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网站推广排名收费标准,网站建设找泉州效率网络,保险资料网站有哪些,网站宣传有文化事业建设费吗基于博弈论的全双工(Full-Duplex, FD)系统无线资源分配。该程序实现了非合作博弈、Stackelberg博弈和合作博弈三种模型#xff0c;并提供了可视化分析功能。 %% 基于博弈论的全双工系统无线资源分配 % 功能: 实现三种博弈模型的全双工资源分配仿真 % 模型: 非合作博弈、Stacke…基于博弈论的全双工(Full-Duplex, FD)系统无线资源分配。该程序实现了非合作博弈、Stackelberg博弈和合作博弈三种模型并提供了可视化分析功能。%% 基于博弈论的全双工系统无线资源分配% 功能: 实现三种博弈模型的全双工资源分配仿真% 模型: 非合作博弈、Stackelberg博弈、合作博弈clear;clc;close all;%% 1. 系统参数设置% 网络拓扑参数numNodes4;% 节点数量areaSize1000;% 区域大小 (m)minDistance100;% 节点间最小距离 (m)% 信道参数c3e8;% 光速 (m/s)fc2.4e9;% 载波频率 (Hz)lambdac/fc;% 波长 (m)pathLossExp3.5;% 路径损耗指数shadowSigma3;% 阴影衰落标准差 (dB)noisePower1e-9;% 噪声功率 (W)% 全双工参数selfInterferenceFactor0.1;% 自干扰因子 (残留干扰比例)maxPower0.1;% 最大发射功率 (W)minPower0.001;% 最小发射功率 (W)% 博弈参数numIterations100;% 最大迭代次数tolerance1e-3;% 收敛容差learningRate0.1;% 学习率 (演化博弈)% 仿真参数numRealizations10;% 蒙特卡洛仿真次数visualizeResultstrue;% 是否可视化结果%% 2. 生成网络拓扑% 随机生成节点位置rng(42);% 固定随机种子positionszeros(numNodes,2);fori1:numNodes validPositionfalse;while~validPosition posareaSize*rand(1,2);ifi1validPositiontrue;elsedistssqrt(sum((positions(1:i-1,:)-pos).^2,2));ifall(distsminDistance)validPositiontrue;endendendpositions(i,:)pos;end% 计算信道增益channelGainzeros(numNodes,numNodes);distanceMatrixzeros(numNodes,numNodes);fori1:numNodesforj1:numNodesifi~jdistanceMatrix(i,j)norm(positions(i,:)-positions(j,:));pathLoss(lambda/(4*pi*distanceMatrix(i,j)))^2;shadowing10^(shadowSigma*randn()/10);% dB转线性channelGain(i,j)pathLoss*shadowing;else% 自干扰信道 (残留干扰)channelGain(i,i)selfInterferenceFactor*(lambda/(4*pi*10))^2;% 假设近距离endendend% 显示网络拓扑ifvisualizeResultsfigure(Name,网络拓扑,Position,[100,100,800,600]);scatter(positions(:,1),positions(:,2),100,filled);text(positions(:,1)20,positions(:,2)20,num2str((1:numNodes)));title(全双工节点分布);xlabel(x (m));ylabel(y (m));grid on;axis equal;end%% 3. 博弈模型实现% 存储性能指标performanceMetricsstruct();% 非合作博弈 (功率分配)[noncoopPower,noncoopThroughput,noncoopConvergence]noncooperativeGame(...channelGain,noisePower,maxPower,minPower,numIterations,tolerance);% Stackelberg博弈 (基站主导)[stackelbergPower,stackelbergThroughput,stackelbergConvergence]stackelbergGame(...channelGain,noisePower,maxPower,minPower,numIterations,tolerance);% 合作博弈 (联盟形成)[coopPower,coopThroughput,coopConvergence]cooperativeGame(...channelGain,noisePower,maxPower,minPower,numIterations,tolerance);% 存储结果performanceMetrics.noncoop.powernoncoopPower;performanceMetrics.noncoop.throughputnoncoopThroughput;performanceMetrics.noncoop.convergencenoncoopConvergence;performanceMetrics.stackelberg.powerstackelbergPower;performanceMetrics.stackelberg.throughputstackelbergThroughput;performanceMetrics.stackelberg.convergencestackelbergConvergence;performanceMetrics.coop.powercoopPower;performanceMetrics.coop.throughputcoopThroughput;performanceMetrics.coop.convergencecoopConvergence;%% 4. 性能分析与可视化% 计算平均吞吐量avgThroughputstruct(...noncoop,mean(noncoopThroughput),...stackelberg,mean(stackelbergThroughput),...coop,mean(coopThroughput));% 计算能效energyEfficiencystruct(...noncoop,avgThroughput.noncoop/mean(sum(noncoopPower,2)),...stackelberg,avgThroughput.stackelberg/mean(sum(stackelbergPower,2)),...coop,avgThroughput.coop/mean(sum(coopPower,2)));% 打印结果fprintf( 性能评估结果 \n);fprintf(模型\t\t平均吞吐量(bps/Hz)\t能效(bps/Hz/W)\n);fprintf(非合作博弈\t%.4f\t\t\t%.4f\n,avgThroughput.noncoop,energyEfficiency.noncoop);fprintf(Stackelberg\t%.4f\t\t\t%.4f\n,avgThroughput.stackelberg,energyEfficiency.stackelberg);fprintf(合作博弈\t%.4f\t\t\t%.4f\n,avgThroughput.coop,energyEfficiency.coop);% 可视化收敛过程ifvisualizeResultsfigure(Name,博弈收敛过程,Position,[100,100,1200,800]);% 非合作博弈收敛subplot(3,1,1);plot(squeeze(noncoopConvergence(:,1,:)),LineWidth,1.5);hold on;plot(squeeze(noncoopConvergence(:,2,:)),LineWidth,1.5);title(非合作博弈收敛过程);xlabel(迭代次数);ylabel(功率 (W));legend(节点1,节点2,节点3,节点4);grid on;% Stackelberg博弈收敛subplot(3,1,2);plot(squeeze(stackelbergConvergence(:,1,:)),LineWidth,1.5);hold on;plot(squeeze(stackelbergConvergence(:,2,:)),LineWidth,1.5);title(Stackelberg博弈收敛过程);xlabel(迭代次数);ylabel(功率 (W));legend(基站,用户1,用户2,用户3);grid on;% 合作博弈收敛subplot(3,1,3);plot(squeeze(coopConvergence(:,1,:)),LineWidth,1.5);hold on;plot(squeeze(coopConvergence(:,2,:)),LineWidth,1.5);title(合作博弈收敛过程);xlabel(迭代次数);ylabel(功率 (W));legend(联盟1,联盟2);grid on;% 吞吐量比较figure(Name,吞吐量比较,Position,[100,100,800,600]);throughputData[avgThroughput.noncoop,avgThroughput.stackelberg,avgThroughput.coop];bar(throughputData);set(gca,XTickLabel,{非合作博弈,Stackelberg,合作博弈});ylabel(平均吞吐量 (bps/Hz));title(不同博弈模型的吞吐量比较);grid on;% 能效比较figure(Name,能效比较,Position,[100,100,800,600]);efficiencyData[energyEfficiency.noncoop,energyEfficiency.stackelberg,energyEfficiency.coop];bar(efficiencyData);set(gca,XTickLabel,{非合作博弈,Stackelberg,合作博弈});ylabel(能效 (bps/Hz/W));title(不同博弈模型的能效比较);grid on;% 功率分配可视化figure(Name,功率分配,Position,[100,100,1200,800]);% 非合作博弈功率分配subplot(3,1,1);imagesc(noncoopPower);colorbar;title(非合作博弈功率分配);xlabel(迭代次数);ylabel(节点);set(gca,XTick,1:numIterations,XTickLabel,[]);set(gca,YTick,1:numNodes);% Stackelberg博弈功率分配subplot(3,1,2);imagesc(stackelbergPower);colorbar;title(Stackelberg博弈功率分配);xlabel(迭代次数);ylabel(节点);set(gca,XTick,1:numIterations,XTickLabel,[]);set(gca,YTick,1:numNodes);% 合作博弈功率分配subplot(3,1,3);imagesc(coopPower);colorbar;title(合作博弈功率分配);xlabel(迭代次数);ylabel(联盟);set(gca,XTick,1:numIterations,XTickLabel,[]);set(gca,YTick,1:size(coopPower,1));end%% 5. 辅助函数非合作博弈function[power,throughput,convergence]noncooperativeGame(...channelGain,noisePower,maxPower,minPower,numIter,tol)numNodessize(channelGain,1);powerzeros(numIter,numNodes);% 记录每次迭代的功率throughputzeros(numIter,numNodes);% 记录吞吐量convergencezeros(numIter,numNodes);% 记录收敛过程% 初始化功率 (随机)currentPowerminPower(maxPower-minPower)*rand(1,numNodes);foriter1:numIter prevPowercurrentPower;% 每个节点更新功率fori1:numNodes% 计算其他节点的干扰interference0;forj1:numNodesifj~iinterferenceinterferencechannelGain(i,j)*currentPower(j);endend% 自干扰selfInterferencechannelGain(i,i)*currentPower(i);% 计算SINRsinr(channelGain(i,mod(i,numNodes)1)*currentPower(mod(i,numNodes)1))/...(interferenceselfInterferencenoisePower);% 效用函数: 吞吐量 - 功率成本utilitylog2(1sinr)-0.01*currentPower(i);% 最佳响应更新 (梯度上升)newPowercurrentPower(i)0.1*(utility-0.5*currentPower(i));newPowermin(max(newPower,minPower),maxPower);currentPower(i)newPower;end% 记录当前状态power(iter,:)currentPower;convergence(iter,:)currentPower;% 计算吞吐量fori1:numNodes interference0;forj1:numNodesifj~iinterferenceinterferencechannelGain(i,j)*currentPower(j);endendselfInterferencechannelGain(i,i)*currentPower(i);sinr(channelGain(i,mod(i,numNodes)1)*currentPower(mod(i,numNodes)1))/...(interferenceselfInterferencenoisePower);throughput(iter,i)log2(1sinr);end% 检查收敛ifiter1max(abs(currentPower-prevPower))tolbreak;endendend%% 6. 辅助函数Stackelberg博弈function[power,throughput,convergence]stackelbergGame(...channelGain,noisePower,maxPower,minPower,numIter,tol)numUserssize(channelGain,1)-1;% 假设节点1是基站powerzeros(numIter,numUsers1);% 记录功率throughputzeros(numIter,numUsers1);% 记录吞吐量convergencezeros(numIter,numUsers1);% 记录收敛过程% 初始化功率baseStationPowerminPower(maxPower-minPower)*rand(1);userPowersminPower(maxPower-minPower)*rand(1,numUsers);currentPower[baseStationPower,userPowers];foriter1:numIter prevPowercurrentPower;% 领导者 (基站) 先决策baseInterference0;foru1:numUsers baseInterferencebaseInterferencechannelGain(1,u1)*currentPower(u1);endbaseSelfInterferencechannelGain(1,1)*currentPower(1);baseSinr(channelGain(1,2)*currentPower(2))/...% 假设基站服务用户2(baseInterferencebaseSelfInterferencenoisePower);baseUtilitylog2(1baseSinr)-0.005*currentPower(1);% 更新基站功率newBasePowercurrentPower(1)0.1*(baseUtility-0.5*currentPower(1));newBasePowermin(max(newBasePower,minPower),maxPower);currentPower(1)newBasePower;% 跟随者 (用户) 更新功率foru1:numUsers userInterference0;forj1:numUsers1ifj~u1j~1% 排除自己和基站userInterferenceuserInterferencechannelGain(u1,j)*currentPower(j);endenduserSelfInterferencechannelGain(u1,u1)*currentPower(u1);userSinr(channelGain(u1,1)*currentPower(1))/...% 用户接收基站信号(userInterferenceuserSelfInterferencenoisePower);userUtilitylog2(1userSinr)-0.01*currentPower(u1);% 更新用户功率newUserPowercurrentPower(u1)0.1*(userUtility-0.5*currentPower(u1));newUserPowermin(max(newUserPower,minPower),maxPower);currentPower(u1)newUserPower;end% 记录当前状态power(iter,:)currentPower;convergence(iter,:)currentPower;% 计算吞吐量fori1:numUsers1interference0;forj1:numUsers1ifj~iinterferenceinterferencechannelGain(i,j)*currentPower(j);endendselfInterferencechannelGain(i,i)*currentPower(i);sinr(channelGain(i,mod(i,numUsers1)1)*currentPower(mod(i,numUsers1)1))/...(interferenceselfInterferencenoisePower);throughput(iter,i)log2(1sinr);end% 检查收敛ifiter1max(abs(currentPower-prevPower))tolbreak;endendend%% 7. 辅助函数合作博弈function[power,throughput,convergence]cooperativeGame(...channelGain,noisePower,maxPower,minPower,numIter,tol)numNodessize(channelGain,1);numCoalitions2;% 分成两个联盟nodesPerCoalitionceil(numNodes/numCoalitions);powerzeros(numIter,numCoalitions,nodesPerCoalition);% 记录功率throughputzeros(numIter,numCoalitions);% 记录吞吐量convergencezeros(numIter,numCoalitions,nodesPerCoalition);% 记录收敛过程% 初始化联盟功率coalitionPowerminPower(maxPower-minPower)*rand(numCoalitions,nodesPerCoalition);foriter1:numIter prevPowercoalitionPower;% 每个联盟更新功率forc1:numCoalitions coalitionMembers(c-1)*nodesPerCoalition(1:nodesPerCoalition);coalitionMemberscoalitionMembers(coalitionMembersnumNodes);numMemberslength(coalitionMembers);% 计算联盟总效用coalitionUtility0;fori1:numMembers nodeIdxcoalitionMembers(i);interference0;forj1:numNodesifj~nodeIdx interferenceinterferencechannelGain(nodeIdx,j)*prevPower(ceil(j/nodesPerCoalition),mod(j-1,nodesPerCoalition)1);endendselfInterferencechannelGain(nodeIdx,nodeIdx)*prevPower(c,i);sinr(channelGain(nodeIdx,mod(nodeIdx,numNodes)1)*prevPower(ceil(mod(nodeIdx,numNodes)1/nodesPerCoalition),mod(mod(nodeIdx,numNodes),nodesPerCoalition)1))/...(interferenceselfInterferencenoisePower);coalitionUtilitycoalitionUtilitylog2(1sinr)-0.01*sum(prevPower(c,:));end% 联盟内功率分配 (按比例)totalPowersum(prevPower(c,:));iftotalPower0powerRatioprevPower(c,:)/totalPower;elsepowerRatioones(1,numMembers)/numMembers;end% 更新联盟功率newPowerprevPower(c,:)0.1*(coalitionUtility*powerRatio-0.5*prevPower(c,:));newPowermin(max(newPower,minPower),maxPower);coalitionPower(c,:)newPower;end% 记录当前状态forc1:numCoalitionsform1:nodesPerCoalitionifmsize(coalitionPower,2)power(iter,c,m)coalitionPower(c,m);convergence(iter,c,m)coalitionPower(c,m);endendend% 计算联盟吞吐量forc1:numCoalitions coalitionMembers(c-1)*nodesPerCoalition(1:nodesPerCoalition);coalitionMemberscoalitionMembers(coalitionMembersnumNodes);numMemberslength(coalitionMembers);coalitionThroughput0;fori1:numMembers nodeIdxcoalitionMembers(i);interference0;forj1:numNodesifj~nodeIdx otherCoalceil(j/nodesPerCoalition);otherMemmod(j-1,nodesPerCoalition)1;interferenceinterferencechannelGain(nodeIdx,j)*coalitionPower(otherCoal,otherMem);endendselfInterferencechannelGain(nodeIdx,nodeIdx)*coalitionPower(c,i);sinr(channelGain(nodeIdx,mod(nodeIdx,numNodes)1)*coalitionPower(ceil(mod(nodeIdx,numNodes)1/nodesPerCoalition),mod(mod(nodeIdx,numNodes),nodesPerCoalition)1))/...(interferenceselfInterferencenoisePower);coalitionThroughputcoalitionThroughputlog2(1sinr);endthroughput(iter,c)coalitionThroughput;end% 检查收敛ifiter1max(abs(coalitionPower(:)-prevPower(:)))tolbreak;endendend%% 8. 高级分析公平性评估functionfairnessIndexcalculateFairness(throughput)% Jain公平性指数numerator(sum(throughput))^2;denominatorlength(throughput)*sum(throughput.^2);fairnessIndexnumerator/denominator;end% 计算各模型的公平性noncoopFairnesscalculateFairness(noncoopThroughput(end,:));stackelbergFairnesscalculateFairness(stackelbergThroughput(end,:));coopFairnesscalculateFairness(coopThroughput(end,:));fprintf(\n 公平性评估 \n);fprintf(模型\t\tJain公平性指数\n);fprintf(非合作博弈\t%.4f\n,noncoopFairness);fprintf(Stackelberg\t%.4f\n,stackelbergFairness);fprintf(合作博弈\t%.4f\n,coopFairness);%% 9. 高级分析能量效率functionefficiencycalculateEfficiency(throughput,power)% 计算系统总吞吐量和总功耗totalThroughputsum(throughput);totalPowersum(power(:));efficiencytotalThroughput/totalPower;end% 计算各模型的能量效率noncoopEfficiencycalculateEfficiency(noncoopThroughput(end,:),noncoopPower(end,:));stackelbergEfficiencycalculateEfficiency(stackelbergThroughput(end,:),stackelbergPower(end,:));coopEfficiencycalculateEfficiency(coopThroughput(end,:),coopPower(end,:));fprintf(\n 能量效率评估 \n);fprintf(模型\t\t能量效率(bps/Hz/W)\n);fprintf(非合作博弈\t%.4f\n,noncoopEfficiency);fprintf(Stackelberg\t%.4f\n,stackelbergEfficiency);fprintf(合作博弈\t%.4f\n,coopEfficiency);博弈论在全双工资源分配中的应用原理1. 全双工系统挑战全双工(FD)系统允许节点同时同频发送和接收理论上可倍增频谱效率但面临两大挑战自干扰(SI)发射信号对自身接收机的强干扰比目标信号高60-100dB双向干扰多FD节点共存时的相互干扰2. 博弈论建模要素要素描述实现方式参与者网络节点基站、用户设备节点数组策略集可调资源参数功率、频谱功率分配向量效用函数衡量策略收益吞吐量、能效Uilog(1SINRi)−c⋅piU_ilog(1SINR_i)−c⋅piUi​log(1SINRi​)−c⋅pi均衡策略稳定状态纳什均衡迭代最佳响应算法3. 三种博弈模型实现(1) 非合作博弈场景节点独立决策追求自身利益最大化策略更新梯度上升法优化效用函数收敛条件功率变化小于容差% 节点策略更新fori1:numNodes% 计算干扰和SINRsinrcalculateSINR(i,currentPower,channelGain);% 效用函数: 吞吐量 - 功率成本utilitylog2(1sinr)-costCoeff*currentPower(i);% 梯度上升更新newPowercurrentPower(i)learningRate*(utility-penalty*currentPower(i));currentPower(i)min(max(newPower,minPower),maxPower);end(2) Stackelberg博弈场景层级结构基站主导用户跟随策略更新 领导者基站先决策 跟随者用户根据领导策略响应均衡子博弈完美均衡% 基站(领导者)决策baseUtilitycalculateBaseUtility(basePower,userPowers);newBasePowerbasePowerlr*(baseUtility-penalty*basePower);% 用户(跟随者)响应foreach user userUtilitycalculateUserUtility(userPower,basePower);newUserPoweruserPowerlr*(userUtility-penalty*userPower);end(3) 合作博弈场景节点形成联盟共享资源策略更新联盟内联合优化效用分配Shapley值或比例分配% 联盟形成coalition1nodes(1:2);coalition2nodes(3:4);% 联盟内功率分配totalUtilitycalculateCoalitionUtility(coalitionPower);powerRatiocoalitionPower/sum(coalitionPower);newPowercoalitionPowerlr*(totalUtility*powerRatio-penalty*coalitionPower);关键技术与创新点1. 信道建模路径损耗PL(d)(λ/(4πd))2PL(d)(λ/(4πd))^2PL(d)(λ/(4πd))2阴影衰落对数正态分布10N(0,σ2)/1010^{N(0,σ2)}/^{10}10N(0,σ2)/10自干扰模型hiiη⋅(λ/(4πdref))2h_{ii}η⋅(λ/(4πd_{ref}))^2hii​η⋅(λ/(4πdref​))22. 效用函数设计吞吐量项log2(1SINR)log_2(1SINR)log2​(1SINR)比例公平成本项c⋅pic⋅pic⋅pi功率消耗干扰处理包含所有节点干扰和自干扰3. 收敛性保障迭代算法最佳响应动态终止条件功率变化小于容差或达最大迭代次数学习率调整自适应学习率加速收敛参考代码 基于博弈论的全双工系统无线资源分配www.youwenfan.com/contentcsn/83490.html性能评估指标1. 系统级指标指标公式意义吞吐量∑log2(1SINRi)∑log_2(1SINR_i)∑log2​(1SINRi​)系统传输能力能效吞吐量/总功耗能源利用效率公平性(∑xi)2/(n∑xi2)(∑x_i)^2/(n∑x_i^2)(∑xi​)2/(n∑xi2​)资源分配公平性2. 可视化分析收敛过程展示功率迭代变化吞吐量比较柱状图对比模型性能功率分配热力图展示资源分布网络拓扑节点位置与信道关系扩展功能与应用1. 多小区扩展% 多小区拓扑生成numCells3;cellRadius200;cellPositionsgenerateCellPositions(numCells,cellRadius);% 跨小区干扰处理interCellInterferencecalculateInterCellInterference(...);2. 动态场景适应% 移动节点更新位置positionsupdateNodePositions(positions,velocities,timeStep);% 信道状态更新channelGainupdateChannelState(positions,shadowing);3. 混合博弈模型% 部分节点合作部分竞争cooperativeNodes[1,2];noncooperativeNodes[3,4];% 差异化效用函数ifismember(i,cooperativeNodes)utilitycoalitionUtility(i);elseutilityindividualUtility(i);end4. 机器学习增强% 强化学习策略优化policyNetworktrainPolicyNetwork(experienceBuffer);% 预测信道状态channelPredictortrainChannelPredictor(historicalData);实际应用场景1. 5G/6G网络密集小区全双工通信D2D通信资源分配超可靠低延迟通信(URLLC)2. 物联网(IoT)传感器网络数据采集低功耗广域网(LPWAN)工业物联网(IIoT)3. 车联网(V2X)车辆间通信(V2V)车路协同(V2I)自动驾驶协同感知4. 应急通信灾区临时网络部署无人机基站中继军事战术通信