基于硬件损伤的异构网络鲁棒安全资源分配算法

针对多蜂窝多用户异构网络中收发机处信号畸变、用户信息泄露和传输中断等问题,该文提出一种基于硬件损伤的异构网络鲁棒安全资源分配算法。考虑小蜂窝用户最小安全速率约束、小蜂窝基站最大发射功率约束和宏用户干扰功率约束,建立了基于有界信道不确定性的能效最大化资源分配模型。基于Dinkelbach法、最坏准则法和连续凸近似理论,将原非凸资源分配问题等价转换为凸优化问题,并利用拉格朗日对偶算法得到解析解。仿真结果表明,与现有算法相比,所提算法具有较好的能效和鲁棒性。     

基于不完美CSI的D2D通信网络鲁棒能效资源分配算法

针对设备到设备(D2D)直连通信网络传统最优资源分配算法在随机信道时延、信道估计误差影响下鲁棒性弱的问题,该文在考虑参数不确定性影响的条件下,提出D2D用户总能效最大的鲁棒资源分配算法.考虑干扰功率门限、用户最小速率需求、最大传输功率和子信道分配约束,建立了下垫式频谱共享模式下多用户D2D网络资源分配模型.基于有界信道不确定性模型,利用最坏准则方法将原非凸鲁棒资源分配问题转换为确定性的凸优化问题.然后利用拉格朗日对偶理论求得资源分配的解析解.仿真结果表明所提出的算法具有很好的鲁棒性.     

基于干扰效率多蜂窝异构无线网络最优基站选择及功率分配算法

针对多蜂窝多用户异构无线网络干扰管理和效率提升问题,该文研究了基于干扰效率最大的下行链路基站(BS)-用户匹配和功率分配问题。首先,考虑宏用户和微蜂窝用户的服务质量,将问题建模为多变量混合整数非线性规划问题。其次将原问题分解为基站选择和功率分配两个子问题。针对基站选择问题,利用凸优化问题获得最优基站选择策略;针对功率分配问题,利用二次变换法和Dinkelbach辅助变量法,将功率分配问题转换为凸优化问题求解。仿真结果表明,与现有算法对比,该算法具有较好的干扰效率和干扰控制性能。     

基于不完美CSI的D2D通信网络鲁棒能效资源分配算法

针对设备到设备(D2D)直连通信网络传统最优资源分配算法在随机信道时延、信道估计误差影响下鲁棒性弱的问题,该文在考虑参数不确定性影响的条件下,提出D2D用户总能效最大的鲁棒资源分配算法。考虑干扰功率门限、用户最小速率需求、最大传输功率和子信道分配约束,建立了下垫式频谱共享模式下多用户D2D网络资源分配模型。基于有界信道不确定性模型,利用最坏准则方法将原非凸鲁棒资源分配问题转换为确定性的凸优化问题。然后利用拉格朗日对偶理论求得资源分配的解析解。仿真结果表明所提出的算法具有很好的鲁棒性。     

面向窃听用户的RIS-MISO系统鲁棒资源分配算法

针对信道不确定性影响、用户信息泄露和能效提升等问题,该文提出一种基于不完美信道状态信息的可重构智能反射面(RIS)多输入单输出系统鲁棒资源分配算法。首先,考虑能量收集最小接收功率约束、合法用户最小保密速率约束、基站最大发射功率约束及RIS相移约束,基于有界信道不确定性,建立一个联合优化基站主动波束、能量波束、RIS相移矩阵的多变量耦合非线性资源分配问题。然后,利用Dinkelbach,S-procedure和交替优化方法,将原非凸问题转换成确定性凸优化问题,并提出一种基于连续凸近似的交替优化算法。仿真结果表明,与传统非鲁棒算法对比,所提算法具有较低的中断概率。     

可重构智能表面辅助的非正交多址接入网络鲁棒能量效率资源分配算法

为提高非正交多址接入(NOMA)网络的鲁棒性和系统能效(EE),考虑了不完美信道状态信息,该文提出一种可重构智能表面(RIS)辅助的NOMA网络鲁棒能效最大资源分配算法。考虑用户信干噪比(SINR)中断概率约束、基站的最大发射功率约束以及连续相移约束,建立了一个非线性的能效最大化资源分配模型。用Dinkelbach方法将分式形式的目标函数转换为线性的参数相减的形式,利用S-procedure方法将含有信道不确定性的SINR中断概率约束转换成确定性形式,利用交替优化算法将多变量耦合的非凸优化问题分解成多个凸优化子问题,最后用CVX对分解出的子问题进行求解。仿真结果表明,在EE方面,所提算法比无可重构智能表面(RIS)算法提高了7.4%。在SINR中断概率方面,所提算法比非鲁棒算法降低了85.5%。     

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.     

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...     

面向安全通信的智能反射面网络能效优化算法

为了提高智能反射面网络传输安全性和能效,提出了一种面向安全通信的智能反射面网络能效最大资源分配算法。针对多输入单输出的智能反射面辅助蜂窝通信系统,考虑用户的安全速率约束、基站的最大发射功率约束以及连续相移约束,建立了非线性、多变量耦合的能效最大化资源分配模型。利用Dinkelbach方法将目标函数转化为辅助变量相减的形式,采用交替迭代算法求解原变量耦合的非凸优化问题。仿真结果表明,所提算法与传统算法对比,能效提升了8.3%,中断概率下降了77%。     

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。     

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.     

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 power and subcarrier allocation algorithm for cognitive network based on interference efficiency maximization

For the cognitive OFDMA uplink communication system,a robust power and subcarrier allocation algorithm based on maximum interference efficiency was proposed.Firstly,considering primary user interference constraint,secondary user transmit power constraint,subcarrier allocation constraint and secondary user minimum rate constraint,a robust resource optimization model based on outage probability was established.Then,by using Bernstein approximation and Dinkelbach’s method,the original non-convex problem based on outage probability was transformed into an equivalent convex optimization one,and the analytical solution was obtained by Lagrangian dual function method.Meanwhile,the computational complexity and robust sensitivity of the algorithm were analyzed.The simulation results show that the proposed algorithm has better interference efficiency and robustness.     

Distributed power control for multiuser cognitive radio networks with quality of service and interference temperature constraints

One of the most challenging problems in dynamic resource allocation for cognitive radio networks is to adjust transmission power of secondary users (SUs) while quality of service needs of both SUs and primary users (PUs) are guaranteed. Most power control algorithms only consider interference temperature constraint in single user scenario while ignoring the interference from PUs to SUs and minimum signal to interference plus noise ratio (SINR) requirement of SUs. In this paper, a distributed power control algorithm without user cooperation is proposed for multiuser underlay CNRs. Specifically, we focus on maximizing total throughput of SUs subject to both maximum allowable transmission power constraint and SINR constraint, as well as interference temperature constraint. To reduce the burden of information exchange and computational complexity, an average interference constraint is proposed. Parameter range and convergence analysis are given for feasible solutions. The resource allocation is transformed into a convex optimization problem, which is solved by using Lagrange dual method. In computer simulations, the effectiveness of our proposed scheme is shown by comparing with distributed constrained power control algorithm and Nash bargaining power control game algorithm. Copyright © 2014 John Wiley & Sons, Ltd.     

一种新的认知无线电功率分配算法（英文）

Most resource allocation algorithms are based on interference power constraint in cognitive radio networks.Instead of using conventional primary user interference constraint,we give a new criterion called allowable signal to interference plus noise ratio(SINR) loss constraint in cognitive transmission to protect primary users.Considering power allocation problem for cognitive users over flat fading channels,in order to maximize throughput of cognitive users subject to the allowable SINR loss constraint and maximum transmit power for each cognitive user,we propose a new power allocation algorithm.The comparison of computer simulation between our proposed algorithm and the algorithm based on interference power constraint is provided to show that it gets more throughput and provides stability to cognitive radio networks.     

Power Allocation with Max–Min and Min–Max Fairness for Cognitive Radio Networks with Imperfect CSI

This paper consider the power allocation strategies in the cognitive radio (CR) system in the presence of channel estimation errors. As the user has different channel condition in CR systems, different amount of power resource is required to meets the QoS request. In order to guarantee the fairness of each CR user, ensure the interference from the primary user and other CR users meet the QoS requirement of the CR user and limit the interference that is caused by CR users on primary user within the range into the level that primary user can tolerate, we proposed some new power allocation schemes. The targets are to minimize the maximum power allocated to CR users, to maximize the minimum signal-to-interference-plus-noise ratio (SINR) among all CR users and to minimize the maximum outage probability over all CR users. The first power allocation scheme can be formulated using Geometric Programming (GP). Since GP problem is equivalent to the convex optimization problem, we can obtain the optimal solutions for the first scheme. The latter two power allocation schemes are not GP problems. We propose iterative algorithms to solve them. Simulation results show that proposed schemes can efficiently guarantee the fairness of CR users under the QoS constraint of the primary user and CR users.     

A self‐organized resource allocation scheme for heterogeneous macro‐femto networks

This paper investigates the radio resource management (RRM) issues in a heterogeneous macro‐femto network. The objective of femto deployment is to improve coverage, capacity, and experienced quality of service of indoor users. The location and density of user‐deployed femtos is not known a‐priori. This makes interference management crucial. In particular, with co‐channel allocation (to improve resource utilization efficiency), RRM becomes involved because of both cross‐layer and co‐layer interference. In this paper, we review the resource allocation strategies available in the literature for heterogeneous macro‐femto network. Then, we propose a self‐organized resource allocation (SO‐RA) scheme for an orthogonal frequency division multiple access based macro‐femto network to mitigate co‐layer interference in the downlink transmission. We compare its performance with the existing schemes like Reuse‐1, adaptive frequency reuse (AFR), and AFR with power control (one of our proposed modification to AFR approach) in terms of 10 percentile user throughput and fairness to femto users. The performance of AFR with power control scheme matches closely with Reuse‐1, while the SO‐RA scheme achieves improved throughput and fairness performance. SO‐RA scheme ensures minimum throughput guarantee to all femto users and exhibits better performance than the existing state‐of‐the‐art resource allocation schemes.Copyright © 2014 John Wiley & Sons, Ltd.