A green radio resource allocation scheme for LTE-A downlink systems with CoMP transmission

In this paper, we propose a green radio resource allocation (GRRA) scheme for LTE-advanced downlink systems with coordinated multi-point (CoMP) transmission to support multimedia traffic. The GRRA scheme defines a green radio utility function, which is composed of the required transmission power, assigned modulation order, and the number of coordinated transmission nodes. By maximizing this utility function, the GRRA scheme can effectively save transmission power, enhance spectrum efficiency, and guarantee quality-of-service requirements. The simulation results show that when the traffic load intensity is greater than 0.7, the GRRA scheme can save transmission power by more than 33.9 and 40.1 %, as compared with the conventional adaptive radio resource allocation (ARRA) scheme (Tsai et al. in IEEE Trans Wireless Commun 7(5):1734–1743, 2008) with CoMP and the utility-based radio resource allocation (URRA) scheme (Katoozian et al. in IEEE Trans Wireless Commun 8(1):66–71, 2009) with CoMP, respectively. Besides, it enhances the system throughput by approximately 5.5 % and improves Jain’s fairness index for best effort users by more than 155 % over these two ARRA and URRA schemes.     

Energy-efficient resource allocation scheme for device-to-device communication underlaying LTE-A uplink

To cope with the co-channel interference between cellular links and device-to-device (D2D) links concurrently transmitting with the long term evolution-advanced (LTE-A) uplink spectrum,a joint resource allocation scheme was pro-posed to maximize the global energy efficiency of D2D links.The above problem can be decomposed into the power control subproblem and the channel assignment subproblem.Specifically,the power control subproblem can be optimally solved with the help of Dinkelbach method and Lagrange duality.Based on the above results,the channel assignment subproblem turns out to be the set packing problem which was generally NP-hard problem,Therefore,a heuristic algo-rithm was further devised to achieve a tradeoff between performance and complexity.Simulations show that the proposed joint resource allocation scheme outperforms the ones where only single resource variable is optimized,and it achieves the polynomial-time complexity at only minor performance loss when compared to the global optimum.     

无线mesh网络中可信协同信道资源分配策略

为了有效提升无线mesh网络信道资源的利用率和网络服务质量,提出基于可信协同的信道资源分配策略.针对节点自适应特点,引入博弈理论、建立节点的信誉机制以实现节点可信协同并优化信道分配结果.仿真实验分别对节点服务等级、网络收益结果作相应评价.实验结果发现节点服务等级对节点网络收益有直接影响,当协同服务等级达到3时,网络收益状况最佳,此时节点跳数与服务等级呈协同关系;对比经典协同算法,在相同网络拓扑环境下,可信协同信道资源分配策略分别是UACRR算法、DMP-MBA算法的1.04倍、1.069倍,明显占优.     

Buffer-aware resource scheduling scheme for LTE-A uplink relay-assisted network

Due to the constraint of single carrier frequency division multiple access （SC-FDMA） adopted in long term evolution （LTE） uplink, subcarriers allocated to single user equipment （UE） must be contiguous. This contiguous allocation constraint limits resource allocation flexibility and makes the resource scheduling problem more complex. Most of the existing work cannot well meet UE＇s quality of service （QoS） requirement, because they just try to improve system performance mainly based on channel condition or buffer size. This paper proposes a novel resource scheduling scheme considering channel condition, buffer size and packet delay when allocating frequency resource. Firstly, optimization function is formulated, which aims to minimize sum of weight for bits still left in UE buffer after each scheduling slot. QoS is the main concern factor here. Then, to get packet delay information, this paper proposes a delay estimation algorithm. Relay node （RN） is introduced to improve overall channel condition. Specific RN selection strategy is also depicted in the scheme. Most important of all, a creative negotiation mechanism is included in the subcarrier allocation process. It can improve the overall system throughput performance in guarantee of user＇s QoS requirement. Simulation results demonstrate that the scheme can greatly enhance system performance like delay, throughput and jitter.     

LTE-A基于压缩感知的信道估计

针对在多普勒环境下LTE-A(改进的长期演进)系统时频域二维稀疏信道的特性,根据导频在时频域的分布以及二者之间的相关性,通过将搜索空间分解为时域上OFDM(正交频分复用)符号间和频域上子载波间范围,提出了一种基于OMP(正交匹配追踪)算法改进压缩感知的信道估计。仿真结果表明,改进的OMP算法较原始算法具有更低的MSE(均方误差)。     

Robust joint optimization for MIMO AF multiple-relay systems with correlated channel uncertainties

In the paper, robust joint optimization of the source/relays precoders and destination equalizer is proposed for non-regenerative dual-hop multiple-input multiple-output (MIMO) amplify-and-forward (AF) multiple-relay systems with correlated channel uncertainties. By taking the imperfect channel state information (CSI) into consideration, the robust transceiver/relays joint optimization is developed based on the minimum mean-squared error (MMSE) criterion under individual power constraints at the source and relays. The optimization problem of precoding and amplifying matrices under power constraints belongs to neither concave nor convex so that a nonlinear matrix-form conjugate gradient (MCG) algorithm is applied to explore local optimal solutions. Simulation results illustrate that the robust transceiver/relays joint architecture for an AF-MIMO multiple-relay system outperforms the non-robust transceiver/relays design.     

中断概率在TD-LTE信道资源分配中的应用

TD-LTE(时分同步码分多址-长期演进)系统业务呈非对称状态,对下行数据率的要求要超过上行数据率,因而对系统信道上下行资源分配的要求更为严格。文章利用中断概率接纳控制算法对系统信道资源进行了智能动态分配,通过系统仿真证明了该策略能够灵活分配系统时隙资源。     

多业务多用户OFDM系统中的资源快速分配方案

本文提出了多用户OFDM系统下行链路中的一种快速实用的资源分配算法.该算法以最大化系统吞吐量为目的,同时满足CBR(固定速率)和VBR(可变速率)用户的QoS(服务质量)要求.算法使用分组代替迭代计算,摒弃复杂的子载波交换操作,以极低的复杂度实现了等功率下的自适应子载波分配和比特加载.本文在选择性衰落信道下对算法进行评估,并与[6]中的算法比较.仿真表明,该算法可以在更低的复杂度D(KN log N 2KN)下实现与[6]同样的吞叶量.     

OFDM系统中考虑信源编码特性的多播资源分配方案

针对OFDM系统单播以及传统多播没有充分发挥无线传播开放性优势的问题,提出了多个多播组保证组间比例公平的资源分配方案.该方案由子载波分配算法及功率分配算法构成,前者通过修正存在的单播子载波分配算法得到;后者基于理论推导的非线性方程组,得到了子载波分配给定条件下的最优功率分配.仿真结果显示,所提方案能够确保完美的多播组间公平性,且和速率容量性能明显优于传统多播方式.结合方案的低复杂度特性,它适合在实际无线系统的音/视频多播中进行应用.     

Robust resource allocation for NOMA-assisted heterogeneous networks

As a promising technology to improve spectrum efficiency and transmission coverage, Heterogeneous Network (HetNet) has attracted the attention of many scholars in recent years. Additionally, with the introduction of the Non-Orthogonal Multiple Access (NOMA) technology, the NOMA-assisted HetNet cannot only improve the system capacity but also allow more users to utilize the same frequency band resource, which makes the NOMA-assisted HetNet a hot topic. However, traditional resource allocation schemes assume that base stations can exactly estimate direct link gains and cross-tier link gains, which is impractical for practical HetNets due to the impact of channel delays and random perturbation. To further improve energy utilization and system robustness, in this paper, we investigate a robust resource allocation problem to maximize the total Energy Efficiency (EE) of Small-Cell Users (SCUs) in NOMA-assisted HetNets under imperfect channel state information. By considering bounded channel uncertainties, the robust resource optimization problem is formulated as a mixed-integer and nonlinear programming problem under the constraints of the cross-tier interference power of macrocell users, the maximum transmit power of small base station, the Resource Block (RB) assignment, and the quality of service requirement of each SCU. The original problem is converted into an equivalent convex optimization problem by using Dinkelbach's method and the successive convex approximation method. A robust Dinkelbach-based iteration algorithm is designed by jointly optimizing the transmit power and the RB allocation. Simulation results verify that the proposed algorithm has better EE and robustness than the existing algorithms.     

Robust neural force control scheme under uncertainties in robotdynamics and unknown environment

The original impedance function is known to lack robustness due to unknown robot dynamic model and the environment. In order to improve that result, a new impedance function is derived which specifies a desired force directly. This results in a new robust robot force tracking impedance control scheme, which employs a neural network as a compensator to cancel out all uncertainties. The proposed neural force control scheme is capable of making the robot track a specified desired force as well as of compensating for uncertainties in environment location and stiffness, and in robot dynamics. Separate training signals for free-space motion and contact-space motion control are developed to train the neural compensator online. The design of the training signals is justified. Simulation studies with a three-link rotary robot manipulator are carried out and the results show excellent force tracking performance     

认知无线电系统的顽健资源分配算法

摘 要：针对多用户下垫式认知无线电网络中参数不确定性问题,提出了一种顽健分布式功率控制算法。在干扰温度门限和次用户信干噪比（SINR）的约束下,考虑信道不确定性,实现认知系统功率消耗最小化。基于欧几里得球形不确定性描述,利用拉格朗日对偶分解理论给出了顽健功率控制问题的解。仿真结果表明,该顽健功率分配算法能同时满足主用户和次用户的QoS需求,与非顽健算法和传统SOCP算法对比可提升系统性能。     

基于比例速率保证的信道误差OFDMA中继系统资源分配

针对存在有信道估计误差的正交频分多址( OFDMA)中继系统,在考虑用户传输中断概率的同时,提出了满足不同用户最小服务质量( QoS)需求和比例公平性约束条件下的中继选择、子载波分配和功率分配的联合优化问题,建立了以最大化系统总容量为目标的优化模型。在此基础上以速率最大化为目标进行最佳中继选择,并通过动态子载波分配来满足用户的最小QoS需求和比例公平性,最后采用拉格朗日乘子法来得到最优功率分配方案。仿真结果表明,此算法在降低用户中断概率的同时,提高了系统吞吐量并保证了用户速率的比例公平性。     

Robust resource allocation algorithm for heterogeneous wireless networkwith SWIPT

To improve energy efficiency and robustness of heterogeneous wireless networks with wireless information and power transfer,the robust joint transmit power and power splitting resource allocation problem was studied.Based on mini-max probability machine and Dinkelbach method,the original NP-hard problem was transformed into a solvable convex optimization form,meanwhile a distributed dual resource allocation algorithm was proposed.Additionally,both computational complexity and robust sensitivity were analyzed.Simulation results show that the proposed algorithm can guarantee the quality of service requirements of macro cellular users and femtocell users under channel uncertainties.     

LTE-A多小区中继网络两跳速率匹配资源分配算法

针对中继网络的两跳特性,提出一种多小区中继网络两跳速率匹配资源分配算法。首先,在第一个子时隙内各小区独立地为小区中所有用户分配子载波,并根据子载波分配结果,建立基于非合作博弈的功率分配模型;然后,由第一跳资源分配结果对第二跳速率匹配,自适应地为中继分配资源以满足边缘用户的速率需求。仿真结果表明,该算法在满足边缘用户速率需求的前提下,进一步提高了中心用户的吞吐量,同时降低了系统的发射功率,从而提升了系统性能。     

基于天线波束成形的OFDMA系统的自适应资源分配

对正交频分复用接入系统在多用户多天线传输情况下的自适应资源分配策略进行了研究,提出了一种基于天线波束成形的,包括动态子载波分配、自适应调制、比特加载的无线资源分配方案.算法的优化设计目标是在满足总的恒定传输比特数和误比特率性能要求的情况下,使得系统总的发射功率最小.仿真结果表明,由于多天线阵列增益和多用户分集增益,系统整体性能得到了明显的优化.     

Dynamic guard channel allocation scheme for calls in WONs

Future wireless networks will provide ubiquitous communication services to a large number of mobile users. The design of such networks is based on heterogeneous wireless overlay networks that allow efficient use of the limited available spectrum, and also cover different ranges of coverage areas. A dynamic guard channel allocation scheme for calls in wireless overlay networks is developed. The new scheme considers the mobility, current location of mobile terminals, and bandwidth status in allocating bandwidth for new calls in order to guarantee the quality of service for all calls     

Robust Kalman filters for linear time-varying systems withstochastic parametric uncertainties

We present a robust recursive Kalman filtering algorithm that addresses estimation problems that arise in linear time-varying systems with stochastic parametric uncertainties. The filter has a one-step predictor-corrector structure and minimizes an upper bound of the mean square estimation error at each step, with the minimization reduced to a convex optimization problem based on linear matrix inequalities. The algorithm is shown to converge when the system is mean square stable and the state space matrices are time invariant. A numerical example consisting of equalizer design for a communication channel demonstrates that our algorithm offers considerable improvement in performance when compared with conventional Kalman filtering techniques     

Robust steady-state filtering for systems with deterministic and stochastic uncertainties

For uncertain systems containing both deterministic and stochastic uncertainties, we consider two problems of optimal filtering. The first is the design of a linear time-invariant filter that minimizes an upper bound on the mean energy gain between the noise affecting the system and the estimation error. The second is the design of a linear time-invariant filter that minimizes an upper bound on the asymptotic mean square estimation error when the plant is driven by a white noise. We present filtering algorithms that solve each of these problems, with the filter parameters determined via convex optimization based on linear matrix inequalities. We demonstrate the performance of these robust algorithms on a numerical example consisting of the design of equalizers for a communication channel.