Efficient Recovery of dSLM in MIMO-OFDM without Side Information

Asseri, Mohammed I.; Goff, S.Y. Le; Tsimenidis, Charalampos C.; Sharif, B.S.

doi:10.1109/aict.2009.33

Cited by 1 publication

(1 citation statement)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…M ASSIVE multiple-input multiple-output (MIMO) systems have recently attracted immense interest in the field of wireless communications due to their ability to increase data throughput and improve link quality [1]- [4]. Orthogonal frequency-division multiplexing (OFDM) is a multicarrier technique with immunity to the channel's frequency selectivity, which can transmit data over large numbers of subcarriers rather than a single carrier transmission [5], [6]. The combination of these two techniques in the form of a massive MIMO-OFDM system is a key technology for next generation wireless communication systems due to its improved performance compared to conventional MIMO systems [1], [7].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of Coded Massive MIMO-OFDM Systems Using Effective Matrix Inversion

Al-Askery

Tsimenidis

Boussakta

et al. 2017

IEEE Trans. Commun.

Self Cite

View full text Add to dashboard Cite

In this paper, we derive the bit error rate (BER) and pairwise error probability (PEP) for massive multipleinput multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems for different M -ary modulations based upon the approximate noise distribution after channel equalization. The PEP is used to obtain the upper-bounds for convolutionally coded and turbo coded massive MIMO-OFDM systems for different code generators and receive antennas. In addition, complexity analysis of the log-likelihood ratio (LLR) values is performed using the approximate noise probability density function (PDF). The derived LLR computations can be timeconsuming when the number of receive antennas is very large in massive MIMO-OFDM systems. Thus, a reduced complexity approximation is introduced using Newton's interpolation with different polynomial orders and the results are compared with the exact simulations. The Neumann large matrix approximation is used to design the receiver for a zero-forcing equalizer (ZFE) by reducing the number of operations required in calculating the channel matrix inverse. Simulations are used to demonstrate that the results obtained using the derived equations match closely the Monte-Carlo simulations.

show abstract