Max–Min Rate of Cell-Free Massive MIMO Uplink With Optimal Uniform Quantization

Abstract

Cell-free Massive multiple-input multiple-output (MIMO) is considered, where distributed access points (APs) multiply the received signal by the conjugate of the estimated channel, and send back a quantized version of this weighted signal to a central processing unit (CPU). For the first time, we present a performance comparison between the case of perfect fronthaul links, the case when the quantized version of the estimated channel and the quantized signal are available at the CPU, and the case when only the quantized weighted signal is available at the CPU. The Bussgang decomposition is used to model the effect of quantization. The max-min problem is studied, where the minimum rate is maximized with the power and fronthaul capacity constraints. To deal with the non-convex problem, the original problem is decomposed into two sub-problems (referred to as receiver filter design and power allocation). Geometric programming (GP) is exploited to solve the power allocation problem whereas a generalized eigenvalue problem is solved to design the receiver filter. An iterative scheme is developed and the optimality of the proposed algorithm is proved through uplink-downlink duality. A user assignment algorithm is proposed which significantly improves the performance. Numerical results demonstrate the superiority of the proposed schemes.

Energy Efficiency of the Cell-Free Massive MIMO Uplink With Optimal Uniform Quantization

“…where a is a constant, n d refers to the distortion noise, z is the input of the quantizer [17], [24], [30]- [32]. The term a is given by a = E{zh(z)}…”

“…where p z = E{|z| 2 } = E{z 2 } denotes the power of z and we drop absolute value as z is a real number, and f z (z) represents the probability distribution function of z. We define the second [17], [24], [30]. We aim to maximize the signalto-distortion noise ratio (SDNR), which is defined as follows:…”

Cooperative Access Networks: Optimum Fronthaul Quantization in Distributed Massive MIMO and Cloud RAN - Invited Paper

“…In [12], the present authors propose to use a quantizer with a fixed step size to model the effect of the quantization. However, in [13], we extend our works in [12], [14]- [16] to a limited-fronthaul cell-free massive MIMO system using the Bussgang decomposition.…”

Channel Estimation in Massive MIMO Under Hardware Non-Linearities: Bayesian Methods Versus Deep Learning

“…The non-linear system behavior is often treated by utilizing the Bussgang decomposition to find an equivalent linear system with uncorrelated distortion [7], [14]- [17], [22], [23]. One can then derive a distortion-aware Bayesian LMMSE estimator that utilizes the first-and second-order distortion statistics to estimate the channels, but in doing so the distortion is treated as independent colored noise, although it depends on the channel.…”