A matching game framework for users clustering and resource allocation with wireless power transfer in a CR-NOMA network

This paper investigates the resource allocation in a massively deployed user cognitive radio enabled non-orthogonal multiple access (CR-NOMA) network considering the downlink scenario. The system performance deteriorates with the number of users who are experiencing similar channel characteristics from the base station (BS) in NOMA. To address this challenge, we propose a framework for maximizing the system throughput that is based on one-to-one matching game theory integrated with the machine learning technique. The proposed approach is decomposed to solve users clustering and power allocation subproblems. The selection of optimal cluster heads (CHs) and their associated cluster members is based on Gale-Shapley matching game theoretical model with the application of Hungarian method. The CHs can harvest energy from the BS and transfer their surplus power to the primary user (PU) through wireless power transfer. In return, they are allowed to access the licensed band for secondary transmission. The power allocation to the users intended for power conservation at CHs is formulated as a probabilistic constraint, which is then solved by employing the support vector machine (SVM) algorithm. The simulation results demonstrate the efficacy of our proposed schemes that enable the CHs to transfer the residual power while ensuring maximum system throughput. The effects of different parameters on the performance are also studied.     

基于Gale-Shapley算法的风储供应链合作伙伴双向选择研究

风电与储能结合形成风储供应链,在风储供应链基础上选择合适的合作伙伴,不仅有助于风电消纳和储能发展,还能提升风储供应链的合作价值。基于风储供应链角度,构建了风储双向评价指标体系,设计了基于不同评价方法与Gale-Shapley算法的风储供应链合作伙伴双向选择模型,对比验证了所建模型结果的可靠性,为风储供应链上的企业合理选择合作伙伴进行科学决策提供参考。     

异构蜂窝网络中联合功率控制的终端直通通信资源分配

针对异构蜂窝网络中终端直通（D2D）用户和小蜂窝用户复用宏蜂窝用户资源产生的干扰问题,提出一种联合功率控制的资源分配方案。首先,在满足用户信号干扰噪声比（SINR）和发射功率约束条件的前提下,根据系统干扰模型推导出每个D2D用户和小蜂窝用户复用宏蜂窝用户信道资源时的最优发射功率;其次,将用户的信道选择规划成用户和信道之间的双边匹配问题,采用延迟接受（Gale-Shapley）算法得到一个稳定的匹配解;最后,以所得的匹配解为初始条件,通过交换搜索算法进一步优化分配方案。仿真结果表明,该方案的系统总容量和能量效率分别是最优解的93.62%和92.14%,与随机资源分配方案、无功率控制和交换搜索的分配方案,以及有功率控制无交换搜索的分配方案相比,系统容量平均增幅分别为48.29%、15.97%和4.8%,系统能量效率平均增幅分别为62.72%、44.48%和4.45%。该方案能够达到近似最优的系统总容量,有效提高频率利用率和能量效率。     

Decision-making method of reconfigurable manufacturing systems’ reconfiguration by a Gale-Shapley model

The companies need to rapid response to new product introduction, mix and demand changes to stay competitive. A reconfigurable manufacturing system can quickly react to changes in products and market. The control method to reconfigure the machines of a reconfigurable manufacturing system is crucial for the performance level. This paper proposes a reconfiguration decision-making method based on a Game-Theory algorithm, and in particular the Gale-Shapley model. A periodic review strategy is used to create two sets: one set of machine over-loaded and one set under-loaded. The Gale-Shapley model forms a coupled of over-loaded and under-loaded machines. The reconfiguration concerns the under-loaded machine of the coupled adding also the task performed by the over-loaded machine. This paper presents a simulation environment developed to evaluate the proposed method and highlight the main topics. The simulation results highlight how the game-theory approach developed improves all the performance measures with controlled number of machines’ reconfigurations.     

基于多对一Gale-Shapley算法的D2D通信资源分配

针对D2D通信复用异构蜂窝网络上行信道产生的干扰问题和频谱资源优化问题进行研究,提出一种基于多对一Gale-Shapley算法的D2D通信资源分配方案。本方案允许多个D2D用户共享一个蜂窝用户信道资源,通过设置信干噪比（SINR）门限保证用户的通信服务质量（QOS）。根据信道分配情况,构建D2D用户和信道的偏好列表,最大化系统总容量。仿真结果表明,该方案收敛较快,复杂度较低,能够有效保证用户的通信服务质量,系统总容量接近最优解。本研究为实现D2D用户和蜂窝用户的频谱资源共享,提高频谱利用率提供了一种有效方案。     

面向移动社交云的资源分配机制

移动社交云是结合了移动云和社交云的一种新型模式,它可以为用户提供安全可靠的资源分享平台.在传统的资源分配中,移动用户从远程数据中心获取资源的时间开销大,显著地降低了用户的体验质量,与此同时也极少考虑用户之间根据社交属性建立的信任关系,从而导致交易机制存在一定信用风险和交易用户信誉度低的问题.为此,在考虑用户的社交效益和经济效益的基础上提出了一种新型的移动社交云资源分配机制.首先,利用改进Gale-Shapley算法为买方在其朋友圈中匹配出合适的卖方,促进社交朋友间资源的共享.其次,利用多对多的买方多标密封拍卖算法,对未成功匹配的用户再次分配资源,最大化利用空闲的移动云资源.最后,实现了提出的资源分配机制,仿真结果表明,与激励与拍卖兼容的移动云资源分配机制相比,提出的资源分配机制在价格满意度、社交信誉满意度和资源交易成功率方面具有更好的性能.     

面向服装供应链平台的订单生产双向推荐策略

针对服装供应链平台的服装多个生产订单与多个制造商间的相互匹配与双向推荐问题,首先分析了下单方和制造商的双方交易需求,然后构建了双方效用评价指标结构和偏好效用函数,最后采用Gale-Shapley(GS)算法策略建立了基于偏好排序的服装生产订单双向匹配模型。实验采用6个订单与6家制造商的数据进行双向匹配模型验证,专家问卷分析结果表明了其匹配结果的稳定性和可接受性,实现了下单方对匹配结果的平均接受率达到94%,接单方对匹配结果的平均接受率达到89%。结果表明:基于订单效用评价体系和GS策略的双向匹配模型能充分反映下单方的交易需求和制造商的接单意愿,在考虑博弈双方的交易需求下,匹配模型能有效地提高服装供应链平台上的下单与接单匹配推荐结果的准确性和稳定性。