cannot transmit in such a severely radioactive environment. This is due to the immediate destruction of any active or passive equipment by radioactivity. The scope of this paper is to determine the feasibility of robust wireless transmission in an underground radioactive tunnel environment. The network infrastructure design to meet the demand will be introduced, and the performance of different wireless technologies will be evaluated.

Segment Routing (SR) is a new routing paradigm based on source routing and provide traffic engineering (TE) capabilities in IP network. By extending interior gateway protocol(IGP), SR can be easily applied to IP network. However, upgrading current IP network to a full SR one can be costly and difficult. Hybrid IP/SR network will last for some time. Aiming at the low flexibility problem of static TE policies in the current SR networks, this paper proposes a Deep Reinforcement Learning (DRL) based TE scheme. The proposed scheme employs multi-path transmission and use DRL to dynamically adjust the traffic splitting ratio among different paths based on the network traffic distribution. As a result, the network congestion can be mitigated and the performance of the network is improved. Simulation results show that our proposed scheme can improve the throughput of the network by up to 9% than existing schemes.

Dispersed computing can link all devices with computing capabilities on a global scale to form a fully decentralized network, which can make full use of idle computing resources. Realizing the overall resource allocation of the dispersed computing system is a significant challenge. In detail, by jointly managing the task requests of external users and the resource allocation of the internal system to achieve dynamic balance, the efficient and stable operation of the system can be guaranteed. In this paper, we first propose a task-resource joint management model, which quantifies the dynamic transformation relationship between the resources consumed by task requests and the resources occupied by the system in dispersed computing. Secondly, to avoid downtime caused by an overload of resources, we introduce intelligent control into the task-resource joint management model. The existence and stability of the positive periodic solution of the model can be obtained by theoretical analysis, which means that the stable operation of dispersed computing can be guaranteed through the intelligent feedback control strategy. Additionally, to improve the system utilization, the task-resource joint management model with bi-directional intelligent control is further explored. Setting control thresholds for the two resources not only reverse restrains the system resource overload, but also carries out positive incentive control when a large number of idle resources appear. The existence and stability of the positive periodic solution of the model are proved theoretically, that is, the model effectively avoids the two extreme cases and ensure the efficient and stable operation of the system. Finally, numerical simulation verifies the correctness and validity of the theoretical results.

The application of information and communications technology(ICT) in the education industry is becoming more and more extensive, and online education realized through ICT is developing in full swing. The influence of ICT on online education consumer's choice behavior is the core issue of online education industry development research. The research on the interactive path and methods of information and online education consumer choice behavior is worth exploring and revealing. This study introduces the word-of-mouth factor as a new research variable under the framework of the Rational Choice Theory model (RCT) and the structural equation method to conduct empirical research and theoretical analysis to verify the validity of the hypothesis and model. The fifth-Generation mobile communication system(5G) analyses the factors affecting online education consumer behavior choices based on the premise of ICT. Research on the path between ICT and choice behavior provides new ideas for online education consumer choice behavior research and ICT and content and provides a new scenario. This article is a cross-disciplinary research content in theory, and its innovation opens up a new path for the management of ICT research. The research results have innovative significance and value at both the theoretical and practical levels.