1 research outputs found
Massive MIMO Pilot Assignment Optimization based on Total Capacity
We investigate the effects of pilot assignment in multi-cell massive
multiple-input multiple-output systems. When deploying a large number of
antennas at base station (BS), and linear detection/precoding algorithms, the
system performance in both uplink (UL) and downlink (DL) is mainly limited by
pilot contamination. This interference is proper of each pilot, and thus system
performance can be improved by suitably assigning the pilot sequences to the
users within the cell, according to the desired metric. We show in this paper
that UL and DL performances constitute conflicting metrics, in such a way that
one cannot achieve the best performance in UL and DL with a single pilot
assignment configuration. Thus, we propose an alternative metric, namely total
capacity, aiming to simultaneously achieve a suitable performance in both
links. Since the PA problem is combinatorial, and the search space grows with
the number of pilots in a factorial fashion, we also propose a low complexity
suboptimal algorithm that achieves promising capacity performance avoiding the
exhaustive search. Besides, the combination of our proposed PA schemes with an
efficient power control algorithm unveils the great potential of the proposed
techniques in providing improved performance for a higher number of users. Our
numerical results demonstrate that with 64 BS antennas serving 10 users, our
proposed method can assure a 95%-likely rate of 4.2 Mbps for both DL and UL,
and a symmetric 95%-likely rate of 1.4 Mbps when serving 32 users