About Adaptive Communications Research
Adaptive Communications Research is a company that received a SBIR Phase I grant for a project entitled: Reconfigurable Sparse Array Smart Antenna System via Multi-Robot Control. Their project develops and evaluates a flexible sparse array smart antenna system that can be reconfigured through the use of multiple mobile robots. Current robotic systems are limited because they cannot utilize beamforming due to their limited number of antennas and the high computational requirement of beamformers. This pioneering research is made possible through recent breakthroughs for ultralow computational complexity beamforming and multi-mobile robot cluster control. Unlike current beamformers, the antennas in the sparse array will not be physically connected together but instead each robot will have a single antenna. By developing new signal processing and robotic control techniques, robotic communications will be enabled where impossible today due to range, dead spots, or interference. Over-the-air measurements will make it possible to finally evaluate how key issues (distance between robots, geometric shape of the sparse array, etc.) affects system performance. The broader impact/commercial potential of this project is that it can revolutionize commercial robotic systems and other applications in the wireless industry. Enabling multi-robot collaborative communications makes reliable communications possible in worst-case environments. Performance evaluation of sparse arrays will provide valuable insight for collaborative communications for other applications such as distributed sensor networks while the beamformer's ultralow computational requirement makes it feasible to be added to current and future wireless systems. Creation of a new class of robotic communications will enable robots to be more effective in current applications and create new markets for the robotic sector. The use of robots has increased exponentially with robots increasingly relied upon for defense, law enforcement, and manufacturing, but communication limitations prevent robots from being effective in many situations. Preventing this critical loss of communications for robots searching for people trapped in collapsed buildings or while on scout missions can save lives and have a great societal impact. This research will foster new fields of scientific and technological understanding by enabling Academia and Industry researchers to evaluate the advances made through this pioneering research, which will enable performance optimization for smart antenna systems whether the antennas are physically connected or at different locations. Adaptive Communications Research is a company that received a SBIR Phase I grant for a project entitled: Non-Eigen Decomposition Beamforming for Smart Antenna Systems. Their 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 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.