Spectral and Energy Efficiency of Distributed Massive MIMO with Low-Resolution ADC

Abstract

In this paper, considering a more realistic case where the low-resolution analog-to-digital convertors (ADCs) are employed at receiver antennas, we investigate the spectral and energy efficiency in multi-cell multi-user distributed massive multi-input multi-output (MIMO) systems with two linear receivers. An additive quantization noise model is provided first to study the effects of quantization noise. Using the model provided, the closed-form expressions for the uplink achievable rates with a zero-forcing (ZF) receiver and a maximum ratio combination (MRC) receiver under quantization noise and pilot contamination are derived. Furthermore, the asymptotic achievable rates are also given when the number of quantization bits, the per user transmit power, and the number of antennas per remote antenna unit (RAU) go to infinity, respectively. Numerical results prove that the theoretical analysis is accurate and show that quantization noise degrades the performance in spectral efficiency, but the growth in the number of antennas can compensate for the degradation. Furthermore, low-resolution ADCs with 3 or 4 bits outperform perfect ADCs in energy efficiency. Numerical results imply that it is preferable to use low-resolution ADCs in distributed massive MIMO systems.

Spectral Efficiency Augmentation in Uplink Massive MIMO Systems by Increasing Transmit Power and Uniform Linear Array Gain

“…We have used horizontal-uniform linear array with antenna spacing from 0 to , wavelength at carrier frequency for LoS case, hence, the spacing of antennas can be calculated in meters as . All user locations are fixed that provides deterministic channel response as Equation ( 13 ) [ 36 , 37 ]. where is an azimuth angle to UE w.r.t the BS array bore sight in cell ranging from , and represents the large-scale fading coefficient.…”

“…We have used horizontal-uniform linear array with d H antenna spacing from 0 to 0.5, wavelength λ at carrier frequency for LoS case, hence, the spacing of antennas can be calculated in meters as λd H . All user locations are fixed that provides deterministic channel response (x i ) as Equation ( 13) [36,37].…”

Joint User Scheduling and Resource Allocation in Distributed MIMO Systems with Multi-Carriers

“…In the D-MIMO system, multiple RAUs are distributed widely in the coverage area and connected to the baseband processing unit (BPU) via backhauls. They cooperate to serve users by transmitting and receiving signals processed collaboratively at the central BPU, for which it can achieve higher spatial diversity/multiplexing gain [7][8][9][10].…”

Massive MIMO Systems: Present and Future

“…Especially, massive array beamforming has a good match to millimeter wave communication [6] which suffers from link budget shortfall due to its high frequency. Further, thanks to its excessive degree of freedom (DoF), massive MIMO has unlimited potentiality to further enhance system capabilities [7] and still expands various research topics with depth. It should be further discussed and believed to break limitations in wireless communications such as spectral and energy efficiencies for better support of continuously increasing mobile data traffic, as well as terminals driven by Internet of things (IoT).…”