Limited-Fronthaul Cell-Free Massive MIMO With Local MMSE Receiver Under Rician Fading and Phase Shifts

Abstract

A cell-free Massive multiple-input multiple-output (MIMO) system is considered, where the access points (APs) are linked to a central processing unit (CPU) via the limited-capacity fronthaul links. It is assumed that only the quantized version of the weighted signals are available at the CPU. The achievable rate of a limited fronthaul cell-free massive MIMO with local minimum mean square error (MMSE) detection is studied. We study the assumption of uncorrelated quantization distortion, which is commonly used in literature. We show that this assumption will not affect the validity of the insights obtained in our work. To investigate this, we compare the uplink per-user rate with different system parameters for two different scenarios; 1) the exact uplink per-user rate and 2) the uplink per-user rate while ignoring the correlation between the inputs of the quantizers. Finally, we present the conditions which imply that the quantization distortions across APs can be assumed to be uncorrelated.

Cell-Free Massive MIMO-OFDM for High-Speed Train Communications

“…3 compares the SE of HST for CF massive MIMO-OFDM with MMSE combining under centralized processing and local processing, respectively. In addition, small cell systems are 6 We estimate the channel every time the train has travelled λ/5, which is frequent enough that the effect of channel aging can be ignored within each coherence block. Besides, Kalman filtering and/or machine learning can be used in the channel prediction for the channel aging reduction [28].…”

“…For example, [5] studied both fully centralized and local processing in CF massive MIMO systems with minimum mean square error (MMSE) combining schemes. Although fully centralized processing can achieve maximal performance in CF massive MIMO systems, the huge processing complexity causes a great burden on the CPU [6], [7]. Then, authors in [8] proposed the sequential processing algorithm that achieves the same performance as the optimal centralized implementation, while reducing the fronthaul requirement.…”

User Rate Optimization for Fronthaul-Constrained Cell-Free Massive MIMO-OFDM Systems: A Quadratic Transform-Based Approach

“…The performance of CF mMIMO systems with variableresolution quantization, i.e., each analog-to-digital converter at the APs and quantizer at the CPU use arbitrary bits for quantization, is investigated in [9]. The achievable rate of a FL cpapacity constrained CF mMIMO with local MMSE detection is studied in [10].…”

“…To maximize the weighted spectrum efficiency, a wideband hybrid beamforming optimization method for multiuser mMIMO systems based on OFDM was studied in [16], which utilized the Riemannian manifold algorithm for analog precoding and the weighted minimum mean square error (WMMSE) algorithm for digital precoding. In [18], the achievable rate of a limited fronthaul CF mMIMO with local MMSE detection is studied. In [17], a quadratic transform (QT) algorithm is proposed to optimize the downlink transmission and obtains better performance than WMMSE, wherein the maximum transmit power constraint of base station is considered.…”

“…In order to meet regulatory requirements, green communication needs, and limitations of the device capabilities, transmit power of the AP is often limited. In this paper, we assume that the transmit power limit of the mth AP can be expressed as (10) where p m denotes the maximum transmission power of the mth AP.…”