| Authors |
Haiyang Zhang, Eliya Reznitskiy, Nimrod Glazer, Weizmann Institute of Science, Israel; Nir Shlezinger, Ben-Gurion University of the Negev, Israel; Moshe Namer, Israel Institute of Technology, Israel; Yonina C. Eldar, Weizmann Institute of Science, Israel |
| Abstract |
In this demo, we present a task-based quantization hardware built for providing an accurate representation of continuous-amplitude signals in a wireless communication application. Specifically, the channel estimation in massive MIMO systems is estimated by applying the developed task-based low-bit quantization board. Quantization plays a critical role in digital signal processing systems, allowing the representation of a continuous-amplitude signal using a finite number of bits. While for high-dimensional input signals such as the signals in massive MIMO systems, accurately representing these types of signals requires huge quantization bits, which will cause severe cost, power consumption, and memory burden. To deal with this challenge, we recently proposed a task-based quantization approach that guarantees the recovery performance of high-dimensional signals with low-bit representation, by accounting for the system task in the design of the quantizer [1]-[2]. In this demo, we design configurable quantization hardware built where the quantization bits are dynamically adjustable. The developed hardware platform is applied to the implementation channel estimation in massive MIMO systems. Our demonstration platform consists of a 16x4 analog combiner and a configurable quantizer, including 2, 3, 4 & 12 bits quantizer. By using a dedicated GUI, our demo will show that the nearly optimal performance of channel estimation can be achieved with a low-bit quantizer by accounting for the task. References: • [1] N. Shlezinger, Y. C. Eldar, and M. R. Rodrigues, “Hardware-limited task-based quantization,” IEEE Trans. Signal Process., vol. 67, no. 20, pp. 5223–5238, 2019 • [2] N. Shlezinger, Y. C. Eldar, and M. R. Rodrigues, “Asymptotic task-based quantization with application to massive MIMO." IEEE Trans. Signal Process., vol. 67, no. 15, pp. 3995-4012, 2019 |
