Channel zooming for the mitigation of pilot contamination in massive MIMO

Abstract

Massive multiple‐input multiple‐output is an attractive technology that aims to ameliorate both the energy and the spectrum efficiencies of fifth‐generation cellular networks. The main problem that limits the performance of a massive multiple‐input multiple‐output system is pilot contamination, which happens due to the limited number of orthogonal pilot sequences. In this paper, we assume that each cell is assigned a set of orthogonal pilot sequences and that these same pilot sequences are reused in other cells. Consequently, the contamination comes only from users in other cells, so that it is less powerful than an active user in the cell of interest. We propose a novel iterative algorithm to ensure the mitigation of the pilot contamination effect. This algorithm, dubbed as the “channel zooming algorithm”, is able to zoom through iterations on the signal of the strongest user by taking advantage of the difference in space signatures and transmitted data sequences. Moreover, we derive an analytical expression of the bit error probability, resulting after a large number of iterations of the proposed algorithm. The analytical results developed in our work are confirmed by simulation results.

Performance analysis and comparison of approximate detectors with linear detectors in massive MIMO systems under imperfect channel state information

“…Meeting these requirements often means increasing bandwidth (spectrum) or densifying the network, which can result in higher latency, congestion, and hardware complexity. Fifth generation (5G) and beyond plan to cater to these needs by employing a third factor, that is, massive MIMO (M‐MIMO) technology 1,2 . M‐MIMO offers high spectral efficiency, less transmission power, link reliability, and high data rates 3,4 .…”

“…Fifth generation (5G) and beyond plan to cater to these needs by employing a third factor, that is, massive MIMO (M-MIMO) technology. 1,2 M-MIMO offers high spectral efficiency, less transmission power, link reliability, and high data rates. 3,4 With a large number of antennas at the base station (BS), M-MIMO achieves prominent performance advantages.…”

Spatial channel correlation for local scattering with linear MMSE‐based estimator and detector in multi‐cell large scale MU‐MIMO networks

“…However, the performance of LS-MU-MIMO has been analyzed in the literature for uncorrelated channel model. [5][6][7] In practice and due to irregularity in the propagation environment, higher power levels are received from some spatial directions more than others. This phenomenon is known as spatial correlation and has a significant influence on the performance of the wireless system.…”

“…These aspects provide insights into the system behavior and can be captured by different tractable models. However, the performance of LS‐MU‐MIMO has been analyzed in the literature for uncorrelated channel model 5‐7 …”