自组织协同网络信道感知接入统计决策优化方法

Optimal statistical decision strategies for channel sensing and access have been considered as crucial research challenges in ad-hoc cooperative communication networks. This research project targets in attentions on the statistical modeling for channel sensing and access process, and optimal statistical decision strategy as well as interaction and impact analysis on network performance in both theoretical and simulation manners. With in-depth study on processes of multi-user channel contention, optional channel sensing, adaptively cooperative transmission and opportunistic channel access, statistical models are formulated for channel sensing and access in ad-hoc cooperative network. New models are further established based on the quality of service and non-saturation of traffic queues. Based on these models, optimal statistical decision strategies are first derived which maximize average network throughput. Then optimal statistical decision strategies under constrained performance are proposed which satisfy delay guarantee requirements. Traffic back-pressure statistical decision strategies are also derived addressing traffic non-saturation in realistic networks. In general, this research will outbreak key techniques including statistical decision optimizations and its associated constrained optimization for sampling procedures based on multi-level multiple stochastic processes as well as distributed stochastic optimization. Under these benefits, the research challenge can be solved and average network throughput be maximized for distributed channel sensing and access in ad-hoc cooperative communication networks with different traffic priories, QoS and queues backlogs. The studies can also provide guidance and technical supports for channel sensing and access in ad-hoc networks.

自组织协同网络信道感知接入统计决策优化方法是当前自组织网络跨层研究领域的关键问题。本项目理论分析和仿真相结合，主要开展分布式信道感知接入统计学模型、统计决策优化方法和网络性能影响分析研究。深入探讨多用户竞争、信道选择感知、自适应协同、信道机会接入等相互作用机制，研究建立自组织协同网络信道感知接入统计学模型，构建面向业务等级和队列非饱和特性的统计学模型；提出网络吞吐量最优的统计决策方法和时延敏感度质量约束的统计决策优化方法，建立面向队列非饱和特性的统计决策优化方法；突破多层级多随机过程采样序列的统计决策优化、统计约束耦合的统计决策优化、分布式随机优化等关键技术，解决不同业务优先级、服务质量要求和队列运行实际的自组织协同网络分布信道感知接入难题，实现网络统计吞吐量最大化。通过上述研究，为自组织协同网络信道感知接入提供理论指导和技术支持。

本项目理论分析和仿真相结合，主要开展分布式信道感知接入统计学模型、统计决策优化方法和网络性能影响分析研究。深入探讨多用户竞争、信道选择感知、自适应协同、信道机会接入等相互作用机制，研究建立自组织协同网络信道感知接入统计学模型，构建面向业务等级和队列非饱和特性的统计学模型；提出网络吞吐量最优的统计决策方法和时延敏感度质量约束的统计决策优化方法，建立面向队列非饱和特性的统计决策优化方法；突破多层级多随机过程采样序列的统计决策优化、统计约束耦合的统计决策优化、分布式随机优化等关键技术，解决不同业务优先级、服务质量要求和队列运行实际的自组织协同网络分布信道感知接入难题，实现网络统计吞吐量最大化。通过上述研究，为自组织协同网络信道感知接入提供理论指导和技术支持。模型与方法研究基础上，设计了信道接入协议，建立了无人集群网络协议栈原型系统，验证了无人集群网络的吞吐量等重要性能。