5G Positioning and Mapping with Diffuse Multipath

5G mmWave communication is useful for positioning due to the geometric connection between the propagation channel and the propagation environment. Channel estimation methods can exploit the resulting sparsity to estimate parameters(delay and angles) of each propagation path, which in turn can be exploited for positioning and mapping. When paths exhibit significant spread in either angle or delay, these methods breakdown or lead to significant biases. We present a novel tensor-based method for channel estimation that allows estimation of mmWave channel parameters in a non-parametric form. The method is able to accurately estimate the channel, even in the absence of a specular component. This in turn enables positioning and mapping using only diffuse multipath. Simulation results are provided to demonstrate the efficacy of the proposed approach

Matrix Completion-Based Channel Estimation for MmWave Communication Systems With Array-Inherent Impairments

Hybrid massive MIMO structures with reduced hardware complexity and power consumption have been widely studied as a potential candidate for millimeter wave (mmWave) communications. Channel estimators that require knowledge of the array response, such as those using compressive sensing (CS) methods, may suffer from performance degradation when array-inherent impairments bring unknown phase errors and gain errors to the antenna elements. In this paper, we design matrix completion (MC)-based channel estimation schemes which are robust against the array-inherent impairments. We first design an open-loop training scheme that can sample entries from the effective channel matrix randomly and is compatible with the phase shifter-based hybrid system. Leveraging the low-rank property of the effective channel matrix, we then design a channel estimator based on the generalized conditional gradient (GCG) framework and the alternating minimization (AltMin) approach. The resulting estimator is immune to array-inherent impairments and can be implemented to systems with any array shapes for its independence of the array response. In addition, we extend our design to sample a transformed channel matrix following the concept of inductive matrix completion (IMC), which can be solved efficiently using our proposed estimator and achieve similar performance with a lower requirement of the dynamic range of the transmission power per antenna. Numerical results demonstrate the advantages of our proposed MC-based channel estimators in terms of estimation performance, computational complexity and robustness against array-inherent impairments over the orthogonal matching pursuit (OMP)-based CS channel estimator.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Technological Evolution from RIS to Holographic MIMO

Multiple-input multiple-output (MIMO) techniques have been widely applied in current cellular networks. To meet the ever-increasing demands on spectral efficiency and network throughput, more and more antennas are equipped at the base station, forming the well-known concept of massive MIMO. However, traditional design with fully digital precoding architecture brings high power consumption and capital expenditure. Cost- and power-efficient solutions are being intensively investigated to address these issues. Among them, both reconfigurable intelligent surface (RIS) and holographic MIMO (HMIMO) stand out. In this chapter, we will focus on the ongoing paradigm shift from RIS to HMIMO, covering both topics in detail. A wide range of closely related topics, e.g., use cases, hardware architectures, channel modeling and estimation, RIS beamforming, HMIMO beamforming, performance analyses of spectral- and energy-efficiency, and challenges and outlook, will be covered to show their potential to be applied in the next-generation wireless networks as well as the rationales for the technological evolution from RIS to holographic MIMO