scholarly journals Energy Efficiency Optimization in Massive MIMO Secure Multicast Transmission

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1145
Author(s):  
Bin Jiang ◽  
Linbo Qu ◽  
Yufei Huang ◽  
Yifei Zheng ◽  
Li You ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Iterative Algorithm ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Secure Multicast ◽  
Efficiency Optimization ◽  
Design Objective ◽  
Multicast Transmission ◽  
Transmission Strategy ◽  
Deterministic Equivalent

Herein, we focus on energy efficiency optimization for massive multiple-input multiple-output (MIMO) downlink secure multicast transmission exploiting statistical channel state information (CSI). Privacy engineering in the field of communication is a hot issue under study. The common signal transmitted by the base station is multicast transmitted to multiple legitimate user terminals in our system, but an eavesdropper might eavesdrop this signal. To achieve the energy efficiency utility–privacy trade-off of multicast transmission, we set up the problem of maximizing the energy efficiency which is defined as the ratio of the secure transmit rate to the power consumption. To simplify the formulated nonconvex problem, we use a lower bound of the secure multicast rate as the molecule of the design objective. We then obtain the eigenvector of the optimal transmit covariance matrix into a closed-form, simplifying the matrix-valued multicast transmission strategy problem into a power allocation problem in the beam domain. By utilizing the Minorize-Maximize method, an iterative algorithm is proposed to decompose the secure energy efficiency optimization problem into a sequence of iterative fractional programming subproblems. By using Dinkelbach’s transform, each subproblem becomes an iterative problem with the concave objective function, and it can be solved by classical convex optimization. We guarantee the convergence of the two-level iterative algorithm that we propose. Besides, we reduce the computational complexity of the algorithm by substituting the design objective with its deterministic equivalent. The numerical results show that the approach we propose performs well compared with the conventional methods.

scholarly journals Energy Efficiency Optimization for Massive MIMO Non-Orthogonal Unicast and Multicast Transmission with Statistical CSI

Electronics ◽  
2019 ◽  
Vol 8 (8) ◽  
pp. 857 ◽  
Author(s):  
Wang ◽  
Huang ◽  
You ◽  
Xiong ◽  
Li ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Power Allocation ◽  
Mimo Systems ◽  
Multiple Input Multiple Output ◽  
Maximization Problem ◽  
Multicast Transmission ◽  
Deterministic Equivalent ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Vector Valued

We study the energy efficiency (EE) optimization problem in non-orthogonal unicast and multicast transmission for massive multiple-input multiple-output (MIMO) systems with statistical channel state information of all receivers available at the transmitter. Firstly, we formulate the EE maximization problem. We reduce the number of variables to be solved and simplify this large-dimensional-matrix-valued problem into a real-vector-valued problem. Next, we lower the computational complexity significantly by replacing the objective with its deterministic equivalent to avoid the high-complex expectation operation. With guaranteed convergence, we propose an iterative algorithm on beam domain power allocation using the minorize maximize algorithm and Dinkelbach’s transform and derive the locally optimal power allocation strategy to achieve the optimal EE. Finally, we illustrate the significant EE performance gain of our EE maximization algorithm compared with the conventional approach through conducting numerical simulations.

scholarly journals Energy Efficiency and Spectral Efficiency Tradeoff in Massive MIMO Multicast Transmission with Statistical CSI

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 1045
Author(s):  
Bin Jiang ◽  
Bowen Ren ◽  
Yufei Huang ◽  
Tingting Chen ◽  
Li You ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Power Allocation ◽  
Spectral Efficiency ◽  
Massive Mimo ◽  
Communication Systems ◽  
Multicast Transmission ◽  
Transmission Strategy ◽  
Trade Offs ◽  
Core Technology ◽  
System Resource

As the core technology of 5G mobile communication systems, massive multi-input multi-output (MIMO) can dramatically enhance the energy efficiency (EE), as well as the spectral efficiency (SE), which meets the requirements of new applications. Meanwhile, physical layer multicast technology has gradually become the focus of next-generation communication technology research due to its capacity to efficiently provide wireless transmission from point to multipoint. The availability of channel state information (CSI), to a large extent, determines the performance of massive MIMO. However, because obtaining the perfect instantaneous CSI in massive MIMO is quite challenging, it is reasonable and practical to design a massive MIMO multicast transmission strategy using statistical CSI. In this paper, in order to optimize the system resource efficiency (RE) to achieve EE-SE balance, the EE-SE trade-offs in the massive MIMO multicast transmission are investigated with statistical CSI. Firstly, we formulate the eigenvectors of the RE optimization multicast covariance matrices of different user terminals in closed form, which illustrates that in the massive MIMO downlink, optimal RE multicast precoding is supposed to be done in the beam domain. On the basis of this viewpoint, the optimal RE precoding design is simplified into a resource efficient power allocation problem. Via invoking the quadratic transform, we propose an iterative power allocation algorithm, which obtains an adjustable and reasonable EE-SE tradeoff. Numerical simulation results reveal the near-optimal performance and the effectiveness of our proposed statistical CSI-assisted RE maximization in massive MIMO.

scholarly journals Optimal precoder designs for sum rate maximization in MIMO multiuser multicells

2015 ◽  
Vol 18 (3) ◽  
pp. 92-101
Author(s):  
Kha Hoang Ha ◽  
Long Dinh Nguyen ◽  
Tuan Hong Do
Keyword(s):  
Design Problem ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Base Stations ◽  
Transmission Strategy ◽  
Difference Of Convex Functions ◽  
Input Multiple Output ◽  
Difference Of Convex ◽  
Sum Rate ◽  
User Interference

This paper is concerned with the joint linear precoder design problem for the multiuser multiple-input multiple-output (MIMO) heterogeneous networks (HetNets) in which multiple femto base stations (FBSs) coexist with a macro base station (MBS). To tackle the inter-user interference in the macrocell, we exploit the blockdiagonalization scheme and then use the convex optimization to maximize the sum rate of the macrocell. The FBS transmission strategy is to maximize the sum-rate of femtocells subject to the transmitted power constraints per FBS and restrictions on the cross-tier interference to macro-users (MUs). Such a design problem is typically nonconvex, and, thus, challenging to find the FBS precoders. We reformulate the design problem of the FBS precoders as a d.c. (difference of convex functions) programming, and develop an efficient iterative algorithm to obtain the optimal precoders. Numerical simulation results show that the proposed algorithm outperforms the other methods in terms of the total sum-rate of the HetNet.

scholarly journals Coordinated Multicast Precoding for Multi-Cell Massive MIMO Transmission Exploiting Statistical Channel State Information

Electronics ◽  
2018 ◽  
Vol 7 (11) ◽  
pp. 338 ◽  
Author(s):  
Li You ◽  
Xu Chen ◽  
Wenjin Wang ◽  
Xiqi Gao
Keyword(s):  
Power Allocation ◽  
Channel State Information ◽  
Matrix Theory ◽  
Multiple Input Multiple Output ◽  
Base Stations ◽  
Channel State ◽  
State Information ◽  
Design Objective ◽  
Deterministic Equivalent ◽  
Input Multiple Output

This paper considers coordinated multi-cell multicast precoding for massive multiple-input-multiple-output transmission where only statistical channel state information of all user terminals (UTs) in the coordinated network is known at the base stations (BSs). We adopt the sum of the achievable ergodic multicast rate as the design objective. We first show the optimal closed-form multicast signalling directions of each BS, which simplifies the coordinated multicast precoding problem into a coordinated beam domain power allocation problem. Via invoking the minorization-maximization framework, we then propose an iterative power allocation algorithm with guaranteed convergence to a stationary point. In addition, we derive the deterministic equivalent of the design objective to further reduce the optimization complexity via invoking the large-dimensional random matrix theory. Numerical results demonstrate the performance gain of the proposed coordinated approach over the conventional uncoordinated approach, especially for cell-edge UTs.

scholarly journals Survey of Green Radio Communications Networks: Techniques and Recent Advances

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Mohammed H. Alsharif ◽  
Rosdiadee Nordin ◽  
Mahamod Ismail
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Cellular Networks ◽  
Multiple Input Multiple Output ◽  
Primary Energy ◽  
Base Station ◽  
Radio Communications ◽  
Improve Energy Efficiency ◽  
Advantages And Disadvantages ◽  
Green Radio

Energy efficiency in cellular networks has received significant attention from both academia and industry because of the importance of reducing the operational expenditures and maintaining the profitability of cellular networks, in addition to making these networks “greener.” Because the base station is the primary energy consumer in the network, efforts have been made to study base station energy consumption and to find ways to improve energy efficiency. In this paper, we present a brief review of the techniques that have been used recently to improve energy efficiency, such as energy-efficient power amplifier techniques, time-domain techniques, cell switching, management of the physical layer through multiple-input multiple-output (MIMO) management, heterogeneous network architectures based on Micro-Pico-Femtocells, cell zooming, and relay techniques. In addition, this paper discusses the advantages and disadvantages of each technique to contribute to a better understanding of each of the techniques and thereby offer clear insights to researchers about how to choose the best ways to reduce energy consumption in future green radio networks.

Fault-tolerant PWM inverter model development for safety electrical drives

2016 ◽  
Vol 7 (2) ◽  
pp. 77-83 ◽  
Author(s):  
Cs. Szász ◽  
R. Şinca
Keyword(s):  
Energy Efficiency ◽  
Mean Square Error ◽  
Minimum Mean Square Error ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Mean Square ◽  
Maximum Ratio Combining ◽  
Multiple Input ◽  
Input Multiple Output ◽  
Better Than

This paper deals with the most recent technology in wireless communication which is massive multiple input multiple output system. The paper studies the performance of massive multiple input multiple output uplink system over Rayleigh fading channel. The performance is measured in terms of spectral and energy efficiency using three schemes of linear detection, maximum-ratio-combining, zero forcing receiver, and minimum mean-square error receiver. The simulation results show that the spectral and energy efficiency increases with increasing the number of base station antennas. Also, the spectral and energy efficiency with minimum mean-square error receiver is better than that withzero forcing receiver, and the latter is better than that with maximum-ratio-combining. Furthermore, the energy efficiency decreases with increasing the spectral efficiency.

A Comparison of Norm Based Antenna Selection and Random Antenna Selection with Regard to Energy Efficiency in Wireless System with Large Number of Users

2020 ◽  
Vol 10 (2) ◽  
pp. 189-196
Author(s):  
Ashu Taneja ◽  
Nitin Saluja
Keyword(s):  
Energy Efficiency ◽  
Mimo Systems ◽  
Antenna Selection ◽  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Base Station ◽  
Transmit Antenna Selection ◽  
Wireless System ◽  
Multiple Input ◽  
Input Multiple Output

Background: The paper considers the wireless system with large number of users (more than 50 users) and each user is assigned large number of antennas (around 200) at the Base Station (BS). Objective: The challenges associated with the defined system are increased power consumption and high complexity of associated circuitry. The antenna selection is introduced to combat these problems while the usage of linear precoding reduces computational complexity. The literature suggests number of antenna selection techniques based on statistical properties of signal. However, each antenna selection technique suits well to specific number of users. Methods: In this paper, the random antenna selection is compared with norm-based antenna selection. It is analysed that the random antenna selection leads to inefficient spectral efficiency if the number of users are more than 50 in Multi-User Multiple-Input Multiple Output (MU-MIMO) system. Results: The paper proposes the optimization of Energy-Efficiency (EE) with random transmit antenna selection for large number of users in MU-MIMO systems. Conclusion: Also the computation leads to optimization of number of transmit antennas at the BS for energy efficiency. The proposed algorithm results in improvement of the energy efficiency by 27% for more than 50 users.

scholarly journals Energy Efficiency Optimization of Massive MIMO Systems Based on the Particle Swarm Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Jing Yang ◽  
Liping Zhang ◽  
Chunhua Zhu ◽  
Xinying Guo ◽  
Jiankang Zhang
Keyword(s):  
Energy Efficiency ◽  
Particle Swarm Optimization ◽  
Single Cell ◽  
Iterative Algorithm ◽  
Massive Mimo ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Base Station ◽  
Imperfect Csi ◽  
Swarm Optimization

As one of the key technologies in the fifth generation of mobile communications, massive multi-input multioutput (MIMO) can improve system throughput and transmission reliability. However, if all antennas are used to transmit data, the same number of radiofrequency chains is required, which not only increases the cost of system but also reduces the energy efficiency (EE). To solve these problems, in this paper, we propose an EE optimization based on the particle swarm optimization (PSO) algorithm. First, we consider the base station (BS) antennas and terminal users and analyze their impact on EE in the uplink and downlink of a single-cell multiuser massive MIMO system. Second, a dynamic power consumption model is used under zero-forcing processing, and it obtains the expression of EE that is used as the fitness function of the PSO algorithm under perfect and imperfect channel state information (CSI) in single-cell scenarios and imperfect CSI in multicell scenarios. Finally, the optimal EE value is obtained by updating the global optimal positions of the particles. The simulation results show that compared with the traditional iterative algorithm and artificial bee colony algorithm, the proposed algorithm not only possesses the lowest complexity but also obtains the highest optimal value of EE under the single-cell perfect CSI scenario. In the single-cell and multicell scenarios with imperfect CSI, the proposed algorithm is capable of obtaining the same or slightly lower optimal EE value than that of the traditional iterative algorithm, but the running time is at most only 1/12 of that imposed by the iterative algorithm.

scholarly journals Energy-Efficient Multicast Precoding for Massive MIMO Transmission with Statistical CSI

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 3175 ◽  
Author(s):  
Li You ◽  
Wenjin Wang ◽  
Xiqi Gao
Keyword(s):  
Power Allocation ◽  
Energy Efficient ◽  
Massive Mimo ◽  
Matrix Theory ◽  
Multiple Input Multiple Output ◽  
Global Optimum ◽  
Base Station ◽  
Optimal Energy ◽  
Deterministic Equivalent ◽  
Mimo Transmission

In this paper, we investigate energy-efficient multicast precoding for massive multiple-input multiple-output (MIMO) transmission. In contrast with most previous work, where instantaneous channel state information (CSI) is exploited to facilitate energy-efficient wireless transmission design, we assume that the base station can only exploit statistical CSI of the user terminals for downlink multicast precoding. First, in terms of maximizing the system energy efficiency, the eigenvectors of the optimal energy-efficient multicast transmit covariance matrix are identified in closed form, which indicates that optimal energy-efficient multicast precoding should be performed in the beam domain in massive MIMO. Then, the large-dimensional matrix-valued precoding design is simplified into an energy-efficient power allocation problem in the beam domain with significantly reduced optimization variables. Using Dinkelbach’s transform, we further propose a sequential beam domain power allocation algorithm which is guaranteed to converge to the global optimum. In addition, we use the large-dimensional random matrix theory to derive the deterministic equivalent of the objective to reduce the computational complexity involved in sample averaging. We present numerical results to illustrate the near-optimal performance of our proposed energy-efficient multicast precoding for massive MIMO.

Sign in / Sign up

Export Citation Format

Share Document