The fifth generation (5G) wireless communication networks are being deployed worldwide from 2020 and more capabilities are in the process of being standardized, such as mass connectivity, ultra-reliability, and guaranteed low latency. However, 5G will not meet all requirements of the future in 2030 and beyond, and sixth generation (6G) wireless communication networks are expected to provide global coverage, enhanced spectral/energy/cost efficiency, better intelligence level and security, etc. To meet these requirements, 6G networks will rely on new enabling technologies, i.e., air interface and transmission technologies and novel network architecture, such as waveform design, multiple access, channel coding schemes, multi-antenna technologies, network slicing, cell-free architecture, and cloud/fog/edge computing. Our vision on 6G is that it will have four new paradigm shifts. First, to satisfy the requirement of global coverage, 6G will not be limited to terrestrial communication networks, which will need to be complemented with non-terrestrial networks such as satellite and unmanned aerial vehicle (UAV) communication networks, thus achieving a space-air-ground-sea integrated communication network. Second, all spectra will be fully explored to further increase data rates and connection density, including the sub-6 GHz, millimeter wave (mmWave), terahertz (THz), and optical frequency bands. Third, facing the big datasets generated by the use of extremely heterogeneous networks, diverse communication scenarios, large numbers of antennas, wide bandwidths, and new service requirements, 6G networks will enable a new range of smart applications with the aid of artificial intelligence (AI) and big data technologies. Fourth, network security will have to be strengthened when developing 6G networks. This article provides a comprehensive survey of recent advances and future trends in these four aspects. Clearly, 6G with additional technical requirements beyond those of 5G will enable faster and further communications to the extent that the boundary between physical and cyber worlds disappears.

In this article, deep learning is applied to estimate the uplink channels for mixed analog-to-digital converters (ADCs) massive multiple-input multiple-output (MIMO) systems, where a portion of antennas are equipped with high-resolution ADCs while others employ low-resolution ones at the base station. A direct-input deep neural network (DI-DNN) is first proposed to estimate channels by using the received signals of all antennas. To eliminate the adverse impact of the coarsely quantized signals, a selective-input prediction DNN (SIP-DNN) is developed, where only the signals received by the high-resolution ADC antennas are exploited to predict the channels of other antennas as well as to estimate their own channels. Numerical results show the superiority of the proposed DNN based approaches over the existing methods, especially with mixed one-bit ADCs, and the effectiveness of the proposed approaches on different ADC resolution patterns.

The bit-flip method has been successfully applied to the successive cancellation (SC) decoder to improve the block error rate (BLER) performance for polar codes in the finite code length region. However, due to the sequential decoding, the SC decoder inherently suffers from longer decoding latency than that of the belief propagation (BP) decoder with efficient early stopping criterion. It is natural to ask how to perform bit-flip in a polar BP decoder. In this paper, bit-flip is introduced into the BP decoder for polar codes. The idea of critical set (CS), that is, originally proposed by Zhang et al. for identifying unreliable bits in a SC bit-flip decoder, is extended to the BP decoder here. After revealing the relationship between CS and the incorrect BP decoding results, critical set with order ω (CS-ω) is constructed to identify unreliable bit decisions in polar BP decoding. The simulation results demonstrate that compared with the conventional BP decoder, the BLER of the proposed bit-flip decoder can achieve significant signal-to-noise ratio (SNR) gain which is comparable to that of a cyclic redundancy check-aided SC list decoder with a moderate list size. In addition, the decoding latency of the proposed BP bit-flip decoder is only slightly higher than that of the conventional BP decoder in the medium and high SNR regions. INDEX TERMS Polar codes, belief propagation, critical set, bit-flip.

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Load imbalance that deteriorates the system performance is a severe problem existing in 3GPP LTE networks. To deal with this problem, we propose in this paper a load balancing framework, which aims at balancing the load in the entire network, while keeping the network throughput as high as possible. In this framework, the objective is formulated as a network-wide utility function balancing network throughput and load distribution, and then it is transformed to an integer optimization problem under resource allocation constraints. After that, the complexity of the problem is analyzed, network structure constraints are presented, and a practical suboptimal algorithm, called Heaviest-First Load Balancing (HFLB), is proposed. Extensive simulation is made and the results show that using the HFLB algorithm the network can get significantly better load balancing while maintaining the same network throughput at the price of a bit more handovers compared with the traditional signal strength-based handover algorithm.

We present an analytic study to support the understanding of collapsing of glass tubes released by local heating and driven by the surface tension only. Our results provide a reliable analytic base for data evaluation if viscosity and surface tension of molten glasses are measured through collapsing. We complete existing 1D approaches to arrive at a consistent 2D description for axial symmetrical arrangements. We focus on advancing, steady-state collapsing profiles for heat sources moving with a constant axial velocity. The glass is considered an incompressible Stokes fluid with temperature-dependent viscosity of Arrhenius type that is ad hoc modeled by an axial, steady-state viscosity course comoving with the heat source. The analysis is carried out in comoving coordinates. Stringent scaling properties of collapsing are derived. We assume two distinct governing length scales L andh in axial and radial directions, respectively, as the base of a joint asymptotic multiscale analysis (AMSA) of the Stokes equation, boundary conditions, and kinematics for smallh/L. Numerical studies of collapsingrelevant parameters in comparison with finite element reference calculations are outlined for axial courses of the reciprocal viscosity idealized as Gaussian. For arbitrary axial viscosity courses, AMSA results in a consistent analytical description of collapsing for smallh/L. Forh/L < 1, precise formulae for collapsing-relevant geometry parameters are outlined.

For ultra-dense networks with wireless backhaul, caching strategy at small base stations (SBSs), usually with limited storage, is critical to meet massive high data rate requests. Since the content popularity profile varies with time in an unknown way, we exploit reinforcement learning (RL) to design a cooperative caching strategy with maximum-distance separable (MDS) coding. We model the MDS coding based cooperative caching as a Markov decision process to capture the popularity dynamics and maximize the long-term expected cumulative traffic load served directly by the SBSs without accessing the macro base station. For the formulated problem, we first find the optimal solution for a small-scale system by embedding the cooperative MDS coding into Q-learning. To cope with the large-scale case, we approximate the state-action value function heuristically. The approximated function includes only a small number of learnable parameters and enables us to propose a fast and efficient action-selection approach, which dramatically reduces the complexity. Numerical results verify the optimality/near-optimality of the proposed RL based algorithms and show the superiority compared with the baseline schemes. They also exhibit good robustness to different environments.

Abstract-In this paper, we investigate load balancing problem in 3GPP Long Term Evolution (LTE) network. Since LTE network aims to serve heterogeneous users with different Quality of Service (QoS) requirements, the influence of load imbalance is quite different. For those users with minimum rate requirements, it may result in high block probability, while for others without minimum rate requirements, the throughput of boundary users may be degraded. In this paper, we take all the differences into account and formulate the problem as a multi-objective optimization problem. Then we analyze its complexity, and propose our solution framework, which includes QoS-guaranteed hybrid scheduling, QoS-aware handover for users with and without QoS requirements, and call admission control. Extensive simulations are conducted and the results show that the proposed framework leads to significantly better load balancing, and thus the decrease in call block probability of users with QoS requirements, and the increase in throughput of boundary best effort users.Index Terms-3GPP LTE, load balancing, Quality of Service (QoS)