MmWave Beamforming and Channel Estimation with 1-bit ADC
Millimeter wave (mmWave) is a technology that can provide high bandwidth communication links in cellular systems. As mmWave uses larger bandwidths, the corresponding sampling rate of the analog-to-digital converter (ADC) scales up. Unfortunately, high speed, high precision (e.g., 8-12 bits) ADCs are costly and power-hungry for portable devices. A possible solution is to use special ADC structures like a timeinterleaved ADC (TI-ADC) architecture where a number of low-speed, high-precision ADCs operate in parallel. The main challenge of the TI-ADC is the mismatch among the sub-ADCs in gain, timing and voltage offset which can cause error floors in receiver performance. An alternative solution is to live with ultra low precision ADCs (1-3 bits), which reduces power consumption and cost.
We investigated the capacity of multiple-input multiple-output (MIMO) system in which a one-bit ADC is used for each inphase and quadrature baseband received signal. The main advantage of this architecture is the ADCs can be implemented with very low power consumption. The architecture also simplifies the overall complexity of the circuit for example automatic gain control may not be required. We studied the channel capacity of the MIMO system with one-bit ADCs with channel state information at the transmitter.
We developed channel estimation algorithms for mmWave MIMO systems with one-bit ADCs. Since the mmWave MIMO channel is sparse due to the propagation characteristics, the estimation problem was formulated as a one-bit compressed sensing problem. We presented a solution using the generalized approximate message passing (GAMP) algorithm to solve this optimization problem.
J. Mo, and R. W. Heath, Jr., “High SNR Capacity of Millimeter Wave MIMO Systems with One-Bit Quantization,” Information Theory and Applications Workshop (ITA), San Diego, CA, Feb. 2014.
J. Mo, P. Schniter, N. G. Prelcic and R. W. Heath, Jr., “Channel Estimation in Millimeter Wave MIMO Systems with One-Bit Quantization,” 2014 Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, Nov. 2014.
The work was supported in part by the National Science Foundation under Grant Nos. NSF-CCF-1218338, NSF-CCF-1319556, NSF-CCF-1018368 and NSF-CCF-1218754.