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

Acquiring channel information in advance is crucial for improving underwater acoustic (UWA) communication networks, yet long propagation delays and severely limited bandwidth make accurate channel acquisition at both the transmitter (Tx) and receiver (Rx) difficult. Channel knowledge maps (CKMs) have recently shown strong benefits in terrestrial wireless systems by providing location-indexed channel information (e.g., gain, delay, AoA/AoD, or even channel state information (CSI) ), but have not been systematically studied for UWA channels, whose propagation characteristics differ fundamentally from radio channels. In this work, we develop an any-to-any (X2X) UWA CKM construction framework by leveraging the normal mode model for low-frequency UWACs. Specifically, we construct a UWA CKM that maps Tx–Rx positions to channel priors, and propose a CKM construction framework together with channel gain map (CGM), CKM-assisted channel estimation and beamforming schemes. Numerical simulations and SWellEx-96 S5 experimental data demonstrate that the reconstruction normalized mean square error (NMSE) remains below 0.05 when the signal-to-noise ratio (SNR) exceeds 12 dB, thereby validating the accuracy of the normal mode model and the efficacy of the mode extraction process. Furthermore, a high-fidelity CGM is constructed even in data-sparse scenarios, requiring only one-fourth of the data volume compared to conventional data-driven CGMs. Simulation results further indicate that the CKM-assisted beamforming achieves a superior array gain over baseline methods. At an SNR of 15 dB, the CKM-assisted channel estimation attains an NMSE of below -16 dB in shallow-water and below -21 dB in deep-water scenarios, significantly outperforming the baselines.

In this paper, we study a system in which a sensor forwards status updates to a receiver through an error-prone channel, while the receiver sends the transmission results back to the sensor via a reliable channel. Both channels are subject to random delays. To evaluate the timeliness of the status information at the receiver, we use the Age of Information (AoI) metric. The objective is to design a sampling policy that minimizes the expected time-average AoI, even when the channel statistics (e.g., delay distributions) are unknown. We first review the threshold structure of the optimal offline policy under known channel statistics and then reformulate the design of the online algorithm as a stochastic approximation problem. We propose a Robbins-Monro algorithm to solve this problem and demonstrate that the optimal threshold can be approximated almost surely. Moreover, we prove that the cumulative AoI regret of the online algorithm increases with rate$\mathcal {O}(\ln K)$, where$K$is the number of successful transmissions. In addition, our algorithm is shown to be minimax order optimal, in the sense that for any online learning algorithm, the cumulative AoI regret up to the$K$-th successful transmissions grows with the rate at least$\Omega (\ln K)$in the worst case delay distribution. Finally, we improve the stability of the proposed online learning algorithm through a momentum-based stochastic gradient descent algorithm. Simulation results validate the performance of our proposed algorithm.

To overcome the performance limitations and computational complexity induced by the unimodular constraint in conventional hybrid precoding designs, this paper proposes a novel optoelectronic base station (OE-BS) architecture, replacing the traditional phase shifter network with an optical network. The proposed OE-BS architecture facilitates simultaneous amplitude and phase control in the analog domain, effectively eliminating the unimodular constraint and significantly enhancing the design flexibility. Based on the proposed OE-BS architecture, a closed-form hybrid precoding solution is first derived for narrowband systems, thus avoiding iterative optimization procedures. For wideband systems, a user-selective hybrid precoding algorithm is developed, where the digital precoder is designed according to the zero-forcing criterion, and the analog precoder and power allocation are jointly optimized using an alternating optimization method. Simulation results demonstrate that the proposed OE-BS schemes outperform conventional methods in both narrowband and wideband scenarios, enhancing overall system performance while reducing computational complexity.

While spatial, temporal, and frequency domains are explored to enhance spectral efficiency (SE) in wireless communications, utilizing the polarization of electromagnetic (EM) waves also contributes to achieving this goal. Unlike conventional reconfigurable intelligent surface (RIS)-aided communications, polarization-transforming RIS (PTRIS) is first applied in this work to assist the line-of-sight (LoS) communication system. We introduce a novel framework for accurately modeling the direct and cascaded channels in the PTRIS-aided LoS communication system. This framework considers the spatial positions of the transmitter and receiver, the radiation patterns, antenna rotations, and the physical propagation mechanisms of EM waves based on antenna theory. The aperture field method is used to model the physical reflection of EM waves by PTRIS. Additionally, we aim to maximize SE by investigating four cases regarding the polarization-transforming capability of the PTRIS. Optimal closed-form solutions are derived for Case 1 and Case 2, while a best-effort approximation-based alternative optimization (BEA-AO) method is proposed for Case 3 to obtain sub-optimal solutions. Case 4 can be solved optimally with the barnch-and-cut algorithm within a reasonable computation time. Numerical results demonstrate that PTRIS can provide a robust and enhanced SE in LoS communications, even with arbitrary antenna rotations, compared to the scenarios without PTRIS.

This paper introduces an interference-free multi-stream transmission architecture leveraging stacked intelligent metasurfaces (SIMs), from a new perspective of interference exploitation. Unlike traditional interference exploitation precoding (IEP) which relies on computational hardware circuitry, we perform the precoding operations within the analog wave domain provided by SIMs. However, the benefits of SIM-enabled IEP are limited by the nonlinear distortion (NLD) caused by power amplifiers. A hardware-efficient interference-free transmitter architecture is developed to exploit SIM’s high and flexible degree of freedom (DoF), where the NLD on modulated symbols can be directly compensated in the wave domain. Moreover, we design a frame-level SIM configuration scheme and formulate a max-min problem on the safety margin function. With respect to the optimization of SIM phase shifts, we propose a recursive oblique manifold (ROM) algorithm to tackle the complex coupling among phase shifts across multiple layers. A flexible DoF-driven antenna selection (AS) scheme is explored in the SIM-enabled IEP system. Using an ROM-based alternating optimization (ROM-AO) framework, our approach jointly optimizes transmit AS, SIM phase shift design, and power allocation (PA), and develops a greedy safety margin-based AS algorithm. Simulations show that the proposed SIM-enabled frame-level IEP scheme significantly outperforms benchmarks. Specifically, the strategy with AS and PA can achieve a 20 dB performance gain compared to the case without any strategy under the 12 dB signal-to-noise ratio, which confirms the superiority of the NLD-aware IEP scheme and the effectiveness of the proposed algorithm.

The rapid expansion of latency-sensitive applications has sparked renewed interest in deploying edge computing capabilities aboard satellite constellations, aiming to achieve truly global and seamless service coverage. On one hand, it is essential to allocate the limited onboard computational and communication resources efficiently to serve geographically distributed users. On the other hand, the dynamic nature of satellite orbits necessitates effective service migration strategies to maintain service continuity and quality as the coverage areas of satellites evolve. We formulate this problem as a spatio-temporal Markov decision process, where satellites, ground users, and flight users are modeled as nodes in a time-varying graph. The node features incorporate queuing dynamics to characterize packet loss probabilities. To solve this problem, we propose a Graph-Aware Temporal Encoder (GATE) that jointly models spatial correlations and temporal dynamics. GATE uses a two-layer graph convolutional network to extract inter-satellite and user dependencies and a temporal convolutional network to capture their short-term evolution, producing unified spatio-temporal representations. The resulting spatial-temporal representations are passed into a Hybrid Proximal Policy Optimization (HPPO) framework. This framework features a multi-head actor that outputs both discrete service migration decisions and continuous resource allocation ratios, along with a critic for value estimation. We conduct extensive simulations involving both persistent and intermittent users distributed across real-world population centers. The results validate that the proposed framework consistently achieves superior performance compared to Proximal Policy Optimization (PPO), Soft Actor Critic (SAC), and ablated baselines in terms of reward, failure rate, and migration overhead, demonstrating the effectiveness of the proposed spatio-temporal modeling and hybrid reinforcement learning approach in dynamic satellite edge environments.

Numerous multicarrier modulation schemes have been proposed recently to enhance the performance in narrowband doubly dispersive channels for emerging high-mobility applications. However, the ultra-reliable modulation framework in wideband linear time-varying (LTV) channels remains an open problem, where the time dilations and contractions brought by the high mobility cannot be ignored for the baseband signal to obtain the constant Doppler shift across the whole transmission band. To solve this problem, we propose the hyperbolic frequency multicarrier (HFMC) waveform in this paper based on the inspiration from affine frequency division multiplexing (AFDM) modulation, where the delay and Doppler shift are absorbed into a 1D shift in the affine domain to provide a compact characterization of doubly dispersive discrete-time channels. By adopting the passband representation of wideband LTV channels and hyperbolic frequency modulated (HFM) signals, we reveal that the Doppler scaling factor brought by the relative mobility can be absorbed into an equivalent delay. The basic principle of HFMC modulation is established by investigating the approximate orthogonality among HFMC subcarriers, which are generated from a basic HFM signal by utilizing uniformly spaced equivalent delay. The spectrum of HFMC subcarriers is also analyzed to evaluate the system capacity, where the overlapping nature in the frequency domain can be observed. The input-output characterization in wideband LTV channels is then executed to confirm the 1D integration of time delay and Doppler scaling factor for each path, which demonstrates the ability to exploit potential multipath diversity. The parameter optimization based on the input-output relation and spectrum analysis is finally developed to balance the efficiency and reliability. Numerical results demonstrate the excellent bit error rate (BER) performance of the proposed HFMC waveform in wideband LTV channels.

Channel knowledge map (CKM) has emerged as a crucial technology for next-generation communication, enabling the construction of high-fidelity mappings between spatial environments and channel parameters via electromagnetic information analysis. Traditional CKM construction methods like ray tracing are computationally intensive. Recent studies utilizing neural networks (NNs) have achieved efficient CKM generation with reduced computational complexity and real-time processing capabilities. Nevertheless, existing research predominantly focuses on single-antenna systems, failing to address the beamforming requirements inherent to MIMO configurations. Given that appropriate precoding vector selection in MIMO systems can substantially enhance user communication rates, this paper presents a TransUNet-based framework for constructing CKM, which effectively incorporates discrete Fourier transform (DFT) precoding vectors. The proposed architecture combines a UNet backbone for multiscale feature extraction with a Transformer module to capture global dependencies among encoded linear vectors. Experimental results demonstrate that the proposed method outperforms state-of-the-art (SOTA) deep learning (DL) approaches, yielding a 17% improvement in RMSE compared to RadioWNet. The code is publicly accessible at https://github.com/github-whh/TransUNet.

The recently proposed orthogonal delay-Doppler division multiplexing (ODDM) modulation has been demonstrated to enjoy excellent reliability over doubly-dispersive channels. However, most of the prior analysis tends to ignore the interactive dispersion caused by the wideband property of ODDM signal, which possibly leads to performance degradation. To solve this problem, we investigate the input-output relation of ODDM systems considering the wideband effect, which is also known as the Doppler squint effect (DSE) in the literature. The extra delay-Doppler (DD) dispersion caused by the DSE is first explicitly explained by employing the time-variant frequency response of multipath channels. Its characterization is then derived for both reduced cyclic prefix (RCP) and zero padded (ZP)-based wideband ODDM systems, where the extra DD spread and more complicated power leakage outside the peak region are presented theoretically. Numerical results are finally provided to confirm the significance of DSE. The derivations in this paper are beneficial for developing accurate signal processing techniques in ODDM-based integrated sensing and communication systems.

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

Recently, with the users' growing demand for communication rate and capacity in wireless networks, Unmanned Aerial Vehicles (UAVs) have attracted widespread attention due to their mobility, flexibility, and robust line-of-sight communication links. By equipping UAVs with multiple communication payloads, we can construct an aerial wireless network with three-dimensional coverage. However, due to the limitations of UAV onboard energy and communication resources, the lifetime and performance of UAV-assisted wireless networks are significantly constrained. This paper mainly focuses on equipping UAVs with mobile base stations to enhance wireless communication coverage and capacity. We propose a Joint Optimization of 3D Trajectory and Resource Allocation (JOTRA) scheme to maximize energy efficiency in complex scenarios with multi-user mobility and diverse requirements (e.g., UAV-assisted post-disaster search and rescue). Specifically, we apply Dinkelbach's iterative method and Block Coordinate Descent (BCD) method to solve the formulated multivariable and non-convex maximization problem. The algorithm's convergence has been analyzed. According to the simulation, the proposed algorithm can converge faster while maximizing energy efficiency in complex wireless communication scenarios.

Integrated sensing and communication (ISAC) has been envisioned as a key enabler for next-generation wireless systems. Meanwhile, fluid antenna system (FAS) has emerged as a promising flexible antenna technology, offering enhanced multiple access capabilities and significant spatial diversity gains. This paper studies the problem of joint port selection and Cramér-Rao Bound (CRB) optimization in an ISAC-enabled FAS. Specifically, the objective is to minimize the CRB while ensuring compliance with communication quality of service (QoS) requirements, transmit power constraints, and port selection limitations. To address this problem, we propose an iterative optimization algorithm leveraging the majorization-minimization (MM) and block coordinate descent (BCD) methods. Numerical results validate the effectiveness of the proposed algorithm, demonstrating its ability to achieve near-optimal solutions with efficient search.

Perception-optimized Learned Image Compression (LIC) methods have recently made significant progress. They surpass both traditional image compression algorithms and non-perceptually optimized LIC methods in image sharpness, detail representation, and subjective perception, even at similar or lower bitrates. However, LIC methods optimized for perception often generate false details and textures in reconstructions, leading to underperformance in objective metrics like PSNR and MS-SSIM, which limits their applicability. In this paper, we introduce a comprehensive loss metric based on saliency detection that aids in achieving exceptional perceptual quality while minimizing distortions. By applying this metric in training, we develop the SaLIC model, i.e., Saliency-Enhanced Learned Image Compression. User study results indicate that, at similar or lower bitrates, SaLIC exhibits better human perceptual quality compared to HiFiC and VVC; quantitative results show that the PSNR of SaLIC significantly outperforms HiFiC (by 1-2dB), and the MS-SSIM of SaLIC even surpasses VVC, achieving a balance between perception and distortion.

In the double-sided near-field extremely large-scale multi-input multi-output (XL-MIMO) systems, due to the spherical-wavefront propagation, the line-of-sight (LoS) path exhibits multiple independent propagation components, leading to a channel rank greater than one. In contrast, the non-line-of-sight (NLoS) path is typically dominated by a single propagation component. Consequently, the unified modeling of mixed LoS and NLoS paths remains unresolved, particularly when with non-parallel and non-coplanar uniform linear arrays (ULAs) at the transceivers. Furthermore, there exist bottlenecks in the beamforming codeword design and low-overhead estimation in double-sided near-field communications. In this paper, we present a unified channel model to characterize both LoS and NLoS paths in extremely large-scale multiple-input multiple-output (XL-MIMO) systems. Apart from the transmitter (Tx)-side and receiver (Rx)-side separate response vectors, an additional Tx/Rx-coupled term with a Vandermonde windowing pattern is studied for the XL-MIMO LoS path. Two codebook-based beamfocusing schemes are proposed, which are termed as the beamspace-projection and diagonal-decomposition schemes. The achievable spectral efficiency and average power are theoretically analyzed in closed form. Under this framework, we further propose a low-overhead unified LoS/NLoS orthogonal matching pursuit (UOMP) algorithm for XL-MIMO channel estimation, which is then extended via 3-stage multiple-measurement-vector (3S-MMV) for complexity reduction. Last, simulation results demonstrate the superiority of the proposed strategies in both beamforming codeword selection and sparse channel estimation.

The orthogonal delay-Doppler division multiplexing (ODDM) modulation has been widely acknowledged as a potential candidate for supporting ultra-reliable wireless communications under high-mobility scenarios. Nevertheless, the downlink transmission utilizing ODDM modulation remains an open problem due to the high complexity of prior channel estimation designs and complicated multi-user interference in the delay-Doppler (DD) domain. To address this issue, the channel estimation and data detection for downlink DD domain multiple access (DDMA) are investigated in this paper. The fast Fourier transform (FFT) interpolation is first presented for estimating each equivalent channel delay tap. To further promote the channel estimation accuracy, an orthogonal matching pursuit (OMP)-enabled scheme is also developed, where the normalized delay time and Doppler shift for each virtual path are determined separately to reduce the computational complexity. To ease the data detection at the user equipment (UE) side, we propose the orthogonal precoding for data symbols to provide the Gaussian distribution as the prior knowledge thanks to the central limit theorem, which can support the highly efficient interference cancellation without sharing the constellation information among UEs. Simulation results confirm the excellent performance of the proposed downlink DDMA schemes, where comparable reliability with optimal interference cancellation by sharing the knowledge of constellation among all UEs while the flexibility and efficiency can be guaranteed.

Traditional channel acquisition faces significant limitations due to ideal model assumptions and scalability challenges. A novel environment-aware paradigm, known as channel twinning, tackles these issues by constructing radio propagation environment semantics using a data-driven approach. In the spotlight of channel twinning technology, a radio map is recognized as an effective region-specific model for learning the spatial distribution of channel information. However, most studies focus on static channel map construction, with only a few collecting numerous channel samples and using deep learning for radio map prediction. In this paper, we develop a novel dynamic radio map twinning framework with a substantially small dataset. Specifically, we present an innovative approach that employs dynamic mode decomposition (DMD) to model the evolution of the dynamic channel gain map as a dynamical system. We first interpret dynamic channel gain maps as spatio-temporal video stream data. The coarse-grained and fine-grained evolving modes are extracted from the stream data using a new ensemble DMD (Ens-DMD) algorithm. To mitigate the impact of noisy data, we design a median-based threshold mask technique to filter the noise artifacts of the twin maps. With the proposed DMD-based radio map twinning framework, numerical results are provided to demonstrate the low-complexity reproduction and evolution of the channel gain maps. Furthermore, we consider four radio map twin performance metrics to confirm the superiority of our framework compared to the baselines.

The underwater Internet of Things (UIoT) is crucial in developing marine resources. However, due to the low data rate of underwater channels, it is difficult to have a central server to process data from numerous devices as using terrestrial communications. Therefore, decentralized federated learning (DFL) with communication-efficient modifications is a promising alternative to empower UIoT with artificial intelligence and collaborative training. However, existing DFL strategies rely on a carefully designed small aggregation weight when aggregating parameters from neighbor nodes to mitigate the compression error, resulting in a slow convergence rate. In addition, the effect of data compression under time-varying topologies is not considered in current DFL algorithms. In response to these problems, this work studies a DFL framework with underwater acoustic channel and time-varying topology. Firstly, considering the low data rate and dynamics of the acoustic channel, we propose a practical scheme for adaptive compression and device connectivity. Moreover, we combine data compression and the error-compensation technique with time-varying topology and propose a DFL algorithm with aggregation weights decaying over time to achieve fast convergence under non-independent and identically distributed (non-IID) data. We derive a convergence bound for the proposed algorithm with respect to compression and time-varying topology and demonstrate that it achieves the same asymptotic convergence rate as centralized FL with perfect communication. Simulation results show that, compared with DFL algorithms without decaying aggregation weights and centralized FL schemes, the proposed algorithm exhibits higher accuracy and faster convergence rate in underwater environments.

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

Beyond 5G mobile networks are required to support the ultra-reliable data transmission with massive connectivity under high-mobility scenarios, where the delay-Doppler domain multiple access (DDMA) has been regarded as one of the potential candidates to avoid the severe performance degradation brought by double-dispersive channels. However, existing DDMA transceiver designs heavily rely on the shared codebooks or large guard space among user equipments (UEs), which restricts the flexibility, complexity, and spectral efficiency significantly. In this paper, we first propose the Zadoff-Chu training sequences-based frame structure and an element-wise iterative successive interference cancellation (SIC)-maximal ratio combining (MRC) detector for single-user transmission, which serves as the basis of flexible resource allocation in the delay-Doppler (DD) domain. The discussion is then extended to the MU downlink scenario, where rate splitting is adopted to effectively balance the noise and interference at the UE side to improve the bit error rate performance. For MU uplink cases, a non-orthogonal pilots-based iterative SIC-least square channel estimator is developed to promote the spectral efficiency while the iterative SIC-MRC detector is also provided. Simulation results demonstrate the excellent performance of the proposed DDMA scheme under typical DDMA patterns without guard space between UEs.

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

The Autonomous Underwater Vehicle (AUV)- assisted Underwater Internet of Things (UIoT) has received much attention due to its potential to develop marine resources with big data analysis. Given the low data rates and distributed data, decentralized federated learning (DFL) emerges as a promising avenue, enabling artificial intelligence integration and collaborative training within the underwater environment. In this paper, we combine DFL with the underwater scenario for the first time. A novel DFL algorithm with decaying aggregation weight is proposed to achieve fast convergence rate under non-independent and identically distributed (non-IID) data. In addition, the DFL algorithm is tailored to underwater acoustic channels, and we integrate adaptive compression, device connectivity, and time-varying topology considerations to enable practical deployment. We provide convergence analysis under convexity and connectivity assumptions. Simulation experiments validate the performance using the MNIST dataset, highlighting its effectiveness for practical UIoT applications.

Recently, Federated Learning (FL) has revolutionized the processing and analysis of vast volumes of data generated by wireless devices, effectively overcoming the traditional cloud computing constraints within Internet of Things (IoT) networks. However, practical challenges arise as data on edge devices dynamically changes, necessitating continuous learning capabilities known as Federated Continual Learning (FCL). One key challenge in FCL is the issue of catastrophic forgetting, which refers to preserving the training performance on old data while training on new data. While common strategies involve retaining a subset of old data to mitigate the issue, privacy concerns limit this approach, and the balance between emphasis on new and old data during the training process remains inadequately studied. To address the above challenges, we propose an Adaptive Federated Continual Learning (AdapFCL) method in heterogeneous environment, which eliminates the need for episodic memory in federated settings. Specifically, the server employs a Deep Convolutional Generative Adversarial Network (DCGAN) model with a data-free knowledge distillation technique, which enables the server to learn representations of old data and generate synthetic data involving only global model. Then clients perform local training by utilizing new data and synthetic data instead of storing old data. Furthermore, we quantify the degree of forgetting on old data for each client, allowing for adaptive adjustment of emphasis weights for old and new data during the training process. Simulation results validate that the proposed method can achieve superior average test accuracy while maintaining communication efficiency compared with baselines, especially in highly heterogeneous data scenarios.

Oceans cover more than 70 percent of the Earth’s surface. In such high-loss medium, conventional acoustic, optical, and electromagnetic (EM) communication strategies fail to establish long-distance, low-latency networks. Very Low Frequency (VLF) communications are capable of achieving extensive, low-latency underwater links using kilometers-size antennas and megawatts of power input. However, such large transceiver dimensions and power requirements are usually not feasible for the networks, which consist of spatially-constrained mobile nodes and base station nodes. To address the aforementioned limitation, this paper introduces metamaterial-enhanced Magnetic Induction (MI) to realize long-distance, low-latency underwater networks. Firstly, we propose a new network architecture and the configuration of underwater mobile and base station nodes. Then, we design and model the metamaterial-enhanced MI transceiver systems for different network nodes. Finally, we quantitatively evaluate the proposed network’s performance in complex multilayered under-water environments. Demonstrations indicate that the proposed underwater network can achieve kb/s-level real-time bi-directional links over 34 km between mobile nodes and base station nodes, meanwhile reducing the transceiver size by up to ten times.

Integrated sensing and communication (ISAC) technology is envisioned as a pivotal component for the next-generation communication networks. Similarly, underwater acoustic ISAC (UWA-ISAC) holds promising prospects for enhancing future UWA sensor networks due to its efficient communication and sensing capabilities. However, the design of UWA-ISAC waveforms and receivers suitable for multi-user scenarios faces formidable challenges, primarily arising from the complex UWA channel and multi-access interference (MAI). In this paper, we propose a UWA-ISAC waveform design scheme based on generalized sinusoidal frequency modulation (GSFM). The proposed waveform provides satisfactory communication and sensing performance and exhibits excellent orthogonality. Furthermore, we design a MAI suppression receiver, leveraging successive interference cancellation based on two-factor compensation and turbo equalization to improve interference suppression capabilities and enhance communication performance. Simulation results validate that the proposed UWA-ISAC waveform has an approximate thumbtack ambiguity function and comparable cross-correlation properties with GSFM under the defined parameters. Moreover, the designed receiver with low training sequence overhead is robust against Doppler, and can iteratively improve the MAI suppression performance.

Federated Learning (FL) enables distributed intelligence in Internet of Things (IoT) networks, facilitating decentralized machine learning without the need for exchanging raw data. However, the growing complexity of training models significantly hinders their deployment on resource-constrained IoT devices. To address this challenge, Split Federated Learning (SFL) has emerged as a promising solution by partitioning the entire model into client-side and server-side sub-models to alleviate the computational burden on IoT devices. Considering that the client-edge-cloud architecture can enhance data privacy, support connections to a wider range of devices, and reduce communication costs, we explore a hierarchical SFL (HierSFL) system. This system is supported by a HierSFL algorithm that allows for different aggregation frequencies between the client-side and server-side sub-models. Then, we present a convergence analysis of HierSFL that quantifies the effects of client-side and server-side model aggregation on learning performance, providing a theoretical foundation. Empirical experiments verify the theoretical analysis and demonstrate the superiority of the hierarchical architecture within a wireless IoT network. In particular, it is validated that adopting different aggregation frequencies can enhance the training performance. Moreover, the HierSFL algorithm outperforms traditional hierarchical FL algorithm, achieving superior test accuracy in a shorter time.

For frequency division duplexing massive multiple-input multiple-output systems, downlink channel state information (CSI) is required to be compressed and fed back to the base station (BS) to support beamforming. Recently, deep learning (DL) has demonstrated overwhelming performance in CSI feed-back, wherein multimodal information is explored to further improve the performance. With the emergence of integrated sensing and communications, radar-equipped BSs exhibit the capability to sense the wireless environment and assist communication design. In this paper, we propose a sensing-aided DL-based CSI feedback method, in which the angle information of scatters in the communication channel is sensed by the BS and utilized to reduce feedback overhead. A novel two-stage feedback scheme with lightweight network structures is carefully designed to improve feedback performance. Experiments demonstrate that compared to previous methods without utilization of sensing information, our sensing-aided methods significantly enhance performance in low-bit scenarios with reduced computational complexity. The open-source codes are available at https://github.com/zhang-xd18/safb.

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

This paper studies the secrecy capacity of the visible light communication (VLC) wiretap channel under a per-antenna peak-intensity constraint and a per-antenna average-intensity constraint. The focus is on the scenario with one transmitter, one legitimate user, and one eavesdropper, all equipped with multiple antennas. Considering various quantity configurations of the number of transmitting and receiving antennas, we first derive a closed-form secrecy rate exploiting the truncated exponential distribution. Based on this, full-connected and sub-connected precoding architectures are proposed to enhance the confidentiality of this multiple-input multiple-output (MIMO) VLC wiretap channel. Simulation results demonstrate that the proposed precoding schemes significantly improve the secrecy performance of the MIMO-VLC wiretap channel.

In this paper, we consider the scenario where the base station (BS) transmits time-sensitive information to various users via shared orthogonal sub-channels. The BS allocates sub-channels and power to different users to complete the data transmission task in each time slot. The metric Age of Information (AoI) is adopted to measure the timeliness of information. We jointly optimize the transmitting decisions, the sub-channel assignments, and the power allocation schemes to minimize the average AoI of the users subject to the average and peak power constraints. We propose a dynamic scheduling policy based on the Lyapunov optimization method and then prove that the proposed policy can satisfy the power constraints and achieve a near-optimal AoI performance. Numerical simulations indicate that the proposed policy is stable and satisfies the power constraints, which validates the theoretical analysis.

This work considers a wireless network transmitting status updates arriving randomly using the Hybrid Automatic Repeat Request (HARQ) protocol under average power con-straints. We measure the information freshness by the Age of Synchronization, which is defined as the time elapsed since the last synchronization. The updates transmitted unsuccessfully can be retransmitted with a higher success probability by the HARQ. However, there is a trade-off between retransmitting the old update with a high success probability and transmitting the new update with a low success probability. We model the time-average AoS minimization problem as a constrained Markov decision process (CMDP) and solve the problem by a relative value iteration algorithm. Numerical simulations show that our scheduling policy outperforms traditional ARQ and HARQ policies on the AoS performance and indicates the stability of our scheduling policy under different channel parameters.

Multiple Input Multiple Output (MIMO) transmitters (Tx) leveraging Channel State Information (CSI) can substantially enhance communication quality, such as increasing channel capacity and Signal-to-Noise Ratio (SNR). However, in Underwater Acoustic (UWA) communications, the extremely low underwater sound speed and the continuously changing sea waves render the conventional CSI acquisition methods. Therefore, the UWA MIMO system only utilize CSI at Receiver (Rx). By analyzing the affect factors of UWA CSI, a model-data dual driven channel prediction framework is proposed. First, the problem and key technologies (historical CSI acquisition scheme, spatial and temporal alignments) of the proposed framework are formulated. Second, a realistic historical CSI acquisition scheme, model-driven spatial and data-driven temporal alignments are proposed. Finally, numerical simulations show that the proposed CSI prediction framework can effectively meet the CSI quality requirements of Tx when the ratio of CSI error variance and CSI variance is lower than 0.4.

Beam squint effect causes severe performance degradation for wideband beamforming inside Internet of thing (IoT) communication, and the true-time-delay (TTD) line has been regarded as a promising enabler to address this issue. However, beam training becomes a challenging puzzle in TTD-aided transceivers, where enormous beam directions bring about unacceptable training overhead, especially for the overhead-efficient IoT devices. In this paper, based on a joint delay-phase beamforming structure, we provide enhanced frequency-scanning-based training schemes for remarkable overhead reduction. Simultaneous beams pointing to different physical directions over a set of OFDM subcarriers can be generated to reduce overhead. The pointing directions can be flexibly controlled following two subcarrier-angular mapping policies: forward-pairing and backward-pairing. Besides, the power leakage problem is retrieved via the compressive phase retrieval (CPR) method to avoid beam mismatch. Furthermore, we adopt it into the multiuser scenario and propose a frequency-scanning-based simultaneous multi-user beam training (FS-MBT) scheme. The different subcarriers’ pencil beams illuminate a broad angular sector, while several sectors are merged into multi-finger wide beams for simultaneous multi-user training. Analytical and numerical results demonstrate the proposed schemes’ superiority over existing methods in both single-user and multi-user scenarios.

Automatic modulation recognition (AMR) has received widespread attention as a crucial aspect of non-cooperative communication. Despite this, large carrier frequency offsets (CFOs) and sample rate offsets (SROs) caused by inaccurate parameter estimation at the receiver are harmful to the recognition accuracy, which is still to be addressed. In this letter, we focus on intelligent modulation recognition tasks under such offsets. A novel transformer-based method named TransGroupNet is designed that can extract deep features of signals from the instantaneous amplitude, phase, and frequency (APF) domain. In addition, lightweight group convolution layers are added ahead of the transformer blocks for better feature preprocessing. Simulations demonstrate that the proposed TransGroupNet achieves better recognition accuracy under large offsets compared with the previous state-of-the-art methods, even though these methods adopt correction modules (CM) to address such offsets.

This paper investigates the capacity region of the optical intensity broadcast channels (OI-BCs), where the input is subject to a peak-intensity constraint, an average-intensity constraint, or both. By leveraging the decomposition results of several random variables, i.e., uniform, exponential, and truncated exponential random variables, and adopting a superposition coding (SC) scheme, the inner bound on the capacity region is derived. Then, the outer bound is derived by applying the conditional entropy power inequality (EPI). In the high signal-to-noise ratio (SNR) regime, the inner bound asymptotically matches the outer bound, thus characterizing the high-SNR asymptotic capacity region. The bounds are also extended to the general$K$-user BCs without loss of high-SNR asymptotic optimality.

With the booming development of Smart Healthcare Systems (SHSs), employing federated learning (FL) in SHS devices has become a research hotspot. FL, as a distributed learning framework, can train models without sharing the original data among users, and then protect the user privacy. Existing research has proposed many methods to improve the security and efficiency of FL, which may not fully consider the characteristics of SHSs. Specifically, the requirements of privacy protection and efficiency pose significant challenges to FL. Current studies have struggled to balance privacy security and efficiency, and the degradation of model training efficiency in SHSs can be critical to patient health. Therefore, to improve the privacy protection of healthcare data and ensure communication efficiency, this work proposes a novel personalized FL framework based on Communication quality and Adaptive Sparsification (pFedCAS). In order to achieve privacy protection, a control unit is proposed and introduced to adjust the sparsity of the local model adaptively. To further improve the training efficiency, a selection unit is added during global model aggregation to select suitable clients for parameter updates. Finally, we validate the proposed method operated on the HAM10000 dataset. Simulation results validate that pFedCAS can not only improve privacy protection, but also gain an improvement of 15% in training accuracy and a reduction of 30% in training costs based on communication quality. The simulation results also validate the excellent robustness of pFedCAS to non-iid data.

Over-the-air computation (AirComp) is considered a transformative enabler for next-generation artificial intelligence (AI) services and wireless data aggregation via the electromagnetic waveform-superposition property of wireless multi-access channels (MAC). However, the conventional distortion metric, minimum square error (MSE), is imperfect and not universally applicable in specific AirComp scenarios in a low-signal-to-noise ratio (SNR) regime and under power budget constraint. Conversely, the average discriminant gain is studied for task-oriented AirComp AI services like classification but with inaccurate performance indication. To solve these problems, this work establishes a novel framework for AirComp systems from the mutual information (MI) perspective. First, we categorize the AirComp model into two distinct classes based on the source (sensing) data independence, namely diverse-targets (DT) AirComp and homogeneous-target (HT) AirComp. Both categories with different inputs like classical Gaussian and classification-based Gaussian mixture model (GMM), can be unified and assessed via MI criterion. Next, for the DT AirComp system, we introduce a novel MI-aided AirComp beamforming scheme employing majorization-minimization (MM) relaxation. As for the HT AirComp, we present a heuristic successive approximation (SA)-based beamforming method considering complex GMM inputs. We also provide the feedback and update protocol for AirComp tracking. Simulations validate the superior performance on AirComp throughput and task-oriented metrics such as classification accuracy with our proposed MI-aided beamforming schemes.

Extremely large-scale multiple-input multiple-output (XL-MIMO) promises to provide ultrahigh data rates in millimeter-wave (mmWave) and Terahertz (THz) spectrum. However, the spherical-wavefront wireless transmission caused by large aperture array presents huge challenges for channel state information (CSI) acquisition and beamforming. Two independent parameters (physical angles and transmission distance) should be simultaneously considered in XL-MIMO beamforming, which brings severe overhead consumption and beamforming degradation. To address this problem, we exploit the near-field channel characteristic and propose two low-overhead hierarchical beam training schemes for near-field XL-MIMO system. Firstly, we project near-field channel into spatial-angular domain and slope-intercept domain to capture detailed representations. Then we point out three critical criteria for XL-MIMO hierarchical beam training. Secondly, a novel spatial-chirp beam-aided codebook and corresponding hierarchical update policy are proposed. Thirdly, given the imperfect coverage and overlapping of spatial-chirp beams, we further design an enhanced hierarchical training codebook via manifold optimization and alternative minimization. Theoretical analyses and numerical simulations are also displayed to verify the superior performances on beamforming and training overhead.

With the rapid development of the Internet of Things (IoT), a large amount of IoT devices are deployed to support various real-time applications, which put forward high requirements for data freshness. However, the limited battery capacity constraints the computation capabilities of terminal devices, thus decreases the timeliness of status updates. Mobile edge computing (MEC) has been considered as a solution to enhance the computing capacity of IoT devices and decrease their power consumption, thus extends their service life. In this paper, we aim to utilize MEC to decrease the power consumption of IoT devices while guaranteeing timeliness performance, which is measured by the metric age of information (AoI). We consider a multi-device scenario with time-varying channels and formulate the power-minimization problem with AoI constraints. Then we propose a dynamic scheduling policy based on Lyapunov optimization and analyze its performance theoretically. Numerical results validate the analysis and indicate that our policy outperforms the baselines.

In recent years, mobile edge computing (MEC) has been proposed as a promising technique to alleviate the challenges faced by delay and computation-intensive applications. However, users in remote and mountainous areas continue to face difficulties obtaining reliable computation services. To overcome this obstacle, unmanned aerial vehicles (UAVs) equipped with MEC servers have emerged as a popular solution. In such a multi-UAV network, the coverage areas of the UAVs might overlap, which would result in resource wastage and interference. To address this issue, we investigate a collaborative UAV-assisted MEC system for both aerial users (AUs) and ground users (GUs) in this work. Specifically, each user is covered by multiple UAV servers, and the resources of UAVs are dynamic over time. The main objective of this work is to reduce the average delay and improve the service success rate by jointly designing the UAV server-user association, bandwidth, and computing resource allocation strategy. To address the non-convex optimization problem mentioned above, we formulate a multi-agent extension of Markov decision processes (MDPs) for the system and design a cooperative Multi-Agent Twin Delayed Deep Deterministic Policy Gradient (MATD3) approach for each UAV server to make decisions using a centralized training approach with distributed execution. Simulation results validate that the proposed approach can achieve a superior success service rate with a lower delay compared with baselines.

XL-MIMO promises to provide ultrahigh data rates in Terahertz (THz) spectrum. However, the spherical-wavefront wireless transmission caused by large aperture array presents huge challenges for channel state information (CSI) acquisition. Two independent parameters (physical angles and transmission distance) should be simultaneously considered in XL-MIMO beamforming, which brings severe overhead consumption and beamforming degradation. To address this problem, we exploit the near-field channel characteristic and propose one low-overhead hierarchical beam training scheme for near-field XL-MIMO system. Firstly, we project near-field channel into spatial-angular domain and slope-intercept domain to capture detailed representations. Secondly, a novel spatial-chirp beam-aided codebook and corresponding hierarchical update policy are proposed. Theoretical analyses and numerical simulations are also displayed to verify the superior performances on beamforming and training overhead.

In this work, we study a system with a sensor forwarding status update to the receiver through an error-prone channel, and the receiver sends the transmission results to the sensor via a reliable link. We assume both transmission links suffer from random delays. We use Age of Information (AoI) to measure the freshness of the status information at the receiver. Our goal is to design a sampling policy that minimizes the expected time average AoI when the channel statistics are unknown. The problem is reformulated into a renewal-reward process optimization, and an online algorithm based on the Robbins-Monro algorithm is proposed. We prove that when the forward and backward transmission delays are bounded, the AoI difference between the online algorithm and the optimal policy decays with rate$\mathcal{O}(\ln K/K)$, where$K$is the number of successful transmissions. Simulation results validate the performance of our proposed algorithm.

This paper revisits the problem of sampling and transmitting status updates through a channel with random delay under a sampling frequency constraint. We use the Age of Information (AoI) to characterize the status information freshness at the receiver. The goal is to design a sampling policy that can minimize the average AoI when the statistics of delay is unknown. We reformulate the problem as the optimization of a renewal-reward process, and propose an online sampling strategy based on the Robbins-Monro algorithm. We prove that the proposed algorithm satisfies the sampling frequency constraint. Moreover, when the transmission delay is bounded and its distribution is absolutely continuous, the average AoI obtained by the proposed algorithm converges to the minimum AoI when the number of samples$K$goes to infinity with probability 1. We show that the optimality gap decays with rate$\mathcal {O}\left ({\ln K/K}\right)$, and the proposed algorithm is minimax rate optimal. Simulation results validate the performance of our proposed algorithm.

The diversity-multiplexing tradeoff (DMT) provides a fundamental performance metric for different multiple-input multiple-output (MIMO) schemes in wireless communications. In this paper, we explore the block fading optical wireless communication (OWC) channels and characterize the DMT in the presence of both optical peak- and average-power constraints. Three different fading distributions are considered, which reflect different channel conditions. In each channel condition, we obtain the optimal DMT when the block length is sufficiently large, and we also derive the lower and upper bounds of the DMT curve when the block length is small. These results are dramatically different from the existing DMT results in radio-frequency (RF) channels. These differences may be due to the fact that the optical input signal is real and bounded, while its RF counterpart is usually complex and unbounded.

Extensive work has demonstrated the excellent performance of orthogonal time frequency space (OTFS) modulation in high-mobility scenarios. Time-variant wideband channel estimation serves as one of the key compositions of OTFS receivers since the data detection requires accurate channel state information (CSI). In practical wideband OTFS systems, the Doppler shift brought by the high mobility is frequency-dependent, which is referred to as the Doppler Squint Effect (DSE). Unfortunately, DSE was ignored in overall prior estimation schemes employed in OTFS systems, which leads to severe performance loss in channel estimation and the consequent data detection. In this paper, we investigate DSE of wideband time-variant channel in delay-Doppler domain and concentrate on the characterization of OTFS channel coefficients considering DSE. The formulation and evaluation of OTFS input-output relationship are provided for both ideal and rectangular waveforms considering DSE. The channel estimation is therefore formulated as a sparse signal recovery problem and an orthogonal matching pursuit (OMP)-based scheme is adopted to solve it. Simulation results confirm the significance of DSE and the performance superiority compared with traditional channel estimation approaches ignoring DSE.

In this work, we study a status update system with a source node sending timely information to the destination through a channel with random delay. We measure the timeliness of the information stored at the receiver via the Age of Information (AoI), the time elapsed since the freshest sample stored at the receiver is generated. The goal is to design a sampling strategy that minimizes the total cost of the expected time average AoI and sampling cost in the absence of transmission delay statistics. We reformulate the total cost minimization problem as the optimization of a renewal-reward process, and propose an online sampling strategy based on the Robbins-Monro algorithm. Denote K to be the number of samples we have taken. We show that, when the transmission delay is bounded, the expected time average total cost obtained by the proposed online algorithm converges to the minimum cost when K goes to infinity, and the optimality gap decays with rate ${\mathcal{O}}$(ln K/K). Simulation results validate the performance of our proposed algorithm.

This paper studies the diversity-multiplexing tradeoff (DMT) for the block fading optical wireless communication (OWC) channel when the number of transmit antennas is not less than that of receive antennas. Inputs in this channel represent optical intensities, and hence are real-valued and non-negative. Moreover, inputs are subject to a per-antenna peak-power and a total average-power constraint. Considering these input constraints and assuming the channel fading follows the negative exponential distribution, we establish the outage diversity and average error probability bounds by using a random truncated exponential coding argument. By these derived bounds, we characterize the optimal DMT curve. Interestingly, the DMT result is fundamentally different from its counterpart in traditional radio frequency (RF) channels. This is due to the fact that inputs in this channel are real-valued and bounded, while in RF channels inputs are usually complex-valued and unbounded.

In this paper, we consider a multi-user multi-channel wireless network with a base station sending random fresh updates. To measure the data freshness of the network, the metric age of synchronization (AoS) is adopted. Our goal is to minimize the expected average AoS of the network. We first obtain a policy-independent lower bound via convex optimization and stochastic analysis under the perfect channel case, where all channels are available for users. Then we propose the perfect matching and maximum weight matching policies to approach the optimal performance. Under certain conditions, we prove theoretically that the gap between the lower bound and the expected average AoS under proposed policies vanishes as the number of channels goes to infinity. Numerical results validate the theoretical analysis and indicate that our policies can reach near-optimal performance.

In this paper, we consider a multi-user interference network where a central controller allocates power resources to multiple base stations for maximizing the entire network utility under a long-term convex power budget constraint. The optimal power allocation strategy depends on the accurate and instant channel state information (CSI). However, due to users’ mobility and the existence of channel fading and interference, timely channel estimation is unavailable. To overcome the difficulty of unknown channel states, we resort to the Lyapunov drift analysis framework and design an online power allocation algorithm based on historical CSI. The algorithm can be proven to achieve sub-linear performance for both cumulative regret and power budget violation. The sub-linear regret indicates the proposed algorithm can asymptotically achieve the optimal static power allocation performance in hindsight. Simulation results are provided to validate the asymptotic optimal performance of the proposed algorithm, as well as its robustness in the presence of adversarial interference.

Millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems with the hybrid precoder employed have been regarded as a reliable option to raise the capacity and coverage of the cellular network. Extensive studies have demonstrated that the capacity achieved by the hybrid precoder with fixed architectures cannot satisfy the requirement of the ever-increasing data traffic. However, the performance of the hybrid precoding can be further promoted by exploiting the flexibility of the precoding structure, where the adaptively-connected architecture can provide a critical enhancement. In this paper, we investigate the adaptively-connected structure and propose a near-optimal hybrid precoding scheme based on the Hungarian algorithm. Simulation results demonstrate the significant advantage of the proposed scheme over existing hybrid precoding algorithms.

This paper proposed a fully integrated MBAN (2360–2400 MHz) continuous phase modulated transmitter (TX) with tunable less than 0dBm output power for medical band. A duty-cycle tuning strategy was proposed for the power amplifier (PA) featuring adaptive optimized efficiency for different output powers. A fully on-chip transformer-based match network was proposed to suppress the 2nd harmonic using a series$LC$resonator and to suppress the 3rd harmonic by introducing a transformer inter-winding capacitor feedback path. A fractional-N all-digital phase locked loop (ADPLL) with a transformer-based digitally controlled oscillator (DCO) is employed to reduce power consumption as well as improve modulation quality. The transmitter was fabricated in 40-nm CMOS technology, occupying an active area of 0.48mm2. Experimental results show a 26% drain efficiency with −10dBm PA output and 4dB tunable range. A 2mW total power consumption was measured with a TX efficiency of 5% and an energy efficiency of 2nJ/bit. The measured 2nd and 3rd harmonic distortion of the output were −44.3dBm and −57.2dBm, respectively, with on-chip matching network. The measured FSK error of CPM was 2.3% with an M of 2 and 1.57% with an M of 4.

Massive multiple-input multiple-output (MIMO) is one of the key techniques to achieve better spectrum and energy efficiency in 5G system. The channel state information (CSI) needs to be fed back from the user equipment to the base station in frequency division duplexing (FDD) mode. However, the overhead of the direct feedback is unacceptable due to the large antenna array in massive MIMO system. Recently, deep learning is widely adopted to the compressed CSI feedback task and proved to be effective. In this paper, a novel network named aggregated channel reconstruction network (ACRNet) is designed to boost the feedback performance with network aggregation and parametric rectified linear unit (PReLU) activation. The practical deployment of the feedback network in the communication system is also considered. Specifically, the elastic feedback scheme is proposed to flexibly adapt the network to meet different resource limitations. Besides, the network binarization technique is combined with the feature quantization for lightweight and practical deployment. Experiments show that the proposed ACRNet outperforms loads of previous state-of-the-art networks, providing a neat feedback solution with high performance, low cost and impressive flexibility.

Reconfigurable intelligent surface (RIS) has been recognized as a potential technology for 5G beyond and attracted tremendous research attention. However, channel estimation for RIS-aided systems is still a critical challenge due to the excessive amount of parameters in the cascaded channel. The existing compressive sensing (CS)-based RIS estimation schemes only adopt incomplete sparsity, which induces redundant pilot consumption. In this paper, we analyze and exploit the specific triple-structured sparsity of the cascaded channel, i.e., the common column sparsity, structured row sparsity after offset compensation and the common offsets among all users. Furthermore, a novel on-grid Multi-user Triple-Structured-Compressive-Sensing simultaneous orthogonal matching pursuit (MTSCS-SOMP) algorithm along with an enhanced super-resolution (gridless) generalized iterative reweighted (MTSCS-IR) scheme are successively proposed. The former is practical and can be easily employed with low computational complexity, and the latter is further proposed to handle the severe power leakage problem encountered in mmWave channel estimations. Besides, we extend the sparsity property and algorithms from uniform linear array (ULA) configuration to uniform planar array (UPA), by transforming cascaded channel from matrix to tensor. Simulation results show that our approaches can significantly reduce pilot overhead over 50% and achieve enhanced performance on estimation accuracy.

Reconfigurable intelligent surface (RIS) has been recognized as a potential technology for 5G beyond and attracted tremendous research attention. However, channel estimation in RIS-aided system is still a critical challenge due to the excessive amount of parameters in cascaded channel. The existing compressive sensing (CS)-based RIS estimation schemes only adopt incomplete sparsity, which induces redundant pilot consumption. In this paper, we exploit the specific triple-structured sparsity of the cascaded channel, i.e., the common column sparsity, structured row sparsity after offset compensation and the common offsets among all users. Then a novel multi-user joint estimation algorithm is proposed. Simulation results show that our approach can significantly reduce pilot overhead in both ULA and UPA scenarios.

In this work, we consider the problem of transmission rate selection for a discrete time point-to-point block fading wire-less communication link. The wireless channel remains constant within the channel coherence time but can change rapidly across blocks. The goal is to design a link rate selection strategy that can identify the best transmission rate quickly and adaptively in quasi-static channels. This problem can be cast into the stochastic bandit framework, and the unawareness of time-stamps where channel changes necessitates running change-point detection simultaneously with stochastic bandit algorithms to improve adaptivity. We present a joint channel change-point detection and link rate selection algorithm based on Thompson Sampling (CD-TS) and show it can achieve a sublinear regret with respect to the number of time steps$T$when the channel coherence time is larger than a threshold. We then improve the CD-TS algorithm by considering the fact that higher transmission rate has higher packet-loss probability. Finally, we validate the performance of the proposed algorithms through numerical simulations.

We study whether and how can we model a joint distribution $p(x,z)$ using two conditional models $p(x|z)$ and $q(z|x)$ that form a cycle. This is motivated by the observation that deep generative models, in addition to a likelihood model $p(x|z)$, often also use an inference model $q(z|x)$ for extracting representation, but they rely on a usually uninformative prior distribution $p(z)$ to define a joint distribution, which may render problems like posterior collapse and manifold mismatch. To explore the possibility to model a joint distribution using only $p(x|z)$ and $q(z|x)$, we study their compatibility and determinacy, corresponding to the existence and uniqueness of a joint distribution whose conditional distributions coincide with them. We develop a general theory for operable equivalence criteria for compatibility, and sufficient conditions for determinacy. Based on the theory, we propose a novel generative modeling framework CyGen that only uses the two cyclic conditional models. We develop methods to achieve compatibility and determinacy, and to use the conditional models to fit and generate data. With the prior constraint removed, CyGen better fits data and captures more representative features, supported by both synthetic and real-world experiments.

In massive multiple-input multiple-output (MIMO) system, user equipment (UE) needs to send downlink channel state information (CSI) back to base station (BS). However, the feedback becomes expensive with the growing complexity of CSI in massive MIMO system. Recently, deep learning (DL) approaches are used to improve the reconstruction efficiency of CSI feedback. In this paper, a novel feedback network named CRNet is proposed to achieve better performance via extracting CSI features on multiple resolutions. An advanced training scheme that further boosts the network performance is also introduced. Simulation results show that the proposed CRNet outperforms the state-of-the-art CsiNet under the same computational complexity without any extra information. The open source codes are available at https://github.com/Kylin9511/CRNet.

We consider updating strategies for a local cache which downloads time-sensitive files from a remote server through a bandwidth-constrained link. The files are requested randomly from the cache by local users according to a popularity distribution which varies over time according to a Markov chain structure. We measure the freshness of the requested time-sensitive files through their Age of Information (AoI). The goal is then to minimize the average AoI of all requested files by appropriately designing the local cache’s downloading strategy. To achieve this goal, the original problem is relaxed and cast into a Constrained Markov Decision Problem (CMDP), which we solve using a Lagrangian approach and Linear Programming. Inspired by this solution for the relaxed problem, we propose a practical cache updating strategy that meets all the constraints of the original problem. Under certain assumptions, the practical updating strategy is shown to be optimal for the original problem in the asymptotic regime of a large number of files. For a finite number of files, we show the gain of our practical updating strategy over the traditional square-root-law strategy (which is optimal for fixed non time-varying file popularities) through numerical simulations.

Cognitive multihop relaying has been widely considered for device-to-device (D2D) communications for applications in the physical layer of the Internet of Things. In this article, we construct a multihop cellular D2D communications system model with energy harvesting (EH) in underlay cognitive radio networks. The locations of primary user equipments (PUEs) and cellular base stations are considered as a Poisson point process in this model. The transmit power of secondary devices is collected from the power beacon with time-switching EH policy. Two charging policies for different applications are considered in this article. Then, the end-to-end outage probability analysis expressions of these two scenarios for the transmission scheme subject to interferences from PUEs are derived. The optimal harvesting time ratio is obtained to get the maximum capacity for end-to-end D2D communications. The analytical results are validated by performing the Monte Carlo simulation of the end-to-end outage probability, which is based on the half-duplex transmission scheme. The results of this article provide a potential pathway to reduce reliance on grid or battery energy supplies and, hence, further strengthen the benefits for the environment and deployment of future smart devices.

This work is motivated by the need of collecting fresh data from power-constrained sensors in the industrial Internet of Things (IIoT) network. A recently proposed metric, the Age of Information (AoI) is adopted to measure data freshness from the perspective of the central controller in the IIoT network. We wonder what is the minimum average AoI the network can achieve and how to design scheduling algorithms to approach it. To answer these questions when the channel states of the network are time-varying and scheduling decisions are restricted to both bandwidth and power consumption constraint, we first decouple the multi-sensor scheduling problem into a single-sensor constrained Markov decision process (CMDP) by relaxing the hard bandwidth constraint. Next we exploit the threshold structure of the optimal policy for the decoupled single sensor CMDP and obtain the optimum solution through linear programming (LP). Finally, an asymptotically optimal truncated policy that can satisfy the hard bandwidth constraint is built upon the optimal solution to each of the decoupled single-sensor. Our investigation shows that to obtain a small average AoI over the network: (1) The scheduler exploits good channels to schedule sensors supported by limited power; (2) Sensors equipped with enough transmission power are updated in a timely manner such that the bandwidth constraint can be satisfied.

In this work, a wireless broadcast network with a base station (BS) sending random time-sensitive information updates to multiple users under bandwidth constraint is considered. To measure the effect of data desynchronization when the updates appear randomly because of external environment, the metric Age of Synchronization (AoS) is adopted in this work. It shows the amount of the time elapsed since freshest information at the receiver becomes desynchronized. The AoS minimization scheduling problem is formulated into a discrete time Markov decision process and the optimal solution is approximated through structural finite state policy iteration. An index based heuristic scheduling policy based on restless multi-arm bandit (RMAB) is provided to further reduce the computational complexity. Simulation results show that the proposed index policy achieves compatible performance with the MDP and is close to the AoS lower bound. Our work indicates that, to obtain a small AoS over the entire network, users with larger transmission success probability and smaller random update probability are more likely to be scheduled at smaller AoS.

The study of frequency-selective hybrid precoding for broadband millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems has recently attracted major research interest. In this paper, we seek to provide an efficient solution to simultaneously conduct user scheduling and frequency-selective hybrid precoding for a broadband multiuser mmWave MIMO system. More specifically, we re-formulate the issue using the framework of stochastic-optimization. By utilizing the powerful tool of Lyapunov optimization, a dynamic weighted sum-rate maximization problem is formulated, towards which we develop an algorithm that simultaneously conducts i) user scheduling, ii) multi-carrier resource allocation, and iii) frequency-selective precoder design. It is also demonstrated that the proposed framework is capable of asymptotically optimizing a given stochastic utility (e.g. long-term sum rate). Simulation results are provided to demonstrate the superiority of the proposed algorithm.

In this work, a wireless broadcast network with a base station (BS) sending random time-sensitive information updates to multiple users with interference constraints is considered. The Age of Synchronization (AoS), namely the amount of time elapsed since the information stored at the network user becomes desynchronized, is adopted to measure data freshness from the perspective of network users. Compared with the more widely used metric-the Age of Information (AoI), AoS accounts for the freshness of the randomly changing content. We formulate the scheduling problem into a discrete time Markov decision process and approximate the optimal solution through finite state policy iteration. An index based heuristic scheduling policy based on restless multi-arm bandit (RMAB) is provided to reduce computational complexity. Numerical results are presented to demonstrate the performance of the proposed policies.

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

As the bandwidth resource in TV white space (TVWS) is permitted to be used for the secondary users, multiple secondary networks coexisting in the same geographical area can provide multiple services without interfering with the primary service, such as the TV broadcasting. To satisfy the minimum rate requirements and maximise the sum rate of the multiple services, the problem of resource allocation can be modelled as a mixed integer non‐linear programming (MINLP). According to this model, the joint bandwidth and power allocation (JBPA) method is proposed to solve the resource allocation for multiple services. The JBPA method is sub‐optimal, which can approximate the performance of the optimal two‐step exhaustive search (TSES) method very well, especially when the number of services is larger than the number of idle channels. Compared with TSES, the JBPA method decreases the complexity from exponential level to polynomial level. Besides, compared with one channel occupied by single service, the JBPA can serve more services and provide stable performance when the number of services varies dynamically. Compared with the algorithm random bandwidth allocation, the JBPA can approximate the optimal performance provided by TSES better with the time complexity of the same order.

Deep learning has a wide application in the area of natural language processing and image processing due to its strong ability of generalization. In this paper, we propose a novel neural network structure for jointly optimizing the transmitter and receiver in communication physical layer under fading channels. We build up a convolutional autoencoder to simultaneously conduct the role of modulation, equalization, and demodulation. The proposed system is able to design different mapping scheme from input bit sequences of arbitrary length to constellation symbols according to different channel environments. The simulation results show that the performance of neural network-based system is superior to traditional modulation and equalization methods in terms of time complexity and bit error rate under fading channels. The proposed system can also be combined with other coding techniques to further improve the performance. Furthermore, the proposed system network is more robust to channel variation than traditional communication methods.

Hybrid precoding is introduced to millimeter wave (mmWave) multiple-input multiple-output (MIMO) System in order to reduce the number of radio frequency (RF) chains. In traditional hybrid precoding scheme, the maximum number of independent data streams is restricted by the number of RF-chains, which limits the achievable spectral efficiency (SE) rate. To further improve the SE rate, the generalized spatial modulation (GenSM) structure is considered. However, current gradient ascent iteration (GAI) algorithm of GenSM aided hybrid precoding is too complicated when the antenna array is large. In this paper we propose a novel low complexity algorithm for the analog and digital precoder design under GenSM hybrid precoding structure. Our approach gives additional unitary constraint to the digital precoder, formulating its design as a masked orthonormal procrustes problem. With this simplification, we accomplish the whole hybrid precoder design via turbo optimization (TO). Finally, numerical simulations are demonstrated to compare the achievable SE performance between our proposed low complexity scheme and some traditional hybrid precoding algorithms including the GAI algorithm.

The layered division multiplexing (LDM) is recently combined with spatial modulation (SM) systems to provide a more efficiency way for broadcasting transmission. In SM aided LDM (SM-LDM) systems, the service of each layer, which is allocated with different power, is transmitted via SM scheme. In this paper, a gradient descent based iterative method is proposed to optimize the injection level, which can enhance the spectral efficiency (SE) in the two-layer SM-LDM systems with maximum ratio combining (MRC). In addition, the concavity analysis of this optimization problem is also conducted. Monte Carlo simulations are also provided to verify the effectiveness of our proposed injection level optimization method.

As a combination of spatial modulation (SM) system and massive multiple‐input multiple‐output (MIMO) system, massive SM aided MIMO (SM‐MIMO) system is recently proposed. In broadband scenarios, single‐carrier (SC) modulation is introduced to massive SM‐MIMO system, thus massive SC‐SM MIMO system is proposed for uplink multi‐user transmission over frequency‐selective fading channels. In this study, the uplink spectral efficiency (SE) of multi‐cell massive SC‐SM MIMO system is analysed, meanwhile the pilot contamination effect is taken into consideration. A tight SE lower bound of multi‐cell massive SC‐SM MIMO system is proposed with maximum ratio (MR) combining, which also takes into account the imperfect channel estimation, transmit antenna correlation and path loss. The tightness of the authors' proposed closed‐form SE lower bound is shown via simulation results. The optimal pilot reuse factor can be determined with different system configurations by simulations, and the pilot reuse factor that is larger than one is more suitable for less TAs and user equipments.

The application of hybrid precoding in millimeter wave (mm-wave) multiple-input multiple-output (MIMO) systems has been proven effective for reducing the number of radio frequency (RF) chains. However, the maximum number of independent data streams is conventionally restricted by the number of RF chains, which limits the spatial multiplexing gain. To further improve the achievable spectral efficiency (SE), in this paper we propose a generalized spatial modulation aided mmwave MIMO system to convey an extra data stream via the index of the active antennas group, while no extra RF chain is required. Moreover, we propose a hybrid analog and digital precoding scheme for SE maximization. Based on a narrowband mmwave-MIMO channel assumption, a closed-form lower bound is derived to quantify the achievable SE of the proposed system. By utilizing this lower bound as the cost function, a two-step algorithm is proposed to optimize the hybrid precoder. The proposed algorithm not only utilizes the concavity of the cost function over the digital power allocation vector but also invokes the convex 1∞relaxation to handle the non-convex constraint imposed by analog precoding. Finally, the proposed scheme is shown via simulations to outperform state-of-the-art mm-wave MIMO schemes in terms of achievable SE.

Multiple-input multiple-output (MIMO) techniques have constituted a key approach to facilitate the rapid data traffic growth in future 5G communication networks. However, the high cost and low efficiency nature of radio frequency (RF) resource has unfortunately reduced the benefit of MIMO. While spatial modulation (SM) technique has recently been proposed to reduce the RF resource for conventional small-scale MIMO systems, it is unclear if future 5G communication network, where conventional MIMOs encounter much different challenges, still embraces the benefits of SM. In order to address this issue, in this paper the application of SM in 5G communications is discussed, where we propose to incorporate SM into i) massive MIMO systems, and ii) millimeter wave (mmWave) MIMO systems, to strike a better tradeoff between energy efficiency and spectral efficiency for future wireless communications. It is revealed in this paper that SM is capable of improving the transmission data rates for 5G communication networks while maintaining an equal scale of RF resource occupancy, which therefore motivates the fruitful application of SM in 5G communications.

In this paper, a novel generalized spatial modulation (GenSM)-aided millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) system is proposed for indoor transmission over line-of-sight (LoS) channels. Unlike the conventional GenSM-based mmWave systems, the application of digital beamforming (DBF) and the impact of spatial-domain codebooks are explored in our work. More specifically, we first propose a closed-form lower bound for quantifying the achievable spectral efficiency (SE) of our system. Then we seek to optimize the spatial-domain codebook and DBF design with respect to SE maximization. Lastly, numerical simulations are provided to substantiate the performance gain achieved by the proposed system compared against the conventional spatial multiplexing (SMX) MIMO schemes with waterfilling precoding.

Due to the high cost and low energy efficiency of the dedicated radio frequency (RF) chains, the number of RF chains in a millimeter wave (mmWave) multiple-input multiple-output (MIMO) system is usually limited from a practical point of view. In this case, the maximum number of independent data streams is also restricted by the number of RF chains, which consequently leads to limiting the potentially attainable spatial multiplexing gain. In order to address this issue, in this paper, a novel generalized spatial modulation (GenSM) aided mmWave MIMO system is proposed, which enables the transmission of an extra data stream via the index of the active antennas group and requires no extra RF chain. Moreover, a two-step algorithm is also proposed to optimize the hybrid precoder design with respect to spectral efficiency (SE) maximization. Finally, numerical simulation results demonstrate the superior SE performance achieved by the proposed scheme.

Flat-fading channel models are usually invoked for analyzing the performance of massive spatial modulation multiple-input multiple-output (SM-MIMO) systems. However, in the context of broadband SM transmission, the severe inter-symbol-interference (ISI) caused by the frequency-selective fading channels can not be ignored, which leads to very detrimental effects on the achievable system performance, especially for single-carrier SM (SC-SM) transmission schemes. To the best of the author's knowledge, none of the previous researchers have been able to provide a thorough analysis on the achievable spectral efficiency (SE) of the massive SC-SM MIMO uplink transmission. In this context, the uplink SE of single-cell massive SC-SM MIMO system is analyzed, and a tight closed-form lower bound is proposed to quantify the SE when the base station (BS) uses maximum ratio (MR) combining for multiuser detection. The impacts of imperfect channel estimation and transmit antenna (TA) correlations are all considered. Monte Carlo simulations are performed to verify the tightness of our proposed SE lower bound. Both the theoretical analysis and simulation results show that the SE of uplink single-cell massive SC-SM MIMO system has the potential to outperform the uplink SE achieved by single-antenna UEs.

In generalised spatial modulation (GenSM) systems, the total number of active antenna groups is always not a power of two, which leaves room for designing an active antenna group (AAG) selection algorithm to achieve the constellation shaping gain and to improve the symbol error rate (SER). In this paper, based on uniform-cost search, a novel iterative AAG selection algorithm for GenSM systems is proposed with channel state information at the transmitter (CSIT). Based on the principle of minimum Euclidean distance maximization, the proposed algorithm reduces the computational complexity while maintaining the global optimality in improving SER comparing with exhaustive search algorithm. Monte Carlo simulations are performed to verify the global optimality in SER performance and favorable reduction in computational complexity striked by the proposed AAG selection algorithm.

Channel estimation for underwater acoustic (UWA) channels in orthogonal frequency division multiplexing (OFDM) systems is one of the most difficult problems in UWA communications. Because UWA channels are both frequency-selective and time-selective, the total number of channel coefficients to be estimated will significantly increase. Besides, the fast time-varying characteristics and strong Doppler effects will deteriorate the orthogonality between the OFDM subcarriers and cause inter-carrier interference (ICI). To handle these problems, in this paper, we firstly transform the discrete doubly selective UWA channel model into an expansion of complex exponential basis. Based on the basis expansion model (BEM) of UWA channels, we then equivalently transform the channel estimation problem into the recovery of sparse BEM coefficients. Finally, a modified sparse signal recovery scheme based on block sparse Bayesian learning is proposed to estimate the doubly selective UWA channel state information (CSI). Simulation results confirm its performance merits.

In spatial modulation (SM) systems, the distribution of amplitude-phase modulation (APM) symbol causes the tradeoff of detection performance between APM symbol and active antenna, which infers the overall capacity of SM system. In this paper, the APM constellation and symbol labeling of SM system is investigated. The optimized APM constellation is designed to maximize the system coded modulation average mutual information (CM-AMI). The symbol labeling of SM system is optimized with binary switching algorithm (BSA) using the symbol detection pairwise error probability (PEP) criteria. Moreover, the proposed APM constellation and symbol labeling are applied in a coded spatial modulation (SM) system with the bit-interleaved coded modulation and iterative demapping (BICM-ID). Simulation result shows the BICM-ID SM system with proposed constellation and symbol labeling has a signal-to-noise ratio (SNR) gain compared to conventional constellation system and independent demapping system.

Spatial modulation (SM) is proposed to both achieve a high energy efficiency (EE) and low implementation complexity, which attracts a lot of academic interests. Generalised SM (GenSM) is proposed to achieve a higher spectral efficiency (SE) than traditional SM system, and there are many researches about detection in GenSM system. In this paper, we propose a novel low-complexity detection algorithm for GenSM system with transmit antenna (TA) correlation, which is based on the polynomial expansion and zero forcing (ZF) equalization algorithm. Our proposed algorithm has a lower computational complexity than traditional ZF detection algorithm, and this computational complexity can be adjusted to achieve a tradeoff with bit error rate (BER) performance. Monte Carlo simulations verify that our proposed low-complexity ZF (LC-ZF) detection algorithm also has a better BER performance than maximum ratio (MR) detection algorithm.

Combined with client pairing on nonorthogonal multiple access (CP-NOMA), carrier sense multiple access/collision avoidance (CSMA/CA) can provide more connectivity for clients in downlink phase and alleviate the low transmission efficiency from rate anomaly problem. Compared with conventional CSMA/CA system, the proposed enhanced CSMA/CA with CP-NOMA can save about half of the time cost to send signaling overhead and provide improvement in the efficiency of data transmission. Similar to saturated throughput, the long-term average rate is defined and used to illustrate the performance of the proposed method. Considering the criterion of the fairness and the received signal to noise ratio (SNR), the selection of client pairing in NOMA could be optimized to maximize the data transmission rate in NOMA. Providing the channel access for more clients, the efficiency of the downlink transmission is enhanced in the proposed scheme. In the application of Internet of Things (IoT), The enabling technology supporting more clients in connectivity really makes a big difference. Besides, the enhanced CSMA/CA with CP-NOMA is compatible with conventional CSMA/CA system.

The non-orthogonal multiple access (NOMA) technology has been proposed and regarded as one of the potential promising technologies for the future 5G network. The extension of generalized spatial modulation multiple-input multiple-output (MIMO) to NOMA system improves both the spectral and energy efficiencies of the system, while maintaining the massive connectivity and low latency advantages, but it puts forward challenges for the multi-user and signal detection as well. In this paper, we propose a joint user activity and signal detection scheme based on the block-sparse compressive sensing (BS-CS) method in the uplink NOMA system, in which the generalized spatial modulation MIMO technology is used. By exploiting the structure and sparsity of the multi-user generalized spatial modulation signals, we formulate the detection problem into a block-sparse recovery problem. Then a BS-CS based detection algorithm, enhanced structured block-sparse compressive sampling matching pursuit (ESB-CoSaMP), is proposed to detect the active users and transmitted data efficiently. Moreover, the information of active antennas at each user is exploited in ESB-CoSaMP to further improve the accuracy. Simulations show that the proposed detection scheme outperforms the conventional CS and BS-CS based schemes.

In this paper, a spectral efficient channel estimation scheme is proposed for spatial-correlated sparse massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems with superimposed time-frequency training sequences (TSs) based on basis expansion model (BEM). This scheme first relies on the identical time-domain TS to acquire the partial channel common support, based on which the frequency-domain TSs inserted into the OFDM block are then utilized for accurate channel state information (CSI) acquisition. Besides, the superimposed TSs are adopted to reduce the TS overhead. Meanwhile, by exploiting the spatial correlations of massive MIMO channels, BEM can be utilized to reduce the number of the frequency-domain TSs and improve the spectral efficiency drastically from 22% to 95%. Simulation results show that the proposed scheme with 200 frequency-domain TSs can achieve almost the same performance as the conventional approach with 3200 frequency-domain TSs in massive MIMO systems with 256 transmit antennas.

LDS-OFDM and SCMA are two examples of non orthogonal multiple access (NOMA), which is considered to be a promising technology for next generation wireless communications due to its larger capacity region compared with orthogonal multiple access (OMA). Iterative receiver structure is used in these two systems to get better performance than the noniterative receiver, but a direct implementation of iterative receiver with conventional message passing algorithm has a prohibitive computational complexity, especially when the system uses high order modulation. In this paper, Gaussian approximation is used to solve this problem, and can reduce the complexity of detector effectively. Simulation results demonstrate that the proposed Gaussian approximation based iterative receiver offers significant performance gains over the traditional non-iterative receiver structure. Even compared with the computationally intractable message passing based iterative receiver, the performance does not degrade much. This means that the proposed method offers a tradeoff between performance and complexity, and is a good candidate for future use in LDS-OFDM and SCMA systems.

In this study, a spectral efficient channel recovery scheme is proposed for spatial‐temporal correlated sparse massive multiple‐input multiple‐output (MIMO) orthogonal frequency division multiplexing systems. By exploiting the spatial‐temporal correlations of massive MIMO channels, superimposed time‐frequency training sequences (TSs) are jointly utilised for accurate channel state information (CSI) acquisition. Moreover, a novel time‐frequency TS pattern design scheme is proposed to improve the recovery performance based on structured compressive sensing theory. Furthermore, the discrete prolate spheroidal basis expansion model of massive MIMO channels in the space‐domain is utilised to reduce the frequency‐domain pilots overhead and improve the spectral efficiency. Simulation results show that the proposed scheme could achieve accurate CSI with great improvement in spectral efficiency and computational complexity.

Generalized spatial modulation (GSM) is an extended architecture to the spatial modulation multiple antenna system. The GSM system uses multiple activated transmit antennas to further exploit the system transmission capacity. In this paper, we consider the GSM systems with channel diagonalization (CD-) transmitter pre-coder (TPC), channel inversion TPC, and without TPC. For each TPC scheme, the pairwise activated antenna detection discriminant and closed-form antenna detection pairwise error probability (PEP) of the GSM system are derived. We use the antenna detection PEP to calculate the analytical antenna detection symbol error rate union bound and the mutual information lower bound. We use asymptotic methods to analyze antenna detection diversity and coding gains of GSM system with or without TPC when the signal-to-noise ratio is high. Together with complexity analysis on the pre-coders and antenna detectors, we discover the CD-TPC trades the antenna detection performance for complexity reduction, while the CD-TPC scheme achieves a balance between the performance and complexity. This paper introduces methods to the performance evaluation of the GSM system, and provides a guideline for the GSM system design such as antenna configuration and TPC schemes selection.

The concept of generalized spatial modulation (GenSM) aided millimeter wave (mm-wave) multiple-input multiple-output (MIMO) has recently attracted substantial research interest, as it benefits from the large bandwidth of mm-wave MIMO, while maintaining a reduced number of radio frequency (RF) chains. However, due to the lack of precoding design, the preceding GenSM-aided mm-wave MIMO schemes suffered from severe performance loss. Inspired by the reduced-RF-chain structure making implementation cost low while maintaining the benefit of GenSM, in this paper, we incorporate the technique of hybrid precoding into GenSM-aided mm-wave MIMO, where a hybrid digital and analog precoding regime is proposed to enhance the system's achievable rate. Moreover, a closed-form expression is proposed to provide an accurate approximation to the spectral efficiency (SE) achieved by the proposed scheme. The proposed expression is further simplified in the region of high signal-to-noise ratio (SNR). By exploiting the proposed SE expressions as low-complexity cost functions, two algorithms, i.e. the gradient ascent algorithm and the high-SNR approximation algorithm, are exploited to optimize the hybrid precoders in terms of SE maximization. Finally, numerical simulations are provided to substantiate the superior SE performance achieved by the proposed scheme over other GenSM-aided mm-wave MIMO schemes as well as the state-of-the-art mm-wave MIMO systems.

Massive spatial modulation aided multiple-input multiple-output (SM-MIMO) systems have recently been proposed as a novel combination of SM and of conventional massive MIMO, where the base station (BS) is equipped with a large number of antennas and simultaneously serves multiple user equipment (TIE) that employ SM for their uplink transmission. Since the massive SM-MIMO concept combines the benefits of both the SM and massive MIMO techniques, it has recently attracted substantial research interest. In this paper, we study the achievable uplink spectral efficiency (SE) of a multi-cell massive SM-MIMO system, and derive closed-form expressions to asymptotically lower-bound the SE yielded by two linear BS combining schemes, including maximum ratio combining and zero forcing combining, when a sufficiently large number of BS antennas are equipped. The derivation takes into account the impact of transmitter spatial correlations, imperfect channel estimations, user-specific power controls, and different pilot reuse factors. The proposed asymptotic bounds are shown to be tight, even when the scale of BS antennas is limited. The new SE results facilitate a system-level investigation of the optimal number of uplink transmit antennas (TAs) N with respect to SE maximization. Explicitly, we provide theoretical insights on the SE of massive SM-MIMO systems. Furthermore, we demonstrate that massive SM-MIMO systems are capable of outperforming the SE of conventional massive MIMOs relying on single-TA TIEs.

The idea of combining spatial modulation (SM) with massive multiple-input multiple-output (MIMO) system, i.e. massive SM-MIMO, has recently attracted plenty of academic interest, as it could simultaneously harness the advantages of SM and massive MIMO. However, the spectral efficiency (SE) analysis for massive SM-MIMO is still lack of exploring. To this end, a novel SE expression is derived in this paper to provide a tight lower bound for massive SM-MIMO systems, in which the impacts of imperfect channel estimation and random user locations are all accounted for. The new SE expression enables an efficient system-level analysis, via which we can investigate the optimal number of user antennas for SE maximization. Interestingly, we found that massive SM-MIMO maintains a higher SE than the conventional massive MIMO with single-antenna users when a limited number of users are scheduled, while conventional massive MIMO is more suitable when a large number of users are served.

Generalized spatial modulation (GSM) is an extended architecture to the spatial modulation (SM) multiple antenna system. Multiple activated transmit antennas (TAs) are applied in the GSM system to further exploit the system transmission capacity. In this paper, we consider the transmitter pre-coding (TPC) GSM systems with channel diagonalization (CD) and channel inversion (CI) pre-coding schemes. The pairwise activated TA detection discriminant and closed-form pairwise error probability (PEP) function are derived. We use the TA detection PEP to calculate the analytical TA detection symbol error rate (SER) union bound and the mutual information lower bound. The analytical results are verified by numerical simulation over Rayleigh fading channels. We investigate the system with different number of antennas and TPC schemes to evaluate the error probability and mutual information performance of the GSM system. The results show that the CD-TPC GSM system with equal power allocation has the most TA detection mutual information, and the CD-TPC system with water-filling power allocation has the most joint APM domain and space domain mutual information.

In conventional generalised spatial modulation (GSM) systems, the antenna combination selection is based on the distribution of channels, without channel state information (CSI). In this paper, an A-Star based heuristic method and a sequential forward selection method for antenna combination selection in GSM systems are proposed to provide a superior symbol error rate (SER) performance with CSI. The A-Star based heuristic method is designed to minimize the union bound of SER rather than to maximize the minimum Euclidean distance between symbols. And the sequential forward antenna combination selection method is designed to reduce the computational complexity compared to the A-Star based heuristic method, but with slight loss in SER performance. Monte Carlo simulations are implemented to verify the performance of these proposed methods.

Channel estimation for MIMO orthogonal frequency division multiplexing (MIMO-OFDM) systems has been improved observably based on compressive sensing (CS) techniques. Recently, some block-structured CS methods have been utilized in MIMO-OFDM systems. However, the block-coherence which characterizes the performance of a block-structured measurement matrix is still not optimized. In this paper, we proposed a two-step scheme to optimize the pilot locations and power of different transmitting antennas, in order to minimize the block-coherence of the aggregate system matrix. The selection of pilot locations and power can be obtained by two genetic algorithms. Then we introduce a block version of the orthogonal matching pursuit (OMP) algorithm, termed BOMP, to obtain the channel estimation consistent with the block-sparse model. Simulation results demonstrate that the proposed pilot allocation scheme with superimposed pilots design and block-structured CS method performs very well in MIMO-OFDM systems.

In this paper, we study the design of frequency domain turbo equalization (FDTE) in single carrier (SC) multiple-input multiple-output (MIMO) systems, and prove the equivalence of two typical FDTE schemes under constant-modulus modulation. In the design of FDTE schemes, received signals and soft decision symbols obtained at the previous iteration are utilized to recover the transmitted symbols under the minimum mean squared error (MMSE) criterion. Frequency domain equalization with frequency domain decision feedback (FDE- FDDF), and frequency domain equalization with parallel interference cancellation (FDE-PIC) are presented with details. Under constant-modulus modulation, the FDE-FDDF scheme and the FDE-PIC scheme are proved to be equivalent mathematically. The equivalence of the two FDTE schemes under constant-modulus modulation has also been verified through numerical simulations.

Generalised spatial modulation (GSM) is extended from the concept of spatial modulation (SM). Due to its superior energy efficiency to classical multiple-input multiple-output (MIMO) techniques, GSM has attracted plenty of interest under the study of large-scale MIMO systems. In this paper, we propose a low-complexity symbol detector for GSM based on the framework of sparse Bayesian learning (SBL), which effectively recovers the GSM symbols by exploiting the inherent sparsity property of GSM. Compared to other sparsity-based symbol detectors, e.g., detectors based on the basis pursuit (BP) method and the orthogonal matching pursuit (OMP), the SBL-based detector is capable of achieving a superior reconstruction accuracy using less receive antennas. Numerical simulations are performed to substantiate the performance of the proposed detector.

Spatial modulation (SM) is a modulation scheme that exploits the spatial dimension of multiple-input multiple-output (MIMO) channels to encode the transmitted information. It is shown in this paper that, by properly rotating the phase of the transmitted symbols, the minimum Euclidean distance (MED) of the constellation of the received symbols can be enlarged and hence the error performance can be improved. Moreover, we propose an algorithm that incorporates an iteration process to design the phase-only pre-scaling factors, which only rotate the phases of different transmitted symbols according to the instantaneous channel realization. Simulation results demonstrate that the proposed method is capable of outperforming the conventional SM scheme and other pre-scaling SM schemes.

In this paper, we propose a low-complexity single- carrier frequency-domain iterative receiver for triply-selective underwater acoustic (UWA) fading channels, which combines frequency domain decision feedback equalization (FD-DFE) with iterative channel estimation. Due to long multipath channels, frequency domain turbo equalization has to use large block size to achieve low computational complexity and high data efficiency, but UWA channel coherence time is often much shorter than block length. We utilize pilots or soft decision symbols obtained at the previous iteration to re-estimate channel at each turbo iteration, thus achieving satisfactory performance. Although having slightly inferior bit error performance to time-domain turbo equalizers, the proposed FDE scheme reduces the complexity by three orders of magnitude, making it affordable for real-time implementation. The proposed iterative receiver has been verified through both numerical simulations and undersea experiment data collected in the Surface Processes and Acoustic Communications Experiment 2008 (SPACE08).

Spatial modulation (SM) is a recently proposed multiple-input multiple-output (MIMO) architecture that exploits the space domain for information transmission. In this paper, SM systems with continuous amplitude-phase modulation (APM) and the corresponding maximum likelihood (ML) active transmit antenna detector are considered. We introduced a method to compute the theoretical transition probability matrix and the mutual information of the ML antenna detector. For SM multiple-input single-output (MISO) system, we derive the error probability and the mutual information of the antenna detector in closed-form. For multiple receive antenna SM system, the union bound of error probability and the lower bound of antenna detection mutual information are proposed. The analytical results are verified by numerical simulation over Rayleigh fading channels. We investigate systems with different number of antennas and APM symbol input distributions to evaluate the performance of SM. The joint APM and antenna detection mutual information of SM systems is compared to the capacity of vertical Bell Labs layered space-time (V-BLAST) spatial multiplexing MIMO scheme. Our research gives achievable transmission rates of SM-MISO/MIMO systems and can be a guideline for suitable application scenarios.

An error transfer chart is introduced to trace the iterative process of the decision feedback equalizer for the single-carrier modulation. In the investigated iterative MF-DFE structure, the forward filter is set to the matched filter to maximize the signal power, while the feedback filter is used to cancel the inter-symbol interference. MMSE detection is applied to minimize the decision error power. The trajectory of iterations is shown in the error transfer chart, in which MF-DFE maps a decision error power to an SINR and MMSE detection further converts the SINR to a new decision error power. The final state of iterations is shown to be the intersection of the MMSE curve and error power transfer curve corresponding to the MF-DFE algorithm. It is shown by simulation that the error transfer chart is a useful tool to analyze the convergence of non-linear iterative equalizers.

Extended spatial modulation (ESM) is proposed as a novel spatial modulation scheme in this paper. Extended spatial modulation is a spatial modulation design where the number of active antennas can be various. The data rate could be improved as more symbols are available for transmitting and thus more information bits can be modulated during each time slot. The minimum distance of the transmitted symbols can also be further maximized as more symbols are available for optimizing selection which further improves the symbol error performance. Moreover, a near-optimal detection scheme with low complexity is also proposed in this paper. An approximated analytical expression for the symbol error probability (SEP) of the proposed detection scheme is derived along with the complexity analysis. Numerical simulations are proposed to demonstrate the performance of the proposed detection scheme and to validate the theoretical analysis.

In this paper, we propose a time-domain oversampled receiver for orthogonal frequency division multiplexing (OFDM) in underwater acoustic communication. Without changing the structure of the transmitter, significant gains are acquired by time- domain oversampling at the receiver. Using time- domain oversampling, the energy of transmitted symbols is concentrated on the first N subcarriers, while additive Gaussian noises are distributed in all the subcarriers. Because of the sparsity of underwater acoustic channels, most energy of the channel impulse responds is concentrated on a few paths, and other positions are submerged by noises. Channel estimation based on the discrete Fourier transform is employed to eliminate noises. After estimating the energy of additive Gaussian noises, the truncation of channel impulse responds eliminates the majority of noises. Simulations are implemented in multipath channels and fading channels. Without changing the structure of the transmitter, the proposed oversampled receiver outperforms the conventional receiver in bit error rate (BER) performances significantly, with a little increased computational complexity.

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%.

Recently, compressive sensing (CS) methods have been considered to improve the accuracy of channel estimation. Since the accurate estimation of the channel relies on the size of the observations, the fixed size of the observations is usually the trade-off between the channel estimation accuracy and the length of the channel delay to handle. In this paper, an adaptive compressive sensing (A-CS) algorithm is proposed to obtain the entire inter-block-interference (IBI)-free observations which can be detected dynamically. The A-CS method can utilize the entire IBI-free observations effectively. Simulation results demonstrate that the proposed method outperforms the state of art solutions whose performances are limited by the fixed size of the IBI-free region.

Frequency domain equalization (FDE), as a linear equalizing method, is widely adopted in many applications with either OFDM or single carrier (SC) modulation. This paper analyzes the the noise transfer effect, a case for noise colorization, after FDE. This process is investigated for CP-OFDM, ZP-OFDM and SC systems where SC is treated as a multi-carrier modulation with the number of subcarriers equal to 1. Based on this analysis and by assuming the de-mapping independence among subcarriers, the average channel capacity for the systems is derived. We have proved that the capacity of the CP-OFDM system is at least equal to that of ZP-OFDM in both low and high SNR scenarios and that the capacity of the SC system is no greater than that of either ZP-OFDM or CP-OFDM systems. In a multi-path channel, if the IDFT size of the OFDM symbol is not equal to the DFT size of the equalizer, the leakage of the enhanced noise in a deeply faded sub-channel will cause the loss of capacity in adjacent sub-channels and the overall average capacity is thus reduced.

In order to realize multi-service application in the time domain synchronous orthogonal frequency division multiplex (TDS-OFDM) system, a layered data transmission scheme is proposed in this paper. This scheme allows for the transmission of different services over the same bandwidth with different transmission qualities. Concretely, the second layer data stream is transmitted through modulation on training sequence, and has lower receiving sensitivity and larger coverage area. So it can be used to support mobile service, such as local news, weather. While the first layer data stream is sent as the conventional TDS-OFDM scheme. Theoretical analysis and computer simulation show that the proposed scheme works well and these two layer data streams are compatible with each other.

A generalised spatial modulation (SM) scheme with multiple active transmit antennas, named as multiple active-spatial modulation (MA-SM), is proposed in this paper. By allowing multiple transmitting antennas in the SM system to transmit different symbols at the same time instant, MA-SM takes advantages of the low complexity of SM and high multiplexing gain of Vertical-Bell Lab Layered Space-Time (V-BLAST) system. In the MA-SM system, the transmitted symbols are mapped into a high dimensional constellation space including the spatial dimension. The general principle for designing the efficient MA-SM for arbitrary number of transmit antennas and modulation scheme is presented. Moreover, a near-optimal detection scheme with low complexity for MA-SM is also proposed and analyzed. A closed form bound for the bit error probability (BEP) of the proposed detection scheme is also derived in this paper. Numerical results with the comparison among the existing multiple-input multiple-output (MIMO) systems such as space time block code (STBC) and V-BLAST demonstrate the efficiency of MA-SM.

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.

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.

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.

Transmit diversity is an attractive technology in broadcasting environment. When it is applied in Chinese Digital Terrestrial Television Broadcasting (DTTB) system, the training sequences (TS) from the different transmit antennas for channel estimation and synchronization will interfere with each other. In order to overcome the problem, two methods are proposed in which the orthogonal TS is designed based on pseudo noise (PN) sequences. One method holds good compatibility with the existing network and the other one obtains good performance under time selective channel. The corresponding channel estimation method is also presented and analyzed in this paper. Simulations show that the proposed schemes can effectively estimate the channel state information and achieve great diversity gain, even more than 3.5dB under fast fading channels. Moreover they can both work well in some cases where the system with single antenna can't work.

To efficiently suppress the noise through a time-domain threshold value after the least square (LS) estimation, the discrete Fourier transform (DFT) based channel estimation (CE) is usually carried out in practical orthogonal frequency division multiplexing (OFDM) systems for its inherent noise immunity. In this paper, an improved DFT based CE method for OFDM systems is proposed. Taking advantage of the wavelet decomposition, the proposed method obtains an optimal threshold value according to the minimum mean-square error (MMSE) optimality criterion. Both theoretical analysis and computer simulation show that, the proposed method not only reduces the mean-square error (MSE) without MSE floor compared with the conventional methods, but also improves the symbol error rate (SER) performance by being more close to that of the ideal known-channel case over both static and dynamic multipath 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.

This paper summarizes some key features for future Chinese digital television terrestrial broadcasting (DTTB) systems including improved frame structure, transmit diversity, advanced channel coding and modulation, multi-service support, broadcasting return channel solution and etc.

In order to improve the system performance over both frequency- and time-selective fading channels, orthogonal frequency division multiplexing (OFDM) system with transmit diversity technique is an effective and attractive solution. However, the received signal is the superposition of the signals from different transmit antennas. Hence, to obtain accurate channel state information (CSI) is difficult but very important to recover transmitted OFDM symbols. Conventional channel estimation methods use subcarrier pilots or training sequences as the channel sounding signal to obtain the estimation of channel frequency response (CFR). Actually, decision-directed channel estimation (DDCE) method which takes advantage of the fact that m-QAM constellation subcarriers in OFDM symbol also contain much valuable hidden-information and can be used to perform the channel estimation as well as tracking of time-varying channel. In this paper, a simple and effective partial hard decision method for m-QAM is investigated. Furthermore, a simplified DDCE method based on an accurate initial CFR and partial decision results of OFDM subcarriers for transmit diversity system is proposed. Simulation results demonstrate that the proposed algorithm works well over the slow-varying frequency-selective fading channel.

To improve the transmission performance of terrestrial digital multimedia/television broadcasting (DMB-T) system, the performance of an efficient space-frequency transmitter diversity scheme is introduced in this article. This scheme maintains the original transmitter branch and the network structure is simple to upgrade. DMB-T system is an important candidate for Chinese digital television terrestrial broadcasting (DTTB) standard. The system was simulated over both slow and fast fading DTTB channels. Simulation results show that the introduced transmitter diversity scheme can efficiently improve the system performance in fading environments.