Joint interference alignment precoding based on the optimization algorithm on the Grassmannian manifold

To solve the interference problem between users of the multiuser MIMO system, we first transform the system sum rate maximization problem into joint optimization of interference signal power and useful signal power. On this basis, we propose a weighted interference alignment objective function, causing the system to obtain a higher sum rate by adjusting the weight with different signal-to-noise ratios. Then, we model the transmit subspace and the interference subspace on the Grassmannian manifold and propose joint interference alignment precoding based on the Grassmannian conjugacy gradient algorithm (GCGA-JIAP algorithm). In contrast to conventional interference alignment algorithms, our proposed algorithm can reduce the computational cost by transforming the constrained optimization of the complex Euclidean space into unconstrained optimization with the degenerate dimension on the Grassmannian manifold. Computer simulation shows that the proposed algorithm improves the convergence of the iterative optimization of the transmitter precoding matrix and the receiver postprocessing matrix and also improves the sum rate performance of the multiuser MIMO interference system.     

一种分布式的OFDMA系统资源分配算法

本文研究OFDMA系统的资源分配问题,把该问题建模为一个在基站的总发射功率一定的条件下,使系统中各个用户的权重速率之和最大化的数学模型。并提出一种基于对偶分解的分布式资源分配算法,将该问题分解为一个关于基站的主问题以及若干个关于用户的子问题。各个用户可以通过对子问题的求解获得各自的子载波以及功率的分配方案;而基站通过对主问题的求解使得满足子载波与功率的分配能够满足约束条件的要求,实现各用户权重速率和最大化的优化目标。所提算法能够把一个复杂的优化问题分解为若干个独立的子问题进行并行求解,因此可以有效地降低计算的复杂度以及基站的运算量。仿真结果表明,该算法能够在较少的迭代步数内得到一个近似最优解。      

Energy‐efficient power control for uplink spectrum‐sharing heterogeneous networks

This article considers energy‐efficient power control schemes for interference management in uplink spectrum‐sharing heterogeneous networks that maximize the energy efficiency of users, protect the macro base station, and support users with QoS consideration. In the first scenario, we define the objective function as the weighted sum of the energy efficiencies and develop an efficient global optimization algorithm with global linear and local quadratic rate of convergence to solve the considered problem. To ensure fairness among individual user equipments (UEs) in terms of energy efficiency, we consider the max‐min problem, where the objective is defined as the weighted minimum of the energy efficiencies, and a fractional programming theory and the dual decomposition method are jointly used to solve the problem and investigate an iterative algorithm. As by‐products, we further discuss the global energy efficiency problem and consider near‐optimal schemes. Numerical examples are provided to demonstrate significant improvements of the proposed algorithms over existing interference management schemes.     

Optimum Power Allocation for Single-User MIMO and Multi-User MIMO-MAC with Partial CSI

We consider both the single-user and the multi-user power allocation problems in MIMO systems, where the receiver side has the perfect channel state information (CSI), and the transmitter side has partial CSI, which is in the form of covariance feedback. In a single-user MIMO system, we consider an iterative algorithm that solves for the eigenvalues of the optimum transmit covariance matrix that maximizes the rate. The algorithm is based on enforcing the Karush-Kuhn-Tucker (KKT) optimality conditions of the optimization problem at each iteration. We prove that this algorithm converges to the unique global optimum power allocation when initiated at an arbitrary point. We, then, consider the multi-user generalization of the problem, which is to find the eigenvalues of the optimum transmit covariance matrices of all users that maximize the sum rate of the MIMO multiple access channel (MIMO-MAC). For this problem, we propose an algorithm that finds the unique optimum power allocation policies of all users. At a given iteration, the multi-user algorithm updates the power allocation of one user, given the power allocations of the rest of the users, and iterates over all users in a round-robin fashion. Finally, we make several suggestions that significantly improve the convergence rate of the proposed algorithms.     

Robust uplink power control for co-channel two-tier femtocell networks

This paper is concerned with uplink interference suppression problem in two-tier femtocell networks through power control. Specifically, we consider the Quality-of-Service (QoS) of the macrocell user and femtocell users in terms of their received Signal to Interference-plus-Noise Ratios (SINRs) at macrocell base station (MBS) and femtocell base stations (FBSs), and we also take femtocell users’ power efficiency into consideration by designing an objective function, which is a weighted sum of transmission power and squared SINR difference between femtocell user's maximum SINR and actual SINR. Due to the error of the SINR at MBS caused by distance errors, a robust uplink power control problem is formulated, and it is equivalent to a robust convex optimization problem with femtocell users’ SINR constraints. Then, the robust convex optimization problem is converted into a general convex optimization problem. Moreover, a distributed power control algorithm combined with admission control is presented to obtain femtocell users’ optimal power allocation. Numerical results show the convergence and effectiveness of the proposed uplink power control algorithm.     

An Iterative Water-Filling Algorithm for Maximum Weighted Sum-Rate of Gaussian MIMO-BC

We consider the maximization of weighted rate sum in Gaussian multiple-input–multiple-output broadcast channels. This problem is motivated by optimal adaptive resource allocation policies in wireless systems with multiple antenna at the base station. In fact, under random packet arrival and transmission queues, the system stability region is achieved by maximizing a weighted rate sum with suitable weights that depend on the queue buffer sizes. Our algorithm is a generalization of the well-known Iterative Multiuser Water-Filling that maximizes the rate sum under a total transmit power constraint and inherits from the latter its simplicity. We propose also a variation on the basic algorithm that makes convergence speed very fast and essentially independent of the number of users.     

Power allocation for F/TDMA multiuser two-way relay networks

We consider a multiuser two-way relay network where multiple pairs of users exchange information with the assistance of a relay node, using orthogonal channels per pair. For a variety of two-way relaying mechanisms, such as decodeand- forward (DF), amplify-and-forward (AF) and compress-andforward (CF), we investigate the problem of optimally allocating relay?s power among the user pairs it assists such that an arbitrary weighted sum rate of all users is maximized, and solve the problem as one or a set of convex problems for each relaying scheme. Numerical results are presented to demonstrate the performance of the optimum relay power allocation as well as the comparison among different two-way relaying schemes.     

On the Sum Rate Capacity of MIMO Broadcast Channels in Cognitive Radio Networks with Interference Power Constraints

This paper investigates the sum rate capacity of MIMO broadcast channels (MIMO-BCs) in cognitive radio networks. A suboptimal user-selection algorithm is proposed to achieve a large sum rate capacity with reduced complexity. This algorithm consists of two steps. First, zero-forcing beamforming is utilized as a downlink precoding technique that precancels inter-user interference. Second, singular value decomposition is applied to the channel matrices of all the secondary users and only consider the singular vectors corresponding to the maximum singular values. The proposed user-selection algorithm chooses singular vectors which are nearly orthogonal to each other and nearly orthogonal to the vector of primary users. With this algorithm, the sum rate capacity of MIMO-BCs in CR networks with interference power constraints and transmit power constraints is derived. We formulate the sum rate capacity as a multi-constraint optimization problem and develop an algorithm to solve the problem in its equivalent form. Finally, numerical simulations are conducted to corroborate our theoretical results in flat Rayleigh fading environments. It is shown that the proposed algorithms are capable of achieving a large sum rate capacity with a very low complexity.     

ROF接入网络认知MAC信道最优迭代注水算法研究

研究基于认知多址接人信道（C—MAC）的光纤无线通信（ROF）接入网络在发射功率以及干扰温度约束下的系统加权总容量（we岫ted—SumRate）最大化问题。由单人多出（SIMO）模型的特殊算法作为切入点,通过部分对偶分解技术,松弛干扰温度约束,将原始问题分解为较易处理的子问题;另外针对文献[133所提方法的不足,提出一种适用于多人多出（MIMO）天线系统的迭代注水算法以求解最大系统加权容量。最后通过仿真表明算法的有效性。     

Coordinated Scheduling and Power Allocation in Downlink Multicell OFDMA Networks

We consider a multicell orthogonal frequency-division multiple-access (OFDMA) wireless network with universal frequency reuse and treat the problem of cochannel interference mitigation via base station coordination in the downlink. Assuming that coordinated access points only share channel quality measurements but not user data symbols, we propose to select the set of cochannel users and the power allocation across tones to maximize the weighted system sum rate subject to per-base-station power constraints. Since this is a nonconvex combinatorial problem, efficient suboptimal algorithms are presented and discussed, each requiring a different level of coordination among base stations and a different feedback signaling overhead. Simulation results are provided to assess the performances of the proposed strategies.      

Weighted Sum Rate Maximization for Downlink Multiuser Relay Network with Direct Link

This paper investigates the joint source, relay precoder and receive filters optimization, aiming to maximize the weighted sum rate (WSR) for non-regenerative downlink multiuser MIMO relay network with source-destination direct link. The joint transceiver is developed taking both the direct link and the relay link into consideration with individual source and relay power constraints. Since the WSR problem is generally non-convex and intractable, it’s difficult to solve directly. Inspired by the recent results of relationship between the mutual information and the mean-square-error, we reformulate the WSR problem into a weighted sum mean square error minimization problem. An iterative algorithm is developed to solve the WSR problem. Simulation results demonstrate that the proposed algorithm offers significant performance gain over existing methods. In addition, we also propose a modified robust joint transceiver design against the imperfect channel state information.     

Resource allocation with proportional rate fairness in orthogonal frequency division multiple access relay networks

We address the problem of subchannel and transmission power allocation in orthogonal frequency division multiple access relay networks with an aim to maximize the sum rate and maintain proportional rate fairness among users. Because the formulated problem is a mixed‐integer nonlinear optimization problem with an extremely high computational complexity, we propose a low‐complexity suboptimal algorithm, which is a two‐step separated subchannel and power allocation algorithm. In the first step, subchannels are allocated to each user, whereas in the second step, the optimal power allocation is carried out on the basis of the given subchannel allocation and the nonlinear interval Gauss–Seidel method. Simulation results have demonstrated that the proposed algorithm can achieve a good trade‐off between the efficiency and the fairness compared with two other existing relevant algorithms. In particular, the proposed algorithm can always achieve 100% fairness under various conditions. Copyright © 2012 John Wiley & Sons, Ltd.     

Energy-Spectral-Efficiency Tradeoff in Interference-Limited Wireless Networks

Spectral efficiency (SE) is an important metric in traditional wireless network design. However, as the development of high-data rate services and rapid increase of energy consumption, energy efficiency (EE) has received more and more attention. In this paper, we investigate the EE–SE tradeoff problem in interference-limited wireless networks. Different from previous researches, we try to optimize EE and SE simultaneously. Firstly, the problem is formulated as a multi-objective optimization problem (MOP), with the constraint of transmit power limit. Then, we convert the MOP to a single-objective optimization problem by the weighted linear sum method. We present an algorithm utilizing difference between two convex functions programming (DCP) to handle with SE optimization problem (SD). EE optimization problem can be solved by an algorithm (EFD) consists of fractional programming embedded with DCP. While for EE–SE tradeoff problem, a particle swarm optimization algorithm is proposed (ESTP) to deal with it. Simulation results validate that the proposed algorithm can efficiently balance EE and SE by adjusting the value of weighted coefficient, which could be used to design a flexible energy efficient network in the future.

     

Penalty function-based precoding for downlink multiuser MIMO systems

In this paper, a novel linear precoding scheme is proposed for downlink multiuser multiple-input multiple-output (MIMO) systems. The new algorithm uses the penalty function method to mitigate the co-channel interference and is formulated as a convex problem with general linear constraints. The constraints can be sum power, per-antenna power or per-antenna-group power constraints, hence the new algorithm is general and can be used in both single-cell and fully cooperative multi-cell scenarios. Moreover, the famous block diagonalization (BD) precoding can be considered as a special case of our method when a very large penalty factor is used. We study the optimal solution of this convex problem and propose an iterative algorithm to obtain the optimum based on the Lagrange dual method. Simulation results show that the proposed method significantly outperforms the BD method at low and moderate SNR values in terms of the weighted sum rate.     

On the use of immune clonal optimization for joint subcarrier and power allocation in OFDMA with proportional fairness rate

Resource allocation in orthogonal frequency division multiple access is a constraint optimization problem. In this paper, we concentrate on maximizing both the sum capacity and proportional fairness rate among users. The optimization model of resource allocation is formulated and then an immune clonalbased algorithm is proposed for it. The resource allocation is solved by separating the subcarrier and power allocation in two steps. Suitable immune operators are designed, such as clonal, mutation, Baldwin learning, selection and so on. Experiments show that, compared with the previous methods, the proposed algorithm obtains higher sum capacity with comparable computational complexity, and keeps the proportional rate more fairly among users. Copyright © 2012 John Wiley & Sons, Ltd.     

Joint power allocation and beamforming with users selection for cognitive radio networks via discrete stochastic optimization

Cognitive radio is a promising technique to dynamic utilize the spectrum resource and improve spectrum efficiency. In this paper, we study the problem of mutual interference cancellation among secondary users (SUs) and interference control to primary users (PUs) in spectrum sharing underlay cognitive radio networks. Multiple antennas are used at the secondary base station to form multiple beams towards individual SUs, and a set of SUs are selected to adapt to the beams. For the interference control to PUs, we study power allocation among SUs to guarantee the interference to PUs below a tolerable level while maximizing SUs?? QoS. Based on these conditions, the problem of joint power allocation and beamforming with SUs selection is studied. Specifically, we emphasize on the condition of imperfect channel sensing due to hardware limitation, short sensing time and network connectivity issues, which means that only the noisy estimate of channel information for SUs can be obtained. We formulate the optimization problem to maximize the sum rate as a discrete stochastic optimization problem, then an efficient algorithm based on a discrete stochastic optimization method is proposed to solve the joint power allocation and beamforming with SUs selection problem. We verify that the proposed algorithm has fast convergence rate, low computation complexity and good tracking capability in time-varying radio environment. Finally, extensive simulation results are presented to demonstrate the performance of the proposed scheme.     

多用户多载波无线携能通信系统的上下行联合资源分配

无线携能通信(SWIPT)技术是解决无线网络能量受限问题的有效方法,该文研究一个由基站(BS)和多用户组成的多载波SWIPT系统,其上行和下行链路均采用正交频分复用(OFDM)技术。在下行链路中,基站向用户同时进行信息与能量传输;在上行链路中,用户利用从基站接收的能量向基站回传信息。该文以最大化上下行加权和速率为目标,联合优化上行和下行的子载波分配和功率分配,提出基于拉格朗日对偶法和椭球法的最优联合资源分配算法。计算机仿真结果证实了该算法的有效性。     

Fast iterative WSVT algorithm in WNN minimization problem for multiuser massive MIMO channel estimation

In this paper, channel estimation for multiuser massive MIMO system is addressed for the scenario, where the number of scatterers is small compared to the base station antennas and single antenna users in the cell. If the number of scatterers is limited, then the corresponding angle of arrivals are finite. Moreover, if all the users share the same angle of arrivals, then the correlation among the channel vector increases. Thus, the high‐dimensional channel is approximated to the low‐rank matrix. This rank minimization problem is formulated as the weighted nuclear norm problem and is estimated using iterative weighted singular value thresholding (IWSVT) algorithm. To increase the convergence rate, fast IWSVT algorithm is proposed and the performance is measured based on mean square error and uplink and downlink achievable sum‐rate. The simulation study shows that the proposed weighted nuclear norm minimization method with fast IWSVT algorithm performs better than the conventional least square and the nuclear norm minimization method for various finite scatterers in different signal‐to‐noise ratio levels.     

Optimal Cross-Layer Scheduling of Transmissions Over a Fading Multiaccess Channel

We consider the problem of several users transmitting packets to a base station, and study an optimal scheduling formulation involving three communication layers, namely, the medium access control, link, and physical layers. We assume Markov models for the packet arrival processes and the channel gain processes. Perfect channel state information is assumed to be available at the transmitter and the receiver. The transmissions are subject to a long-run average transmitter power constraint. The control problem is to assign power and rate dynamically as a function of the fading and the queue lengths so as to minimize a weighted sum of long run average packet transmission delays.     

Power-Efficient Resource Allocation for Time-Division Multiple Access Over Fading Channels

We investigate resource allocation policies for time-division multiple access (TDMA) over fading channels in the power-limited regime. For frequency-flat block-fading channels and transmitters having full channel state information (CSI), we first minimize power under a weighted sum average rate constraint and show that the optimal rate and time allocation policies can be obtained by a greedy water-filling approach with linear complexity in the number of users. Subsequently, we pursue power minimization under individual average rate constraints and establish that the optimal resource allocation also amounts to a greedy water-filling solution. Our approaches not only provide fundamental power limits when each user can support an infinite-size capacity-achieving codebook (continuous rates), but also yield guidelines for practical designs where users can only support a finite set of adaptive modulation and coding modes (discrete rates).