With a ten-year horizon from concept to reality, it is time now to start thinking about what will the sixth-generation (6G) mobile communications be on the eve of the fifth-generation (5G) deployment. To pave the way for the development of 6G and beyond, we provide 6G visions in this paper. We first introduce the state-of-the-art technologies in 5G and indicate the necessity to study 6G. By taking the current and emerging development of wireless communications into consideration, we envision 6G to include three major aspects, namely, mobile ultra-broadband, super Internet-of-Things (IoT), and artificial intelligence (AI). Then, we review key technologies to realize each aspect. In particular, teraherz (THz) communications can be used to support mobile ultra-broadband, symbiotic radio and satellite-assisted communications can be used to achieve super IoT, and machine learning techniques are promising candidates for AI. For each technology, we provide the basic principle, key challenges, and state-of-the-art approaches and solutions.

Across the world, we are currently witnessing the deployments of 4G LTE-Advanced and the 5G research is reaching its peak point. The 5G research mainly concentrates on addressing some of the existing OFDM based LTE problems along with use of non-contiguous fragmented spectrum. Universal Filtered Multi Carrier (UFMC) has been considered as one of the candidate waveform for the 5G communications because it provides robustness against the Inter Symbol Interference (ISI), and Inter Carrier Interference (ICI) and is suitable for low latency scenarios. In this paper, a novel approach is proposed to use Kaiser-Bessel filter based pulse shaping instead of standard Dolph-Chebyshev filter for UFMC based waveform to reduce the spectral leakage into nearby sub-bands. In this paper, UFMC system is simulated using MATLAB software, a comparative study for Dolph-Chebyshev and Kaiser-Bessel filters are performed and the results are also presented in terms of power spectrum density (PSD) analysis, Complementary Cumulative Distribution Function (CCDF) analysis, and Adjacent Channel Power Ratio (ACPR) analysis. The simulated results show a better power spectral density and lower sidebands for UFMC (Kaiser Based window), when compared with UFMC (Dolph-Chebyshev) and conventional OFDM.

Data transmission among multicast trees is an efficient routing method in mobile ad hoc networks (MANETs). Genetic algorithms (GAs) have found widespread applications in designing multicast trees. This paper proposes a stable quality-of-service (QoS) multicast model for MANETs. The new model ensures the duration time of a link in a multicast tree is always longer than the delay time from the source node. A novel GA is designed to solve our QoS multicast model by introducing a new crossover mechanism called leaf crossover (LC), which outperforms the existing crossover mechanisms in requiring neither global network link information, additional encoding/decoding nor repair procedures. Experimental results confirm the effectiveness of the proposed model and the efficiency of the involved GA. Specifically, the simulation study indicates that our algorithm can obtain a better QoS route with a considerable reduction of execution time as compared with existing GAs.

This paper focuses on how to use consortium blockchain to improve the regulation of peer-to-peer (P2P) lending market. The partial decentralized consortium blockchain with limited pre-set nodes can well improve transparency and security, which is suitable for financial regulation. Considering irregularities of the P2P lending market, the Hyperledger-based Peer-to-Peer Lending System (HyperP2PLS) is proposed. First elaborate the application scenario and business logic of the system, where a national P2P Lending Trading Center will be established to integrate all transactions and information of P2P lending market. Then construct the system architecture consisting of the blockchain network, HTTP server, and applications. The algorithm of implementation process and the web application for users have been well illustrated. The performance analysis shows that HyperP2PLS can guarantee the reliability, safety, transparency and efficiency.

Carrying out pilot project to provide broadband universal service nationwide, especially in rural impoverished areas, is a major policy decision in China. To accelerate implementation and ensure quality of the constructed network, it is of great significance to conduct comprehensive and scientific evaluation of the network status. In this paper, we present the evaluation of the broadband network constructed in rural China with several key indicators. It shows that with stepped-up efforts of the telecom industry, broadband networks have been introduced into more and more villages. The average network speed reaches 60 Mbps, which is far exceeding 12 Mbps’ obligation.

This paper studies the effect of phase noise and fronthaul compression on a downlink cloud radio access network (C-RAN), where several remote radio heads (RRHs) are coordinated to communicate with users by a baseband unit (BBU) on the cloud server. In the system, the baseband signals are precoded at BBU, and then compressed before being transmitted to RRHs through capacity-limited fronthaul links which results in the compressive quantization noise. We assume the regularized zero-forcing precoding is performed with an imperfect channel state information and a compression strategy is applied at BBU. The effect of phase noise arising from nonideal local oscillators both at RRHs and users is considered. We propose an approximate expression for the downlink ergodic sum-rate of considered C-RAN utilizing large dimensional random matrix theory in the large-system regime. From simulation results, the accuracy of the approximate expression is validated, and the effect of phase noise and fronthaul compression can be analyzed theoretically based on the approximate expression.

We study the effects of quantization and additive white Gaussian noise (AWGN) in transmitting latent representations of images over a noisy communication channel. The latent representations are obtained using autoencoders (AEs). We analyze image reconstruction and classification performance for different channel noise powers, latent vector sizes, and number of quantization bits used for the latent variables as well as AEs’ parameters. The results show that the digital transmission of latent representations using conventional AEs alone is extremely vulnerable to channel noise and quantization effects. We then propose a combination of basic AE and a denoising autoencoder (DAE) to denoise the corrupted latent vectors at the receiver. This approach demonstrates robustness against channel noise and quantization effects and enables a significant improvement in image reconstruction and classification performance particularly in adverse scenarios with high noise powers and significant quantization effects.

To reinforce the coverage and QoS (quality of service) of on-ground cellular communication system, unmanned aerial vehicles which are carrying small cells are deployed in some emergency and disaster areas. Although ASCs (aerial small cells) can provide a higher probability of LoS (line-of-sight) transmission, the wireless backhaul link will bring extra interference to the whole system if not well designed. Therefore, in this paper, we study the backhaul scheme for UAV-assist cellular network. We first analyze the interference environments of UAV-assist cellular network considering the IBOG (In-Band to On-Ground), OBOG (Out-of-Band to On-Ground) and IBTU (In-Band to Tethered-UAV) backhauling mode, and give the descriptions of the performance metrics for each mode. Then, the considered problem is formulated as an optimization of achievable rate. We derive the optimal solutions for the involved three backhauling modes for ASCs respectively, and closed-form optimal value for each mode is acquired with proof. We also give a pseudo-code form of our proposed optimal access/backhaul spectrum allocation algorithm. The simulation results indicate that the proposed scheme can deliver a significant gain, while IBTU performs best among proposed backhauling modes.

Mobile operators face the challenge of how to best design a service-centric network that can effectively process the rapidly increasing number of bandwidth-intensive user requests while providing a higher quality of experience (QoE). Existing content distribution networks (CDN) and mobile content distribution networks (mCDN) have both latency and throughput limitations due to being multiple network hops away from end-users. Here, we first propose a new Personalized Edge Caching System (PECS) architecture that employs big data analytics and mobile edge caching to provide personalized service access at the edge of the mobile network. Based on the proposed system architecture, the edge caching strategy based on user behavior and trajectory is analyzed. Employing our proposed PECS strategies, we use data mining algorithms to analyze the personalized trajectory and service usage patterns. Our findings provide guidance on how key technologies of PECS can be employed for current and future networks. Finally, we highlight the challenges associated with realizing such a system in 5G and beyond.

In this paper, we conduct research on the dynamic demand response problem in smart grid to control the energy consumption. The objective of the energy consumption control is constructed based on differential game, as the dynamic of each users’ energy state in smart gird can be described based on a differential equation. Concept of electricity sharing is introduced to achieve load shift of main users from the high price hours to the low price hours. Nash equilibrium is given based on the Hamilton equation and the effectiveness of the proposed model is verified based on the numerical simulation results.

Artificial intelligence technology has revolutionized every industry and trade in recent years. However, its own development is encountering bottlenecks that it is unable to implement empathy with human emotions. So affective computing is getting more attention from researchers. In this paper, we propose an amygdala-inspired affective computing framework to realize the recognition of all kinds of human personalized emotions. Similar to the amygdala, the instantaneous emergency emotion is first computed more quickly in a low-redundancy convolutional neural network compressed by pruning and weight sharing with hashing trick. Then, the real-time process emotion is identified more accurately by the memory level neural networks, which is good at handling time-related signals. Finally, the intracranial emotion is recognized in personalized hidden Markov models. We demonstrate on Facial Expression of Emotion Dataset and the recognition accuracy of external emotions (including the emergency emotion and the process emotion) reached 85.72%. And the experimental results proved that the personalized affective model can generate desired intracranial emotions as expected.

Faced with the evolving attacks in recommender systems, many detection features have been proposed by human engineering and used in supervised or unsupervised detection methods. However, the detection features extracted by human engineering are usually aimed at some specific types of attacks. To further detect other new types of attacks, the traditional methods have to re-extract detection features with high knowledge cost. To address these limitations, the method for automatic extraction of robust features is proposed and then an Adaboost-based detection method is presented. Firstly, to obtain robust representation with prior knowledge, unlike uniform corruption rate in traditional mLDA (marginalized Linear Denoising Autoencoder), different corruption rates for items are calculated according to the ratings’ distribution. Secondly, the ratings sparsity is used to weight the mapping matrix to extract low-dimensional representation. Moreover, the uniform corruption rate is also set to the next layer in mSLDA (marginalized Stacked Linear Denoising Autoencoder) to extract the stable and robust user features. Finally, under the robust feature space, an Adaboost-based detection method is proposed to alleviate the imbalanced classification problem. Experimental results on the Netflix and Amazon review datasets indicate that the proposed method can effectively detect various attacks.

Considering that there is no single full reference image quality assessment method that could give the best performance in all situations, some multi-method fusion metrics were proposed. Machine learning techniques are often involved in such multi-method fusion metrics so that its output would be more consistent with human visual perceptions. On the other hand, the robustness and generalization ability of these multi-method fusion metrics are questioned because of the scarce of images with mean opinion scores. In order to comprehensively validate whether or not the generalization ability of such multi-method fusion IQA metrics are satisfying, we construct a new image database which contains up to 60 reference images. The newly built image database is then used to test the generalization ability of different multi-method fusion IQA metrics. Cross database validation experiment indicates that in our new image database, the performances of all the multi-method fusion IQA metrics have no statistical significant different with some single-method IQA metrics such as FSIM and MAD. In the end, a thorough analysis is given to explain why the performance of multi-method fusion IQA framework drop significantly in cross database validation.

Cloud-as-the-center computing paradigms face multiple challenges in the 5G and Internet of Things scenarios, where the service requests are usually initiated by the end-user devices located at network edge and have rigid time constraints. Therefore, Fog computing, or mobile edge computing, is introduced as a promising solution to the service provision in the tiered IoT infrastructure to compensate the shortage of traditional cloud-only architecture. In this cloud-to-things continuum, several cloudlet or mobile edge server entities are placed at the access network to handle the task offloading and processing problems at the network edge. This raises the resource scheduling problem in this tiered system, which is vital for the promotion of the system efficiency. Therefore, in this paper, a scheduling mechanism for the cloudlets or fog nodes are presented, which takes the mobile tasks’ deadline and resources requirements at the same time while promoting the overall profit of the system. First, the problem at the cloudlet, to which IoT devices offload their tasks, is formulated as a multi-dimensional 0-1 knapsack problem. Second, based on ant colony optimization, a scheduling algorithm is presented which treat this problem as a subset selection problem. Third, to promote the performance of the system in the dynamic environments, a churn-refined algorithm is further put forward. A series of simulation experiments have shown that out proposal outperforms many state-of-the-art algorithms in both profit and guarantee ratio.

Distributed antenna arraying is a promising technology for weak signal reception. The received signals from different antennas are aligned and combined to improve the receiving signal-to-noise ratio (SNR). However, the combining performance is serious degraded by the difference of sampling frequency between antennas. In this paper, a frequency domain based signal combining method is proposed to solve this problem. The unaligned sampled data in time domain of the received signals are transformed to frequency domain using fast Fourier transform (FFT). The received signals can be aligned in frequency domain when their spectrum resolutions are the same. Therefore the received signals with the same total sampling time can be aligned and combined in frequency domain and then the combined signal is recovered using inverse fast Fourier transform (IFFT). Numerical simulations with two typical modulation types, i.e., PSK and PCM/BPSK/PM, prove the validity and robustness of this method.

A novel online Differential Mode Group Delay(DMGD)monitoring method based on four-wave mixing (FWM) in few mode fiber (FMF) transmission system is proposed, and the DMGD monitoring is achieved on the whole range of 15—50 ps/km. Detection principle is deduced and relationship of the power of idler waves and DMGD is analyzed . With various chromatic dispersion(CD)values, different line widths and different optical signal noise ratio(OSNR) values, the simulations are carried out. The simulation results show that this new DMGD monitoring method is less affected by different line widths and has a high tolerance for OSNR.