Energy‐Efficiency Power Allocation for Cognitive Radio MIMO‐OFDM Systems

This paper studies energy‐efficiency (EE) power allocation for cognitive radio MIMO‐OFDM systems. Our aim is to minimize energy efficiency, measured by “Joule per bit” metric, while maintaining the minimal rate requirement of a secondary user under a total power constraint and mutual interference power constraints. However, since the formulated EE problem in this paper is non‐convex, it is difficult to solve directly in general. To make it solvable, firstly we transform the original problem into an equivalent convex optimization problem via fractional programming. Then, the equivalent convex optimization problem is solved by a sequential quadratic programming algorithm. Finally, a new iterative energy‐ efficiency power allocation algorithm is presented. Numerical results show that the proposed method can obtain better EE performance than the maximizing capacity algorithm.     

An energy-efficient power allocation scheme for millimeter-wave MIMO-NOMA systems

There are many challenges in fifth generation (5G) telecommunication systems, due to the increasing demands and applications. The most important of which are need to have higher energy efficiency (EE) and spectral efficiency (SE). They are critical in the practical multiple-input multiple-output (MIMO) telecommunication systems. Non-orthogonal multiple access (NOMA) methods and millimeter-waves can be used in conjunction with MIMO systems to improve their EE and SE performance. In this paper, we investigate the application of NOMA and mm-Wave transmission in the downlink of MIMO systems. Then, we formulate the optimization problem for users in MIMO-NOMA systems to maximize the EE that is subject to minimum data rate to satisfy required quality of service (QoS) and maximum transmission power. To achieve the optimal power allocation for users, we reach a problem for the EE maximization that is non-convex and solution of the optimization problem is not trivial. We exploit a lower bound of the data rate and the Lagrange dual function to convert it to a convex and unconstrained problem, which is easy to solve. In the next step, we derive a relation for determining the optimal power allocation of users. In addition, a numerical algorithm is presented that can be used to solve the problem. According to the simulation results of the proposed algorithm, our method performs better and provides higher EE than both orthogonal multiple access and equal power allocation schemes.     

Massive MIMO异构网的高能效资源分配

为满足第五代移动通信系统高频谱效率和高能量效率的需求,提出一种工作在不同频段下行两层异构网中的高能量效率资源分配方法,考虑用户数据率需求和基站最大发射功率。天线和传输带宽是影响系统能量效率的关键因素。通过研究宏基站和小基站的天线资源和带宽分配发现:当系统天线数很大时,发射功耗的影响可以忽略不计;给定带宽分配因子时,达到宏基站或微基站最大发射功率的天线分配因子几乎可以达到最高能效;给定天线分配因子时,系统平均总功耗是关于带宽分配因子的下凸函数,存在全局最优带宽分配因子使能效最高。仿真结果表明,与给定带宽和天线资源的异构网和小小区网络相比,所提出的异构网可以显著提高系统能量效率,而且在大量用户、高数据率需求时能效提升更明显。      

On the Ergodic Capacity and Energy Efficiency for Fading MIMO NOMA Systems

Non-orthogonal multiple access (NOMA) is expected to be a promising multiple access techniques for 5G networks due to its superior spectral efficiency (SE). Previous research mainly focus on the design to improve the SE performance with instantaneous channel state information (CSI). In this paper, we consider the fading MIMO channels with only statistical CSI at the transmitter, and explore the potential gains of MIMO NOMA scheme in terms of both ergodic capacity and energy efficiency (EE). The ergodic capacity maximization problem is first studied for the fading multiple-input multiple-output (MIMO) NOMA systems. We derive the optimal input covariance structure and propose both optimal and low complexity suboptimal power allocation schemes to maximize the ergodic capacity of MIMO NOMA system. For the EE maximization, the optimization problem is formulated to maximize the system EE (defined by ergodic capacity under unit power consumption) under the total transmit power constraint and the minimum rate constraint of the weak user. By transforming the EE maximization problem into an equivalent one-dimensional optimization problem, the optimal power allocation for EE design is proposed. To further reduce the computation complexity, a near-optimal solution based on golden section search and suboptimal closed form solution are proposed as well. Numerical results show that the proposed NOMA schemes significantly outperform the traditional orthogonal multiple access scheme with traditional orthogonal multiple access transmission in terms of both SE and EE.     

一种新的时变衰落信道下MIMO系统的功率分配与自适应调制算法

本文提出一种在时变衰落信道下,MIMO系统的功率分配和自适应调制方法.该方法采用空域注水定理,在发送端天线的平均功率受限的条件下,按照信道传输矩阵的奇异值对发端的多天线进行最优功率分配和自适应MQAM调制.本文从频谱效率方面对其性能进行分析.给出了信道估计的误差和反馈时延对该方法的影响.理论分析和仿真结果表明,该方法实现简单,且与传统的总功率受限的自适应调制方法相比,具有更高的频谱效率.     

大规模MIMO系统中基于能效最大化的资源联合优化算法

针对信道条件未知的多小区大规模多输入多输出（MIMO）系统,提出一种对导频序列长度、导频符号功率以及数据符号功率进行联合优化的资源分配算法。采用最大比合并（MRC）接收,考虑电功率和导频污染的影响,并对最大传输功率进行约束从而建立起以能效（EE）最大化为目标的非凸函数模型。根据分数规划的性质,首先将分数形式转化成减式形式,进而分解成一系列凸函数之差（DC）的问题,最后采用交替优化算法联合调整 3 个变量从而达到能效最大化的目标。仿真结果表明,随着最大符号传输功率的增加,所提方案仍然能保持良好系统能效性能。     

Modified-rate-quantization algorithm for multiple-input multiple-output systems under imperfect channel knowledge

In this paper, a modified-rate-quantization algorithm for multiple input multiple output (MIMO) systems is proposed using singular-value decomposition (SVD). This low complexity scheme adapts the subchannel transmit power and spectral efficiency in the spatial and temporal domains under transmit power and instantaneous bit error rate (BER) constraints. It is shown that with five discrete-rate levels, the proposed scheme reaches a spectral efficiency performance similar to the scheme with a continuous rate. The robustness of the proposed scheme to channel state information (CSI) imperfections is also studied. The obtained results show that the spectral efficiency is unaffected up to a certain level, but the bit error rate (BER) performance is particularly sensitive to these imperfections, especially at high SNR levels. Indeed, this ideally designed MIMO system over-estimates the subchannels, which leads to a deterioration of the BER performance. A new version of this algorithm, which is suitable for vertical Bell Labs layered space–time (V-BLAST) systems, is also presented. Through simulation results, it appears that the extended algorithm allows to reach a better performance in terms of spectral efficiency than other known schemes, but it is more sensitive to imperfect CSI than the first version.     

Energy efficient power allocation in MIMO relaying systems with the MRT precoding

Multiple-input multiple-output (MIMO) systems perform well from the energy efficiency (EE) and the spectral efficiency (SE) points of view in fifth generation (5G) communication systems. This paper considers the operation of a MIMO system with a relay. The optimization problem relates to the EE maximization. This problem has two types of limitations, which provide a maximum transmission power and a minimum data rate for users. The encountered objective function is in a fractional form and thus it is a non-convex function. Besides, the problem is constrained. We utilize a lower bound analysis for the data rates, some properties of the linear programming, and the maximum ratio transmission (MRT) precoding scheme to obtain a convex objective function. Using the Lagrange dual function, we obliterate the constraints of the problem and then it is easy to solve. To improve system performance, users are divided into two groups based on their channel gains, and the maximum transmission power is reasonably divided between them. Two iterative algorithms are proposed to solve the optimization problem numerically, and finally we investigate performance of the proposed method.     

非对称毫米波大规模MIMO系统信道特性研究

在大规模多输入多输出（multiple-input multiple-output,MIMO）系统中,合理的天线选择、用户调度以及用户功率分配方案,对提升系统能效、节省资源成本有着重要的作用. 针对大规模MIMO下行链路通信场景,基于能效最大化准则,提出了一种联合天线选择、用户调度以及功率分配的低复杂度优化算法. 首先,针对天线选择和用户调度问题,结合递增递减的选择思想,以最大化系统能效为目标,对天线和用户进行双向交替搜索;其次,对于搜索过程中的用户功率分配问题,采用分式规划理论和拉格朗日对偶算法得到最优能效功率的闭式解,三个参数进行迭代优化,从而得到系统最优能效. 仿真结果表明,本文所提算法不仅具有低复杂度而且具有较好性能,能够有效降低大规模MIMO系统的能耗.

     

Energy efficiency of massive MIMO wireless communication systems with antenna selection

Massive multiple-input multiple-output (MIMO) requires a large number (tens or hundreds) of base station antennas serving for much smaller number of terminals, with large gains in energy efficiency and spectral efficiency compared with traditional MIMO technology. Large scale antennas mean large scale radio frequency (RF) chains. Considering the plenty of power consumption and high cost of RF chains, antenna selection is necessary for Massive MIMO wireless communication systems in both transmitting end and receiving end. An energy efficient antenna selection algorithm based on convex optimization was proposed for Massive MIMO wireless communication systems. On the condition that the channel capacity of the cell is larger than a certain threshold, the number of transmit antenna, the subset of transmit antenna and servable mobile terminals (MTs) were jointly optimized to maximize energy efficiency. The joint optimization problem was proved in detail. The proposed algorithm is verified by analysis and numerical simulations. Good performance gain of energy efficiency is obtained comparing with no antenna selection.     

Reliability feedback–aided low‐complexity detection in uplink massive MIMO systems

Massive multiple‐input multiple‐output (MIMO) plays a crucial role in realizing the demand for higher data rates and improved quality of service for 5G and beyond communication systems. Reliable detection of transmitted information bits from all the users is one of the challenging tasks for practical implementation of massive‐MIMO systems. The conventional linear detectors such as zero forcing (ZF) and minimum mean square error (MMSE) achieve near‐optimal bit error rate (BER) performance. However, ZF and MMSE require large dimensional matrix inversion which induces high computational complexity for symbol detection in such systems. This motivates for devising alternate low‐complexity near‐optimal detection algorithms for uplink massive‐MIMO systems. In this work, we propose an ordered sequential detection algorithm that exploits the concept of reliability feedback for achieving near‐optimal performance in uplink massive‐MIMO systems. In the proposed algorithm, symbol corresponding to each user is detected in an ordered sequence by canceling the interference from all the other users, followed by reliability feedback‐based decision. Incorporation of the sequence ordering and the reliability feedback‐based decision enhances the interference cancellation, which reduces the error propagation in sequential detection, and thus, improves the BER performance. Simulation results show that the proposed algorithm significantly outperforms recently reported massive‐MIMO detection techniques in terms of BER performance. In addition, the computational complexity of the proposed algorithm is substantially lower than that of the existing algorithms for the same BER. This indicates that the proposed algorithm exhibits a desirable trade‐off between the complexity and the performance for massive‐MIMO systems.     

基于能效优化的大规模MIMO FDD下行系统导频和功率资源分配算法

该文针对大规模MIMO FDD下行系统,联合考虑信道估计与数据传输两个阶段的资源分配问题,提出一种能效资源分配算法。该方法以最大化能效为目标,以导频时长、导频功率和数据功率为参量,在指定发射功率和频谱效率约束的条件下建立优化模型。由于目标函数无精确解析形式,借助确定性等价原理对其近似并求得闭合表达式。进而,利用分数规划将原分式形式目标函数等价转换为减式形式,再利用目标函数的下界将非凸优化问题逐步释放为相对容易求解的等效问题,最终获得一种3层迭代能效资源优化算法。仿真结果验证了所提算法的有效性,且具有较快的收敛速度。     

Bandwidth‐Efficient Selective Retransmission for MIMO‐OFDM Systems

In this work, we propose an efficient selective retransmission method for multiple‐input and multiple‐output (MIMO) wireless systems under orthogonal frequency‐division multiplexing (OFDM) signaling. A typical received OFDM frame may have some symbols in error, which results in a retransmission of the entire frame. Such a retransmission is often unnecessary, and to avoid this, we propose a method to selectively retransmit symbols that correspond to poor‐quality subcarriers. We use the condition numbers of the subcarrier channel matrices of the MIMO‐OFDM system as a quality measure. The proposed scheme is embedded in the modulation layer and is independent of conventional hybrid automatic repeat request (HARQ) methods. The receiver integrates the original OFDM and the punctured retransmitted OFDM signals for more reliable detection. The targeted retransmission results in fewer negative acknowledgements from conventional HARQ algorithms, which results in increasing bandwidth and power efficiency. We investigate the efficacy of the proposed method for optimal and suboptimal receivers. The simulation results demonstrate the efficacy of the proposed method on throughput for MIMO‐OFDM systems.     

基于天线选择的MIMO系统能效功率分配优化

对MIMO系统在毫米波衰落信道下的能效功率分配进行研究,以期实现绿色传输。考虑发送端采用天线选择技术,接收端采用最大比合并技术,给出系统能量效率公式,并构建系统基于能效最大化的目标优化函数。利用拉格朗日乘子法,获得迭代功率分配方案。利用排序法及朗伯函数给出简单的闭式功率分配方案。利用Matlab软件验证所提功率分配方案的有效性。仿真结果表明,文章提出的功率分配方案较穷举搜索法EE性能得到了明显提升,是一种复杂度低的优化算法,能快速收敛到EE最大值。     

Capacity of multiuser diversity systems with adaptive transmission and different MIMO schemes

In this paper, we present a comprehensive capacity analysis of the downlink of multiuser diversity (MD) systems with adaptive transmission over Rayleigh fading channels. First, the exact capacity of the single‐input single‐output (SISO) systems with MD and adaptive transmission technique is derived. The optimal power allocation scheme for such a system is shown to be a water‐filling algorithm. Next, we derive the exact closed‐form capacity expressions for different multiple‐input multiple‐output (MIMO) schemes, including the selective combining (SC), maximum ratio combining (MRC) and space‐time block codes (STBC). In order to avoid the cumbersome numerical root finding techniques in solving the optimal cutoff SNR level below which the channel is not used, we also provide the approximate expressions for the cutoff level. For the MD MIMO systems, it is observed that the optimal power allocation strategy is to focus transmit power on a single transmit antenna (e.g. Tx‐MRC/Rx‐MRC scheme) or selecting the best transmit antennas (e.g. Tx‐SC/Rx‐MRC scheme). Copyright © 2007 John Wiley & Sons, Ltd.     

Compressive sensing power control for interference management in D2D underlaid massive MIMO systems

This paper introduces a sparsity controlled multiple random access scheme for efficient user scheduling in Device-to-Device (D2D) underlaid massive MIMO systems. In order to both avoid collision and enhance the Energy Efficiency (EE) of two-tier Heterogeneous Cellular Networks (HCNs), a unified Compressed Sensing (CS) based interference management strategy is proposed which guarantees concurrent cellular and D2D multi-user transmissions without collision. Specifically, supposing the natural sparsity in practical fifth generation (5G) scenarios and employing the sparse signal processing techniques, an analytical random access based model is adopted where provides several user scheduling and channel gain constraints to permit user identification, channel estimation and data decoding simultaneously. Furthermore, by developing a tractable tradeoff between the total power consumption and overall throughput of D2D tier, the transmission power is optimized such that the EE of D2D tier is maximized. Numerical simulations demonstrate the effectiveness of suggested approach to improve the collision avoidance capability and EE of D2D underlaid massive MIMO systems, even for crowded scenarios where the sparsity constraint does not meet sufficiently.     

Spectral efficiency and energy efficiency of distributed antenna systems with virtual cells

In this paper, we consider user centric virtual cells model in distributed antenna systems (DAS). We investigate different power allocation optimization problems with interferences in DAS with and without user centric virtual cells model, respectively. The first objective problem is maximizing spectral efficiency (SE) of the DAS with user centric virtual cells model under the constraints of the minimum SE requirements of each user equipment (UE), maximum transmit power of each remote access unit (RAU). We firstly transform this non-convex objective function into a difference of convex functions (D.C.) problem, and then we obtain the optimal solutions by using the concave-convex procedure (CCCP) algorithm. The second objective problem is maximizing energy efficiency (EE) of the DAS with user centric virtual cells model under the same constraints as the first objective problem. Firstly, we exploit fractional programming theory to obtain the equivalent objective function of the second problem with subtract form, and then transform it into a D.C. problem and use CCCP algorithm to obtain the optimal power allocation. In each part, we propose the corresponding optimal power allocation algorithm and also use similar method to obtain optimal solutions of the same optimization problems in DAS without using user centric virtual cells model. Simulation results are provided to demonstrate the effectiveness of the DAS with user centric virtual cells model, which can significantly improve the SE and the EE of the communication systems.     

浅述一种应用于MIMO系统的自适应调制方案

探讨了时变信道里MIMO系统的自适应编码调制问题并且提出一个低复杂率量化方案，被称为增强型码率量化方案，也叫ERQ。不需要大量计算，ERQ可以通过最佳连续码率和功率适应提高频谱效率。除此之外，ERQ还满足误码率和平均传送能量限制条件。     

Unsupervised Optimization for Universal Spatial Image Steganalysis

This paper presents adaptive per-spatial stream power allocation algorithms for Single User Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (SU MIMO-OFDM) systems. Three efficient and low-complexity Greedy Power Allocation (GPA) algorithms are proposed to maximize the throughput and spectral efficiency of the SU MIMO-OFDM systems. Firstly, the low-complexity pre-coded GPA algorithms are developed for the MIMO systems. The spatial sub-channels are created by applying the so-called Singular Value Decomposition (SVD) technique on the MIMO channel matrix, and then the Pre-GPA algorithms are applied to exploit the multi-path and spatial diversities. Secondly, the spatial and frequency diversities are exploited by adaptively allocating the system sub-carriers to the spatial sub-channels followed by Per-Spatial GPA (PSGPA). Finally, spatial multiplexing-based GPA algorithms are proposed to optimize the spectral efficiency of the SU MIMO-OFDM system. An optimal two-dimensional Spatial-Frequency GPA (SFGPA) algorithm is proposed to efficiently improve the average system spectral efficiency. The high computational complexity of the optimal SFGPA solution is simplified by proposing a low-complexity Per-Spatial GPA with Excess Power Moving down (PSGPA-EPMd) algorithm, which moves the per-spatial excess power downwards to enhance the spectral efficiency of the spatial multiplexing-based SU MIMO-OFDM systems. The proposed algorithms achieve better spectral efficiency and maximize the throughput in comparison with conventional algorithms.     

Peak‐to‐average power ratio reduction in space–time coded MIMO‐OFDM via preprocessing

In this paper, we propose a trellis exploration algorithm based preprocessing strategy to lower the peak‐to‐average power ratio (PAPR) of precoded MIMO‐OFDM. We first illustrate the degradation in PAPR due to optimal linear precoding in MIMO‐OFDM systems. Then we propose two forms of multi‐layer precoding (MLP) schemes to reduce PAPR. In both schemes, the inner‐layer precoder is designed to optimize system capacity/BER performance. In the first MLP scheme (MLP‐I), a common outer‐layer polyphase precoding matrix is employed. In the second MLP scheme (MLP‐II), data stream corresponding to every transmit antenna is precoded with a different outer‐layer polyphase precoding matrix. Both outer‐layer precoders are custom designed using the trellis exploration algorithm by applying the aperiodic autocorrelation of OFDM data symbols as the metric to minimize. Simulation results indicate that both MLP schemes show superior PAPR performance over conventional MIMO‐OFDM with and without precoding. In addition, MLP better exploits frequency diversity resulting in BER performance gains in multi‐path environments. Copyright © 2010 John Wiley & Sons, Ltd.