一种人与人和机器到机器共存下能效最大化的上行用户分配算法

针对5G超密集异构蜂窝网络中人与人(H2H)和机器到机器(M2M)共存场景下有服务质量(QoS)保障和负载均衡的上行用户分配问题,该文提出一种基于匹配理论的用户分配算法。该算法在用户分配过程中同时考虑不同类型节点的接入控制策略,在最大化网络能效的同时,实现节点QoS保障。仿真结果表明,与其他算法相比,该算法不仅能够在能效、负载均衡以及QoS保障方面获得更好的性能,并且能获得与穷举法相近的性能。此外,所提算法的收敛速度很快且不受节点和基站数目变化的影响,适合解决H2H和M2M共存场景中的用户分配问题。     

一种人与人和机器到机器共存下能效最大化的上行用户分配算法

针对5G超密集异构蜂窝网络中人与人(H2H)和机器到机器(M2M)共存场景下有服务质量(QoS)保障和负载均衡的上行用户分配问题,该文提出一种基于匹配理论的用户分配算法.该算法在用户分配过程中同时考虑不同类型节点的接入控制策略,在最大化网络能效的同时,实现节点QoS保障.仿真结果表明,与其他算法相比,该算法不仅能够在能效、负载均衡以及QoS保障方面获得更好的性能,并且能获得与穷举法相近的性能.此外,所提算法的收敛速度很快且不受节点和基站数目变化的影响,适合解决H2H和M2M共存场景中的用户分配问题.     

基于学习的能量采集认知M2M通信资源分配算法

本文针对能量采集认知机器到机器（Machine-to-Machine,M2M）通信的能量效率问题，在保证服务质量（Quality of Service,QoS）的条件下，提出了一种能效优化算法.以最大化网络中用户能效为目标，综合考虑传输功率控制、时隙分配、传输模式选择、中继选择以及每个设备的能量状态为约束，将优化问题建模为一个混合整数非线性规划问题.将该能效优化问题转化为离散时间有限状态马尔科夫决策过程（Discrete-time and Finite-state Markov Decision Process,DFMDP）进行求解.提出一种基于深度强化学习的算法寻找最优策略.仿真结果表明，所提算法在平均能效方面优于其他方案，且收敛速度在可接受范围内.     

多标签无线供电反向散射通信网络能效优化算法

为了提高物联网(IoT)节点的运行周期和能量利用率,该文提出一种多标签无线供电反向散射通信网络能效最大化资源分配算法。考虑传输速率约束、能量收集约束以及发射功率约束,建立了基于系统能效最大化的资源分配模型。利用Dinkelbach理论、2次变换以及变量替换法,将原分式非凸问题转化为可求解的凸优化问题。通过拉格朗日对偶理论求得优化问题的全局最优解。仿真结果表明,该算法具有较好的收敛性和能效。     

基于能量收集的UAV-D2D网络资源分配算法

为更好地利用周围环境中的射频信号能量,提升终端直连(D2D)通信的运行时间和无人机(UAV)通信的频谱利用率,该文提出一种基于能量收集的UAV-D2D网络资源分配算法.考虑UAV最大发射功率和移动性约束,蜂窝用户和D2D用户的最小速率约束,建立了系统和速率最大化的多变量耦合资源分配问题.利用连续凸近似和变量替换方法将混...     

LTE-A无线接入网随机接入优化分析

移动通信产业即将走进LTE-A时代的今天,随着物物(M2M)通信业务的快速发展,M2M业务融入到LTE-A移动通信网络已成为了必然的发展趋势。然而,大量MTC设备同时接入网络又会造成无线接入网络的过载问题,从而影响了人与人(H2H)之间的正常通信。为了解决这一问题,3GPP提出了解决因大量机器通信(MTC)设备同时涌入而造成的无线网拥塞问题的候选方案。本文通过对这些解决方案进行分析与研究,并在此基础上对融入M2M业务的LTE-A无线网络侧的随机接入提出优化方案。     

基于通感融合的无人机预编码及飞行轨迹设计

无人机(UAVs)具有机动性强,低成本及易部署等特性,通过搭载通信及感知设备,支持通信与感知技术的高效资源共享,无人机可作为融合通信与传感技术的高性能空中平台。该文针对多输入多输出(MIMO)无人机使能的联合通信、感知场景,综合考虑无人机飞行能量、多天线传输及用户业务需求等限制条件,建模无人机通信、感知预编码及飞行轨迹联合优化问题为多目标优化问题,以实现通信用户最低速率最大化及目标最小发现概率最大化。由于通信用户最低速率最大化问题为非凸优化问题,难以直接求解,将原优化问题分解为通信预编码设计子问题及无人机轨迹设计子问题,并采用交替迭代法依次求解两个子问题直至算法收敛,其中,对于通信预编码设计子问题,提出一种基于迫零(ZF)算法的求解策略;对于无人机轨迹设计子问题,提出一种基于连续凸逼近(SCA)算法的求解策略。基于所得到的无人机最优轨迹,将无人机感知位置选择问题建模为加权距离和最小化问题,进而应用泛搜索算法优化确定目标感知位置,并设计基于ZF算法的通信感知预编码联合优化策略,以实现通信感知性能的联合优化。最后通过仿真验证了该文所提算法的有效性。     

多小区下行链路系统的资源效率最大化协同波束成形算法

本文研究了基于资源效率优化的多小区多用户协同波束成形算法设计。为了权衡系统频谱效率与能源效率,考虑在单基站发射功率约束以及达到用户服务质量需求条件下最大化系统资源效率,即系统能源效率与归一化系统频谱效率的加权和。由于优化变量之间的耦合性以及约束条件的非凸特性,该优化问题是一种非凸优化问题并且难以直接获得最优解。为了求解所考虑的优化问题,本文联合利用凸近似方法和分数规化理论,提出一种多小区下行链路系统中最大化资源效率的交替优化算法。所提算法的收敛性可以由凸近似方法和单调有界理论保证。同时,数值仿真验证了所提算法的有效性。      

蜂窝网络中基于D2D用户总能量效率保证的资源分配策略

在基于终端直连(D2D)通信的蜂窝网络中,文章提出了一种基于D2D用户能量效率保证的资源分配策略,我们的目标是尽量增大系统传输速率,并在同一时间,保证目标D2D用户能量效率。在文章提出的策略的第一阶段,采用加权二分匹配算法最大化系统传输速率,在目标D2D用户能效需求较低时,一阶段也可以满足需求,但D2D用户总能效需求变高时就需要进入第二阶段。因此,在第二阶段,先利用加权二分匹配算法最大化D2D用户能效并得到初始复用策略,然后使用局部搜索技术来改善初始复用策略。仿真结果表明,与已存在的基准策略相比,所提出的策略能够保证D2D用户能效并获得较高的系统传输速率性能收益。     

基于NOMA的无线携能D2D通信鲁棒能效优化算法

针对频谱短缺、基站负荷过高、通信系统功耗较大等问题,考虑不完美的信道状态信息,该文提出一种基于非正交多址接入的无线携能(SWIPT)D2D网络鲁棒能效(EE)最大化资源分配算法(SREA)。考虑用户的服务质量约束以及最大发射功率约束,基于随机信道不确定性建立鲁棒能效最大化资源分配模型。利用Dinkelbach和变量替换方法,将原NP-hard问题转换为确定性的凸优化问题,通过拉格朗日对偶理论求得解析解。仿真结果表明,所提算法在保证蜂窝用户通信质量的同时,能够有效提高D2D用户的能效性和鲁棒性能。     

Modeling and performance analysis of unlicensed bands MAC strategy in multi-channel LTE-A networks with M2M/H2H coexistence

With the growing use of the machine-to-machine (M2M) communication and the unlicensed band by advanced long term evolution (LTE-A) networks, known as LTE unlicensed (LTE-U), demand for resource access strategy is rapidly increasing and has recently been attracting considerable attention of mobile operators. The requirement set by 3rd generation partnership project in the release 11 about LTE standards will allow LTE-U and other unlicensed band access technology to peacefully coexist and operate in the same unlicensed band. LTE-U supports not only the human-to-human (H2H) communication but also the M2M communication. In this paper, a new MAC protocol for LTE-U that allow friendly co-existence of H2H with M2M communications working in unlicensed bands is presented. The proposed MAC mechanisms is designed to ensure an efficient and fair channel access as well as enabling better H2H/M2M coexistence. The throughput performance of both H2H and M2M systems is evaluated analytically and by simulation. The impact of H2H/M2M transmissions periods and spectrum sensing time on the throughput performance of H2H and M2M systems are also studied.     

M2M业务和H2H业务共存策略研究

随着M2M业务发展扩张,网络在H2H业务原本的矛盾上又增加了一系列新问题,研究怎样在解决新问题的同时满足M2M业务需求是一个迫切的工作。文章在对M2M业务充分分析的基础上,重点讨论M2M和H2H业务的共存策略,并针对共存策略中的过载技术进行详细论述,最后对M2M业务的市场发展进行了初步分析,并为移动运营商在M2M领域的发展提出建议。     

Energy and Latency-Aware Scheduling Under Channel Uncertainties in LTE Networks for Massive IoT

The machine-to-machine (M2M) communication is an enabler technology for internet of things (IoT) that provides communication between machines and devices without human intervention. One of the main challenges in IoT is managing a large number of machine-type communications co-existing with the human to human (H2H) or human type communications. Long term evolution (LTE) and LTE-advanced (LTE-A) technologies due to their inherent characteristics like high capacity and flexibility in data access management are appropriate choices for M2M/IoT systems. In this paper, a two-phase intelligent scheduling mechanism based on interval type-2 fuzzy logic to (1) satisfy QoS requirements, (2) ensure fair resource allocation and (3) control energy level of devices for coexistence of M2M/H2H traffics in LTE-A networks, is presented. The proposed interval type-2 fuzzy Logic mechanism enhances data traffic efficiency by predicting and handling the network uncertainties. The performance of the proposed algorithm is evaluated in terms of various metrics such as delay, throughput, and bandwidth utilization.     

基于NOMA的无线携能D2D通信鲁棒能效优化算法

针对频谱短缺、基站负荷过高、通信系统功耗较大等问题,考虑不完美的信道状态信息,该文提出一种基于非正交多址接入的无线携能(SWIPT)D2D网络鲁棒能效(EE)最大化资源分配算法(SREA).考虑用户的服务质量约束以及最大发射功率约束,基于随机信道不确定性建立鲁棒能效最大化资源分配模型.利用Dinkelbach和变量替换方法,将原NP-hard问题转换为确定性的凸优化问题,通过拉格朗日对偶理论求得解析解.仿真结果表明,所提算法在保证蜂窝用户通信质量的同时,能够有效提高D2D用户的能效性和鲁棒性能.     

Multiple access based on different lengths super orthogonal codes to support QoS of M2M communications in next generation networks

Global connectivity, low latency, and ready‐to‐use infrastructure of next generation wireless (NGW) networks provide a platform for machine‐to‐machine (M2M) communications on a large scale. However, M2M communications over NGW networks pose significant challenges because of different data rates, diverse applications, and a large number of connections. In this paper, we address M2M challenges over NGW networks, and in particular, we focus on random access overload issue and diverse quality‐of‐service (QoS) requirements to enable M2M communications in the context of NGW networks. To enable massive M2M access while QoS guarantees, we propose group‐based M2M communications on the basis of identical transmission protocols and QoS requirements. Furthermore, to guarantee low energy consumption for M2M devices in the same group, we propose a decentralized group‐head selection scheme. In addition, a solution is proposed by using an effective capacity concept to provide QoS guarantees for M2M devices with a strict time constraint. A new random access approach based on different lengths super orthogonal codes is proposed to ease massive random access challenges with provisioning diverse QoS requirements of M2M communications in heterogeneous NGW networks.     

Reducing Energy Consumption by Data Aggregation in M2M Networks

Machine-to-machine (M2M) communications have emerged as a new technology for next-generation communications. As the number of M2M devices and the amount of transmitted data increase, applying data aggregation is an efficient way to improve energy efficiency of M2M networks. In this paper, we devise an analytical model to compute the energy consumption and delivery delay in packet delivery by using data aggregation. Then we develop an extensive simulation to validate our proposed analytical model. Numerical results show that it is essential to smartly configure the parameters for data aggregation in M2M networks. Our study provides guidelines to determine the parameters in terms of the buffering time and the maximum number of buffered packets for data aggregation.     

Graph-Based Radio Resource Sharing Schemes for MTC in D2D-based 5G Networks

Apart from the the increasing demand of smartphones in human-to-human (H2H) communications, the introduction of machine-to-machine (M2M) devices poses significant challenges to wireless cellular networks. In order to offer the ability to connect billion of devices to propel the society into a new era of connectivity in our homes, officies and smart cities, we design novel radio resource sharing algorithms in a H2H/M2M coexistence case to accommodate M2M communications while not severely degrading existing H2H services. We propose group-based M2M communications that share the same spectrum with H2H communications through device-to-device (D2D) communication, as one of the technology components of 5G architecture. First, we formulate radio resource sharing problem as a sum-rate maximization, problem for which the optimal solution is non-deterministic polynomial-time hard (NP-hard). To overcome the computational complexity of the optimal solution, we model the resource sharing problem as a bipartite graph, then propose a novel interference-aware graph-based resource sharing scheme using a fixed M2M transmit power. To further enhance the protection of H2H services, we introduce an adaptive power control mechanism into the interference-aware graph-based resource sharing scheme. M2M transmit power is efficiently adjusted using one among the two following alternative controllers, namely, either the proportional integral derivative (PID) or the fuzzy logic. The latter is proposed within the aim to assure the desired quality-of-service (QoS) of H2H users and increase the efficiency of M2M spectrum usage. In both cases (fixed and adaptive), a centralized and a semi-distributed instantiations are given. Simulation results show that adaptive M2M radio resource sharing scheme using fuzzy logic is the one that achieves the best compromise. In fact, it guarantees H2H performance in terms of throughput and fairness while maximizing the efficiency of M2M spectrum usage. Simulation results also show that in spite of its quite good performance, semi-distributed M2M resource sharing instantiation achieves them with a decline of up to 10% in terms of H2H throughput compared to the centralized instantiation. This is achieved through a markedly lower communication overhead.

     

多频带混合场景下终端直通通信用户最优用户密度与功率分配研究

该文分析了蜂窝与终端直通(Device-to-Device, D2D)混合网络中多频带资源的场景下D2D用户最佳密度和功率分配问题。在混合网络中包含一个或者多个蜂窝网络,D2D用户复用蜂窝系统上行频谱资源。通过采用随机几何理论,上述问题可以建模成一个以最大化D2D网络容量为目标并以蜂窝用户和D2D用户的中断概率为约束条件的优化问题。由于上述优化问题非凸,因此分成两步解决原问题:首先证明当D2D用户密度确定的时候原问题对于功率分配是凸问题,并通过拉格朗日对偶方法得到了最优功率分配方案;随后证明中断约束条件将D2D用户密度的定义域分成有限个子区间,在每个子区间上可以通过求导的方式得到D2D传输容量局部最优解,基于上述两个结论,文中设计了一种子区间最优值搜索算法。通过仿真验证了算法的有效性,并且反映出D2D传输容量主要由中断约束条件和来自蜂窝网络的干扰决定。     

A Survey of IoT Key Enabling and Future Technologies: 5G,Mobile IoT,Sematic Web and Applications

The Internet of Things (IoT) is the communications paradigm that can provide the potential of ultimate communication. The IoT paradigm describes communication not only human to human (H2H) but also machine to machine (M2M) without the need of human interference. In this paper, we examine, review and present the current IoT technologies starting from the physical layer to the application and data layer. Additionally, we focus on future IoT key enabling technologies like the new fifth generation (5G) networks and Semantic Web. Finally, we present main IoT application domains like smart cities, transportation, logistics, and healthcare.