Showing papers by "Beijing University of Posts and Telecommunications published in 2015"

Very deep convolutional neural network based image classification using small training sample size

Abstract: Since Krizhevsky won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) 2012 competition with the brilliant deep convolutional neural networks(D-CNNs), researchers have designed lots of D-CNNs. However, almost all the existing very deep convolutional neural networks are trained on the giant ImageNet datasets. Small datasets like CIFAR-10 has rarely taken advantage of the power of depth since deep models are easy to overfit. In this paper, we proposed a modified VGG-16 network and used this model to fit CIFAR-10. By adding stronger regularizer and using Batch Normalization, we achieved 8.45% error rate on CIFAR-10 without severe overfitting. Our results show that the very deep CNN can be used to fit small datasets with simple and proper modifications and don't need to re-design specific small networks. We believe that if a model is strong enough to fit a large dataset, it can also fit a small one.

Detect Rumors Using Time Series of Social Context Information on Microblogging Websites

Abstract: Automatically identifying rumors from online social media especially microblogging websites is an important research issue. Most of existing work for rumor detection focuses on modeling features related to microblog contents, users and propagation patterns, but ignore the importance of the variation of these social context features during the message propagation over time. In this study, we propose a novel approach to capture the temporal characteristics of these features based on the time series of rumor's lifecycle, for which time series modeling technique is applied to incorporate various social context information. Our experiments using the events in two microblog datasets confirm that the method outperforms state-of-the-art rumor detection approaches by large margins. Moreover, our model demonstrates strong performance on detecting rumors at early stage after their initial broadcast.

Heterogeneous Vehicular Networking: A Survey on Architecture, Challenges, and Solutions

Abstract: With the rapid development of the Intelligent Transportation System (ITS), vehicular communication networks have been widely studied in recent years. Dedicated Short Range Communication (DSRC) can provide efficient real-time information exchange among vehicles without the need of pervasive roadside communication infrastructure. Although mobile cellular networks are capable of providing wide coverage for vehicular users, the requirements of services that require stringent real-time safety cannot always be guaranteed by cellular networks. Therefore, the Heterogeneous Vehicular NETwork (HetVNET), which integrates cellular networks with DSRC, is a potential solution for meeting the communication requirements of the ITS. Although there are a plethora of reported studies on either DSRC or cellular networks, joint research of these two areas is still at its infancy. This paper provides a comprehensive survey on recent wireless networks techniques applied to HetVNETs. Firstly, the requirements and use cases of safety and non-safety services are summarized and compared. Consequently, a HetVNET framework that utilizes a variety of wireless networking techniques is presented, followed by the descriptions of various applications for some typical scenarios. Building such HetVNETs requires a deep understanding of heterogeneity and its associated challenges. Thus, major challenges and solutions that are related to both the Medium Access Control (MAC) and network layers in HetVNETs are studied and discussed in detail. Finally, we outline open issues that help to identify new research directions in HetVNETs.

In-Band Full-Duplex Relaying: A Survey, Research Issues and Challenges

Abstract: Recent advances in self-interference cancellation techniques enable in-band full-duplex wireless systems, which transmit and receive simultaneously in the same frequency band with high spectrum efficiency. As a typical application of in-band full-duplex wireless, in-band full-duplex relaying (FDR) is a promising technology to integrate the merits of in-band full-duplex wireless and relaying technology. However, several significant research challenges remain to be addressed before its widespread deployment, including small-size full-duplex device design, channel modeling and estimation, cross-layer/joint resource management, interference management, security, etc. In this paper, we provide a brief survey on some of the works that have already been done for in-band FDR, and discuss the related research issues and challenges. We identify several important aspects of in-band FDR: basics, enabling technologies, information-theoretical performance analysis, key design issues and challenges. Finally, we also explore some broader perspectives for in-band FDR.

System architecture and key technologies for 5G heterogeneous cloud radio access networks

Abstract: Compared with fourth generation cellular systems, fifth generation wireless communication systems are anticipated to provide spectral and energy efficiency growth by a factor of at least 10, and the area throughput growth by a factor of at least 25. To achieve these goals, a H-CRAN is presented in this article as the advanced wireless access network paradigm, where cloud computing is used to fulfill the centralized large-scale cooperative processing for suppressing co-channel interferences. The state-of-the-art research achievements in the areas of system architecture and key technologies for H-CRANs are surveyed. Particularly, Node C as a new communication entity is defined to converge the existing ancestral base stations and act as the base band unit pool to manage all accessed remote radio heads. Also, the software-defined H-CRAN system architecture is presented to be compatible with software-defined networks. The principles, performance gains, and open issues of key technologies, including adaptive large-scale cooperative spatial signal processing, cooperative radio resource management, network function virtualization, and self-organization, are summarized. The major challenges in terms of fronthaul constrained resource allocation optimization and energy harvesting that may affect the promotion of H-CRANs are discussed as well.

Fronthaul-constrained cloud radio access networks: insights and challenges

Abstract: As a promising paradigm for fifth generation wireless communication systems, cloud radio access networks (C-RANs) have been shown to reduce both capital and operating expenditures, as well as to provide high spectral efficiency (SE) and energy efficiency (EE). The fronthaul in such networks, defined as the transmission link between the baseband unit and the remote radio head, requires a high capacity, but is often constrained. This article comprehensively surveys recent advances in fronthaul-constrained CRANs, including system architectures and key techniques. Particularly, major issues relating to the impact of the constrained fronthaul on SE/EE and quality of service for users, including compression and quantization, large-scale coordinated processing and clustering, and resource allocation optimization, are discussed together with corresponding potential solutions. Open issues in terms of software-defined networking, network function virtualization, and partial centralization are also identified.

Inferring social status and rich club effects in enterprise communication networks.

Abstract: Social status, defined as the relative rank or position that an individual holds in a social hierarchy, is known to be among the most important motivating forces in social behaviors. In this paper, we consider the notion of status from the perspective of a position or title held by a person in an enterprise. We study the intersection of social status and social networks in an enterprise. We study whether enterprise communication logs can help reveal how social interactions and individual status manifest themselves in social networks. To that end, we use two enterprise datasets with three communication channels — voice call, short message, and email — to demonstrate the social-behavioral differences among individuals with different status. We have several interesting findings and based on these findings we also develop a model to predict social status. On the individual level, high-status individuals are more likely to be spanned as structural holes by linking to people in parts of the enterprise networks that are otherwise not well connected to one another. On the community level, the principle of homophily, social balance and clique theory generally indicate a “rich club” maintained by high-status individuals, in the sense that this community is much more connected, balanced and dense. Our model can predict social status of individuals with 93% accuracy.

Integrating network function virtualization with SDR and SDN for 4G/5G networks

Abstract: The rapidly diversified market demands have presented a huge challenge to the conventional mobile broadband network architecture. On one hand, the limited machine room space and insufficient power supply make it impossible to accommodate exponentially growing amount of network equipment of operators. On the other hand, net heterogeneity caused by different specifications of wireless access equipment causes costly trouble related to management and optimization. This article, correspondingly, proposes a holistic solution involving different technologies, i.e. network function virtualization (NFV), software defined radio (SDR), and software defined network (SDN). In particular, we investigate both existing standards and possible extensions for 4G/5G mobile networks, followed by a few open issues for future research.

Survey of Large-Scale MIMO Systems

Abstract: The escalating teletraffic growth imposed by the proliferation of smartphones and tablet computers outstrips the capacity increase of wireless communications networks. Furthermore, it results in substantially increased carbon dioxide emissions. As a powerful countermeasure, in the case of full-rank channel matrices, MIMO techniques are potentially capable of linearly increasing the capacity or decreasing the transmit power upon commensurately increasing the number of antennas. Hence, the recent concept of large-scale MIMO (LS-MIMO) systems has attracted substantial research attention and been regarded as a promising technique for next-generation wireless communications networks. Therefore, this paper surveys the state of the art of LS-MIMO systems. First, we discuss the measurement and modeling of LS-MIMO channels. Then, some typical application scenarios are classified and analyzed. Key techniques of both the physical and network layers are also detailed. Finally, we conclude with a range of challenges and future research topics.

Semantic Path based Personalized Recommendation on Weighted Heterogeneous Information Networks

Abstract: Recently heterogeneous information network (HIN) analysis has attracted a lot of attention, and many data mining tasks have been exploited on HIN. As an important data mining task, recommender system includes a lot of object types (e.g., users, movies, actors, and interest groups in movie recommendation) and the rich relations among object types, which naturally constitute a HIN. The comprehensive information integration and rich semantic information of HIN make it promising to generate better recommendations. However, conventional HINs do not consider the attribute values on links, and the widely used meta path in HIN may fail to accurately capture semantic relations among objects, due to the existence of rating scores (usually ranging from 1 to 5) between users and items in recommender system. In this paper, we are the first to propose the weighted HIN and weighted meta path concepts to subtly depict the path semantics through distinguishing different link attribute values. Furthermore, we propose a semantic path based personalized recommendation method SemRec to predict the rating scores of users on items. Through setting meta paths, SemRec not only flexibly integrates heterogeneous information but also obtains prioritized and personalized weights representing user preferences on paths. Experiments on two real datasets illustrate that SemRec achieves better recommendation performance through flexibly integrating information with the help of weighted meta paths.

A Survey of Mobile Information-Centric Networking: Research Issues and Challenges

Abstract: Recently, a series of innovative information-centric networking (ICN) architectures have been designed to better address the shift from host-centric end-to-end communication to requester-driven content retrieval. With the explosive increase of mobile data traffic, the mobility issue in ICN is a growing concern and a number of approaches have been proposed to deal with the mobility problem in ICN. Despite the potential advantages of ICN in mobile wireless environments, several significant research challenges remain to be addressed before its widespread deployment, including consistent routing, local cached content discovery, energy efficiency, privacy, security and trust, and practical deployment. In this paper, we present a brief survey on some of the works that have already been done to achieve mobile ICN, and discuss some research issues and challenges. We identify several important aspects of mobile ICN: overview, mobility enabling technologies, information-centric wireless mobile networks, and research challenges.

Physarum Optimization: A Biology-Inspired Algorithm for the Steiner Tree Problem in Networks

Abstract: Using insights from biological processes could help to design new optimization techniques for long-standing computational problems. This paper exploits a cellular computing model in the slime mold physarum polycephalum to solve the Steiner tree problem which is an important NP-hard problem in various applications, especially in network design. Inspired by the path-finding and network formation capability of physarum, we develop a new optimization algorithm, named as the physarum optimization, with low complexity and high parallelism. To validate and evaluate our proposed models and algorithm, we further apply the physarum optimization to the minimal exposure problem which is a fundamental problem corresponding to the worst-case coverage in wireless sensor networks. Complexity analysis and simulation results show that our proposed algorithm could achieve good performance with low complexity. Moreover, the core mechanism of our physarum optimization also may provide a useful starting point to develop some practical distributed algorithms for network design.

An SMDP-Based Resource Allocation in Vehicular Cloud Computing Systems

Abstract: Vehicular ad hoc networks are expected to significantly improve traffic safety and transportation efficiency while providing a comfortable driving experience. However, available communication, storage, and computation resources of the connected vehicles are not well utilized to meet the service requirements of intelligent transportation systems. Vehicular cloud computing (VCC) is a promising approach that makes use of the advantages of cloud computing and applies them to vehicular networks. In this paper, we propose an optimal computation resource allocation scheme to maximize the total long-term expected reward of the VCC system. The system reward is derived by taking into account both the income and cost of the VCC system as well as the variability feature of available resources. Then, the optimization problem is formulated as an infinite horizon semi-Markov decision process (SMDP) with the defined state space, action space, reward model, and transition probability distribution of the VCC system. We utilize the iteration algorithm to develop the optimal scheme that describes which action has to be taken under a certain state. Numerical results demonstrate that the significant performance gain can be obtained by the SMDP-based scheme within the acceptable complexity.

Energy-Efficient Resource Assignment and Power Allocation in Heterogeneous Cloud Radio Access Networks

Abstract: Taking full advantage of both heterogeneous networks and cloud access radio access networks, heterogeneous cloud radio access networks (H-CRANs) are presented to enhance both spectral and energy efficiencies, where remote radio heads (RRHs) are mainly used to provide high data rates for users with high quality of service (QoS) requirements, whereas the high-power node (HPN) is deployed to guarantee seamless coverage and serve users with low-QoS requirements. To mitigate the intertier interference and improve energy efficiency (EE) performances in H-CRANs, characterizing user association with RRH/HPN is considered in this paper, and the traditional soft fractional frequency reuse (S-FFR) is enhanced. Based on the RRH/HPN association constraint and the enhanced S-FFR, an energy-efficient optimization problem with the resource assignment and power allocation for the orthogonal-frequency-division-multiple-access-based H-CRANs is formulated as a nonconvex objective function. To deal with the nonconvexity, an equivalent convex feasibility problem is reformulated, and closed-form expressions for the energy-efficient resource allocation solution to jointly allocate the resource block and transmit power are derived by the Lagrange dual decomposition method. Simulation results confirm that the H-CRAN architecture and the corresponding resource allocation solution can enhance the EE significantly.

A Survey of Heterogeneous Information Network Analysis

Abstract: Most real systems consist of a large number of interacting, multi-typed components, while most contemporary researches model them as homogeneous networks, without distinguishing different types of objects and links in the networks. Recently, more and more researchers begin to consider these interconnected, multi-typed data as heterogeneous information networks, and develop structural analysis approaches by leveraging the rich semantic meaning of structural types of objects and links in the networks. Compared to widely studied homogeneous network, the heterogeneous information network contains richer structure and semantic information, which provides plenty of opportunities as well as a lot of challenges for data mining. In this paper, we provide a survey of heterogeneous information network analysis. We will introduce basic concepts of heterogeneous information network analysis, examine its developments on different data mining tasks, discuss some advanced topics, and point out some future research directions.

Multi-manifold deep metric learning for image set classification

Abstract: In this paper, we propose a multi-manifold deep metric learning (MMDML) method for image set classification, which aims to recognize an object of interest from a set of image instances captured from varying viewpoints or under varying illuminations. Motivated by the fact that manifold can be effectively used to model the nonlinearity of samples in each image set and deep learning has demonstrated superb capability to model the nonlinearity of samples, we propose a MMDML method to learn multiple sets of nonlinear transformations, one set for each object class, to nonlinearly map multiple sets of image instances into a shared feature subspace, under which the manifold margin of different class is maximized, so that both discriminative and class-specific information can be exploited, simultaneously. Our method achieves the state-of-the-art performance on five widely used datasets.

An intelligent SDN framework for 5G heterogeneous networks

Abstract: In fifth-generation (5G) mobile networks, a major challenge is to effectively improve system capacity and meet dynamic service demands. One promising technology to solve this problem is heterogeneous networks (HetNets), which involve a large number of densified low power nodes (LPNs). This article proposes a software defined network (SDN) based intelligent model that can efficiently manage the heterogeneous infrastructure and resources. In particular, we first review the latest SDN standards and discuss the possible extensions. We then discuss the advantages of SDN in meeting the dynamic nature of services and requirements in 5G HetNets. Finally, we develop a variety of schemes to improve traffic control, subscriber management, and resource allocation. Performance analysis shows that our proposed system is reliable, scalable, and implementable.

Structured Sparse Subspace Clustering: A unified optimization framework

Abstract: Subspace clustering refers to the problem of segmenting data drawn from a union of subspaces. State of the art approaches for solving this problem follow a two-stage approach. In the first step, an affinity matrix is learned from the data using sparse or low-rank minimization techniques. In the second step, the segmentation is found by applying spectral clustering to this affinity. While this approach has led to state of the art results in many applications, it is sub-optimal because it does not exploit the fact that the affinity and the segmentation depend on each other. In this paper, we propose a unified optimization framework for learning both the affinity and the segmentation. Our framework is based on expressing each data point as a structured sparse linear combination of all other data points, where the structure is induced by a norm that depends on the unknown segmentation. We show that both the segmentation and the structured sparse representation can be found via a combination of an alternating direction method of multipliers with spectral clustering. Experiments on a synthetic data set, the Hopkins 155 motion segmentation database, and the Extended Yale B data set demonstrate the effectiveness of our approach.

A Survey of Incentive Mechanisms for Participatory Sensing

Abstract: Participatory sensing is now becoming more popular and has shown its great potential in various applications. It was originally proposed to recruit ordinary citizens to collect and share massive amounts of sensory data using their portable smart devices. By attracting participants and paying rewards as a return, incentive mechanisms play an important role to guarantee a stable scale of participants and to improve the accuracy/coverage/timeliness of the sensing results. Along this direction, a considerable amount of research activities have been conducted recently, ranging from experimental studies to theoretical solutions and practical applications, aiming at providing more comprehensive incentive procedures and/or protecting benefits of different system stakeholders. To this end, this paper surveys the literature over the period of 2004–2014 from the state of the art of theoretical frameworks, applications and system implementations, and experimental studies of the incentive strategies used in participatory sensing by providing up-to-date research in the literature. We also point out future directions of incentive strategies used in participatory sensing.

Recent Advances in Underlay Heterogeneous Networks: Interference Control, Resource Allocation, and Self-Organization

Abstract: By deploying additional low power nodes (LPNs) within the coverage area of traditional high power nodes (HPNs) and bringing them closer to users, underlay heterogeneous networks (HetNets) can significantly boost the overall spectral efficiency (SE) and energy efficiency (EE) through a full spatial resource reuse. Considering that the severe intra-tier interference among dense LPNs and inter-tier interference between LPNs and HPNs are challenging the successful rollout and commercial operations of underlay HetNets, a great emphasis is given towards advanced techniques that take interference control, radio resource allocation, and self-organization into account to enhance both SE and EE in this paper. The interference control techniques presented in this paper are classified as the spatial interference coordination at the transmitter and the interference cancelation at the receiver. For the radio resource allocation, the multi-dimensional optimization, cross-layer optimization, and cooperative radio resource management are comprehensively summarized. The self-configuration, self-optimization, and self-healing techniques for the self-organized underlay HetNets are surveyed. Furthermore, this paper outlines the potential open issues for underlay HetNets to improve SE and EE when combining with energy harvesting and cloud computing.

Low power wide area machine-to-machine networks: key techniques and prototype

Abstract: As one of the fastest growing technologies, machine-to-machine (M2M) communications are expected to provide ubiquitous connectivity. M2M devices can be used for a wide range of emerging applications that have various communications requirements. While M2M communications have been developed for many years, major challenges still remain with their efficient implementation from the perspective of low energy consumption and wide coverage. To address these challenges, low power wide area (LPWA) technology is investigated as one of the potential candidate solutions. In this article, we first introduce some typical LPWA M2M application scenarios. Given their requirements, we highlight key techniques and standards that are explicitly designed for LPWA M2M communications. Finally, we present an LPWA prototype system to evaluate its performance and demonstrate its potential in bridging a technological gap for future Internet-of-Things (IoT) applications.

Dual yolk-shell structure of carbon and silica-coated silicon for high-performance lithium-ion batteries

Abstract: Silicon batteries have attracted much attention in recent years due to their high theoretical capacity, although a rapid capacity fade is normally observed, attributed mainly to volume expansion during lithiation. Here, we report for the first time successful synthesis of Si/void/SiO2/void/C nanostructures. The synthesis strategy only involves selective etching of SiO2 in Si/SiO2/C structures with hydrofluoric acid solution. Compared with reported results, such novel structures include a hard SiO2-coated layer, a conductive carbon-coated layer, and two internal void spaces. In the structures, the carbon can enhance conductivity, the SiO2 layer has mechanically strong qualities, and the two internal void spaces can confine and accommodate volume expansion of silicon during lithiation. Therefore, these specially designed dual yolk-shell structures exhibit a stable and high capacity of 956 mA h g(-1) after 430 cycles with capacity retention of 83%, while the capacity of Si/C core-shell structures rapidly decreases in the first ten cycles under the same experimental conditions. The novel dual yolk-shell structures developed for Si can also be extended to other battery materials that undergo large volume changes.

Distortion-Aware Concurrent Multipath Transfer for Mobile Video Streaming in Heterogeneous Wireless Networks

Abstract: The massive proliferation of wireless infrastructures with complementary characteristics prompts the bandwidth aggregation for Concurrent Multipath Transfer (CMT) over heterogeneous access networks. Stream Control Transmission Protocol (SCTP) is the standard transport-layer solution to enable CMT in multihomed communication environments. However, delivering high-quality streaming video with the existing CMT solutions still remains problematic due to the stringent quality of service (QoS) requirements and path asymmetry in heterogeneous wireless networks. In this paper, we advance the state of the art by introducing video distortion into the decision process of multipath data transfer. The proposed distortion-aware concurrent multipath transfer (CMT-DA) solution includes three phases: 1) per-path status estimation and congestion control; 2) quality-optimal video flow rate allocation; 3) delay and loss controlled data retransmission. The term ‘flow rate allocation’ indicates dynamically picking appropriate access networks and assigning the transmission rates. We analytically formulate the data distribution over multiple communication paths to minimize the end-to-end video distortion and derive the solution based on the utility maximization theory. The performance of the proposed CMT-DA is evaluated through extensive semi-physical emulations in Exata involving H.264 video streaming. Experimental results show that CMT-DA outperforms the reference schemes in terms of video peak signal-to-noise ratio (PSNR), goodput, and inter-packet delay.

Achievable Throughput Optimization in Energy Harvesting Cognitive Radio Systems

Abstract: In this paper, we consider an energy harvesting cognitive radio (CR) system operating in slotted mode, where the secondary user (SU) has no wired power supplies and is powered exclusively by energy harvested from ambient environment. The SU can only perform either energy harvesting, spectrum sensing or data transmission at a time due to hardware limitation such that a timeslot is segmented into three non-overlapping fractions. Considering a generalized multi-slot spectrum sensing paradigm and two types of fusion rules: data fusion and decision fusion, we focus on the “harvesting-sensing-throughput” tradeoff and joint optimization for save-ratio, sensing duration, sensing threshold as well as fusion rule to maximize the SU's expected achievable throughput while keeping primary users (PUs) sufficiently protected. For data-fusion spectrum sensing, we translate the original problem into a convex one and show that the optimal solutions for sample number, mini-slot number as well as sensing threshold are non-unique. For decision-fusion spectrum sensing, we propose a two-level algorithm to solve the original problem with in-depth analysis on the convexity of a simplified problem and experiments show that the proposed algorithm is more efficient than differential evolution algorithm. We find that despite the inherent difference between the two types of fusion rules, the optimal data-fusion and decision-fusion strategies both converge to single-slot spectrum sensing while the SU's maximal expected achievable throughput is attained. Simulation results show that the optimal single-slot spectrum sensing strategy outperforms three other multi-slot strategies as well as two existing strategies while the empirical probability of detection is limited under a predefined level.

Data Gathering with Compressive Sensing in Wireless Sensor Networks: A Random Walk Based Approach

Abstract: In this paper, we study the problem of data gathering with compressive sensing (CS) in wireless sensor networks (WSNs). Unlike the conventional approaches, which require uniform sampling in the traditional CS theory, we propose a random walk algorithm for data gathering in WSNs. However, such an approach will conform to path constraints in networks and result in the non-uniform selection of measurements. It is still unknown whether such a non-uniform method can be used for CS to recover sparse signals in WSNs. In this paper, from the perspectives of CS theory and graph theory, we provide mathematical foundations to allow random measurements to be collected in a random walk based manner. We find that the random matrix constructed from our random walk algorithm can satisfy the expansion property of expander graphs. The theoretical analysis shows that a k-sparse signal can be recovered using `1 minimization decoding algorithm when it takes m = O(k log(n=k)) independent random walks with the length of each walk t = O(n=k) in a random geometric network with n nodes. We also carry out simulations to demonstrate the effectiveness of the proposed scheme. Simulation results show that our proposed scheme can significantly reduce communication cost compared to the conventional schemes using dense random projections and sparse random projections, indicating that our scheme can be a more practical alternative for data gathering applications in WSNs.

Toward 5G densenets: architectural advances for effective machine-type communications over femtocells

Abstract: Ubiquitous, reliable and low-latency machinetype communication, MTC, systems are considered to be value-adds of emerging 5G cellular networks. To meet the technical and economical requirements for exponentially growing MTC traffic, we advocate the use of small cells to handle the massive and dense MTC rollout. We introduce a novel 3GPP-compliant architecture that absorbs the MTC traffic via home evolved NodeBs, allowing us to significantly reduce congestion and overloading of radio access and core networks. A major design challenge has been to deal with the interference to human-type traffic and the large degree of freedom of the system, due to the unplanned deployments of small cells and the enormous amount of MTC devices. Simulation results in terms of MTC access delay, energy consumption, and delivery rate corroborate the superiority of the proposed working architecture.

A survey: Several technologies of non-orthogonal transmission for 5G

Abstract: One key advantage of 4G OFDM system is the relatively simple receiver implementation due to the orthogonal resource allocation. However, from sum-capacity and spectral efficiency points of view, orthogonal systems are never the achieving schemes. With the rapid development of mobile communication systems, a novel concept of non-orthogonal transmission for 5G mobile communications has attracted researches all around the world. In this trend, many new multiple access schemes and waveform modulation technologies were proposed. In this paper, some promising ones of them were discussed which include Non-orthogonal Multiple Access (NOMA), Sparse Code Multiple Access (SCMA), Multi-user Shared Access (MUSA), Pattern Division Multiple Access (PDMA) and some main new waveforms including Filter-bank based Multicarrier (FBMC), Universal Filtered Multi-Carrier (UFMC), Generalized Frequency Division Multiplexing (GFDM). By analyzing and comparing features of these technologies, a research direction of guiding on future 5G multiple access and waveform are given.

10 Gb/s hetsnets with millimeter-wave communications: access and networking - challenges and protocols

Abstract: Heterogeneous and small cell networks (Het- SNets) increase spectral efficiency and throughput via hierarchical deployments. In order to meet the increasing requirements in capacity for future 5G wireless networks, millimeter-wave (mmWave) communications with unprecedented spectral resources have been suggested for 5G HetSNets. While the mmWave physical layer is well understood, major challenges remain for its effective and efficient implementation in Het- SNets from an access and networking point of view. Toward this end, we introduce a novel but 3GPP backwards-compatible frame structure, based on time-division duplex, which facilitates both high-capacity access and backhaul links. We then discuss networking issues arising from the multihop nature of the mmWave backhauling mesh. Finally, system-level simulations evaluate the performance of HetSNets with mmWave communications and corroborate the possibility of having capacities of tens of gigabits per second in emerging 5G systems.