Interference Management and the Capacity of Wireless Networks

项目摘要：干扰管理和无线网络的容量 干扰被认为是在无线网络中实现更高数据速率的主要障碍。最近出现的干扰对齐思想表明，无线网络的容量可以显着高于先前的估计。关键思想是巧妙地设计信号，使其占据一半带宽，从而使它们在构成干扰的每个接收器上投射重叠的阴影，同时在需要它们的接收器上保持清晰。令人惊讶的结论是“每个人都获得一半的蛋糕”，即无论有多少用户竞争相同的无线频谱，每个用户都可能能够访问一半的带宽，而不会受到其他用户的干扰。这项研究将干扰对齐观点整合到现有的无线网络容量结果中，并利用集体见解、工具和技术来开发最佳干扰管理算法。这项研究得出的干扰管理综合观点对无线网络设计的理论和实践都有重大贡献。它提供的分析见解不仅对于估计无线网络的性能限制至关重要，而且对于寻找解决这些限制的有效方法也至关重要。了解无线网络的功能对于行业、学术界、政府机构和整个社会对未来无线网络抱有现实的期望至关重要。研究人员采用了具有以下三个主旨的分层方法：(1) 基本场景的容量表征，(2) 不同类别网络的广义自由度表征，以及 (3) 大型网络的分布式干扰管理算法。在微观层面上，该研究探索了基本网络参数化连续体的容量，该连续体捕获了干扰对齐容量最优的场景与经典强干扰、极强干扰和噪声干扰情况之间的过渡机制。在粗略但更大的范围内，容量近似值以无线网络的广义自由度特征的形式获得，包括中继、反馈和协作的影响。第三阶段从宏观角度看待无线网络，将从容量和自由度角度获得的见解结合到分布式干扰管理算法中。还研究了网络管理算法的稳定性及其对信道不确定性的鲁棒性。

Project Summary: Interference Management and the Capacity of Wireless NetworksInterference is believed to be the principal barrier to achieving higher data rates in wireless networks. The recent emergence of the idea of interference alignment has shown that the capacity of wireless networks can be significantly higher than prior estimates. The key idea is to design signals cleverly to occupy half the bandwidth in such a manner that they cast overlapping shadows at each receiver where they constitute interference, while they remain distinct at the receivers where they are desired. The surprising conclusion is that "everyone gets half the cake", i.e., regardless of the number of users competing for the same wireless spectrum, potentially every user is able to access half the bandwidth with no interference from other users. This research integrates the interference alignment perspective into the existing array of wireless network capacity results and uses the collective insights, tools and techniques to develop optimal interference management algorithms. The integrated view of interference management resulting from this research is a significant contribution to both the theory and practice of wireless network design. It contributes analytical insights that are pivotal in not only estimating the performance limits of wireless networks but also in finding efficient ways to approach these limits. Understanding the capabilities of wireless networks is essential for the industry, the academia, the government agencies and the society in general to have realistic expectations from the wireless networks of the future.The investigators adopt a layered approach with the following three thrusts - (1) Capacity characterizations for elemental scenarios, (2) Generalized degrees of freedom characterizations for different classes of networks, and (3) Distributed interference management algorithms for large networks. At the microscopic level, the research explores the capacity of a parameterized continuum of elemental networks that capture the transitional regime between scenarios where interference alignment is capacity optimal and the classical strong interference, very strong interference and noisy interference cases. On a coarse but larger scale, capacity approximations are obtained in the form of generalized degrees of freedom characterizations for wireless networks, including the impact of relays, feedback and cooperation. The third stage takes a macroscopic view of wireless networks and combines the insights obtained from the capacity and degrees of freedom perspectives into distributed interference management algorithms. The stability of network management algorithms and their robustness to channel uncertainty is also investigated.