CIF: Small: Low Complexity Massive MIMO Systems: Synergistic use of Array Geometry, Modeling and Learning

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The project addresses the challenges of next-generation wireless communication systems. A key enabling technology for reliable and high-data-rate communication is the deployment of Multiple Input Multiple Output (MIMO) systems, which consist of multiple antennas for transmission and reception. With the use of the millimeter-wave (mmWave) frequencies in next-generation systems, the shorter wavelength enables deployment of many antennas in a small physical area, leading to massive MIMO systems. Massive MIMO systems, however, tend to have high complexity, high power consumption and high cost. This project seeks to do more with less: “Less” refers to limited hardware (fewer radio-frequency chains, one-bit analog to digital converters, etc.) and “more” to being able to extract the benefits (with minimal degradation) of massive MIMO systems by working around these hardware limitations. To do more with less, the project adopts a synergistic approach where innovations in system architecture and algorithms (model-based and data-driven) complement each other via judicious exploitation of structure (antenna array geometry and modeling) aided by powerful inference frameworks (sparse Bayesian learning and machine-learning techniques). The project will lead to state-of-the-art wireless communication systems that should help with maintaining US leadership in this important technology as well to train the next generation of researchers in this area of strategic importance.To develop low-complexity, low-cost, next-generation mmWave massive MIMO systems, this project has two major components. One is to harness antenna array geometry, both for one-dimensional and two-dimensional arrays, for rich channel sensing with fewer sensors complemented by robust inference. A key aspect of this work is embedding a nested array into a massive MIMO architecture employing fewer radio-frequency chains. The rich sensing capability of the nested array is being maximally exploited using the sparse Bayesian learning method. The channel sensing is also complemented with enhanced channel models incorporating variable and unknown angular spreads. A further component is the use of learning through deep neural networks to compensate for nonlinearities introduced to reduce power and cost. Models complemented by learning as well as fully data-driven techniques are being developed that address the specific and unique needs of wireless systems, such as variable numbers of users and channel coherence.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.