Underwater acoustic (UWA) communications are essential for high-speed marine data transmission but remain severely constrained by limited bandwidth, significant propagation loss, and sparse multipath structures. Conventional underwater acoustic multiple-input multiple-output (MIMO) systems primarily utilize spatial diversity but suffer from limited array resolution, causing angular ambiguity and insufficient spatial degrees of freedom (DoFs). This paper addresses these limitations through acoustic Reconfigurable Intelligent Surfaces (aRIS) to actively generate orthogonally distinguishable virtual paths, significantly enhancing spatial DoFs and channel capacity. An ocean-specific DoF-channel coupling model is established, explicitly deriving conditions for spatial rank enhancement. Subsequently, the optimal geometric locus, termed the Light-Point, is analytically identified, where deploying a single aRIS maximizes DoFs by introducing two and three additional resolvable paths in deep-sea and shallow-sea environments, respectively. Furthermore, an active simultaneous transmitting and reflecting (ASTAR) aRIS architecture with independent beam control and adaptive beam-tracking mechanism integrating unmanned underwater vehicles (UUVs) and acoustic intensity gradient sensing is proposed. Extensive simulations validate the proposed joint aRIS deployment and beamforming framework, demonstrating substantial UWA channel capacity improvements-up to 265% and 170% in shallow-sea and deep-sea scenarios, respectively.

Underwater acoustic communication (UWA) faces significant coverage challenges due to the depth-varying sound speed gradients and the presence of sound shadow zones. Acoustic reconfigurable intelligent surface (RIS) is promising as an enabler to enhance acoustic signal quality and reliability. In this paper, we propose a novel full-phase-range acoustic RIS with effective acoustic beamforming scheme. Electrical unit parameters are carefully designed with Tonpilz hardware and equivalent circuit architecture. The reflective magnitude-phase coupling is analytically modelled by dual-quadratic expression. Furthermore, we propose one Majorization-Minimization (MM)-based acoustic RIS beamforming scheme, where alternative maximization approach is coordinated with fractional programming and MM methods to achieve the convex relaxation and near-optimal solutions.

Though meaningful progress has been achieved for mobile wireless networks with the development of orthogonal frequency division multiplexing (OFDM) modulation, the reliability and transmission efficiency of underwater acoustic (UWA) communications are still limited, which cannot support the ever-increasing requirements of underwater applications. The major barrier lies in the wideband time-varying channels with extremely large time scales. Since the time dilation or contraction of baseband signals cannot be ignored in UWA communications, the orthogonality between subcarriers of OFDM is destroyed more significantly than in narrowband high-mobility channels, which leads to severe performance degradation. To solve this problem, a novel multicarrier modulation scheme referred to as the orthogonal hyperbolic frequency division multiplexing (OHFDM) modulation is proposed in this paper inspired by the scale-invariance of hyperbolic frequency signals, where a series of orthogonal narrowband hyperbolic frequency subcarriers (HFSs) is adopted to load data symbols. The input-output relation is then characterized by jointly processing the carrier and subcarrier signals, and selecting the appropriate sampling time of the output of matched filters at the receiver corresponding to the time scale of the wideband time-varying channel. The analysis reveals that the approximate orthogonality can be guaranteed, i.e., much smaller inter-carrier-interference (ICI) than OFDM systems, which enhances the system reliability and reduces the processing complexity at the receiver notably. The robustness of the proposed OHFDM modulation when path-specific scales are involved is also confirmed theoretically in this paper. Simulation results demonstrate that the proposed OHFDM modulation outperforms OFDM in terms of bit error rate (BER) under typical UWA channels with large time scales.

Communication objectives with high mobility bring severe Doppler shifts, causing the inter-carrier interference of orthogonal frequency division multiplexing (OFDM) system, which raises the requirements of novel modem schemes. However, existing modem schemes for high-mobility communications face challenges in adapting to diverse channel environments, involving complex channel estimation and etc. Fortunately, the potential of deep learning (DL) has been exploited in various communication applications. In order to design the consistent and robust modem scheme for different channel environments, we propose the DL-based architecture termed the closer twins (CTs) model, which borrows the idea from the Siamese structure in contrastive learning. In specific, two identical network backbones like twins can simultaneously process different channel inputs and make outputs consistent. We design a convlutional neural network called modem network (ModNet) as the backbone for the design of consistent and robust modem scheme. Moreover, to make traditional channel estimation and interpolation methods applicable to the designed modem scheme, a training-aided strategy called random-pilot (R-P) is proposed. In R-P strategy, we simulate the process of conventional channel estimation to modify the objective function of the modem scheme design. Furthermore, the performance of traditional channel estimation can be further improved by DL-based methods. We utilize the CTs model and design the backbone called estimation matrix network (EMNet) to optimize a linear channel estimation method, who outperforms the traditional methods with a similar complexity. Simulation results demonstrate that the proposed modem scheme outperforms OFDM, especially with high Doppler spread. The channel estimation strategy, supported by the R-P strategy and EMNet, achieves lower normalized mean square error compared with traditional methods, contributing to more reliable transmission.

Extremely large-scale antenna array (ELAA) is promising as one of the key ingredients for the sixth generation (6G) of wireless communications. The electromagnetic propagation of spherical wavefronts introduces an additional distance-dependent dimension beyond conventional beamspace. In this paper, we first present one concise closed-form channel formulation for extremely large-scale multiple-input multiple-output (XL-MIMO). All line-of-sight (LoS) and non-line-of-sight (NLoS) paths, far-field and near-field scenarios, and XL-MIMO and XL-MISO channels are unified under the framework, where additional Vandermonde windowing matrix is exclusively considered for LoS path. Under this framework, we further propose one low-complexity unified LoS/NLoS orthogonal matching pursuit (XL-UOMP) algorithm for XL-MIMO channel estimation. The simulation results demonstrate the superiority of the proposed algorithm on both estimation accuracy and pilot consumption.

Terahertz (THz) communication has been considered as one of the promising technologies for future 6G wireless systems. In order to cope with the high path loss in THz systems, reconfigurable intelligent surface (RIS) with low-complexity reflecting elements has been proposed to strengthen signals and improve the spectrum and energy efficiency. In order to acquire accurate direction of the user equippment (UE) to send directional beams, beam training is usually utilized. However, existing beam training frameworks have not taken the wideband beam split effect into consideration, so the beam training accuracy decreases a lot in wideband scenario. To solve the problems mentioned above, we propose an analytical beam training framework in RIS-assisted wideband THz communication systems. Specifically, we firstly propose a power distribution pattern (PDP) based direction estimation scheme, where the exact value of the received power is utilized to analytically calculate the direction. Then, we design the analytical codebook for the proposed framework based on the inherent parameters of the wideband THz system. Simulation results show that the proposed framework can achieve the near-optimal achievable rate performance with a lower beam training overhead.

Terahertz (THz) communications have been widely considered as one of the promising technologies for future 6G wireless systems. In order to cope with the high path loss in THz systems, reconfigurable intelligent surface (RIS) composed of low-complexity reflecting elements can be deployed to generate directional beams. In order to acquire the direction of user equipment (UE) to send directional beams, the acquisition of accurate channel state information (CSI) is very important. Beam training is widely utilized to acquire the CSI. However, existing beam training schemes have not taken the wideband beam split effect into consideration, so the beam training accuracy decreases a lot in wideband scenarios. To solve this problem, in this paper, we propose an analytical beam training framework in RIS-assisted wideband THz communication systems. Specifically, we first propose a power distribution pattern (PDP) based direction estimation scheme, where the exact value of the received power is utilized to analytically calculate the UE direction. Then, we design the analytical codebook for the proposed framework based on the inherent parameters of the wideband THz system. Simulation results show that the proposed framework can achieve the near-optimal achievable rate performance with a lower beam training overhead than existing schemes.

Benefiting from tens of GHz of bandwidth, terahertz (THz) communication has become a promising technology for future 6G network. To deal with the serious propagation loss of THz signals, massive multiple-input multiple-output (MIMO) with hybrid precoding is utilized to generate directional beams with high array gains. However, the standard hybrid precoding architecture based on frequency-independent phase-shifters cannot cope with the beam split effect in THz massive MIMO caused by the large bandwidth and the large number of antennas, where the beams split into different physical directions at different frequencies. The beam split effect will result in a serious array gain loss across the entire bandwidth, which has not been well investigated in THz massive MIMO. In this paper, we first quantify the seriousness of the beam split effect in THz massive MIMO by analyzing the array gain loss it causes. Then, we propose a new precoding architecture called delay-phase precoding (DPP) to mitigate this effect. Specifically, the proposed DPP introduces a time delay network composed of a small number of time delay elements between radio-frequency chains and phase-shifters in the standard hybrid precoding architecture. Unlikefrequency-independentphase shifts, the time delay network introduced in the DPP can realizefrequency-dependentphase shifts, which can be designed to generate frequency-dependent beams towards the target physical direction across the entire bandwidth. Due to the joint control of delay and phase, the proposed DPP can alleviate the array gain loss caused by the beam split effect. Furthermore, we propose a hardware structure by using true-time-delayers to realize frequency-dependent phase shifts for realizing the concept of DPP. A corresponding precoding algorithm is proposed to realize the precoding design. Theoretical analysis and simulations show that the proposed DPP can mitigate the beam split effect and achieve near-optimal rate with higher energy efficiency.

Abstract Millimetre‐wave (mmWave) massive multiple‐input multiple‐output (MIMO) is one of the promising techniques for 5G wireless communications and beyond. Low‐resolution hybrid precoding using low‐resolution phase shifters (PSs) is considered to be promising for mmWave massive MIMO, since it can realize an acceptable performance with significantly reduced energy consumption. However, to realize accurate channel state information acquisition, the traditional channel feedback codebooks that quantize the channel with high resolution are not suitable for low‐resolution hybrid precoding. To solve this problem, angle‐based codebook is proposed here. In the proposed codebook, the analog codebook is designed based on the channel angle‐of‐departures (AoDs) and the digital codebook is generated by the random vector quantization. Specifically, the analog codewords are optimized by a neighbour search algorithm under the constraint of low‐resolution PSs. These analog codewords are designed to be aligned with channel AoDs. In this way, they can remain unchanged in a much larger time scale, since the angle‐coherence time is much longer than the channel‐coherence time. Therefore, the channel feedback overhead can be significantly reduced. Both theoretical analyses and simulation results illustrate that the proposed codebook can achieve the acceptable achievable rate performance with low channel feedback overhead.

Terahertz (THz) massive multiple-input multiple-output (MIMO) has been considered as one of the promising technologies for future 6G wireless communications. It is essential to obtain channel information by beam tracking scheme to track mobile users in THz massive MIMO systems. However, the existing beam tracking schemes designed for narrowband systems with the traditional hybrid precoding structure suffer from a severe performance loss caused by the beam split effect, and thus cannot be directly applied to wideband THz massive MIMO systems. To solve this problem, in this paper we propose a beam zooming based beam tracking scheme by considering the recently proposed delay-phase precoding structure for THz massive MIMO. Specifically, we firstly prove the beam zooming mechanism to flexibly control the angular coverage of frequency-dependent beams over the whole bandwidth, i.e., the degree of the beam split effect, which can be realized by the elaborate design of time delays in the delay-phase precoding structure. Then, based on this beam zooming mechanism, we propose to track multiple user physical directions simultaneously in each time slot by generating multiple beams. The angular coverage of these beams is flexibly zoomed to adapt to the potential variation range of the user physical direction. After several time slots, the base station is able to obtain the exact user physical direction by finding out the beam with the largest user received power. Unlike traditional schemes where only one frequency-independent beam can be usually generated by one radio-frequency chain, the proposed beam zooming based beam tracking scheme can simultaneously track multiple user physical directions by using multiple frequency-dependent beams generated by one radio-frequency chain. Theoretical analysis shows that the proposed scheme can achieve the near-optimal achievable sum-rate performance with low beam training overhead, which is also verified by extensive simulation results.

Reconfigurable intelligent surface (RIS) is an emerging technology to improve the spectral efficiency of wireless communication systems. However, the increase of RIS elements results in the non-negligible overhead of channel estimation and channel feedback, as well as the high complexity of beam design. Therefore, how to improve the system capacity with a limited number of RIS elements becomes a challenge. In this paper, we propose a brand new irregular RIS structure to enhance the capacity of RIS-aided wireless communications. The key idea is to irregularly configure a given number of RIS elements on an enlarged surface, which provides extra degrees of freedom and spatial diversity compared with the classical regular RIS. For the proposed irregular RIS-aided communication, we then formulate the joint topology and beamforming design problem to maximize the system capacity. Accordingly, we propose a joint optimization framework with low complexity to alternately optimize the RIS topology and the corresponding beamforming design. Finally, simulation results demonstrate that the proposed irregular RIS with a limited number of RIS elements can significantly enhance the system capacity compared with the traditional regular RIS.

Terahertz (THz) multiple-input multiple-output (MIMO) is becoming a promising technology for future 6G network, where using beam tracking scheme to track mobile users is essential. However, existing beam tracking schemes designed for narrowband systems with the traditional hybrid precoding structure suffer from severe performance loss caused by the wideband beam split effect. To solve this problem, we propose a beam zooming based beam tracking scheme by considering the recently proposed delay-phase precoding structure. At first, we prove the beam zooming mechanism to flexibly control the angular coverage of frequency -dependent beams over the whole bandwidth, i.e., the degree of the wideband beam split effect, which is achieved by the elaborate design of time delays in the delay-phase precoding structure. Based on this mechanism, we then propose to track multiple physical directions in each time slot by generating multiple beams. The angular coverage of these beams are flexibly zoomed to match the angular variation range of user physical direction. After several time slots, the base station can obtain the new physical direction by finding out the beam with the largest received power. The proposed scheme can track multiple physical directions simultaneously with reduced training overhead, which is verified by simulation results.

Benefiting from tens of GHz bandwidth, Terahertz (THz) communications has been considered as one of the promising technologies for the future 6G wireless communications. To compensate the serious attenuation in THz band and avoid huge power consumption, massive multiple input multiple output (MIMO) with hybrid precoding is widely considered. However, the traditional phase-shifter (PS) based hybrid precoding architecture cannot cope with the effect of beam split in THz communications, which means that the path components of THz channel split into different spatial directions at different subcarrier frequencies, leading serious array gain loss. In this paper, we first point out the seriousness of beam split effect in THz massive MIMO by analyzing the array gain loss caused by the beam split effect. To compensate this array gain loss, we propose a new hybrid precoding architecture called delay-phase precoding (DPP). In the proposed DPP, a time delay (TD) network is introduced between radio- frequency chains and the traditional PS network, which converts phase-controlled analog precoding into delay-phase controlled analog precoding. When carrying out precoding, the time delays in the TD network are dedicatedly designed to generate frequency-dependent beams which are aligned with the spatial directions over the whole bandwidth. Thanks to the joint control of delay and phase, the proposed DPP can significantly alleviate the beam split effect. Simulation results reveal that the proposed DPP can generate beams with the near- optimal array gain over the whole bandwidth, and achieve the near-optimal achievable rate performance.

Massive multiple-input-multiple-output (MIMO) has been considered as one of the promising technologies for the future communications due to its high spectrum efficiency. In massive MIMO systems, hybrid precoding is widely adopted for it can reduce the unacceptable power consumption with a small number of radio- frequency (RF) chains. However, one RF chain can serve at most one user. Consequently, the number of simultaneously served users are strictly limited. To break this limitation, the idea of beamforming MIMO non-orthogonal multiple access (NOMA) has been proposed. In beamforming MIMO NOMA systems, one RF chain can support two or more users, where users with the same beam selection are paired into one cluster and served by an identical RF chain. However, the traditional beams are so narrow that only a few users can find a match while most of the users still remain single and perform orthogonal multiple access. To solve the problem, we propose a new scheme called multi-resolution beamforming and user clustering scheme. We first utilize beams with narrow angle range and higher array gain for user clustering. Those users which cannot be paired with these beams are clustered by a new beam codebook whose beams have a wider angle range. The selected wide beams are well-designed to avoid bringing interference to the previously paired users. The proposed scheme raises the possibility for users being paired into clusters while keeping a satisfying array gain. Therefore, more users in the cell can take advantages of NOMA to achieve a higher spectral efficiency. The simulation results demonstrate that the proposed scheme outperforms existing schemes under different scenarios.

Recently, mobile social networks (MSNs) has been playing an increasingly large proportion in people's daily life, and consequently attracted enormous amount of researches in this area, including network data collection, user behavior analysis and so on. Many of them are based on the community structure of the MSNs, the detection of which has attracted academic attention. In this paper, we propose an inter-community detection scheme under the framework of the emerging compressive sensing (CS) over graphs. Firstly, we extract the social structure among users by calculating the probability of two users encountering with each other. Then, the proposed scheme utilizes the encounter probability and the edge-clustering coefficient to define a novel additive property to make the CS algorithm applicable. Simulation results demonstrate that the proposed detection scheme can detect inter- community links more accurately than the conventional random walk based method.

As a cluster between Internet of Things (IoT) and mobile social networks (MSNs), social IoT allows close interaction between humans and things. Many applications of IoT and MSN are based on the community structure to realize efficient services, and thus the detection of the community structure has become a key problem and attracted much academic attention. In this paper, a compressive sensing (CS) over graphs-based intercommunity detection scheme is proposed for the social IoT. By exploiting the probability of two nodes encountering each other, the encounter probability and the edge-clustering coefficient are utilized to define a novel metric for each connection and construct the measurement matrix. Then a CS-based detection algorithm is proposed to detect the intercommunity links. Moreover, the edge-clustering coefficient is exploited as the prior information to further improve accuracy and reduce complexity. Simulations show that the proposed detection scheme outperforms the conventional random walk-based scheme in the social IoT.