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Register a new userChannel Estimation for Extremely Large-Scale Massive MIMO: Far-Field, Near-Field, or Hybrid-Field?
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Extremely large-scale massive MIMO (XL-MIMO) is a promising technique for future 6G communications. The sharp increase of BS antennas leads to the unaffordable channel estimation overhead. Existing low-overhead channel estimation schemes are based on the far-field or near-field channel model. However, the far-field or near-field channel model mismatches the practical XL-MIMO channel feature, where some scatters are in the far-field region while others may locate in the near-field region, i.e., hybrid-field channel. Thus, existing far-field and near-field channel estimation schemes cannot be directly used to accurately estimate the hybrid-field XL-MIMO channel. To solve this problem, we propose an efficient hybrid-field channel estimation scheme by accurately modeling the XL-MIMO channel. Specifically, we firstly reveal the hybrid-field channel feature of the XL-MIMO channel. Then, we propose a hybrid-field channel model to capture this feature, which contains both the far-field and near-field path components. Finally, we propose a hybrid-field channel estimation scheme, where the far-field and near-field path components are respectively estimated. Simulation results show the proposed scheme performs better than existing schemes.
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Extremely large-scale multiple-input-multiple-output (XL-MIMO) with hybrid precoding is a promising technique to meet the high data rate requirements for future 6G communications. To realize efficient hybrid precoding, it is essential to obtain accurate channel state information. Existing channel estimation algorithms with low pilot overhead heavily rely on the channel sparsity in the angle domain, which is achieved by the classical far-field planar wavefront assumption. However, due to the non-negligible near-field spherical wavefront property in XL-MIMO systems, this channel sparsity in the angle domain is not available anymore, and thus existing far-field channel estimation schemes will suffer from severe performance loss. To address this problem, in this paper we study the near-field channel estimation by exploiting the polar-domain sparse representation of the near-field XL-MIMO channel. Specifically, unlike the classical angle-domain representation that only considers the angle information of the channel, we propose a new polar-domain representation, which simultaneously accounts for both the angle and distance information. In this way, the near-field channel also exhibits sparsity in the polar domain. By exploiting the channel sparsity in the polar domain, we propose the on-grid and off-grid near-field channel estimation schemes for XL-MIMO. Firstly, an on-grid polar-domain simultaneous orthogonal matching pursuit (P-SOMP) algorithm is proposed to efficiently estimate the near-field channel. Furthermore, to solve the resolution limitation of the on-grid P-SOMP algorithm, an off-grid polar-domain simultaneous iterative gridless weighted (P-SIGW) algorithm is proposed to improve the estimation accuracy, where the parameters of the near-field channel are directly estimated. Finally, numerical results are provided to verify the effectiveness of the proposed schemes.
Terahertz (THz) communication is widely considered as a key enabler for future 6G wireless systems. However, THz links are subject to high propagation losses and inter-symbol interference due to the frequency selectivity of the channel. Massive multiple-input multiple-output (MIMO) along with orthogonal frequency division multiplexing (OFDM) can be used to deal with these problems. Nevertheless, when the propagation delay across the base station (BS) antenna array exceeds the symbol period, the spatial response of the BS array varies across the OFDM subcarriers. This phenomenon, known as beam squint, renders narrowband combining approaches ineffective. Additionally, channel estimation becomes challenging in the absence of combining gain during the training stage. In this work, we address the channel estimation and hybrid combining problems in wideband THz massive MIMO with uniform planar arrays. Specifically, we first introduce a low-complexity beam squint mitigation scheme based on true-time-delay. Next, we propose a novel variant of the popular orthogonal matching pursuit (OMP) algorithm to accurately estimate the channel with low training overhead. Our channel estimation and hybrid combining schemes are analyzed both theoretically and numerically. Moreover, the proposed schemes are extended to the multi-antenna user case. Simulation results are provided showcasing the performance gains offered by our design compared to standard narrowband combining and OMP-based channel estimation.
Terahertz (THz) communication is considered to be a promising technology for future 6G network. To overcome the severe attenuation and relieve the high power consumption, massive MIMO with hybrid precoding has been widely considered for THz communication. However, accurate wideband channel estimation is challenging in THz massive MIMO systems. The existing wideband channel estimation schemes based on the ideal assumption of common sparse channel support will suffer from a severe performance loss due to the beam split effect. In this paper, we propose a beam split pattern detection based channel estimation scheme to realize reliable wideband channel estimation. Specifically, a comprehensive analysis on the angle-domain sparse structure of the wideband channel is provided by considering the beam split effect. Based on the analysis, we define a series of index sets called as beam split patterns, which are proved to have a one-to-one match to different physical channel directions. Inspired by this one-to-one match, we propose to estimate the physical channel direction by exploiting beam split patterns at first. Then, the sparse channel supports at different subcarriers can be obtained by utilizing a support detection window. This support detection window is generated by expanding the beam split pattern which is determined by the obtained physical channel direction. The above estimation procedure will be repeated path by path until all path components are estimated. The proposed scheme exploits the wideband channel property implied by the beam split effect, which can significantly improve the channel estimation accuracy. Simulation results show that the proposed scheme is able to achieve higher accuracy than existing schemes.
We consider a cell-free hybrid massive multiple-input multiple-output (MIMO) system with $K$ users and $M$ access points (APs), each with $N_a$ antennas and $N_r< N_a$ radio frequency (RF) chains. When $Kll M{N_a}$, efficient uplink channel estimation and data detection with reduced number of pilots can be performed based on low-rank matrix completion. However, such a scheme requires the central processing unit (CPU) to collect received signals from all APs, which may enable the CPU to infer the private information of user locations. We therefore develop and analyze privacy-preserving channel estimation schemes under the framework of differential privacy (DP). As the key ingredient of the channel estimator, two joint differentially private noisy matrix completion algorithms based respectively on Frank-Wolfe iteration and singular value decomposition are presented. We provide an analysis on the tradeoff between the privacy and the channel estimation error. In particular, we show that the estimation error can be mitigated while maintaining the same privacy level by increasing the payload size with fixed pilot size; and the scaling laws of both the privacy-induced and privacy-independent error components in terms of payload size are characterized. Simulation results are provided to further demonstrate the tradeoff between privacy and channel estimation performance.
Massive multiple-input multiple-output is a very important technology for future fifth-generation systems. However, massive massive multiple input multiple output systems are still limited because of pilot contamination, impacting the data rate due to the non-orthogonality of pilot sequences transmitted by users in the same cell to the neighboring cells. We propose a channel estimation with complete knowledge of large-scale fading by using an orthogonal pilot reuse sequence to eliminate PC in edge users with poor channel quality based on the estimation of large-scale fading and performance analysis of maximum ratio transmission and zero forcing precoding methods. We derived the lower bounds on the achievable downlink DR and signal-to-interference noise ratio based on assigning PRS to a user grouping that mitigated this problem when the number of antenna elements approaches infinity The simulation results showed that a high DR can be achieved due to better channel estimation and reduced performance loss
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