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大小: 2KB文件类型: .zip金币: 1下载: 0 次发布日期: 2021-03-24
- 语言: Matlab
- 标签: energy efficiency Massive MIMO
资源简介
this code is related to Energy efficiency in massive mimo system
代码片段和文件信息
function [EEratepower] = functionEnergyEfficiency(powerhetaUEetaBSnoiseRkappaUEkappaBSomegarhozetatrafficPortionULDLratio)
%This is an implementation of energy efficiency metric used in the article:
%
%Emil Bj鰎nson Jakob Hoydis Marios Kountouris M閞ouane Debbah 揗assive
%MIMO Systems with Non-Ideal Hardware: Energy Efficiency Estimation and
%Capacity Limits?To appear in IEEE Transactions on Information Theory.
%
%Download article: http://arxiv.org/pdf/1307.2584
%
%This is version 1.0 (Last edited: 2014-08-26)
%
%License: This code is licensed under the GPLv2 license. If you in any way
%use this code for research that results in publications please cite our
%original article listed above.
%
%INPUT:
%power = Transmit power
%h = Channel realizations (dimension: N x nbrOfMonteCarloRealizations)
%etaUE = Distortion noise realization at UE
% (dimension 1 x nbrOfMonteCarloRealizations)
%etaBS = Distortion noise realization at UE
% (dimension N x nbrOfMonteCarloRealizations)
%noise = Matrix with noise realizations in UL
% (dimension: N x nbrOfMonteCarloRealizations)
%R = N x N channel covariance matrix
%kappaUE = Level of hardware impairments at UE
%kappaBS = Level of hardware impairments at BS
%omega = Efficiency of power amplifiers at UE and BS
%rho = Circuit power that scales with N
%zeta = Circuit power independent of N
%trafficPortion = Portion of the total resources available for data
% transmission in the direction under study (smaller than
% ULDLratio)
%ULDLratio = Fraction of total resources allocated to the transmission
% direction under study (set it to 0.5 to study both UL/DL)
%
%OUTPUT:
%EE = Energy efficiency according to model in Definition 1
%A typical pilot signal
d = sqrt(power);
%Extract number of antennas
N = size(h1);
%Extract number of Monte Carlo simulations
nbrOfMonteCarloRealizations = size(h2);
%Compute matrix A in the LMMSE estimator (see Eq. (9))
A_LMMSE = conj(d) * R / (abs(d)^2*(1+kappaUE)*R + abs(d)^2*kappaBS*diag(diag(R))+eye(N));
%Placeholders for storing Monte Carlo simulation results
firstMoment = zeros(nbrOfMonteCarloRealizations1);
distortionTerm = zeros(nbrOfMonteCarloRealizations1);
%Go through all Monte Carlo realizations
for k = 1:nbrOfMonteCarloRealizations
%Compute received signal
z = h(:k) * ( d + d*etaUE(k) ) + d*etaBS(:k) + noise(:k);
%Compute channel estimates
hhat = A_LMMSE*z;
%Compute the beamforming vector (MRT/MRC)
beamforming = sqrt(power)*hhat/norm(hhat);
%Compute a realization of the first moment of the inner product between
%beamforming and channel
firstMoment(k) = h(:k)‘*beamforming;
%The elementwise product between channel and beamforming vectors (and
%sum over these ele
属性 大小 日期 时间 名称
----------- --------- ---------- ----- ----
文件 3531 2014-08-26 13:05 functionEnergyEfficiency.m
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