马里兰大学的新书。主要作者 K. J. R. Liu, A. K. Sadek, W. Su, A. Kwasinski; Presenting the fundamental principles of cooperative communications and networking, this book treats the concepts of space, time, frequency diversity, and MIMO, with a holistic approach to principal topics where significant improvements can be obtained. Beginning with background and MIMO systems, Part I includes a review of basic principles of wireless communications, space–time diversity and coding, and broadband space–time–frequency diversity and coding. Part II then goes on to present topics on physical layer cooperative communications, such as relay channels and protocols, performance bounds, optimum power control, multi-node cooperation, distributed space–time and space–frequency coding, relay selection, differential cooperative transmission, and energy efficiency. Finally, Part III focuses on cooperative networking including cooperative and content–aware multiple access, distributed routing, source–channel coding, source–channel diversity, coverage expansion, broadband cooperative communications, and network lifetime maximization.

Non-orthogonal multiple access (NOMA), an emerging technology to improve system capacity and spectrum efficiency, has attracted significant attention. In this study, the authors propose a NOMA-based downlink cooperative cellular system, where the base station communicates with two paired mobile users simultaneously through the help of a half-duplex amplify-and-forward relay. The outage performance of the system is investigated and closed-form expressions for their respective exact and asymptotic outage scheme are derived. Furthermore, they study the ergodic sum rate of the two paired users and the upper bound of the ergodic sum rate is obtained. By comparing the NOMA with conventional multiple access (MA) via numerical simulations, they have shown that NOMA can obtain the same diversity order with conventional MA, and achieve nearly the same sum rate with conventional MA. Furthermore, NOMA can offer better spectral efficiency and user fairness since more users are served at the same time, frequency, and spreading code.

针对空中群的作战任务分配问题，首先对战场环境进行了假设。 然后分别分析了战场的两个多属性主体，群飞机和攻击目标。第二，在综合考虑隐身和反隐身，攻击和反击的基础上，多车通过分析SA承担侦察，攻击和评估任务的成本和收益，建立了目标功能。 然后，在考虑了弹药限度等约束条件的基础上，建立了具有多目标，多任务，。多约束，异构多飞机特性的任务分配模型。为了更好地传达模型信息，提出了一种新的整数编码方法，提出了一种整数编码的狼群算法（ICWPA）来求解任务分配模型。 仿真结果表明，该模型和算法可以有效地解决空中群的作战任务分配问题。

J.A.Fax教授的博士毕业论文 optimal and cooperative control of vehicle formation

博弈论书籍 Cooperative.Stochastic.Differential.Games David.W.K.Yeung pdf

本文讨论了合作博弈论如何在实际情况中发挥作用的问题。 首先对实践中出现的共享问题进行了调查，这些问题也有助于发展或支持合作博弈论。 然后讨论了一些集装箱运输和电信领域的合作案例，其中涉及蒂尔堡大学的博弈论者。

合作发展 合作协同进化 该算法出现在Potter M.和De Jong K.的“一种用于功能优化的协作式协同进化方法”中，《从自然中解决并行问题》，第1页。 249-257，1994年。 共有五种功能：RASTRIGIN，SCHWEFEL，GRIEWANGK，ACKLEY和ROSENBROCK 编译：gcc合作伙伴_coevolution.c -lm -o合作伙伴_coevolution 演示：./cooperative_coevolution RASTRIGIN

matlab代码粒子群算法合作PSO-LA 基于学习自动机（CPSOLA）算法和Matlab的协同粒子群优化算法的Matlab代码。 抽象的 本文提出了一种基于群体协同行为和自动机学习能力的粒子群优化（PSO）技术。 这种方法称为基于学习自动机的合作粒子群优化（CPSOLA）。 CPSOLA算法使用三层协作：群内，群内和群间。 CPSOLA中有两个活跃的种群。 在主要种群中，粒子被放置在所有群体中，每个群体都包含搜索空间的多个维度。 此外，CPSOLA中还有一个二级人口，使用的是常规PSO的更新格式。 在合作的上层，嵌入式学习自动机（LA）负责决定是否在人群之间进行合作。 在五个基准功能上组织了实验，结果显示了CPSOLA的显着性能和鲁棒性，群体的协作行为以及成功的种群自适应控制。 参考 [1] Mohammad Hasanzadeh，Mohammad Reza Meybodi和Mohammad Mehdi Ebadzadeh，“”，在2012年第20届伊朗电气工程大会（ICEE）上，2012年，第656至661页。 [2] Mohammad Hasanzadeh，Mohammad R

Cooperative Collision Avoidance at Intersections.pdf

Ren Wei的协同控制导论，值得一读。