Abstract

This letter studies the multiple-input single-output (MISO) non-orthogonal multiple-access (NOMA) downlink using regularized zero-forcing (RZF) precoding with imperfect channel state information (CSI). We first propose a new user scheduling scheme based on imperfect CSI and a model to characterize the channel correlation between the weak and strong users. Then we derive an approximate expression of the ergodic sum-rate using large-system random matrix theory. This approximation permits us to derive the optimal power allocation scheme that satisfies the rate requirement of the weak users. Simulation results are presented to confirm the accuracy of the approximation and reveal the relationships between the ergodic sum-rate, the channel correlation, and other system parameters.

Highlights

  • Non-orthogonal multiple-access (NOMA) is regarded as the radio access technique to address the massive connectivity problem for Internet of Things (IoT) [1, 2]

  • We show the significance of this correlation and reveal that for high correlation between the channels, NOMA can achieve a higher gain than orthogonal multiple access (OMA); otherwise, OMA is better

  • We see that when the correlation between the channels of the weak and strong users θk increases, the ergodic sum-rate of NOMA increases while αopt decreases monotonically

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Summary

INTRODUCTION

User clustering, scheduling, power allocation, and multiuser beamforming are among the methods that can further improve the sum-rate and energy efficiency of the multiple-input singleoutput (MISO)-NOMA downlink [4, 5]. In [4], the authors provided a user clustering method based on the correlation between the user channels of the MISO-NOMA system. We consider a downlink MISO-NOMA system with imperfect CSI using RZF precoding and aim to obtain a closedform expression of the optimal power allocation by maximizing an approximate expression of the achievable ergodic sumrate. By proposing a model to characterize the channel correlation, we derive a large-system approximate expression for the ergodic sum-rate This approximation permits us to obtain a closed-form expression for the optimal power allocation, which depends only on the statistical CSI. The simulation results reveal that the optimal regularization scalar monotonically increases with the channel correlation, does not depend on the rate requirement of the weak user, and monotonically increases with the amount of channel uncertainty

System Model
Problem Formulation
Large System Analysis
NUMERICAL RESULTS
CONCLUSION
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