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adaptive-regularized-zero-forcing-beamforming-in-massive-mimo-with-multi-antenna-users

Modern wireless cellular networks use massive multiple-input multiple-output (MIMO) technology. This technology involves operations with an antenna array at a base station that simultaneously serves multiple mobile devices which also use multiple antennas on their side. For this, various precoding and detection techniques are used, allowing each user to receive the signal intended for him from the base station. There is an important class of linear precoding called Regularized Zero-Forcing (RZF). In this work, we propose Adaptive RZF (ARZF) with a special kind of regularization matrix with different coefficients for each layer of multi-antenna users. These regularization coefficients are defined by explicit formulas based on SVD decompositions of user channel matrices. We study the optimization problem, which is solved by the proposed algorithm, with the connection to other possible problem statements. We also compare the proposed algorithm with state-of-the-art linear precoding algorithms on simulations with the Quadriga channel model. The proposed approach provides a significant increase in quality with the same computation time as in the reference methods.

advanced-pandas

bayesian-collage

minimization of pair-separable energy based on graph cut

business-intelligence

diploma specializing in data analysis and visualization

carvana-segmentation

just small cnn machine vision project

contest-dota

some of my friends started to play after this

deep-feature-interpolation

A new data-driven baseline for automatic high-resolution image transformation

ldpc-codes

loopy belief propagation in error-correction coding

lectures-mmp

The most advantage lectures of the Forecasting Department

mmp_practicum_fall_2020

Лекции и материалы по курсу "Практикум на ЭВМ 2020/2021" осеннего семестра для студентов 3 курса кафедры ММП, ВМК МГУ

mmro-background-subtraction

The third practicum task of MSU ML course

molecules-biodegradability

advanced logistic regression

nitrogen-plants

analysis of variance as statistical provement

on-probabilistic-qam-shaping-for-5g-mimo-wireless-channel-with-realistic-ldpc-codes

On Probabilistic QAM Shaping for 5G MIMO Wireless Channel with Realistic LDPC Codes

power-allocation-algorithms-for-massive-mimo-systems-with-multi-antenna-users

Modern 5G wireless cellular networks use massive multiple-input multiple-output (MIMO) technology. This concept entails using an antenna array at a base station to concurrently service many mobile devices that have several antennas on their side. In this field, a significant role is played by the precoding (beamforming) problem. During downlink, an important part of precoding is the power allocation problem that distributes power between transmitted symbols. In this paper, we consider the power allocation problem for a class of precodings that asymptotically work as regularized zero-forcing. Under some realistic assumptions, we simplify the sum spectral efficiency functional and obtain tractable expressions for it. We prove that equal power allocation provides optimum for the simplified functional with total power constraint (TPC). Also, low-complexity algorithms that improve equal power allocation in the case of per-antenna power constraints (PAPC) are proposed. On simulations using Quadriga, the proposed algorithms show a significant gain in sum spectral efficiency while using a similar computing time as the reference solutions.

practical-rl

Based on https://github.com/yandexdataschool/Practical_RL

pzad2017

cmc msu course

sql-course

cmc msu course

vision-hack

artificial intelligence and computer vision international student hackathon