能量高效超密集网络的最优部署

为使能量效率最大化,采用随机几何方法对多小区多用户MIMO蜂窝网络的上行链路进行建模,改进系统功耗模型,得到一个关于基站密度、发送信号功率、基站天线数、小区用户数以及导频复用因子的能量效率最大化问题.求解该问题,可得到最优的网络部署方案以及各最优参数与硬件特性、传播环境之间的关系.仿真与数值计算结果显示,超密集网络部署可以显著提高能量效率,但是随着基站密度的进一步增加,能量效率的提升很快饱和.更有趣的是,能量效率最优化所确定的部署方案恰为大规模MIMO情形.     

Asymptotic RZF cooperative beamforming algorithm based on energy efficiency

A low complexity asymptotic regularized zero forcing cooperative beamforming algorithm based on energy efficiency in heterogeneous massive MIMO system was proposed,aiming at the problem that the current multi-flow regularization zero forcing beamforming algorithm sets the power constraint of each antenna in the regularization term as a fixed value and ignores the influences of factors such as the number of antennas,the number of users and QoS.The algorithm selects the optimal antenna power constraint set through the optimization method,and the optimal beamforming was asymptotically ob-tained to balance the interference among users to achieve the optimal energy efficiency,considering the impact of the number of antennas and users with the constraints of the antenna power and QoS.In view of the importance of backhaul in massive MIMO system,a backhaul power consumption model and the impact of backhaul power consumption on system performance was analyzed.Analysis and simulation results show that the proposed algorithm has great improvement of the performance,especially when the number of antennas is large.The algorithm is close to optimal performance,especially suitable for massive MIMO system of next generation communication.     

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。      

Performance optimization of cell-free massive MIMO system with power control approach

The cell-free massive MIMO (multiple-input multiple-output) system involves a large number of access points serving a smaller number of mobile users (MUs) over identical time/ frequency resource. By providing large number of service antennas closer to the MUs, the cell-free massive MIMO can offer great spectral efficiency, better macro-diversity and minimal path loss. Despite several advantages, the cell-free massive MIMO suffers from energy overloading caused by uncontrolled backhaul power consumption for large number of distributed access points (APs) and pilot contamination during channel estimation. In this paper, we have taken into consideration a cell-free massive MIMO system with APs equipped with multiple antennas performing time-division-duplex (TDD) operation. Here, all the APs coordinate through a constrained backhaul network for joint transmission of signals to all the users simultaneously by multiplying the received signal with the normalized conjugate of the estimated channel state information (CSI) and send back a rounded off version of the weighted pattern to the central processing unit (CPU). Finally, an effective user defined algorithm is presented involving selection and grouping of various APs based on their individual contributions for a particular MU to improve the overall performance of the system.     

Extended Length and Error-Correcting-Capacity of First-Order Reed–Muller Code in Extended Length Range

The massive multiple-input multiple-output (massive MIMO) system is the major section of the fifth generation (5G) future wireless cellular systems. It consists of hundreds of antennas in the base station that serves more number of users, concurrently. Thus, this system will get optimized energy usage, high data rate, and more precision because of their larger degrees of freedom. The computation power to the total power consumption ratio is considered for rapid increment owing to the more data traffic at the baseband unit that seeks more attention in the exploitation of massive MIMO systems for 5G wireless systems. The main intent of this paper is to develop the multi-user massive MIMO systems by deriving the joint optimization problem of computation and communication power. In the existing energy efficiency analysis, there is a negative effect on energy efficiency when increasing the count of RF chains and antennas by considering only computation power or communication power in massive MIMO. In order to overwhelm this problem, this paper focuses on two optimization problems. The first problem is focusing on the improvement of upper bound on energy efficiency with the optimal baseband and RF precoding matrices based on a new hybrid meta-heuristic algorithm. The combination of two well-performing meta-heuristic algorithms like electric fish optimization and dragonfly algorithm is used as the new algorithm, which is named as hybrid dragonfly with electric fish optimization (HD-EFO) for enhancing the efficiency of massive MIMO system. In the second phase, the joint optimization of both computation and communication power is performed by the same HD-EFO for developing the optimized hybrid precoding matrix. The extensive results have shown that the implemented multi-user massive MIMO systems with partially-connected structures using HD-EFO increase the cost and energy efficiencies, and save the maximum power.

     

A lower bound on achievable rate of MRT precoding in multicell multiuser massive MIMO networks with Rician flat fading

A multicell multiuser massive multiple‐input‐multiple‐output (MIMO) network with Rician flat fading is considered. Given channel reciprocity, non‐orthogonal uplink channel training in conjunction with minimum mean square error channel estimation at the base stations are used to acquire channel state information. In the forward link, using maximal ratio transmission precoding, base stations send data to corresponding users. In this paper, first, a closed‐form expression for signal to interference and noise ratio and a lower bound on achievable rate are obtained for arbitrary number of base station antennas. Then, using random matrix theory, a simplified approximate expression for large number of base station antennas (i.e., massive MIMO scenario) are calculated. This simplified expression shows that in a multicell multiuser massive MIMO network with Rician flat fading, like Rayleigh fading, as the number of base station antennas goes to infinity, the effects of uncorrelated noise and intercell and intracell interferences tend to zero. The only factor limiting the performance of system is the correlated intercell interference, that is, pilot contamination, due to non‐orthogonality of channel training sequences in adjacent cells. Numerical results show that our obtained closed‐form expression is a good lower bound on sum‐rate for various system parameters. Copyright © 2016 John Wiley & Sons, Ltd.     

基于低分辨率模数转换的全双工大规模MIMO中继系统频谱效率分析

为了降低由于大规模基站天线阵列模数转换(analog-to-digital converters, ADCs)所造成的巨大硬件损耗,同时有效地提高系统的能量和频谱效率,基于迫零传输/迫零接收(zero-forcing transmitting/ zero-forcing receiving, ZFT/ZFR)预处理方案,文章提出了低分辨率模数转换的多用户全双工大规模多入多出(massive multiple-input multiple-output, massive MIMO)中继系统,基站采用放大转发(amplify-and-forward, AF)协议,并对系统频谱效率进行了分析。文章首先获得了任一用户对频谱效率的闭式表达式,然后分别对三种不同功率缩放方案下系统的频谱效率进行了渐近分析。研究结果表明,当基站天线数量足够大时,三种不同的功率缩放方案对系统的环路干扰和量化误差有不同的影响,且当信源功率固定、基站的传输功率与发送天线数量成反比时,系统能够有效地抑制系统的环路干扰和量化误差,这对低分辨率全双工massive MIMO 中继系统的部署具有一定的指导意义。      

A novel antenna allocation technique for green single cell MIMO and MIMO-CoMP downlink transmission

To meet the increasing traffic and energy consumption demands of wireless networks, energy efficiency and energy efficient transmission techniques have become an urgent need for cellular networks. In this work, the problem of base station (BS) power consumption reduction for increased network energy efficiency of downlink TDMA-based transmission is considered. To meet network’s high traffic demand due to high data rates required by large numbers of users, multiple-input multiple-output (MIMO) and coordinated multi-point (CoMP) transmission have been considered. By adopting realistic power consumption models for single cell MIMO and multi-cell MIMO-CoMP networks, enhanced antenna allocation techniques are proposed and their energy efficiency is compared to the conventional power allocation schemes. It is shown that for a target signal to interference plus noise ratio (SINR), the proposed techniques consume less total power compared to traditional schemes, which leads to higher energy efficiency. In addition, for same power level, the symbol error rate (SER) is reduced and system’s sum rate increases, which leads to higher spectral efficiency.     

Energy-efficient hybrid beamforming for millimeter-wave-based massive multiple-input multiple-output system

The advanced wireless communication system requires abridged energy consumption, enhanced data rate, and good signal coverage. The massive MIMO technology for 5G systems has been developed to accommodate several users simultaneously with superior throughput. The claim for high data rate wireless communication services is expanding quickly as time goes. Thus, the key difficulty is that as the number of users grows, the number of phase shifters grows as well, causing the system to consume more power; as a result, the system's energy efficiency decreases. Hybrid beamforming has recently emerged as an attractive technique for millimeter-wave (mmWave) communication systems. The analog beamformer in the RF domain and digital beamformer in the baseband are coupled through a minimal number of RF chains in hybrid beamforming architecture. Hybrid beamforming utilizes fewer RF (radio frequency) chains than the total number of antennas to have a lower energy consumption design. The hybrid beamforming for a mmWave-based massive MIMO system through different phase shifter selection mechanisms is proposed to achieve the highest energy efficiency for mmWave communications systems. The fully connected with phase shifter selection, sub-connected with phase shifter selection (SPSS), and fully connected and sub-connected with phase shifter selection with halved and doubled switches are considered for this research. The simulation results show the SPSS with halved switch outperforms on energy efficiency.     

基于绿色分布式光载无线系统的无线传感器网络的使能技术(英文)

Novel enabling technologies from physical layer to Medium Access Control (MAC) layer are proposed to provide energy efficient Radio-over-Fiber (RoF) Distributed Antenna System (DAS) based Wireless Sensor Networks (WSN). The power consumption performance of the network is evaluated in terms of the total network power consumption based on the proposed power consumption models from the physical layer. The results illustrate that for a given power consumption value, the tradeoff among the number of Remote Acces...     

Energy and spectral efficiency improvement using improved shark smell-coyote optimization for massive MIMO system

Massive multiple-input multiple-output (MIMO) can considerably enhance the “spectral efficiency and energy efficiency” since it is a major technique for future wireless networks. Thus, the performance needs a huge count of base station antennas to serve a smaller number of terminals in conventional MIMO methodology. Large-scale radio frequency (RF) chains represent the large-scale antennas. There is a need of implementing an effective massive MIMO system for maximizing the efficient performance of the system with high “spectral efficiency and energy efficiency” owing to the high cost of RF chains, and the higher power consumption. In this paper, a massive MIMO communication system is implemented to satisfy the requirements regarding “energy efficiency and spectral efficiency.” Here, the number of base station antennas, the transmit power, and beam forming vectors are optimized to maximize “energy efficiency and spectral efficiency” when the channel capacity is known to be higher than some threshold values. The novelty of this work is a new hybrid optimization adaptive shark smell-coyote optimization (ASS-CO) algorithm is developed for improving energy efficiency. The optimization is done with the help of the hybrid optimization ASS-CO Algorithm. The proposed ASS-CO algorithm-based massive MIMO communication system is evaluated by experimental analysis. From the result analysis, the maximum resource efficiency is observed by SS-WOA, which is 6.6%, 50%, 6.6%, 6.6%, and 6.6% maximized than rider optimization algorithm (ROA), spotted hyena optimization (SHO), lion algorithm (LA), Shark Smell Optimization (SSO), and Coyote Optimization Algorithm (COA) by taking the count of base stations as 4. The superior performance enhancement regarding “spectral efficiency and energy efficiency” is accomplished over the traditional systems.     

基于天线选择和功率分配的联合迭代优化算法

当使用所有天线进行无线数据传输时，大规模多输入多输出（Multiple-Input multiple-Output,MIMO）系统中的基站需要使用与天线数相同的射频链路，导致系统的实现复杂度增加，降低了系统的能效。针对能效降低的问题，提出了一种天线选择和功率分配的联合迭代优化算法。该算法在给定初始发送功率的条件下，随机生成一个天线集合作为内循环的初始值，内循环每次从余下的天线集合中选择一根具有最大能效的天线进行替换，得出最优天线集合，求出相应的最优发送功率，并以此作为下次外循环发送功率的初始值。仿真结果表明，所提算法在降低计算复杂度的前提下，几乎可以达到近似于最优穷举搜索算法的能效性能。     

Power optimization using optimal small cell arrangements in different deployment scenarios

For smooth transition from 4G to 5G, there is a need for optimizing the power in the wireless communication. In 5G, it is expected that the number of users will increase drastically that correspondingly increase the utilization of power in the transmitter and receiver sides. So researchers and academicians are now finding ways to optimize the power. Some optimizing methods like convex optimization are very helpful for optimized algorithm design. In this paper, a proposed mathematical approach for deployment of small cell access point is used for optimizing the power consumption in the massive multiple input and multiple output and small cell scenario. The new proposed mathematical approach will also help in deciding the optimal number of small cell access points and optimal location of these small cell access points for the particular deployment scenario like urban macro heterogeneous deployment scenario in the 3GPP LTE standard and different macro deployment scenario in the ITU‐R M.2135 standard like urban macro, suburban macro, and rural macro, for optimizing the power.     

基于联合用户分组及天线选择的大规模MIMO波束成形

大规模MIMO系统中,当小区用户数与基站天线数较大时,各用户的信道条件不尽相同,提出一种适用于大规模MIMO下行链路的基于联合用户分组及天线选择的迫零波束成形算法。将用户分成两组,选择信道条件较优的一组用户来接收信号,并为每一个发送数据流选择最优的基站天线组合进行通信,以较小的性能损失,换取大规模MIMO 射频电路的成本与功耗的大幅度降低。仿真结果证明,该算法能够较好地实现系统性能与硬件复杂度的折中。     

Multiuser MIMO receivers for space frequency block coded SC‐FDMA systems

Single carrier‐frequency division multiple access (SC‐FDMA) has been adopted as the uplink transmission standard in fourth generation cellular network to enable the power efficiency transmission in mobile station. Because multiuser MIMO (MU‐MIMO) is a promising technology to fully exploit the channel capacity in mobile radio network, this paper investigates the uplink transmission of SC‐FDMA systems with orthogonal space frequency block codes (SFBC). Two linear MU‐MIMO receivers, orthogonal SFBC (OSFBC) and minimum mean square error (MMSE), are derived for the scenarios with limited number of users or adequate receive antennas at base station. In order to effectively eliminate the multiple access interference (MAI) and fully exploit the capacity of MU‐MIMO channel, we propose a turbo MU‐MIMO receiver, which iteratively utilizes the soft information from maximum a posteriori decoder to cancel the MAI. By the simulation results in several typical MIMO channels, we find that the proposed MMSE MU‐MIMO receiver outperforms the OSFBC receiver over 1 dB at the cost of higher complexity. However, the proposed turbo MU‐MIMO receivers can effectively cancel the MAI under overloaded channel conditions and really achieve the capacity of MU‐MIMO channel. Copyright © 2014 John Wiley & Sons, Ltd.     

基于成对用户大规模MIMO中继系统的频谱效率性能分析

该文针对成对用户大规模MIMO中继系统,在中继采用最大比合并/最大比发射（MRC/MRT）预编码方案下,利用大数定律,推导出系统频谱效率的闭合表达式,显式的描述了频谱效率与中继天线数、该文针对成对用户大规模MIMO中继系统,在中继采用最大比合并/最大比发射（MRC/MRT）预编码方案下,利用大数定律,推导出系统频谱效率的闭合表达式,显式的描述了频谱效率与中继天线数、用户对个数以及信源用户和中继发射功率的关系。基于此,分析了当天线数增大时,在用户对个数固定不变和等比例增长两种情况下,频谱效率的渐进性能和所能获得的发射功率增益。结果表明,当用户数固定时,频谱效率将随天线数呈近似对数增长规律;当用户数随天线数等比例增长时,通过调整比例值,可满足平均每个用户的频谱效率达到任意指定值,且无需额外发射功率消耗。最后,通过数值仿真验证了所推导的频谱效率闭合表达式的精确性以及所有渐进性能的分析结果。      

时间相关信道下分布式大规模MIMO系统频谱效率分析

为了分析时变信道特性对多用户分布式大规模MIMO系统的频谱效率性能影响,通过引入一阶高斯马尔科夫过程来建模时变信道,以时间相关性系数表征时变快慢程度。当系统采用最大比合并（MRC）接收和最大比发送（MRT）预编码方案时,借助于确定性等价原理以及Gamma分布随机变量的性质,推导出了含有信道时变信息的上行和下行频谱效率闭合表达式。同时,给出了当基站总发送天线数与用户个数之比趋于无穷大时,频谱效率的极限表达式。分析表明,频谱效率随时间相关系数减小而降低,但并不影响系统所获得的发射功率增益。数值仿真验证了所推导的频谱效率闭合表达式和极限值的有效性和精确性,并比较得出时变信道下分布式大规模MIMO比集中式大规模MIMO具有更好的性能。      

莱斯信道下具有硬件损伤的多对双向大规模 MIMO AF 中继系统及性能分析

本文研究了莱斯信道条件下具有硬 件损伤的多对双向大规模多输入多输出放大转发(massive MIMO amplify-and-forward,MM-AF)中继系统,多对用户通过配置有大规模天线阵列的中继实现用户对内的信息交换,并分析了其频谱效率和能量效率。首先,考虑了硬件损伤对多对双向MM-AF中继系统性能的影响,并将其建模为发送和接收失真噪声。然后,给出了半双工中继(half-duplex relaying,HDR)和全双工中继(full-duplex relaying,FDR)模式下的迫零发送/迫零接收处理矩阵。最后,分别给出了两种模式下的信干噪比渐近表达式。理论分析和仿真结果表明,在中继天线数较大情况下FDR的频谱和能量效率优于HDR;随着信噪比的增加,两种模式下系统的频谱效率先增大后收敛于一个固定值;当中继天线数较大时,系统的频谱效率主要受限于用户处的硬件损伤而非中继处或其它干扰。      

Performance analysis of achievable sum‐rate for downlink massive MIMO systems

Massive multiple‐input and multiple‐output (MIMO) has been recognized as a promising technology in the fifth‐generation wireless networks. Under perfect channel state information, we derive three tractable closed‐form expressions that corresponding to the lower bound, approximation, and upper bound on the achievable rate in a massive MIMO downlink system with maximum‐ratio transmission precoding. Based on the proposed closed‐form expressions, the power efficiency of the system is investigated as the number of transmit antennas increases. Simulation results demonstrate the tightness of our proposed closed‐form expressions for the achievable sum‐rate.     

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.