Different types of intelligent reflecting surfaces (IRS) are exploited for assisting wireless communications. The joint use of passive IRS (PIRS) and active IRS (AIRS) emerges as a promising solution owing to their complementary advantages. They can be integrated into a single hybrid active-passive IRS (HIRS) or deployed in a distributed manner, which poses challenges in determining the IRS element allocation and placement for rate maximization. In this paper, we investigate the capacity of an IRS-aided wireless communication system with both active and passive elements. Specifically, we consider three deployment schemes: 1) base station (BS)$\rightarrow $HIRS$\rightarrow $user (BHU); 2) BS$\rightarrow $AIRS$\rightarrow $PIRS$\rightarrow $user (BAPU); 3) BS$\rightarrow $PIRS$\rightarrow $AIRS$\rightarrow $user (BPAU). Under the line-of-sight channel model, we formulate a rate maximization problem via a joint optimization of the IRS element allocation and placement. We first derive the optimized number of active and passive elements for BHU, BAPU, and BPAU schemes, respectively. Then, low-complexity HIRS/AIRS placement strategies are provided. To obtain more insights, we characterize the system capacity scaling orders for the three schemes with respect to the large total number of IRS elements, amplification power budget, and BS transmit power. Finally, simulation results are presented to validate our theoretical findings and show the performance difference among the BHU, BAPU, and BPAU schemes with the proposed joint design under various system setups.

This paper proposes a millimeter-wave (mmWave) multiple-input multiple-output (MIMO) transmission scheme termed spatial scattering shift keying (SSSK), which exploits spatial scattering modulation (SSM) to encode information through the indices of channel scatterers rather than conventional symbol constellations. The proposed SSSK achieves superior reliability compared to amplitude-phase modulation (APM) schemes, while simultaneously reducing hardware complexity. Specifically, the scatterer-index-based signaling mechanism mitigates the detection complexity inherent in APM systems by avoiding explicit symbol-level demodulation. In addition, we illustrate the advantages of SSSK by investigating the interaction between SSSK and fading channels. We derive closed-form expressions for the average bit error probability (ABEP) tight upper bound of the proposed scheme using two different approaches based on the greedy detection algorithm. To gain more insights, we further derive the asymptotic ABEP expression and diversity gain. To characterize the performance, we rigorously derive tight upper bounds on the ABEP using two complementary approaches: union bound and pairwise error probability analysis under a greedy detection framework. Furthermore, asymptotic ABEP expressions are established to reveal the achievable diversity gain. Moreover, we design maximum likelihood (ML) detectors with serial and parallel architectures and corresponding ABEP upper bounds. Simulations validate the analytical derivations and demonstrate SSSK outperforms APM in ABEP at high signal-to-noise ratios. The proposed greedy detector reduces computational complexity compared to the serial ML detector while maintaining comparable ABEP performance.

Reconfigurable intelligent surface (RIS) has emerged as a promising solution to overcome the challenges of high path loss and easy signal blockage in millimeter-wave (mmWave) and terahertz (THz) communication systems. With the increase of RIS aperture and system bandwidth, the near-field beam split effect emerges, which causes beams at different frequencies to focus on distinct physical locations, leading to a significant gain loss of beamforming. To address this problem, we leverage the property of Fresnel zone that the beam split disappears for RIS elements along a single Fresnel zone and propose beamforming design on the two dimensions of along and across the Fresnel zones. The phase shift of RIS elements along the same Fresnel zone are designed aligned, so that the signal reflected by these element can add up in-phase at the receiver regardless of the frequency. Then the expression of equivalent channel is simplified to the Fourier transform of reflective intensity across Fresnel zones modulated by the designed phase. Based on this relationship, we prove that the uniformly distributed in-band gain with aligned phase along the Fresnel zone leads to the upper bound of achievable rate. Finally, we design phase shifts of RIS to approach this upper bound by adopting the stationary phase method. Simulation results validate the effectiveness of our proposed Fresnel zone-based method in mitigating the near-field beam split effect.

Extremely large-scale massive multiple-input multiple-output (XL-MIMO) is a potential enabling technology for 6th-generation (6G) communication. The XL-MIMO channel research will be important for XL-MIMO system development. In this paper, the measurement of XL-MIMO channels from 32 to 512 transmitting antenna elements in the 6 GHz band is carried out in an indoor corridor scenario. The delay spread, angular spread, and channel capacity are investigated. The results are compared with the indoor channel model in the Third Generation Partnership Project (3GPP) TR 38.901. We find that the number of antenna elements has a small impact on the delay spread and angular spread. So the spatial non-stationary effect does not need to be considered specifically in the far-field range in this scenario. In addition, the special structure of the corridor leads to a difference in the comparison of the angular spread with the 3GPP model in each dimension. The closed environment of the corridor also results in a significant gap in channel capacity performance from the i.i.d. channel. This work can provide insights into XL-MIMO applications in the 6G era.

Extremely Large-Scale Multiple-Input-Multiple-Output (XL-MIMO) communication, is recognized as a potential enabling technology for sixth-generation (6G) communication. Due to the large antenna aperture of XL-MIMO, spatial non-stationary (SnS) phenomena may occur on the array domain during deployment. This paper, relying on Ray-tracing (RT) simulations, analyzes the SnS phenomena from various perspectives of channel characteristics. Meanwhile, to accurately model the SnS phenomenon, this paper proposes a method for stationary sub-interval partitioning based on channel characteristics. It is assumed that the channel is stationary within each sub-interval, while it is non-stationary across different intervals. The method comprehensively considers factors such as channel correlation, delay spread (DS), azimuth angle spread of departure (ASD), and multipath components (MPCs) birth-death for sub-interval partitioning. By analyzing the independence of sub-intervals, this paper demonstrates that the proposed method performs better in sub-interval partitioning compared to the traditional averaging approach.

Abstract Near-field technology is increasingly recognized due to its transformative potential in communication systems, establishing it as a critical enabler for sixth-generation (6G) telecommunication development. This paper presents a comprehensive survey of recent advancements in near-field technology research. First, we explore the near-field propagation fundamentals by detailing definitions, transmission characteristics, and performance analysis. Next, we investigate various near-field channel models—deterministic, stochastic, and electromagnetic information theory based models, and review the latest progress in near-field channel testing, highlighting practical performance and limitations. With evolving channel models, traditional mechanisms such as channel estimation, beamtraining, and codebook design require redesign and optimization to align with near-field propagation characteristics. We then introduce innovative beam designs enabled by near-field technologies, focusing on non-diffractive beams (such as Bessel and Airy) and orbital angular momentum (OAM) beams, addressing both hardware architectures and signal processing frameworks, showcasing their revolutionary potential in near-field communication systems. Additionally, we highlight progress in both engineering and standardization, covering the primary 6G spectrum allocation, enabling technologies for near-field propagation, and network deployment strategies. Finally, we conclude by identifying promising future research directions for near-field technology development that could significantly impact system design. This comprehensive review provides a detailed understanding of the current state and potential of near-field technologies.

Intelligent reflecting surfaces (IRSs) have recently attained growing popularity in wireless networks owning to their capability to customize the wireless channel via smartly configured passive reflections. In addition to optimizing IRS reflection patterns, the flexible deployment of IRSs offers another design degree of freedom (DoF) to reconfigure the wireless propagation environment in favour of signal transmission. To unveil the impact of IRS deployment on the system capacity, we investigate the capacity of a broadcast channel with a multi-antenna base station (BS) sending independent messages to multiple users, aided by IRSs with N elements. In particular, both the distributed and centralized IRS deployment architectures are considered. Regarding the distributed IRS, the N IRS elements form multiple IRSs and each of them is installed near a user cluster; while for the centralized IRS, all IRS elements are located in the vicinity of the BS. To draw essential insights, we first derive the maximum capacity achieved by the distributed IRS and centralized IRS, respectively, under the assumption of line-of-sight (LoS) propagation and homogeneous channel setups. By carefully capturing the fundamental tradeoff between the spatial multiplexing gain and passive beamforming gain, we rigourously prove that the capacity of the distributed IRS is higher than that of the centralized IRS provided that the total number of IRS elements is above a threshold. Motivated by the superiority of the distributed IRS, we then focus on the transmission and element allocation design under the distributed IRS. By exploiting the user channel correlation of intra-clusters and inter-clusters, an efficient hybrid multiple access scheme relying on both spatial and time domains is proposed to fully exploit both the passive beamforming gain and spatial DoF. Moreover, the IRS element allocation problem is investigated for the objectives of the sum-rate maximization and the minimum user rate maximization, respectively. Finally, extensive numerical results are provided to validate our theoretical finding and also to unveil the effectiveness of the distributed IRS for improving the system capacity under various system setups.

The design of the Internet of Things (IoT) system over space–air–ground-integrated network (SAGIN) is still in its infancy. It is critical to get 6G technology involved, in order to address the current problems in the combined SAGIN and IoT ecosystem. This work utilizes a reconfigurable intelligent surface (RIS) to enhance the existing SAGIN infrastructure. We highlight channel modeling and estimate as a major component of RIS beam control and present a sparse Bayesian super-resolution estimation approach to achieve a good balance of accuracy and computing complexity. Our numerical results justify that, with our proposed models and algorithms, RIS will become a cost-effective and spectral-efficient solution for SAGIN-based 6G networks.

Reconfigurable intelligent surface (RIS) can improve the communications between a source and a destination. Recently, continuous aperture RIS is proved to have better communication performance than discrete aperture RIS and has received much attention. However, the conventional continuous aperture RIS is designed to convert the incoming planar waves into the outgoing planar waves, which is not the optimal reflecting scheme when the receiver is not a planar array and is located in the near field of the RIS. In this paper, we consider two types of receivers in the radiating near field of the RIS: (1) when the receiver is equipped with a uniform linear array (ULA), we design RIS coefficient to convert planar waves into cylindrical waves; (2) when the receiver is equipped with a single antenna, we design RIS coefficient to convert planar waves into spherical waves. We then propose the maximum likelihood (ML) method and the focal scanning (FS) method to sense the location of the receiver based on the analytic expression of the reflection coefficient, and derive the corresponding position error bound (PEB). Simulation results demonstrate that the proposed scheme can reduce energy leakage and thus enlarge the channel capacity compared to the conventional scheme. Moreover, the location of the receiver could be accurately sensed by the ML method with large computation complexity or be roughly sensed by the FS method with small computation complexity.

The reconfigurable intelligent surface (RIS) is recognized as a critical enabler for the next generation wireless networks with the capacity to configure the wireless propagation environment dynamically. In this paper, a practical phase response model is proposed to give a more accurate description of the response on RIS elements induced by incident waves and external signals. This model is employed in the optimization problem to analyze the practical system performance. RISs are able to establish artificial and enhanced radio links among network nodes, while the low-rank property of the base station (BS)-RIS channel restricts the spatial multiplexing gain. By balancing among the data streams in terms of reducing the condition number of the multiple input multiple output (MIMO) channel matrix, the multiplexing gain can also be improved. Different criteria, including the condition number, the minimum singular value, the effective rank and capacity, are exploited to identify an optimized RIS reflection coefficient matrix to obtain higher spatial multiplexing gain. Simulation results are provided to compare singular values of composite channels and the achievable rate under different objective functions. The suitable scenarios for different objective functions are discussed as a conclusion.

This paper studies the reliable support for massive machine-type communications and proposes an efficient semi-blind detection scheme for grant-free massive connectivity. In the proposed scheme, each active device directly transmits a very short reference signal along with its payload data in the uplink, without any access scheduling in advance. At the base station (BS), we develop a successive interference cancellation (SIC)-based semi-blind detection algorithm to detect active devices and their payload data. Specifically, benefitting from the large spatial dimensionality of the BS antenna array, the bilinear generalized approximate message passing algorithm is employed for joint channel and signal estimation (JCSE). In particular, we introduce an a priori refining strategy to leverage the structured sparsity of the massive access channel matrix for improved JCSE performance. Moreover, the inserted reference signal is utilized to resolve the inherent phase and permutation ambiguities. Besides, the idea of SIC is adopted for further improved detection accuracy, where the cyclic redundancy check and soft pilot-based channel refining are incorporated to prevent error propagation. Numerical results demonstrate that the proposed semi-blind detection scheme outperforms the state-of-the-art training-based coherent detection scheme when the same number of physical resources are occupied.

As a promising solution for massive machine-type communication, grant-free non-orthogonal multiple access (GF-NOMA) has received considerable attention in recent years. However, the multidimensional constellation design (MCD) and multiuser detection (MUD) in GF-NOMA are usually optimized in adivide and conquerway, leading to local optima and performance degradation. To address this issue, we investigate the joint optimization of MCD and MUD for GF-NOMA. The formulated joint optimization is based on variational inference, which is intractable due to the signal superimposition that makes the optimization variables intricately coupled. Then, we resort to end-to-end deep learning (DL) to obtain the optimal solution. Specifically, we propose a DL-based multi-task variational autoencoder (Mul-VAE) that adopts a variational autoencoder network to optimize the distribution of the constellation points. We further derive the loss function of the proposed network and analyze it from an information-theoretic perspective. On this basis, multi-task learning is employed to deal with mutually conflicting yet related detection processes. Besides, taking heterogeneous transmission rates of users into account, a multi-task prioritizing strategy is designed to balance training performance. Simulation results reveal that the proposed method enables significant gains compared to state-of-the-art techniques.

As one of the potential 6G technologies, terahertz positively affects the data rate, supports ultra-dense connection, and realizes low-latency transmission. How to make better use of the terahertz frequency is one focus of the future 6G research. Moreover, orbital angular momentum (OAM) is promised to effectively increase the system capacity with reduced hardware complexity. The application of OAM in the terahertz frequency band is investigated in this paper, giving the pros and cons. First, the characteristics of the terahertz channel are analyzed, and the feasibility of applying terahertz OAM is confirmed. Then, the investigation of terahertz OAM proves that working at terahertz frequency can effectively solve the beam divergence, and a novel receiving antenna design is proposed to reduce the receiving aperture size further. Finally, the capacity characteristics of OAM and MIMO in the terahertz frequency band are analyzed to prove the effectiveness and the necessity of applying terahertz OAM.

Terahertz has an emphatic effect on increasing data rates, supporting ultra-dense connections, and realizing low-latency transmission. In this paper, we investigate the peculiarity of the terahertz spectrum, study the propagation characteristics of terahertz technology, and discuss its channel modeling feature. Given the unique spectral characteristics of terahertz bands, physical layer waveforms, modulation, coding schemes are designed accordingly to reduce the peak-to-average power ratio (PAPR), increase the spectral flexibility, meet the backward compatibility and improve the system performance. The baseband signal processing of terahertz signals is analyzed and discussed in this paper, inspiring the design of future terahertz communication systems.

With the explosive growth of the Internet of Things (IoT), a massive number of IoT devices are deployed so as to realize a variety of advanced applications, i.e., environmental monitoring and smart traffic. There are two main characteristics in these typical applications, namely the massive connectivity and the sporadic transmission. The massive connectivity usually leads the IoT system to be spatially undersampled, since the number of devices is much larger than the number of receiver antennas, which brings difficulties and challenges to symbol detection. Fortunately, the sporadic transmission in IoT communication introduces sparsity into transmitted symbols, thanks to which we are able to perform symbol detection even in a spatially undersampled scenario. In this paper, we attempt to incorporate deep learning (DL) into the symbol detection of spatially undersampled IoT with sporadically transmitting devices. Specifically, we propose a novel DL-based detector named DeepIoT that employs a variant autoencoder network to recover both the indices of active devices and their transmitting symbols by using only the received signal. Simulation results show that the DeepIoT can outperform various existing methods and has only a 1.5- 2dB signal-to-noise ratio (SNR) loss compared to the optimal maximum likelihood (ML) detector.

In this paper, we consider the multipath effect in orbital angular momentum (OAM) MIMO transmission systems and develop a pre-processing scheme to effectively detect the received signal with low computational complexity. The non-line-of-sight (NLOS) OAM-MIMO channel that contains a LOS path and a specular reflected path is mathematical modelled and predigested to characterize the transmission progress. A SVD based pre-transmitting and pre-receiving scheme is proposed to mitigate the interference between different OAM modes. By this mean, the severe intra-channel and inter-channel crosstalk can be relived greatly and a simple discrete Fourier transform (DFT) operation can work efficiently to estimate the transmitted signal. Moreover, the BER and capacity performance of NLOS OAM-MIMO is analyzed and some conclusions are drawn to guide the system design.

In this paper, we design a compressed sensing based Uplink/Downlink (UL/DL) channel estimation (CE) scheme for RIS-aided terahertz MIMO systems and develop an integral transmission strategy. Specifically, we adopt the off-grid sparse Bayesian learning (SBL) approach to work directly on the continuous AOA/AOD domain and avoid the severe grid mismatch. Compared with most state-of-the-art algorithms, i.e., LS, MMSE and on-grid compressive sensing (CS) approaches, the proposed CE method achieves better channel estimation accuracy. The angle-domain reciprocity is exploited to obtain a much simplified overall transmission scheme with significantly reduced training and feedback overhead. A novel frame structure is developed to successfully handle the RIS-aided transmission problem. Moreover, a multi-user downlink strategy is developed where the limited scattering nature of terahertz channel is exploited and a region-separation aided user grouping algorithm is explained. Simulation results are provided to demonstrate the superior performance of the proposed method over existing ones.

In this paper, we design an uplink/downlink channel estimation method for millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems and investigate the impact of beam squint effect that accompanies large array configuration. Specifically, we adopt the off-grid sparse Bayesian learning (SBL) that directly works on the continuous angle-delay parameter domain and avoids the grid mismatch problem. Hence, the proposed method achieves good channel estimation accuracy and handles the wideband direction of arrival (DOA) estimation problem for mmWave massive MIMO communications, where beam squint effect was previously ignored by many existing literatures. The Cramér-Rao bound for unknown parameters is derived to make the proposed study complete. More importantly, a much simplified downlink channel estimation scheme is designed with the aid of angle-delay reciprocity, which significantly reduces training and feedback overhead. The simulation results are provided to demonstrate the superior performance of the proposed method over existing ones.

In this paper, we design a compressed sensing (CS) based channel estimation method for millimeter wave (mmWave) massive MIMO systems and investigate the impact of dual-wideband effect (frequency-wideband and spatial-wideband) that appears in large array communications. Specifically, we adopt the off-grid sparse Bayesian learning (SBL) that directly works on the continuous angle-delay parameter domain and avoids the basis mismatch problem. Hence, the proposed method achieves better channel estimation accuracy compared to most state-of-the-art algorithms that rely on on-grid CS approach. Moreover, the proposed method could successfully handle the spatial-wideband effect (sometimes known as beam squint effect) for wideband massive MIMO communications that was previously ignored by many existing literatures. Simulation results are provided to demonstrate the superior performance of the proposed method.