Spatial Channel Covariance Estimation and Two-Timescale Beamforming for IRS-Assisted Millimeter Wave Systems

Abstract

Recently, intelligent reflecting surface (IRS)-assisted communication has gained considerable attention due to its advantage in extending the coverage and compensating the path loss with low-cost passive metasurface. This paper considers the uplink channel estimation for IRS-aided multiuser massive multiinput single-output (MISO) communications with one-bit ADCs at the base station (BS). The use of one-bit ADC is impelled by the low-cost and power efficient implementation of massive antennas techniques. However, the passiveness of IRS and the lack of signal level information after one-bit quantization make the IRS channel estimation challenging. To tackle this problem, we exploit the structured sparsity of the user-IRS-BS cascaded channels and develop three channel estimators, each of which utilizes the structured sparsity at different levels. Specifically, the first estimator exploits the elementwise sparsity of the cascaded channel and employs the sparse Bayesian learning (SBL) to infer the channel responses via the type-II maximum likelihood (ML) estimation. However, due to the one-bit quantization, the type-II ML in general is intractable. As such, a variational expectation-maximization (EM) algorithm is custom-derived to iteratively compute an ML solution. The second estimator utilizes the common row-structured sparsity induced by the IRS-to-BS channel shared among the users, and develops another type-II ML solution via the block SBL (BSBL) and the variational EM. To further improve the performance of BSBL, a third two-stage estimator is proposed, which can utilize both the common rowstructured sparsity and the column-structured sparsity arising from the limited scattering around the users. Simulation results show that the more diverse structured sparsity is exploited, the better estimation performance is achieved, and that the proposed estimators are superior to state-of-the-art one-bit estimators.

Channel Customization for RISs-assisted mmWave MIMO communication systems

“…As obtaining the BS-RIS and RIS-user subchannel matrices in RIS-assisted systems is challenging, two-timescale designs with the statistical and instant CSI were proposed in [23]- [27]. Exploiting the fact that the BS-RIS subchannel is quasi-static while the RIS-user subchannel is mobile, channel estimation [23] and channel feedback [24] were performed in the long-term and short-term timescale to simplify the channel acquisition process.…”

“…Exploiting the fact that the BS-RIS subchannel is quasi-static while the RIS-user subchannel is mobile, channel estimation [23] and channel feedback [24] were performed in the long-term and short-term timescale to simplify the channel acquisition process. In the two-timescale transmission protocol [25]- [27], the passive beamforming at the RIS and the active beamforming at the BS were designed with the statistical CSI in the long-term timescale and the instant CSI in the short-term timescale, respectively. All the aforementioned studies [7]- [27] designed the RIS in a single timescale, ignoring the fact that the RIS can tailor the channel in different timescales to improve system performance.…”

“…In the two-timescale transmission protocol [25]- [27], the passive beamforming at the RIS and the active beamforming at the BS were designed with the statistical CSI in the long-term timescale and the instant CSI in the short-term timescale, respectively. All the aforementioned studies [7]- [27] designed the RIS in a single timescale, ignoring the fact that the RIS can tailor the channel in different timescales to improve system performance.…”