Relationship Between Two Distortion Measures for Memoryless Nonlinear Systems

Cumanan

et al. 2019

IEEE Trans. Commun.

117

146

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.

Section: B=mentioning

confidence: 99%

“…Equations (2)-(4) come from[12] but we include them here for completeness, and to define the terms we used.…”

mentioning

confidence: 99%

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

Cumanan

et al. 2019

IEEE Trans. Commun.

117

146

“…, [9], [24]. We aim to maximize the signal-to-distortion noise ratio (SDNR), which is defined as follows: SDNR = E{(az) 2 }…”

Section: B Optimal Uniform Quantization Modelmentioning

confidence: 99%

“…At each AP, MRC weighting is performed. Using Bussgang's theorem [24], a nonlinear output can be represented as a linear function as follows:…”

Section: The Signal Received At the Cpumentioning

confidence: 99%

“…Proposition 1. Using Bussgang decomposition the elements of the quantization error are uncorrelated with the input of the quantizer [24], i.e., E ĝ H mk y m H n d,mk = 0. Hence, exploiting the analysis in [1], it can be shown that terms DS k .s k , BU k .s k , IUI kk ′ .s ′ k , TN k and TQE k are mutually uncorrelated.…”

Section: The Signal Received At the Cpumentioning

confidence: 99%

See 1 more Smart Citation

On the Energy Efficiency of Limited-Backhaul Cell-Free Massive MIMO

Cumanan

ICC 2019 - 2019 IEEE International Conference on Communications (ICC)

et al. 2019

We investigate the energy efficiency performance of cell-free Massive multiple-input multiple-output (MIMO), where the access points (APs) are connected to a central processing unit (CPU) via limited-capacity links. Thanks to the distributed maximum ratio combining (MRC) weighting at the APs, we propose that only the quantized version of the weighted signals are sent back to the CPU. Considering the effects of channel estimation errors and using the Bussgang theorem to model the quantization errors, an energy efficiency maximization problem is formulated with per-user power and backhaul capacity constraints as well as with throughput requirement constraints. To handle this non-convex optimization problem, we decompose the original problem into two sub-problems and exploit a successive convex approximation (SCA) to solve original energy efficiency maximization problem. Numerical results confirm the superiority of the proposed optimization scheme.

On the Performance of Backhaul Constrained Cell-Free Massive MIMO with Linear Receivers

Ngo

2018 52nd Asilomar Conference on Signals, Systems, and Computers

et al. 2018

Limited-backhaul cell-free Massive multiple-input multiple-output (MIMO), in which the fog radio access network (F-RAN) is implemented to exchange the information between access points (APs) and the central processing unit (CPU), is investigated. We introduce a novel approach where the APs estimate the channel and send back the quantized version of the estimated channel and the quantized version of the received signal to the central processing unit. The Max algorithm and the Bussgang theorem are exploited to model the optimum uniform quantization. The ergodic achievable rates are derived. We show that exploiting microwave wireless backhaul links and using a small number of bits to quantize the estimated channel and the received signal, the performance of limited-backhaul cell-free Massive MIMO closely approaches the performance of cell-free Massive MIMO with perfect backhaul links.