Research on Energy Efficiency Optimization in Heterogeneous Network

2014 ◽  
Vol 687-691 ◽  
pp. 2462-2465
Author(s):  
Hui Bao ◽  
Pan Shi
Keyword(s):  
Energy Efficiency ◽  
Heterogeneous Network ◽  
Optimization Problem ◽  
Beam Forming ◽  
Block Diagonalization ◽  
Mathematical Methods ◽  
Zero Forcing ◽  
Convex Optimization Problem ◽  
Single Antenna ◽  
Inter Cell Interference

The biggest challenge for heterogeneous network is co-channel interference problem, and due to the presence of interference, the system energy efficiency reduced greatly. In previous studies that use of space resources to eliminate interference between heterogeneous networks , most are based on the receiving end with a single antenna case to use zero forcing (ZF) beam-forming algorithm , however in practice the receiving end often configure multiple antennas, so this article use Block Diagonalization (BD) beam-forming algorithm to eliminate inter-cell interference after considering the coordinated multi-point transmission processing technology. This paper defines a energy efficiency metrics and convert it into a convex optimization problem by mathematical methods, and the improved energy efficiency programs is obtained ultimately.

scholarly journals Clustering and Auction-Based Power Allocation Algorithm for Energy Efficiency Maximization in Multi-Cell Multi-Carrier NOMA Networks

2019 ◽  
Vol 9 (23) ◽  
pp. 5034 ◽  
Author(s):  
Abuzar B. M. Adam ◽  
Xiaoyu Wan ◽  
Zhengqiang Wang
Keyword(s):  
Energy Efficiency ◽  
Power Allocation ◽  
Cooperative Game ◽  
Multiple Access ◽  
Optimization Problem ◽  
Interference Mitigation ◽  
Base Stations ◽  
Frequency Division ◽  
Simulation Results ◽  
Inter Cell Interference

In this paper, we investigate the energy efficiency (EE) maximization in multi-cell multi-carrier non-orthogonal multiple access (MCMC-NOMA) networks. To achieve this goal, an optimization problem is formulated then the solution is divided into two parts. First, we investigate the inter-cell interference mitigation and then we propose an auction-based non-cooperative game for power allocation for base stations. Finally, to guarantee the rate requirements for users, power is allocated fairly to users. The simulation results show that the proposed scheme has the best performance compared with the existing NOMA-based fractional transmit power allocation (FTPA) and the conventional orthogonal frequency division multiple access (OFDMA).

scholarly journals Energy Efficiency Maximization with Optimal Beamforming in Secure MISO CRNs with SWIPT

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Liang Xue ◽  
Yao Ma ◽  
Miao Zhang ◽  
Wanqiang Qin ◽  
Jin-Long Wang ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Optimization Problem ◽  
Type Inequality ◽  
Optimal Solution ◽  
Maximization Problem ◽  
Subtraction Problem ◽  
Channel State ◽  
Convex Optimization Problem ◽  
Single Output ◽  
Optimal Beamforming

In this paper, the optimal beamforming problem of multi-input single-output (MISO) cognitive radio (CR) downlink networks with simultaneous wireless information and power transfer is studied. Due to the nonconvexity of the objective function, the considered nonconvex optimization problem is firstly transformed to an equivalent subtraction problem and then an approximated convex optimization problem is obtained by using the successive convex approximation (SCA). When the instantaneous channel state information (CSI) of the eavesdropping link is unknown to the legitimate transmitter, another interruption-constrained energy efficiency optimization problem is proposed and the Bernstein-type inequality (BTI) is used to conservatively approximate the probability constraint. The paper proposes a two-level iterative algorithm based on Dinkelbach to find the optimal solution of the EE maximization problem. Numerical results validate the effectiveness and convergence of the proposed algorithm.

An Improved Precoding Algorithm for LTE-Advanced CoMP System

2013 ◽  
Vol 427-429 ◽  
pp. 2519-2522
Author(s):  
Qiong Wang ◽  
Zhao Xia Zhang ◽  
Jia Liu
Keyword(s):  
Performance Improvement ◽  
Block Diagonalization ◽  
Communication Quality ◽  
Zero Forcing ◽  
Cell Edge ◽  
Lte Advanced ◽  
Simulation Results ◽  
Noise Ratio ◽  
Inter Cell Interference

In LTE-Advanced (LTE-A) system, coordinated multi-point (CoMP) technology can reduce inter-cell interference effectively, and improve the communication quality of the cell edge users. The main purpose of this paper is to optimize the precoding algorithm and enhance the overall cell throughput in LTE-A CoMP downlink. Based on CoMP-JP, we focus on zero-forcing (ZF), block diagonalization (BD) and signal-to-leakage-plus-noise-ratio (SLNR). We propose an improved precoding algorithm (ZF-SLNR) which combines the advantages of ZF and SLNR . Simulation results suggest that ZF-SLNR algorithm provides appreciable performance improvement.

scholarly journals Inverse reinforcement learning in contextual MDPs

2021 ◽  
Author(s):  
Stav Belogolovsky ◽  
Philip Korsunsky ◽  
Shie Mannor ◽  
Chen Tessler ◽  
Tom Zahavy
Keyword(s):  
Reinforcement Learning ◽  
Optimization Problem ◽  
Decision Processes ◽  
Inverse Reinforcement Learning ◽  
Convex Optimization Problem ◽  
Reward Function ◽  
Dynamic Treatment Regime ◽  
Markov Decision ◽  
Dynamic Treatment ◽  
Recorded Data

AbstractWe consider the task of Inverse Reinforcement Learning in Contextual Markov Decision Processes (MDPs). In this setting, contexts, which define the reward and transition kernel, are sampled from a distribution. In addition, although the reward is a function of the context, it is not provided to the agent. Instead, the agent observes demonstrations from an optimal policy. The goal is to learn the reward mapping, such that the agent will act optimally even when encountering previously unseen contexts, also known as zero-shot transfer. We formulate this problem as a non-differential convex optimization problem and propose a novel algorithm to compute its subgradients. Based on this scheme, we analyze several methods both theoretically, where we compare the sample complexity and scalability, and empirically. Most importantly, we show both theoretically and empirically that our algorithms perform zero-shot transfer (generalize to new and unseen contexts). Specifically, we present empirical experiments in a dynamic treatment regime, where the goal is to learn a reward function which explains the behavior of expert physicians based on recorded data of them treating patients diagnosed with sepsis.

scholarly journals Implementation of a Zero-Forcing Precoding Algorithm Combined with Adaptive Beamforming Based on WiMAX System

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Hyunwook Yang ◽  
Seungwon Choi
Keyword(s):  
Mimo System ◽  
Multiple Input Multiple Output ◽  
Experimental System ◽  
Multiple Access Interference ◽  
Adaptive Beamforming ◽  
Base Station ◽  
Zero Forcing ◽  
Single Antenna ◽  
Input Multiple Output ◽  
Wimax System

We propose a novel precoding algorithm that is a zero-forcing (ZF) method combined with adaptive beamforming in the Worldwide Interoperability for Microwave Access (WiMAX) system. In a Multiuser Multiple-Input Multiple-Output (MU-MIMO) system, ZF is used to eliminate the Multiple Access Interference (MAI) in order to allow several users to share a common resource. The adaptive beamforming algorithm is used to achieve the desired SNR gain. The experimental system consists of a WiMAX base station that has 2 MIMO elements, each of which is composed of three-array antennas and two mobile terminals, each of which has a single antenna. Through computer simulations, we verified that the proposed method outperforms the conventional ZF method by at least 2.4 dB when the BER is 0.1%, or 1.7 dB when the FER is 1%, in terms of the SNR. Through a hardware implementation of the proposed method, we verified the feasibility of the proposed method for realizing a practical WiMAX base station to utilize the channel resources as efficiently as possible.

Extended state observer-based robust non-linear integral dynamic surface control for triaxial MEMS gyroscope

Robotica ◽  
2018 ◽  
Vol 37 (3) ◽  
pp. 481-501 ◽  
Author(s):  
Mehran Hosseini-Pishrobat ◽  
Jafar Keighobadi
Keyword(s):  
Optimization Problem ◽  
Dynamic Surface Control ◽  
Extended State ◽  
Gain Function ◽  
Dynamic Surface ◽  
Convex Optimization Problem ◽  
Mems Gyroscope ◽  
Surface Control ◽  
Non Linear ◽  
Linear Integral

SUMMARYThis paper reports an extended state observer (ESO)-based robust dynamic surface control (DSC) method for triaxial MEMS gyroscope applications. An ESO with non-linear gain function is designed to estimate both velocity and disturbance vectors of the gyroscope dynamics via measured position signals. Using the sector-bounded property of the non-linear gain function, the design of an $\mathcal{L}_2$-robust ESO is phrased as a convex optimization problem in terms of linear matrix inequalities (LMIs). Next, by using the estimated velocity and disturbance, a certainty equivalence tracking controller is designed based on DSC. To achieve an improved robustness and to remove static steady-state tracking errors, new non-linear integral error surfaces are incorporated into the DSC. Based on the energy-to-peak ($\mathcal{L}_2$-$\mathcal{L}_\infty$) performance criterion, a finite number of LMIs are derived to obtain the DSC gains. In order to prevent amplification of the measurement noise in the DSC error dynamics, a multi-objective convex optimization problem, which guarantees a prescribed $\mathcal{L}_2$-$\mathcal{L}_\infty$ performance bound, is considered. Finally, the efficacy of the proposed control method is illustrated by detailed software simulations.

scholarly journals Shearlet-based regularized reconstruction in region-of-interest computed tomography

2018 ◽  
Vol 13 (4) ◽  
pp. 34
Author(s):  
T.A. Bubba ◽  
D. Labate ◽  
G. Zanghirati ◽  
S. Bonettini
Keyword(s):  
Optimization Problem ◽  
Ad Hoc ◽  
Iterative Scheme ◽  
Tomographic Reconstruction ◽  
Region Of Interest ◽  
Projection Data ◽  
Reconstruction Problem ◽  
Convex Optimization Problem ◽  
Scanning Time ◽  
Novel Approach

Region of interest (ROI) tomography has gained increasing attention in recent years due to its potential to reducing radiation exposure and shortening the scanning time. However, tomographic reconstruction from ROI-focused illumination involves truncated projection data and typically results in higher numerical instability even when the reconstruction problem has unique solution. To address this problem, bothad hocanalytic formulas and iterative numerical schemes have been proposed in the literature. In this paper, we introduce a novel approach for ROI tomographic reconstruction, formulated as a convex optimization problem with a regularized term based on shearlets. Our numerical implementation consists of an iterative scheme based on the scaled gradient projection method and it is tested in the context of fan-beam CT. Our results show that our approach is essentially insensitive to the location of the ROI and remains very stable also when the ROI size is rather small.

scholarly journals Low Complexity Sparse Beamspace DOA Estimation Via Single Measurement Vectors for Uniform Circular Array

2021 ◽  
Author(s):  
Di Zhao ◽  
Weijie Tan ◽  
Zhongliang Deng ◽  
Gang Li
Keyword(s):  
Optimization Problem ◽  
Signal To Noise Ratio ◽  
Estimation Method ◽  
Doa Estimation ◽  
Low Complexity ◽  
Estimation Methods ◽  
Circular Array ◽  
Single Measurement ◽  
Signal Model ◽  
Convex Optimization Problem

Abstract In this paper, we present a low complexity beamspace direction-of-arrival (DOA) estimation method for uniform circular array (UCA), which is based on the single measurement vectors (SMVs) via vectorization of sparse covariance matrix. In the proposed method, we rstly transform the signal model of UCA to that of virtual uniform linear array (ULA) in beamspace domain using the beamspace transformation (BT). Subsequently, by applying the vectorization operator on the virtual ULA-like array signal model, a new dimension-reduction array signal model consists of SMVs based on Khatri-Rao (KR) product is derived. And then, the DOA estimation is converted to the convex optimization problem. Finally, simulations are carried out to verify the eectiveness of the proposed method, the results show that without knowledge of the signal number, the proposed method not only has higher DOA resolution than subspace-based methods in low signal-to-noise ratio (SNR), but also has much lower computational complexity comparing other sparse-like DOA estimation methods.

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