The unceasing demands on wireless networks in support of increasingly bandwidth hungry applications is leading to a drastic change in the modern communication landscape. The need to support aggregate data transfer rate of tens or hundreds of Gigabits/sec (Gbps) has led to new approaches for efficient use of the spectrum using multi-antenna technology. Millimeter-wave (mm-wave) communications is an emerging technology for next-generation communication systems with abundant bandwidth. However, at these mm-wave frequencies, free-space transmission loss is vulnerable to blockage. As a result, large antenna arrays are required to combat severe propagation loss. Traditional multi-antenna arrays employing phase-shifting however leads to a problem of beam squinting that becomes worse as the antenna array size is increased. This problem is particularly evident in airborne communication systems, unmanned aerial vehicles, satellite and missiles comprising of hundreds and often thousands of antenna elements with gigahertz bandwidth.
Extremely large antenna arrays (LAA) comprising hundreds of antenna elements promise to provide unprecedented spatial resolutions that can not only enable many critical infrastructure technologies using millimeter-wave wireless communications but also usher in exciting concepts such as holographic surfaces for multi-user wireless communications, six-dimensional positioning for autonomous vehicles, high-speed communication links for deep-space planetary explorations, and automobile radars for detecting multiple objects. However, the signal processing at these large-scale arrays bring challenges of higher energy consumption and less accurate localization. Conventional phased array transceivers, which interface with the real-world signals, face several impediments in low-latency tracking and scaling due to highly complex signal processing and imperfect spatial filtering. Such imperfections result in drastic performance degradation endangering evolution of emerging wireless technologies. To overcome these fundamental challenges, this research seeks to use discrete-time delay-compensating techniques incorporating scalable time-based circuits and systems so that future LAAs can estimate direction-of-arrival precisely, cancel multiple interferences efficiently, and optimize the physical front-end transceivers autonomously. This research effort is integrated with the principal investigator’s educational career goal of enhancing high-school and undergraduate learning experience by increasing education, awareness and preparation of the students through active collaborations with national labs and industry.
Recent work by Washington State University researchers has identified sampled time-delay based discrete-time signal processing techniques to overcome beam-squint problems for large-scale arrays without using expensive discrete components (or mismatch-prone mm-wave time-delay implementations). The techniques we propose are broadly applied to many applications in the airborne communications and sensing industry and are particularly applicable to location sensing using large-array systems. The outcomes from this work should lead to a family of integrated circuit design tools that could be applied to other areas of aviation electronics including imaging, radar, coexistence and interference cancellation in other communication applications.
This project is supported by National Science Foundation award (#1705026/1944668 and NSF I/UCRC Center for Analog and Digital Integrated Circuits (CDADIC).
- Subhanshu Gupta, Washington State University (PI (#1944688)/Co-PI) – mmWave data converters
- Erfan Ghaderi, Ph.D. student, Washington State University
- Chase Puglisi, Ph.D. student, Washington State University
- Shrestha Bansal, Ph.D. student, Washington State University
- Qiuyan Xu, Ph.D. student, Washington State University
- Chung-Ching Lin, Ph.D., Washington State University
Goal 1: Development of a discrete-time delay-compensating spatial signal processor will be demonstrated with variable gain and delay ranges for near-field and far-field large-antenna arrays.
Goal 2: Instituting delay compensating technique in linear time-based matrix-multiplying data converters optimized using artificial-intelligence based self-initializing bias optimization techniques to demonstrate faster and energy-efficient convergence
Goal 3: Scalable system-level models for spatial arrays incorporating wide scan angles, high-speed signal bandwidth, large number of antenna elements, low-latency direction-of-arrival, and segmentation in true-time-delay arrays will be developed to study their effects on both spectral efficiency and energy efficiency for future LAAs.
- Erfan Ghaderi, Chase Puglisi, Shrestha Bansal, Subhanshu Gupta, “10.8 A 4-Element 500MHz-Modulated-BW 40mW 6b 1GS/s Analog-Time-to-Digital-Converter-Enabled Spatial Signal Processor in 65nm CMOS”, IEEE Intl. Solid-State Circuits Conf. (ISSCC), February 2020.
- Erfan Ghaderi, Ajith S. Ramani, Arya A. Rahimi, Deuk Heo, Sudip Shekhar and Subhanshu Gupta, “A 4-Element Wide Modulated Bandwidth MIMO Receiver with >35 dB Interference Cancellation,” under review.
- Erfan Ghaderi, Ajith S. Ramani, Arya A. Rahimi, Deuk Heo, Sudip Shekhar and Subhanshu Gupta, “An Integrated Discrete-Time Delay-Compensating Technique for Large-Array Beamformers,” IEEE Trans. on Circuits and Systems – I: Regular Papers, vol. 66, no. 9, pp. 3296-3306, Sept. 2019.