Optimal Joint Channel Estimation and Data Detection for Massive SIMO Wireless Systems: A Polynomial Complexity Solution
Type
ArticleAuthors
Xu, WeiyuAlshamary, Haider Ali
Al-Naffouri, Tareq Y.
Zaib, Alam
KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionElectrical Engineering Program
Online Publication Date
2019-12-02Print Publication Date
2020-03Date
2019-12-02Abstract
By exploiting large antenna arrays, massive MIMO (multiple input multiple output) systems can greatly increase spectral and energy efficiency over traditional MIMO systems. However, increasing the number of antennas at the base station (BS) makes the uplink joint channel estimation and data detection (JED) challenging in massive MIMO systems. In this paper, we consider the JED 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 Generalized Likelihood Ratio Test (GLRT) optimal JED algorithms with low expected complexity, for both constant-modulus and nonconstant-modulus constellations. We show that, despite the large number of unknown channel coefficients, the expected computational complexity of these algorithms is polynomial in channel coherence time ( T ) and the number of receive antennas ( N ), even when the number of receive antennas grows polynomially in the channel coherence time ( N=O(T^{11}) suffices to guarantee an expected computational complexity cubic in T and linear in N ). Simulation results show that the GLRT-optimal JED algorithms achieve significant performance gains (up to 5 dB improvement in energy efficiency) with low computational complexity.Citation
Xu, W., Alshamary, H. A., Al-Naffouri, T., & Zaib, A. (2020). Optimal Joint Channel Estimation and Data Detection for Massive SIMO Wireless Systems: A Polynomial Complexity Solution. IEEE Transactions on Information Theory, 66(3), 1822–1844. doi:10.1109/tit.2019.2957084Publisher
Institute of Electrical and Electronics Engineers (IEEE)Journal
IEEE Transactions on Information TheoryDOI
10.1109/TIT.2019.295708410.1109/tit.2020.3004903