[OFDM communication] simulation of OFDM multi-user resource allocation based on MATLAB [including Matlab source code 1902]

One 、OFDM brief introduction

In order to alleviate the increasingly tense situation of wireless spectrum , Cognitive radio technology has received more and more attention . Compared with traditional transmission technology , Cognitive radio technology has higher flexibility and intelligence . Its biggest feature is that it can sense the surrounding spectrum environment ( Mainly spectrum usage ), Find the spectrum hole that can be used [1], Use the idle frequency band in a specific time and in a specific geographical location , Thus, the spectrum efficiency and system capacity are greatly improved .

Cognitive radio technology is the development trend of communication technology after software radio technology , It reflects the development of communication technology from network to intelligence . Radio technology has gone through a fixed pattern , After adaptive mode, it develops to cognitive mode . Cognitive radio has the function of perception , And by perceiving the results , Select transmission scheme , At the same time, the wireless terminal has “ Study ” function , And the ability to generate new alternative transmission schemes .

OFDM Compared with other transmission technologies, this technology has the advantage of flexible allocation of subcarriers , As a promising technology in the next generation communication standard , It also has a broad application prospect in cognitive radio . The frequency band used by cognitive users is not fixed , Is random , And may be discontinuous .OFDM The bandwidth can be adjusted by adaptively allocating zero power to individual subcarriers , Its advantage is that it can flexibly allocate subcarriers and control their power . The author aims at OFDM The system presents a multi-user subcarrier and its power allocation algorithm .

1 Cognitive radio system model
Cognitive radio system embodies higher intelligence , It can actively sense the wireless environment , Sensing the surrounding spectrum usage , Therefore, the cognitive radio system must have a spectrum analysis and channel estimation unit to detect the spectrum , Look for spectrum holes that can be used . The spectrum hole used by cognitive radio refers to the spectrum hole that has been allocated to authorized users , The frequency band not used by the authorized user within a specific time or used at a specific geographical location far away .

chart 1 Schematic diagram of cognitive loop

The classical cognitive radio system includes the perception of wireless environment 、 Spectrum resource estimation 、 Resource allocation algorithm , It forms a closed loop , Pictured 1 Shown .

Simon Haykin Put forward the concept of cognitive ring , In the cognitive loop, it can be seen that cognitive radio systems need to consider a new physical quantity —— Disturbance temperature . The interference temperature limits the power distribution in the spectrum , That is, the upper limit of channel capacity is limited . Cognitive radio system can detect the spectrum usage and the interference temperature on the corresponding spectrum , Dynamically allocate resources among multiple users , To make full use of the spectrum .

How to make multiple cognitive users make full use of the detected spectrum holes , How to allocate these subcarriers among multiple cognitive users , And allocating transmission power on these subcarriers . This is the focus of this paper . The principle block diagram of transmitter with multi-user adaptive resource allocation is shown in the figure below 2 Shown . In traditional multi-user OFDM Resource detection is added to the system 、 Power allocation algorithm and power control unit .

2 Multi user resource allocation algorithm
Resource allocation refers to cognition AP(Access Point) The downlink of the node is for multiple types of cognitive users ( The type can be mobile phone , Car radio ,PDA Or others ) Research on the algorithm of . cognition AP Nodes efficiently use the detected spectrum holes to dynamically allocate the spectrum resources in the downlink in real time . The resource allocation algorithm mainly considers two factors: power constraints and device types .

cognition AP After the node receives the cognitive user request information that requires communication , First, detect the surrounding spectrum usage , Find the spectrum hole that can be used , At the same time, it is necessary to obtain the fading characteristics of all cognitive users on the available spectrum holes , And the information of authorized users within the power coverage of the whole system . These are cognitions AP The basis for node resource allocation algorithm .

cognition AP The node shall complete power allocation and subcarrier assignment in the downlink according to the information obtained above , The power allocation algorithm of cognitive radio should consider the power interference to authorized users . When allocating resources , The allocated power of power limited subcarriers shall not exceed their respective upper power limits , This requires the allocation algorithm to allocate a pre allocated power to each subcarrier , So as not to interfere with the normal communication of authorized users .

Follow the principle of proportional fairness in the process of resource allocation , Prevent users with good channel characteristics from occupying most spectrum holes , Other users, especially those with poor channel characteristics, cannot guarantee normal communication . In this paper, only the interference of cognitive users to authorized users is considered . The interference of cognitive users on authorized users is mainly reflected in the interference on the signal to interference ratio of authorized users .

set up RSI Indicates the signal to interference ratio at the authorized user ,

It can be seen that this is a nonlinear optimization problem , The solution of the optimal value is very complicated , This paper presents a two-step suboptimal value algorithm which first allocates subcarriers and then allocates power .

Two 、 Partial source code

clear all;
worstpower=1.1565;
N0=worstpower*1e-8;
Ptotal=1;
BER = 1e-3;
Gap = -log(5*BER)/1.6;

channelnum=10;
samplenum=10;
numuser = 8;

epsilong=1e-3;
maxNumOfNewton=0;
maxTotalNewtonIteNum=0;

avenonlintime = zeros(1,numuser);
aveshentime = zeros(1,numuser);
aveiantime = zeros(1,numuser);


diffuservector = [];
iancapamat = [];
shencapamat = [];

cc = 1;

clear shencapa iancapa
N=64;            %  Number of subcarriers 
B=1000000;       %  Bandwidth is 1MHz

noise=B*N0/N;
clear ch


%for different user number
for ii=1:numuser,
    diffuser=2*ii;
    diffuservector(ii)=diffuser;
    K=diffuser;

    totaliancapa=0;
    totalshencapa=0;
    totalnonlincapa = 0;

    shencapavec = zeros(1,diffuser);
    iancapavec = zeros(1,diffuser);

    iannorm = 0;
    shennorm = 0;
    nonlinnorm = 0;


    %for different channel
    for chan=1:channelnum,
        chan
        [env,I,Q]=chtry(K,samplenum,30);
        h = rand(1,K);
        gamma = .064*(h < .5) + .128*((h >= .5) & (h < .8)) + .256*(h>.8);%%how to generate the coefficient

        %for diff samples
        for diffsamp=1:samplenum,
            diffsamp
            for i=1:K
                user=I(i,:,diffsamp)+sqrt(-1)*Q(i,:,diffsamp);
                ch(i,:)=abs(fft(user,N)).^2/Gap;%%channel in frequency domain
            end


            % Shen's subcarrier allocation
            [rheecapa,rheesuballo]=rheesub(Ptotal,ch, N, K, noise, gamma);

            % Shen's power allocation
            t=cputime;  shenp = shenpowerallo(ch,rheesuballo,N,K,Ptotal,noise,gamma); shentime = cputime-t;
            aveshentime(ii) = aveshentime(ii) + shentime;

            % Ian's subcarrier allocation
            [iancapa,iansuballo]=wongsuballo(Ptotal, ch, N, K, noise, gamma);

            % Ian's power allocation
            t=cputime; ianp = wongpowerallo(ch,iansuballo,N,K,Ptotal,noise,gamma); iantime = cputime-t;
            aveiantime(ii) = aveiantime(ii) + iantime;

            for i=1:K,
                shencapa(i) =  waterfilling(shenp(i),rheesuballo(i,:).*ch(i,:)/noise)/N;
                iancapa(i) = waterfilling(ianp(i),iansuballo(i,:).*ch(i,:)/noise)/N;
            end;

            totalshencapa=totalshencapa+sum(shencapa);
            totaliancapa=totaliancapa+sum(iancapa);

            if (chan == 1),
                shencapavec = shencapavec + shencapa;
                iancapavec = iancapavec + iancapa;
                if (chan ==  1 & diffsamp == 1),
                    figure(2);
                    bar([gamma/sum(gamma); iancapavec/sum(iancapavec); shencapavec/sum(shencapavec)]', 'grouped');%; nonlincapavec/sum(nonlincapavec)]', 'grouped');
                    title(' snapshot ');
                end;
            end;
            iannorm = iannorm + norm(iancapa/sum(iancapa) - gamma/sum(gamma), inf);
            shennorm = shennorm + norm(shencapa/sum(shencapa) - gamma/sum(gamma), inf);
        end

        if (chan == 1),
            iancapavec = iancapavec/(channelnum*samplenum);
            shencapavec = shencapavec/(channelnum*samplenum);
            figure(5);
            bar([gamma/sum(gamma); iancapavec/sum(iancapavec); shencapavec/sum(shencapavec)]', 'grouped');
            legend(' gamma ', ' linear ', ' Search for roots ');
        end;
        %end diff channel
    end
    maxNumOfNewton;
    maxTotalNewtonIteNum;
    totalshencapavec(ii)=totalshencapa/(channelnum*samplenum);
    totaliancapavec(ii)=totaliancapa/(channelnum*samplenum);
    iannormvec(ii) = iannorm/(channelnum*samplenum);
    shennormvec(ii) = shennorm/(channelnum*samplenum);
end

% total capacities plot
figure(1)
plot(diffuservector,totaliancapavec, 'ko--', diffuservector, totalshencapavec, 'b+-.');%, diffuservector, totalnonlincapavec, 'rx-.');
iansumcapa = sum(totaliancapavec)
shensumcapa = sum(totalshencapavec)

grid on
xlabel(' The number of users ')
ylabel(' Capacity  (bit/s/Hz)')
legend(' linear ', ' Search for roots ');     %, 'NONLIN');
hold off

aveshentime = aveshentime/(channelnum*samplenum)
aveiantime = aveiantime/(channelnum*samplenum)

figure(3);
semilogy(diffuservector,aveiantime, 'ko--', diffuservector,aveshentime, 'b+-.');%, diffuservector, avenonlintime, 'rx-.');
grid on
xlabel(' The number of users ')
ylabel(' Average CPU Time (s)')
legend(' linear ', ' Search for roots ');      %, 'NONLIN');
title(' Average CPU Time comparison ');

3、 ... and 、 Running results

Four 、matlab Edition and references

1 matlab edition
2014a

2 reference
[1] Shen Zaiyang . Master MATLAB signal processing [M]. tsinghua university press ,2015.
[2] Gao Baojian , Peng Jinye , Wang Lin , Pan Jianshou . Signals and systems —— Use MATLAB Analysis and Implementation [M]. tsinghua university press ,2020.
[3] Wang Wenguang , Wei Shaoming , Ren Xin . Signal processing and system analysis MATLAB Realization [M]. Electronic industry press ,2018.
[4] Li Weiying , Chen2 dong , Xingchengwen , Wang Ning . In cognitive radio systems OFDM Multi user resource allocation algorithm [J]. Journal of Xi'an University of Electronic Science and Technology . 2007,(03)

3 remarks
This part of the introduction is taken from the Internet , For reference only , If infringement , Contact deletion