  Antenna selection (AS) is regarded as the key promising technology to reduce
hardware cost but keep relatively high spectral efficiency in multi-antenna
systems. By selecting a subset of antennas to transceive messages, AS greatly
alleviates the requirement on Radio Frequency (RF) chains. This paper studies
receive antenna selection in massive multiple-input multiple-output (MIMO)
systems. The receiver, equipped with a large-scale antenna array whose size is
much larger than that of the transmitter, selects a subset of antennas to
receive messages. A low-complexity asymptotic approximated upper capacity bound
is derived in the limit of massive MIMO systems over independent and identical
distributed (i.i.d.) Rayleigh flat fading channel, assuming that the channel
side information (CSI) is only available at the receiver. Furthermore,
numerical simulations are provided to demonstrate the approximation precision
of the asymptotic results and the tightness of the capacity bound. Besides the
asymptotic analysis of the upper bound, more discussions on the ergodic
capacity of the antenna selection systems are exhibited. By defining the number
of corresponding rows in the channel matrix as the amount of acquired CSI, the
relationship between the achievable channel capacity and the amount of acquired
CSI is investigated. Our findings indicate that this relationship approximately
follows the Pareto principle, i.e., most of the capacity can be achieved by
acquiring a small portion of full CSI. Finally, on the basis of this observed
law, an adaptive AS algorithm is proposed, which can achieve most of the
transmission rate but requires much less CSI and computation complexity
compared to state-of-the-art methods.
