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.

Next Generation Reconfigurable MetaSurfaces: When Wave Propagation Control Meets Computing

The envisioned sixth-generation (6G) of wireless networks will involve an intelligent integration of communications and computing, thereby meeting the urgent demands of diverse applications. To realize the concept of the smart radio environment, reconfigurable intelligent surfaces (RISs) are a promising technology for offering programmable propagation of impinging electromagnetic signals via external control. However, the purely reflective nature of conventional RISs induces significant challenges in supporting computation-based applications, e.g., wave-based calculation and signal processing. To fulfil future communication and computing requirements, new materials are needed to complement the existing technologies of metasurfaces, enabling further diversification of electronics and their applications. In this event, we introduce the concept of reconfigurable intelligent computational surface (RICS), which is composed of two reconfigurable multifunctional layers: the `reconfigurable beamforming layer' which is responsible for tunable signal reflection, absorption, and refraction, and the `intelligence computation layer' that concentrates on metamaterials-based computing. By exploring the recent trends on computational metamaterials, RICSs have the potential to make joint communication and computation a reality. We further demonstrate two typical applications of RICSs for performing wireless spectrum sensing and secrecy signal processing. Future research challenges arising from the design and operation of RICSs are finally highlighted.

Demo: Real-Time Semantic Communications with a Vision Transformer

Semantic communications are expected to enable the more effective delivery of meaning rather than a precise transfer of symbols. In this paper, we propose an end-to-end deep neural network-based architecture for image transmission and demonstrate its feasibility in a real-time wireless channel by implementing a prototype based on a field-programmable gate array (FPGA). We demonstrate that this system outperforms the traditional 256-quadrature amplitude modulation system in the low signal-to-noise ratio regime with the popular CIFAR-10 dataset. To the best of our knowledge, this is the first work that implements and investigates real-time semantic communications with a vision transformer.

Double-Side Near-Field Channel Estimation for Extremely Large-Scale MIMO System

Accurate channel estimation is essential to empower extremely large-scale MIMO (XL-MIMO) in 6G networks with ultra-high spectral efficiency. Unfortunately, most of the existing channel estimation methods designed for XL-MIMO fail to consider a double-side near-field scenario, where both transmitter and receiver are equipped with extremely large-scale antenna arrays. The existing channel estimation schemes cannot be directly applied to the double-side near-field scenario. In this paper, based on this scenario, we first derive double-side near-field Rayleigh distance (DS-RD) and effective double-side near-field Rayleigh distance (EDS-RD) to determine the range of the double-side near-field region. Then, a double-side near-field channel model is proposed to match this scenario, where the distance of the transmitter from the receiver is smaller than EDS-RD. In the proposed channel model, the line of sight (LoS) path component is modeled by the geometric free assumption while non-line of sight (NLoS) path components are modeled by the near-field array response vectors. Finally, a double-side near-field channel estimation algorithm is proposed to solve the channel estimation problem in this scenario, where the LoS path component and NLoS path components are estimated separately. Numerical simulation results demonstrate that, the proposed channel estimation algorithm is able to outperform the existing methods.

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.

Sensing RISs: Enabling Dimension-Independent CSI Acquisition for Beamforming

Reconfigurable intelligent surfaces (RISs) are envisioned as a potentially transformative technology for future wireless communications. However, RIS's inability to process signals and their attendant increased channel dimension have brought new challenges to RIS-assisted systems, which greatly increases the pilot overhead required for channel estimation. To address these problems, several prior contributions that enhance the hardware architecture of RISs or develop algorithms to exploit the channels' mathematical properties have been made, where the required pilot overhead is reduced to be proportional to the number of RIS elements. In this paper, we propose a dimension-independent channel state information (CSI) acquisition approach in which the required pilot overhead is independent of the number of RIS elements. Specifically, in contrast to traditional signal transmission methods, where signals from the base station (BS) and the users are transmitted in different time slots, we propose a novel method in which signals are transmitted from the BS and the user simultaneously during CSI acquisition. Under this method, an electromagnetic interference random field (IRF) will be induced on the RIS, and we employ a sensing RIS to capture its features. Moreover, we develop three algorithms for parameter estimation in this system, and also derive the Cramer-Rao lower bound (CRLB) and an asymptotic expression for it. Simulation results verify that our proposed signal transmission method and the corresponding algorithms can achieve dimension-independent CSI acquisition for beamforming.

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.

Pattern-Division Multiplexing for Continuous-Aperture MIMO

In recent years, continuous-aperture multiple-input multiple-output (CAP-MIMO) is reinvestigated to achieve improved communication performance with limited antenna apertures. Unlike the classical MIMO composed of discrete antennas, CAP-MIMO has a continuous antenna surface, which is expected to generate any current distribution (i.e., pattern) and induce controllable spatial electromagnetic waves. In this way, the information can be modulated on the electromagnetic waves, which makes it promising to approach the ultimate capacity of finite apertures. The pattern design for CAP-MIMO is the key factor to determine the communication performance, but it has not been well studied in the literature. In this paper, we propose the pattern-division multiplexing to design the patterns for CAP-MIMO. Specifically, we first derive the system model of a typical multi-user CAP-MIMO system, which allows us to formulate the sum-rate maximization problem. Then, we propose a general pattern-division multiplexing technique to transform the design of continuous pattern functions to the design of their projection lengths on finite orthogonal bases. Based on this technique, we further propose a pattern design scheme to solve the formulated sum-rate maximization problem. Simulation results show that, the sum-rate achieved by the proposed scheme is about 260% higher than that achieved by the benchmark scheme.

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.