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Antenna selection (AS) is regarded as the key promising technology to reducehardware cost but keep relatively high spectral efficiency in multi-antennasystems. By selecting a subset of antennas to transceive messages, AS greatlyalleviates the requirement on Radio Frequency (RF) chains. This paper studiesreceive antenna selection in massive multiple-input multiple-output (MIMO)systems. The receiver, equipped with a large-scale antenna array whose size ismuch larger than that of the transmitter, selects a subset of antennas toreceive messages. A low-complexity asymptotic approximated upper capacity boundis derived in the limit of massive MIMO systems over independent and identicaldistributed (i.i.d.) Rayleigh flat fading channel, assuming that the channelside information (CSI) is only available at the receiver. Furthermore,numerical simulations are provided to demonstrate the approximation precisionof the asymptotic results and the tightness of the capacity bound. Besides theasymptotic analysis of the upper bound, more discussions on the ergodiccapacity of the antenna selection systems are exhibited. By defining the numberof corresponding rows in the channel matrix as the amount of acquired CSI, therelationship between the achievable channel capacity and the amount of acquiredCSI is investigated. Our findings indicate that this relationship approximatelyfollows the Pareto principle, i.e., most of the capacity can be achieved byacquiring a small portion of full CSI. Finally, on the basis of this observedlaw, an adaptive AS algorithm is proposed, which can achieve most of thetransmission rate but requires much less CSI and computation complexitycompared to state-of-the-art methods.

Massive MIMO Antenna Selection: Asymptotic Upper Capacity Bound and Partial CSI