Adaptive antenna selection and Tx/Rx beamforming for large-scale MIMO systems in 60 GHz channels

Abstract

We consider a large-scale MIMO system operating in the 60 GHz band employing beamforming for high-speed data transmission. We assume that the number of RF chains is smaller than the number of antennas, which motivates the use of antenna selection to exploit the beamforming gain afforded by the large-scale antenna array. However, the system constraint that at the receiver, only a linear combination of the receive antenna outputs is available, which together with the large dimension of the MIMO system makes it challenging to devise an efficient antenna selection algorithm. By exploiting the strong line-of-sight property of the 60 GHz channels, we propose an iterative antenna selection algorithm based on discrete stochastic approximation that can quickly lock onto a nearoptimal antenna subset. Moreover, given a selected antenna subset, we propose an adaptive transmit and receive beamforming algorithm based on the stochastic gradient method that makes use of a low-rate feedback channel to inform the transmitter about the selected beams. Simulation results show that both the proposed antenna selection and the adaptive beamforming techniques exhibit fast convergence and near-optimal performance.

Massive MIMO in Real Propagation Environments: Do All Antennas Contribute Equally?

“…However, to the best of our knowledge, there are only few studies on antenna selection for massive MIMO available. In [43], antenna selection in massive MIMO was addressed for short-range wireless communications at 60 GHz. In [44], a simulation study using the Kronecker channel model [45] showed that significantly higher performance can be achieved with antenna selection than without.…”

Eliminating Out-Of-Cell Interference in Cellular Massive Mimo with a Single Additional Transceiver

“…Selection methods for reducing the complexity of massive MU-MIMO systems is a widely explored field [8], [12]- [14]. There are many works related to antenna selection in LOS channels, see e.g., [15], [16]. The optimal selection of antennas is very complicated.…”

Joint Beam Adaptation in 60GHz Interference Channel via Sequential Stochastic Approximation

“…whereh q,p (ω) is the flat-fading CIR defined in (9) and v (m) q refers to the noise with zero mean and power density N 0 .…”

“…Searching in brute force is simple and effective for single link, but it is impractical in our case because the total search space spanned by Q systems is extremely large even the multistage strategy is used. To improve the search efficiency, we resort to the stochastic approximation (SA) algorithm, which originated in operation research [8] and applied in MIMO antenna selection [9] and sensor network encoding [10].…”