SBIR Phase I: Non-Eigen Decomposition Beamforming for Smart Antenna Systems

SBIR 第一阶段：智能天线系统的非本征分解波束形成

• 批准号: 0810790
• 负责人: Garret Okamoto
• 金额: $ 10万
• 依托单位: Adaptive Communications Research Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-07-01 至 2008-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0810790&HistoricalAwards=false
• 关键词: SBIR; Phase; Non; Eigen; Decomposition

This Small Business Innovation Research (SBIR) Phase I project proposed the development and evaluation of a new class of adaptive interference mitigation techniques via smart antenna beamforming algorithms. Current blind (no user or interference information required) beamforming algorithms require computational complexity too high for many target applications; consequently, the proposed work focuses on a promising new technique for blind beamforming that does not rely on the eigenvalues and eigenvectors utilized by standard algorithms. Current blind interference mitigation research focuses on incrementally improving previous techniques fundamentally limited by unnecessary assumptions and their basis in Eigen Decomposition techniques. This new category of Non-Eigen Decomposition beamforming techniques achieves comparable performance (approaching theoretical maximums for SINR gain) to conventional blind algorithms in nulling interference sources while reducing computational requirements by an order of magnitude or more (order M instead of M2 or M3, where M is the number of antennas). Unlike most conventional techniques, the beamforming weight for this new technique does not require the weight at the previous snapshot because it is only a function of the cross correlation vector and initial guess. When the array autocorrelation matrix is known, the optimal solution is found with zero transition time, resulting in fast convergence and excellent tracking ability. If successful this SBIR Phase I project will have a significant impact on commercial applications and will foster a new field of scientific and technological understanding. By significantly reducing computational requirements for blind beamforming algorithms this work will make it feasible for low-cost commercial applications to eliminate co-channel interference signals despite limited computational resources. Current blind beamforming algorithms cannot be used in many applications due to their heavy computational loads and nonblind algorithms require significant overhead to obtain spatial information for the user and interference sources. If feedback is also required for non-blind beamforming techniques then significant throughput and bandwidth are wasted. Creation of a new class of adaptive blind interference mitigation techniques for smart antenna systems will enhance scientific and technological understanding. Published works over the past decade made incremental advances in blind beamforming algorithms, but those techniques are based on past works and do not have the potential for revolutionary improvements in this research area. Academia and Industry researchers will be able to evaluate the simulations and over-the-air measurement results from this work and adapt these algorithms for their purposes.

这个小企业创新研究（SBIR）第一阶段项目提出了一个新的类的自适应干扰缓解技术，通过智能天线波束形成算法的开发和评估。目前盲（没有用户或干扰信息的要求）波束形成算法需要计算复杂度太高，许多目标应用程序，因此，所提出的工作集中在一个有前途的新技术盲波束形成，不依赖于标准算法所利用的特征值和特征向量。目前的盲干扰抑制研究主要集中在逐步改进以前的技术，从根本上限制了不必要的假设和他们的基础，在本征分解技术。这种新类别的非本征分解波束成形技术在使干扰源归零方面实现了与传统盲算法相当的性能（接近SINR增益的理论最大值），同时将计算要求降低了一个数量级或更多（阶数M而不是M2或M3，其中M是天线的数量）。与大多数传统技术不同，这种新技术的波束成形权重不需要先前快照处的权重，因为它仅是互相关向量和初始猜测的函数。在已知阵列自相关矩阵的情况下，以零过渡时间求出最优解，收敛速度快，跟踪能力强。如果成功，SBIR第一阶段项目将对商业应用产生重大影响，并将促进科学和技术理解的新领域。通过显着降低盲波束形成算法的计算要求，这项工作将使其可行的低成本的商业应用，以消除同信道干扰信号，尽管有限的计算资源。当前的盲波束形成算法由于计算量大而无法在许多应用中使用，而非盲算法需要大量开销来获取用户和干扰源的空间信息。如果非盲波束成形技术也需要反馈，则浪费了大量的吞吐量和带宽。为智能天线系统创建一类新的自适应盲干扰抑制技术将提高科学和技术的理解。在过去的十年中发表的作品在盲波束形成算法方面取得了渐进的进步，但这些技术是基于过去的作品，并没有在这一研究领域进行革命性改进的潜力。学术界和工业界的研究人员将能够评估这项工作的模拟和空中测量结果，并根据其目的调整这些算法。