Ultra-Massive MIMO Research Articles

SM-VBLAST Aided Ultra-Massive MIMO Over Generalized Fading Channel

SM-VBLAST Aided Ultra-Massive MIMO Over Generalized Fading Channel

Bridging the Gap to 6G: Leveraging the Synergy of Standardization and Adaptability

The field of wireless network and communication technology is evolving from generation to generation from 1G to 6G as of now till expected to be deployed and used by 2030. It is to succeed in 5G and bring significant improvements in terms of connectivity, speed, and size in next-generation communication technology. 6G aims to deal with the rising need for more rapid information speed, low latency, and wider network coverage. This intelligent communication is proposed to meet these demands and enable new services and applications. This review paper highlights the key enablers and challenges involved in implementing intelligent communication beyond 5G. The paper identifies the research gaps for incorporating beyond 5G communication networks and outlines the possible 6G key objectives from a flexibility standpoint. It reviews infrastructure deployment, network densification, spectrum capacity and network energy efficiency in predecessors to 6G. This paper emphasizes the need for standardization and adaptation of research areas to revolutionize 6G wireless communication, focusing on areas like, ultra massive MIMO, Terahertz Communications, Cell-Free Communications, Intelligent Reflecting Surface, Visible Light Communication, Internet of Things, Big Data management, Artificial Intelligence, and network connectivity techniques.

RIS-Based IMT-2030 Testbed for MmWave Multi-Stream Ultra-Massive MIMO Communications

RIS-Based IMT-2030 Testbed for MmWave Multi-Stream Ultra-Massive MIMO Communications

The Channel Modeling of Ultra-Massive MIMO Terahertz-Band Communications in the Presence of Mutual Coupling

The Channel Modeling of Ultra-Massive MIMO Terahertz-Band Communications in the Presence of Mutual Coupling

6G Networks and the AI Revolution-Exploring Technologies, Applications, and Emerging Challenges.

In the rapidly evolving landscape of wireless communication, each successive generation of networks has achieved significant technological leaps, profoundly transforming the way we connect and interact. From the analog simplicity of 1G to the digital prowess of 5G, the journey of mobile networks has been marked by constant innovation and escalating demands for faster, more reliable, and more efficient communication systems. As 5G becomes a global reality, laying the foundation for an interconnected world, the quest for even more advanced networks leads us to the threshold of the sixth-generation (6G) era. This paper presents a hierarchical exploration of 6G networks, poised at the forefront of the next revolution in wireless technology. This study delves into the technological advancements that underpin the need for 6G, examining its key features, benefits, and key enabling technologies. We dissect the intricacies of cutting-edge innovations like terahertz communication, ultra-massive MIMO, artificial intelligence (AI), machine learning (ML), quantum communication, and reconfigurable intelligent surfaces. Through a meticulous analysis, we evaluate the strengths, weaknesses, and state-of-the-art research in these areas, offering a wider view of the current progress and potential applications of 6G networks. Central to our discussion is the transformative role of AI in shaping the future of 6G networks. By integrating AI and ML, 6G networks are expected to offer unprecedented capabilities, from enhanced mobile broadband to groundbreaking applications in areas like smart cities and autonomous systems. This integration heralds a new era of intelligent, self-optimizing networks that promise to redefine the parameters of connectivity and digital interaction. We also address critical challenges in the deployment of 6G, from technological hurdles to regulatory concerns, providing a holistic assessment of potential barriers. By highlighting the interplay between 6G and AI technologies, this study maps out the current landscape and lights the path forward in this rapidly evolving domain. This paper aims to be a cornerstone resource, providing essential insights, addressing unresolved research questions, and stimulating further investigation into the multifaceted realm of 6G networks. By highlighting the synergy between 6G and AI technologies, we aim to illuminate the path forward in this rapidly evolving field.

Integration of Hybrid Networks, AI, Ultra Massive-MIMO, THz Frequency, and FBMC Modulation Toward 6G Requirements: A Review

Integration of Hybrid Networks, AI, Ultra Massive-MIMO, THz Frequency, and FBMC Modulation Toward 6G Requirements: A Review

PRINCE: A Pruned AMP Integrated Deep CNN Method for Efficient Channel Estimation of Millimeter-Wave and Terahertz Ultra-Massive MIMO Systems

Millimeter-wave (mmWave) and Terahertz (THz)-band communications exploit the abundant bandwidth to fulfill the increasing data rate demands of 6G wireless communications. To compensate for the high propagation loss with reduced hardware costs, ultra-massive multiple-input multiple-output (UM-MIMO) with a hybrid beamforming structure is a promising technology in the mmWave and THz bands. However, channel estimation (CE) is challenging for hybrid UM-MIMO systems, which requires recovering the high-dimensional channels from severely few channel observations. In this paper, a Pruned Approximate Message Passing (AMP) Integrated Deep Convolutional-neural-network (DCNN) CE (PRINCE) method is firstly proposed, which enhances the estimation accuracy of the AMP method by appending a DCNN network. Moreover, by truncating the insignificant feature maps in the convolutional layers of the DCNN network, a pruning method including training with regularization, pruning and refining procedures is developed to reduce the network scale. Simulation results show that the PRINCE achieves a good trade-off between the CE accuracy and significantly low complexity, with normalized-mean-square-error (NMSE) of -10 dB at signal-to-noise-ratio (SNR) as 10 dB after eliminating 80% feature maps.

Reconfigurable Holographic Surfaces for Ultra-Massive MIMO in 6G: Practical Design, Optimization and Implementation

Reconfigurable Holographic Surfaces for Ultra-Massive MIMO in 6G: Practical Design, Optimization and Implementation

An Adaptive and Robust Deep Learning Framework for THz Ultra-Massive MIMO Channel Estimation

Terahertz ultra-massive MIMO (THz UM-MIMO) is envisioned as one of the key enablers of 6G wireless networks, for which channel estimation is highly challenging. Traditional analytical estimation methods are no longer effective, as the enlarged array aperture and the small wavelength result in a mixture of far-field and near-field paths, constituting a hybrid-field channel. Deep learning (DL)-based methods, despite the competitive performance, generally lack theoretical guarantees and scale poorly with the size of the array. In this paper, we propose a general DL framework for THz UM-MIMO channel estimation, which leverages existing iterative channel estimators and is with provable guarantees. Each iteration is implemented by a fixed point network (FPN), consisting of a closed-form linear estimator and a DL-based non-linear estimator. The proposed method perfectly matches the THz UM-MIMO channel estimation due to several unique advantages. First, the complexity is low and adaptive. It enjoys provable linear convergence with a low per-iteration cost and monotonically increasing accuracy, which enables an adaptive accuracy-complexity tradeoff. Second, it is robust to practical distribution shifts and can directly generalize to a variety of heavily out-of-distribution scenarios with almost no performance loss, which is suitable for the complicated THz channel conditions. For practical usage, the proposed framework is further extended to wideband THz UM-MIMO systems with beam squint effect. Theoretical analysis and extensive simulation results are provided to illustrate the advantages over the state-of-the-art methods in estimation accuracy, convergence rate, complexity, and robustness.

Dynamic-Subarray With Fixed Phase Shifters for Energy-Efficient Terahertz Hybrid Beamforming Under Partial CSI

Terahertz (THz) communications are regarded as a pillar technology for the 6G systems, by offering multi-ten-GHz bandwidth. To overcome the huge propagation loss, THz ultra-massive MIMO systems with hybrid beamforming are proposed to offer high array gain. Notably, the adjustable phase shifters considered in most existing hybrid beamforming studies are power-hungry and difficult to realize in the THz band. Moreover, due to the ultra-massive antennas, full channel-state-information (CSI) is challenging to obtain. To address these practical concerns, in this paper, an energy-efficient dynamic-subarray with fixed phase shifters (DS-FPS) architecture is proposed for THz hybrid beamforming. To compensate for the spectral efficiency loss caused by the fixed phase of FPS, a switch network is inserted to enable dynamic connections. In addition, by considering the partial CSI, we propose a row-successive-decomposition (RSD) algorithm to design the hybrid beamforming matrices for DS-FPS. A row-by-row (RBR) algorithm is further proposed to reduce the computational complexity. Extensive simulation results show that, the proposed DS-FPS architecture with the RSD and RBR algorithms achieves much higher energy efficiency than the existing architectures. Moreover, the spectral efficiency of the DS-FPS architecture with the proposed algorithms is robust to the CSI error.

Deep Joint Source-Channel Coding for CSI Feedback: An End-to-End Approach

The increased throughput brought by MIMO technology relies on the knowledge of channel state information (CSI) acquired in the base station (BS). To make the CSI feedback overhead affordable for the evolution of MIMO technology (e.g., massive MIMO and ultra-massive MIMO), deep learning (DL) is introduced to deal with the CSI compression task. In traditional communication systems, the compressed CSI bits is treated equally and expected to be transmitted accurately over the noisy channel. While the errors occur due to the limited bandwidth or low signal-to-noise ratios (SNRs), the reconstruction performance of the CSI degrades drastically. As a branch of semantic communications, deep joint source-channel coding (DJSCC) scheme performs better than the separate source-channel coding (SSCC) scheme—the cornerstone of traditional communication systems—in the limited bandwidth and low SNRs. In this paper, we propose a DJSCC based framework for the CSI feedback task. In particular, the proposed method can simultaneously learn from the CSI source and the wireless channel. Instead of truncating CSI via Fourier transform in the delay domain in existing methods, we apply non-linear transform networks to compress the CSI. Furthermore, we adopt an SNR adaption mechanism to deal with wireless channel variations. The extensive experiments demonstrate the validity, adaptability, and generality of the proposed framework.

Beamforming Technologies for Ultra-Massive MIMO in Terahertz Communications

Terahertz (THz) communications with a frequency band 0.1-10 THz are envisioned as a promising solution to future high-speed wireless communication. Although with tens of gigahertz available bandwidth, THz signals suffer from severe free-spreading loss and molecular-absorption loss, which limit the wireless transmission distance. To compensate for the propagation loss, the ultra-massive multiple-input-multiple-output (UM-MIMO) can be applied to generate a high-gain directional beam by beamforming technologies. In this paper, a review of beamforming technologies for THz UM-MIMO systems is provided. Specifically, we first present the system model of THz UM-MIMO and identify its channel parameters and architecture types. Then, we illustrate the basic principles of beamforming via UM-MIMO and discuss the far-field and near-field assumptions in THz UM-MIMO. Moreover, an important beamforming strategy in THz band, i.e., beam training, is introduced wherein the beam training protocol and codebook design approaches are summarized. The intelligent-reflecting-surface (IRS)-assisted joint beamforming and multi-user beamforming in THz UM-MIMO systems are studied, respectively. The spatial-wideband effect and frequency-wideband effect in the THz beamforming are analyzed and the corresponding solutions are provided. Further, we present the corresponding fabrication techniques and illuminate the emerging applications benefiting from THz beamforming. Open challenges and future research directions on THz UM-MIMO systems are finally highlighted.

A CNN-LSTM-Based Fusion Separation Deep Neural Network for 6G Ultra-Massive MIMO Hybrid Beamforming

In the sixth-generation (6G) cellular networks, hybrid beamforming would be a real-time optimization problem that is becoming progressively more challenging. Although numerical computation-based iterative methods such as the minimal mean square error (MMSE) and the alternative manifold optimization (Alt-Min) can already attain near-optimal performance, their computational cost renders them unsuitable for real-time applications. However, recent studies have demonstrated that machine learning techniques like deep neural networks (DNN) can learn the mapping done by those algorithms between channel state information (CSI) and near-optimal resource allocation and then approximate this mapping in near real-time. In light of this, we investigate various DNN architectures for beamforming challenges in the terahertz (THz) band for ultra-massive multiple-input multiple-output (UM-MIMO) and explore their contextual mathematical modeling. Specifically, we design a sophisticated 1D convolutional neural network, and long short-term memory (1D CNN-LSTM) based fusion-separation scheme, which can approach the performance of the Alt-Min algorithm in terms of spectral efficiency (SE) for fully connected structures and, at the same time, use significantly less computational effort. Simulation results indicate that the proposed system can attain almost the same level of SE as that of the numerical iterative algorithms while incurring a substantial reduction in computational cost. Our DNN-based approach also exhibits exceptional adaptability to diverse network setups and high scalability. Although the current model only addresses the fully connected hybrid architecture, our approach can also be expanded to address a variety of other beamforming topologies.

Hybrid Spherical- and Planar-Wave Modeling and DCNN-Powered Estimation of Terahertz Ultra-Massive MIMO Channels

The Terahertz band is envisioned to meet the demanding 100 Gbps data rates for 6G wireless communications. Aiming at combating the distance limitation problem with low hardware-cost, ultra-massive MIMO with hybrid beamforming is promising. However, relationships among wavelength, array size and antenna spacing give rise to the inaccuracy of planar-wave channel model (PWM), while an enlarged channel matrix dimension leads to excessive parameters of applying spherical-wave channel model (SWM). Moreover, due to the adoption of hybrid beamforming, channel estimation (CE) needs to recover high-dimensional channels from severely compressed channel observation. In this paper, a hybrid spherical- and planar-wave channel model (HSPM) is investigated and proved to be accurate and efficient by adopting PWM within subarray and SWM among subarrays. Furthermore, a two-phase HSPM CE mechanism is developed. A deep convolutional-neural-network (DCNN) is designed in the first phase for parameter estimation of reference subarrays, while geometric relationships of the remaining channel parameters between reference subarrays are leveraged to complete CE in the second phase. Extensive numerical results demonstrate the HSPM is accurate at various communication distances, array sizes and carrier frequencies. The DCNN converges fast and achieves high accuracy with 5.2 dB improved normalized-mean-square-error compared to literature methods, and owns substantially low complexity.

Security and Privacy for 6G: A Survey on Prospective Technologies and Challenges

Sixth-generation (6G) mobile networks will have to cope with diverse threats on a space-air-ground integrated network environment, novel technologies, and an accessible user information explosion. However, for now, security and privacy issues for 6G remain largely in concept. This survey provides a systematic overview of security and privacy issues based on prospective technologies for 6G in the physical, connection, and service layers, as well as through lessons learned from the failures of existing security architectures and state-of-the-art defenses. Two key lessons learned are as follows. First, other than inheriting vulnerabilities from the previous generations, 6G has new threat vectors from new radio technologies, such as the exposed location of radio stripes in ultra-massive MIMO systems at Terahertz bands and attacks against pervasive intelligence. Second, physical layer protection, deep network slicing, quantum-safe communications, artificial intelligence (AI) security, platform-agnostic security, real-time adaptive security, and novel data protection mechanisms such as distributed ledgers and differential privacy are the top promising techniques to mitigate the attack magnitude and personal data breaches substantially.

Ultramassive MIMO Systems at Terahertz Bands: Prospects and Challenges

Terahertz (THz)-band communications are currently being celebrated as a key technology that could fulfill the increasing demands for wireless data traffic in 6G wireless communications. Many challenges, such as high propagation losses and power limitations, which result in short communication distances, have yet to be addressed for this technology to be realized. Ultramassive multiple-input, multiple-output (UMMIMO) antenna systems have emerged as practical means for combatting this distance problem, thereby increasing system capacity. Toward that end, graphene-based nanoantennas have recently been proposed, as they can be individually tuned and collectively controlled in compact UM-MIMO array-of-subarrays (AoSA) architectures. In this article, we present a holistic overview of THz UM-MIMO systems. We assess recent advancements in transceiver design and channel modeling and discuss the major challenges and shortcomings of such designs by deriving the relationships among communication range, array dimensions, and system performance. We further highlight several research advances that could enhance resource allocation at the THz band, including waveform designs, multicarrier configurations, and spatial modulations (SMs). Based on this discussion, we highlight prospective use cases that can bring THz UM-MIMO into reality in the context of sensing, data centers, cell-free systems, and mid-range wireless communications.

Технология Ultra Massive MIMO как альтернатива сверхплотной сети: преимущества и проблемы.

Технология Ultra Massive MIMO как альтернатива сверхплотной сети: преимущества и проблемы.

Mutual Coupling Reduction for Ultra-Dense Multi-Band Plasmonic Nano-Antenna Arrays Using Graphene-Based Frequency Selective Surface

Terahertz (THz) band provides huge bandwidth but the molecular absorptions in these frequencies cause high path losses in long-distance communications. Recently, multi-band ultra-massive MIMO (UM-MIMO) systems based on graphene-based plasmonic nano-antennas have been proposed to overcome the distance problem. In the UM MIMO systems, the mutual coupling effect is a challenging problem because of the ultra-dense integration of the multi-band nano-antenna arrays. In this paper, a graphene-based frequency selective surface (FSS) is proposed to reduce the coupling effects in dense plasmonic nano-antenna arrays for multi-band UM MIMO systems. The performance of the proposed structure is evaluated by full-wave simulation for different cases. The results show that the FSS structure has a wide stopband (-15 dB bandwidth, approximately 43%-50%) from 1.1 to 1.7 THz. By inserting the FSS structure between nano-antennas, a high isolation coefficient of -25 dB with a 15 dB fall and an envelope correlation coefficient of less than 0.01 are achieved. The field distributions and the radiation patterns are also presented to confirm that the proposed FSS structure improves the performance of the array with negligible influence on the antenna itself. Moreover, the receiving mode simulation of the array is performed with the FSS structure. It is also asymptotically shown that the mutual coupling experienced by the nano-antenna with the FSS structure is negligible even in the presence of a very large number of closely integrated elements. Finally, the technological issues are discussed for practical implementations.

Propagation Modeling for Wireless Communications in the Terahertz Band

Terahertz band (0.1-10 THz) communication is envisioned as a key technology to support ultra-broadband wireless systems for beyond 5G. For realization of efficient wireless communication networks in the THz band, it is imperative to develop channel models that can accurately and efficiently characterize the THz spectrum peculiarities. This article provides an in-depth view of channel modeling in the THz band, based on the deterministic, statistical, and hybrid methods. The state-of-the-art THz channel models in single-antenna and ultra-massive MIMO systems are extensively reviewed, respectively. Furthermore, the open challenges and potential research directions are highlighted regarding THz propagation modeling. Associated with the channel models, key physical parameters of the THz channel and their implications for wireless communication design are analyzed. The provided analysis lays the foundation for reliable and efficient ultra-broadband wireless communications in the THz band.