多小区多用户无线网络下行链路协同波束成形设计

在频率复用的多小区多用户无线网络中,为了获得较好的和速率性能,研究了降低同频干扰的协同波束成形设计问题。博弈论分析结果表明,性能最优的协同波束成形矢量是自私和利他策略的线性组合。提出了一种最大化和速率的波束成形矢量迭代算法,给出了基于信道状态统计量的组合系数的估计方法。最后,仿真评估了波束成形算法的性能,表明了所提迭代算法的收敛性。     

基于智能反射面辅助的无人机中继系统安全通信方法

为了提高无人机中继系统的安全通信性能,解决无线信道受障碍物遮挡问题,该文提出一种基于智能反射面(IRS)辅助的无人机(UAV)中继系统安全通信方法。在所提方法中,通过联合优化UAV的位置、基站波束成形和IRS相移,最大化系统的最小保密速率。为了解决这个复杂的非凸优化问题,该文将原问题分解为UAV位置优化子问题、波束成形和IRS相移优化两个子问题。使用1阶泰勒展开处理优化问题中的非凸项,然后提出一种交替优化的算法进行求解。仿真结果表明该文提出的算法能提高系统的最小保密速率,并且具有良好的收敛性。     

User clustering and robust beamforming design in multicell MIMO-NOMA system for 5G communications

In this paper, we present a robust beamforming design to tackle the weighted sum-rate maximization (WSRM) problem in a multicell multiple-input multiple-output (MIMO) – non-orthogonal multiple access (NOMA) downlink system for 5G wireless communications. This work consider the imperfect channel state information (CSI) at the base station (BS) by adding uncertainties to channel estimation matrices as the worst-case model i.e., singular value uncertainty model (SVUM). With this observation, the WSRM problem is formulated subject to the transmit power constraints at the BS. The objective problem is known as non-deterministic polynomial (NP) problem which is difficult to solve. We propose an robust beamforming design which establishes on majorization minimization (MM) technique to find the optimal transmit beamforming matrix, as well as efficiently solve the objective problem. In addition, we also propose a joint user clustering and power allocation (JUCPA) algorithm in which the best user pair is selected as a cluster to attain a higher sum-rate. Extensive numerical results are provided to show that the proposed robust beamforming design together with the proposed JUCPA algorithm significantly increases the performance in term of sum-rate as compared with the existing NOMA schemes and the conventional orthogonal multiple access (OMA) scheme.     

认知MIMO MAC最优迭代注水算法

研究发送功率以及干扰温度约束下的认知多入多出多址接入信道(MIMO MAC)的速率和最大化问题。通过部分对偶分解技术松弛干扰温度约束,原始问题被分解为较易处理的子问题,并提出一种迭代算法,通过交替进行对偶变量更新与迭代注水运算求解使得速率和最大的最优发送协方差矩阵。最后通过仿真表明算法的有效性。     

Energy efficiency optimization algorithm for heterogeneous NOMA network based on imperfect CSI

In order to improve the suppression capability of parametric perturbation and energy efficiency (EE) of heterogeneous networks (HetNets),a robust resource allocation algorithm was proposed to maximize system EE for reducing cross-tier interference power in non-orthogonal multiple access (NOMA) based HetNets.Firstly,the resource optimization problem was formulated as a mixed integer and nonlinear programming one under the constraints of the interference power of macrocell users,maximum transmit power of small cell base station (BS),resource block assignment and the quality of service (QoS) requirement of each small cell user.Then,based on ellipsoid bounded channel uncertainty models,the original problem was converted into the equivalent convex optimization problem by using the convex relaxation method,Dinkelbach method and the successive convex approximation (SCA) method.The analytical solutions were obtained by using the Lagrangian dual approach.Simulation results verifiy that the proposed algorithm had better EE and robustness by comparing it with the existing algorithm under perfect channel state information.     

Distributed energy efficient beamforming algorithm for multicell multiuser downlink system

A distributed energy efficient beamforming algorithm was studied for multicell multiuser downlink communication system.Firstly,the energy efficient optimization problem was first considered as the maximization of the ratio between the system sum rate and the system power consumption.The fractional programming optimization target was then transformed into a subtractive form via using the fractional programming theorem.Secondly,the problem was decomposed into some subproblems that can be solved respectively by introducing the concept of the interference temperature in cognitive radio networks.Finally,an effective distributed energy efficient beamforming algorithm was proposed by exploiting the Lagrangian duality theorem and optimization theorem.Compared to the classical energy efficient optimization algorithm,the proposed algorithm can achieve an obvious energy efficiency performance gain.     

基于改进遗传算法的C-RAN网络动态无线资源分配

针对云无线网络(Cloud Radio Access Network,C-RAN)中传统静态资源分配效率低下以及动态无线资源分配中资源种类单一的问题,提出了一种基于用户服务质量(Qulity of Service,QoS)约束的动态无线资源分配方案,对无线资源从无线射频单元(Remote Radio Head,RRH)选择、子载波分配和RRH功率分配三个维度进行研究.首先,根据传统的C-RAN系统传输模型和QoS约束在时变业务环境下建立了以发射功率为变量,以吞吐量最大为优化目标的优化问题;然后,基于改进的遗传算法,将原优化方案转变为通过优化RRH选择、子载波分配和RRH功率分配来达到提高系统吞吐量的目的;最后,将改进的遗传算法与其他智能算法在种群规模变化下进行了时间复杂度对比.实验结果表明,所提算法具有较低时间复杂度,所提资源分配方案下的平均吞吐量增益为17％.     

基于分布式天线的全双工中继系统最大化和速率波束成形设计

基于分布式天线的全双工中继系统结合了全双工中继两跳同时同频传输的能力和分布式天线高效覆盖的特性,为提升小区边缘和严重阴影衰落区域的频谱效率提供了一种有效途径。在自干扰抵消非理想的多用户场景下,利用分布式多天线波束成形可实现对系统中自干扰和多用户干扰的联合抑制。为此,该文首先建立了在各分布式天线节点独立发射功率约束下最大化多用户端到端和速率的最优化系统模型,进而提出一种双层迭代算法,解决原问题的非凸性求解难题。仿真结果验证了算法的有效性,表明在多用户分布式天线全双工中继系统中,所提波束成形设计能够有效抑制自干扰和多用户干扰,显著提高系统频谱效率。     

基于IRS辅助的异构网络中超可靠低时延通信波束成形算法设计

为了增强微小区内超可靠低时延通信(URLLC)业务在异构网络场景下的传输性能,该文提出一种基于智能超表面(IRS)辅助通信网络下最大化用户和速率的波束成形算法。异构网络中微小区采用短包通信技术,在保证宏小区用户通信质量的前提下,使用IRS提高微小区用户在一定解码错误概率下的短数据包传输性能,建立一个联合优化波束向量和IRS相移向量的微小区用户和速率最大化问题模型。通过交替固定优化变量的方式,将该非凸优化问题拆分为两个子问题,利用逐次凸逼近(SCA)的方法将原问题转换成凸优化问题,并利用交替优化算法对该问题进行求解。仿真结果表明,该算法通过部署IRS可以有效减弱异构场景下对于微小区用户的干扰,同时由于IRS的部署能够有效优化波束成形向量进而提高微小区用户的短包传输性能,并且IRS的通信增强效果与微小区用户的解码错误概率以及IRS反射单元的数量有直接关系。     

Joint Beamforming and Power Allocation for Multiple Access Channels in Cognitive Radio Networks

A cognitive radio (CR) network refers to a secondary network operating in a frequency band originally licensed/allocated to a primary network consisting of one or multiple primary users (PUs). A fundamental challenge for realizing such a system is to ensure the quality of service (QoS) of the PUs as well as to maximize the throughput or ensure the QoS, such as signal-to-interference-plus-noise ratios (SINRs), of the secondary users (SUs). In this paper, we study single-input multiple output multiple access channels (SIMO-MAC) for the CR network. Subject to interference constraints for the PUs as well as peak power constraints for the SUs, two optimization problems involving a joint beamforming and power allocation for the CR network are considered: the sum-rate maximization problem and the SINR balancing problem. For the sum-rate maximization problem, zero-forcing based decision feedback equalizers are used to decouple the SIMO-MAC, and a capped multi-level (CML) water-filling algorithm is proposed to maximize the achievable sum-rate of the SUs for the single PU case. When multiple PUs exist, a recursive decoupled power allocation algorithm is proposed to derive the optimal power allocation solution. For the SINR balancing problem, it is shown that, using linear minimum mean-square-error receivers, each of the interference constraints and peak power constraints can be completely decoupled, and thus the multi-constraint optimization problem can be solved through multiple single-constraint sub-problems. Theoretical analysis for the proposed algorithms is presented, together with numerical simulations which compare the performances of different power allocation schemes.     

一种分布式天线系统的最大化能源效率传输优化算法

为实现绿色通信,考虑在每个远端射频头的发送功率受限、前端链路容量受限和保证用户目标速率的情况下,研究分布式天线系统的最大化能源效率传输优化算法。受分布式前端链路容量表达式及能源效率分式形式的约束,所考虑优化问题是非凸优化问题,难以获得其最优解。针对此优化问题,通过分式规划和凸近似方法将原始非凸问题转化为凸优化问题,提出了有效的二层迭代优化算法,并理论分析证明了所提算法的收敛性。仿真结果表明,所提算法优于传统算法,且随着远端射频头调度的用户数减少,能效压缩预编码策略的能源效率增大。      

Service function chain embedding algorithm with wireless multicast in mobile edge computing network

To resolve the excessive system overhead and serious traffic congestion in user-oriented service function chain (SFC) embedding in mobile edge computing (MEC) networks,a content-oriented joint wireless multicast and SFC embedding algorithm was proposed for the multi-base station and multi-user edge networks with MEC servers.By involving four kinds of system overhead,including service flow,server function sustaining power,server function service power and wireless transmission power,an optimization model was proposed to jointly design SFC embedding with multicast beamforming.Firstly,with Lagrangian dual decomposition,the problem was decoupled into two independent subproblems,namely,SFC embedding and multicast beamforming.Secondly,with the L_p norm penalty term-based successive convex approximation algorithm,the integer programming-based SFC embedding problem was relaxed to an equivalent linear programming one.Finally,the non-convex beamforming optimization problem was transformed into a series of convex ones via the path following technique.Simulation results revealed that the proposed algorithm has good convergence,and is superior to both the optimal SFC embedding with unicasting and random SFC embedding with multicasting in terms of system overhead.     

引入D2D通信的蜂窝网上行资源分配算法

该文研究了引入Device-to-Device (D2D)通信的蜂窝网系统中的上行资源分配问题。首先将该问题建模为一个简洁的二值整数规划问题。然而整数规划仍是NP难问题。该文利用Canonical对偶理论,得到其对偶形式。该对偶问题是一个连续域内的凸问题。证明了在特定的条件下,可以通过求解对偶问题得到原问题的最优解,且对偶间隙为零。提出了一个基于Barrier方法的算法来求解对偶问题。仿真结果表明,该文的算法优于现有算法,且性能接近最优。     

Power allocation in small cell networks with full-duplex self-backhauls and massive MIMO

With the dense deployment of small cell networks, low-cost backhaul schemes for small cell base stations (SBSs) have attracted great attentions. Self-backhaul using cellular communication technology is considered as a promising solution. Although some excellent works have been done on self-backhaul in small cell networks, most of them do not consider the recent advances of full-duplex (FD) and massive multiple-input and multiple-output (MIMO) technologies. In this paper, we propose a self-backhaul scheme for small cell networks by combining FD and massive MIMO technologies. In our proposed scheme, the macro base station (MBS) is equipped with massive MIMO antennas, and the SBSs have the FD communication ability. By treating the SBSs as special macro users, we can achieve the simultaneous transmissions of the access link of users and the backhaul link of SBSs in the same frequency. Furthermore, considering the existence of inter-tier and intra-tier interference, we formulate the power allocation problem of the MBS and SBSs as an optimization problem. Because the formulated power allocation problem is a non-convex problem, we transform the original problem into a difference of convex program by successive convex approximation method and variable transformation, and then solve it using a constrained concave convex procedure based iterative algorithm. Finally, extensive simulations are conducted with different system configurations to verify the effectiveness of the proposed scheme.     

非对称信道下双向中继网络中最大化和速率的波束成形设计

双向中继网络的波束成形设计大都基于对称信道的假设。该文考虑非对称信道的情况,通过合理设计波束成形因子,使得系统在中继总功率约束下,其和速率最大化。为解决此非凸的最优化问题,首先提出了一种基于分支定界(branch-and-bound)思想的优化算法,并证明了该算法可获得全局最优解。然后提出了一种次优算法,该算法假设次优解为两个已知向量的线性组合,因而求解次优解只需优化两个实数系数,具有较低的复杂度。仿真表明,分支定界算法性能优于现有其他算法;次优算法相对于分支定界算法有一定的性能损失,但是在节点数较小或中继节点平均功率较低时,次优算法优于其他现有算法。     

Energy-efficient power allocation for NOMA heterogeneous networks with imperfect CSI

In this paper, an optimal user power allocation scheme is proposed to maximize the energy efficiency for downlink non-orthogonal multiple access (NOMA) heterogeneous networks (HetNets). Considering channel estimation errors and inter-user interference under imperfect channel state information (CSI), the energy efficiency optimization problem is formulated, which is non-deterministic polynomial (NP)-hard and non-convex. To cope with this intractable problem, the optimization problem is converted into a convex problem and address it by the Lagrangian dual method. However, it is difficult to obtain closed-form solutions since the variables are coupled with each other. Therefore, a Lagrangian and sub-gradient based algorithm is proposed. In the inner layer loop, optimal powers are derived by the sub-gradient method. In the outer layer loop, optimal Lagrangian dual variables are obtained. Simulation results show that the proposed algorithm can significantly improve energy efficiency compared with traditional power allocation algorithms.     

一种基于GNC和增广拉格朗日对偶的非凸非光滑图像恢复方法

逐步非凸方法（GNC）和增广拉格朗日对偶在非凸非光滑图像恢复中有较高的恢复性能.然而分别使用这两种方法时GNC不能够保证全局收敛,增广拉格朗日对偶不能获得有效的初始值.为克服上述缺陷,本文通过转换原始问题为等式约束优化问题推出了一种基于GNC和增广拉格朗日对偶的组合图像恢复方法,并对其收敛性严格证明.该方法不仅可以获得有效的初始值,同时不要求问题具有凸性和光滑性.更多地,一个自适应能量函数通过对偶迭代而得到.实验结果表明推出的方法可以有效地提高图像恢复质量和算法效率.     

Joint admission control,cell association,power allocation and throughput maximization in decoupled 5G heterogeneous networks

Downlink (DL)-uplink (UL) coupled cell association scheme was adopted in 3rd generation (3G) homogeneous network and 4th generation (4G) heterogeneous network (HetNet). In the coupled cell association scheme, a user is associated to single base station (BS) in DL & UL based on the strongest received signal power (SRSP) in DL from a macro base station (MBS) and multiple small base stations (SBS) in HetNet. This is a sub-optimal solution for cell association as most of the users are associated to a MBS due to dominant transmit power and brings challenges like multiple interference issues and imbalanced user traffic load which leads to a degraded throughput in HetNet. In this paper, we investigate downlink uplink decoupled cell association scheme to address these issues and formulate a sum-rate maximization problem in terms of admission control, cell association and power allocation for MBS only, coupled and decoupled HetNet. The formulated optimization problem falls into class of a mixed integer non linear programming (MINLP) problem which is NP-hard and requires exhaustive search to find the optimal solution. However, computational complexity of the exhaustive search increases exponentially with the increase in number of users. Therefore, an outer approximation algorithm (OAA) is proposed as a solution to find near optimal solution with less computation complexity. Extensive simulations work has been done to evaluate the decoupled cell association scheme in HetNet vs the coupled cell association scheme in traditional MBS only and HetNet. Results show the effectiveness of decoupled cell association scheme in terms of KPIs, such as throughput, addressing user traffic load imbalances and number of users attached.

     

异构无线网络干扰效率最大顽健资源分配算法

针对能效提升、宏用户干扰减小的问题,该文研究了基于干扰效率最大的异构无线网络顽健资源分配算法.首先,考虑宏用户干扰约束、微蜂窝用户速率需求约束和最大发射功率约束,将资源优化问题建模为多变量非线性规划问题.其次,考虑有界信道不确定性模型,利用Dinkelbach辅助变量方法和连续凸近似方法结合对数变换方法,将原分式规划顽健资源分配问题转换为等价的确定性凸优化问题,并利用拉格朗日对偶算法获得解析解.理论分析了计算复杂度和参数不确定性对性能的影响.仿真结果表明该算法具有较好的干扰效率和鲁棒性.     

Joint backhaul bandwidth and power allocation in heterogeneous cellular networks: A two‐level approach

We investigate the problem of joint downlink wireless backhaul bandwidth (WBB) and power allocation in heterogeneous cellular networks (HCNs). A WBB partitioning scheme is considered, which allocates the whole bandwidth between the macrocell and small cells for data transmission and backhauling. We formulate an optimization problem to maximize the weighted sum logarithmic utility function by jointly optimizing WBB portion and fronthaul power allocation of each base station with consideration of the backhaul capacity limitation on each small cell. In order to solve this joint optimization problem, we propose a hierarchical two‐level approach and decompose the original problem into two independent subproblems: the WBB allocation at the macrocell base station (MBS) and the power allocation at both the MBS and small cell base stations (SBSs). Accordingly, the optimal WBB portion and power allocation solutions are obtained, respectively. Furthermore, we develop a distributed algorithm to implement the joint WBB and power allocation. Numerical results verify the effectiveness of the proposed approach and analyze the impact of the weighted coefficient and backhaul capacity limitation on the network performance. In addition, significant performance gains can be achieved by the proposed approach over the benchmark.