Abstract
The concept of reconfiguring wireless propagation environments using intelligent reflecting surfaces (IRS)s has recently emerged, where an IRS comprises of a large number of passive reflecting elements that can smartly reflect the impinging electromagnetic waves for performance enhancement. Previous works have shown promising gains assuming the availability of perfect channel state information (CSI) at the base station (BS) and the IRS, which is impractical due to the passive nature of the reflecting elements. This paper makes one of the preliminary contributions of studying an IRS-assisted multi-user multiple-input single-output (MISO) communication system under imperfect CSI. Different from the few recent works that develop least-squares (LS) estimates of the IRS-assisted channel vectors, we exploit the prior knowledge of the large-scale fading statistics at the BS to derive the Bayesian minimum mean squared error (MMSE) channel estimates under a protocol in which the IRS applies a set of optimal phase shifts vectors over multiple channel estimation sub-phases. The resulting mean squared error (MSE) is both analytically and numerically shown to be lower than that achieved by the LS estimates. Joint designs for the precoding and power allocation at the BS and reflect beamforming at the IRS are proposed to maximize the minimum user signal-to-interference-plus-noise ratio (SINR) subject to a transmit power constraint. Performance evaluation results illustrate the efficiency of the proposed system and study its susceptibility to channel estimation errors.
Highlights
M ASSIVE multiple-input multiple-output (MIMO) communication, millimeter wave communication, and network densification are some of the main technological advancements that are leading the emergence of Fifth Generation (5G) mobile communication systems
These technologies face two main practical limitations. They consume a lot of power, which is a critical issue for practical implementation and second, they struggle to provide the users with uninterrupted connectivity and a guaranteed quality of service (QoS) in harsh propagation environments, due to the lack of control over the wireless propagation channel
We propose an optimal minimum mean squared error (MMSE) based channel estimation protocol to estimate the direct base stations (BS)-to-users channel vectors as well as the cascaded channel vectors consisting of the BS-to-intelligent reflecting surfaces (IRS) link and the IRS-to-users links
Summary
M ASSIVE multiple-input multiple-output (MIMO) communication, millimeter wave (mmWave) communication, and network densification are some of the main technological advancements that are leading the emergence of Fifth Generation (5G) mobile communication systems. A vast majority of the existing works assume the availability of perfect channel state information (CSI) to design the precoding vectors at the BS and phase shifts matrix at the IRS. This assumption is highly unlikely to hold in practice for an IRS-assisted system. The contributions of most of these works are limited to developing channel estimation protocols and numerically evaluating them in terms of the mean squared error (MSE) They do not utilize the estimates to develop joint precoding and reflect beamforming designs for different downlink communication scenarios of interest, where the downlink rate loss caused by channel training is an important issue especially in IRS-assisted systems. The Kronecker product of two matrices X and Y is denoted as X ⊗ Y
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