Distributed DNN Power Allocation in Cell-Free Massive MIMO

2018 52nd Asilomar Conference on Signals, Systems, and Computers, pp. 722–726

Abstract

This paper considers a cell-free massive MIMO (multiple-input multiple-output) system that consists of a large number of geographically distributed access points (APs) simultaneously serving multiple user equipments (UEs) on the same time-frequency resources via coherent joint transmission. The performance of the system is evaluated, with maximum ratio and regularized zero-forcing precoding, in terms of the achievable spectral efficiency (SE) under two optimization objectives for the downlink power allocation problem: sum-SE and proportional fairness. Aiming at a less computationally complex as well as a distributed scalable solution, we train a deep neural network (DNN) to perform approximately the same network-wide power allocation. Instead of training our DNN to mimic the actual optimization procedure, we use a heuristic power allocation based on large-scale fading parameters as the input to the DNN. The heuristic input provides better dynamic range while preserving the ratios among the DNN inputs. This allows the use of a simplified structure for the DNN while achieving higher SEs compared to the heuristic scheme.

Authors 4

1. Mahmoud Zaher

   KTH Royal Institute of Technology

   Affiliation as printed

   KTH Royal Institute of Technology,Department of Computer Science,Sweden

   Department of Computer Science, KTH Royal Institute of Technology, Sweden

2. Özlem Tuğfe Demir

   KTH Royal Institute of Technology

   Affiliation as printed

   KTH Royal Institute of Technology,Department of Computer Science,Sweden

   Department of Computer Science, KTH Royal Institute of Technology, Sweden

3. Emil Björnson

   KTH Royal Institute of Technology

   Affiliation as printed

   KTH Royal Institute of Technology,Department of Computer Science,Sweden

   Department of Computer Science, KTH Royal Institute of Technology, Sweden

4. Marina Petrova Aachen Mobile Communications and Computing (MCC)

   KTH Royal Institute of Technology · RWTH Aachen University

   Affiliation as printed

   KTH Royal Institute of Technology,Department of Computer Science,Sweden

   Department of Computer Science, KTH Royal Institute of Technology, Sweden

   Mobile Communications and Computing, RWTH Aachen University, Germany