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

该小型企业创新研究（SBIR）第一阶段项目提出了通过智能天线波束形成算法开发和评估一类新的自适应干扰缓解技术。目前的盲波束形成算法（不需要用户或干扰信息）对于许多目标应用来说，计算复杂度太高；因此，提出的工作重点是一种有前途的盲波束形成新技术，该技术不依赖于标准算法所使用的特征值和特征向量。目前的盲干扰缓解研究主要集中在增量改进以前的技术，这些技术基本上受到不必要的假设的限制，它们的基础是特征分解技术。这种新型的非本征分解波束形成技术在消除干扰源方面达到了与传统盲算法相当的性能（接近SINR增益的理论最大值），同时将计算需求减少了一个数量级或更多（M阶而不是M2或M3，其中M是天线的数量）。与大多数传统技术不同，这种新技术的波束形成权重不需要前一个快照的权重，因为它只是相互关联向量和初始猜测的函数。在阵列自相关矩阵已知的情况下，以零过渡时间找到最优解，收敛速度快，跟踪能力强。如果成功，SBIR第一期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。通过显著降低盲波束形成算法的计算需求，这项工作将使低成本的商业应用在计算资源有限的情况下消除同信道干扰信号成为可能。目前的盲波束形成算法由于计算量大，而非盲波束形成算法在获取用户和干扰源的空间信息时需要很大的开销，因此不能用于许多应用。如果非盲波束形成技术也需要反馈，那么将浪费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缓解技术将增强科学和技术的理解。过去十年中发表的作品在盲波束形成算法方面取得了渐进式的进展，但这些技术是基于过去的工作，在该研究领域没有革命性改进的潜力。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，并根据他们的目的调整这些算法。

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.