CoMP-JT中预编码与功率联合分配算法

在CoMP(协作多点发送/接收)联合传输资源分配中,研究发现在单天线功率约束条件下,传统的迫零波束赋形预编码技术会降低平均天线发射功率利用率。为了进一步提升系统吞吐量,文章提出一种预编码与功率联合分配算法,该算法采用一种预编码迭代计算方式,提高了天线发射功率利用率。仿真结果表明,所提算法与传统的预编码相比性能有较大提高。     

最大化瑞利熵的干扰对齐优化算法

干扰对齐技术常采用迭代的方法进行预编码矩阵与干扰抑制矩阵的设计,然而目前采用的迭代方法并不能做到完全的干扰对齐。针对这种情况,提出了一种新的干扰对齐优化算法,避开了反转信道的操作。该算法在最大化信干噪比算法的基础上加入完全干扰对齐的约束条件,将系统中各用户的所有干扰对齐约束条件进行奇异值分解,最终通过瑞利熵最大化的求解方法联合迭代求解发送侧的预编码矩阵与接收侧的干扰抑制矩阵。仿真结果表明,所提优化算法在较高信噪比时不同天线数下的和速率、不同迭代次数下的和速率等方面都优于原最大信干噪比算法。     

MIMO多中继系统中基于不完全信道状态信息的预编码算法研究

针对配置多个中继节点的多输入多输出（Multiple-input Multiple-Output,MIMO）系统,提出一种不完全信道状态信息（Imperfect channel state information,CSI）下基于最小均方误差（Minimum Mean-Squared Error,MMSE）准则的预编码设计方案,该方案综合考虑各个节点天线之间的相关性及信道估计误差。由于各个中继端预编码矩阵之间可视为相互独立,故将约束优化问题转化为若干半正定规划问题,继而通过内点法或CVX工具箱求解预编码矩阵,然后采用求导法推导出接收端处理矩阵。最后利用迭代算法交替迭代来联合优化预编码矩阵和接收端处理矩阵,直到算法收敛。仿真结果表明,与未能综合考虑各个节点的预编码算法相比,本文提出的算法能明显改善系统BER性能。      

放大转发双向中继系统中降低总功率的波束成形设计

本文研究了多天线放大转发双向中继系统中在满足源节点信噪比要求条件下最小化系统总功率的波束设计问题。该问题是非凸优化问题,为了有效求解该问题,采用分层优化方法将原问题分解成发送波束成形向量优化、接收波束成形向量优化和中继波束成形矩阵优化三类子问题。发送/接收波束成形向量通过求解Rayleigh商最小化问题来获得。中继波束成形矩阵优化问题通过半正定松弛方法转化成半正定优化问题来求解。在求解这三类优化问题的基础上,提出了一种迭代波束成形算法,并采用单调有界序列定理证明了所提算法的收敛性。计算机仿真表明:所提算法经过若干次迭代即可收敛到稳定点;相比于已有算法,本文算法能显著降低系统总功率。      

基于人工噪声的多用户MIMO系统加密算法

当多用户MIMO系统中的用户数多于或等于发射端天线数时,现有的基于人工噪声的物理层加密算法会导致合法用户无法正常接收。为提高此时的多用户MIMO系统的安全性,该文提出一种新的基于人工噪声的多用户MIMO系统加密算法。首先,发送端对多个合法用户进行联合处理,建立多用户联合信道状态矩阵;然后,将联合信道状态矩阵进行奇异值分解,并根据最小的奇异值进行预编码,以消除人工噪声对合法用户的影响;最后,提出一种优化功率分配的方案。仿真结果表明,该算法将多用户MIMO系统的保密容量平均增加了0.1 bit/(s?Hz),从而提高多用户MIMO系统的安全性。     

OFDMA中继通信系统中非对称的资源分配

传统的OFDMA协作通信系统,中继经过一一对应的两跳子载波接收和发送数据信息,这一对称的资源分配模式限制了系统吞吐量的提高;本文放宽对称约束,研究了非对称模式下的资源分配问题。自由度的增加,增大了系统资源分配的复杂度,这是一个结合中继选择、子载波分配和功率控制等多维自由度的混合离散型优化问题,很难得到满足实时性要求的近似最优算法。针对该模型,本文提出基于拉格朗日松弛的联合优化算法,算法的关键在于OFDMA系统的渐进强对偶性,这个性质使得多项式时间算法的设计成为可能。算法的复杂度与子载波数目成线性关系。仿真结果表明,该算法的性能是近似最优的,而且与对称模式相比,非对称模式下的资源分配进一步提升了系统的吞吐量性能。      

基于非冗余线性预编码的MIMO—OFDM系统的盲信道估计算法

本文在分析得出MIMO-OFDM系统中的子空间盲信道估计方法不能用在发射天线数大于接收天线数这种情况下,引出了基于非冗余线性预编码的子空间盲估计算法,分析了预编码矩阵的设计方法.最后通过算法的仿真得出,只要P的取值合适,该算法就会有着很好的估计性能,并且通过分析发现,该算法的计算复杂度较小.     

采用天线选择的多用户MIMO-OFDM系统容量研究

对于采用线性预编码进行空分复用的多用户MIMO-OFDM系统的下行链路,分析了使用户间干扰完全消除的预编码矩阵设计实现过程,提出了用户端联合天线选择算法,以增大发送的数据流数,提高系统和容量,在此基础上提出了逐次递增确定用户天线的选择算法.仿真结果表明,本文所提的天线选择算法使系统和容量远高于未经天线选择时的和容量,并且次优的逐次选择算法在计算量可接受的情况下与最优联合选择算法相比获得的容量增益相差不大.     

异构网络特征子信道干扰对准算法优化

包含多种低功率发射节点的异构网络因其在系统容量提升和解决室内及热点场景覆盖上的优势,正成为新一代无线通信系统组网的趋势。但异构网络干扰问题也因小区数目的增加而复杂化。近年提出的干扰对准技术因能实现系统自由度的最大化而成为解决异构网络干扰问题的有效手段。文章在对经典干扰对准算法进行分析的基础上,对特征子信道干扰对准方案进行了改进,优化了发送预编码矩阵和接收滤波矩阵算法,并提出算法在宏蜂窝——毫微微蜂窝异构网络场景下的应用方案。     

基于两跳匹配的OFDMA中继网络联合资源分配算法

针对OFDMA中继网络的两跳特性,提出一种基于两跳匹配的中继网络联合资源分配算法。首先根据中继网络的两跳性建立两跳速率匹配模型,然后利用对偶分解理论将中继网络资源分配的主问题分解为：中继选择、子载波分配和功率分配三个主要的子问题并进行联合优化,同时基于中继网络两跳性在子载波分配的过程中考虑两跳子载波配对,以逼近系统最优解。最后为了保证算法的公平性,考虑子载波分配因子约束以优化子载波分配。仿真结果表明：所提算法将中继选择、两跳子载波配对与功率分配联合优化以进一步提升系统吞吐量,同时引入子载波分配因子约束,保证了算法的公平性。     

Joint Optimum Linear Precoding and Power Control Strategies for Downlink MC-CDMA Systems

In this paper we derive two iterative joint precoding and power allocation methods for downlink MC-CDMA systems with multiple transmit antennas at the base station. The first method optimally solves the so called assigned target SINRs problem, i.e. determines the precoding coefficients and the minimum sum-power allocation under a constraint on the SINR of each user. The second method instead solves the maximum sum-rate problem, i.e. maximizes the system throughput under a constraint on the overall transmit power. The main result of this paper is that the proposed joint precoding and power control solutions provide an advantage with respect to suboptimal solutions for a fully loaded system, i.e. when the number of users is equal to the spreading factor times the number of transmit antennas. For lower loads, performance of the various schemes becomes closer.     

Precoding and decoding design for two-way MIMO AF multiple-relay system

In this paper, a joint precoding and decoding design scheme is proposed for two-way Multiple-Input Multiple-Output (MIMO) multiple-relay system. The precoding and decoding matrices are jointly optimized based on Minimum Mean-Square-Error (MMSE) criteria under transmit power constraints. The optimization problem is solved by using a convergent iterative algorithm which includes four sub-problems. It is shown that due to the difficulty of the block diagonal nature of the relay precoding matrix, sub-problem two cannot be solved with existing methods. It is then solved by converting sub-problem two into a convex optimization problem and a simplified method is proposed to reduce the computational complexity. Simulation results show that the proposed scheme can achieve lower Bit Error Rate (BER) and larger sum rate than other schemes. Furthermore, the BER and the sum rate performance can be improved by increasing the number of antennas for the same number of relays or increasing the number of relays for the same number of antennas.     

Graph‐based resource allocation algorithms for multiuser downlink MIMO‐OFDMA networks

Multiuser multiple‐input multiple‐output orthogonal frequency division multiple access (MIMO‐OFDMA) is considered as the practical method to attain the capacity promised by multiple antennas in the downlink direction. However, the joint calculation of precoding/beamforming and resource allocation required by the optimal algorithms is computationally prohibitive. This paper proposes computationally efficient resource allocation algorithms that can be invoked after the precoding and beamforming operations. To support stringent and diverse quality of service requirements, previous works have shown that the resource allocation algorithm must be able to guarantee a specific data rate to each user. The constraint matrix defined by the resource allocation problem with these data rate constraints provides a special structure that lends to efficient solution of the problem. On the basis of the standard graph theory and the Lagrangian relaxation, we develop an optimal resource allocation algorithm that exploits this structure to reduce the required execution time. Moreover, a lower‐complexity suboptimal algorithm is introduced. Extensive simulations are conducted to evaluate the computational and system‐level performance. It is shown that the proposed resource allocation algorithms attain the optimal solution at a much lower computational overhead compared with general‐purpose optimization algorithms used by previous MIMO‐OFDMA resource allocation approaches. Copyright © 2012 John Wiley & Sons, Ltd.     

基于对偶分解的OFDMA系统资源分配算法

该文针对多业务OFDMA系统资源分配问题,建立了考虑业务服务质量、数据到达、系统约束的最优化问题。分析了不同业务的速率约束、延时约束和队列长度之间的关系,并利用对偶分解方法将原问题分解为若干独立子问题,分别得到了最优资源块与最优功率分配规则,进而提出了基于对偶分解的最优资源分配算法。仿真结果表明,该算法在业务违反概率较低、公平性较好、算法复杂度略有上升的情况下,可以实现非实时业务吞吐量最大化。     

Delay-aware cross-layer optimization method for FANET

An asynchronous distributed cross-layer optimization (ADCO) method was proposed to solve the problem of jointly considering real-time routing,rate allocation and power control in FANET (flying ad hoc network).And a delay-constrained cross-layer optimization framework was designed to formally represent proposed problem.Then Lagrangian relaxation and dual decomposition methods was used to divide joint optimization problem into several sub-problems.ADCO allowed each relay node to perform the optimization operation for different sub-problems with local information,and the relay nodes could update the dual variables based on asynchronous update mechanism.The simulation results show that the proposed algorithm can improve the network performance effectively in terms of energy efficiency,packet timeout ratio and network throughput.     

Optimal scheduling of coordinated multipoint transmissions in cellular networks

In this paper, we study the optimal scheduling problem in coordinated multipoint (CoMP) transmission–based cellular networks. We consider joint transmission and coordinated scheduling together in CoMP transmission–based cellular networks and develop an optimization framework to compute the optimal max‐min throughput and the optimal scheduling of the transmissions to the users. The optimization problem is found to be a complex linear program with number of variables in for a cellular network of N users and K cells. We solve the optimization problem for several network instances using an optimization tool. The numerical results show that the optimal CoMP transmission provides a significant throughput gain over a traditional transmission. We find that in optimal scheduling the fraction time of coordinated scheduling is higher than that of joint transmission. To solve the optimization problem without any optimization tool, we propose a heuristic algorithm. The performance of the heuristic algorithm is evaluated and found to be provided throughput around 97% of the optimal throughput. Further, we extend the optimization framework to study joint scheduling and power allocation (JSPA) problem in CoMP transmission–based cellular networks. We numerically solve the JSPA problem for the network instances and demonstrate that the optimal power allocation at the base stations is not binary for a significant fraction of time of scheduling. However, the gain in max‐min throughput by the optimal JSPA technique over the optimal scheduling technique is not significant.     

Resource optimization for dual-hop device to device networks

In this paper, we consider an orthogonal frequency division multiplexing based device to device network. The communication between source and destination is facilitated by a dual-hop transmission under amplify and forward relaying protocol. The sum throughput of the system is maximized with joint optimization over power allocation at each transmitting node and sub-carrier pairing over two hops. A dual decomposition based solution is proposed subject to separate transmit power constraint at each node. Simulation results are presented to validate the performance of the proposed algorithm where the results are compared with the existing works in literature as well as with a sub-optimal power optimization solution.     

基于可再生能量协作的雾无线接入网络资源分配

针对雾无线接入网络(Fog Radio Access Network,F-RAN)中能耗开销巨大的问题,提出了一种基于能量收集(Energy Harvesting,EH)约束的资源分配算法,从联合模式选择与功率分配两个方面进行了研究.首先建立传输模型和能量采集模型,根据功率约束和电费支出约束建立最优化问题;再使用分枝定界法对通信模式进行选择,利用吞吐量注水法对不同传输模式下的发射功率进行分配.仿真结果表明,提出的基于可再生能量协作的F-RAN的吞吐量和电网能量效率均高于传统F-RAN,具有经济和环境双重效益.     

Cognitive radio network with coordinated multipoint joint transmission

Cognitive radio (CR) is considered to be a promising technology for future wireless networks to make opportunistic utilization of the unused or underused licensed spectrum. Meanwhile, coordinated multipoint joint transmission (CoMP JT) is another promising technique to improve the performance of cellular networks. In this paper, we propose a CR system with CoMP JT technique. We develop an analytical model of the received signal‐to‐noise ratio at a CR to determine the energy detection threshold and the minimum number of required samples for energy detection–based spectrum sensing in a CR network (CRN) with CoMP JT technique. The performance of energy detection–based spectrum sensing under the developed analytical model is evaluated by simulation and found to be reliable. We formulate an optimization problem for a CRN with CoMP JT technique to configure the channel allocation and user scheduling for maximizing the minimum throughput of the users. The problem is found to be a complex mixed integer linear programming. We solve the problem using an optimization tool for several CRN instances by limiting the number of slots in frames. Further, we propose a heuristic‐based simple channel allocation and user scheduling algorithm to maximize the minimum throughput of the users in CRNs with CoMP JT technique. The proposed algorithm is evaluated via simulation and found to be very efficient.     

Joint estimation of the channel and I/Q imbalance for two-way relay networks

In modern day communication systems, the massive MIMO architecture plays a pivotal role in enhancing the spatial multiplexing gain, but vice versa the system energy efficiency is compromised. Consequently, resource allocation in-terms of antenna selection becomes inevitable to increase energy efficiency without having any obvious effect or compromising the system spectral efficiency. Optimal antenna selection can be performed using exhaustive search. However, for a massive MIMO architecture, exhaustive search is not a feasible option due to the exponential growth in computational complexity with an increase in the number of antennas. We have proposed a computationally efficient and optimum algorithm based on the probability distribution learning for transmit antenna selection. An estimation of the distribution algorithm is a learning algorithm which learns from the probability distribution of best possible solutions. The proposed solution is computationally efficient and can obtain an optimum solution for the real time antenna selection problem. Since precoding and beamforming are also considered essential techniques to combat path loss incurred due to high frequency communications, so after antenna selection, successive interference cancellation algorithm is adopted for precoding with selected antennas. Simulation results verify that the proposed joint antenna selection and precoding solution is computationally efficient and near optimal in terms of spectral efficiency with respect to exhaustive search scheme. Furthermore, the energy efficiency of the system is also optimized by the proposed algorithm, resulting in performance enhancement of massive MIMO systems.