Channel coding is vital for reliable sixth-generation (6G) data transmission, employing diverse error correction codes for various application scenarios. Traditional decoders require dedicated hardware for each code, leading to high hardware costs. Recently, artificial intelligence (AI)-driven approaches, such as the error correction code Transformer (ECCT) and its enhanced version, the foundation error correction code Transformer (FECCT), have been proposed to reduce the hardware cost by leveraging the Transformer to decode multiple codes. However, their excessively high computational complexity ofO(N2) due to the self-attention mechanism in the Transformer limits scalability, whereNrepresents the sequence length. To reduce computational complexity, we propose a unified Transformer-based decoder that handles multiple linear block codes within a single framework. Specifically, a standardized unit is employed to align code length and code rate across different code types, while a redesigned low-rank unified attention module, with computational complexity ofO(N), is shared across various heads in the Transformer. Additionally, a sparse mask, derived from the parity-check matrix’s sparsity, is introduced to enhance the decoder’s ability to capture inherent constraints between information and parity-check bits, improving decoding accuracy and further reducing computational complexity by 86%. Extensive experimental results demonstrate that the proposed unified Transformer-based decoder outperforms existing methods and provides a high-performance, low-complexity solution for next-generation wireless communication systems.

Electromagnetic information theory (EIT) is an interdisciplinary subject that serves to integrate deterministic electromagnetic theory with stochastic Shannon's information theory. Existing EIT analysis operates in the continuous space domain, which is not aligned with the practical algorithms working in the discrete space domain. This mismatch leads to a significant difficulty in application of EIT methodologies to practical discrete space systems, which is called as thediscrete-continuous gapin this paper. To bridge this gap, we establish the discrete-continuous correspondence with a prolate spheroidal wave function (PSWF)-based ergodic capacity analysis framework. Specifically, we state and prove some discrete-continuous correspondence lemmas to establish a firm theoretical connection between discrete information-theoretic quantities to their continuous counterparts. With these lemmas, we apply the PSWF ergodic capacity bound to advanced MIMO architectures such as continuous-aperture MIMO (CAP-MIMO) and extremely large-scale MIMO (XL-MIMO). From this PSWF capacity bound, we discover the capacity saturation phenomenon both theoretically and empirically. Although the growth of MIMO performance is fundamentally limited in this EIT-based analysis framework, we reveal new opportunities in MIMO channel estimation by exploiting the EIT knowledge about the channel. Inspired by the PSWF capacity bound, we utilize continuous PSWFs to improve the pilot design of discrete MIMO channel estimators, which is called as the PSWF channel estimator (PSWF-CE). Simulation results demonstrate improved performance of the proposed PSWF-CE, compared to traditional minimum mean squared error (MMSE) and compressed sensing-based estimators.

Neural network-based decoding methods show promise in enhancing error correction performance but face challenges with punctured codes. In particular, existing methods struggle to adapt to variable code rates or meet protocol compatibility requirements. This paper proposes a unified long short-term memory (LSTM)-based neural decoder for punctured convolutional and Turbo codes to address these challenges. The key component of the proposed LSTM-based neural decoder is puncturing-aware embedding, which integrates puncturing patterns directly into the neural network to enable seamless adaptation to different code rates. Moreover, a balanced bit error rate training strategy is designed to ensure the decoder’s robustness across various code lengths, rates, and channels. In this way, the protocol compatibility requirement can be realized. Extensive simulations in both additive white Gaussian noise (AWGN) and Rayleigh fading channels demonstrate that the proposed neural decoder outperforms conventional decoding techniques, offering significant improvements in decoding accuracy and robustness.

The advance of Artificial Intelligence (AI) is continuously reshaping the future 6G wireless communications. Particularly, the development of Large Language Models (LLMs) offers a promising approach to effectively improve the performance and generalization of AI in different physical-layer (PHY) tasks. However, most existing works finetune dedicated LLM networks for a single wireless communication task separately. Thus, performing diverse PHY tasks requires extremely high training resources, memory usage, and deployment costs. To solve the problem, we propose a LLM-enabled multi-task PHY network to unify multiple tasks with a single LLM, by exploiting the excellent semantic understanding and generation capabilities of LLMs. Specifically, we first propose a multi-task LLM framework, which finetunes LLM to perform multiple tasks including multi-user precoding, signal detection, and channel prediction. Besides, the multi-task instruction module, input encoders, as well as output decoders, are elaborately designed to distinguish different tasks and adapt LLM for different tasks in the wireless domain. Moreover, low-rank adaptation (LoRA) is utilized for LLM fine-tuning. To reduce the memory requirement during LLM fine-tuning, a LoRA fine-tuning-aware quantization method is introduced. Extensive numerical simulations are also displayed to verify the effectiveness of the proposed method.

Electromagnetic information theory (EIT) is one of the emerging topics for 6G communication due to its potential to reveal the performance limit of wireless communication systems. For EIT, one of the most important research directions is degree of freedom (DoF) analysis. Existing research works on DoF analysis for EIT focus on asymptotic conclusions of DoF, which do not well fit the practical wireless communication systems with finite spatial regions and finite frequency bandwidth. In this paper, we provide mathematical definitions of the DoF for continuous electromagnetic fields. Moreover, we theoretically prove that the channel DoF is upper-bounded by the proposed functional DoF of electromagnetic fields. Furthermore, we use the theoretical analyzing tools from the Slepian concentration problem and extend them to three-dimensional space domains and four-dimensional space-time domains under electromagnetic constraints. Then we provide asymptotic DoF conclusions and non-asymptotic DoF analyzing scheme, which suits practical scenarios better, under different scenarios like three-dimensional antenna array. Finally, we use numerical analysis to provide some insights about the optimal spatial sampling interval of the antenna array, the DoF of three-dimensional antenna array, the impact of unequal antenna spacing, the orthogonal space-time patterns, etc.

Channel estimation in reconfigurable intelligent surface (RIS) assisted communications requires high pilot overhead due to numerous RIS elements incapable of signal processing. Recently, research on sensing RIS has provided a dimension-independent channel estimation scheme with merely three pilots. Nevertheless, it assumes that the BS-RIS channel is perfectly known to the RIS and remains invariant over a prolonged period, while inducing high hardware and power consumption. To address these issues, this paper introduces a generalized approximate message passing (GAMP) based channel estimation framework to achieve dimension-independent estimation of separate channels, without the assumption of known BS-RIS channel. Specifically, we first formulate the channel estimation problem in RIS assisted communications as a compressive phase retrieval problem. Based on the phaseless power observations, we leverage the GAMP algorithm to retrieve original sparse signals, which inherently supports the sparse-sampling sensing RIS architecture. Furthermore, by exploiting the intrinsic mapping between the sparse representations of the channels and the power observations in the angular domain, we propose a learned GAMP network to enhance the convergence stability and estimation accuracy. Finally, simulation results demonstrate that the proposed approach can efficiently estimate both the BS-RIS and UE-RIS channels with four pilots, while eliminating the requirement for prior knowledge of the BS-RIS channel and significantly reducing hardware and power consumption.

Accurate channel prediction is essential for addressing channel aging caused by user mobility. However, the actual channel variations over time are highly complex in high-mobility scenarios, which makes it difficult for existing predictors to obtain future channels accurately. The low accuracy of channel predictors leads to difficulties in supporting reliable communication. To overcome this challenge, we propose a channel predictor based on spatio-temporal electromagnetic (EM) kernel learning (STEM-KL). Specifically, inspired by recent advancements in electromagnetic information theory (EIT), the STEM kernel function is derived. The velocity and the concentration kernel parameters are designed to reflect the time-varying propagation of the wireless signal. We obtain the parameters through kernel learning. Then, the future channels are predicted by computing their Bayesian posterior, with the STEM kernel acting as the prior. To further improve the stability and model expressibility, we propose a grid-based EM mixed kernel learning (GEM-KL) scheme. We design the mixed kernel to be a convex combination of multiple sub-kernels, where each sub-kernel corresponds to a grid point in the set of pre-selected parameters. This approach transforms the non-convex STEM kernel learning problem into a convex grid-based problem that can be easily solved by weight optimization. Finally, simulation results verify that the proposed STEM-KL and GEM-KL schemes can achieve more accurate channel prediction. This indicates that EIT can improve the performance of wireless systems efficiently.

To satisfy the increasing demands for wireless transmission rates, it is necessary to exploit spatial resources of electromagnetic (EM) waves. In this context, EM information theory (EIT) has emerged as a hot topic by integrating the theoretical framework of deterministic mathematics and stochastic statistics to explore the transmission mechanisms of continuous EM waves. However, the previous studies were primarily focused on the analysis of the spatial degrees of freedom, with limited exploration of a comprehensive understanding of the essential physical characteristics of EIT. In this paper, a three-dimensional (3D) line-of-sight channel capacity formula is proposed, which captures the vector EM physics and accommodates both near- and far-field scenes. A novel channel model is established based on the rigorous mathematical equation and the physical mechanism of fast multipole algorithm, and it is revealed that the scattered EM waves have finite angular spectral bandwidth, which determines the eigenvalue distributions of the communication system. Furthermore, a series of orthogonal basis are constructed for the currents on the transmitter and the fields on the receiving aperture, thus supporting the optimal design of the spatial precoder and combiner. Comprehensive analyses are made to investigate the relationship among the noise, transmitted power, and spatial degree of freedom, thereby establishing a rigorous upper bound of channel capacity. Finally, a series of simulations are conducted to validate the theoretical model and numerical method. This work offers a novel perspective and methodology for comprehending and leveraging spatial resources of wireless channels, and provides a theoretical foundation for the design and optimization of the holographic communications.

Recent advancements in foundation models (FMs) have attracted increasing attention in the wireless communication domain. Leveraging the powerful multi-task learning capability, FMs hold the promise of unifying multiple tasks of wireless communication with a single framework. Nevertheless, existing wireless FMs face limitations in the uniformity to address multiple tasks with diverse inputs/outputs across different communication scenarios. In this paper, we propose a MUlti-taSk Environment-aware FM (MUSE-FM) with a unified architecture to handle multiple tasks in wireless communications, while effectively incorporating scenario information. Specifically, to achieve task uniformity, we propose a unified prompt-guided data encoder-decoder pair to handle data with heterogeneous formats and distributions across different tasks. Besides, we integrate the environmental context as a multi-modal input, which serves as prior knowledge of environment and channel distributions and facilitates cross-scenario feature extraction. Simulation results illustrate that the proposed MUSE-FM outperforms existing methods for various tasks, and its prompt-guided encoder-decoder pair facilitates few-shot adaptation to new task configurations. Moreover, the incorporation of environment information improves the ability to adapt to different scenarios.

Rydberg atomic receivers represent a transformative approach to achieving high-sensitivity, broadband, and miniaturized radio frequency (RF) reception. However, existing static signal models for Rydberg atomic receivers rely on the steady-state assumption of atomic quantum states, which cannot fully describe the signal reception process of dynamic signals. To fill in this gap, in this paper, we present a general model to compute the dynamic signal response of Rydberg atomic receivers in closed form. Specifically, by applying small-signal perturbation techniques to the quantum master equation, we derive closed-form Laplace domain transfer functions that characterize the receiver’s dynamic responses to time-varying signal fields. To gain more insights into the quantum-based RF-photocurrent conversion process, we further introduce the concept of quantum transconductance that describes the quantum system as an equivalent classical system. By applying quantum transconductance, we quantify the influence of in-band blackbody radiation (BBR) noise on the atomic receiver sensitivity. Extensive simulations for Rydberg atomic receivers validate the proposed signal model, and demonstrate the possibility of quantum receivers to outperform classical electronic receivers through the improvement of quantum transconductance.

The low-altitude economy (LAE) has recently received widespread attention from both academia and industry. To facilitate and support the successful implementation of the LAE, we fortunately find that the LAE and near-field communications in extremely large-scale MIMO (XL-MIMO) are a natural combination. Specifically, the LAE can utilize the near-field beamfocusing characteristic to accurately focus the beam energy to the positions of different UAVs, and utilize the new distance dimension to further enhance the entire spectrum efficiency. However, most existing works on near-field communications only consider the ideal scenario in a 2D horizontal plane and how to efficiently achieve near-field communications for LAE is still a blank in the literature and faces several challenges. To fill in this blank, inspired by the powerful large language models (LLM) which can act as a general wireless communications optimization solver, in this paper, we first apply LLM to solve the spectrum efficiency maximization problem of near-field communications for LAE. Specifically, our proposed LLM-based scheme can accurately distinguish far-field and near-field users and achieve joint optimization of precoding and power allocation through elaborately designing adapters and finetuning the pretrained GPT2. Simulation results substantiate the efficacy and excellence of our proposed scheme compared to the existing benchmark schemes.

In wireless communication, accurate and efficient channel prediction is essential for addressing channel aging caused by user mobility. However, the actual channel variations over time are complex in high-mobility scenarios. This complexity makes it difficult for existing channel predictors to obtain future channels accurately. To overcome channel aging, we propose a channel prediction scheme based on spatial-temporal electromagnetic kernel learning (STEM-KL). Specifically, the STEM correlation function can capture the fundamental propagation characteristics of the wireless channel, making it suitable to use as a kernel function that incorporates prior information. For the channel prediction problem especially, we redesign the hyperparameters of the STEM kernel, including user velocity and concentration, which characterize the direction of the EM wave. The hyperparameters are obtained through kernel learning. Then, we use Bayesian inference to predict the future channels, employing the STEM kernel as the required covariance. To further improve the stability and model expressiveness, we propose a grid-based electromagnetic mixed kernel learning (GEM-KL) scheme. We design the mixed kernel to be a convex combination of multiple sub-kernels, where each of the sub-kernels corresponds to a grid point in the parameter space. This approach transforms the learning of concentration and speed hyperparameters into the learning of weights for different subkernels, helping the kernel learning process avoid local optima. Finally, simulation results verify that the proposed STEM-KL schemes outperform the baseline schemes.

In extremely large-scale multiple input multiple output (XL-MIMO) systems, near-field beam training is an essential way to acquire channel state information. To reduce the high training overhead brought by the additional distance dimension of the near-field codebook, some overhead-reduced near-field beam training schemes were proposed in the literature. However, existing schemes ignore the correlation between different near-field beams. In this paper, we propose a Bayesian regression-based near-field beam training scheme, which fully utilizes the correlation between near-field code-words to reduce the training overhead. Specifically, inspired by Bayesian regression, we model the received signal corresponding to different near-field codewords as a Gaussian process and determine the optimal codeword by iteratively updating the posterior distribution and designing the codeword searching order. Besides, different searching strategies are analysed and compared. The proposed scheme only requires searching for a few codewords instead of the entire codebook, which reduces the high training overhead. Simulation results verify the effectiveness of the proposed Bayesian regression-based near-field beam training scheme, which significantly reduces the training overhead while maintaining the high achievable rate performance.

Rydberg atomic quantum receivers represent a transformative approach to achieving high-sensitivity, broadband, and miniaturized radio frequency (RF) reception. However, existing static signal models for Rydberg atomic receivers rely on the steady-state assumption of atomic quantum states, which cannot fully describe the signal reception process of dynamic signals. To fill in this gap, in this paper, we present a transfer function-based model to compute the dynamic signal response of Rydberg atomic receivers in closed form. Specifically, by applying small-signal perturbation techniques to the quantum master equation, we derive closed-form Laplace domain transfer functions that characterize the receiver’s dynamic responses to time-varying signal fields. To gain more insights into the quantum-based RF-photocurrent conversion process, we further introduce the concept of quantum transconductance that describes the quantum system as an equivalent classical system. Simulations for Rydberg atomic receivers validate the proposed signal model, and demonstrate the possibility of quantum receivers to outperform classical electronic receivers through the improvement of quantum transconductance.

In extremely large-scale multiple-input-multiple-output (XL-MIMO) systems for future sixth-generation (6G) communications, codebook-based beam training stands out as a promising technology to acquire channel state information (CSI). Despite their effectiveness, existing beam training methods suffer from significant achievable rate degradation for remote users with low signal-to-noise ratio (SNR). To tackle this challenge, leveraging the error-correcting capability of channel codes, we incorporate channel coding theory into beam training to enhance the training accuracy, thereby extending the coverage area. Specifically, we establish the duality between hierarchical beam training and channel coding, and build on it to propose a general coded beam training framework. Then, we present two specific implementations exemplified by coded beam training methods based on Hamming codes and convolutional codes, during which the beam encoding and decoding processes are refined respectively to better accommodate to the beam training problem. Simulation results have demonstrated that, the proposed coded beam training method can enable reliable beam training performance for remote users with low SNR, while keeping training overhead low.

Extremely large-scale multiple-input multiple-output (XL-MIMO) has been viewed as a potential key technology for spectrum efficiency enhancement in 6G communications. With the spherical-wave channel model in XL-MIMO systems, the direct-link channels tend to be orthogonal as the number of antennas scales up. In this context, extra distance dimension can be utilized for multiple access and the location division multiple access (LDMA) was proposed for spectrum efficiency enhancement. However, the spectrum efficiency enhancement in near-field LDMA communications is limited when users are situated far away from the base station and the channels become highly correlated. To tackle this problem, we apply non-orthogonal multiple access (NOMA) in near-field communications to further enhance spectrum efficiency. Specifically, for two-user near-field NOMA, we analyse its closed-form solution for spectrum efficiency and the impact of channel correlation on its spectrum efficiency. Besides, we compare near-field NOMA with LDMA which employs zero-forcing (ZF) precoders. Furthermore, the criteria for applying near-field NOMA is formulated and the impact of near-field beamfocusing position on near-field NOMA performance is analyzed. Moreover, we extend the two-user near-field NOMA communications to the multi-user scenarios and give a overall framework of applying NOMA in near-field multi-user communications. Finally, simulation results verify the effectiveness of applying NOMA in near-field communications to enhance spectrum efficiency.

The low-altitude economy (LAE) has recently received widespread attention from both academia and industry. To facilitate and support the successful implementation of the LAE, we fortunately find that the LAE and near-field communications in extremely large-scale MIMO (XL-MIMO) systems are a natural combination. Specifically, the LAE can utilize the near-field beamfocusing characteristic to accurately focus the beam energy to the positions of different unmanned aerial vehicles, and utilize the new distance dimension to further enhance the entire spectrum efficiency. However, most existing works on near-field communications only consider the ideal scenario in a horizontal plane and how to efficiently achieve near-field communications for LAE is still a blank in the literature and faces several challenges. To fill in this blank, inspired by the powerful large language models (LLM) which can act as a general wireless communications optimization solver, in this paper, we first apply LLM to solve the spectrum efficiency maximization problem of near-field communications for LAE. Specifically, our proposed LLM-based scheme can accurately distinguish far-field and near-field users and achieve joint optimization of precoding and power allocation through elaborately designing adapters and finetuning the pretrained GPT-2. Simulation results substantiate the efficacy and excellence of our proposed scheme compared to the existing benchmark schemes.

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 the Fresnel zone that the beam split disappears for RIS elements along a single Fresnel zone and propose a beamforming design on the two dimensions along and across the Fresnel zones. The phase shifts of RIS elements along the same Fresnel zone are designed aligned so that the signal reflected by these elements can add up in phase at the receiver regardless of the frequency. Then the expression of the 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 and the Gerchberg-Saxton (GS) algorithm. Simulation results validate the effectiveness of our proposed Fresnel zone-based method in mitigating the near-field beam split effect.

Electromagnetic information theory (EIT) is an emerging interdisciplinary subject that integrates classical Maxwell electromagnetics and Shannon information theory. The goal of EIT is to uncover the information transmission mechanisms from an electromagnetic (EM) perspective in wireless systems. Existing works on EIT are mainly focused on the analysis of EM channel characteristics, degrees-of-freedom, and system capacity. However, these works do not clarify how to integrate EIT knowledge into the design and optimization of wireless systems. To fill in this gap, in this paper, we propose an EIT-based statistical channel model with simplified parameterization. Thanks to the simplified closed-form expression of the EMCF, it can be readily applied to various channel modeling and inference tasks. Specifically, by averaging the solutions of Maxwell’s equations over a tunable von Mises distribution, we obtain a spatio-temporal correlation function (STCF) model of the EM channel, which we name as the EMCF. Furthermore, by tuning the parameters of the EMCF, we propose an EIT-based covariance estimator (EIT-Cov) to accurately capture the channel covariance. Since classical MMSE estimators can exploit prior information contained in the channel covariance matrix, we further propose the EIT-MMSE channel estimator by substituting EMCF for the covariance matrix. Simulation results show that both the proposed EIT-Cov covariance estimator and the EIT-MMSE channel estimator outperform their baseline algorithms, thus proving that EIT is beneficial to wireless communication systems.

Reconfigurable intelligent surface (RIS) is considered as one of the key technologies for future 6G communications. To fully unleash the performance of RIS, accurate channel state information (CSI) is crucial. Beam training is widely utilized to acquire the CSI. However, before aligning the beam correctly to establish stable connections, the signal-to-noise ratio (SNR) at UE is inevitably low, which reduces the beam training accuracy. To deal with this problem, we exploit the coded beam training framework for RIS systems, which leverages the error correction capability of channel coding to improve the beam training accuracy under low SNR. Specifically, we first extend the coded beam training framework to RIS systems by decoupling the base station-RIS channel and the RIS-user channel. For this framework, codewords that accurately steer to multiple angles is essential for fully unleashing the error correction capability. In order to realize effective codeword design in RIS systems, we then propose a new codeword design criterion, based on which we propose a relaxed Gerchberg-Saxton (GS) based codeword design scheme by considering the constant modulus constraints of RIS elements. In addition, considering the two dimensional structure of RIS, we further propose a dimension reduced encoder design scheme, which can not only guarentee a better beam shape, but also enable a stronger error correction capability. Simulation results reveal that the proposed scheme can realize effective and accurate beam training in low SNR scenarios.

By deploying a large number of antennas with subhalf- 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 designing the observation matrix to exploit the high spatial correlation of DAS channels is crucial for realizing near-optimal Bayesian channel estimation. Specifically, we prove that the observation matrix design for channel estimation is equivalent to a time-domain duality of point-to-point multipleinput multiple-output precoding, except for the change in the total power constraint on the precoding matrix to the pilot-wise discrete power constraint on the observation matrix. Inspired by Bayesian regression, 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 interpreted as a “quantized” water-filling algorithm, wherein the latter’s continuous power-allocation process is converted into the former’s discrete pilot-assignment process. To support the near-optimality of the proposed designs, we provide comprehensive analyses on the achievable mean square errors and their asymptotic expressions. Finally, numerical results confirm that our proposed designs achieve the near-optimal channel estimation performance and outperform existing approaches significantly.

In extremely large-scale multiple input multiple output (XL-MIMO) systems, near-field beam training (NFBT) is an essential way to acquire channel state information (CSI) knowledge. To reduce the high training overhead caused by the distance dimension of the near-field codebook, some overhead-reduced NFBT schemes were proposed in the literature. However, existing schemes ignore the correlation between different near-field beams, which promises to provide prior knowledge for the reduction of training overhead. Aligned with this vision, this paper proposes a Bayesian regression (BAR)-based NFBT scheme, which fully utilizes the strong correlation between near-field codewords to achieve near-optimal and low-overhead NFBT. Specifically, inspired by Bayesian regression, we model the received signal corresponding to different codewords as a Gaussian process. Then, the optimal codeword can be determined by iteratively updating the posterior distribution and designing the codeword searching order. Besides, different codeword inference strategies are analyzed and compared. The proposed scheme only requires searching for a few codewords instead of the entire codebook thus avoiding the high training overhead. Simulation results verify that, compared to the existing schemes, the proposed scheme can significantly reduce the training overhead while maintaining a near-optimal achievable rate performance.

Fluid antenna systems (FASs) can reconfigure their antenna locations freely within a spatially continuous space. To keep favorable antenna positions, the channel state information (CSI) acquisition for FASs is essential. While some techniques have been proposed, most existing FAS channel estimators require several channel assumptions, such as slow variation and angular-domain sparsity. When these assumptions are not reasonable, the model mismatch may lead to unpredictable performance losses. In this paper, we propose the successive Bayesian reconstructor (S-BAR) as a general solution to estimate FAS channels. Unlike model-based estimators, the proposed S-BAR is prior-aided, which builds the experiential kernel for CSI acquisition. Inspired by Bayesian regression, the key idea of S-BAR is to model the FAS channels as a stochastic process, whose uncertainty can be successively eliminated by kernel-based sampling and regression. In this way, the predictive mean of the regressed stochastic process can be viewed as a Bayesian channel estimator. Simulation results verify that, in both model-mismatched and model-matched cases, the proposed S-BAR can achieve higher estimation accuracy than the existing schemes.

Near-field beam training is essential for acquiring channel state information in 6G extremely large-scale multiple input multiple output (XL-MIMO) systems. To achieve low-overhead beam training, existing method has been proposed to leverage the near-field beam split effect, which deploys true-time-delay arrays to simultaneously search multiple angles of the entire angular range in a distance ring with a single pilot. However, the method still requires exhaustive search in the distance domain, which limits its efficiency. To address the problem, we propose a distance-dependent beam-split-based beam training method to further reduce the training overheads. Specifically, we first reveal the new phenomenon of distance-dependent beam split, where by manipulating the configurations of time-delay and phase-shift, beams at different frequencies can simultaneously scan the angular domain in multiple distance rings. Leveraging the phenomenon, we propose a near-field beam training method where both different angles and distances can simultaneously be searched in one time slot. Thus, a few pilots are capable of covering the whole angle-distance space for wideband XL-MIMO. Theoretical analysis and numerical simulations are also displayed to verify the superiority of the proposed method on beamforming gain and training overhead.

Extremely large-scale multiple-input multiple-output (XL-MIMO) has been considered as a potential key technology for spectrum efficiency enhancement in 6G communications. The XL-MIMO systems introduce spherical- wave based near-field communications and a new multiple access scheme named location division multiple access (LDMA) is adopted. The near-field LDMA communications provides a novel distance dimension for enhancing spectrum efficiency. However, the spectrum efficiency enhancement in near-field LDMA communications is limited, when user are located far away from the base station and the channels become highly correlated. To solve this problem, we apply non-orthogonal multiple access (NOMA) in near-field communications to further enhance spectrum efficiency. Specifically, for two-user near-field NOMA communications, the closed-form solutions for spectrum efficiency in near-field LDMA and near-field NOMA systems and the impact of channel correlation on them are analyzed. Besides, the criteria for applying near-field NOMA is formulated. Moreover, we extend the two-user near-field NOMA to the multi-user scenarios and give a overall framework of applying NOMA in near-field multi-user communications. Finally, simulation results are provided to verify the feasibility and superiority of applying NOMA in near-field communications to enhance spectrum efficiency.

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.

Electromagnetic information theory (EIT) is an emerging interdisciplinary subject that integrates classical Maxwell electromagnetics and Shannon information theory. The goal of EIT is to uncover the information transmission mechanisms from an electromagnetic (EM) perspective in wireless systems. Existing works on EIT are mainly focused on the analysis of degrees-of-freedom (DoF), system capacity, and characteristics of the electromagnetic channel. However, these works do not clarify whether EIT can improve wireless communication systems. To answer this question, in this paper, we provide a novel example of how to improve channel estimators by integrating EM knowledge into the classical MMSE channel estimator. Specifically, the EM knowledge is first encoded into a spatial correlation function (SCF) of the channel, which we term the EM kernel. This EM kernel plays the role of side information to the channel estimator. Since the EM kernel takes the form of Gaussian processes (GP), we propose the EIT-based Gaussian process regression (EIT-GPR) to derive the channel estimations. Furthermore, we propose EM kernel learning to fit the EM kernel to channel observations. Simulation results show that EIT benefits the channel estimator and enables it to outperform traditional isotropic MMSE algorithm, thus proving the practical values of EIT.

Electromagnetic information theory (EIT) is one of the important topics for 6G communication due to its potential to reveal the performance limit of wireless communication systems. For EIT, the research foundation is reasonable and accurate channel modeling. Existing channel modeling works for EIT in non-line-of-sight (NLoS) scenario focus on far-field modeling, which can not accurately capture the characteristics of the channel in near-field. In this paper, we propose the near-field channel model for EIT based on electromagnetic scattering theory. We model the channel by using non-stationary Gaussian random fields and derive the analytical expression of the correlation function of the fields. Furthermore, we analyze the characteristics of the proposed channel model, including how the parameters of the scattering field affect the degrees of freedom (DoF).

It is well known that there is inherent radiation pattern distortion for the commercial base station antenna array, which usually needs three antenna sectors to cover all space. To eliminate pattern distortion and further enhance beamforming performance, we propose an electromagnetic hybrid beamforming (EHB) algorithm based on 3D superdirective holographic antenna arrays. Specifically, EHB consists of antenna excitation current vectors (analog beamforming) and digital precoding matrices, where the implementation of analog beamforming involves real-time adjustments to the radiation pattern to adapt to the wireless environment. Meanwhile, the digital beamforming is optimized based on the channel characteristics of analog beam-forming to further improve the achievable rate of communication systems. An electromagnetic channel model incorporating array radiation pattern and coupling effect is also developed to evaluate the benefits of our proposed scheme. Simulation results show that the proposed scheme achieves a sum rate gain of over 150 % compared to traditional beamforming algorithms.

In extremely large-scale multiple input multiple output (XL-MIMO) systems for future 6G communications, codebook-based beam training stands out as a promising technology to acquire channel state information. Due to sharp increase in the number of antennas, the transition of electromagnetic propagation from the far-field to the near-field introduces extremely high overhead when employing exhaustive search in both angle and distance domain. To reduce the overwhelming overheads, existing method has been proposed to utilize the dispersed directional beams produced by controllable beam split to simultaneously search multiple directions in a distance ring in wideband communications. However, the method still requires exhaustive search in the distance domain and thus cannot achieve low-overhead beam training for XL-MIMO. To address the problem, achieving a transition from distance-independent beam split to distance-dependent beam split, we propose a near-field beam training method where both different angles and distances can be searched in a pilot simultaneously, and thus a few pilots are capable of covering the whole two-dimensional space for wideband XL-MIMO. Numerical simulations are displayed to verify the performance of the proposed low-overhead method.

Fluid antenna systems (FASs) can reconfigure their locations freely within a spatially continuous space. To keep favorable antenna positions, the channel state information (CSI) acquisition for FASs is essential. While some techniques have been proposed, most existing FAS channel estimators require several channel assumptions, such as slow variation and angular-domain sparsity. When these assumptions are not reasonable, the model mismatch may lead to unpredictable performance loss. In this paper, we propose the successive Bayesian reconstructor (S- BAR) as a general solution to estimate FAS channels. Unlike model-based estimators, the proposed S- BAR is prior-aided, which builds the experiential kernel for CSI acquisition. Inspired by Bayesian regression, the key idea of S- BAR is to model the FAS channels as a stochastic process, whose uncertainty can be successively eliminated by kernel-based sampling and regression. In this way, the predictive mean of the regressed stochastic process can be viewed as the maximum a posterior (MAP) estimator of FAS channels. Simulation results verify that, in both model-mismatched and model-matched cases, the proposed S-BAR can achieve higher estimation accuracy than the existing schemes.

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.

传统无线通信系统广泛利用了远场空间资源。随着6G网络的出现, 近场资源的探索和利用势在必行。这些资源为无线通信系统引入了新的物理空间维度。通过利用更高频段并结合智能超表面(RIS)、超大规模多入多出(XL-MIMO)和无蜂窝网络等技术, 近场通信将成为6G网络的关键推动因素。这种范式转变挑战了传统的远场平面波假设, 需要重新评估空间资源管理策略。 尽管传统系统已有效利用远场空间资源, 但在6G网络中采用近场空间资源为重新定义无线通信系统提供了机会。这种向近场通信的转变促进了对创新技术范式的研究。近场通信有可能显著提高频谱效率、数据传输速率和空间分辨率, 从而在增强现实、高精度定位、通感一体化以及安全无线能量传输等领域实现先进应用。影响近场通信开发和应用的关键因素包括近场传播和信道建模、提高空间资源利用率、硬件挑战以及工程实践与标准化。这些领域强调了近场通信的多面性, 反映了在标准化工作的同时, 对建模、技术、硬件开发和工程实践进步的需求。近场通信具有推动无线技术发展的变革潜力, 为消费者、工业和安全等领域的应用提供了新的可能。 在此背景下, 中国工程院院刊《信息与电子工程前沿(英文)》邀请张平院士担任主编, 赵亚军总工、戴凌龙教授、Marco di Renzo教授担任执行主编, 组织出版了“近场通信理论与应用”专刊。专刊收录12篇文章, 包括2篇综述、5篇研究、5篇通讯, 内容涵盖近场传播基本原理、信道模型的发展、传统机制在近场环境中面临的限制等, 此外包含XL-MIMO信道研究、同时无线信息和能量传输(SWIPT)系统以及RIS的最新研究进展, 以及它们在增强通信系统中的应用。

Extremely large-scale MIMO (XL-MIMO) is a promising technique for future 6G communications. The sharp increase in the number of antennas results in a transition of electromagnetic propagation from the far-field to the near-field. Due to the near-field effect, the exhaustive near-field beam training at all angles and distances requires very high overhead. The improved fast near-field beam training scheme based on time-delay structure can reduce the overhead, but it suffers from very high hardware costs and energy consumption caused by time-delay circuits. In this paper, we propose a near-field two dimension (2D) hierarchical beam training scheme to reduce the overhead without the need for extra hardware circuits. Specifically, we first formulate the multi-resolution near-field codewords design problem covering different angle and distance coverages. Next, inspired by phase retrieval problems in digital holography imaging technology, we propose a Gerchberg-Saxton (GS)-based algorithm to acquire the theoretical codeword by considering the fully digital architecture. Based on the theoretical codeword, an alternating optimization algorithm is proposed to acquire the practical codeword considering the hybrid digital-analog architecture. Finally, with the designed multi-resolution codebooks, we propose a near-field 2D hierarchical beam training scheme to significantly reduce the training overhead, which is verified by extensive simulation results.

Millimeter-wave (mmWave) and terahertz (THz) communications with hybrid precoding architectures have been regarded as energy-efficient solutions to fulfill the vision of high-speed transmissions for 6G communications. Benefiting from the advantages of providing a wide scan-range and flat array gain, the uniform circular array (UCA) has attracted much attention. However, the growing bandwidth of mmWave and THz communications require frequency-dependent phase shifts, which can not be perfectly realized through frequency-independent phase shifters (PSs) in classical hybrid precoding architectures. This mismatch causes the beam defocus effect in UCA wideband communications, where high-gain beams could not form at non-central frequencies in any direction. In this paper, we first investigate the characteristics of the beam defocus effect distinguishing itself from the beam split effect in uniform linear array (ULA) systems. The beamforming gain in both frequency domain and angular domain is analyzed, characterizing the beamforming loss caused by the beam defocus effect. Then, the delay-phase precoding (DPP) architecture leveraging true-time-delays (TTDs) to generate frequency-dependent phase shifts is employed to mitigate the beam defocus effect. Finally, performance analysis and extensive simulation results are provided to evaluate the effectiveness of DPP architecture in UCA systems.

Extremely large-scale multiple-input multiple-output (XL-MIMO) is considered as a key technology for future 6G communications. To realize effective precoding, channel estimation schemes are essential to acquire precise channel state information (CSI), while most existing schemes work relying on the spatial stationary assumption. However, in XL-MIMO systems, the spatial non-stationary effect naturally exists. Such an effect can hardly be recognized by most existing channel estimation schemes, leading to a severe accuracy loss of channel estimation. In order to deal with this problem, we study the spatial non-stationary channel estimation in XL-MIMO systems in this paper. Specifically, the spatial non-stationary channel in an XL-MIMO system is converted to a series of spatial stationary channels by a proposed group time block code (GTBC) based signal extraction scheme. The key idea is to artificially create the time-domain relevance of non-stationary effect, which brings XL-MIMO the ability to recognize such effect in the space domain. Based on the extracted signals, an on-grid GTBC-based polar-domain simultaneous orthogonal matching pursuit (GP-SOMP) algorithm and an off-grid GTBC-based polar-domain simultaneous iterative gridless weighted (GP-SIGW) algorithm are proposed to effectively estimate the non-stationary channel. Then, analyses of the time complexity and performances of the above two algorithms are carried out and the Cramér-Rao lower bound is derived. Finally, numerical results reveal that the proposed algorithms can recognize the spatial non-stationary effect and realize a much more accurate channel estimation than existing schemes.

The natural integration of extremely large antenna arrays (ELAAs) and terahertz (THz) communications can potentially establish Tbps data links for 6G networks. However, due to the extremely large array aperture and wide bandwidth, a new phenomenon termed as “near-field beam split” emerges. This phenomenon causes beams at different frequencies to focus on distinct physical locations, leading to a significant loss of the beamforming gain. To address this challenging problem, we first harness a piecewise-far-field channel model to approximate the complicated near-field wideband channel. In this model, the entire large array is partitioned into several small sub-arrays. While the wireless channel’s phase discrepancy across the entire array is modeled as near-field spherical, the phase discrepancy within each sub-array is approximated as far-field planar. Built on this approximation, a phase-delay focusing (PDF) method employing delay phase precoding (DPP) architecture is proposed. Our PDF method could compensate for the intra-array far-field phase discrepancy and the inter-array near-field phase discrepancy via the joint control of phase shifters and time delayers, respectively. Theoretical and numerical results are provided to demonstrate the efficiency of the proposed PDF method in mitigating the near-field beam split effect. Finally, we define and derive a novel metric termed as the “effective Rayleigh distance” by the evaluation of beamforming gain loss. Compared to classical Rayleigh distance, the effective Rayleigh distance is more accurate in determining the near-field range for practical communications.

It is well known that there is inherent radiation pattern distortion for the commercial base station antenna array, which usually needs three antenna sectors to cover the whole space. To eliminate pattern distortion and further enhance beamforming performance, we propose an electromagnetic hybrid beamforming (EHB) scheme based on a three-dimensional (3D) superdirective holographic antenna array. Specifically, EHB consists of antenna excitation current vectors (analog beamforming) and digital precoding matrices, where the implementation of analog beamforming involves the real-time adjustment of the radiation pattern to adapt it to the dynamic wireless environment. Meanwhile, the digital beamforming is optimized based on the channel characteristics of analog beamforming to further improve the achievable rate of communication systems. An electromagnetic channel model incorporating array radiation patterns and the mutual coupling effect is also developed to evaluate the benefits of our proposed scheme. Simulation results demonstrate that our proposed EHB scheme with a 3D holographic array achieves a relatively flat superdirective beamforming gain and allows for programmable focusing directions throughout the entire spatial domain. Furthermore, they also verify that the proposed scheme achieves a sum rate gain of over 150% compared to traditional beamforming algorithms.

Electromagnetic information theory (EIT) is one of the emerging topics for 6G communication due to its potential to reveal the performance limit of wireless communication systems. For EIT, the research foundation is reasonable and accurate channel modeling. Existing channel modeling works for EIT in non-line-of-sight (NLoS) scenario focus on far-field modeling, which can not accurately capture the characteristics of the channel in near field. In this paper, we propose the near-field channel modeling scheme for EIT based on electromagnetic scattering theory. We model the channel by using non-stationary Gaussian random fields and derive the analytical expression of the correlation function of the random fields. Furthermore, we analyze the characteristics of the proposed channel model, e.g., channel degrees of freedom (DoF). Finally, we design a channel estimation scheme for near-field scenario by integrating the electromagnetic prior information of the proposed model. Numerical analysis verifies the correctness of the proposed scheme and shows that it can outperform existing schemes like least square (LS) and orthogonal matching pursuit (OMP).

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, the near-field region at large incidence/emergence angles is greatly reduced with the widely studied uniform linear array (ULA). Thus, many randomly distributed users 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 regions at all angles, enabling more users to benefit from near-field communications. Specifically, by exploiting the geometrical relationship between UCA and users, the near-field beamforming technique for UCA is developed. 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, a concentric-ring codebook is designed to realize efficient codebook-based beamforming in the near-field region. In addition, we find out that UCA could generate orthogonal near-field beams along the same direction when the focal point of the near-field beam is exactly the zeros of other beams, which has the potential to further improve spectrum efficiency in multi-user communications compared with ULA. Simulation results are provided to verify the effectiveness of theoretical analysis and feasibility of UCA to enable more users to benefit from near-field communications by broadening the near-field region.

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.

The evolution of multi-input multi-output (MIMO) will develop toward extremely large-scale MIMO (XL-MIMO) for future 6G communications. With the extension of the antenna array, the electromagnetic propagation change from far-field to near-field. Because of the near-field effect, the exhaustive near-field beam training scanning all angles and distances involves very high overhead. The existing fast near-field beam training scheme with extra time-delay circuits can reduce the overhead, but it suffers from very high hardware costs and energy consumption. To solve this issue, we propose a low-overhead near-field two dimension (2D) hierarchical beam training after carefully designing the near-field multi-resolution codebooks. Specifically, we first formulate the problem of designing near-field multi-resolution codewords, which have various angle coverage and distance coverage. Next, we propose a Gerchberg-Saxton (GS)-based algorithm to obtain the theoretical codeword by considering the ideal fully digital architecture, and an alternating optimization algorithm is then proposed to acquire the practical codeword by considering the hybrid digital-analog architecture. Finally, we generate multi-resolution codebooks and propose a near-field 2D hierarchical beam training scheme. Simulation results demonstrate that the proposed scheme can provide a tradeoff between the achievable rate performance and overhead in near-field XL-MIMO beam training.

The concept of continuous-aperture multiple-input multiple-output (CAP-MIMO) technology has been proposed recently, which aims at achieving high spectrum efficiency by deploying extremely dense antennas or even continuous antennas in a given aperture. The fundamental question of CAP-MIMO is whether it can achieve much better performance than the traditional discrete MIMO system. In this paper, we propose a non-asymptotic performance comparison scheme between continuous and discrete MIMO systems based on the analysis of mutual information. We show the consistency of the proposed scheme by proving that the mutual information between discretized transceivers converges to that between continuous transceivers. Numerical analysis verifies the theoretical results, and suggests that the mutual information obtained from the discrete MIMO with widely adopted half-wavelength spaced antennas almost achieves the mutual information obtained from CAP-MIMO.

Millimeter-wave (mmWave) and terahertz (THz) communications with hybrid precoding architectures have been regarded as energy-efficient methods to fulfill the vision of high-speed transmissions for 6G communications. Benefiting from the advantages of providing a wide scan range and uniform array pattern, uniform circular array (UCA) has attracted much attention. However, the growing bandwidth of mmWave and THz communications require frequency-independent phase shifts to perform beamforming, which can not be perfectly realized through frequency-independent phase shifters (PSs) in hybrid precoding schemes. This mismatch causes the beam defocus effect in UCA systems, where high-gain beams disappear at non-central frequencies. In this paper, we first investigate the characteristics of the beam defocus effect distinguishing from beam split effect in uniform linear array (ULA) systems. The beam pattern of UCA in both frequency and angular domain is analyzed, characterizing the beamforming loss caused by beam defocus effect. Then, the delay-phase precoding (DPP) architecture leveraging true-time-delay (TTD) is employed to mitigate the beam defocus effect. Finally, performance analysis and simulations are provided to evaluate the performance improvement with DPP architectures.

Accurate channel estimation is essential to empower extremely large-scale MIMO (XL-MIMO) with ultra-high spectral efficiency in 6G networks. With the sharp increase in the antenna array aperture of the XL-MIMO system, the electromagnetic propagation field will change from far-field to near-field. Unfortunately, due to the near-field effect, the existing near-field XL-MIMO channel model mismatches the practical mixed line-of-sight (LoS) and non-line-of-sight (NLoS) channel feature. In this paper, a mixed LoS/NLoS near-field XL-MIMO channel model is proposed to accurately describe the LoS and NLoS path components simultaneously, where the LoS path component is modeled by the geometric free space propagation assumption while NLoS path components are modeled by the near-field array response vectors. Then, to define the range of near-field for XL-MIMO, the MIMO Rayleigh distance (MIMO-RD) is derived. Next, a two stage channel estimation algorithm is proposed, where the LoS path component and NLoS path components are estimated separately. Numerical simulation results demonstrate that, the proposed two stage scheme is able to outperform the existing methods.

Spatial division multiple access (SDMA) is essential to improve the spectrum efficiency for multi-user multiple-input multiple-output (MIMO) communications. The classical SDMA for massive MIMO with hybrid precoding heavily relies on the angular orthogonality in the far field to distinguish multiple users at different angles, which fails to fully exploit spatial resources in the distance domain. With dramatically increasing number of antennas, extremely large-scale antenna array (ELAA) introduces additional resolution in the distance domain in the near field. In this paper, we propose the concept of location division multiple access (LDMA) to provide a new possibility to enhance spectrum efficiency. The key idea is to exploit extra spatial resources in the distance domain to serve different users at different locations (determined by angles and distances) in the near field. Specifically, the asymptotic orthogonality of beam focusing vectors in the distance domain is proved, which reveals that near-field beam focusing is able to focus signals on specific locations to mitigate inter-user interferences. Simulation results verify the superiority of the proposed LDMA over classical SDMA in different scenarios.

Reconfigurable intelligent surface (RIS) has been envisioned as a promising research direction for future wireless communications. As an important indicator of RIS-assisted communication systems in green wireless communications, energy efficiency (EE) receives intensive research interest as an optimization target. Recently, an increasing number of works have been devoted to EE maximization in RIS-assisted systems, in which the power consumptions of the base station and RIS are jointly optimized. However, most existing works ignored the different power consumption between the ON and OFF states of the RIS elements, which mismatches the characteristics of the realworld physical components. To construct a more correct power dissipation model for EE optimization in RIS-aided systems, in this paper, we establish a novel ON-OFF aware power dissipation model for the RIS elements. Based on this new model, the EE optimization problem is re-formulated, and corresponding lowcomplexity algorithms are proposed by leveraging the mathematical structures of the problem. Simulation results verify the effectiveness of the proposed algorithms across a wide range of scenarios.

Extremely large-scale multiple-input multiple-output (XL-MIMO) is a promising technology for future 6G communications. To realize effective precoding, channel estimation schemes are essential to acquire precise channel state information (CSI), while most existing schemes work relying on the spatial stationary assumption. In XL-MIMO systems, however, the spatial non-stationary effect naturally exists. Such effect can hardly be recognized by existing channel estimation schemes, leading to a severe accuracy loss of channel estimation. To address this problem, in this paper, we study the spatial non-stationary channel estimation for XL-MIMO systems. Specifically, we propose a group time block code (GTBC) based signal extraction scheme. The key idea is to artificially create and exploit the time-domain relevance of non-stationary effect, which allows XL-MIMO to recognize such effect in the space domain. In this way, the spatial non-stationary channel is converted to a series of spatial stationary channels. To effectively estimate these channels, a GTBC-based polar-domain simultaneous orthogonal matching pursuit (GP-SOMP) algorithm is proposed as a solution. Simulation results reveal that the proposed GP-SOMP algorithm can recognize the spatial non-stationary effect in XL-MIMO systems and realize a much more accurate channel estimation than existing schemes.

Spatial division multiple access (SDMA) is essential to improve the spectrum efficiency for multi-user multiple-input multiple-output (MIMO) communications. The classical SDMA for massive MIMO with hybrid precoding heavily relies on the angular orthogonality in the far field to distinguish multiple users at different angles, which fails to fully exploit spatial resources in the distance domain. With the dramatically increasing number of antennas, the extremely large-scale antenna array (ELAA) introduces additional resolution in the distance domain in the near field. In this paper, we propose the concept of location division multiple access (LDMA) to provide a new possibility to enhance spectrum efficiency compared with classical SDMA. The key idea is to exploit extra spatial resources in the distance domain to serve different users at different locations (determined by angles and distances) in the near field. Specifically, the asymptotic orthogonality of near-field beam focusing vectors in the distance domain is proved, which reveals that near-field beam focusing is able to focus signals on specific locations with limited leakage energy at other locations. This special property could be leveraged in hybrid precoding to mitigate inter-user interferences for spectrum efficiency enhancement. Moreover, we provide the spherical-domain codebook design method for LDMA communications with the uniform planar array, which provides the sampling method in the distance domain. Additionally, performance analysis of LDMA is provided to reveal that the asymptotic optimal spectrum efficiency could be achieved with the increasing number of antennas. Finally, simulation results verify the superiority of the proposed LDMA over SDMA in different scenarios.

In recent years, thanks to the advances in meta-materials, the concept of continuous-aperture MIMO (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 quasi-continuous antenna surface, which is expected to generate any current distribution (i.e., pattern) and induce controllable spatial electromagnetic (EM) waves. In this way, the information is directly modulated on the EM waves, which makes it promising to approach the ultimate capacity of finite apertures. The pattern design is the key factor to determine the communication performance of CAP-MIMO, but it has not been well studied in the literature. In this paper, we develop pattern-division multiplexing (PDM) to design the patterns for CAP-MIMO. Specifically, we first study and model a typical multi-user CAP-MIMO system, which allows us to formulate the sum-rate maximization problem. Then, we develop a general PDM technique to transform the design of the continuous pattern functions to the design of their projection lengths on finite orthogonal bases, which can overcome the challenge of functional programming. Utilizing PDM, we further propose a block coordinate descent (BCD) based pattern design scheme to solve the formulated sum-rate maximization problem. Simulation results show that, the sum-rate achieved by the proposed scheme is higher than that achieved by benchmark schemes, which demonstrates the effectiveness of the developed PDM for CAP-MIMO.

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.

Accurate channel model and channel estimation are essential to empower extremely large-scale MIMO (XL-MIMO) in 6G networks with ultra-high spectral efficiency. With the sharp increase in the antenna array aperture of the XL-MIMO scenario, the electromagnetic propagation field will change from far-field to near-field. Unfortunately, due to the near-field effect, most of the existing XL-MIMO channel models fail to describe mixed line-of-sight (LoS) and non-line-of-sight (NLoS) path components simultaneously. In this paper, a mixed LoS/NLoS near-field XL-MIMO channel model is proposed to match the practical near-field XL-MIMO scenario, where the LoS path component is modeled by the geometric free space propagation assumption while NLoS path components are modeled by the near-field array response vectors. Then, to define the range of near-field for XL-MIMO, the MIMO Rayleigh distance (MIMO-RD) and MIMO advanced RD (MIMO-ARD) is derived. Next, a two stage channel estimation algorithm is proposed, where the LoS path component and NLoS path components are estimated separately. Moreover, the Cramér-Rao lower bound (CRLB) of the proposed algorithm is derived in this paper. Numerical simulation results demonstrate that, the proposed two stage scheme is able to outperform the existing methods in both the theoretical channel model and the QuaDRiGa channel emulation platform.

Reconfigurable intelligent surface (RIS)-aided terahertz (THz) communication has been considered as a promising technology for enabling future sixth-generation (6G) wireless systems. Due to the exploitation of extremely large bandwidth and large scale of RIS, RIS-aided THz communications would suffer from the beam split effect, where the generated beams cannot be aligned with the target physical direction in the whole bandwidth, so a severe array gain loss will be introduced. In this paper, the beam split effect is first analyzed in the existence of RIS. Then, a novel sub-connected RIS architecture is proposed to mitigate the beam split effect. The crux is to introduce additional time-delay (TD) modules and phase shifters into RIS elements so as to convert the classical phase-only precoding to the joint phase and delay precoding. Accordingly, a wideband precoding design is proposed to compensate for the severe array gain loss, and the performance analysis on the array gain is also provided. After that, we extend our discussions to the emerging scenarios with massive antennas equipped at the base station (BS), where the effect of “double beam split”, i.e., the coupling of beam split at the BS and the RIS, occurs. We prove the decomposability of the array gain, based on which the double beam split effect can be addressed by separately optimizing the wideband precoding at the BS and the RIS. Simulation results demonstrate that our proposed sub-connected RIS significantly alleviates the beam split effect with a small number of TD modules, and it is capable of achieving sub-optimal achievable rate performance with acceptable hardware cost and power consumption.

Reconfigurable intelligent surface (RIS) has been widely considered as a key technique to improve spectral efficiency for the 5th generation (5G) and beyond 5G (B5G) communications. Compared with most existing research that only focuses on the reflective RIS, the design and prototyping of a novel transmissive RIS are presented in this paper, and its enhancement to the RIS-aided communication system is experimentally demonstrated. The 2-bit transmissive RIS element utilizes the penetration structure, which combines a 1-bit current reversible dipole and a 90° digital phase shifter based on a quadrature hybrid coupler. A transmissive RIS prototype with$16\times16$elements is designed, fabricated, and measured to verify the proposed design. The measured phase shift and insertion loss of the RIS element validate the 2-bit phase modulation capability. Being illuminated by a horn feed, the prototype achieves a maximum broadside gain of 22.0 dBi at 27 GHz, and the two-dimensional beamforming capability with scan angles up to ±60° is validated. The experimental results of the RIS-aided communication system verify that by introducing the extra gain and beam steering capability, the transmissive RIS is able to achieve a higher data rate, reduce the transmit power, improve the transmission capability through obstacles, and dynamically adapt to the signal propagation direction.

Traditional channel capacity based on the discrete spatial dimensions mismatches the continuous electromagnetic fields. For the wireless communication system in a limited region, the spatial discretization may results in information loss because the continuous field can not be perfectly recovered from the sampling points. Therefore, electromagnetic information theory based on spatially continuous electromagnetic fields becomes necessary to reveal the fundamental theoretical capacity bound of communication systems. In this paper, we propose analyzing schemes for the performance limit between continuous transceivers. Specifically, we model the communication process between two continuous regions by random fields. Then, for the white noise model, we use Mercer expansion to derive the mutual information between the source and the destination. For the close-form expression, an analytic method is introduced based on autocorrelation functions with rational spectrum. Moreover, the Fredholm determinant is used for the general autocorrelation functions to provide the numerical calculation scheme. Further works extend the white noise model to colored noise and discuss the mutual information under it. Finally, we build an ideal model with infinite-length source and destination which shows a strong correpsondence with the time-domain model in classical information theory. The mutual information and the capacity are derived through the spatial spectral density.

As a revolutionary paradigm for controlling wireless channels, reconfigurable intelligent surfaces (RISs) have emerged as a candidate technology for future 6G networks. However, due to the “multiplicative fading” effect, the existing passive RISs only achieve limited capacity gains in many scenarios with strong direct links. In this paper, the concept of active RISs is proposed to overcome this fundamental limitation. Unlike passive RISs that reflect signals without amplification, active RISs can amplify the reflected signals via amplifiers integrated into their elements. To characterize the signal amplification and incorporate the noise introduced by the active components, we develop and verify the signal model of active RISs through the experimental measurements based on a fabricated active RIS element. Based on the verified signal model, we further analyze the asymptotic performance of active RISs to reveal the substantial capacity gain they provide for wireless communications. Finally, we formulate the sum-rate maximization problem for an active RIS aided multi-user multiple-input single-output (MU-MISO) system and a joint transmit beamforming and reflect precoding scheme is proposed to solve this problem. Simulation results show that, in a typical wireless system, passive RISs can realize only a limited sum-rate gain of 22%, while active RISs can achieve a significant sum-rate gain of 130%, thus overcoming the “multiplicative fading” effect.

Reconfigurable intelligent surfaces (RISs) are envisioned as a potentially transformative technology for future wireless communications. However, RISs’ inability to process signals and the attendant increased channel dimension have brought new challenges to RIS-assisted systems, including significantly increased 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. With this method, an electromagnetic interference random field (IRF) will be induced on the RIS, and we propose the structure of sensing RIS to capture its features. Moreover, we develop three algorithms for parameter estimation in this system, in which one of the proposed vM-EM algorithm is analyzed with the fixed-point perturbation method to obtain an asymptotic achievable bound. In addition, we also derive the Cramér-Rao lower bound (CRLB) and an asymptotic expression for characterizing the best possible performance of the proposed algorithms. Simulation results verify that our proposed signal transmission method and the corresponding algorithms can achieve dimension-independent CSI acquisition for beamforming.

Wideband extremely large-scale multiple-input-multiple-output (XL-MIMO) plays an important role in boosting the data rate for 6G networks. Because of the huge bandwidth and the large number of antennas, wideband XL-MIMO introduces a significant near-field beam split effect, where beams at different frequencies are focused on different locations. This effect results in a severe array gain loss, and existing works mainly consider to compensate for this loss by utilizing time-delay (TD) beamforming. This paper demonstrates that despite degrading the array gain, the near-field beam split effect can also contribute to the fast near-field beam training. Specifically, we first reveal the controllable near-field beam split effect. This effect indicates that TD beamforming can 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, we also call this effect as “near-field rainbow”. Then, by taking advantage of the near-field rainbow, a fast wideband beam training scheme is proposed to generate beams focusing on multiple locations at multiple frequencies with the help of TD beamforming. Finally, simulation results demonstrate that the proposed scheme is able to realize efficient near-field beam training with low training overheads.

Massive multiple-input multiple-output (MIMO) and full-duplex (FD) are promising candidates for achieving the spectral efficiency to meet the needs of 5G communications. One essential key to realizing practical FD massive MIMO systems is how to effectively mitigate the self-interference (SI). Conventionally, however, the performance comparison of different SI methods by reflecting the actual channel characteristics was insufficient in the literature. Accordingly, this paper presents a performance analysis of SI cancellation (SIC) methods in FD massive MIMO systems. Analytical and numerical results confirm that, in an imperfect channel-estimation case, the ergodic rates performance of the spatial suppression in the uplink outperforms those of the SI subtraction, due to the correlation between the precoder and the estimation error of the SI channel. In addition, we discuss which method performs better under different given system constraints such as uplink and downlink sum rates, the total transmit power, and the power scaling law.

Reconfigurable intelligent surfaces (RISs) have emerged as a candidate technology for future 6G networks. However, due to the “multiplicative fading” effect, the existing passive RISs only achieve a negligible capacity gain in environments with strong direct links. In this paper, the concept of active RISs is studied to overcome this fundamental limitation. Unlike the existing passive RISs that reflect signals without amplification, active RISs can amplify the reflected signals via amplifiers integrated into their elements. To characterize the signal amplification and incorporate the noise introduced by the active components, we verify the signal model of active RISs through the experimental measurements on a fabricated active RIS element. Based on the verified signal model, we formulate the sum-rate maximization problem for an active RIS aided multi-user multiple-input single-output (MU-MISO) system and a joint transmit precoding and reflect beamforming algorithm is proposed to solve this problem. Simulation results show that, in a typical wireless system, the existing passive RISs can realize only a negligible sum-rate gain of 3%, while the active RISs can achieve a significant sum-rate gain of 62%, thus over coming the “multiplicative fading” effect. Finally, we develop a 64-element active RIS aided wireless communication prototype, and the significant gain of active RISs is validated by field test.

The development of reconfigurable intelligent surface (RIS) will evolve towards the extremely large-scale RIS (XL-RIS) to overcome the “multiplicative fading effect”. With the increase in array aperture, this evolution leads to the near-field propagation becoming dominant. To achieve the performance gain of XL-RIS, it is effective to explore the near-field beam design with a codebook via beam training. Unfortunately, due to the constant modulus constraint of XL-RIS, most of the existing works in the near-field scenario focus on single-beam design. Hence, these works will face a serious loss for the quality of service in the multiple user equipments case. In this paper, we propose a block coordinate descent (BCD) based scheme with majorization-minimization (MM) algorithm for the multi-beam design. The proposed scheme handles the constant modulus constraint from two aspects. Firstly, the multi-beam design is an intractable non-convex quadratic programming problem due to this constraint. We utilize the MM algorithm to decompose this problem as a series of tractable sub-problems to be iteratively solved. Secondly, the solution space for the multi-beam design is limited due to this constraint, we introduce the phases for beam gains as an extra optimizable variable to enrich the degree of freedom for optimization. Simulation results show that the proposed multi-beam design can achieve a superior quality of service 50% higher than the existing schemes.

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 CAPMIMO. 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.

Shannon-Hartley theorem can accurately calculate the channel capacity when the signal observation time is infinite. However, the calculation of finite-time mutual information, which remains unknown, is essential for guiding the design of practical communication systems. In this paper, we investigate the mutual information between two correlated Gaussian processes within a finite-time observation window. We first derive the finite-time mutual information by providing a limit expression. Then we numerically compute the mutual information within a single finite-time window. We reveal that the number of bits transmitted per second within the finite-time window can exceed the mutual information averaged over the entire time axis, which is called the exceed-average phenomenon. Furthermore, we derive a finite-time mutual information formula under a typical signal autocorrelation case by utilizing the Mercer expansion of trace class operators, and reveal the connection between the finite-time mutual information problem and the operator theory. Finally, we analytically prove the existence of the exceed-average phenomenon in this typical case, and demonstrate its compatibility with the Shannon capacity.

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 significant. Unlike the classical far-field line-of-sight (LoS) channel with only one available data stream, 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 fully utilize the extra DoFs in the near-field region. In this paper, to exploit the near-field effect as a new possibility for capacity improvement, the distance-aware precoding (DAP) architecture is developed, where each RF chain can be flexibly configured to active or inactive according to the distance-related DoFs. Moreover, based on the developed DAP architecture, a DAP algorithm is proposed to optimize the number of activated RF chains and precoding matrices to match the increased DoFs. Finally, simulation results verify that, the proposed DAP scheme can efficiently utilize the extra DoFs in the near-field region to improve the spectrum efficiency.

Accurate channel prediction is vital to address the channel aging issue in mobile communications with fast time-varying channels. Existing channel prediction schemes are generally based on the sequential signal processing, i.e., the channel in the next frame can only be sequentially predicted. Thus, the accuracy of channel prediction rapidly degrades with the evolution of frame due to the error propagation problem in the sequential operation. To overcome this challenging problem, we propose a transformer-based parallel channel prediction scheme to predict future channels in parallel. Specifically, we first formulate the channel prediction problem as a parallel channel mapping problem, which predicts the channels in next several frames in parallel. Then, inspired by the recently proposed parallel vector mapping model named transformer, a transformer-based parallel channel prediction scheme is proposed to solve this formulated problem. Relying on the attention mechanism in machine learning, the transformer-based scheme naturally enables parallel signal processing to avoid the error propagation problem. The transformer can also adaptively assign more weights and resources to the more relevant historical channels to facilitate accurate prediction for future channels. Moreover, we propose a pilot-to-precoder (P2P) prediction scheme that incorporates the transformer-based parallel channel prediction as well as pilot-based channel estimation and precoding. In this way, the dedicated channel estimation and precoding can be avoided to reduce the signal processing complexity. Finally, simulation results verify that the proposed schemes are able to achieve a negligible sum-rate performance loss for practical 5G systems in mobile scenarios.

Extremely large-scale multiple-input-multiple-output (XL-MIMO) is promising to meet the high rate requirements for future 6G. To realize efficient precoding, accurate channel state information is essential. Existing channel estimation algorithms with low pilot overhead heavily rely on the channel sparsity in the angular domain, which is achieved by the classical far-field planar-wavefront assumption. However, due to the non-negligible near-field spherical-wavefront property in XL-MIMO, this channel sparsity in the angular domain is not achievable. Therefore, existing far-field channel estimation schemes will suffer from severe performance loss. To address this problem, in this paper, we study the near-field channel estimation by exploiting the polar-domain sparsity. Specifically, unlike the classical angular-domain representation that only considers the angular information, we propose a polar-domain representation, which simultaneously accounts for both the angular and distance information. In this way, the near-field channel also exhibits sparsity in the polar domain, based on which, we propose on-grid and off-grid near-field XL-MIMO channel estimation schemes. Firstly, an on-grid polar-domain simultaneous orthogonal matching pursuit (P-SOMP) algorithm is proposed to efficiently estimate the near-field channel. Furthermore, an off-grid polar-domain simultaneous iterative gridless weighted (P-SIGW) algorithm is proposed to improve the estimation accuracy. Finally, simulations are provided to verify the effectiveness of our schemes.

The downlink channel estimation requires a huge pilot overhead in the reconfigurable intelligent surface (RIS) assisted communication system. By exploiting the powerful learning ability of the neural network, the machine learning (ML) technique can be used to estimate the high-dimensional channel from a few received pilot signals at the user. However, since the training dataset collected by the single user only contains the information of part of the channel scenarios of a cell, the neural network trained by the single user is not able to work when the user moves from one channel scenario to another. To solve this challenge, we propose to leverage the distributed machine learning (DML) technique to enable the reliable downlink channel estimation. Specifically, we firstly build a downlink channel estimation neural network shared by all users, which can be collaboratively trained by the BS and the users with the help of the DML technique. Then, we further propose a hierarchical neural network architecture to improve the channel estimation accuracy, which can extract different channel features for different channel scenarios. Simulation results show that compared with the neural network trained by the single user, the proposed DML based neural networks can achieve better estimation performance with the reduced pilot overhead for all users from different scenarios.

Large-scale antenna arrays employed by the base station (BS) constitute an essential next-generation communications technique. However, due to the constraints of size, cost, and power consumption, it is usually considered unrealistic to use a large-scale antenna array at the user side. Inspired by the emerging technique of reconfigurable intelligent surfaces (RIS), we firstly propose the concept of user-specific RIS (US-RIS) for facilitating the employment of a large-scale antenna array at the user side in a cost- and energy-efficient way. In contrast to the existing employments of RIS, which belong to the family of base-station-specific RISs (BSS-RISs), the US-RIS concept by definition facilitates the employment of RIS at the user side for the first time. This is achieved by conceiving a multi-layer structure to realize a compact form-factor. Furthermore, our theoretical results demonstrate that, in contrast to the existing single-layer structure, where only the phase of the signal reflected from RIS can be adjusted, the amplitude of the signal penetrating multi-layer US-RIS can also be partially controlled, which brings about a new degree of freedom (DoF) for beamformer design that can be beneficially exploited for performance enhancement. In addition, based on the proposed multi-layer US-RIS, we formulate the signal-to-noise ratio (SNR) maximization problem of US-RIS-aided communications. Due to the non-convexity of the problem introduced by this multi-layer structure, we propose a multi-layer transmit beamformer design relying on an iterative algorithm for finding the optimal solution by alternately updating each variable. Finally, our simulation results verify the superiority of the proposed multi-layer US-RIS as a compact realization of a large-scale antenna array at the user side for uplink transmission.

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.

With the help of non-orthogonal multiple access (NOMA), the number of connections of the beamspace multiple-input multiple-output (MIMO) systems can be improved with enhanced sum-rate performance, which constitutes beamspace MIMO-NOMA. Thus, most relevant papers focus on improving the system sum rate, which may inflict unbearable rate loss to weak users. To ensure the achievable rates of weak users, we maximize and analyze the minimal rate of the system in the single-beam case as well as the multi-beam case, where two completely different phenomena are revealed. Particularly, in the single-beam case, the maximized minimal rate of the beamspace MIMO-NOMA always grows rapidly with the signal-to-noise-ratio (SNR), and is larger than that of the beamspace MIMO using orthogonal multiple access (beamspace MIMO-OMA). However, in the multi-beam case, the maximized minimal rate of the beamspace MIMO-NOMA grows slower and slower in the high-SNR region, where it is smaller than that of the beamspace MIMO-OMA. To explain this difference, it is disclosed that the intra-beam interference in the single-beam case is ofsuccessive pattern, which is proved to have no limit on the max-min rate. In contrast, the inter-beam interference in the multi-beam case is ofmutual pattern, which is proved to restrict the max-min rate to a derived upper bound.

Extremely large-scale multiple-input-multiple-output (XL-MIMO) with hybrid precoding is a promising technique to meet the high rate requirements for future 6G. To realize efficient precoding, accurate channel estimation is essential. Existing channel estimation algorithms with low pilot overhead heavily rely on the channel sparsity in angle domain, which is achieved by the classical far-field planar-wavefront assumption. However, this sparsity is not available, due to the non-negligible near-field spherical-wavefront property in XL-MIMO. Therefore, existing far-field estimation schemes will suffer from severe performance loss. To address this problem, in this paper, we study the near-field channel estimation by exploiting the polar-domain sparsity. Specifically, unlike the classical angle-domain representation that only considers the angle information of channel, we propose a polar-domain representation, which simultaneously accounts both the angle and distance information. In this way, the near-field channel also exhibits sparsity in polar domain. Exploiting this polar-domain sparsity, we propose an polar-domain simultaneous orthogonal matching pursuit (P-SOMP) algorithm to efficiently estimate the near-field channel. Finally, simulations are provided to verify the effectiveness of our schemes.

Massive multiple-input multiple-output (MIMO) with a large-scale antenna array at the base station (BS) side is one of the most essential techniques for 5G wireless communications. However, due to the forbidden hardware cost and mismatched size, it is physically limited to deploy massive MIMO at the user side. To break this limitation, inspired by the promising technique called reconfigurable intelligent surface (RIS), we firstly propose the concept of user-side RIS (US-RIS) which is a cost- and energy-efficient realization for large-scale array at the user side. Different from the existing RISs that work as base-station-side RISs (BSS-RISs), US-RIS is the first usage of RIS at the user side. Then, we propose a novel architecture of user with the aid of US-RIS with a multi-layer structure for compact implementation. Based on the proposed multi-layer US-RIS, we formulate the signal-to-noise ratio (SNR) maximization problem in the US-RIS-aided communications. To tackle the challenge of solving this non-convex problem, we propose a multi-layer precoding design that can obtain the optimal parameters at transceivers and US-RIS by iterative optimization. Finally, simulation results are shown to verify the practicability and superiorities of the proposed multi-layer US-RIS as a realization of the large-scale array at the user side.

Leveraging powerful deep learning techniques, the end-to-end (E2E) learning of communication system is able to outperform the classical communication system. Unfortunately, this communication system cannot be trained by deep learning without known channel. To deal with this problem, a generative adversarial network (GAN) based training scheme has been recently proposed to imitate the real channel. However, the gradient vanishing and overfitting problems of GAN will result in the serious performance degradation of E2E learning of communication system. To mitigate these two problems, this paper proposes a residual aided GAN (RA-GAN) based training scheme. Particularly, inspired by the idea of residual learning, we propose a residual generator to mitigate the gradient vanishing problem by realizing a more robust gradient back propagation. Moreover, to cope with the overfitting problem, we reconstruct the loss function for training by adding a regularizer, which limits the representation ability of RA-GAN. Simulation results show that the trained residual generator has better generation performance than conventional generator, and the proposed RA-GAN based training scheme can achieve the near-optimal block error rate (BLER) performance.

Hybrid precoding design is a high-complexity problem due to the coupling of analog and digital precoders as well as the constant modulus constraint for the analog precoder. Fortunately, the deep learning based hybrid precoding methods can significantly reduce the complexity, but the performance remains limited. In this paper, inspired by the attention mechanism recently developed for machine learning, we propose an attention-based hybrid precoding scheme for millimeter-wave (mmWave) MIMO systems with improved performance and low complexity. The key idea is to design each user’s beam pattern according to its attention weights to other users’. Specifically, the proposed attention-based hybrid precoding scheme consists of two parts, i.e., the attention layer and the convolutional neural network (CNN) layer. The attention layer is used to identify the features of inter-user interferences. Then, these features are processed by the CNN layer for the analog precoder design to maximize the achievable sum-rate. Simulation results demonstrate that the attention layer could mitigate the inter-user interferences, and the proposed attention-based hybrid precoding with low complexity can achieve higher achievable sum-rate than the existing deep learning based method.

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.

Channel estimation is challenging for the reconfigurable intelligent surface (RIS)-aided wireless communications. Since the number of coefficients of the cascaded channel among the base station (BS), the RIS, and the user equipment (UE), is the product of the number of BS antennas, the number of RIS elements, and the number of UEs, the pilot overhead can be prohibitively high. In this paper, we propose a two-timescale channel estimation framework to exploit the property that the BS-RIS channel is high-dimensional but quasi-static, while the RIS-UE channel is mobile but low-dimensional. Specifically, to estimate the quasi-static BS-RIS channel, we propose a dual-link pilot transmission scheme, where the BS transmits downlink pilots and receives uplink pilots reflected by the RIS. Then, we propose a coordinate descent-based algorithm to recover the BS-RIS channel. Since the quasi-static BS-RIS channel is estimated less frequently than the mobile channel is, the average pilot overhead can be reduced from a long-term perspective. Although the mobile RIS-UE channel has to be frequently estimated in a small timescale, the associated pilot overhead is low thanks to its low dimension. Simulation results show that the proposed two-timescale channel estimation framework can achieve accurate channel estimation with low pilot overhead.

Reconfigurable intelligent surface (RIS) has recently received extensive research interest due to its capability to intelligently change the wireless propagation environment. For RIS-aided wireless communications in frequency division duplex (FDD) mode, channel feedback at the user equipment (UE) is essential for the base station (BS) to acquire the downlink channel state information (CSI). In this paper, a dimension reduced channel feedback scheme is proposed to reduce the channel feedback overhead by exploiting the single-structured sparsity of BS-RIS-UE cascaded channel. Since different UEs share the same sparse BS-RIS channel but have their respective RIS-UE channels, there are only limited non-zero column vectors in the BS-RIS-UE cascaded channel matrix, and different UEs share the same indexes of the non-zero columns. Thus, the downlink CSI can be decomposed intouser-independentchannel information (i.e., the indexes of non-zero columns) anduser-specificchannel information (i.e., the non-zero column vectors), where the former for all UEs can be fed back by only one UE, while the latter can be fed back with a fairly low overhead by different UEs, respectively. Simulation results show that, compared with the conventional method, the proposed scheme can reduce channel feedback overhead by more than 80% for RIS-aided wireless communications.

Millimeter-wave massive multiple-input multiple-output (MIMO) can use a lens antenna array to considerably reduce the number of radio frequency (RF) chains, but channel estimation is challenging due to the number of RF chains is much smaller than that of antennas. By exploiting the sparsity of beamspace channels, the beamspace channel estimation can be formulated as a sparse signal recovery problem, which can be solved by the classical iterative algorithm named approximate message passing (AMP), and its corresponding version learned AMP (LAMP) realized by a deep neural network (DNN). However, these existing schemes cannot achieve satisfactory estimation accuracy. To improve the channel estimation performance, we propose a prior-aided Gaussian mixture LAMP (GM-LAMP) based beamspace channel estimation scheme in this paper. Specifically, based on the prior information that beamspace channel elements can be modeled by the Gaussian mixture distribution, we first derive a new shrinkage function to refine the AMP algorithm. Then, by replacing the original shrinkage function in the LAMP network with the derived Gaussian mixture shrinkage function, a prior-aided GM-LAMP network is developed to estimate the beamspace channel more accurately. Simulation results by using both the theoretical channel model and the ray-tracing based channel dataset show that, the proposed GM-LAMP network can achieve better channel estimation accuracy than existing schemes.

Large intelligent surface (LIS) has recently emerged as a potential low-cost solution to reshape the wireless propagation environment for improving the spectral efficiency. In this article, we consider a downlink millimeter-wave (mmWave) multiple-input-multiple-output (MIMO) system, where an LIS is deployed to assist the downlink data transmission from a base station (BS) to a user equipment (UE). Both the BS and the UE are equipped with a large number of antennas, and a hybrid analog/digital precoding/combining structure is used to reduce the hardware cost and energy consumption. We aim to maximize the spectral efficiency by jointly optimizing the LIS's reflection coefficients and the hybrid precoder (combiner) at the BS (UE). To tackle this non-convex problem, we reformulate the complex optimization problem into a much more friendly optimization problem by exploiting the inherent structure of the effective (cascade) mmWave channel. A manifold optimization (MO)-based algorithm is then developed. Simulation results show that by carefully devising LIS's reflection coefficients, our proposed method can help realize a favorable propagation environment with a small channel matrix condition number. Besides, it can achieve a performance comparable to those of state-of-the-art algorithms, while at a much lower computational complexity.

Reconfigurable intelligent surface (RIS) has recently received increasing interest due to the superiority of changing the wireless propagation environments intelligently. Channel feedback is essential in frequency division duplex (FDD) RIS-assisted wireless communications, since the downlink channel state information (CSI) has to be acquired by the base station (BS) for the joint beamforming at the BS and the RIS. In this paper, we first investigate the single-structured sparsity of RIS channel, which means that the angular-domain sparse channels of different users share the same non-zero element indices only in the column dimension, but not in the row dimension. Based on this channel characteristic, we then propose a dimension-reduced channel feedback scheme with a low overhead for channel feedback. Specifically, the downlink CSI can be decomposed into the structured CSI and unstructured CSI. The structured CSI of the multi-user channels can be fed back to the BS by only one user according to the single-structured sparsity, while the unstructured CSI can be fed back with a fairly low overhead by different users, respectively. Moreover, by utilizing the angle coherence time, the per-user overhead can be reduced further, while the near-optimal per-user rate can still be guaranteed.

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.

In this paper, we study a multi-beam millimeter- wave beamspace multiple-input multiple-output (MIMO) system with non-orthogonal multiple access (NOMA) to simultaneously accommodate multiple users in a single beam. To improve the data rate while maintaining user fairness, we analyze the max-min rate of the system via power allocation. The challenge is that the existence of both the intra- beam and inter-beam interference makes the power- allocation problem non-convex. To address this issue, we devise a bisection approach to calculate the max-min rate and the corresponding power allocation. We prove that the max-min rate can be achieved when all the users are assigned the same rate. Furthermore, our endeavors reveal that beamspace MIMO-NOMA outperforms the traditional beamspace MIMO in terms of the minimal rate when the power or the number of users is relatively small. When the power or the number of users is relatively large, traditional beamspace MIMO can outperform beamspace MIMO-NOMA since the former is free of inter-beam interference, which has been 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.

Orthogonal time frequency space (OTFS) modulation outperforms orthogonal frequency division multiplexing (OFDM) in high-mobility scenarios. One challenge for OTFS massive MIMO is downlink channel estimation due to the required high pilot overhead. In this paper, we propose a 3D structured orthogonal matching pursuit (3D-SOMP) algorithm based channel estimation technique. First, we show that the OTFS MIMO channel exhibits 3D structured sparsity: normal sparsity along the delay dimension, block sparsity along the Doppler dimension, and burst sparsity along the angle dimension. Based on the 3D structured channel sparsity, we then formulate the downlink channel estimation problem as a sparse signal recovery problem. Simulation results show that the proposed 3D-SOMP algorithm can achieve accurate channel state information with low pilot overhead.

We introduce a full duplex non-orthogonal multiple access (FD-NOMA)-based decentralized vehicle to everything (V2X) system model and focus on its capacity performance analysis. In order to solve the computation complicated problems of the involved exponential integral functions and infinite factorial expressions, we give approximate closed-form expressions with controllable arbitrary small errors. We find the accuracy of our approximate expressions is controlled by the division of $\frac{\pi}{2}$ in the urban and crowded (UC) scenario, and the truncation point $T$ in the suburban and remote (SR) scenario. Numerical results manifest 1) Increasing the number of V2X device, NOMA power and Rician factor value yields better capacity performance. 2) Effect of FD-NOMA is determined by the FD self-interference and the channel noise. 3) FD-NOMA has better latency performance compared to other schemes.

Non-orthogonal multiple access (NOMA) has been recently considered in millimeter-wave (mmWave) massive MIMO systems to further enhance the spectrum efficiency. In addition, simultaneous wireless information and power transfer (SWIPT) is a promising solution to maximize the energy efficiency. In this paper, for the first time, we investigate the integration of SWIPT in mmWave massive MIMO-NOMA systems. As mmWave massive MIMO will likely use hybrid precoding (HP) to significantly reduce the number of required radio-frequency (RF) chains without an obvious performance loss, where the fully digital precoder is decomposed into a high-dimensional analog precoder and a low-dimensional digital precoder, we propose to apply SWIPT in HP-based MIMO-NOMA systems, where each user can extract both information and energy from the received RF signals by using a power splitting receiver. Specifically, the cluster-head selection algorithm is proposed to select one user for each beam at first, and then the analog precoding is designed according to the selected cluster heads for all beams. After that, user grouping is performed based on the correlation of users' equivalent channels. Then, the digital precoding is designed by selecting users with the strongest equivalent channel gain in each beam. Finally, the achievable sum rate is maximized by jointly optimizing power allocation for mmWave massive MIMO-NOMA and power splitting factors for SWIPT, and an iterative optimization algorithm is developed to solve the non-convex problem. Simulation results show that the proposed HP-based MIMO-NOMA with SWIPT can achieve higher spectrum and energy efficiency compared with HP-based MIMO-OMA with SWIPT.

In this paper, we investigate the resource allocation algorithm design for multicarrier solar-powered unmanned aerial vehicle (UAV) communication systems. In particular, the UAV is powered by the solar energy enabling sustainable communication services to multiple ground users. We study the joint design of the 3D aerial trajectory and the wireless resource allocation for maximization of the system sum throughput over a given time period. As a performance benchmark, we first consider an off-line resource allocation design assuming non-causal knowledge of the channel gains. The algorithm design is formulated as a mixed-integer non-convex optimization problem taking into account the aerodynamic power consumption, solar energy harvesting, a finite energy storage capacity, and the quality-of-service requirements of the users. Despite the non-convexity of the optimization problem, we solve it optimally by applying monotonic optimization to obtain the optimal 3D-trajectory and the optimal power and subcarrier allocation policy. Subsequently, we focus on the online algorithm design that only requires real-time and statistical knowledge of the channel gains. The optimal online resource allocation algorithm is motivated by the off-line scheme and entails a high computational complexity. Hence, we also propose a low-complexity iterative suboptimal online scheme based on the successive convex approximation. Our simulation results reveal that both the proposed online schemes closely approach the performance of the benchmark off-line scheme and substantially outperform two baseline schemes. Furthermore, our results unveil the tradeoff between solar energy harvesting and power-efficient communication. In particular, the solar-powered UAV first climbs up to a high altitude to harvest a sufficient amount of solar energy and then descends again to a lower altitude to reduce the path loss of the communication links to the users it serves.

High power consumption and expensive hardware are two bottlenecks for practical massive multiple-input multiple-output (mMIMO) systems. One promising solution is to employ low-resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs). In this paper, we consider a general multipair mMIMO relaying system with a mixed-ADC/DAC architecture, in which some antennas are connected to low-resolution ADCs/DACs, while the rest of the antennas are connected to high-resolution ADCs/DACs. Leveraging on the additive quantization noise model, both exact and approximate closed-form expressions for the achievable rate are derived. It is shown that the achievable rate can approach the unquantized one by using only 2-3 bits of resolutions. Moreover, a power scaling law is presented to reveal that the transmit power can be scaled down inversely proportional to the number of antennas at the relay. We further propose an efficient power allocation scheme by solving a complementary geometric programming problem. In addition, a tradeoff between the achievable rate and power consumption for different numbers of low-resolution ADCs/DACs is investigated by deriving the energy efficiency. Our results reveal that the large antenna array can be exploited to enable the mixed-ADC/DAC architecture, which significantly reduces the power consumption and hardware cost for practical mMIMO systems.

In order to meet the requirements of massively connected devices, different quality of services (QoS), various transmit rates, and ultra-reliable and low latency communications (URLLC) in vehicle-to-everything (V2X) communications, we introduce a full duplex non-orthogonal multiple access (FD-NOMA)-based decentralized V2X system model. We, then, classify the V2X communications into two scenarios and give their exact capacity expressions. To solve the computation complicated problems of the involved exponential integral functions, we give the approximate closed-form expressions with arbitrary small errors. Numerical results indicate the validness of our derivations. Our analysis has that the accuracy of our approximate expressions is controlled by the division of π/2 in the urban and crowded scenarios, and the truncation point T in the suburban and remote scenarios. Numerical results manifest that: 1) increasing the number of V2X device, NOMA power, and Rician factor value yields a better capacity performance; 2) effect of FD-NOMA is determined by the FD self-interference and the channel noise; and 3) FD-NOMA has a better latency performance compared with other schemes.

In time-division duplexing (TDD) systems, massive multiple-input multiple-output (MIMO) relies on the channel reciprocity to obtain the downlink (DL) channel state information (CSI) with the acquired uplink (UL) CSI at the base station (BS). However, the mismatches in the radio frequency (RF) analog circuits at different antennas at the BS break the end-to-end UL and DL channel reciprocity. To restore the channel reciprocity, it is necessary to calibrate all the antennas at the BS. This paper addresses the interconnection strategy for the internal self-calibration at the BS where different antennas are interconnected via hardware transmission lines. Specifically, the paper reveals the optimality of the star interconnection and the daisy chain interconnection respectively. From the results, we see the star interconnection is the optimal interconnection strategy when the B S are given the same number of measurements. On the other hand, the daisy chain interconnection outperforms the star interconnection when the same amount of time resources are consumed. Numerical results corroborate our theoretical analyses.

Beamspace channel estimation is essential for wideband millimeter-wave (mmWave) MIMO with lens antenna array to achieve substantial increase in data rates with considerably reduced number of radio-frequency chains. However, most of existing beamspace channel estimation schemes are designed for narrowband mmWave systems, while rather scarce wideband schemes ideally assume beamspace channel enjoys the common support in frequency-domain. In this paper, inspired by the classical successive interference cancellation for multi-user detection, we propose an efficient successive support detection (SSD) based scheme without the assumption of common support. Specifically, we first demonstrate that each path component of the wideband beamspace channel exhibits a unique frequency-varying sparse structure. Based on this, we then successively estimate all sparse path components. For each path component, its supports at different frequencies are jointly estimated to improve the estimation accuracy, and then its influence is removed to estimate the remained path components. Once all path components have been estimated, the wideband beamspace channel can be recovered at a low complexity. Simulation results verify that the proposed SSD based beamspace channel estimation scheme achieves higher accuracy than existing wideband schemes.

In this paper, we design a robust resource allocation algorithm for a wireless-powered communication network (WPCN) taking into account residual hardware impairments (HWIs) at the transceivers, the imperfectness of the channel state information, and the non-linearity of practical radio frequency energy harvesting circuits. In order to ensure power-efficient secure communication, physical layer security techniques are exploited to deliberately degrade the channel quality of a multiple-antenna eavesdropper. The resource allocation algorithm design is formulated as a non-convex optimization problem for minimization of the total power consumption in the network, while guaranteeing the quality of service of the information receivers in terms of secrecy rate. The globally optimal solution of the optimization problem is obtained via a 2-D search and semidefinite programming relaxation. To strike a balance between computational complexity and system performance, a low-complexity iterative suboptimal resource allocation algorithm is also proposed. Numerical results demonstrate that both the proposed optimal and suboptimal schemes can significantly reduce the total system power consumption required for guaranteeing secure communication, and unveil the impact of HWIs on the system performance: 1) residual HWIs create a system performance bottleneck in WPCN in the high transmit/receive power regimes; 2) increasing the number of transmit antennas can effectively reduce the power consumption of wireless power transfer and alleviate the performance degradation due to residual HWIs; and 3) imperfect CSI exacerbates the impact of residual HWIs, which increases the power consumption of both wireless power and wireless information transfer.

Channel feedback is essential in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems. Unfortunately, prior work on multiuser MIMO has shown that the feedback overhead scales linearly with the number of base station (BS) antennas, which is large in massive MIMO systems. To reduce the feedback overhead, we propose an angle-of-departure (AoD) adaptive subspace codebook for channel feedback in FDD massive MIMO systems. Our key insight is to leverage the observation that path AoDs vary more slowly than the path gains. Within the angle coherence time, by utilizing the constant AoD information, the proposed AoD-adaptive subspace codebook is able to quantize the channel vector in a more accurate way. From the performance analysis, we show that the feedback overhead of the proposed codebook only scales linearly with a small number of dominant (path) AoDs instead of the large number of BS antennas. Moreover, we compare the proposed quantized feedback technique using the AoD-adaptive subspace codebook with a comparable analog feedback method. Extensive simulations show that the proposed AoD-adaptive subspace codebook achieves good channel feedback quality, while requiring low overhead.

Hardware impairments, such as phase noise, quantization errors, non-linearities, and noise amplification, have baneful effects on wireless communications. In this paper, we investigate the effect of hardware impairments on multipair massive multiple-input multiple-output (MIMO) two-way full-duplex relay systems with amplify-and-forward scheme. More specifically, novel closed-form approximate expressions for the spectral efficiency are derived to obtain some important insights into the practical design of the considered system. When the number of relay antennas N increases without bound, we propose a hardware scaling law, which reveals that the level of hardware impairments that can be tolerated is roughly proportional to √N. This new result inspires us to design low-cost and practical multipair massive MIMO two-way full-duplex relay systems. Moreover, the optimal number of relay antennas is derived to maximize the energy efficiency. Finally, Motor-Carlo simulation results are provided to validate our analytical results.

Hybrid precoding is a promising technique for mmWave massive MIMO systems, as it can considerably reduce the number of required radio-frequency (RF) chains without obvious performance loss. However, most of the existing hybrid precoding schemes require a complicated phase shifter network, which still involves high energy consumption. In this paper, we propose an energy-efficient hybrid precoding architecture, where the analog part is realized by a small number of switches and inverters instead of a large number of high-resolution phase shifters. Our analysis proves that the performance gap between the proposed hybrid precoding architecture and the traditional one is small and keeps constant when the number of antennas goes to infinity. Then, inspired by the cross-entropy (CE) optimization developed in machine learning, we propose an adaptive CE (ACE)-based hybrid precoding scheme for this new architecture. It aims to adaptively update the probability distributions of the elements in hybrid precoder by minimizing the CE, which can generate a solution close to the optimal one with a sufficiently high probability. Simulation results verify that our scheme can achieve the near-optimal sum-rate performance and much higher energy efficiency than traditional schemes.

Channel feedback is essential for frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems to realize precoding and power allocation. Traditional codebooks for channel feedback, where the required number of feedback bits is proportional to the number of base station (BS) antennas, can not scale up with massive MIMO due to the large number of BS antennas. To solve this problem, in this paper, we propose an angle-of-departure (AoD) adaptive subspace codebook to reduce the codebook size and feedback overhead. Specifically, by leveraging the concept of angle coherence time, which implies that the path AoDs vary much slower than path gains, we propose an AoD-adaptive subspace codebook to quantize the channel vector in a more accurate way. We also provide performance analysis of the proposed AoD-adaptive subspace codebook, where we prove that the required number of feedback bits only scales linearly with the number of resolvable AoDs, which is much smaller than the number of BS antennas. This quantitative result is also verified by simulations.

We study the beamforming design for multiuser systems with simultaneous wireless information and power transfer (SWIPT). Employing a practical non-linear energy harvesting (EH) model, the design is formulated as a non-convex optimization problem for the maximization of the minimum harvested power across several energy harvesting receivers. The proposed problem formulation takes into account imperfect channel state information (CSI) and a minimum required signal-to-interference-plus-noise ratio (SINR). The globally optimal solution of the design problem is obtained via the semidefinite programming (SDP) relaxation approach. Interestingly, we can show that at most one dedicated energy beam is needed to achieve optimality. Numerical results demonstrate that with the proposed design a significant performance gain and improved fairness can be provided to the users compared to two baseline schemes.

Heterogeneous networks (HetNets), which involve densely deployed femtocells underlaid traditional macrocell network, is a promising solution to the extremely high data rate requirements of the future 5G communications. In this paper, we analyze the closed-form optimal deployment of femtocells in HetNets to maximize the network throughput under the outage constraints from both macrocells and femtocells. Specifically, we model the random distribution of macro cell users (MUEs) and femtocell base stations (FBSs) as Poisson Point Processes (PPPs). Then, the closed form expressions for outage probabilities in both uplink and downlink transmissions are derived. Further, we study the network throughput maximization problem under the outage probability constraints. Finally, With the help of convex optimization, the interval of FBS density, which contains the maximum network throughput is obtained in closed form. Simulation results validate the impact of the system parameters on the different optimal FBS density as well as the influence of interference to the maximum network throughput.

The recent concept of beamspace multiple-input multiple-output (MIMO) is capable of significantly reducing the number of radio-frequency (RF) chains required by millimeter-wave (mmWave) massive MIMO systems. However, the fundamental limit of the existing beamspace MIMO is that, the number of supported users cannot be higher than the number of RF chains using the same time-frequency resources. To break this limit, beamspace MIMO is integrated with non-orthogonal multiple access (NOMA) in the proposed MIMO-NOMA system in this paper, where the number of supported users can be higher than the number of RF chains. To reduce the inter-beam interference, a transmit precoding (TPC) scheme based on the principle of zero-forcing (ZF) is designed. Furthermore, a dynamic power allocation scheme is proposed for maximizing the achievable sum rate. Moreover, a low-complexity iterative optimization algorithm is conceived for dynamic power allocation. Simulation results show that the proposed beamspace MIMO-NOMA achieves a higher spectrum and energy efficiency than the existing beamspace MIMO for mmWave communications.

This paper proposes a novel hybrid downlinkuplink cooperative NOMA (HDU-CNOMA) scheme to achieve a better tradeoff between spectral efficiency and signal reception reliability than the conventional cooperative NOMA schemes. In particular, the proposed scheme enables the strong user to perform a cooperative transmission and an interference-free uplink transmission simultaneously during the cooperative phase, at the expense of a slightly decrease in signal reception reliability at the weak user. We analyze the outage probability, diversity order, and outage throughput of the proposed scheme. Simulation results not only confirm the accuracy of the developed analytical results, but also unveil the spectral efficiency gains achieved by the proposed scheme over a baseline cooperative NOMA scheme and a non-cooperative NOMA scheme.

The recently proposed concept of beamspace MIMO can significantly reduce the number of power- hungry radio frequency (RF) chains in millimeter- wave (mmWave) massive MIMO systems. However, most existing studies ignore the power leakage problem in beamspace MIMO systems, which results in an obvious loss in the achievable sum rate. In this paper, a phase shifter network (PSN)-based precoding structure is proposed to solve this problem. Its key idea is to employ multiple phase shifters from each RF chain to select multiple instead of only one beam to collect most of the leaked power. Based on the proposed structure, a rotation-based precoding algorithm is further designed to maximize the signal-to-noise-ratio (SNR) of each user by rotating the channel gains of the selected beams to the same direction. Simulation results show that the proposed PSN- based precoding can effectively collect the leaked power to achieve the near-optimal sum rate, and enjoys a higher energy efficiency than the conventional precoding solutions.

The recent concept of beamspace multiple input multiple output (MIMO) can significantly reduce the number of required radio frequency (RF) chains in millimeter-wave (mmWave) massive MIMO systems without obvious performance loss. However, the fundamental limit of existing beamspace MIMO is that the number of supported users cannot be larger than the number of RF chains at the same time-frequency resources. To break this fundamental limit, in this paper, we propose a new spectrum and energy-efficient mmWave transmission scheme that integrates the concept of non-orthogonal multiple access (NOMA) with beamspace MIMO, i.e., beamspace MIMO-NOMA. By using NOMA in beamspace MIMO systems, the number of supported users can be larger than the number of RF chains at the same time-frequency resources. In particular, the achievable sum rate of the proposed beamspace MIMO-NOMA in a typical mmWave channel model is analyzed, which shows an obvious performance gain compared with the existing beamspace MIMO. Then, a precoding scheme based on the principle of zero forcing is designed to reduce the inter-beam interferences in the beamspace MIMO-NOMA system. Furthermore, to maximize the achievable sum rate, a dynamic power allocation is proposed by solving the joint power optimization problem, which not only includes the intra-beam power optimization, but also considers the inter-beam power optimization. Finally, an iterative optimization algorithm with low complexity is developed to realize the dynamic power allocation. Simulation results show that the proposed beamspace MIMO-NOMA can achieve higher spectrum and energy efficiency compared with the existing beamspace MIMO.

Millimeter wave (mmWave) communications have recently attracted large research interest, since the huge available bandwidth can potentially lead to the rates of multiple gigabit per second per user. Though mmWave can be readily used in stationary scenarios, such as indoor hotspots or backhaul, it is challenging to use mmWave in mobile networks, where the transmitting/receiving nodes may be moving, channels may have a complicated structure, and the coordination among multiple nodes is difficult. To fully exploit the high potential rates of mmWave in mobile networks, lots of technical problems must be addressed. This paper presents a comprehensive survey of mmWave communications for future mobile networks (5G and beyond). We first summarize the recent channel measurement campaigns and modeling results. Then, we discuss in detail recent progresses in multiple input multiple output transceiver design for mmWave communications. After that, we provide an overview of the solution for multiple access and backhauling, followed by the analysis of coverage and connectivity. Finally, the progresses in the standardization and deployment of mmWave for mobile networks are discussed.

For the potential of providing rates of multiple Giga-bps in a single channel, millimeter wave (mmWave) communications have recently attracted substantial research interest. While mmWave technology is already being used in stationary scenarios such as indoor hotspots or backhaul, it is challenging to use mmWave frequencies in mobile networks, where transmitting/receiving nodes may be moving, channels may have a complicated structure, and the coordination among multiple nodes is difficult. To fully exploit the high potential rates of mmWave in mobile networks, many significant technical challenges must be tackled. The main objective of this IEEE JSAC Special Issue on “Millimeter wave communications for future mobile networks” is to collect the most recent technical advances in mmWave for future mobile networks. The response from the community to the call has been overwhelming. We received 96 submissions with a call period short than 4 months. Many of the submissions are from the most well known research groups in the field. After a strict review process, we decided to accept 38 papers, which will be published in two issues. The papers were selected based on the technical relevance and merits. Unfortunately, due to space limitations, a number of interesting papers were not selected, despite the merits that they had. We sincerely hope those papers can find other publishing venues.

The practical deployment of massive multiple-input multiple-output (MIMO) in the future fifth generation (5G) wireless communication systems is challenging due to its high-hardware cost and power consumption. One promising solution to address this challenge is to adopt the low-resolution analog-to-digital converter (ADC) architecture. However, the practical implementation of such architecture is challenging due to the required complex signal processing to compensate the coarse quantization caused by low-resolution ADCs. Therefore, few high-resolution ADCs are reserved in the recently proposed mixed-ADC architecture to enable low-complexity transceiver algorithms. In contrast to previous works over Rayleigh fading channels, we investigate the performance of mixed-ADC massive MIMO systems over the Rician fading channel, which is more general for the 5G scenarios like Internet of Things. Specially, novel closed-form approximate expressions for the uplink achievable rate are derived for both cases of perfect and imperfect channel state information (CSI). With the increasing Rician K-factor, the derived results show that the achievable rate will converge to a fixed value. We also obtain the power-scaling law that the transmit power of each user can be scaled down proportionally to the inverse of the number of base station (BS) antennas for both perfect and imperfect CSI. Moreover, we reveal the tradeoff between the achievable rate and the energy efficiency with respect to key system parameters, including the quantization bits, number of BS antennas, Rician K-factor, user transmit power, and CSI quality. Finally, numerical results are provided to show that the mixed-ADC architecture can achieve a better energy-rate tradeoff compared with the ideal infinite-resolution and low-resolution ADC architectures.

Device-to-device (D2D) communication can effectively meet the demanding high data rate by providing direct links among mobile users in cellular networks. In this paper, we analyze the transmission capacity of relay-assisted D2D communication coexisting with cellular networks in both overlay and underlay modes. D2D users can use the delicate spectrum resources in the overlay mode, while reuse the cellular resources in the underlay mode. Based on stochastic geometry, cellular users, D2D transmitters, and relay nodes (RNs) in the networks are all modeled as Poisson point process. Then, we calculate the RN existence probability and the expectation of relay link distance to obtain the successful transmission probabilities for D2D communication. According to two relay mechanisms for enhancing D2D transmission distance, we further obtain the transmission capacities of D2D communication with the assistance of RNs in both two modes, which reflect the influence from D2D density and power. In addition, the D2D transmission capacities with variable D2D link distance are also analyzed in two modes. Simulation results verify that D2D transmission capacity can be enhanced by relay transmission and influenced by a multitude of factors, including the user density, power, the D2D link distance, and the way of using RNs.

Millimeter-wave (mm-wave) massive MIMO with lens antenna array can considerably reduce the number of required radio-frequency (RF) chains by beam selection. However, beam selection requires the base station to acquire the accurate information of beamspace channel. This is a challenging task as the size of beamspace channel is large, while the number of RF chains is limited. In this paper, we investigate the beamspace channel estimation problem in mm-wave massive MIMO systems with lens antenna array. Specifically, we first design an adaptive selecting network for mm-wave massive MIMO systems with lens antenna array, and based on this network, we further formulate the beamspace channel estimation problem as a sparse signal recovery problem. Then, by fully utilizing the structural characteristics of the mm-wave beamspace channel, we propose a support detection (SD)-based channel estimation scheme with reliable performance and low pilot overhead. Finally, the performance and complexity analyses are provided to prove that the proposed SD-based channel estimation scheme can estimate the support of sparse beamspace channel with comparable or higher accuracy than conventional schemes. Simulation results verify that the proposed SD-based channel estimation scheme outperforms conventional schemes and enjoys satisfying accuracy even in the low SNR region as the structural characteristics of beamspace channel can be exploited.

Millimeter-wave (mmWave) massive MIMO used for access and backhaul in ultra-dense network (UDN) has been considered as the promising 5G technique. We consider such an heterogeneous network (HetNet) that ultra-dense small base stations (BSs) exploit mmWave massive MIMO for access and backhaul, while macrocell BS provides the control service with low frequency band. However, the channel estimation for mmWave massive MIMO can be challenging, since the pilot overhead to acquire the channels associated with a large number of antennas in mmWave massive MIMO can be prohibitively high. This paper proposes a structured compressive sensing (SCS)-based channel estimation scheme, where the angular sparsity of mmWave channels is exploited to reduce the required pilot overhead. Specifically, since the path loss for non-line-of-sight paths is much larger than that for line-of-sight paths, the mmWave massive channels in the angular domain appear the obvious sparsity. By exploiting such sparsity, the required pilot overhead only depends on the small number of dominated multipath. Moreover, the sparsity within the system bandwidth is almost unchanged, which can be exploited for the further improved performance. Simulation results demonstrate that the proposed scheme outperforms its counterpart, and it can approach the performance bound.

In this paper, we investigate the spectral efficiency (SE) of massive multiple-input multiple-output (MIMO) systems with a large number of antennas at the base station (BS) accounting for physical space constraints. In contrast to the vast body of related literature, which considers fixed inter-element spacing, we elaborate on a practical topology in which an increase in the number of antennas in a fixed total space induces an inversely proportional decrease in the inter-antenna distance. For this scenario, we derive exact and approximate expressions, as well as simplified upper/lower bounds, for the SE of maximum-ratio combining (MRC), zero-forcing (ZF) and minimum mean-squared error receivers (MMSE) receivers. In particular, our analysis shows that the MRC receiver is non-optimal for space-constrained massive MIMO topologies. On the other hand, ZF and MMSE receivers can still deliver an increasing SE as the number of BS antennas grows large. Numerical results corroborate our analysis and show the effect of the number of antennas, the number of users, and the total antenna array space on the sum SE performance.

Sparse adaptive filtering algorithms are utilized to exploit potential sparse structure information as well as to mitigate noises in many unknown sparse systems. Sparse recursive least square (RLS) algorithms have been attracted intensely attentions due to their low-complexity and easy- implementation. Basically, these algorithms are constructed by standard RLS algorithm and sparse penalty functions (e.g., l_1-norm). However, existing sparse RLS algorithms do not exploit the sparsity efficiently. In this paper, an improved adaptive filtering algorithm is proposed by incorporating a novel correntropy induced metric (CIM) constraint into RLS, which is termed as RLS- CIM algorithm. Specifically, we adopt a well-known Gaussian kernel in CIM and further devise a novel variable kernel width to control the sparse penalty in different transient-error scenarios. Numerical simulation results are given to corroborate the proposed algorithm via mean square deviation (MSD).

Massive MIMO has become a promising key technology for future 5G wireless communications to increase the channel capacity and link reliability. However, with greatly increased number of transmit antennas at the base station (BS) in massive MIMO systems, the pilot overhead for accurate acquisition of channel state information (CSI) will be prohibitively high. To address this issue, we propose a block iterative support detection (block-ISD) based algorithm for channel estimation to reduce the pilot overhead. The proposed block-ISD algorithm fully exploits the block sparsity inherent in the block-sparse equivalent channel impulse response (CIR) generated by considering the spatial correlations of MIMO channels. Furthermore, unlike conventional greedy compressive sensing (CS) algorithms that rely on prior knowledge of the channel sparsity level, block-ISD relaxes this demanding requirement and is thus more practically appealing. Simulation results demonstrate that block-ISD yields better normalized mean square error (NMSE) performance than classical CS algorithms, and achieve a reduction of 87.5% pilot overhead than conventional channel estimation techniques.

Millimeter wave (mmWave) MIMO will likely use hybrid analog and digital precoding, which uses a small number of RF chains to reduce the energy consumption associated with mixed signal components like analog-to-digital components not to mention baseband processing complexity. However, most hybrid precoding techniques consider a fully connected architecture requiring a large number of phase shifters, which is also energy-intensive. In this paper, we focus on the more energy-efficient hybrid precoding with subconnected architecture, and propose a successive interference cancelation (SIC)-based hybrid precoding with near-optimal performance and low complexity. Inspired by the idea of SIC for multiuser signal detection, we first propose to decompose the total achievable rate optimization problem with nonconvex constraints into a series of simple subrate optimization problems, each of which only considers one subantenna array. Then, we prove that maximizing the achievable subrate of each subantenna array is equivalent to simply seeking a precoding vector sufficiently close (in terms of Euclidean distance) to the unconstrained optimal solution. Finally, we propose a low-complexity algorithm to realize SIC-based hybrid precoding, which can avoid the need for the singular value decomposition (SVD) and matrix inversion. Complexity evaluation shows that the complexity of SIC-based hybrid precoding is only about 10% as complex as that of the recently proposed spatially sparse precoding in typical mmWave MIMO systems. Simulation results verify that SIC-based hybrid precoding is near-optimal and enjoys higher energy efficiency than the spatially sparse precoding and the fully digital precoding.

Massive MIMO is a promising technique for future 5G communications due to its high spectrum and energy efficiency. To realize its potential performance gain, accurate channel estimation is essential. However, due to massive number of antennas at the base station (BS), the pilot overhead required by conventional channel estimation schemes will be unaffordable, especially for frequency division duplex (FDD) massive MIMO. To overcome this problem, we propose a structured compressive sensing (SCS)-based spatio-temporal joint channel estimation scheme to reduce the required pilot overhead, whereby the spatio-temporal common sparsity of delay-domain MIMO channels is leveraged. Particularly, we first propose the nonorthogonal pilots at the BS under the framework of CS theory to reduce the required pilot overhead. Then, an adaptive structured subspace pursuit (ASSP) algorithm at the user is proposed to jointly estimate channels associated with multiple OFDM symbols from the limited number of pilots, whereby the spatio-temporal common sparsity of MIMO channels is exploited to improve the channel estimation accuracy. Moreover, by exploiting the temporal channel correlation, we propose a space-time adaptive pilot scheme to further reduce the pilot overhead. Additionally, we discuss the proposed channel estimation scheme in multicell scenario. Simulation results demonstrate that the proposed scheme can accurately estimate channels with the reduced pilot overhead, and it is capable of approaching the optimal oracle least squares estimator.

The effective rate is an important performance metric of real-time applications in next generation wireless networks. In this paper, we present an analysis of the effective rate of multiple-input single-output (MISO) systems over α-μ fading channels under a maximum delay constraint. More specifically, novel and highly accurate closed-form approximate expressions of the effective rate are derived for such systems assuming the generalized α-μ channel model. In order to examine the impact of system and channel parameters on the effective rate, we also derive closed-form expressions of the effective rate in asymptotically high and low signal-to-noise ratio (SNR) regimes. Furthermore, connections between our derived results and existing results from the literature are revealed for the sake of completeness. Our results demonstrate that the effective rate is a monotonically increasing function of channel fading parameters α and μ, as well as the number of transmit antennas, while it decreases to zero when the delay constraint becomes stringent.

In this paper, by exploiting the special features of temporal correlations of dynamic sparse channels that path delays change slowly over time but path gains evolve faster, we propose the structured matching pursuit (SMP) algorithm to realize the reconstruction of dynamic sparse channels. Specifically, the SMP algorithm divides the path delays of dynamic sparse channels into two different parts to be considered separately, i.e., the common channel taps and the dynamic channel taps. Based on this separation, the proposed SMP algorithm simultaneously detects the common channel taps of dynamic sparse channels in all time slots at first, and then tracks the dynamic channel taps in each single time slot individually. Theoretical analysis of the proposed SMP algorithm provides a guarantee that the common channel taps can be successfully detected with a high probability, and the reconstruction distortion of dynamic sparse channels is linearly upper bounded by the noise power. Simulation results demonstrate that the proposed SMP algorithm has excellent reconstruction performance with competitive computational complexity compared with conventional reconstruction algorithms.

Millimeter wave (mmWave) massive MIMO can achieve orders of magnitude increase in spectral and energy efficiency, and it usually exploits the hybrid analog and digital precoding to overcome the serious signal attenuation induced by mmWave frequencies. However, most of hybrid precoding schemes focus on the full-array structure, which involves a high complexity. In this paper, we propose a near-optimal iterative hybrid precoding scheme based on the more realistic subarray structure with low complexity. We first decompose the complicated capacity optimization problem into a series of ones easier to be handled by considering each antenna array one by one. Then we optimize the achievable capacity of each antenna array from the first one to the last one by utilizing the idea of successive interference cancelation (SIC), which is realized in an iterative procedure that is easy to be parallelized. It is shown that the proposed hybrid precoding scheme can achieve better performance than other recently proposed hybrid precoding schemes, while it also enjoys an acceptable computational complexity.

Linear precoding techniques, such as zero forcing precoding, can achieve the near-optimal capacity due to the favorable channel propagation in large-scale MIMO systems, but involve complicated matrix inversion of large size. In this paper, we propose a low-complexity linear precoding scheme based on the Gauss-Seidel (GS) method. The proposed scheme can achieve the capacity-approaching performance of the classical linear precoding schemes in an iterative way without complicated matrix inversion, which can reduce the overall complexity by one order of magnitude. We also prove that the proposed GSbased precoding scheme has a faster convergence rate than the recently proposed Neumann-based precoding scheme. Simulation results demonstrate that the proposed scheme can achieve the exact capacity-approaching performance of the classical linear precoding schemes with only a small number of iterations.

In this paper, we consider the low density parity check (LDPC) coded multi-input multi-output (MIMO) system with iterative detection and decoding (IDD). Since the traditional frame-by-frame receiver scheme suffers from a huge decoding delay, we propose an efficient scheme with a shuffled structure between the demapper and decoder, which adopts group vertical shuffled belief propagation (BP) algorithm. The proposed shuffled iterative receiver converges faster and significantly reduces the delay introduced by the IDD process. Simulation results demonstrate that our proposed shuffled iterative receiver exhibits several tenths dB of signal-to-noise ratio gain in comparison to the existing schemes, while imposing a much lower average number of iterations for the IDD process.

Non-orthogonal multiple access (NOMA) has become one of the promising key technologies for future 5G wireless communications to improve spectral efficiency and support massive connectivity. However, in the uplink grant-free NOMA system, the current near-optimal multi-user detection (MUD) based on message passing algorithm (MPA) assumes that the user activity information is exactly known at the receiver, which is impractical yet challenging due to anyone of massive users can randomly enter or leave the system. In this paper, inspired by the observation of user sparsity, we jointly use compressive sensing (CS) and MPA to propose a CS-MPA detector to realize both user activity and data detection for uplink grant-free NOMA. Specifically, the MUD problem is firstly formulated under CS framework by exploiting user sparsity, and then user activity can be detected by sparse signal recovery algorithms in CS. Then, MPA can be performed to reliably detect active users' data. It is shown that the proposed CS-MPA detector with affordable complexity not only outperforms the conventional MPA detector without user activity information, but also achieves very close performance to the genie- knowledge MPA detector with exact knowledge of user activity, especially when the signal-to-noise ratio (SNR) is high.

Optical wireless communication (OWC) has been a rapidly growing research area in recent years. Applying multiple-input multiple-output (MIMO), particularly large-scale MIMO, into OWC is very promising to substantially increase spectrum efficiency. However, one challenging problem to realize such an attractive goal is the practical signal detection algorithm for optical MIMO systems, whereby the linear signal detection algorithm like minimum mean square error (MMSE) can achieve satisfying performance but involves complicated matrix inversion of large size. In this paper, we first prove a special property that the filtering matrix of the linear MMSE algorithm is symmetric positive definite for indoor optical MIMO systems. Based on this property, a low-complexity signal detection algorithm based on the successive overrelaxation (SOR) method is proposed to reduce the overall complexity by one order of magnitude with a negligible performance loss. The performance guarantee of the proposed SOR-based algorithm is analyzed from the following three aspects. First, we prove that the SOR-based algorithm is convergent for indoor large-scale optical MIMO systems. Second, we prove that the SOR-based algorithm with the optimal relaxation parameter can achieve a faster convergence rate than the recently proposed Neumann-based algorithm. Finally, a simple quantified relaxation parameter, which is independent of the receiver location and signal-to-noise ratio, is proposed to guarantee the performance of the SOR-based algorithm in practice. Simulation results verify that the proposed SOR-based algorithm can achieve the exact performance of the classical MMSE algorithm with a small number of iterations.

Free-space optical (FSO) communications can achieve high capacity with huge unlicensed optical spectrum and low operational costs. The corresponding performance analysis of FSO systems over turbulence channels is very limited, particularly when using multiple apertures at both transmitter and receiver sides. This paper aims to provide the ergodic capacity characterization of multiple-input-multiple-output (MIMO) FSO systems over atmospheric turbulence-induced fading channels. The fluctuations of the irradiance of optical channels distorted by atmospheric conditions is usually described by a gamma-gamma (rr) distribution, and the distribution of the sum of rr random variables (RVs) is required to model the MIMO optical links. We use an α - μ distribution to efficiently approximate the probability density function (pdf) of the sum of independent and identical distributed ΓΓ RVs through moment-based estimators. Furthermore, the pdf of the sum of independent, but not necessarily identically distributed ΓΓ RVs can be efficiently approximated by a finite weighted sum of pdfs of ΓΓ distributions. Based on these reliable approximations, novel and precise analytical expressions for the ergodic capacity of MIMO FSO systems are derived. Additionally, we deduce the asymptotic simple expressions in high signal-to-noise ratio regimes, which provide useful insights into the impact of the system parameters on the ergodic capacity. Finally, our proposed results are validated via Monte Carlo simulations.

Conventional time domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) systems have the difficulty to support 256QAM or higher-order modulations and suffer from performance loss, especially under the severe fading channels with long delays. In this letter, a simultaneous time-frequency channel estimation method based on compressive sensing (CS) is proposed to solve this problem. First, the auxiliary channel information is obtained by exploiting the signal structure of TDS-OFDM. Then, a very few frequency-domain pilots in the OFDM block are used to acquire the accurate channel impulse response under the framework of CS. Besides, the auxiliary channel information is utilized to further reduce the complexity of the classical CS algorithm. Simulation results show that the proposed scheme can well support 256QAM under fading channels with long delays and have better performance than the conventional schemes.

For uplink large-scale MIMO systems, linear minimum mean square error (MMSE) signal detection algorithm is near-optimal but involves matrix inversion with high complexity. In this paper, we propose a low-complexity signal detection algorithm based on the successive overrelaxation (SOR) method to avoid the complicated matrix inversion. We first prove a special property that the MMSE filtering matrix is symmetric positive definite for uplink large-scale MIMO systems, which is the premise for the SOR method. Then a low-complexity iterative signal detection algorithm based on the SOR method as well as the convergence proof is proposed. The analysis shows that the proposed scheme can reduce the computational complexity from O(K3) to O(K2), where K is the number of users. Finally, we verify through simulation results that the proposed algorithm outperforms the recently proposed Neumann series approximation algorithm, and achieves the near-optimal performance of the classical MMSE algorithm with a small number of iterations.

Compared with standard cyclic prefix OFDM (CP-OFDM), time domain synchronous OFDM (TDS-OFDM) can achieve a higher spectrum efficiency by using the known training sequence instead of CP as the guard interval. However, TDS-OFDM suffers from reduced energy efficiency and performance loss due to the existing mutual inferences. In this paper, based on the newly emerging theory of structured compressive sensing (SCS), we propose a reliable and energy-efficient TDS-OFDM transmission scheme with reduced guard interval power (which is impossible for CP-OFDM) by designing a channel estimation scheme with high accuracy. The wireless channel properties including channel sparsity and inter-channel correlation, which are usually not considered in conventional OFDM schemes, have been exploited. We further exploit the worst-case system design principle to extract multiple interference-free regions of small size to simultaneously reconstruct multiple channels of large size without iterative interference cancellation. In this way, the guard interval power in TDS-OFDM can be reduced to achieve a 20% higher energy efficiency than standard CP-OFDM, and the system reliability can be also improved in fast fading channels.

Accurate channel estimation is essential for broadband wireless communications. Adaptive sparse channel estimation schemes based on normalized least mean square (NLMS) have been proposed to exploit channel sparsity for improved performance. However, their performance bound as derived in this paper indicates that the invariable step size (ISS) usually used for iteration in these schemes would lead to performance loss or/and slow convergence speed as well as high computational cost. To solve this problem, based on the observation that a large step size is preferred for fast convergence while a small step size is preferred for accurate estimation, we then propose to replace the ISS by the variable step size (VSS) to improve the performance of sparse channel estimation. The key idea is that the VSS can be adaptive to the estimation error in each iteration, i.e., a large step size is used in the case of large estimation error to accelerate the convergence speed, while a small step size is used when the estimation error is small to improve the steady-state estimation accuracy. Finally, simulation results verify that better mean square error (MSE) and bit error rate (BER) performance could be achieved by the proposed scheme.

Coherent optical orthogonal frequency division multiplexing (CO-OFDM) is a promising technique for future elastic optical transport networks. In CO- OFDM systems, the preamble is usually used for timing and frequency synchronization, and dedicated pilots are adopted to carry signaling for flexible system configurations. In this paper, we propose a judicious signaling-embedded preamble design to simultaneously achieve the exact timing synchronization based on the ideal Delta-like timing metric, the accurate frequency synchronization with sufficient estimation range, as well as reliable signaling transmission. This is achieved by designing the preamble in the frequency, i.e., two identical training sequences with a specific distance occupy the even subcarriers of the preamble, whereby the system signaling is carried by the combination of different training sequences and different distances between two sequences. Such frequency-domain design would result in the time-domain preamble having conjugate symmetric property and two repetitive parts. The former property is used to produce the ideal Delta-like correlation function for exact timing synchronization, while the latter one is used for accurate frequency synchronization. Simulation results also show that the improved signaling detection performance can be achieved.

Minimum mean square error (MMSE) signal detection is near-optimal for uplink multi-user large-scale MIMO systems with hundreds of antennas at the base station, but involves matrix inversion with high complexity. In this paper, we first prove that the filtering matrix of the MMSE algorithm in large-scale MIMO is symmetric positive definite, based on which we propose a low-complexity signal detection algorithm by exploiting the Richardson method to avoid the complicated matrix inversion. The proof of the convergence of the proposed scheme is also provided. We then propose a zone-based initial solution by simply checking the values of the received signals, which can accelerate the convergence rate of the Richardson method for high-order modulations to reduce the complexity further. The analysis shows that the complexity can be reduced from O(K3) to O(K2) by the proposed signal detection algorithm, where K is the number of users. Simulation results indicate that the proposed algorithm outperforms the recently proposed Neumann series approximation algorithm and achieves the near-optimal performance of the classical MMSE algorithm.

Time domain synchronous OFDM (TDS-OFDM) can achieve a higher spectrum efficiency than standard cyclic prefix OFDM (CP-OFDM). Currently, it can support constellations up to 64QAM, but cannot support higher-order constellations like 256QAM due to the residual mutual interferences between the pseudorandom noise (PN) guard interval and the OFDM data block. To solve this problem, we break the traditional approach of iterative interference cancellation and propose the idea of using multiple inter-block-interference (IBI)-free regions of very small size to realize simultaneous multi-channel reconstruction under the framework of structured compressive sensing, whereby the sparsity nature of wireless channels as well as the characteristic that path delays vary much slower than path gains are jointly exploited. In this way, the mutually conditional time-domain channel estimation and frequency-domain data demodulation in TDS-OFDM can be decoupled without the use of IBI removal. We then propose the adaptive simultaneous orthogonal matching pursuit (A-SOMP) algorithm with low complexity to realize accurate multi-channel reconstruction, whose performance is close to the Cramér-Rao lower bound (CRLB). Simulation results confirm that the proposed scheme can support 256QAM without changing the current signal structure, so the spectrum efficiency can be increased by about 30%.

The large number of pilots commonly used in OFDM MIMO systems reduces the spectral efficiency in practice. This paper proposes the time-frequency training OFDM (TFT-OFDM) transmission scheme for MIMO systems to solve this problem. The transmission frame is composed of one preamble and the following TFT-OFDM symbols, where each TFT-OFDM symbol without cyclic prefix adopts the time-domain training sequence (TS) and the frequency-domain orthogonal grouped pilots as the time-frequency training information. At the receiver, the time-frequency joint channel estimation directly exploits the “contaminated” time-domain TS to estimate the path delays only, while the path gains are acquired by the frequency-domain grouped pilots. The Cramer-Rao lower bound (CRLB) of the proposed estimator is also derived. Compared with standard OFDM MIMO systems in typical applications, the proposed scheme has about 17% higher spectral efficiency, and has better performance over doubly selective fading channels as indicated by the simulation results.

Large-scale orthogonal frequency division multiplexing (OFDM) multiple-input multiple-output (MIMO) is a promising candidate to achieve the spectral efficiency up to several tens of bps/Hz for future wireless communications. One key challenge to realize practical large-scale OFDM MIMO systems is high-dimensional channel estimation in mobile multipath channels. In this paper, we propose the time-frequency training OFDM (TFT-OFDM) transmission scheme for large-scale MIMO systems, where each TFT-OFDM symbol without cyclic prefix adopts the time-domain training sequence (TS) and the frequency-domain orthogonal grouped pilots as the time-frequency training information. At the receiver, the corresponding time-frequency joint channel estimation method is proposed to accurately track the channel variation, whereby the received time-domain TS is used for path delays estimation without interference cancellation, while the path gains are acquired by the frequency-domain pilots. The channel property that path delays vary much slower than path gains is further exploited to improve the estimation performance, and the sparse nature of wireless channel is utilized to acquire the path gains by very few pilots. We also derive the theoretical Cramer-Rao lower bound (CRLB) of the proposed channel estimator. Compared with conventional large-scale OFDM MIMO systems, the proposed TFT-OFDM MIMO scheme achieves higher spectral efficiency as well as the coded bit error rate performance close to the ergodic channel capacity in mobile environments.

Time domain synchronous OFDM (TDS-OFDM) has higher spectral efficiency and faster synchronization than standard cyclic prefix OFDM (CP-OFDM), but suffers from the difficulty of supporting 256QAM in low-speed vehicular channels with long delay spread and the performance loss over fast time-varying vehicular channels. This paper addresses how to efficiently use the compressive sensing (CS) theory to solve those problems. First, we break through the conventional concept of cancelling the interferences if present, and propose the idea of using the inter-block-interference (IBI)-free region of small size to reconstruct the high-dimensional sparse multipath channel, whereby no interference cancellation is required any more. In this way, without changing the current signal structure of TDS-OFDM at the transmitter, the mutually conditional time-domain channel estimation and frequency-domain data detection in conventional TDS-OFDM receivers can be decoupled. Second, we propose the parameterized channel estimation method based on priori aided compressive sampling matching pursuit (PA-CoSaMP) algorithm to achieve reliable performance over vehicular channels, whereby partial channel priori available in TDS-OFDM is used to improve the performance and reduce the complexity of the classical CoSaMP signal recovery algorithm. Simulation results demonstrate that the proposed scheme can support the 256QAM and gain improved performance over fast fading channels.

Orthogonal frequency-division multiplexing (OFDM) has been widely used in fiber-wireless (FiWi) networks, but it suffers from reduced spectral efficiency, high peak-to-average power ratio (PAPR), and severe sensitivity to carrier frequency offset (CFO). In this paper, we propose a flexible multi-block OFDM (MB-OFDM) transmission scheme to simultaneously solve those problems. First, one guard interval is shared by multiple blocks by exploiting the slow time-varying property of the fiber-wireless channel, so the spectral efficiency could be typically improved by about 10%. Second, the proposed scheme further divides every data block into multiple small sub-blocks, whereby each sub-block is generated by an inverse fast Fourier transform (IFFT) of smaller size accordingly. It thus provides a flexible compromise between the multi-carrier and single-carrier transmissions, and reduces the PAPR and the sensitivity to CFO. In addition, a hybrid-domain channel equalization method is proposed to detect the MB-OFDM signal by utilizing the single-carrier and multi-carrier transmission formats successively. Simulation results are provided to demonstrate the enhanced performance of the proposed scheme.

Orthogonal frequency division multiplexing (OFDM) is a promising technology for the next-generation optical transmission systems beyond 100 Gb/s. To further improve the spectral efficiency and system reliability, we propose a flexible coherent zero padding OFDM (CO-ZP-OFDM) scheme with signaling-embedded preamble and polarization-time-frequency (PTF) coded pilots for high-speed optical transport networks. Our judicious design embeds signaling in the specially designed preamble whose Delta-like correlation function helps to simultaneously achieve very accurate timing and frequency synchronization. Unlike the periodically inserted training symbols, the PTF-coded pilots are properly distributed within the time-frequency grid of the ZP-OFDM payload symbols and used to realize low-complexity multiple-input multiple-output (MIMO) channel estimation at high accuracy. Compared with the conventional optical OFDM systems, CO-ZP-OFDM increases payload by about 6.68%, and the low-density parity-check (LDPC) coded bit error rate only suffers from no more than 0.3 dB compared with the back-to-back case even when the channel dispersion impairments are severe.

By fully exploiting the optical channel properties, we propose in this paper the coherent optical zero padding orthogonal frequency division multiplexing (CO-ZP-OFDM) for future high-speed optical transport networks to increase the spectral efficiency and improve the system reliability. Unlike the periodically inserted training symbols in conventional optical OFDM systems, we design the polarization-time-frequency (PTF) coded pilots scattered within the time-frequency grid of the ZP-OFDM payload symbols to realize low-complexity multiple-input multiple-output (MIMO) channel estimation with high accuracy. Compared with conventional optical OFDM systems, CO-ZP-OFDM improves the spectral efficiency by about 6.62%. Simulation results indicate that the low-density parity-check (LDPC) coded bit error rate of the proposed scheme only suffers from no more than 0.3 dB optical signal-to-noise ratio (OSNR) loss compared with the ideal back-to-back case even when the optical channel impairments like chromatic dispersion (CD) and polarization mode dispersion (PMD) are severe.

This paper exploits compressive sensing (CS) theory to solve the open problems of time domain synchronous OFDM (TDS-OFDM): the difficulty of supporting 256QAM in long-delay channels and the obvious performance loss over fast fading channels. First, we break through the conventional concept of cancelling the interferences if present in TDS-OFDM, and propose the idea of using the small-size inter-block-interference (IBI)-free region of the received training sequence to reconstruct the high-dimensional sparse multipath channel without any interference cancellation under the CS framework. This new perspective could decouple the mutually conditional time-domain channel estimation and frequency-domain data detection in conventional TDS-OFDM without changing its signal structure. Second, we propose the parameterized channel estimation method based on priori aided compressive sampling matching pursuit (PA-CoSaMP) algorithm, whereby partial channel priori available in TDS-OFDM is used to improve the performance and reduce the complexity of the classical CoSaMP signal recovery algorithm.

In this paper, three novel efficient power control algorithms are proposed for V-BLAST system with both total power constraint (TPC) and per-antenna power constraints (PAPC). Through analysis of the Karush-Kuhn-Tucker (KKT) conditions of the optimization problem, we find out that the antennas with the optimal power control can be divided into two sets. The optimal allocated power (OAP) of each antenna in the first set is its per-antenna power constraint, and the OAPs of the antennas in the second set can be obtained easily through a new optimization problem with TPC only. Based on the above analysis, three novel efficient power allocation algorithms are developed, which have much lower computational complexities compared with the existing dual update method. Simulation results are presented to demonstrate the efficiency of the proposed power control schemes.

Orthogonal frequency division multiplexing (OFDM) is widely recognized as the key technology for the next generation broadband wireless communication (BWC) systems. Besides high spectral efficiency, reliable performance over fast fading channels is becoming more and more important for OFDM-based BWC systems, especially when high speed cars, trains and subways are playing an increasingly indispensable role in our daily life. The time domain synchronous OFDM (TDS-OFDM) has higher spectral efficiency than the standard cyclic prefix OFDM (CP-OFDM), but suffers from severe performance loss over high speed mobile channels since the required iterative interference cancellation between the training sequence (TS) and the OFDM data block. In this paper, a fundamentally distinct OFDM-based transmission scheme called time-frequency training OFDM (TFT-OFDM) is proposed, whereby every TFT-OFDM symbol has training information both in the time and frequency domains. Unlike TDS-OFDM or CP-OFDM where the channel estimation is solely dependent on either time-domain TS or frequency-domain pilots, the joint time-frequency channel estimation for TFT-OFDM utilizes the time-domain TS without interference cancellation to merely acquire the path delay information of the channel, while the path coefficients are estimated by using the frequency-domain grouped pilots. The redundant grouped pilots only occupy about 3% of the total subcarriers, thus TFT-OFDM still has much higher spectral efficiency than CP-OFDM by about 8.5% in typical applications. Simulation results also demonstrate that TFT-OFDM outperforms CP-OFDM and TDS-OFDM in high speed mobile environments.

Time domain synchronous OFDM (TDS-OFDM) has higher spectral efficiency than cyclic prefix OFDM (CP-OFDM), but suffers from severe performance loss over fast fading channels. In this paper, a novel transmission scheme called time-frequency training OFDM (TFT-OFDM) is proposed. The time-frequency joint channel estimation for TFT-OFDM utilizes the time-domain training sequence without interference cancellation to merely acquire the time delay profile of the channel, while the path coefficients are estimated by using the frequency-domain group pilots. The redundant group pilots only occupy about 1% of the useful subcarriers, thus TFT-OFDM still has much higher spectral efficiency than CP-OFDM by about 10%. Simulation results also demonstrate that TFT-OFDM outperforms CP-OFDM and TDS-OFDM over time-varying channels.

Due to wide coverage and high transmission power of digital television (DTV) transmitters, DTV based wireless positioning is a promising complementary to global positioning system. For Chinese DTV broadcasting network whose key technology is time-domain synchronous orthogonal frequency division multiplexing (TDS-OFDM), this paper proposes a time-frequency joint positioning scheme by utilising TDS-OFDM signal properties in both the time and frequency domains. The proposed scheme needs no modification of current infrastructures, and has no impact on the normal TV program reception. Simulation results show that the positioning accuracy of less than 0.1 m can be achieved when the signal-to-noise ratio is higher than 20 dB over the realistic simulated channels.

To reduce the complexity of existing transmit diversity solutions for time domain synchronous OFDM (TDS-OFDM), a simple transmit diversity scheme is proposed in this paper. The space shifted constant amplitude zero autocorrelation (CAZAC) sequence is used for time-domain channel estimation. Two types of flexible frame structures are investigated for cyclicity reconstruction of the received inverse discrete Fourier transform (IDFT) block. Regarding to channel estimation and cyclicity reconstruction, the complexity of the proposed scheme is only about 7% of the conventional solutions. With the penalty of small loss in spectral efficiency, the proposed scheme achieves better bit error rate performance over doubly selective channels, which is demonstrated by the simulation results.

This contribution proposes a novel time-domain synchronous frequency division multiple access (TDS-FDMA) scheme to support multi-user uplink application. A unified frame structure for both single-carrier and multi-carrier transmissions and the corresponding low-complexity receiver design are derived. Compared with standard cyclic prefix based orthogonal frequency division multiple access systems, the proposed TDS-FDMA scheme improves the spectral efficiency by about 5% to 10% as well as imposes a similarly low computational complexity, while obtaining a slightly better bit error rate performance over Rayleigh fading channels.

The existing transmit diversity schemes for time domain synchronous OFDM (TDS-OFDM) is only suitable either for fast time-varying but weakly frequency-selective channels, or strongly frequency-selective but slow fading channels. In this paper, the space-time shifted constant amplitude zero autocorrelation (CAZAC) sequence based TDS-OFDM transmit diversity scheme is proposed for doubly selective channels. The spaceshifted CAZAC sequence is used for channel estimation, and the time-shifted sequence is utilized for the cyclicity reconstruction of the received inverse discrete Fourier transform (IDFT) block. Compared with the state-of-the-art solutions, the proposed scheme has lower complexity irrelative to the transmit antenna number, and it achieves better bit error rate (BER) performance under various multi-path fading channels.

Time domain synchronous OFDM (TDS-OFDM) with higher spectral efficiency than cyclic prefix OFDM (CP-OFDM)was originally proposed for downlink broadcasting transmission. To support multi-user uplink scenarios, this paper proposes a novel multiple access scheme called time domain synchronous frequency division multiple access (TDS-FDMA), wherein an uniform frame structure and its corresponding receiver algorithms are presented for both single-carrier and multicarrier signal transmission. Compared with typical OFDMA systems, TDS-FDMA has higher spectral efficiency. The multi-user TDS-FDMA receiver has lower complexity than the conventional single-user TDS-OFDM receiver, and it can achieve better bit error rate (BER) performance under the slow to medium time-varying channels.

Compared with the global positioning system (GPS), the digital television (DTV) broadcasting signal is a promising candidate for position location due to low implementation cost and strong signal reception. Without changing the current infrastructure of the Chinese DTV broadcasting network, this paper proposes a novel positioning scheme using the multi-carrier pseudo-noise (PN-MC) training sequence in the guard interval of the time domain synchronous OFDM (TDS-OFDM) signal frame. Different from the existing positioning methods based on timing synchronization or super resolution algorithms, the joint time-frequency estimation utilizing the properties of the received PN-MC sequence both in the time and frequency domain with respect to transmission delay, results in the accurate time of arrival (TOA) estimation. Performance of the proposed scheme is evaluated by Monte Carlo simulations in comparison with other the-state-of-art methods. The positioning accuracy of less than 0.1 m when the signal-to-noise ratio (SNR) is greater than 15 dB is achieved, under both the additive white Gaussian noise (AWGN) and the simulated multi-path channels.

The unavoidable Doppler frequency shift reduces the correlation peak for code acquisition in global positioning system (GPS). In contrast to conventional methods where the code phase and Doppler frequency shift are separately treated, this paper proposes a novel three-step scheme for joint code acquisition and Doppler estimation, whereby not only the Doppler effect on the correlation peak is removed, but also the reduced detection probability due to noise enhancement in low signal-to-noise ratio (SNR) environments is avoided. The theoretical analysis shows that the proposed method has low complexity and fast acquisition speed. Computer simulations demonstrate that code acquisition with high detection probability and Doppler frequency shift estimation with high accuracy can be simultaneously achieved.

Time-domain synchronous orthogonal frequency division multiplexing (TDS-OFDM) outperforms cyclic prefixed-OFDM (CP-OFDM) in spectrum efficiency at the expense of higher complexity. Up to now, TDS-OFDM has only been used in unidirectional transmission, such as broadcasting applications. The overwhelming complexity of removing the implicitly superposed interferences between the PN sequences and IDFT information blocks, caused by multiple users, hinders the application of time-domain synchronous orthogonal frequency division multiple access (TDS-OFDMA) in multi-user communication scenarios. To solve this problem, this paper proposes a novel TDS-OFDMA scheme, including joint cyclicity reconstruction and joint channel estimation, utilizing the new "time-space two-dimensional frame structure". Theoretical analysis and computer simulation show that the proposed scheme not only can achieve the purpose of multiple access but also has an even better system performance in mobile environment than the conventional single-user TDS-OFDM system.