Abstract
The increasing demand for higher data rates motivates the exploration of advanced techniques for future wireless networks. To this end, massive multiple-input multiple-output (mMIMO) is envisioned as the most essential technique to meet this demand. However, the expansion of the number of antennas in mMIMO systems with short coherence time makes the downlink channel estimation (DCE) overhead potentially overwhelming. As such, the number of training sequence (TS) needs to be significantly reduced. However, reducing the number of TS reduces the mean-squared error (MSE) accuracy significantly and to date it is not clear to what extend can this TS reduction affects the achievable sum rate performance. Therefore, this paper develops a low complexity and tractable TS solution for DCE and establishes an analytical framework for the optimum TS. Furthermore, the tradeoff between the achievable sum rate maximization criteria and the MSE minimization criteria is investigated. This investigation is essential to characterize the optimum TS length and the actual performance of mMIMO systems when the channel exhibits a limited coherence time. To this end, the statistical structure of mMIMO channels is exploited. In addition, this paper utilizes a random matrix theory (RMT) method to characterize the downlink achievable sum rate and MSE in a closed-form. This paper shows that maximizing the downlink sum rate criterion is more important than minimizing the MSE of the SINR only, which is typically considered in the conventional MIMO systems and/or in the time division duplex (TDD) mMIMO systems. The results demonstrate that a feasible downlink achievable sum rate can be achieved in an frequency division duplex (FDD) mMIMO system. This finding is necessary to extend the benefit of mMIMO systems to high frequency bands such as millimeter-wave (mmWave) and Terahertz (THZ) communications.
Highlights
Future wireless networks aim to maximize the data-rate to support the rapidly increasing demands for data traffic and meet the envision needs of the Internet of Things (IoT) and artificial intelligence (AI) applications [1], [2]
To capture a realistic performance assessment of an frequency division duplex (FDD) Massive multiple-input-multiple-output (mMIMO) system and to characterize the impact of spatial correlation on the DL achievable sum rate and mean-squared error (MSE), three different correlation models are considered in this paper
We present analytical and simulation results, which characterize the FDD operation in the mMIMO systems in terms of the MSE and the achievable sum rate for both BF and RZF precoding
Summary
Future wireless networks aim to maximize the data-rate to support the rapidly increasing demands for data traffic and meet the envision needs of the Internet of Things (IoT) and artificial intelligence (AI) applications [1], [2]. Obtaining a feasible solution for DCE based on downlink (DL) TS design with limited TS length is essential with limited coherence time To this end, the vast majority of research studies on mMIMO systems have focused on the time division duplex (TDD) operation mode. Characterizing the tradeoff between achievable sum rate maximization and the MSE minimization in the FDD mMIMO systems with limited coherence time is crucial. In order to design a feasible TS for DL FDD mMIMO system, the secondorder channel statistic is exploited and an objective function based on maximizing the achievable sum rate is used instead of minimizing the MSE of DCE. Other mathematical operators such as trace, transpose, Hermitian transpose and inverse are denoted by tr(·), (·)T, (·)H, and (·)−1, respectively
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
