Spatially-Correlated Rician-Faded Multi-Relay Multi-Cell Massive MIMO NOMA Systems

We consider the downlink of a relay-aided multi-cell massive multi-input multi-output (mMIMO) system, where in each cell the base station (BS) serves its users via multiple relays by employing non-orthogonal multiple access (NOMA). We model this system by considering spatially-correlated Rician-fade...

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Bibliographic Details
Published in:IEEE transactions on communications 2022-08, Vol.70 (8), p.5317-5335
Main Authors: Amudala, Dheeraj Naidu, Kumar, Bibhor, Budhiraja, Rohit
Format: Article
Language:English
Subjects:
Algorithms
Block optimization
Channel estimation
Complexity
Downlink
energy efficiency
Interference cancellation
Lower bounds
massive MIMO
multiple relays
NOMA
Nonorthogonal multiple access
Optimization
Relay
Relays
Rician channels
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Summary:We consider the downlink of a relay-aided multi-cell massive multi-input multi-output (mMIMO) system, where in each cell the base station (BS) serves its users via multiple relays by employing non-orthogonal multiple access (NOMA). We model this system by considering spatially-correlated Rician-faded channels and their estimation errors. The users, consequently, perform imperfect successive interference cancellation (SIC). We derive a lower bound on the spectral efficiency (SE) of this system. We then optimize the non-convex global energy efficiency (GEE) metric, which is a fractional function of the optimization variables. We solve this problem by considering a low-complexity alternating minimization maximization approach, which splits a complex joint problem into multiple simpler convex surrogate sub-problems. We propose a novel surrogate function to exploit this framework, and analytically show that it satisfies the desirable properties of a valid surrogate function. We numerically show that 1) reusing the pilots in each cell, when the channel has sufficiently hardened, provides higher SE than using orthogonal pilots in all cells and 2) the proposed GEE algorithm provides similar GEE as that of an existing joint optimization framework, but with much less complexity.
ISSN:0090-6778
1558-0857
DOI:10.1109/TCOMM.2022.3180066