5G has been developing at high speed since 2012 and has become a global economic driver. In this paper, we offer a survey of 5G covering visions, requirements, roadmap, key technologies, standardization, frequency management, technology trials, industrial ecology, and a list of main 5G contributors. We also point out the contributions to 5G from China, aiming to be ‘globally leading in 5G’ by acting as a main 5G contributor in standardization and promoting/enhancing the Chinese 5G industry. Finally, progress on 5G is reviewed mixed with our rethinking of 5G.
This paper provides a comprehensive survey of the impact of the emerging communication technique, non-orthogonal multiple access (NOMA), on future wireless networks. Particularly, how the NOMA principle affects the design of the generation multiple access techniques is introduced first. Then the applications of NOMA to other advanced communication techniques, such as wireless caching, multiple-input multiple-output techniques, millimeter-wave communications, and cooperative relaying, are discussed. The impact of NOMA on communication systems beyond cellular networks is also illustrated, through the examples of digital TV, satellite communications, vehicular networks, and visible light communications. Finally, the study is concluded with a discussion of important research challenges and promising future directions in NOMA.
Uplink non-orthogonal multiple access (NOMA) is a promising technique to meet the requirements of the fifth generation (5G) and beyond systems. Various NOMA schemes have been proposed in both academia and industry. However, most existing schemes assume equal average received power, which limits the performance. We propose three enhancements of uplink NOMA to achieve the requirements of massive connectivity and high reliability in 5G, where unequal average received power is exploited as part of the multiple access signature. First, the optimal sequences targeting to generalized Welch-bound equality (GWBE) are obtained for unequal average received power. Then user grouping with multi-level received powers is proposed for better successive interference cancellation (SIC) at the receiver. Finally, sequence grouping based on the cross-correlation properties of sequences is proposed to reduce inter- and intra-group interference. Simulation results show that by incorporating multi-level received powers and sequence grouping into existing NOMA schemes, for an NOMA system with 400% overloading and fixed signature allocation, 3 dB and 10 dB signal-to-noise ratio (SNR) gains at 0.1 block error rate (BLER) target can be achieved compared with existing NOMA schemes and orthogonal multiple access (OMA), respectively. Besides, 0.01 BLER target can be achieved while an error floor exists in existing NOMA schemes. Under random sequence selection, collision probability is reduced by multi-level powers. In addition, GWBE sequences achieve lower BLER than existing sequences and the gain is large especially for low BLER requirements. This shows that the proposed scheme can support larger connectivity and higher reliability.
We study the performance of space division multiple access (SDMA) under a non-ideal engineering situation. When the SDMA channel is of high inter-layer correlation and the condition number is large, the multiple user multiple input and multiple output user equipment (MUMIMOUE) grouping should be optimized, and in some cases further dimension-reduction should be applied. As the channel measuring is always non-ideal, we use two methods, feedback mode and non-feedback mode, in terms of performance and overhead. It is proposed that the non-feedback mode is preferable even for some non-reciprocal channels. Principle analysis and test results are given.
The fifth generation (5G) networks have been envisioned to support the explosive growth of data demand caused by the increasing traditional high-rate mobile users and the expected rise of interconnections between human and things. To accommodate the ever-growing data traffic with scarce spectrum resources, cognitive radio (CR) is considered a promising technology to improve spectrum utilization. We study the power control problem for secondary users in an underlay CR network. Unlike most existing studies which simplify the problem by considering only a single primary user or channel, we investigate a more realistic scenario where multiple primary users share multiple channels with secondary users. We formulate the power control problem as a non-cooperative game with coupled constraints, where the Pareto optimality and achievable total throughput can be obtained by a Nash equilibrium (NE) solution. To achieve NE of the game, we first propose a projected gradient based dynamic model whose equilibrium points are equivalent to the NE of the original game, and then derive a centralized algorithm to solve the problem. Simulation results show that the convergence and effectiveness of our proposed solution, emphasizing the proposed algorithm, are competitive. Moreover, we demonstrate the robustness of our proposed solution as the network size increases.
We consider a single-cell network with a hybrid full-/half-duplex base station. For the practical scenario with N channels, K uplink users, and M downlink users (max{K,M}≤N ≤K + M), we tackle the issue of user admission and power control to simultaneously maximize the user admission number and minimize the total transmit power when guaranteeing the quality-of-service requirement of individual users. We formulate a 0–1 integer programming problem for the joint-user admission and power allocation problem. Because finding the optimal solution of this problem is NP-hard in general, a low-complexity algorithm is proposed by introducing the novel concept of adding dummy users. Simulation results show that the proposed algorithm achieves performance similar to that of branch and bound algorithm and significantly outperforms the random pairing algorithm.
Direct-conversion transceivers are gaining increasing attention due to their low power consumption. However, they suffer from a serious in- and quadrature-phase (I/Q) imbalance problem. The I/Q imbalance can severely limit the achievable operating signal-to-noise ratio (SNR) at the receiver and, consequently, the supported constellation sizes and data rates. In this paper, we first investigate the effects of I/Q imbalance on orthogonal frequency division multiplexing (OFDM) receivers, and then propose a new I/Q imbalance compensation scheme. In the proposed method, a new statistic, which is robust against channel distortion, is used to estimate the I/Q imbalance parameters, and then the I/Q imbalance is corrected in the frequency domain. Simulations are performed to verify the effectiveness of the proposed method for I/Q imbalance compensation. The results show that the proposed I/Q imbalance compensation method can achieve bit error rate (BER) performance close to that in the ideal case without I/Q imbalance in additive white Gaussian noise (AWGN) or multipath environments. Furthermore, because no pilot information is required, this method can be applied in various standard communication systems.
We investigate the problem of resource allocation in a downlink orthogonal frequency-division multiple access (OFDMA) broadband network with an eavesdropper under the condition that both legitimate users and the eavesdropper are with imperfect channel state information (CSI). We consider three kinds of imperfect CSI: (1) noise and channel estimation errors, (2) feedback delay and channel prediction, and (3) limited feedback channel capacity, where quantized CSI is studied using rate-distortion theory because it can be used to establish an informationtheoretic lower bound on the capacity of the feedback channel. The problem is formulated as joint power and subcarrier allocation to optimize the maximum-minimum (max-min) fairness criterion over the users’ secrecy rate. The problem considered is a mixed integer nonlinear programming problem. To reduce the complexity, we propose a two-step suboptimal algorithm that separately performs power and subcarrier allocation. For a given subcarrier assignment, optimal power allocation is achieved by developing an algorithm of polynomial computational complexity. Numerical results show that our proposed algorithm can approximate the optimal solution.
The distinctive characteristics of unmanned aerial vehicle networks (UAVNs), including highly dynamic network topology, high mobility, and open-air wireless environments, may make UAVNs vulnerable to attacks and threats. In this study, we propose a novel trust model for UAVNs that is based on the behavior and mobility pattern of UAV nodes and the characteristics of inter-UAV channels. The proposed trust model consists of four parts: direct trust section, indirect trust section, integrated trust section, and trust update section. Based on the trust model, the concept of a secure link in UAVNs is formulated that exists only when there is both a physical link and a trust link between two UAVs. Moreover, the metrics of both the physical connectivity probability and the secure connectivity probability between two UAVs are adopted to analyze the connectivity of UAVNs. We derive accurate and analytical expressions of both the physical connectivity probability and the secure connectivity probability using stochastic geometry with or without Doppler shift. Extensive simulations show that compared with the physical connection probability with or without malicious attacks, the proposed trust model can guarantee secure communication and reliable connectivity between UAVs and enhance network performance when UAVNs face malicious attacks and other security risks.
Electroencephalogram (EEG) based brain-computer interfaces allow users to communicate with the external environment by means of their EEG signals, without relying on the brain’s usual output pathways such as muscles. A popular application for EEGs is the EEG-based speller, which translates EEG signals into intentions to spell particular words, thus benefiting those suffering from severe disabilities, such as amyotrophic lateral sclerosis. Although the EEG-based English speller (EEGES) has been widely studied in recent years, few studies have focused on the EEG-based Chinese speller (EEGCS). The EEGCS is more difficult to develop than the EEGES, because the English alphabet contains only 26 letters. By contrast, Chinese contains more than 11 000 logographic characters. The goal of this paper is to survey the literature on EEGCS systems. First, the taxonomy of current EEGCS systems is discussed to get the gist of the paper. Then, a common framework unifying the current EEGCS and EEGES systems is proposed, in which the concept of EEG-based choice acts as a core component. In addition, a variety of current EEGCS systems are investigated and discussed to highlight the advances, current problems, and future directions for EEGCS.
The cohort intelligence (CI) method has recently evolved as an optimization method based on artificial intelligence. We use the CI method for the first time to optimize the parameters of the fractional proportionalintegral-derivative (PID) controller. The performance of the CI method in designing the fractional PID controller was validated and compared with those of some other popular algorithms such as particle swarm optimization, the genetic algorithm, and the improved electromagnetic algorithm. The CI method yielded improved solutions in terms of the cost function, computing time, and function evaluations in comparison with the other three algorithms. In addition, the standard deviations of the CI method demonstrated the robustness of the proposed algorithm in solving control problems.
Efficient resource scheduling and allocation in radiological examination process (REP) execution is a key requirement to improve patient throughput and radiological resource utilization and to manage unexpected events that occur when resource scheduling and allocation decisions change due to clinical needs. In this paper, a Tabu search based approach is presented to solve the resource scheduling and allocation problems in REP execution. The primary objective of the approach is to minimize a weighted sum of average examination flow time, average idle time of the resources, and delays. Unexpected events, i.e., emergent or absent examinations, are also considered. For certain parameter combinations, the optimal solution of radiological resource scheduling and allocation is found, while considering the limitations such as routing and resource constraints. Simulations in the application case are performed. Results show that the proposed approach makes efficient use of radiological resource capacity and improves the patient throughput in REP execution.
The latest video coding standard High Efficiency Video Coding (HEVC) can achieve much higher coding efficiency than previous video coding standards. Particularly, by exploiting the hierarchical B-picture prediction structure, temporal redundancy among neighbor frames is eliminated remarkably well. In practice, videos available to consumers usually contain many repeated shots, such as TV series, movies, and talk shows. According to our observations, when these videos are encoded by HEVC with the hierarchical B-picture structure, the temporal correlation in each shot is well exploited. However, the long-term correlation between repeated shots has not been used. We propose a long-term prediction (LTP) scheme to use the long-term temporal correlation between correlated shots in a video. The long-term reference (LTR) frames of a source video are chosen by clustering similar shots and extracting the representative frames, and a modified hierarchical B-picture coding structure based on an LTR frame is introduced to support long-term temporal prediction. An adaptive quantization method is further designed for LTR frames to improve the overall video coding efficiency. Experimental results show that up to 22.86% coding gain can be achieved using the new coding scheme.