Near-Optimal Channel Estimation for Dense Array Systems

By deploying a large number of antennas with sub-half-wavelength spacing in a compact space, dense array systems(DASs) can fully unleash the multiplexing-and-diversity gains of limited apertures. To acquire these gains, accurate channel state information acquisition is necessary but challenging due to the large antenna numbers. To overcome this obstacle, this paper reveals that exploiting the high spatial correlation of DAS channels is crucial while designing the observation matrix for optimal/near-optimal channel estimation. Firstly, we prove that the observation matrix design is equivalent to a time-domain duality of multiple-input multiple-output precoding, which can be ideally addressed by the water-filling principle. For practical realizations, a novel ice-filling algorithm is proposed to design amplitude-and-phase controllable observation matrices, and a majorization-minimization algorithm is proposed to address the phase-only controllable case. Particularly, we prove that the ice-filling algorithm can be viewed as a ``quantized" water-filling algorithm. To support the sub-optimality of the proposed designs, we provide comprehensive analyses on the achievable mean square errors and their asymptotic expressions. Finally, numerical simulations verify that our proposed channel estimation designs can achieve the near-optimal performance and outperform existing approaches significantly.

Continuous-Time Channel Prediction Based on Tensor Neural Ordinary Differential Equation

Channel prediction is critical to address the channel aging issue in mobile scenarios. Existing channel prediction techniques are mainly designed for discrete channel prediction, which can only predict the future channel in a fixed time slot per frame, while the other intra-frame channels are usually recovered by interpolation. However, these approaches suffer from a serious interpolation loss, especially for mobile millimeter wave communications. To solve this challenging problem, we propose a tensor neural ordinary differential equation (TN-ODE) based continuous-time channel prediction scheme to realize the direct prediction of intra-frame channels. Specifically, inspired by the recently developed continuous mapping model named neural ODE in the field of machine learning, we first utilize the neural ODE model to predict future continuous-time channels. To improve the channel prediction accuracy and reduce computational complexity, we then propose the TN-ODE scheme to learn the structural characteristics of the high-dimensional channel by low dimensional learnable transform. Simulation results show that the proposed scheme is able to achieve higher intra-frame channel prediction accuracy than existing schemes.

Enabling More Users to Benefit from Near-Field Communications: From Linear to Circular Array

Massive multiple-input multiple-output (MIMO) for 5G is evolving into the extremely large-scale antenna array (ELAA) to increase the spectrum efficiency by orders of magnitude for 6G communications. ELAA introduces spherical-wave-based near-field communications, where channel capacity can be significantly improved for single-user and multi-user scenarios. Unfortunately, for the widely studied uniform linear array (ULA), the near-field regions at large incidence angles will be reduced. Thus, many users randomly distributed in a cell may fail to benefit from near-field communications. In this paper, we leverage the rotational symmetry of uniform circular array (UCA) to provide uniform and enlarged near-field region for all users in a cell, enabling more users to benefit from near-field communications. Specifically, by exploiting the geometrical relationship between UCA and user with the spherical-wave model, the near-field beamforming technique for UCA is developed for the first time. Based on the analysis of near-field beamforming, we reveal that UCA is able to provide a larger near-field region than ULA in terms of the effective Rayleigh distance. Moreover, based on the UCA beamforming property, a concentric-ring codebook is designed to realize efficient beamforming in the near-field region of UCA. In addition, we find out that UCA could generate orthogonal near-field beams along the same direction, which has the potential for further improvement of multi-user capacity compared with ULA. Simulation results are provided to verify the feasibility of UCA to enable more users to benefit from near-field communications by broadening the near-field region.

Near-Field Wideband Channel Estimation for Extremely Large-Scale MIMO

Extremely large-scale multiple-input-multiple-output (XL-MIMO) at millimeter-wave (mmWave) and terahertz (THz) bands plays an important role in supporting extreme high beamforming gain as well as ultra-wideband spectrum resources. Unfortunately, accurate wideband XL-MIMO channel estimation suffers from the new challenge called as the near-field beam split effect. Prior works either neglect the accurate near-field channel model or fail to exploit the beam split effect, resulting in poor channel estimation accuracy for wideband XL-MIMO. To tackle this problem, this paper proposes a bilinear pattern detection (BPD) based approach to accurately recover the wideband XL-MIMO channel. Specifically, by analyzing the characteristics of near-field wideband channels, we first reveal the bilinear pattern of the near-field beam split effect, which implies that the sparse support set of near-field channels in both the angle and the distance domains can be regarded as a linear function against frequency. Then, inspired by the classical simultaneously orthogonal matching pursuit technique, we use the bilinear pattern to estimate the angle-of-arrival (AoA) and distance parameters of each near-field path component at all frequencies. In this way, the entire wideband XL-MIMO channel can be recovered by compressed sensing algorithms. Moreover, we provide the computational complexity of the proposed algorithm compared with existing algorithms. Finally, simulation results demonstrate that our scheme can achieve the accurate estimation of the near-field wideband XL-MIMO channel in the presence of near-field beam split effect.

Multiple access for near-field communications: SDMA or LDMA?

Spatial division multiple access (SDMA) is essential in supporting multiple user transmissions in multiple-input multiple-output (MIMO) communications. Classical 5G massive MIMO SDMA relies on the orthogonality in far-field areas to distinguish multiple users residing at different angles, which fails to make full use of the spatial resources. With the dramatically increasing number of antennas, the extremely large-scale antenna array (ELAA) introduces the additional spatial resolution in the distance domain, which provides a new dimension for enhancing multiple accessibility in the near-field region. In this paper, we propose the concept of location division multiple access (LDMA), which elaborately exploit the spatial resources to serve multiple users at different locations. Specifically, unlike classical far-field beamsteering vectors which focus on specific angles, near-field beamfocusing vectors are capable of focusing on specific locations with managed leakage energy on other locations. The near-field focusing property could be leveraged to mitigate the interference from users at the same angle to enhance the multiple accessibility. Similar to the asymptotic angular orthogonality of far-field beamsteering vectors, the asymptotic orthogonality of near-field beamfocusing in the distance domain is investigated. Near-field codebook design is critical to support multiple transmission services. An alternate algorithm based on generalized Lloyd algorithm (GLA) and heuristic spherical sampling method is proposed for uniform planar array (UPA) codebook design. Based on the near-field codebook, LDMA scheme is investigated, comprising the beam training, uplink channel estimation and downlink transmission procedure. Simulation results verify the superiority of proposed LDMA scheme on spectral efficiency over different scenarios.

Near-Field Rainbow: Wideband Beam Training for XL-MIMO

Wideband extremely large-scale multiple-input-multiple-output (XL-MIMO) is a promising technique to achieve Tbps data rates in future 6G systems through beamforming and spatial multiplexing. Due to the extensive bandwidth and the huge number of antennas for wideband XL-MIMO, a significant near-field beam split effect will be induced, where beams at different frequencies are focused on different locations. The near-field beam split effect results in a severe array gain loss, so existing works mainly focus on compensating for this loss by utilizing the time delay (TD) beamformer. By contrast, this paper demonstrates that although the near-field beam split effect degrades the array gain, it also provides a new possibility to realize fast near-field beam training. Specifically, we first reveal the mechanism of the near-field controllable beam split effect. This effect indicates that, by dedicatedly designing the delay parameters, a TD beamformer is able to control the degree of the near-field beam split effect, i.e., beams at different frequencies can flexibly occupy the desired location range. Due to the similarity with the dispersion of natural light caused by a prism, this effect is also termed as the near-field rainbow in this paper. Then, taking advantage of the near-field rainbow effect, a fast wideband beam training scheme is proposed. In our scheme, the close form of the beamforming vector is elaborately derived to enable beams at different frequencies to be focused on different desired locations. By this means, the optimal beamforming vector with the largest array gain can be rapidly searched out by generating multiple beams focused on multiple locations simultaneously through only one radio-frequency (RF) chain. Finally, simulation results demonstrate the proposed scheme is able to realize near-optimal nearfield beam training with a very low training overhead.

Near-Field Communications for 6G: Fundamentals, Challenges, Potentials, and Future Directions

Extremely large antenna array (ELAA) is a common feature of several key candidate technologies for 6G, such as ultra-massive multiple-input-multiple-output (UM-MIMO), cell-free massive MIMO, reconfigurable intelligent surface (RIS), and terahertz communications. Since the number of antennas is very large for ELAA, near-field communications will become essential in 6G wireless networks. In this article, we systematically investigate the emerging near-field communication techniques. Firstly, the fundamental of near-field communications is explained, and the metric to determine the near-field ranges in typical communication scenarios is introduced. Then, we investigate recent studies on near-field communication techniques by classifying them into two categories, i.e., techniques addressing the challenges and those exploiting the potentials in near-field regions. Their principles, recent progress, pros and cons are discussed. More importantly, several open problems and future research directions for near-field communications are pointed out. We believe that this article would inspire more innovations for this important research topic of near-field communications for 6G.

Distance-Aware Precoding for Near-Field Capacity Improvement in XL-MIMO

Extremely large-scale MIMO (XL-MIMO) communication is a promising technology to improve the capacity for future 6G networks. With a very large number of antennas, the near-field property of XL-MIMO systems becomes dominant. Unlike the classical far-field line-of-sight (LoS) channel with rank one, the significantly increased degrees of freedom (DoFs) are available in the near-field LoS channel. However, limited by the small number of radio frequency (RF) chains, the existing hybrid precoding architecture widely used for 5G is not able to utilize the extra DoFs in the near-field region. In this paper, we propose the distance-aware precoding (DAP) scheme to exploit the near-field effect as a new possibility for capacity improvement. Firstly, the DAP architecture is developed, where each RF chain can be flexibly controlled as active or inactive according to the distance-related DoFs. Then, a dedicated selection circuit is inserted to connect phase shifters and RF chains. Moreover, based on the developed DAP architecture, a DAP algorithm is proposed to jointly optimize the number of activated RF chains and precoding matrices to match the increased DoFs in the near-field region. Finally, simulation results verify that, the proposed DAP scheme can efficiently utilize the extra DoFs in the near-field region to improve the capacity.

Near-Field Wideband Beamforming for Extremely Large Antenna Array

Extremely large antenna array for wideband communications is a promising technology to achieve a Tbps data rate for next-generation communications. However, due to the extremely large bandwidth and antenna array aperture, the near-field beam split effect will severely decrease the actual transmission rates, which has not been investigated in existing works. To solve this challenging problem, we first reveal the near-field beam split effect and analyze the corresponding array gain loss. Then, a piecewise-far-field model with piecewise-linear phase property is proposed to approximate the near-field channel, based on which we propose a phase-delay focusing method to effectively mitigate the near-field beam split effect. Moreover, a new metric called effective Rayleigh distance is defined, which is more accurate than the classical Rayleigh distance to distinguish the far-field and near-field regions for practical communications. Finally, theoretical and numerical results are provided to demonstrate the effectiveness of our methods.