时延QoS保证的D2D-based V2V车载通信功率分配方案

OFDMA蜂窝网络中采用Underlay模式的基于终端直通技术的车-车(D2D-based V2V)通信能够提高频谱利用率、增大系统吞吐量,但随之产生的同频干扰问题严重影响通信质量。为了解决该问题,同时满足车辆通信的低时延服务质量(QoS)需求,提出一种基于时延QoS保证的D2D-based V2V车载通信功率分配方案。该方案同时考虑了蜂窝用户和D2D-based V2V用户所受到的干扰,并采用有效容量提供QoS保障,通过拉格朗日对偶分解法求解优化问题。数值仿真表明,相比于其他方案,该方案能够很好地提高系统有效容量。     

应急通信系统中协作中继选择与功率分配优化

为提高应急通信系统的信道容量和能量效率,提出一种协作中继选择与功率分配的联合优化算法。在动态的通信环境中,通过最佳期望选取参与数据传输的协作中继集,提高通信系统的信道容量。为使每个用户及整个网络的能量消耗降到最低,采用凸优化方法,合理分配源节点和多个中继节点之间合理的发射功率。理论分析与仿真实验证明,该算法能以较低的复杂度明显提高系统的信道容量和能量 效率。     

D2D通信中联合资源分配与功率控制算法

终端直通(D2D)技术引入移动蜂窝网络虽然能够提高蜂窝系统性能,但却带来了很大的干扰和能量消耗.为了降低干扰,提升频谱效率,同时又兼顾能量效率,提出了一种联合资源分配和功率控制的方法,用以实现高能效的D2D通信.仿真结果表明:本文提出的迭代资源分配和功率控制方案,相比已有方案能量效率有了明显提高.     

安全能量效率优化的传感器网络传输研究

研究了射频无线充能传感器网络通信系统的安全传输及能量效率问题,针对传感器网络能量受限而无线通信容易被窃听的问题,利用物理层安全技术,提出了一种两阶段的安全传输协议。通过联合优化发送功率、射频能量协方差矩阵、敏感信息波束成型以及协作干扰协方差矩阵,使得传感器网络的安全能量效率达到最大。针对所提出的非凸问题,利用Dinkelbach方法将包含分式形式的目标函数简化,利用连续参数凸近似方法将非凸问题转化为可解形式,进而使用收敛迭代算法得到原问题的近似最优解。仿真结果表明,与对比方案相比,提出的传输方案可以在保证安全通信的基础上有效提高安全能量效率。     

基于信息能量同传的异构小蜂窝网络能效优化

研究无线信息和能量协同传输(simultaneous wireless information and power transfer,SWIPT)异构小蜂窝网络的能效优化问题。保证小蜂窝用户和宏用户通信质量、小蜂窝用户能量采集和小蜂窝基站传输功率等约束下,为实现小蜂窝系统下行能效的最大化,对小蜂窝基站发射功率和小蜂窝用户端功率分流系数进行联合优化。该问题属于非凸优化问题,通过变量替换对原问题作等价转换,然后采用基于拉格朗日乘子的次梯度算法求解。计算机仿真结果表明,该联合优化算法简单有效。     

基于NOMA的D2D用户和蜂窝用户通信方案

LTE网络采用非正交多址来增强网络容量,采用D2D来提高频谱效率;让D2D用户使用非正交多址技术共享蜂窝用户的频谱资源可以缓解当下频谱资源短缺的问题,但也会在D2D用户和蜂窝用户之间引入干扰。提出了一种基于非正交多址技术（Non-orthogonal Multiple Access,NOMA）的D2D用户和蜂窝用户的共享研究方案,对于蜂窝用户和D2D用户之间的干扰采用SIC技术消除。该方案不仅通过调整用户的传输功率来优化系统的吞吐量,而且提高了边缘用户的性能。仿真结果表明,基于NOMA的D2D用户和蜂窝用户之间的通信方案在性能上优于传统的蜂窝资源分配方案和干扰控制方案。     

基于最优连通功率控制的WSNs跨层路由优化算法

针对非连通区域节点空洞效应和热点区域节点间通信干扰导致的路由服务质量（QoS）下降问题,提出了一种基于最优连通功率控制的无线传感器网络（WSNs）跨层路由优化算法。算法采用自适应最优连通功率控制策略,在避免路由空洞产生和保证网络连通性条件下,降低热点区域节点数据转发竞争干扰;通过位置信息、剩余能量和干扰等级的跨层信息交互,动态选取最优转发节点,提高网络整体性能。仿真实验表明：算法能够提高路由（QoS）、优化网络生命周期和降低热点区域通信干扰。     

无线携能通信下mmWave协作通信小单元的能效最优策略

针对无线携能通信(SWIPT)网络中的能量与信息同时传输阶段的优化问题,以最大化链路能量效率为目标,提出一种SWIPT下mmWave协作通信小单元的能效最优策略。该策略在最小链路传输速率和最小收集能量的联合约束下,在能量受限型用户设备的接收端采用功率分流工作模式,通过优化发射功率控制和功率分流因子,最大化系统链路能量效率。针对原问题是一个非凸的分式规划问题,具有NP难性质,采用Dinkelbach方法将目标函数转化为易于求解的凸优化问题,并设计一个交叉迭代的算法求出最优解。仿真实验结果表明,提出的策略在优化系统能量效率性能上要优于传统功率控制方法和最大发射功率法。     

基于H∞滤波能量预测中继选择协作传输技术*

研究了一种适用于分布式移动无线传感器网络数据传输基于能量预测的协作通信技术。针对无线传感器网络,协作数据传输技术难以有效解决无线网络动态拓扑结构、带宽和能量受限等问题,建立了一种基于H∞滤波器相邻节点剩余能量预测的分布式中继选择机制,并在此基础上建立数据协作传输技术。该技术充分利用无线信道的广播特性,首先根据H∞滤波器预测节点剩余能量,然后根据剩余能量值选择最大者作为协作节点,最后根据无线信道质量在放大重传和解码重传机制之间进行自适应调整。数学分析表明,H∞滤波器可以准确地预测节点的剩余能量,同时该协作传输技术能够显著提高无线传感器网络的资源利用率和系统吞吐率,有效延长网络寿命。     

Research of cooperative transmission technology based on H ∞ filter energy prediction

研究了一种适用于分布式移动无线传感器网络数据传输基于能量预测的协作通信技术.针对无线传感器网络,协作数据传输技术难以有效解决无线网络动态拓扑结构、带宽和能量受限等问题,建立了一种基于H∞滤波器相邻节点剩余能量预测的分布式中继选择机制,并在此基础上建立数据协作传输技术.该技术充分利用无线信道的广播特性,首先根据H∞滤波器预测节点剩余能量,然后根据剩余能量值选择最大者作为协作节点,最后根据无线信道质量在放大重传和解码重传机制之间进行自适应调整.数学分析表明,H∞滤波器可以准确地预测节点的剩余能量,同时该协作传输技术能够显著提高无线传感器网络的资源利用率和系统吞吐率,有效延长网络寿命.     

无线传感器网络RCF-MAC协议研究

射频充电（ RFC）技术是一种通过基站发射射频电波为传感器节点补充电量的新兴技术。但在RFC无线传感器网络中,多基站同时发射能量时产生的能量干涉衰减现象会明显降低充电效率,此外,能量传输与数据通信共享信道会降低整个网络的吞吐量。针对上述问题,提出了RFC—MAC协议,为提高充电效率,将请求充电传感器节点周围的基站按距离分为两组,两组基站分时能量传输,以避免能量干涉衰减现象;传感器网络中设双信道,分别用于能量传输与数据通信,以提高网络的吞吐量。仿真结果表明：RFC—MAC协议显著提高了传感器节点的充电效率和平均网络吞吐量。     

Joint base station activation,user admission control and beamforming in downlink green networks

Consider a multicell downlink network, where the base stations (BSs) in different cells cooperate in the precoder level, while those in the same cell are coordinated for joint processing (JP). To achieve green communication, we optimize the network power consumption under some quality-of-service (QoS) constraints. Different from the conventional approaches based on BS activation and beamforming, we further integrate user admission control into network management, and thus minimize the power consumption by jointly optimizing the active BSs, the admissible users and the transmit beamformers. This strategy has two advantages. First, it ensures that the network still works even when some users cannot achieve the QoS requirements. Second, it helps select a subset of users whose QoS requirements can be satisfied with relatively low power cost, thereby improving the power efficiency. However, this problem is challenging due to the mixed-integer programming. To pursue some efficient approximate solution, we first reformulate it as a convex sparse optimization problem and then develop a distributed algorithm to iteratively solve the problem, utilizing the alternating direction method of multipliers (ADMM). The proposed algorithm has very low complexity since each step can be computed in closed form. Its efficacy and efficiency are demonstrated by numerical simulations.     

认知中继网络中的功率分配算法

无人机（Unmanned aerial vehicle, UAV）通信是当前无线通信领域的研究热点。为了保证地面移动端与UAV通信的可靠性,提出了基于空时块码（Space-time block code, STBC）的协作中继传输方案。为了提升频谱效率,本文利用认知无线电技术,于协作中继处分别采用放大转发(Amplify-and-forward, AF)和解码转发(Decode-and-forward, DF)两种协议进行传输,在主用户通信服务质量得到保证和认知用户传输功率受限的条件下,建立以认知中继网络的吞吐量最大化为目标函数的优化问题。采取拉格朗日乘子法与Karush-Kuhn-Tucker (KKT)条件相结合的方案来实现优化问题中最优功率的分配。最后,仿真结果不仅验证了所提方案的有效性,还表明了DF中继协议下认知中继网络的吞吐量优于AF中继协议。     

Ant based Pareto optimal solution for QoS aware energy efficient multicast in wireless networks

Most of the group communication technologies support real-time multimedia applications such as video conferencing and distributed gaming. These applications require quality-of-service (QoS) aware multicast routing protocol to deliver the same data stream to a predefined group of receivers. Since nodes in wireless networks are severely energy constrained due to finite battery source, hence it is of paramount importance that QoS aware multicast routing protocol be energy efficient. Transmission power control is one of the methods used to save energy. In this method, the nodes dynamically adjust the transmission power so that energy consumption in the tree is minimized. However, reduction in the transmission power increases the number of forwarding nodes in the multicast tree. This negatively impacts the QoS in terms of propagation delay, delay jitter, and packet loss etc. In wireless networks, there is a trade-off between the energy consumption and the QoS guarantees provided by the network. We unify these requirements into a multiobjective framework referred to as Energy Efficient QoS Multicast Routing (E²QoSMR). The goal is to simultaneously optimize the total power consumption and the QoS parameters in the multicast tree. We extend two algorithms based on metaphor of swarm intelligence for finding an energy efficient multicast tree satisfying the QoS guarantees. Extensive simulations have been conducted to validate the correctness and efficiency of the algorithms. The simulation result of the algorithms is compared with the nondominated sorting genetic algorithm, NSGA-III. The experimental results are consolidated by statistical analyses that demonstrate the ability of the algorithms to generate the Pareto optimal solution set.     

基于分簇协作频谱感知的认知无线网能量效率研究

针对认知无线网能量消耗引起的能量效率较低问题,在研究认知无线网分簇协作频谱感知能量效率的基础上,提出了一种最优功率分配算法来最大化次用户系统的能量效率。通过建立基于次用户能量效率最大化传输优化模型,在考虑传输功率、感知时间以及干扰限制的情况下,利用拉格朗日函数及KKT条件,得到最优次用户发射功率分配算法,以达到系统能量效率最大化的目的,同时分析了不同参数对能量效率的影响。仿真结果表明,本文所提算法能有效提高次用户系统的能量效率、减少系统开销。     

A Cross-Layer Optimization and Design approach under QoS constraints for green IP over WDM networks

Energy consumption of communication networks is growing very fast due to the rapidly increasing traffic demands. It is important and valuable to find a way to save power for such networking systems. In this paper, we propose a Cross-Layer Optimization and Design (CLOD) approach to improve the energy efficiency of Internet Protocol (IP) over Wavelength Division Multiplexing (WDM) backbone networks under QoS constraints. CLOD makes use of the skew spatial distribution (i.e. 80–20 law) of traffic demands to construct the virtual topology of IP layer with a consideration of the cross-layer resource constraints. Also, CLOD takes advantage of the constraint-based routing to satisfy the QoS constraints in terms of allowed maximum hop count and allowed maximum link utilization. Taking into account the connection between base network design and network operation, CLOD dimensions the base network by using pattern of network operation and optimizes network power consumption by reconfiguring network to adapt traffic variation and the available resources. Simulation results indicate that CLOD can save power significantly and achieve near energy-proportional networks. In addition, an analysis on trade-off performance between the hop count and power consumption is given.     

基于能效的多天线中继网络波束形成设计

为了提高放大转发多天线中继网络的能量效率,提出了一种基于能量效率的波束形成算法,在满足中继功率约束条件的同时,最大化系统归一化信噪比.最终得到的优化问题是一个非凸的优化问题,难以求解.将其简化为一个一维的优化问题,可以利用求导的方法进行求解.仿真结果表明:与基于信噪比的波束形成算法和未使用波束形成的放大转发方式相比,所提算法可显著提高系统的能量效率.     

Energy-aware deployment of dense heterogeneous cellular networks with QoS constraints

The base station (BS) configuration is a key factor to improve energy efficiency (EE). In this paper, we focus on designing the network deployment parameters (i.e., BS densities) for biased K-tier heterogeneous cellular network (HCN) with quality of service (QoS) provisioning. Using appropriate approximations, we derive the closed-form expressions of optimal BS density across all tiers to minimize the area power consumption (APC) by applying the stochastic geometry theory, while satisfying the users’ QoS requirements. These results are used to obtain some new insights into the EE performance of biased HCN deployment. With the aid of this approach, the best type of BSs to be deployed or switched off for energy saving purposes can be identified from the perspectives of BS transmission power. More precisely, if the BS transmission power ratio between an arbitrary pair of tiers of K-tier HCN, e.g., the small cell BS and macro BS tiers, is higher than a threshold which is a function of path loss exponent, bias factor and power consumption, the small cell BSs are preferred. The opposite situation takes place otherwise. Furthermore, it is also shown that, compared to the unbiased HCN scenario, significant energy savings are possible by appropriately biasing the HCN and optimizing the BS density, subject to the QoS constraints among all tiers.