Optimal Non-coherent Data Detection for Massive SIMO Wireless Systems with General Constellations: A Polynomial Complexity Solution

Massive MIMO systems can greatly increase spectral and energy efficiency over traditional MIMO systems by exploiting large antenna arrays. However, increasing the number of antennas at the base station (BS) makes the uplink non-coherent data detection very challenging in massive MIMO systems. In thi...

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Bibliographic Details
Published in:arXiv.org 2015-07
Main Authors: Haider Ali Jasim Alshamary, Md Fahim Anjum, Al-Naffouri, Tareq, Alam Zaib, Xu, Weiyu
Format: Article
Language:English
Subjects:
Algorithms
Antenna arrays
Antennas
Coherence
Complexity
Computation
Computer simulation
Constellations
Maximum likelihood estimation
MIMO (control systems)
Polynomials
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Summary:Massive MIMO systems can greatly increase spectral and energy efficiency over traditional MIMO systems by exploiting large antenna arrays. However, increasing the number of antennas at the base station (BS) makes the uplink non-coherent data detection very challenging in massive MIMO systems. In this paper we consider the joint maximum likelihood (ML) channel estimation and data detection problem for massive SIMO (single input multiple output) wireless systems, which is a special case of wireless systems with large antenna arrays. We propose exact ML non-coherent data detection algorithms for both constant-modulus and nonconstant-modulus constellations, with a low expected complexity. Despite the large number of unknown channel coefficients for massive SIMO systems, we show that the expected computational complexity of these algorithms is linear in the number of receive antennas and polynomial in channel coherence time. Simulation results show the performance gains (up to 5 dB improvement) of the optimal non-coherent data detection with a low computational complexity.
ISSN:2331-8422