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Millimeter wave (mmWave) spectrum has drawn attention due to its tremendous available bandwidth. The high propagation losses in the mmWave bands necessitate beamforming with a large number of antennas. Traditionally each antenna is paired with a high-speed analog-to-digital converter (ADC), which results in high power consumption. A hybrid beamforming architecture and one-bit resolution ADCs have been proposed to reduce power consumption. However, analog beamforming and one-bit quantization make channel estimation more challenging.

We investigate the practical realization of energy beamforming gains in the downlink wireless power transfer from a massive antenna radio frequency (RF) source to multiple single antenna energy harvesting (EH) users. Assuming channel reciprocity for the uplink and downlink channels undergoing Rician fading, we first obtain the least-squares and linear-minimum-mean-square-error channel estimates using the energy-constrained pilot signal transmission from EH users.

Millimeter wave (mmWave) multiple-input multiple-output (MIMO) transceivers employ narrow beams to obtain a large array-gain, rendering them sensitive to changes in the angles of arrival and departure of the paths. Since the singular vectors that span the channel subspace are used to design the precoder and combiner, we propose a method to track the receiver-side channel subspace during data transmission using a separate radio frequency (RF) chain dedicated for channel tracking.

In spectrum sharing networks, a base station (BS)
needs to mitigate the interference to users associated with other
coexisting network in the same band. The BS can achieve this by
transmitting its downlink signal in the null space of channels
to such users. However, under a wideband scenario, the BS
needs to estimate null space matrices using the received signal
from such non-cooperative users in each frequency bin where
the users are active. To reduce the computational complexity

In this paper, we consider the design of optimal transceiver and relay processing algorithms for a full-duplex (FD) two-way amplify-and-forward (AF) multiple-input multiple-output (MIMO) relaying system. We assume the channel state information of loopback self-interference (SI) channels to be imperfect. The nodes employ precoders and receive filters for suppressing the residual SI. The optimal precoders at transceivers are designed by equalising the signal-to-interference-plus-noise ratio at the relay and transceiver.

Millimeter wave (mmWave) systems will likely employ large antennas at both the transmitter and receiver for directional beamforming. Hybrid analog/digital MIMO architectures have been proposed previously for leveraging both array gain and multiplexing gain, while reducing the power consumption in analog-to-digital converters. Channel knowledge is needed to design the hybrid precoders/combiners, which is difficult to obtain due to the large antenna arrays and the frequency selective nature of the channel.

Hybrid analog/digital precoding in the downlink of multiuser massive MIMO systems can reduce the number of RF chains hence reducing total cost and improving power efficiency. Having few RF chains, however, makes it difficult for a base station to acquire instantaneous channel state information across all antennas. We develop a hybrid technique that uses only long-term (slowly changing) channel statistics in computing the analog precoding matrix. The proposed analog precoder is designed to maximize signal-to-leakage-plusnoise ratio (SLNR) when combined with a baseband precoder.

Compressed beam-selection (CBS) exploits the limited scattering of the millimeter wave (mmWave) channel using compressed sensing (CS) and finds the best beam-pair with limited overhead. The CBS procedure can further benefit from the knowledge of some additional structure in the channel. As mmWave systems are envisioned to be deployed in conjunction with sub-6 GHz systems, we use the spatial information extracted at sub 6 GHz as out-of-band side information about the mmWave channel.