DeepAI AI Chat
Log In Sign Up

Cooperative Limited Feedback Design for Massive Machine-Type Communications

05/17/2018
by   Jiho Song, et al.
Incheon National University
Soongsil University
0

Multiuser multiple-input multiple-output (MIMO) systems have been in the spotlight since it is expected to support high connection density in internet of things (IoT) networks. Considering the massive connectivity in IoT networks, the challenge for the multiuser MIMO systems is to obtain accurate channel state information (CSI) at the transmitter in order that the sum-rate throughput can be maximized. However, current communication mechanisms relying upon frequency division duplexing (FDD) might not fully support massive number of machine-type devices due to the rate-constrained limited feedback and complicated time-consuming scheduling. In this paper, we develop a cooperative feedback strategy to maximize the benefits of massive connectivity under limited resource constraint for the feedback link. In the proposed algorithm, two neighboring users form a single cooperation unit to improve the channel quantization performance by sharing some level of channel information. To satisfy the low-latency requirement in IoT networks, the cooperation process is conducted without any transmitter intervention. In addition, we analyze the sum-rate throughput of the multiuser MIMO systems relying upon the proposed feedback strategy to study a cooperation decision-making framework. Based on the analytical studies, we develop a network-adapted cooperation algorithm to turn the user cooperation mode on and off according to network conditions.

READ FULL TEXT

page 1

page 2

page 3

page 4

12/13/2021

CSI Feedback with Model-Driven Deep Learning of Massive MIMO Systems

In order to achieve reliable communication with a high data rate of mass...
09/20/2020

A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback

Forward channel state information (CSI) often plays a vital role in sche...
09/28/2020

Recursive CSI Quantization of Time-Correlated MIMO Channels by Deep Learning Classification

In frequency division duplex (FDD) multiple-input multiple-output (MIMO)...
02/05/2022

Predicting Future CSI Feedback For Highly-Mobile Massive MIMO Systems

Massive multiple-input multiple-output (MIMO) system is promising in pro...
02/14/2019

A Covariance-Based Hybrid Channel Feedback in FDD Massive MIMO Systems

In this paper, a novel covariance-based channel feedback mechanism is in...
07/06/2018

Enabling Covariance-Based Feedback in Massive MIMO: A User Classification Approach

In this paper, we propose a novel channel feedback scheme for frequency ...
09/23/2018

Generalized Low-Rank Optimization for Topological Cooperation in Ultra-Dense Networks

Network densification is a natural way to support dense mobile applicati...