IEEE ICDM 2015 Call For Demos

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2015电工杯数学建模竞赛论文D一、问题重述近年来乘坐邮轮旅游的人越来越多，邮轮公司的发展也非常迅速。

如何通过合理的定价吸引更多的旅游者，从而为邮轮公司创造更多的收益，这也是众多邮轮公司需要探讨和解决的问题。

邮轮采用提前预订的方式进行售票，邮轮出发前0周至14周为有效预定周期，邮轮公司为了获得每次航行的预期售票收益，希望通过历史数据预测每次航行0周至14周的预定舱位人数、预订舱位的价格，为保证价格的平稳性，需要限定同一航次相邻两周之间价格浮动比，意愿预定人数（填写信息表未交款的人数）转化为实际预定人数（填写信息表并交款的人数）与定价方案密切相关。

已知某邮轮公司拥有一艘1200个舱位的邮轮，舱位分为三种，250个头等舱位，450个二等舱位，500个三等舱位。

该邮轮每周往返一次，同一航次相邻两周之间价格浮动比不超过20%。

现给出10次航行的实际预订总人数、各航次每周实际预订人数非完全累积表、每次航行预订舱位价格表、各舱位每航次每周预订平均价格表及意愿预订人数表、每次航行升舱后最终舱位人数分配表（详见附件中表sheet1- sheet5），邀请你们为公司设计定价方案，需解决以下问题：1.预测每次航行各周预订舱位的人数，完善各航次每周实际预订人数非完全累积表sheet2。

（至少采用三种预测方法进行预测，并分析结果。

）2.预测每次航行各周预订舱位的价格，完善每次航行预订舱位价格表sheet3。

3.依据附件中表sheet4给出的每周预订价格区间以及每周意愿预订人数，预测出公司每周给出的预订平均价格。

4.依据附件中表sheet1-sheet4，建立邮轮每次航行的最大预期售票收益模型，并计算第8次航行的预期售票收益。

5.在头等、二等舱位未满的情况下，游客登船后，可进行升舱（即原订二等舱游客可通过适当的加价升到头等舱，三等舱游客也可通过适当的加价升到头等舱、二等舱）。

请建立游客升舱意愿模型，为公司制定升舱方案使其预期售票收益最大。

ｄｏｉ：１０．３９６９／ｊ．ｉｓｓｎ．１００３－３１０６．２０２３．０７．０１０引用格式：张志晨，王昱凯，王荣，等．基于深度学习的ＯＦＤＭ ＩＭ信号检测方法［Ｊ］．无线电工程，２０２３，５３（７）：１５７２－１５７７．［ＺＨＡＮＧＺｈｉｃｈｅｎ，ＷＡＮＧＹｕｋａｉ，ＷＡＮＧＲｏｎｇ，ｅｔａｌ．ＯＦＤＭ ＩＭＳｉｇｎａｌＤｅｔｅｃｔｉｏｎＭｅｔｈｏｄＢａｓｅｄｏｎＤｅｅｐＬｅａｒｎｉｎｇ［Ｊ］．ＲａｄｉｏＥｎｇｉｎｅｅｒｉｎｇ，２０２３，５３（７）：１５７２－１５７７．］基于深度学习的ＯＦＤＭ ＩＭ信号检测方法张志晨，王昱凯，王　荣，李　军，郑文静（齐鲁工业大学（山东省科学院）信息与自动化学院，山东济南２５０３５３）摘　要：正交频分复用（ＯｒｔｈｏｇｏｎａｌＦｒｅｑｕｅｎｃｙＤｉｖｉｓｉｏｎＭｕｌｔｉｐｌｅｘｉｎｇ，ＯＦＤＭ）技术在无线通信领域中拥有着重要地位，但ＯＦＤＭ系统中存在子载波间干扰和较高的峰均比的缺点，使得ＯＦＤＭ系统在信号检测方面的表现不太理想。

针对ＯＦＤＭ系统中信号检测性能较差的问题，提出一种基于自归一化网络的索引调制（ＩｎｄｅｘＭｏｄｕｌａｔｉｏｎｆｏｒＳｅｌｆＮｏｒｍａｌｉｚｉｎｇＮｅｔｗｏｒｋ，ＩＭ ＳＮＮ）算法，并采用４ＱＡＭ、８ＱＡＭ、１６ＱＡＭ的调制方式验证系统的信号检测性能。

结果表明，所提出的算法提高了接收端解调信号的性能，有效增强了信号检测的能力，并表现出优于传统技术中最大似然检测（ＭａｘｉｍｕｍＬｉｋｅｌｉｈｏｏｄＤｅｔｅｃｔｉｏｎ，ＭＬＤ）算法及现有技术中基于深度神经网络的索引调制（ＩｎｄｅｘＭｏｄｕｌａｔｉｏｎｉｎＤｅｅｐＮｅｕｒａｌＮｅｔｗｏｒｋ，ＩＭ ＤＮＮ）算法的系统误码率及网络损失。

在３种调制方式下，性能改善０．６～８ｄＢ。

关键词：正交频分复用；索引调制；信号检测；自归一化网络；深度学习中图分类号：ＴＮ９２文献标志码：Ａ开放科学（资源服务）标识码（ＯＳＩＤ）：文章编号：１００３－３１０６（２０２３）０７－１５７２－０６ＯＦＤＭ ＩＭＳｉｇｎａｌＤｅｔｅｃｔｉｏｎＭｅｔｈｏｄＢａｓｅｄｏｎＤｅｅｐＬｅａｒｎｉｎｇＺＨＡＮＧＺｈｉｃｈｅｎ，ＷＡＮＧＹｕｋａｉ，ＷＡＮＧＲｏｎｇ，ＬＩＪｕｎ，ＺＨＥＮＧＷｅｎｊｉｎｇ（ＳｃｈｏｏｌｏｆＩｎｆｏｒｍａｔｉｏｎａｎｄＡｕｔｏｍａｔｉｏｎ，ＱｉｌｕＵｎｉｖｅｒｓｉｔｙｏｆＴｅｃｈｎｏｌｏｇｙ（ＳｈａｎｄｏｎｇＡｃａｄｅｍｙｏｆＳｃｉｅｎｃｅ），Ｊｉｎａｎ２５０３５３，Ｃｈｉｎａ）Ａｂｓｔｒａｃｔ：ＯｒｔｈｏｇｏｎａｌＦｒｅｑｕｅｎｃｙＤｉｖｉｓｉｏｎＭｕｌｔｉｐｌｅｘｉｎｇ（ＯＦＤＭ）ｔｅｃｈｎｏｌｏｇｙｐｌａｙｓａｎｉｍｐｏｒｔａｎｔｒｏｌｅｉｎｔｈｅｆｉｅｌｄｏｆｗｉｒｅｌｅｓｓｃｏｍｍｕｎｉｃａｔｉｏｎ．Ｈｏｗｅｖｅｒ，ｔｈｅＯＦＤＭｓｙｓｔｅｍｈａｓｔｈｅｄｉｓａｄｖａｎｔａｇｅｓｏｆｉｎｔｅｒ ｃａｒｒｉｅｒｉｎｔｅｒｆｅｒｅｎｃｅａｎｄｈｉｇｈｐｅａｋ ｔｏ ａｖｅｒａｇｅｒａｔｉｏ．ＴｈｅｐｅｒｆｏｒｍａｎｃｅｏｆＯＦＤＭｓｙｓｔｅｍｉｎｓｉｇｎａｌｄｅｔｅｃｔｉｏｎｉｓｎｏｔｉｄｅａｌ．ＴｏｄｅａｌｗｉｔｈｔｈｅｐｒｏｂｌｅｍｏｆｐｏｏｒｓｉｇｎａｌｄｅｔｅｃｔｉｏｎｐｅｒｆｏｒｍａｎｃｅｉｎＯＦＤＭｓｙｓｔｅｍ，ａｎＩｎｄｅｘＭｏｄｕｌａｔｉｏｎｆｏｒＳｅｌｆＮｏｒｍａｌｉｚｉｎｇＮｅｔｗｏｒｋ（ＩＭ ＳＮＮ）ａｌｇｏｒｉｔｈｍｉｓｐｒｏｐｏｓｅｄ．Ｔｈｅｓｉｇｎａｌｄｅｔｅｃｔｉｏｎｐｅｒｆｏｒｍａｎｃｅｏｆｔｈｅｓｙｓｔｅｍｉｓｖｅｒｉｆｉｅｄｂｙ４ＱＡＭ，８ＱＡＭａｎｄ１６ＱＡＭｍｏｄｕｌａｔｉｏｎｍｏｄｅｓ．Ｔｈｅｒｅｓｕｌｔｓｓｈｏｗｔｈａｔｔｈｅｐｒｏｐｏｓｅｄａｌｇｏｒｉｔｈｍｉｍｐｒｏｖｅｓｔｈｅｐｅｒｆｏｒｍａｎｃｅｏｆｔｈｅｒｅｃｅｉｖｅｒｄｅｍｏｄｕｌａｔｉｏｎｓｉｇｎａｌ，ｅｆｆｅｃｔｉｖｅｌｙｅｎｈａｎｃｅｓｔｈｅｃａｐａｂｉｌｉｔｙｏｆｓｉｇｎａｌｄｅｔｅｃｔｉｏｎ，ａｎｄｐｅｒｆｏｒｍｓｂｅｔｔｅｒｉｎｔｅｒｍｓｏｆｂｉｔｅｒｒｏｒｒａｔｅａｎｄｎｅｔｗｏｒｋｌｏｓｓｔｈａｎｔｈｅｔｒａｄｉｔｉｏｎａｌｔｅｃｈｎｉｑｕｅｏｆＭａｘｉｍｕｍＬｉｋｅｌｉｈｏｏｄＤｅｔｅｃｔｉｏｎ（ＭＬＤ）ａｌｇｏｒｉｔｈｍａｎｄｔｈｅｃｕｒｒｅｎｔｔｅｃｈｎｉｑｕｅｏｆＩｎｄｅｘＭｏｄｕｌａｔｉｏｎｉｎＤｅｅｐＮｅｕｒａｌＮｅｔｗｏｒｋ（ＩＭ ＤＮＮ）ａｌｇｏｒｉｔｈｍ．Ｔｈｅｐｅｒｆｏｒｍａｎｃｅｉｍｐｒｏｖｅｍｅｎｔｉｓ０．６～８ｄＢｉｎｔｈｅｔｈｒｅｅｍｏｄｕｌａｔｉｏｎｍｏｄｅｓ．Ｋｅｙｗｏｒｄｓ：ＯＦＤＭ；ｉｎｄｅｘｍｏｄｕｌａｔｉｏｎ；ｓｉｇｎａｌｄｅｔｅｃｔｉｏｎ；ｓｅｌｆｎｏｒｍａｌｉｚｉｎｇｎｅｔｗｏｒｋ；ｄｅｅｐｌｅａｒｎｉｎｇ收稿日期：２０２３－０１－１６基金项目：国家自然科学基金（１２００５１０８）；山东省自然科学基金（ＺＲ２０２０ＱＦ０１６）ＦｏｕｎｄａｔｉｏｎＩｔｅｍ：ＮａｔｉｏｎａｌＮａｔｕｒａｌＳｃｉｅｎｃｅＦｏｕｎｄａｔｉｏｎｏｆＣｈｉｎａ（１２００５１０８）；ＳｈａｎｄｏｎｇＰｒｏｖｉｎｃｉａｌＮａｔｕｒａｌＳｃｉｅｎｃｅＦｏｕｎｄａｔｉｏｎｏｆＣｈｉｎａ（ＺＲ２０２０ＱＦ０１６）０　引言正交频分复用（ＯｒｔｈｏｇｏｎａｌＦｒｅｑｕｅｎｃｙＤｉｖｉｓｉｏｎＭｕｌｔｉｐｌｅｘｉｎｇ，ＯＦＤＭ）［１］技术已广泛应用于无线通信领域，其衍生技术也正在发展。