The forecasting of mobile data not only helps operators proactively perceive the network status, enabling them to arrange and schedule network resources in advance to improve user service quality, but also allows for the on-demand, flexible extrapolation of network changes under different strategies, effectively reducing the trial-and-error costs in the live network. Traditional prediction methods with tailored models for exclusive data types undoubtedly increase the design complexity and deployment costs. In this paper, we propose a Mobile Foundation Model (MobiFM) for data forecasting, which adopts a unified framework to forecast mobile data with diverse types (mobile traffic, users, and wireless channel), various time granularities (hourly and minute-level), and multiple spatial scales (cell-level and grid-level). MobiFM is a generative model built on diffusion and Transformer backbones. It incorporates a memory-network core that flexibly stores large amounts of contextual knowledge from urban environments, network configuration parameters, and spatio-temporal features. In parallel, MobiFM employs Mixture-of-Experts (MoE) networks to specialize and exploit the distinct characteristics of heterogeneous mobile data. We train the MobiFM using 10 real-world datasets with over 200,000 time-series points, totaling more than 1 billion tokens. The experimental results demonstrate that MobiFM achieves improvements of 20.24%, 10.92%, and 6.52% in the forecasting of mobile traffic, users, and wireless channel data, respectively, exhibiting good generalization performance compared to the baselines. Furthermore, based on MobiFM’s forecasting capability, we formulate an energy-saving optimization case, where the experimental results show the MobiFM-based scheme can improve energy efficiency up to 17.9%.

Radio maps play a crucial role in optimizing wireless network performance and configuration, providing insights into the spatial distribution of radio frequency signal power. Existing solutions often face challenges in generalizing and adapting across various environments, frequency bands, and vertical dimensions. To overcome these limitations, we propose UniRM, a universal large model designed for constructing multiband 3D radio maps. UniRM leverages large-scale pre-training and prompt learning techniques to accurately generate radio maps across diverse environments, altitudes, and frequency bands. Specifically, UniRM employs a UNet-based encoder-decoder architecture during pre-training to extract universal latent representations that capture shared features across different environmental conditions. A prompt learning module further enhances this by transforming auxiliary inputs, such as environmental descriptions, frequency bands, and altitudes, into discriminative embeddings, thereby enabling effective cross-domain generalization and ensuring robustness in unseen scenarios. Extensive experiments using a large, diverse dataset covering numerous scenarios demonstrate that UniRM outperforms state-of-the-art baselines by over 10% in key metrics, including mean squared error, normalized mean squared error, root mean squared error, and peak signal-to-noise ratio. Notably, zero-shot evaluations highlight UniRM’s strong ability to generalize to new environments without retraining. The code for UniRM is available at: https://github.com/Shirleyue/UniRM.

The widespread use of uncrewed aerial vehicles (UAVs) has propelled the development of advanced techniques on countering unauthorized UAV flights. However, the resistance of legal UAVs to illegal interference remains under-addressed. This paper proposes radiation pattern reconfigurable fluid antenna systems (RPR-FAS)-empowered interference-resilient UAV communication scheme. This scheme integrates the reconfigurable pixel antenna technology, which provides each antenna with an adjustable radiation pattern. Therefore, RPR-FAS can enhance the angular resolution of a UAV with a limited number of antennas, thereby improving spectral efficiency (SE) and interference resilience. Specifically, we first design dedicated radiation pattern adapted from 3GPP-TR-38.901, where the beam direction and half power beamwidth are tailored for UAV communications. Furthermore, we propose a low-storage-overhead orthogonal matching pursuit multiple measurement vectors algorithm, which accurately estimates the angle-of-arrival (AoA) of the communication link, even in the single antenna case. Particularly, by utilizing the Fourier transform to the radiation pattern gain matrix, we design a dimension-reduction technique to achieve 1–2 order-of-magnitude reduction in storage requirements. Meanwhile, we propose a maximum likelihood interference AoA estimation method based on the law of large numbers, so that the SE can be further improved. Finally, alternating optimization is employed to obtain the optimal uplink radiation pattern and combiner, while an exhaustive search is applied to determine the optimal downlink pattern, complemented by the water-filling algorithm for beamforming. Comprehensive simulations demonstrate that the proposed schemes outperform traditional methods in terms of angular sensing precision and spectral efficiency1.

Wireless jamming identification, which detects and classifies electromagnetic jamming from non-cooperative devices, is crucial for emerging low-altitude wireless networks consisting of many drone terminals that are highly susceptible to electromagnetic jamming. However, jamming identification schemes adopting deep learning (DL) are vulnerable to attacks involving carefully crafted adversarial samples, resulting in inevitable robustness degradation. To address this issue, we propose a differential transformer framework for wireless jamming identification. Firstly, we introduce a differential transformer network in order to distinguish jamming signals, which overcomes the attention noise when compared with its traditional counterpart by performing self-attention operations in a differential manner. Secondly, we propose a randomized masking training strategy to improve network robustness, which leverages the patch partitioning mechanism inherent to transformer architectures in order to create parallel feature extraction branches. Each branch operates on a distinct, randomly masked subset of patches, which fundamentally constrains the propagation of adversarial perturbations across the network. Additionally, the ensemble effect generated by fusing predictions from these diverse branches demonstrates superior resilience against adversarial attacks. Finally, we introduce a novel consistent training framework that significantly enhances adversarial robustness through dual-branch regularization. Simulation results demonstrate that our proposed methodology is superior to existing methods in boosting robustness to adversarial samples.

We provide new recovery bounds for hierarchical compressed sensing (HCS) based on prior support information (PSI). A detailed PSI-enabled reconstruction model is formulated using various forms of PSI. The hierarchical block orthogonal matching pursuit with PSI (HiBOMP-P) algorithm is designed in a recursive form to reliably recover hierarchically block-sparse signals. We derive exact recovery conditions (ERCs) measured by the mutual incoherence property (MIP), wherein hierarchical MIP concepts are proposed, and further develop reconstructible sparsity levels to reveal sufficient conditions for ERCs. Leveraging these MIP analyses, we present several extended insights, including reliable recovery conditions in noisy scenarios and the optimal hierarchical structure for cases where sparsity is not equal to zero. Our results further confirm that HCS offers improved recovery performance even when the prior information does not overlap with the true support set, whereas existing methods heavily rely on this overlap, thereby compromising performance if it is absent.

The coordinated deployment of multiple Base Stations (BS) and tuning of antenna configuration plays a crucial role in ensuring high-quality communication services, especially in the context of dense 5G BS deployment in megacities. However, traditional optimization methods, such as heuristics and Reinforcement Learning (RL), face challenges in addressing such problems involving the coordination of hundreds of BSs due to their limitations in handling the complexity and scale of large-scale scenarios. To address these challenges, this article proposes the Hierarchical Multi-Agent Proximal Policy Optimization with Representation Learning (HMAPPO-RL). By employing a hierarchical structure, we effectively decouple the optimization problem into two sub-problems: BS deployment and antenna parameter tuning. Different from the step-by-step method of optimizing the BS location and antenna, HMAPPO-RL achieves joint optimization of the two problems through an ingenious interactive mechanism, fully considering the mutual influence of the BS location and antenna. To address the large-scale challenge posed by hundreds of BSs, we utilize the upsampling and downsampling mechanisms of the UNet network to integrate global and local information from large-scale state information for performance enhancement. Since complex environmental information will cause great difficulties for the agent to evaluate the state value in large-scale scenarios, we add a representation learning module to enhance the accuracy of the agent’s state value estimation. The experiments using a precise mobile network simulator demonstrate the superiority of the proposed HMAPPO-RL, offering a comparative analysis with existing state-of-the-art methods. HMAPPO-RL achieves a coverage rate of 91.66% and an average throughput of 4,983,537 bit/s. These results represent improvements of 3.62% and 6.75% in coverage rate and throughput, respectively, when compared with the MAPPO algorithm.

With the widespread deployment of 5G networks, efficient network optimization and planning have become increasingly important. The generation of mobile traffic and user data can help network operators understand and grasp the network’s operational status from different perspectives, enabling customized strategies such as wireless resource allocation and user access control. However, existing research primarily focuses on the generation of single-type data and lacks exploration of the interplay between mobile traffic and user data. Moreover, current generative models struggle to capture the spatio-temporal correlations between multimodal environmental data and mobile data. In this paper, we propose a Multimodal Context-aware Diffusion Model (MCDiff) for simultaneously generating mobile traffic and users. The model incorporates an interplay perception module to capture the correlation between mobile traffic and users. To better characterize the complex and dynamic features of urban environments, we innovatively propose extracting both spatial and temporal variations from multimodal contextual data and employing contrastive learning to align multimodal contextual features with mobile data. Extensive experiments on two real-world datasets demonstrate that MCDiff can accurately generate both mobile traffic and users, achieving up to a 23.11% improvement in fidelity metrics. Facilitated by our multimodal contextual fusion module, MCDiff exhibits strong controllable and generalization capabilities, with a minimal transfer gap of only 1.19%. Furthermore, by leveraging the generated mobile traffic and user data, we formulate network planning and optimization strategies. Experimental results highlight the superiority and practicality of our method.

This paper introduces a sensing-centric joint communication and millimeter-wave radar paradigm to facilitate collaboration among intelligent vehicles. We first propose a chirp waveform-based delay-Doppler quadrature amplitude modulation (DD-QAM) that modulates data across delay, Doppler, and amplitude dimensions. Building upon this modulation scheme, we derive its achievable rate to quantify the communication performance. We then introduce an extended Kalman filter-based scheme for four-dimensional (4D) parameter estimation in dynamic environments, enabling the active vehicles to accurately estimate orientation and tangential-velocity beyond traditional 4D radar systems. Furthermore, in terms of communication, we propose a dual-compensation-based demodulation and tracking scheme that allows the passive vehicles to effectively demodulate data without compromising their sensing functions. Simulation results underscore the feasibility and superior performance of our proposed methods, marking a significant advancement in the field of autonomous vehicles. Simulation codes are provided to reproduce the results in this paper: https://github.com/LiZhuoRan0.

Deep learning based channel prediction has garnered significant attention to mitigate channel aging in high-mobility multiple-input multiple-output (MIMO) systems. However, existing channel prediction methods extract the temporal correlations from the channel sequences estimated at uniform pilots, which require dense pilot configuration to mitigate Doppler aliasing in high-mobility scenarios and incur substantial estimation overhead. To tackle this problem, we propose a channel prediction method based on continuous-time transformer with the non-uniform pilot pattern, thereby enabling accurate prediction across arbitrary time scales with only a small number of pilots. Specifically, we first design the non-uniform pilot pattern based on Chebyshev polynomial roots and then prove its optimality under Doppler-dominated channel variations with relatively stable user velocity, wherein a subset of pilots are densely configured to provide a finer resolution of Doppler phase estimation. To adapt to the non-uniform pattern, a continuous-time transformer is further proposed, which integrates the superior feature extraction capability of transformer with the continuous-time modeling strength of neural ordinary differential equation (ODE) for flexibly processing the estimated channel sequences with non-uniform time scales. More concretely, the attention mechanism is extended to the continuous-time domain by incorporating neural ODE, while a high-frequency temporal encoding is designed to fit rapidly time-varying channels. Besides, an element-wise prediction mechanism is proposed to efficiently capture temporal correlations and prevent overfitting. Simulation results demonstrate that our proposed method can realize accurate continuous-time channel prediction in high-mobility scenarios, and significantly outperforms existing channel prediction methods.

Frequency-domain channel extrapolation is effective in reducing pilot overhead for massive multiple-input multiple-output (MIMO) systems. Recently, deep learning (DL) based channel extrapolators have become promising candidates for modeling complex frequency-domain dependency. Nevertheless, current DL extrapolators fail to operate in unseen environments under distribution shift, which poses challenges for large-scale deployment. In this paper, environment generalizable learning for channel extrapolation is achieved by realizing distribution alignment from a physics perspective. Firstly, the distribution shift of wireless channels is rigorously analyzed, which comprises the distribution shift of multipath structure and single-path response. Secondly, a physics-based progressive distribution alignment strategy is proposed to address the distribution shift, which includes successive path-oriented design and path alignment. Path-oriented DL extrapolator decomposes multipath channel extrapolation into parallel extrapolations of the extracted paths, which can mitigate the distribution shift of multipath structure. Path alignment is proposed to address the distribution shift of single-path response in path-oriented DL extrapolators, which eventually enables generalizable learning for channel extrapolation. In the simulation, distinct wireless environments are generated using the precise ray-tracing tool. Based on extensive evaluations, the proposed path-oriented DL extrapolator with path alignment can reduce extrapolation error by more than 6 dB in unseen environments compared to the state-of-the-arts.

Deep learning is promising to enhance the accuracy and reduce the overhead of channel state information (CSI) feedback, which can boost the capacity of frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) systems. Nevertheless, the generalizability of current deep learning-based CSI feedback algorithms cannot be guaranteed in unseen environments, which induces a high deployment cost. In this paper, the generalizability of deep learning-based CSI feedback is promoted with physics interpretation. Firstly, the distribution shift of the cluster-based channel is modeled, which comprises the multi-cluster structure and single-cluster response. Secondly, the physics-based distribution alignment is proposed to effectively address the distribution shift of the cluster-based channel, which comprises multi-cluster decoupling and fine-grained alignment. Thirdly, the efficiency and robustness of physics-based distribution alignment are enhanced. Explicitly, an efficient multi-cluster decoupling algorithm is proposed based on the Eckart–Young-Mirsky (EYM) theorem to support real-time CSI feedback. Meanwhile, a hybrid criterion to estimate the number of decoupled clusters is designed, which enhances the robust-ness against channel estimation error. Fourthly, environment-generalizable neural network for CSI feedback (EG-CsiNet) is proposed as a novel learning framework with physics-based distribution alignment. Based on extensive simulations and sim-to-real experiments in various conditions, the proposed EG-CsiNet can robustly reduce the generalization error by more than 3 dB compared to the state-of-the-arts.

Larger antenna arrays, combined with higher transmission frequencies, are prospective in fulfilling the demands of the sixth-generation (6G) communications, enabling a 10-fold increase in overall spectral efficiency. However, such configurations give rise to near-field effects, requiring spherical rather than planar wave modeling. In practical multi-user communications, it is typical that part of the user equippments (UEs) resides in the near-field region, while others are located in the far-field region, thereby leading to a unified near/far-field scenario. Conventional codebooks tailored to either regime alone thus become mismatched, resulting in notable spectral efficiency degradation. In view of this, the ringwise codebook based on the slope-intercept formulation is proposed to address the unified near/far-field communication scenario. Specifically, the slope-intercept domain is first illustrated as the foundation of our codebook design, where the correlation between near/far-field channel steering vectors is exploited by mapping the angle-distance into the slope-intercept parameters. In the slope-intercept domain, the correlation pattern of steering vectors exhibits a dual triangle structure, supported by rigorous analyses on axial symmetry and correlation width, which paves the way for the subsequent codebook development. Secondly, the ringwise codebook is proposed where all UEs coarsely estimate its own slope parameter, based on which ring-wise codebooks composed of orthogonal codewords derived from the slope-intercept domain are constructed for different UEs. The resulting codebooks are then fed back to the base station through specific slope parameters, which are leveraged in the subsequent precoding procedure. Finally, rigorous analysis demonstrates that both the computational complexity and hardware cost of the proposed ringwise codebook design are acceptable, while numerical results validate its effectiveness and feasibility, achieving gains in both spectral efficiency and complexity compared to conventional counterparts.

Beamforming is a key technology for achieving integrated sensing and communication (ISAC). However, most existing works focus on mono-static sensing, which has limited sensing accuracy and strong self-interference. To address these issues, this paper investigates hybrid beamforming (HBF) design for millimeter-wave (mmWave) multiple-input multiple-output (MIMO) ISAC system with multi-static cooperative localization. Specifically, one access point (AP) simultaneously forms communication beams to serve multiple user equipments (UEs) and a sensing beam towards one target, and other multiple distributed APs perform cooperative localization on the target by estimating the angle-of-arrivals (AOAs) of received echo signals. First, to characterize the target localization accuracy, we derive the squared position error bound (SPEB) of AOA-based multi-static cooperative localization. Then, two HBF optimization problems are formulated to investigate the performance tradeoff between sensing and communication. For the sensing-centric design, we aim to minimize the SPEB of target localization while ensuring the signal-to-interference-plus-noise ratio (SINR) requirements of individual UEs. To tackle this nonconvex problem, we propose a semidefinite relaxation (SDR)-based alternating optimization algorithm. For the communication-centric design, a fractional programming (FP)-based alternating optimization algorithm is proposed for solving the communication sum-rate maximization problem under the sensing SPEB constraint. Simulation results demonstrate that the proposed two HBF algorithms can achieve localization accuracy and sum-rate performance close to fully-digital beamforming counterparts and outperform other baseline schemes.

With the rapid development of Internet of Things (IoT), efficient and reliable massive IoT device connections need to be widely supported in the upcoming next-generation communication networks, especially for emerging high-mobility scenarios. In this context, this paper investigates massive grant-free random access (GF-RA) in high mobility scenarios, focusing on active device detection (ADD) and channel estimation (CE) under fast time-varying channels. By exploiting the inherent low-rank structure of the observed pilot-signal-tensor, a tensor-based GF-RA transmission scheme is provided. On this basis, we propose a joint ADD and CE method based on the canonical polyadic (CP) model for both sourced and unsourced RA frameworks. More specifically, by remodelling the observation signal as a third-order tensor, the channel parameters can be grouped in the factor matrices of the CP model. However, the excessive number of potential device connections in massive GF-RA scenarios lead to excessively large dimensions of the factor matrices, thus resulting in severe ill-condition. To solve this problem, the Vandermonde structure of factor matrices is developed, which enables the effective exploitation of the tensor subspace for CP decomposition. Then, by utilizing the pre-allocated training precoders, an effective two-dimensional search method is proposed to jointly detect active devices and initialize the iterative estimation of channel parameters. Finally, due to the grouping situation, independent and coupled channel parameters are estimated by appropriate methods based on maximum likelihood (ML) and iterative updating, respectively. Moreover, the pre-allocation of training precoders can be unified to the unsourced RA scenarios, where the joint ADD and CE can be regard as a simple degenerate method compared to sourced RA. Simulation results demonstrate that the proposed tensor-based GF-RA framework outperforms the state-of-the-art schemes in terms of both ADD and CE performance.

Near-space airship-borne communication network is recognized to be an indispensable component of the future integrated ground-air-space network thanks to airships’ advantage of long-term residency at stratospheric altitudes, but it urgently needs reliable and efficient Airship-to-X link. To improve the transmission efficiency and capacity, this paper proposes to integrate semantic communication with massive multiple-input multiple-output (MIMO) technology. Specifically, we propose a deep joint semantic coding and beamforming (JSCBF) scheme for airship-based massive MIMO image transmission network in space, in which semantics from both source and channel are fused to jointly design the semantic coding and physical layer beamforming. First, we design two semantic extraction networks to extract semantics from image source and channel state information, respectively. Then, we propose a semantic fusion network that can fuse these semantics into complex-valued semantic features for subsequent physical-layer transmission. To efficiently transmit the fused semantic features at the physical layer, we then propose the hybrid data and model-driven semantic-aware beamforming networks. At the receiver, a semantic decoding network is designed to reconstruct the transmitted images. Finally, we perform end-to-end deep learning to jointly train all the modules, using the image reconstruction quality at the receivers as a metric. The proposed deep JSCBF scheme fully combines the efficient source compressibility and robust error correction capability of semantic communication with the high spectral efficiency of massive MIMO, achieving a significant performance improvement over existing approaches.

In frequency division duplex (FDD) massive multiple input multiple output (MIMO) systems, reliable downlink channel estimation is essential but requires huge pilot overhead due to hundreds of antennas at base station (BS). In order to reduce pilot overhead without compromising the channel estimation, compressive sensing (CS) has been widely applied for channel estimation by exploiting the inherent sparse structure of massive MIMO channel in angular domain. However, it still suffers from high complexity during the optimization process and the requirement of prior information on the number of spatial paths (PINP). To overcome these challenges, this paper develops a novel hybrid channel estimation scheme by integrating model-driven CS and data-driven deep unrolling techniques. The proposed scheme is composed of a coarse estimation part and a fine correction part, which is implemented in a two-stage manner by exploiting both inter- and intra-frame sparsities of channels in angular domain. Additionally, a threshold function is proposed to eliminate the requirement of the number of spatial paths. Theoretical results are provided to indicate the convergence of both fine correction and coarse estimation. Numerical results demonstrate that our scheme can achieve high accuracy with less pilot overhead and low complexity.

Cell-free network has emerged as a promising candidate to enhance both sum rate and coverage in wireless communications. Unlike traditional cellular networks, cell-free network deploys multiple access points (APs) to jointly serve all users within a predefined geographic area, offering significant spatial diversity and interference reduction. However, the optimal deployment of APs remains a challenging problem due to its dependence on user distribution, pilot contamination, and channel estimation errors, etc. Since existing methods usually rely on simplistic models, such as Poisson point processes (PPP), they fail to capture real-world user distribution and dynamic environmental conditions. In this paper, we address this challenge by modeling the AP deployment problem as a Markov decision process (MDP). We construct multiple heterogeneous graphs to represent the interactions between APs and users and propose a graph neural network (GNN) with multiple graph inputs and an inter-graph pooling layer, referred to as GNNMG, to extract deployment state features. Subsequently, GNNMG is coupled with proximal policy optimization (PPO) to learn the optimal deployment policy. Our proposed GNNMG with PPO (GNNMG-PPO) is compared against its traditional optimization counterparts, and its superior performance is validated in terms of sum rate. Additionally, GNNMG-PPO exhibits strong generalization ability to different deployment scenarios and varying numbers of APs and users.

For multiple-input multiple-output systems, channel prediction is crucial for mitigating channel aging in mobile scenarios. The existing channel prediction schemes typically require strictly equal sampling intervals of historical and predicted channel sequences, which imposes enormous pilot overhead in high-mobility scenarios with frequent channel estimation. To tackle this problem, we investigate the super-resolution channel prediction, where the future channel sequence is predicted at a finer temporal resolution without additional channel estimation. Specifically, we theoretically analyze the physics process underlying super-resolution channel prediction to show that the measurement of Doppler phase rotation faces the challenging issue of phase ambiguity in high-mobility and high-frequency scenarios. To address this issue, a deep learning-based dual-band fusion approach is proposed to adaptively integrate the low-frequency information for accurate Doppler phase measurement. To realize accurate channel prediction at a finer temporal resolution, we propose the physics feature-inspired neural ordinary differential equation with modulated-periodic-based multi-layer perceptron for effectively learning the dynamics of fast time-varying channels. Simulation results verify that our proposed scheme outperforms existing channel prediction schemes and it maintains robust performance in high-mobility scenarios.

In unlicensed spectrum, wireless communication systems are vulnerable to electromagnetic attacks and interference from non-cooperating parties, thus amplifying the significance of wireless communication security. The identification of wireless interference serves as a pivotal technology for spectrum sensing and is crucial to facilitate anti-interference communications, where wireless interference identification adopting deep learning technology has been extensively explored and has exhibited exceptional performance benefits. In this paper, we propose a lightweight transformer network (LTN) for interference identification, which can solve the computational complexity challenge from the conventional transformer networks while preserving their global feature extraction proficiency. LTN comprises three lightweight modules, namely low-complexity linear embedding (LCLE), integral and refined feature extraction (IRFE) and attention matrix reuse (AMR). Firstly, the LCLE module is obtained through the utilization of reparameterization techniques. The incorporation of reparameterization enables the decoupling of the network architecture during the training and testing phases, thereby enhancing the performance and reducing the complexity concurrently. Secondly, we propose the IRFE module, which leverages the discrete wavelet transform to partition the input into integral and refined components. For feature extraction, multi-head self-attention (MSA) is utilized for the refined part while window-based MSA is employed for the integral part, ensuring an optimized allocation of computational resources. Finally, we present a novel AMR mechanism, which takes advantage of the similarity of attention matrices in adjacent MSA layers. AMR can effectively circumvent the computational complexity by saving subsequent attention matrix computations. Simulation results validate that our proposed methodology has higher recognition accuracy.

Near-field communication comes to be an indispensable part of the future sixth generation (6G) communications at the arrival of the forth-coming deployment of extremely large-scale multiple-input-multiple-output (XL-MIMO) systems. Due to the huge array aperture and high-frequency bands, the electromagnetic radiation field is modeled by the spherical waves instead of the conventional planar waves, leading to severe weak sparsity to angular-domain near-field channel. Therefore, the channel estimation reminiscent of the conventional compression sensing (CS) approaches in the angular domain, judiciously utilized for low pilot overhead, may result in unprecedented challenges. To this end, this paper proposes a brand-new near-field channel estimation scheme by exploiting the naturally occurring useful side information. Specifically, we formulate the dual-band near-field communication model based on the fact that high-frequency systems are likely to be deployed with lower-frequency systems. Representative side information, i.e., the structural characteristic information derived by the sparsity ambiguity and the out-of-band spatial information stemming from the lower-frequency channel, is explored and tailored to materialize exceptional near-field channel estimation. Furthermore, in-depth theoretical analyses are developed to guarantee the minimum estimation error, based on which a suite of algorithms leveraging the elaborating side information are proposed. Numerical simulations demonstrate that the designed algorithms provide more assured results than the off-the-shelf approaches in the context of the dual-band near-field communications in both on- and off-grid scenarios, where the angle of departures/arrivals are discretely or continuously distributed, respectively.

Accurately predicting mobile traffic and accessed user amount is of great importance to network resource allocation, energy saving, etc. However, due to the complicated environmental contexts and complex interaction between mobile traffic and connected users, mobile network prediction is still challenging. Besides, the existing works could not be applied to large-scale networks because of the limited hardware resources and unacceptable time cost. In this work, we propose the spatiotemporal transformer framework for the multi-task mobile network prediction. Our proposed model contains three key parts. First, to capture the complex interaction between mobile traffic and connected users, we propose the temporal cross-attention encoder. Then, to identify and extract the most relevant information from various semantic relationships, we propose the hierarchical spatial encoder. This information is then used to create a more comprehensive representation of the network. Finally, the subgraph sampling method could significantly reduce the amount of computing power required and have comparable performance to the methods that input the whole network, enabling the model for real-world applications. Extensive experiments demonstrate that our proposed model significantly outperforms the state-of-the-art models by over 17% in both mobile traffic prediction and connected user prediction.

This paper proposes a fast and robust graph-based wavenumber-domain approach for channel estimation in holo-graphic MIMO (HMIMO) systems. Unlike conventional angulardomain methods—prone tomutual coupling, power leakage, andsampling redundancy—our framework resolves HMIMO’s high-dimensional challenges by introducing a wavenumber-domain basis via orthogonal Fourier harmonics (FHs), eliminating dependencies on antenna density. By reformulating channel estimation as its sparse recovery counterpart, we model clustered sparsity using an elliptic Markov random field (EMRF), upon which a graph-cut swap expansion (GCSE) algorithm is developed, leveraging graph-theoretic optimizations for fast convergence and low complexity. Simulations demonstrate that our method achieves robust performance against mutual coupling, varying SNRs, and antenna density with drastically less computing time.

As a superior multicarrier technique utilizing chirp signals for high-mobility communications, affine frequency division multiplexing (AFDM) is envisioned to be a promising candidate for sixth-generation (6G) wireless networks. AFDM is based on the discrete affine Fourier transform (DAFT) with two adjustable parameters of the chirp signals, termed the pre-chirp and post-chirp parameters, respectively. Whilst the post-chirp parameter complies with stringent constraints to combat the time-frequency doubly selective channel fading, we show that the pre-chirp counterpart can be flexibly manipulated for an additional degree of freedom. Therefore, this paper proposes a novel AFDM scheme with the pre-chirp index modulation (PIM) philosophy (AFDM-PIM), which can implicitly convey extra information bits through dynamic pre-chirp parameter assignment, thus enhancing both spectral and energy efficiency. Specifically, we first demonstrate that the subcarrier orthogonality is still maintained by applying distinct pre-chirp parameters to various subcarriers in the AFDM modulation process. Inspired by this property, we allow each AFDM subcarrier to carry a unique pre-chirp signal according to the incoming bits. By such an arrangement, extra bits can be embedded into the index patterns of pre-chirp parameter assignment without additional energy consumption. We derive asymptotically tight upper bounds on the average bit error probability (BEP) of the proposed schemes with the maximum-likelihood detection, and validate that the proposed AFDM-PIM can achieve full diversity under doubly dispersive channels. Based on the derived result, we further propose an optimal pre-chirp alphabet design to enhance the bit error rate (BER) performance via intelligent optimization algorithms. Simulation results demonstrate that the proposed AFDM-PIM outperforms the classical benchmarks.

Benefiting from a large amount of unallocated bandwidth, millimeter-wave (mmWave) communications have been regarded as one of the most promising technologies. To overcome high pathloss of mmWave signals, the beamforming technique plays a fundamental role. In recent years, with the success of deep learning (DL), DL-based beam prediction methods have been widely studied to reduce the training overhead of traditional beam scanning methods. In this paper, a novel DL-based low-overhead beam prediction scheme is proposed, which is motivated by two important observations: (1) The optimal beam prediction is difficult for non-line of sight (NLOS) scenario, which limits the overall prediction accuracy. (2) On the contrary, the optimal beam can be precisely predicted with low computational costs under line of sight (LOS) scenario. Therefore, we propose a flexible network architecture, namely multi-stage network (MSN), to conduct the optimal beam prediction. Firstly, MSN contains multiple branches with gradually increasing computational complexity, and each branch carries with a classifier, which enables the MSN to have the capability of adaptively and dynamically allocating computational resources. Meanwhile, to combine the advantages of convolutional neural network (CNN) and transformer for feature extraction in MSN, we design joint CNN and transformer (JCT) module and its simplified module, namely Ghost-JCT. Secondly, we propose two pre-training strategies to effectively improve the performance of classifiers without additional computational costs. Finally, we propose confidence-based and Markov-based classifier selection strategies, which could select the appropriate classifier to strike a balance between accuracy and computational complexity. Simulation results demonstrate that MSN enjoys significant superiority in terms of computational complexity and prediction accuracy compared to its traditional counterparts.

The fifth-generation (5G) mobile network has significantly enhanced people's lives with faster internet speed and more reliable connections. However, there is still insufficient coverage of 5G networks worldwide, requiring telecom operators to deploy more base stations to meet the increasing demand for 5G's further commercialization. In this regard, a major challenge is understanding user network behaviors and traffic demands in target areas where 5G has not yet been deployed, which is crucial for developing a more efficient base station deployment strategy. Mobile traffic generation is a potential approach that enables operators to preemptively estimate user network demands in target areas, thereby specifying corresponding deployment strategies to enhance network performance. However, existing methods have limitations in capturing spatio-temporal features of 5G mobile traffic, particularly in areas with insufficient 5G coverage and limited historical 5G traffic data. To fill this gap, we introduce a regional feature conditioned diffusion framework for 5G network traffic generation. Our models explore the relationship between 5G traffic and existing 4G traffic, utilizing a customized cross attention mechanism and graph convolutional networks (GCN) to capture the correlation between network traffic and regional features. Based on this relationship, the framework can characterize mobile network traffic demands, thereby achieving high-fidelity 5G traffic generation in target regions with insufficient 5G coverage. Extensive experiments on real-world datasets have shown that the proposed scheme outperforms state-of-the-art baselines by more than 10%, demonstrating its high-fidelity generation capability, controllability, and generalizability. Moreover, we have deployed our scheme on China Mobile's Jiutian Platform as a network traffic simulator to improve 5G base station deployment strategies.

In the near-field context, the Fresnel approximation is typically employed to mathematically represent solvable functions of spherical waves. However, these efforts may fail to take into account the significant increase in the lower limit of the Fresnel approximation, known as the Fresnel distance. The lower bound of the Fresnel approximation imposes a constraint that becomes more pronounced as the array size grows. Beyond this constraint, the validity of the Fresnel approximation is broken. As a potential solution, the wavenumber-domain paradigm characterizes the spherical wave using a spectrum composed of a series of linear orthogonal bases. However, this approach falls short of covering the effects of the array geometry, especially when using Gaussian-mixed-model (GMM)-based von Mises-Fisher distributions to approximate all spectra. To fill this gap, this paper introduces a novel wavenumber-domain ellipse fitting (WD-EF) method to tackle these challenges. Particularly, the channel is accurately estimated in the near-field region, by maximizing the closed-form likelihood function of the wavenumber-domain spectrum conditioned on the scatterers’ geometric parameters. Simulation results are provided to demonstrate the robustness of the proposed scheme against both the distance and angles of arrival.

In this paper, we investigate the sparse channel estimation in holographic multiple-input multiple-output (HMIMO) systems. The conventional angular-domain representation fails to capture the continuous angular power spectrum characterized by the spatially -stationary electromagnetic random field, thus leading to the ambiguous detection of the significant angular power, which is referred to as the power leakage. To tackle this challenge, the HMIMO channel is represented in the wavenumber domain for exploring its cluster-dominated sparsity. Specifically, a finite set of Fourier harmonics acts as a series of sampling probes to encapsulate the integral of the power spectrum over specific angular regions. This technique effectively eliminates power leakage resulting from power mismatches induced by the use of discrete angular-domain probes. Next, the channel estimation problem is recast as a sparse recovery of the significant angular power spectrum over the continuous integration region. We then propose an accompanying graph-cut-based swap expansion (GCSE) algorithm to extract beneficial sparsity inherent in HMIMO channels. Numerical results demonstrate that this wavenumber-domain-based GCSE approach achieves robust performance with rapid convergence.

The latest TypeII codebook selects partial strongest angular-delay ports for the feedback of downlink channel state information (CSI), whereas its performance is limited due to the deficiency of utilizing the correlations among the port coefficients. To tackle this issue, we propose a tailored autoencoder named TypeII-CsiNet to effectively integrate the TypeII codebook with deep learning, wherein three novel designs are developed for sufficiently boosting the sum rate performance. Firstly, a dedicated pre-processing module is designed to sort the selected ports for reserving the correlations of their corresponding coefficients. Secondly, a position-filling layer is developed in the decoder to fill the feedback coefficients into their ports in the recovered CSI matrix, so that the corresponding angular-delay-domain structure is adequately leveraged to enhance the reconstruction accuracy. Thirdly, a two-stage loss function is proposed to improve the sum rate performance while avoiding the trapping in local optimums during model training. Simulation results verify that our proposed TypeII-CsiNet outperforms the TypeII codebook and existing deep learning benchmarks.

Integrated sensing and communication (ISAC) is recognized as one of key enabling technologies for the Meta-verse. To enhance both communication data rate and sensing accuracy, the exploitation of millimeter wave (mmWave) and terahertz (THz) frequencies becomes mandatory due to huge amount of spectrum resources. To combat the severe path-loss at mmWave/THz band, large-scale antenna arrays are usually employed to form directional beams. However, the ultra-broad bandwidth induces undesirable beam squint (BS) effects, where the beams from different subcarriers point to diverse angles, leading to the communication performance loss. Fortunately, this BS effect can be leveraged to facilitate the multi-angle super-resolution sensing with minimal beam sweeping overhead. Against this background, we propose a novel multicarrier waveform design methodology for the BS-assisted ISAC systems, which optimizes the frequency resource allocation between sensing and communications to reach a good dual-functional performance trade-off. To mitigate mutual interference, both functions are assigned non-overlapping subcarriers. The subcarrier assignment design is formulated as a mixed integer programming problem to maximize the communication throughput while ensuring the required sensing range and resolution, which involves high complexity to get an exact solution. To this end, we propose a two-stage iterative update algorithm to obtain a quasi-optimal solution with low computational complexity. Numerical results demonstrate that our proposed methodology achieves high-rate communication and high-resolution sensing simultaneously with relatively low overhead.

Affine frequency division multiplexing (AFDM) is a promising waveform due to its excellent performance in doubly dispersive channels. Compared with OFDM, AFDM has two more adjustable parameters with better flexibility. By analyzing the diversity gain of AFDM system and deducing the Cramér-Rao lower bounds (CRLB) of unknown parameters, this paper studies the influence of the parameter $c_{1}$ of AFDM on the bit error rate (BER) and parameter estimation. In addition, an adaptive waveform approach is proposed to adapt to different communication and sensing requirements based on AFDM parameter $c_{1}$ and a demand factor. The simulation results confirm the validity of our analysis, which shows that $c_{1}$ can be adjusted according to the demand factor to achieve the optimal performance compromise.

Aiming at the problem of weak coverage resulting from interference and obstructions in wireless networks, we propose a gridding based Reconfigurable Intelligent Surface (RIS)-assisted optimization scheme that divides problem area and RIS deployment area into several grids for better performance and lower complexity. The objective is to maximize the signal strength received in weak coverage area, with optimization variables comprising location, angle of incidence and angle of reflection of RIS. Then Particle Swarm Optimization (PSO) and Alternating Optimization (AO) algorithms are used to solve this non-convex problem. Simulation results show that our proposed scheme can achieve more than 25% improvement in terms of average SNR than conventional ones when the number of reflection elements of RIS is more than 100. Moreover, we analyze the impact of different sizes of RIS on its optimal location and the average SNR received in the weak coverage area, finding out when RIS is closer to the weak coverage area, the performance is not always better, which is different to existed observations.

Integrated sensing and communication (ISAC) is expected to be a promising technology in the sixth-generation (6G) wireless networks for its ability to alleviate resources shortage and excessive hardware expenses. One typical representative for ISAC waveforms is the orthogonal frequency division multiplexing (OFDM) waveform, which divides the time-frequency resources into orthogonal resource elements (REs). In order to satisfy their diverse design requirements and mitigate mutual interference, the communication and sensing subsystems can be assigned with different REs, which necessitates effective allocation strategies of different resources across time and frequency domains. In this article, a cross-domain multicarrier waveform design method-ology is proposed, which optimizes the RE assignment and power allocation strategies for the OFDM-based ISAC system. Specifically, for sensing performance enhancement, the unit cells of the ambiguity function (AF) of the sensing components are spe-cially shaped to achieve a “locally” perfect auto-correlation (AC) property within a predefined region of interest (RoI) in the Delay-Doppler domain. Afterwards, the irrelevant cells outside the RoI, which can determine the sensing power allocation strategy, are optimized alternatively with the communication power allocation strategy to maximize the throughput for the communication purpose. Numerical results demonstrate the superiority of the cross-domain multicarrier waveform design, which also provides useful guidelines for parameter settings of the proposed OFDM-based ISAC system.

Leveraging the concept of the electromagnetic signal and information theory, holographic multiple-input multiple-output (MIMO) technology opens the door to an intelligent and endogenously holography-capable wireless propagation environment, with their unparalleled capabilities for achieving high spectral and energy efficiency. Less examined are the important issues such as the acquisition of accurate channel information by accounting for holographic MIMO’s peculiarities. To fill this knowledge gap, this paper investigates the channel estimation for holographic MIMO systems by unmasking their distinctions from the conventional one. Specifically, we elucidate that the channel estimation, subject to holographic MIMO’s electromagnetically large antenna arrays, has to discriminate not only the angles of a user/scatterer but also its distance information, namely the three-dimensional (3D) azimuth and elevation angles plus the distance (AED) parameters. As the angular-domain representation fails to characterize the sparsity inherent in holographic MIMO channels, the tightly coupled 3D AED parameters are firstly decomposed for independently constructing their own covariance matrices. Then, the recovery of each individual parameter can be structured as a compressive sensing (CS) problem by harnessing the covariance matrix constructed. This pair of techniques contribute to a parametric decomposition and compressed deconstruction (DeRe) framework, along with a formulation of the maximum likelihood estimation for each parameter. Then, an efficient algorithm, namely DeRe-based variational Bayesian inference and message passing (DeRe-VM), is proposed for the sharp detection of the 3D AED parameters and the robust recovery of sparse channels. Finally, the proposed channel estimation regime is confirmed to be of great robustness in accommodating different channel conditions, regardless of the near-field and far-field contexts of a holographic MIMO system, as well as an improved performance in comparison to the state-of-the-art benchmarks.

Orthogonal frequency division multiplexing (OFDM) has been widely recognized as the representative waveform for 5G wireless networks, which can directly support sensing/positioning with existing infrastructure. To guarantee superior sensing/positioning accuracy while supporting high-speed communication simultaneously, the dual functions tend to be assigned with different resource elements (REs) due to their diverse design requirements. This motivates optimization of resource allocation/waveform design across time, frequency, power and delay-Doppler domains. Therefore, this article proposes two cross-domain waveform optimization strategies for effective convergence of OFDM-based communication and sensing/positioning, following communication- and sensing-centric criteria, respectively. For the communication-centric design, to maximize the achievable data rate, a fraction of REs are optimally allocated for communication according to prior knowledge of the communication channel. The remaining REs are then employed for sensing/positioning, where the sidelobe level and peak-to-average power ratio are suppressed by optimizing its power-frequency and phase-frequency characteristics for sensing performance improvement. For the sensing-centric design, a ‘locally’ perfect auto-correlation property is ensured for accurate sensing and positioning by adjusting the unit cells of the ambiguity function within its region of interest (RoI). Afterwards, the irrelevant cells beyond RoI, which can readily determine the sensing power allocation, are optimized with the communication power allocation to enhance the achievable data rate. Numerical results demonstrate the superiority of the proposed waveform designs.

We demonstrate that the proposed optical orthogonal time frequency space (O-OTFS) is capable of improving the bandwidth-/power-/energy-efficiencies of optical orthogonal frequency-division multiplexing (O-OFDM). The bandwidth-efficiency is improved because only a single cyclic prefix (CP) is needed for an entire O-OTFS frame. The power-efficiency is enhanced thanks to the diversity gain achieved by its symplectic finite Fourier transform (SFFT), which also leads to a reduced peak-to-average power ratio (PAPR), hence improving its energy-efficiency. These features are facilitated by the proposed layered asymmetrically clipped O-OTFS (LACO-OTFS), which is capable of removing the direct current (DC) bias while retaining the full optical throughput. Nonetheless, there exists an inherent trade-off, where increasing the O-OTFS frame size leads to a commensurately reduced CP percentage at the cost of an increased PAPR. In order to mitigate this, we propose to perform discrete Fourier transform based spreading (DFT-S) in the delay-Doppler (DD)-domain. Furthermore, we demonstrate that regardless of the choice of domain in which the information is modulated (i.e. O-OFDM/O-OTFS with/without DFT-S), the frequency-selectivity of the quasi-static but dispersive optical channel can always be equalized by single-tap frequency-domain equalization (FDE). Moreover, the channel estimation techniques are conceived to operate in the time-/frequency-/DD-domains for both O-OFDM and O-OTFS. Our simulation results demonstrate that for a multi-user optical wireless system associated withM= 64 subcarriers and the OTFS frame length ofN= 64, LACO-OTFS is capable of achieving a 7 dB power-efficiency gain over LACO-OFDM, where the CP overhead is reduced by a factor ofN= 64. DFT-S-LACO-OTFS is also capable of providing a 7 dB power-efficiency gain over DFT-S-LACO-OFDM, where the low PAPR of single-carrier transmission is retained.

In next-generation communications, sub-6GHz and millimeter-wave (mmWave) links typically coexist, with the sub-6GHz link always active and the mmWave link active when high-rate transmission is required. Due to the spatial similarities between sub-6GHz and mmWave channels, sub-6GHz channel information can be utilized to support hybrid beamforming in mmWave communications to reduce overhead costs. We consider a multi-cell heterogeneous communication network where both sub-6GHz and mmWave communications co-exist. Multiple mmWave base stations (BSs) in the heterogeneous network simultaneously transmit signals to multiple users in their own mmWave cells while interfering with each other. The challenging problem is to design hybrid beamformers in the mmWave band that can maximize the system spectral efficiency. To address this highly complex programming using sub-6GHz information, a novel heterogeneous graph neural network (HGNN) architecture is proposed to learn the intrinsic relationship between sub-6GHz and mmWave and design the hybrid beamformers for mmWave BSs. The proposed HGNN consists of two different node types, namely, BS nodes and user equipment (UE) nodes, and two different edge types, namely, desired link edge and interfering link edge. In addition, the attention mechanism and the residual structure are utilized in the HGNN architecture to improve the performance. Simulation results show that the proposed HGNN can successfully achieve better performances with sub-6GHz information than traditional learning methods. The results also demonstrate that the attention mechanism and residual structure improve the performances of the HGNN compared to its unmodified counterparts.

Deep learning based channel state information (CSI) acquisition and feedback in frequency division duplex systems have drawn much attention in the beyond fifth-generation (B5G) wireless systems. In this paper, we focus on exploiting the CSI codebook in B5G wireless standards with deep learning to enhance the performance of CSI acquisition and feedback. Specifically, the angular-delay-domain partial reciprocity between uplink and downlink channels is considered, and part of angular-delay-domain ports are selected for measuring and feeding back the downlink CSI, where the performance of the conventional deep learning methods is limited due to the deficiency of sparse structures. To address this issue, we propose the new paradigm of adopting deep learning to improve the performance of CSI codebook. Firstly, considering the relatively low signal-to-noise ratio of uplink channels, deep learning is utilized to refine the selection of the dominant angular-delay-domain ports, where the focal loss is harnessed to solve the class imbalance problem. Secondly, we propose to reconstruct the downlink CSI by way of deep learning based on the feedback of CSI codebook at the base station, where the information of sparse structures can be effectively leveraged. Finally, a weighted shortcut module is designed to facilitate the accurate reconstruction, and a two-stage loss function with the combination of the mean squared error and sum rate is proposed for adapting to actual multi-user scenarios. Simulation results demonstrate that our proposed angular-delay-domain port selection and CSI reconstruction paradigm can improve the sum rate performance by more than 10% compared with the standard CSI codebook and traditional deep learning benchmarks.

This work explores the fundamental problem of the recoverability of a sparse tensor being reconstructed from its compressed embodiment. We present a generalized model of block-sparse tensor recovery as a theoretical foundation, where concepts involving a holistic mutual incoherence property (MIP) of the measurement matrix set are defined. A representative algorithm based on the orthogonal matching pursuit (OMP) framework, called tensor generalized block OMP (T-GBOMP), is applied to the theoretical framework for analyzing both noiseless and noisy recovery conditions. Specifically, we present an exact recovery condition (ERC) and sufficient conditions for establishing it with consideration of different degrees of restriction. Reliable reconstruction conditions, in terms of the residual convergence, the estimated error and a signal-to-noise ratio bound, are established to reveal the computable theoretical interpretability based on the newly defined MIP. The flexibility of tensor recovery is highlighted, i.e., the reliable recovery can be guaranteed by optimizing the MIP of the measurement matrix set. Analytical comparisons demonstrate that the theoretical results developed are tighter and less restrictive than existing ones (if any). Further discussions provide tensor extensions for several classic greedy algorithms, indicating that the results derived are universal and applicable to all these tensorized variants.

With the exponential growth of mobile users, ensuring high-quality network coverage has become paramount. Large-scale mobile networks consist of numerous base stations (BSs), each with adjustable parameters such as angles and beam widths. Automatically optimizing network coverage can be difficult due to environmental factors and the interdependence of the adjustable parameters. Due to the inherent uncertainties and unpredictable nature of large-scale wireless networks, traditional methods such as heuristics and meta-heuristics lack the adaptability and scalability required to cope with their dynamic environment. To address these challenges, we propose utilizing digital twin and reinforcement learning (RL) techniques within mobile networks characterized by multiple collaborating agents. We initially introduce DT-SimNet, a digital twin-enabled mobile network simulator to facilitate optimization evaluation. DT-SimNet can efficiently simulate communication behaviors of network elements within a complex environment while revealing user mobility patterns. Moreover, to address challenges arising from multifaceted relationships among users, BSs, and the parameters across BSs, we introduce an innovative strategy named Optimized Multi-Agent Proximal Policy Optimization with Self-supervised Prediction (OMAPPO-SSP). Compared to MAPPO, which leads to limited applicability and inferior performance due to the dynamic characteristics of 5G networks, this approach leverages network structure optimization and a self-supervised prediction mechanism, employing multi-agent reinforcement learning (MARL) principles to enhance efficiency. By harnessing collaborative neural networks, OMAPPO-SSP facilitates the explicit learning of behavioral interactions among all BSs, enabling effective decision-making in environments characterized by intricate spatial relationships, dynamic user behaviors, and diverse interactions. Extensive experiments are conducted to validate the efficiency and effectiveness of the OMAPPO-SSP. Within the target area, OMAPPO-SSP achieves a coverage ratio of 94.66% and an average throughput of 89746 bits per second (bps), demonstrating significant improvements compared to competing methods.

Reconfigurable intelligent surface (RIS) has drawn extensive attentions as a promising alternative for classical phased-array antennas at the massive multiple-input multiple-output (MIMO) transmitter, leading to cost-effective data transmission that is especially desirable at terahertz (THz) frequencies. In this article, we consider a multi-user MIMO (MU-MIMO) system equipped with a RIS-assisted transmitter: A RIS array is illuminated by unmodulated THz carriers through a feeding antenna, which is equally divided into a number of subarrays (SAs). Each activated SA serves one unique user equipment (UE) via directional beams. Then we develop a spectrum- and energy- efficient MU-MIMO scheme for THz communications by performing index modulation (IM) on the array-of-SA structure of the RIS, abbreviated as RIS-SA-IM. Specifically, the indices of the RIS-SAs allocated to different UEs, defined as SA allocation pattern (SAPs), are flexibly controlled by the information bits at each symbol period. Hence, aside from classical amplitude/phase modulation, additional energy-free bits (referred to asindex bits) can be conveyed implicitly by the chosen SAP at the transmitter, thus attaining superior enhancement on spectrum- and energy-efficiencies. Furthermore, we design a distributed mapping rule between the SAPs and index bits, which guarantees that the index information for each UE is exclusively determined by the index of its allocated RIS-SA. Hence, the proposed mapping rule can enable localized demodulation of the index bits without inter-UE data exchange. In particular, a general form of the distributed mapping rule is provided based on the binary-tree structure, which can be extended to arbitrary number of UEs and index bits. Additionally, the error performance of the proposed RIS-SA-IM is evaluated through pairwise error probability (PEP) calculations. Theoretical and simulation results demonstrate the superiority of our proposed RIS-SA-IM over its classical non-IM-aided counterpart.

Acquiring accurate channel state information (CSI) at an access point (AP) is challenging for wideband millimeter wave (mmWave) ultra-massive multiple-input and multiple-output (UM-MIMO) systems, due to the high-dimensional channel matrices, hybrid near- and far- field channel feature, beam squint effects, and imperfect hardware constraints, such as low-resolution analog-to-digital converters, and in-phase and quadrature imbalance. To overcome these challenges, this paper proposes an efficient downlink channel estimation (CE) and CSI feedback approach based on knowledge and data dual-driven deep learning (DL) networks. Specifically, we first propose a data-driven residual neural network de-quantizer (ResNet-DQ) to pre-process the received pilot signals at user equipment (UEs), where the noise and distortion brought by imperfect hardware can be mitigated. A knowledge-driven generalized multiple measurement vector learned approximate message passing (GMMV-LAMP) network is then developed to jointly estimate the channels by exploiting the approximately same physical angle shared by different subcarriers. In particular, two wideband redundant dictionaries (WRDs) are proposed such that the measurement matrices of the GMMV-LAMP network can accommodate the far-field and near-field beam squint effect, respectively. Finally, we propose an encoder at the UEs and a decoder at the AP by a data-driven CSI residual network (CSI-ResNet) to compress the CSI matrix into a low-dimensional quantized bit vector for feedback, thereby reducing the feedback overhead substantially. Simulation results show that the proposed knowledge and data dual-driven approach outperforms conventional downlink CE and CSI feedback methods, especially in the case of low signal-to-noise ratios.

In non-cooperative communications, malicious electromagnetic interference attacks communication systems and causes higher probability of communication disruption. In order to address the challenges posed by electromagnetic interference, the wireless interference recognition technique has emerged, which identifies the interference signals without priori information. In recent years, the success of deep learning (DL) has sparked interest in introducing DL in the field of wireless interference recognition. However, most DL-based interference identification methods improve accuracy by dramatically increasing network sizes while ignoring the important effect of network inputs. For this reason, we extensively investigate the impact of different signal transformation forms of interference (called signal modalities) on performance. The artificial features of the interference signal are also utilized as one of the refined modalities, which breaks the inherent concept that artificial features are only used in the methods of feature extraction. Convolution and transformer are combined in the extraction of different modal features. In order to reduce the complexity of transformer, a dual transformer module (DTM) is proposed. Furthermore, to overcome the imbalance of modal optimization during the training process, an adaptive gradient modulation (AGM) strategy is proposed, which leads to better convergence for the multimodal training. Finally, modal information selection mechanism (MISM) selects the most appropriate modalities for each input sample, which saves computational costs. Extensive experiments demonstrate that combining multiple interference modalities is more effective than trying different networks.

In cognitive radio (CR), compressive spectrum sensing (CSS) has drawn much attention since it enjoys decent performance and facilitates fast implementation. Due to the spectrum's inherent block sparsity and the joint spectrum sampling, CSS is further modeled as a block multiple measurement vector (BMMV) problem, which can be solved by joint block greedy-iterative algorithms such as block simultaneous orthogonal matching pursuit (BSOMP). However, the feasibility of such methods are shadowed by their inflexible sampling rates and dependence on accurate sparsity information. To address this issue, this paper proposes an adaptive-blind block simultaneous orthogonal matching pursuit (AB-BSOMP) algorithm based on the BMMV model. The blind halting criterion for BSOMP is first derived, allowing spectrum recovery to be independent of a priori sparsity information. Furthermore, to guarantee the reliable and adaptive spectrum recovery, a sampling-controlled algorithm (SCA) is developed to calculate an optimal number of measurements dynamically. Finally, AB-BSOMP is proposed by combining the developed blind halting criterion and the SCA. Simulation results demonstrate that the elaborating algorithm performs reliable recovery under various signal-to-noise ratios (SNRs), and reduces computational complexity in high SNR conditions while maintaining exact detection.

The Type-II codebook in Release 17 (R17) exploits the angular-delay-domain partial reciprocity between uplink and downlink channels to select part of angular-delay-domain ports for measuring and feeding back the downlink channel state information (CSI), where the performance of existing deep learning enhanced CSI feedback methods is limited due to the deficiency of sparse structures. To address this issue, we propose two new perspectives of adopting deep learning to improve the R17 Type-II codebook. Firstly, considering the low signal-to-noise ratio of uplink channels, deep learning is utilized to accurately select the dominant angular-delay-domain ports, where the focal loss is harnessed to solve the class imbalance problem. Secondly, we propose to adopt deep learning to reconstruct the downlink CSI based on the feedback of the R17 Type-II codebook at the base station, where the information of sparse structures can be effectively leveraged. Besides, a weighted shortcut module is designed to facilitate the accurate reconstruction. Simulation results demonstrate that our proposed methods could improve the sum rate performance compared with its traditional R17 Type-II codebook and deep learning benchmarks.

The programmable metasurface (MTS), which can enhance the signal quality by flexibly manipulating the electromagnetic (EM) responses of reflected waves, has emerged as a promising technology for multiple-input multiple-output (MIMO) transmission due to its superior energy efficiency and cost-effective hardware implementations. In this paper, we consider a multi-user MIMO (MU-MIMO) system equipped with a MTS-based multi-feed transmitter, where an array of metallic elements is split into several subarrays (SAs), each irradiated by a unique radio-frequency (RF) feed. Additionally, each user is allocated with one unique RF-feed-SA pair for data services. Then we develop a novel index modulation (IM) scheme based on this array-of-SA (AoSA) structure of the MTS, named as MTS-SA -IM. More specifically, the allocation strategy of the SAs to different users, termed as SA allocation pattern (SAP), is flexibly controlled by the information bits. In other words, aside from classical amplitude/phase modulation bits, additional binary bits, referred to as index bits, can be embedded into the indices of the allocated SAs without extra power consumption, leading to enhancement of both spectrum and energy efficiencies. Furthermore, we propose a distributed mapping rule design between the index bits and SAPs, which guarantees that the index bits at each user are exclusively dependent on the index of its own allocated SA. By constructing a binary-tree structure, a general form of the proposed distributed mapping rule is generated recursively, which can be extended to the cases of arbitrary number of users. Simulation results demonstrate that the proposed MTS is capable of achieving desirable performance gain over its classical counterpart.

Multiple-input multiple-output (MIMO) and low-density parity check (LDPC) codes are two of the fundamental technologies in the fifth-generation (5G) networks, where an efficient power allocation scheme is desired to minimize the bit error rate (BER) of the LDPC-coded MIMO system. However, the conventional power allocation methods do not take into account the constraint of modulation and coding scheme (MCS), which may degrade the BER performance. To solve this issue, we propose a deep learning based method to predict the efficient power allocation scheme in coded MIMO systems. Specifically, a neural network is built to learn the complex BER-SNR function to derive the power allocation ratio between the parallel MIMO streams, where the training label is acquired based on the exhaustive searching algorithm. Simulation results show that our proposed method could achieve better BER performance than its conventional counterparts.

Unmanned aerial vehicle (UAV) equipped with large-scale antenna array constitutes a promising relaying candidate for reliable and secure satellite-terrestrial communication. Due to the limitation of energy consumption, a novel UAV architecture with one-bit analog-to-digital converters (ADCs) and one-bit digital-to-analog converters (DACs) is proposed firstly. Leveraging the additive quantization noise model, the exact closed-form expressions of both ergodic capacity and ergodic achievable secrecy rate are derived for UAV-assisted satellite-terrestrial communication systems using large-scale antenna array with one-bit ADCs/DACs. To enhance the transmission capacity and combat the eavesdropper simultaneously, maximum-ratio combining (MRC) is used by UAV to receive signals from satellite and location-based beamforming (LBB) is adopted by UAV to forward signals to destination, where the beamformer is optimized based on the derived expression of the ergodic achievable secrecy rate. Simulation results validate the accuracy of our analytical ergodic achievable secrecy rate, and demonstrate that our proposed MRC/LBB scheme has better secrecy rate than its conventional location-based counterpart.

In this paper, motivated by the inter-base station (BS) channel dependence due to the shared wireless environment, we propose to fuse sub-6 GHz channel information and mmWave low-overhead measurement to predict the optimal mmWave beam in heterogeneous networks (HetNets) and reduce the overhead of both mmWave BS selection and beam training. Moreover, deep learning is adopted to extract the complex dependence between sub-6 GHz and mmWave channels for achieving high prediction accuracy. Specifically, we propose to leverage a few user equipment (UE)-specific high-quality mmWave wide beams predicted by the sub-6 GHz channel state information (CSI) as the mmWave low-overhead measurement. In order to adapt to different confidences of the mmWave wide beam prediction for diverse UE, the sum-probability criterion is proposed to flexibly adjust the number of measured wide beams. Besides, to fully fuse the diversified features extracted from the sub-6 GHz CSI and mmWave wide beams, the attention mechanism is further exploited to adaptively weight the features for improving the prediction accuracy. Simulation results show that our proposed scheme achieves higher beamforming gain while imposing smaller mmWave measurement overhead over the conventional deep learning based schemes.

Although reconfigurable intelligent surface (RIS) has been touted as a technology star for future wireless networks, the critical bottleneck still lies in the accurate acquisition of channel state information (CSI). The vast majority of state-of-the-art cascaded channel estimates entail the pilot overhead typically proportional to the number of RIS elements, which results in a long training time and thus may not be tolerable in high-mobility scenarios. In this paper, we investigate the channel tracking for RIS-aided high-mobility millimeter wave (mmWave) communications. By leveraging the angular domain representation of cascaded channels, we initially demonstrate the dynamic dual-structured sparsity (DDS), i.e., i) the angular cascaded channel matrices associated with different users share the identical non-zero rows while differ in their non-zero columns and ii) the cascaded channel support exhibits temporal correlation inherent to the dynamic nature of mobile channels. Then, a layered processing with dynamic dual-structured sparsity (LP-DDS) framework is customized to provide sparse priors for the exact distributions of cascaded channels. In this case, the joint estimate of the angular cascaded channel and Doppler shift is formulated as a compressive sensing (CS) problem while taking into account the temporal correlation of dynamic channels, which, however, is highly intractable due to the ill-conditioned sensing matrix. To tackle this issue, we propose an efficient algorithm, namely DDS-VBIMP, where in particular, both variational Bayesian inference (VBI) and message passing techniques are complemented each other to achieve parameter updates by taking full advantage of the sparse priors as captured by LP-DDS. We demonstrate through our analyses that the proposed DDS-VBIMP can significantly reduce pilot overhead. Simulation results reveal the superiority and robustness of the proposed DDS-VBIMP as compared to various benchmark schemes.

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

In this paper, an adaptive millimeter-wave (mmWave) beam tracking scheme is proposed to flexibly adjust the angular range of beam tracking based on the user-specific speeds for reducing the tracking overhead, where deep learning is exploited to accurately extract the user movement features. Specifically, long short-term memory network is utilized to predict the possible optimal beams according to the received signals of previous beam tracking. Based on the predicted probabilities, the sum-probability criterion is proposed to track the subset of maximum-probability beams whose sum-probability is larger than the predefined threshold, where the beam with the highest received power is selected as the optimal one. Considering the limited number of received beam tracking signals, a two-stage training strategy is further proposed to stabilize the model optimization. Simulation results demonstrate that our proposed scheme could effectively reduce the overhead of beam tracking in guarantee of high beamforming gains, compared with the conventional schemes.

Two key impediments to reconfigurable intelligent surface (RIS)-aided vehicular communications are, respectively, the double fading experienced by the signal on RIS-aided cascaded links and the high-mobility-induced intractability of acquiring channel state information (CSI). To overcome these challenges, a novel kind of RIS is presented in this paper, namely active reconfigurable intelligent omni-surface (RIOS), each element of which is supported by active loads, that concurrently transmits and reflects the incident signal amplified rather than just reflecting it as compared to the case of a passive reflecting-only RIS. We consider the use of an active RIOS to a vehicular communication system for mitigating double fading effect. Specifically, the active RIOS is mounted on the vehicle window to enhance transmission for users in the vehicle and for adjacent vehicles. We aim to jointly optimize the transmit precoding matrix at the base station (BS) and RIOS coefficient matrices to minimize the BS’s transmit power relying exclusively upon the imperfect knowledge of the large-scale CSI. To significantly relax the frequency of channel information updates, initially an efficient transmission protocol is put forward to reap the high active RIOS beamforming gain with low channel training overhead by appropriately tailoring the time-scale of CSI acquisition. Then, two algorithms, namely an alternating optimization (AO)-based algorithm and a constrained stochastic successive convex approximation (CSSCA)-based algorithm, are developed to tackle with the investigated resource allocation problem, whose pros and cons are elaborated, respectively. Simulation results substantiate the significant performance improvement of active RIOS as well as determine the validity and robustness of our proposed algorithms over various benchmark schemes.

The convergence of radar sensing and communication applications in the millimeter-wave (mmWave) and low terahertz (THz) bands has been envisioned as a promising technology, since it incorporates high-rate data transmission of hundreds of gigabits per second (Gbps) and mm-level radar sensing in a spectrum- and cost-efficient manner, by sharing both the frequency and hardware resources. However, the joint radar sensing and communication (JRC) system faces considerable challenges in the mmWave and low-THz scale, due to the peculiarities of the propagation channel and radio-frequency (RF) front ends. To this end, the waveform design for the JRC systems in mmWave and low-THz bands with ultra-broad bandwidth is investigated in this paper. Firstly, by considering the JRC design based on the co- existence concept, where both functions operate in a time-domain duplex (TDD) manner, a novel multi-subband quasi-perfect (MS-QP) sequence, composed of multiple perfect subsequences on different subbands, is proposed for target sensing, which achieves accurate target ranging and velocity estimation, whilst only requiring cost-efficient low-rate analog-to-digital converters (A/Ds) for sequence detection. Furthermore, the root index of each perfect subsequence is designed to eliminate the influence of strong Doppler shift on radar sensing. Finally, a data-embedded MS-QP (DE-MS-QP) waveform is constructed through time-domain extension of the MS-QP sequence, generating null frequency points on each subband for data transmission. Unlike the co- existence-based JRC system in TDD manner, the proposed DE-MS-QP waveform enables simultaneous interference-free sensing and communication, whilst inheriting all the merits from MS-QP sequences. Numerical results validate the superiority of the proposed waveforms regarding the communication and sensing performances, hardware cost as well as flexibility of the resource allocation between the dual functions.

The highly directional beams applied in millimeter wave (mmWave) cellular networks make it possible to achieve near interference-free (NIF) transmission under judiciously designed space-time user scheduling, where the power of intra-/ inter-cell interference between any two users is below a predefined threshold. In this paper, we investigate two aspects of the NIF space-time user scheduling in a multi-cell mmWave network with multi-RF-chain base stations. Firstly, given that each user has a requirement on the number of space-time resource elements, we study the NIF user scheduling problem to minimize the unfulfilled user requirements, so that the space-time resources can be utilized most efficiently and meanwhile all strong interferences are avoided. A near-optimal scheduling algorithm is proposed with performance close to the lower bound of unfulfilled requirements. Furthermore, we study the joint NIF user scheduling and power allocation problem to minimize the total transmit power under the constraint of rate requirements. Based on our proposed NIF scheduling, an energy-efficient joint scheduling and power allocation scheme is designed with limited channel state information, which outperforms the existing independent set based schemes, and has near-optimal performance as well.

Huge overhead of beam training poses a significant challenge to mmWave communications. To address this issue, beam tracking has been widely investigated whereas existing methods are hard to handle serious multipath interference and non-stationary scenarios. Inspired by the spatial similarity between low-frequency and mmWave channels in non-standalone architectures, this paper proposes to utilize prior low-frequency information to predict the optimal mmWave beam, where deep learning is adopted to enhance the prediction accuracy. Specifically, periodically estimated low-frequency channel state information (CSI) is applied to track the movement of user equipment, and timing offset indicator is proposed to indicate the instant of mmWave beam training relative to low-frequency CSI estimation. Meanwhile, long-short term memory networks based dedicated models are designed to implement the prediction. Simulation results show that our proposed scheme can achieve higher beamforming gain than the conventional methods while requiring little overhead of mmWave beam training.

In this paper, we investigate secure relay selection for finite-state Markov channel and propose a Q-learning assisted relay selection scheme. Specifically, we firstly analyze the achievable effective secrecy throughput of random selection scheme and optimal selection scheme, respectively, showing that the secrecy performance is highly determined by relay selection methodology. Then, we leverage the Q-learning to learn how to select relay for finite-state Markov channel, which is capable of selecting proper relay with outdated channel state information. Numerical results demonstrate that our proposed Q-learning assisted relay selection scheme can achieve a significant improvement of effective secrecy throughput even with outdated channel information.

Beamforming (BF) is usually adopted in millimeter wave wireless systems to compensate high path loss. To increase the received power at user equipment (UE), beam training (BT) is performed to align the beam direction between base station (BS) and UE, where good trade-off between BT overhead and BF gain is preferred. In this letter, two memory-less statistical BT algorithms with finite slots are proposed to reduce the overhead and failure probability of BT, including minimum BT overhead (MO-BT) algorithm and minimum failure probability of BT (MFP-BT) algorithm. Both algorithms make the assumption that memory-less BS randomly select a beam in each slot, based on the prior probability of UE preference, and UE feeds back the beam index to BS to inform which beam should be adopted. By carefully adjusting the probability mass function of beam scanning at BS, MFP-BT can reduce the failure probability of BT, and MO-BT can reduce BT time consumption when compared to their traditional BT counterpart. Numerical results demonstrate the superior performance of our proposed algorithms.

Terahertz (THz) wireless communication is envisioned as a promising technology, which is capable of providing ultra-high-rate transmission up to Terabit per second. However, some hardware imperfections, which are generally neglected in the existing literature concerning lower data rates and traditional operating frequencies, cannot be overlooked in the THz systems. Hardware imperfections usually consist of phase noise, in-phase/quadrature imbalance, and nonlinearity of power amplifier. Due to the time-variant characteristic of phase noise, frequent pilot insertion is required, leading to decreased spectral efficiency. In this paper, to address this issue, a novel pilot design strategy is proposed based on index modulation (IM), where the positions of pilots are flexibly changed in the data frame, and additional information bits can be conveyed by indices of pilots. Furthermore, a turbo receiving algorithm is developed, which jointly performs the detection of pilot indices and channel estimation in an iterative manner. It is shown that the proposed turbo receiver works well even under the situation where the prior knowledge of channel state information is outdated. Analytical and simulation results validate that the proposed schemes achieve significant enhancement of bit-error rate performance and channel estimation accuracy, whilst attaining higher spectral efficiency in comparison with its classical counterpart.

The radio frequency (RF) impairments of analog devices have been regarded as an important factor degrading the performance of Terahertz (THz) communications, where in-phase/quadrature (IQ) imbalance and phase noise (PN) are the two typical RF impairments at THz transceiver. In this paper, we investigate the channel estimation (CE) and equalization for THz receiver in the presence of wideband IQ imbalance and PN, where single-carrier frequency-domain equalization is used. Since PN is inserted between channel impulse response (CIR) and wideband IQ imbalance at the receiver, the CIR and IQ imbalance cannot be treated together as an effective channel. Therefore, a novel two-stage CE method is derived to separately estimate the CIR and wideband IQ imbalance parameters, and its corresponding channel equalization method is designed to compensate both wideband IQ imbalance and PN, and equalizes the CIR. Theoretical and simulation results validate the efficiency of our proposed CE, and the proposed equalization method is shown to outperform the state-of-the-art methods.

In intelligent transportation systems, vehicular ad-hoc network (VANET) is an important technique for data exchange among vehicles and other mobile terminals (MTs) on the roads. In VANET, radio frequency (RF) and visible light communication (VLC) links could cooperate to improve the communication quality of MTs. However, in order to fully exploit the benefits of these two kinds of communication links and improve the VANET performance, a topology control (TC) scheme should be developed to help MTs establish VLC and RF links with their neighbors properly based on the distribution of MTs in the VANET. Since there is usually not a central controller in the VANET, the TC scheme should be conducted locally at each MT. As the size of VANET increases, distributed TC becomes more challenging since MTs usually need to make decisions with only local knowledge about the VANET. In addition, the frequent changes of VANET structure due to MT movements require that the TC scheme can adapt to the dynamic changes quickly. In consideration of those challenges, an adaptive TC scheme is proposed in this paper, aiming to achieve a balance between connectivity and power consumption for MTs in the hybrid VLC/RF VANET. In the proposed TC scheme, MTs can adjust their related links locally and iteratively based on merely its knowledge about the local VANET topology. Moreover, the TC problem is modelled by the coordination game, which guarantees that the proposed TC scheme can converge to a Nash Equilibrium state and can also adapt to the changes in VANET structure dynamically.

TeraHertz (THz) wireless communication constitutes a promising technique of satisfying the ever-increasing appetite for high-rate services. However, the ultra-wide bandwidth of THz communications requires high-speed, high-resolution analog-to-digital converters, which are hard to implement due to their high complexity and power consumption. In this paper, a deep learning-assisted THz receiver is designed, which relies on single-bit quantization. Specifically, the imperfections of THz devices, including their in-phase/quadrature-phase imbalance, phase noise and nonlinearity are investigated. The deflection ratio of the maximum-likelihood detector used by our single-bit-quantization THz receiver is derived, which reveals the effect of phase offset on the demodulation performance, guiding the architecture design of our proposed receiver. To combat the performance loss caused by the above-mentioned distortions, a twin-phase training strategy and a neural network based demodulator are proposed, where the phase offset of the received signal is compensated before sampling. Our simulation results demonstrate that the proposed deep learning-assisted receiver is capable of achieving a satisfactory bit error rate performance, despite the grave distortions encountered.

Huge overhead of beam training imposes a significant challenge in millimeter-wave (mmWave) wireless communications. To address this issue, in this paper, we propose a wide beam based training approach to calibrate the narrow beam direction according to the channel power leakage. To handle the complex nonlinear properties of the channel power leakage, deep learning is utilized to predict the optimal narrow beam directly. Specifically, three deep learning assisted calibrated beam training schemes are proposed. The first scheme adopts convolution neural network to implement the prediction based on the instantaneous received signals of wide beam training. We also perform the additional narrow beam training based on the predicted probabilities for further beam direction calibrations. However, the first scheme only depends on one wide beam training, which lacks the robustness to noise. To tackle this problem, the second scheme adopts long-short term memory (LSTM) network for tracking the movement of users and calibrating the beam direction according to the received signals of prior beam training, in order to enhance the robustness to noise. To further reduce the overhead of wide beam training, our third scheme, an adaptive beam training strategy, selects partial wide beams to be trained based on the prior received signals. Two criteria, namely, optimal neighboring criterion and maximum probability criterion, are designed for the selection. Furthermore, to handle mobile scenarios, auxiliary LSTM is introduced to calibrate the directions of the selected wide beams more precisely. Simulation results demonstrate that our proposed schemes achieve significantly higher beamforming gain with smaller beam training overhead compared with the conventional and existing deep-learning based counterparts.

Reconfigurable intelligent surfaces (RISs), consisting of many low-cost elements that reflect the incident waves by an adjustable phase shift, have attracted sudden attention for their potential of reconfiguring the signal propagation environment and enhancing the performance of wireless networks. The passive nature of RISs is indeed beneficial, but the lack of radio frequency (RF) chains at the RIS has made channel estimation extremely challenging. We face this challenge by proposing a joint channel estimation and transmit precoding framework for RIS-aided multiple-input multiple-output (MIMO) systems. Specifically, the effective cascaded channel of the reflected transmitter-RIS-receiver link is decomposed into multiple subchannels, each of which corresponds to a single RIS element. Then our joint RIS-transmitter precoding model is formulated for the individual subchannels of each reflecting element. Finally, we develop a two-stage precoding design for successively determining the required phase shifts of each reflecting element of the RIS and the digital baseband precoder of the transmitter, only relying on the channel state information (CSI) of the subchannels. The performance of the proposed subchannel estimation and joint precoding method is evaluated by extensive simulations. Our numerical results show that the proposed designs provide an attractive solution to RIS-aided MIMO systems.

As the enabling technologies move up to the mmWave and even to the TeraHertz bands for the next-generation wireless systems, the signal processing of high-bandwidth orthogonal frequency division multiplexing (OFDM) becomes increasingly power-thirsty, owing to the following OFDM deficiencies: (1) the high peak-to-average power ratio (PAPR); (2) the bandwidth efficiency loss due to the cyclic prefix (CP) overhead; (3) the sensitivity to carrier frequency offset; (4) the complex out-of-band (OOB) filtering. Over the past six decades, a variety of waveforms have been developed in order to mitigate these deficiencies, which are generally achieved at the cost of compromising some of OFDM’s beneficial properties, such as its subcarrier (SC) orthogonality, its high throughput and its straighforward adoption to multiple-input multiple-output (MIMO) systems. Against this background, we propose a new waveform termed as multi-band discrete Fourier transform spread-OFDM with index modulation (MB-DFT-S-OFDM-IM), where the component multi-carrier techniques are conceived to constructively function together in order to mitigate the OFDM deficiencieswithout compromising the beneficial OFDM properties. More explicitly, first of all, the PAPR is reduced by the DFT-precoding. Secondly, thanks to the IM design, MB-DFT-S-OFDM-IM is capable of achieving a high throughput that is strictly equal to or higher than the OFDM throughput. Thirdly, MB-DFT-S-OFDM-IM achieves a beneficial frequency diversity gain, which leads to a higher tolerance to carrier frequency offset. Fourthly, the OOB filters are placed in each sub-band before DFT, so that the SC orthogonality remains intact, which is unique to the proposed MB-DFT-S-OFDM-IM structure. Last but not least, we extend the proposed MB-DFT-S-OFDM-IM to support a variety of MIMO schemes, where the IM philosophy is integrated with the space-, time- and frequency-domains within a singleunifiedplatform.

In this paper, we consider the impact of turbulence on performance of UWOC systems and investigate capacity and bit-error rate (BER) of underwater wireless optical links under weak and strong turbulence by deriving the expressions of average capacity and BER. Numerical results suggest that turbulence degrades both capacity and BER performance as expected. This work provides a theoretical analysis tool for system design and performance evaluation of UWOC systems.

TeraHertz (THz) communication has drawn increasing interest from both academic and industrial fields due to its capability of attaining data transmission over 100 Gb/s. To generate THz-band carriers, one promising low-cost electronic approach is the frequency-multiplier-last architecture, which however induces undesirable nonlinearity. Another substantial issue in THz communication systems is the residual hardware impairment in the mixer and power amplifier (PA), which are usually neglected under low-rate scenarios, but considered to be pronounced in the ultra-high-speed THz wireless links. With the existence of all these radio-frequency (RF) imperfections at the transmitter, conventional receiver design for lower-frequency bands is no longer applicable. In this paper, a single-carrier THz communication system utilizing the frequency-multiplier-last architecture is investigated. By jointly considering the overall RF imperfections of the mixer, the PA, and the frequency multiplier at the transmitter, a sophisticated mathematical model of the induced distortions on transmitted THz signals is provided. Based on the model, a low-complexity closed-form minimum mean Euclidean distance (MMED) channel estimator is firstly derived. Afterwards, the aggregate distortions plus thermal noise on the THz signals are approximated as a signal-dependent and spatially-colored noise term, where a closed-form quasi-maximum-likelihood (quasi-ML) detector is developed for the THz communication system. Simulation results demonstrate that our proposed receiver design is capable of significantly enhancing the performance of the THz system compared with its conventional counterparts, in terms of channel estimation accuracy and bit-error rate (BER).

Reconfigurable intelligent surfaces (RISs), which can deliberately adjust the phase of incident waves, have shown enormous potentials to reconFigure the signal propagation for performance enhancement. In this paper, we investigate the RIS-aided offshore system to provide a cost-effective coverage of high-speed data service. The shipborne RIS is placed offshore to improve the signal quality at the vessels, and the coastal base station is equipped with low-cost reconfigurable reflect-arrays (RRAs), instead of the conventional costly fully digital antenna arrays (FDAAs), to reduce the hardware cost. In order to meet the rate requirements of diversified maritime activities, the effective sum rate (ESR) is studied by jointly optimizing the beamforming scheme and the service time allocated to each vessel. The optimal allocation scheme is derived, and an efficient fixed-point based alternating ascent method is developed to obtain a suboptimal solution to the non-convex beamforming problem. Numerical results show that the ESR is considerably improved with the aid of the RIS, and the proposed scheme using the hardware-efficient RRAs has only a slight performance loss, compared to its FDAA-based counterpart.

Vehicular ad hoc network (VANET) is a promising technology for intelligent transportation systems, where dedicated short range communication (DSRC) is usually used for inter-vehicle communications. When the vehicle density is high, the randomly access feature of the carrier sense multiple access with collision avoidance (CSMA/CA) mechanism in DSRC will lead to the increased channel contention delay. Therefore, visible light communication (VLC) can be introduced to form a heterogeneous VLC-DSRC network for delay reduction. Although VLC has no contention delay, one VLC link can only be established between two adjacent vehicles, which might increase the delay caused by multi-hop communications. In this paper, a delay-minimization link selection scheme is proposed to select appropriate links for vehicles according to actual situations and minimize average delay. Simulation results demonstrate that the proposed scheme outperforms the considered benchmarks in terms of average transmission delay.

The low-frequency and mmWave links usually co-exist in the next generation wireless terminals, where the low-frequency link is always on and the mmWave link becomes active when high rate transmission is required. Since low-frequency and mmWave channels have spatial similarities, it is feasible to utilize low-frequency channel information to reduce the beam training overhead in mmWave communications. In this paper, we propose a deep learning assisted beam prediction scheme using out-of-band information extracted from low-frequency channel state information (CSI). To overcome the inaccuracy in estimating spatial features due to small number of antennas in low-frequency band, deep learning is introduced to extract robust channel features and increase the prediction accuracy. Moreover, dedicated pre-processing algorithm and network architecture are derived to improve the performance. Simulation results demonstrate that the proposed scheme is robust to various CSI matrix sizes and signal-to-noise ratio. By exploiting low-frequency CSI, it could successfully predict the optimal beam direction to facilitate the initial beam training in mmWave communications with over 94% accuracy in line-of-sight scenarios, which can reduce the overhead of beam training significantly.

Vehicular ad-hoc network (VANET) is a promising technology to realize communications among vehicles in intelligent transportation systems. Conventional VANET utilizes radio frequency (RF) for vehicle to vehicle communications. However, as the vehicle density increases, RF-based VANET suffers from limited bandwidth and unavoidable interference. Visible light communication (VLC) is emerging as a complement candidate, which possesses the advantages of wide spectrum, high power efficiency and low interference, but also has the drawback of limited communication range. Therefore, the hybrid VLC/RF structure is beneficial to an efficient and reliable VANET. Since there are usually no center infrastructures in VANET and different vehicles could have different preferences, the distributed and non-cooperative topology control (TC) should be investigated for the establishment of VLC and RF links among various vehicles to satisfy some properties, e.g., interference level, power consumption, connectivity and delay requirements. Conventional TC schemes designed particularly for RF-based ad-hoc network can not be applied directly due to the distinctive features of VLC and RF links. In this paper, the TC problem for the hybrid VLC/RF VANET is modeled as a potential game. When considering the different features of the two kinds of links, a distributed TC algorithm is proposed to handle the TC game, in which the RF and VLC transmitting power of each vehicle is locally adapted based on the vehicle's local knowledge about the VANET topology and can converge to a Nash Equilibrium state.

Visible light communication (VLC) is a promising technology for short-range communications with the benefits of wide spectrum, low power consumption, and high security. Since access points (APs) in VLC network are usually densely and randomly located, cell-free network model can be utilized, where APs cooperate to serve users without frequent handover. However, when massive APs transmitting signals simultaneously, managing the inter-user interference is critical to ensure the network performance. Precoding schemes can eliminate inter- user interference in spatial domain. Nevertheless, the number of supportable users in each time slot cannot exceed the number of APs and it can be further reduced if the VLC channel correlation is high, which occurs commonly when APs or users are closely located. Therefore, user scheduling scheme should be developed to decide the proper user group for precoding in each time slot, in order to mitigate the impact of high channel correlation and provide satisfying service for users. In this paper, an interference management (IM) algorithm for cell-free VLC network is proposed via the joint design of user scheduling and precoding. Specifically, to adapt to the various channel conditions, feasible user groups with different group sizes are considered. Given a user group, SLNR-based precoding is employed to reduce the inter-user interference and improve users' data rates. Moreover, the user scheduling scheme is proposed to optimize the time proportions for different user groups being served based on the VLC channel conditions, aiming to improve users' data rates while considering user fairness.

In millimeter-wave communications, full-dimension multiple-input multiple-output has been widely utilized to generate both horizontal and vertical beams by equipping uniform planar array (UPA) at the base station. However, considering the antenna radiation pattern for each sector, UPA offers nonuniform coverage in horizontal domain, where UEs on the boundary of each sector possess lower beamforming gains and hence the loss of spectral efficiency compared to the UEs at the center. In this paper, uniform cylindrical array (UCA) based hybrid precoding is proposed to improve the uniformity of beam coverage in horizontal domain and the fairness of spectral efficiency for UEs at different locations. Firstly, the UCA-based codebook is designed to generate uniform beams in horizontal domain. Next, the sector-free sub- connection transmitter architecture is designed to reduce the hardware complexity. Meanwhile, to serve multi-user transmission, the corresponding cyclic UE scheduling along with grouped digital precoding are introduced to suppress the inter-user interference. Theoretical analyses and simulation results demonstrate that the proposed scheme can improve the fairness of spectral efficiency compared to its conventional UPA counterpart.

As a benefit of using highly directional beams in millimeter wave systems, the downlink inter-cell interference (ICI) imposed on the users can be avoided, provided that the beams of neighbor cells do not point towards the user. We exploit this by designing a protocol for network-coordinated time- domain beam scheduling. Specifically, every pair of two neighbor cells maintains a beam collision table for recording the beam pairs that may inflict ICI upon each other. Then, to avoid beam-collision, the two neighbor cells exchange the necessary information to avoid the simultaneous activation of two beams recorded in one pair. More explicitly, our protocol supports a distributed cell coordination method without requiring any information exchanged between the user and the base station, once the beam collision table has been established. Furthermore, our theoretical analysis and numerical simulations demonstrate that the proposed protocol is capable of efficiently mitigating the ICI between the adjacent cells and hence improves the overall network performance.

Three-dimensional visible light positioning (3D-VLP) is capable of achieving superior locating accuracy in comparison with other existing positioning techniques, such as global positioning system (GPS) and Wi-Fi-based method, which draws much attention from the researchers. In this paper, a novel 3D-VLP scheme using regression neural network is proposed to provide accurate and real-time positioning service. In the proposed method, the angle of arrival (AOA) vectors corresponding to the light-emitting diodes (LEDs) are obtained by the image sensor of the receiver and then fed into a regression neural network, which directly outputs the positioning results. Simulations are carried out to validate the superiority of the proposed method. It’s observed that, in spite of the inevitable quantization error in the positioning process, the mean positioning error is still as accurate as 1.1 cm. In addition, the proposed positioning method is more robust to camera’s height, and takes only 0.27ms to calculate the position, which could be used for real-time locating.

In indoor environment, a hybrid network consisting of light fidelity (LiFi) and wireless fidelity (WiFi) is capable of retaining both the high-speed data transmission and the ubiquitous coverage, where the network access type of users can be optimized to improve the performance. Since visible light communication mainly depends on the line-of-sight (LoS) transmission, it is susceptible to channel blockage, which should be considered by users to select the appropriate type of network access. In the existing literature, LiFi channel blockage parameters are regarded as known for users to determine the access type. However, in practical scenarios, the estimation of blockage parameters lags behind their variations, and users can not get the real-time blockage information. In this paper, a support-vector-machine- based (SVM-based) network access type decision scheme is proposed in hybrid LiFi and WiFi networks. By taking the correlation of blockage parameters between adjacent periods into account, SVM is adopted to achieve high equivalent data rate when accurate blockage parameters are unknown. Simulation results demonstrate that the proposed scheme outperforms the considered benchmarks under different scenarios in terms of the equivalent data rate performance.

For multi-user millimeter wave (mmWave) communications, different users can be served using various timefrequency resources through their corresponding preferred beams which can provide the highest beamforming gain among all candidate beams. However, when the non-uniform distribution of users is considered, beams selected by fewer users can provide more time-frequency resources to each user than the beams serving more users, which indicates that the conventional uniform beam pattern may lead to unfair resource allocation and hence the loss of system spectral efficiency. In this paper, a novel non-uniform beam design is proposed, wherein a two-level codebook which supports both wide and sharp beams is adopted for data transmission. Specifically, two neighboring sharp beams selected by fewer users can be merged into one wide beam which can cover the same angular space. Thus, the saved resources can be assigned to other sharp beams selected by relatively more users. Theoretical analysis and numerical simulations demonstrate the superiority of our proposal on fairness of resource allocation as well as the spectral efficiency compared with its conventional counterpart.

Due to the inherent high path-loss of millimeterwave signals, beamforming combined with massive MIMO is crucial for increasing coverage range, whereby beam alignment via beam training is necessary to select appropriate beamforming/combining vectors at both base station (BS) and user equipment (UE) in order to acquire sufficient beamforming gain. To reduce the beam training overhead and keep the good beam alignment for millimeter-wave systems, a calibrated beam training is proposed in this paper. Firstly, BS provides a wide beam for coarse beam training and UE calculates the wide beam index by checking the received power levels. Secondly, UE refines the beam training by calculating the narrow beam index based on the ratio of beamforming gain between the selected wide beam and its neighboring wide beams. Simulation results demonstrate that the proposed scheme achieves almost the same beamforming gain as the optimal exhaustive search scheme with much reduced beam training overhead.

Two decades of full-diversity high-rate MIMO research has created perfect Space-Time Block Codes (STBCs), including the Golden code. However, the major stumbling block of their wide-spread employment is their limited energy-efficiency. On one hand, the superposition of their signals results in a high Peak-to-Average Power Ratio (PAPR). On the other hand, the total number of equivalent Inter-Antenna Interference (IAI) contributions that the receiver has to deal with is increased to IAI = M2upon using M Transmit Antennas (TAs), which is a substantial extra price compared to the IAI = M of V-BLAST. Against this background, we propose a new family of Finite-Cardinality Generalized Space-Time Shift Keying (FC-GSTSK). More explicitly, the proposed FC-GSTSK is capable of outperforming both V-BLAST and STBC, which is the ultimate objective of full-diversity high-rate MIMO design. Furthermore, following the index modulation philosophy, the proposed FC-GSTSK replaces the signal-additions by the data-carrying signal-selection process. As a benefit, the FC-GSTSK substantially reduces the PAPR of signal transmission. As a further advantage, the equivalent IAI imposed on signal detection is reduced back to the same level as that of the V-BLAST. Moreover, the proposed FC-GSTSK is even capable of consistently outperforming the perfect STBCs in terms of its Peak Signal to Noise-power Ratio (PSNR) that takes into account the power consumption at the transmitter. As a further advance, the reduced-RF-chain based version of FC-GSTSK is also capable of outperforming both Generalized Spatial Modulation (GSM) and Space-Time Block Coded Spatial Modulation (STBC-SM) without increasing the PAPR and the equivalent IAI.

The narrow beamwidth characteristic in millimeter wave (mmWave) makes the level of inter-cell interference depend on the beams used by adjacent cells, which indicates that a well-designed schedule of the beams in neighboring cells can reduce the inter-cell interference. In this paper, we investigate a time-domain mmWave beam scheduling problem for interference suppression in a two-cell scenario, where each cell may transmit one or multiple beams simultaneously. Specifically, the beams are one-to-one paired between two adjacent cells, and the simultaneous transmission of two beams in one pair can cause severe inter-cell interference, which is referred to as a pair-wise collision. Instead of optimizing the sum rate of network, we minimize the number of pair-wise collision time slots under the constraint of the service demand of each beam. In this way, the channel state information is not required, and the global optimal solution can be found by our proposed least pair-wise collision (LPC) algorithm, which is a recursive algorithm with linear computational complexity. After that, we extend LPC algorithm to more complicated models, including an asymmetric model and a full-dimension model. Finally, simulations are conducted to verify the efficiency of our methodology.

Media-based modulation (MBM) has become a promising technology for wireless communications, which offers significant throughput enhancement and superior performance due to the channel diversity gain achieved by RF mirrors. In this paper, the physical-layer security (PLS) issue is investigated for the single-input-multiple-output MBM (SIMO-MBM) system, and a secure SIMO-MBM system is proposed. Especially, by considering time-division duplexing (TDD) mode, based on its channel reciprocity property, both the data symbol bits and the index bits are protected from the eavesdropper by using the amplitude and phase information of the legitimate channel states as the secret key. For performance evaluation, the bit error rate (BER) of the eavesdropper as well as the system secrecy mutual information are investigated, and Monte Carlo simulations are carried out. Simulation results demonstrate that, the proposed secure SIMO-MBM system is capable of efficiently preventing data leakage whilst ensuring reliable data transmission.

Hybrid analog/digital precoding provides an energy- efficient solution for multi-user millimeter-wave (mmWave) systems by utilizing a small number of radio frequency (RF) chains for digital precoding and a phase shifter (PS) network for analog precoding. However, the conventional fully-connected and partially-connected architectures exist the problem of terrible trade-off between performance and hardware complexity. In this paper, we propose a joint user scheduling and hybrid precoding scheme for multi-user mmWave systems with two-layer PS network at the base station. Specifically, we propose a rank-one approximation based hybrid precoding algorithm to handle the hardware constraints on analog precoder. Moreover, a vertical user grouping scheduling algorithm is proposed to improve the achievable-rate performance by simultaneously serving users with similar vertical angle of departures (AoDs) but different horizontal AoDs. Simulation results demonstrate that the proposed scheme approaches the performance of fully- connected architecture with much lower hardware complexity.

Absorption, scattering and turbulence are the three main characteristics for underwater optical wireless communications (UOWC). Among these three factors, absorption and scattering, respectively, characterize the energy loss and direction change when photons propagate through underwater wireless channels interacting with water molecules or suspended particles. In recent years, several analytical methods originated from free space optical communications are used to model various underwater channels, while only statistical models are employed to consider the effects of absorption and scattering containing all the scattering components. To facilitate in-depth theoretical analysis, we propose an expression to model the spatial probability density function of optical intensity for all scattering components in UOWC links in this paper. After that, we model the photodiode receiving process and give a clear explanation on the related parameters. Numerical results indicate that the bit-error rate performance deteriorates as the turbulence gets stronger, and larger multiple-input multiple-output array can alleviate the negative effect caused by fading. Besides, the effect of turbulence on BER is more important than that of the link geometry, and the increase in transmitted power will weaken the diversity gain from MIMO configuration.

Absorption and scattering are traditionally considered as the most important factors to affect the performance of underwater optical wireless communications (UOWC). Recently, the theoretical models from free space optical (FSO) communications are applied to model the underwater turbulence, and the turbulence-induced fading may introduce fluctuations to the light intensity. However, the effect of turbulence on UOWC channels might be different from FSO channels due to the interference from absorption and scattering. In this work, we first introduce the log-normal distribution to represent the weak turbulence. After that, we deduce the average capacity of turbulent buoy- based multiple-input multiple-output (MIMO) systems. Numerical results demonstrate that turbulence will boost the average capacity under low transmitted signal-to-noise ratio (SNR) and reduce the average capacity when the transmitted SNR which is defined as the transmitted power divided by the noise at the receiver is sufficiently high enough. Besides, stronger turbulence exerts more influence on the capacity, and the increasing attenuation length will eliminate the effect of turbulence. Moreover, MIMO could offset the impact of turbulence-induced fading, which indicates that it cannot improve the capacity performance under low SNR but could bring positive effects when SNR becomes high.

A 3D positioning method based on visible light communication is proposed. Compared to the previous methods, we only need three light emitting diodes (LEDs) with known coordinates to obtain the object position without the priori knowledge of the receiver's height and tile angle. The gradient descent method and vector method are used to obtain the coordinate and inclination of the object (i.e., camera). To validate the effectiveness of our methods, the relation of mean positioning error caused by the discrete camera sensor pixel and the system parameters (i.e., focal length of the camera, pixel density and height of the receiver) is analyzed. Simulation results show the quantization error is about 5 cm.

As a complementary technology for conventional radio frequency communication, visible light communication (VLC) is a potential form of the optical wireless communication, which can provide both communication and illumination simultaneously. Since load balancing and power control for interference management are key challenges in the network deployment, we consider a joint user association and power allocation scheme in a cell-free VLC network to improve the system performance. It is mathematically formulated as a non-convex network utility maximization problem in consideration of the user fairness, load balancing, and power control. To tackle this non-convex problem, we divide it into two subproblems (i.e., the user association subproblem and the power allocation subproblem) and solve them with the dual projected gradient algorithm and successive convex approximation algorithm iteratively until a stationary point is found. Simulation results verify that significant gain can be achieved with the proposed scheme compared with the user association schemes without consideration of the power control.

As a promising non-orthogonal multiple access scheme, sparse code multiple access (SCMA) technology has attracted much attention. Because inter-user interference is present in code domain and multi-user iterative detection is required, user capacity and outage probability analysis for uplink SCMA systems are challenging and have not been presented in the literature. In this paper, the capacity region for uplink SCMA systems is analyzed, based on which the common and individual outage probability regions are calculated. Optimizing the outage probability within the outage probability region can be casted as an Lagrangian duality problem and solved by an iterative descent algorithm, which however imposes high complexity since the expectation operation is required in each iteration. To reduce the computational complexity of solving this Lagrangian duality problem, an adaptive algorithm is developed, which is capable of providing the optimal outage probability and adaptively updating it. Furthermore, a power allocation policy is naturally obtained to achieve the optimized outage probability in the outage probability region.

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

Orthogonal frequency division multiplexing (OFDM) with index modulation has emerged as a promising complementary technique for next-generation networks due to its high energy efficiency. In this paper, zero-padded tri-mode index modulation aided OFDM (ZTM-OFDM) is proposed, where subcarriers are partitioned into subblocks. Explicitly, only a fraction of subcarriers are utilized for modulation with two distinguishable constellation alphabets in each OFDM subblock, whilst the others remain empty. By such strategy, additional bits can be conveyed by the subcarrier indices corresponding to the two constellation sets (denoted as index pattern). At the receiver, a maximum-likelihood (ML) detector and a two-stage log-likelihood ratio (LLR) detector with reduced complexity are proposed for demodulation. The proposed ZTM-OFDM is capable of enhancing the spectral and energy efficiency compared with other index modulated OFDM schemes. Theoretical analysis based on the minimum Euclidean distance and Monte Carlo simulation results validate that the proposed ZTM-OFDM can attain performance gain over conventional OFDM and other index modulated OFDM schemes.

Hybrid analog/digital precoding provides an effective solution for millimeter wave (mmWave) multi-input multi-output (MIMO) systems since it reduces the number of radio frequency (RF) chains significantly. Meanwhile, full-dimension MIMO (FD-MIMO) has attracted considerable interests due to its spatial separation ability in vertical direction. However, the hardware complexity of phase shifter (PS) feeding network in the conventional full-connection architecture is too large. In this paper, we propose a novel two-layer PS feeding network architecture for mmWave FD-MIMO systems. Specifically, the two-layer PS network consists of a vertical PS layer and a horizontal PS layer, which greatly reduces the number of PSs required. Furthermore, an iterative precoding design algorithm is proposed, in which the vertical and horizontal beamformers are alternately configured in an iterative fashion. Asymptotic analysis proves that our proposed scheme could exactly approach the performance of full- connection architecture when vertical channel angle spread tends to zero. Simulation results demonstrate that the proposed scheme achieves satisfactory performance while only imposing relatively low hardware complexity compared with its conventional counterparts.

Due to the limited modulation bandwidth of light emitting diodes (LEDs), the imaging optical multiple-input multiple-output (MIMO) technology is applied in visible light communications (VLC) for high data rate transmission. As an imaging lens with a wide angle/field-of-view (FOV) is favorable in the imaging optical MIMO VLC system, a fisheye lens can be utilized to concentrate the light. To avoid inter-user interference among indoor users and satisfy their target bit error rate (BER) requirements with the minimum number of LEDs simultaneously, an interference-free LED allocation scheme for the fisheye lens based imaging optical MIMO VLC system is investigated in this paper. Since the formulated problem is NP-hard, a location-based greedy algorithm is proposed, where the LEDs are allocated to the users sequentially based on their distances to the center of the LED array. Simulation results verify that there exists no interference among all users while the target BER requirements for all users are satisfied with our proposed algorithm.

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

The huge training overhead for obtaining channel state information (CSI) at the BS has been recognized as a major challenge in frequency division duplex (FDD) massive multiple-input multiple-output (MIMO) cellular networks. To solve this problem, we propose an angular domain pilot design and channel estimation scheme to reduce the required overhead by exploiting the angle domain channel sparsity. Specifically, we firstly propose the downlink dominant angular set estimation by utilizing the directional reciprocity of FDD channels, where an index calibration algorithm is introduced to handle the effect of different wavelengths in FDD systems. Then, two kinds of angular domain pilots design schemes, named complete orthogonal pilot design and partial orthogonal pilot design, together with their corresponding feedback frameworks are proposed for channel estimation. Simulation results demonstrate that our proposed angular domain pilot design and channel estimation scheme could provide good mean square error (MSE) performance with much reduced pilot overhead, and consequently achieve much larger downlink throughput in comparison to the conventional scheme adopted in LTE.

Visible light communication (VLC) has become a promising complement to its radio-frequency (RF) counterpart. In VLC systems, orthogonal frequency division multiplexing (OFDM) has drawn much attention due to its high data rate, simple equalization and robustness to the inter-symbol interference (ISI). In this paper, we present a comparative performance evaluation of several classical optical OFDM schemes, including DC-biased optical OFDM (DCO-OFDM), asymmetrically clipped optical OFDM (ACO-OFDM), pulse-amplitude-modulated discrete multitone (PAM-DMT), unipolar OFDM (U-OFDM) and Flip OFDM. Since DCO-OFDM suffers from energy efficiency loss due to addition of DC-bias, whilst ACO-OFDM, PAM-DMT and U-OFDM/Flip OFDM are spectrally inefficient due to their unique frame structures, the state-of-the-art energy- and spectrum- efficient optical OFDM schemes are investigated, including asymmetrically clipped DC-biased optical OFDM (ADO-OFDM), hybrid ACO-OFDM (HACO-OFDM), asymmetrically clipped absolute value optical OFDM (AAO-OFDM), the spectral and energy efficient OFDM (SEE-OFDM), layered ACO-OFDM (LACO-OFDM), enhanced U-OFDM (eU-OFDM), optical OFDM with index modulation (O-OFDM-IM) and optical dual-mode index modulation aided OFDM (DM-OFDM). In this paper, their principles are firstly illustrated, then performance comparisons are conducted for those optical OFDM schemes in terms of spectral efficiency, energy efficiency and computational complexity. Since light emitting diodes (LEDs) need to support both the illumination and communication simultaneously, dimming control should be considered in VLC systems. Therefore, dimmable optical OFDM for practical VLC systems incorporating illumination is also addressed.

Digital television terrestrial broadcasting (DTTB) networks can help to alleviate the congestion problem in cellular networks by delivering rich contents to a large number of clients simultaneously. In particular, recently, there is a strong interest of extending current DTTB systems to support multimedia broadcasting services. The lack of return channel and long transmission time interval however impose great challenge to the resource allocation for this application in DTTB networks. The reliable resource allocation is studied for multi‐services with data delivery delay constraints in the second generation digital video broadcasting terrestrial (DVB‐T2) system. To solve this challenging problem, the data cells of a T2‐frame are divided into data slices which are indexed by binary numbers. These data slices are organised in a binary tree, and each node in the tree is associated with a certain number of non‐adjacent data slices. Then a node can be allocated to a service by using the predefined policies. Based on this scheme, this study proposes a heuristic algorithm to allocate resources to multi‐services. Simulation results validate the effectiveness of the proposed algorithm and demonstrate its advantage over the current resource allocation scheme in DVB‐T2 networks.

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

Full-dimensional multiple-input multiple-output (FD-MIMO) systems, whereby each base station is equipped with a uniformly spaced rectangular antenna array (URA), provides a practical means of realizing massive MIMO systems. However, the spectral efficiency of URA is considerably lower than that of its uniformly spaced linear array counterpart having the same number of antenna elements. In this paper, we first introduce a discrete angular resolution metric for quantifying the low resolution of URA in the antenna-elevation domain. This motivates us to propose a novel antenna device design, referred to as the structured non-uniformly spaced rectangular array (NURA), in which the antenna elements are non-uniformly distributed in the elevation-angle domain. Specifically, we conceive a structured NURA device for which the nonuniform distribution of the elevation-domain antenna elements is controlled by a single parameter. The design of the optimally structured NURA for the given nonlinear antenna-element-positioning function then becomes a single-parameter optimization, namely, that of maximizing the spectral efficiency of the FD-MIMO system, which can be solved efficiently. Our simulation results demonstrate that our structured NURA design significantly outperforms the standard URA in terms of achievable spectral efficiency. Our proposed structured NURA design therefore offers an effective practical framework for enhancing the achievable performance of FD-MIMO systems.

Compared with the conventional amplitude phase modulation (APM), spatial modulation (SM) is a low- complexity, yet energy-efficient transmission technique, whereby transmit antenna (TA) indices are utilized to convey the information. However, the number of the required TAs grows exponentially with the number of transmitted bits, which leads to unacceptable pilot overhead for channel estimation in practical systems. To reduce the number of TAs whereas keep the data rate unchanged, virtual spatial modulation (VSM) is proposed in the first time. Specifically, by activating multiple TAs with their corresponding analog phase shifters (APSs), massive equivalent channel vectors could be constructed based on the combinations of original channel vectors from different TAs and their phase rotations. By way of mapping each equivalent channel vector to a virtual transmit antenna (VTA) index which might convey the information, the number of the required TAs could grow linearly with the number of transmitted bits. Furthermore, the selection of a VTA subset from all available VTAs is formulated as a combinatorial optimization problem to maximize the minimal Euclidean distance (ED) among the equivalent channel vectors. A spatial constellation optimizing (SCO) algorithm is proposed to obtain a near-optimal solution to this problem with low-complexity. Simulation results demonstrate that the proposed VSM is able to achieve lower bit error rate (BER) under the same transmit rate compared with the conventional SM and APM schemes.

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

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

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

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

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

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

In this paper, a two-dimensional location-based channel estimation algorithm is proposed for massive full-dimensional multiple-input multiple-output (FD-MIMO) systems with intracell pilot reuse, which serves more users using the same timefrequency resource and significantly improves the sum capacity of users compared with conventional schemes. By utilizing the two-dimensional antenna arrays, different users assigned with the same pilot could be distinguished by their non-overlapping azimuth angle-of-arrivals (A-AOAs) and elevation angle-of-arrivals (EAOAs), and thus the interference caused by pilot reuse could be removed. Simulation results show that the sum capacity of the proposed algorithm could be improved as the number of antennas grows large.

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

Max‐log‐maximum a posterior (MAP) detection is preferred in practical systems rather than log‐MAP because of its lower complexity, but also suffers from a considerable performance loss. In this study, the authors focus on the performance optimisation for (doped) bit‐interleaved coded modulation with iterative demapping schemes where max‐log‐MAP detection is employed. First, they study the effects of max‐log‐MAP detection to the extrinsic information transfer curves of the (doped) demapper and decoder, and reselect the constellation labelling. Second, they find that the small difference between max‐log‐MAP and log‐MAP detection in each iteration would be accumulated during the whole iterative procedure referred to as error accumulation, which causes a large performance loss, and consequently they propose some methods to control the error accumulation. Owing to the labelling reselection and error‐accumulation control, the proposed scheme exhibits several tenths to 1 dB gains, compared with the conventional schemes, while maintaining low complexity.

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

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

This paper develops a low-complexity channel estimation (CE) scheme based on compressive sensing (CS) for time-domain synchronous (TDS) orthogonal frequency-division multiplexing (OFDM) to overcome the performance loss under doubly selective fading channels. Specifically, an overlap-add method of the time-domain training sequence is first proposed to obtain the coarse estimates of the channel length, path delays, and path gains of the wireless channel, by exploiting the channel's temporal correlation to improve the robustness of the coarse CE under the severe fading channel with long delay spread. We then propose the priori-information aided (PA) iterative hard threshold (IHT) algorithm, which utilizes the priori information of the acquired coarse estimate for the wireless channel and therefore is capable of obtaining an accurate channel estimate of the doubly selective fading channel. Compared with the classical IHT algorithm whose convergence requires the l2norm of the measurement matrix being less than 1, the proposed PA-IHT algorithm exploits the priori information acquired to remove such a limitation and to reduce the number of required iterations. Compared with the existing CS-based CE method for TDS-OFDM, the proposed PA-IHT algorithm significantly reduces the computational complexity of CE and enhances the CE accuracy. Simulation results demonstrate that, without sacrificing spectral efficiency and changing the current TDS-OFDM signal structure, the proposed scheme performs better than the existing CE schemes for TDS-OFDM in various scenarios, particularly under severely doubly selective fading channels.

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

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

A low complexity detection algorithm based on list sphere decoding (LSD) is proposed for under-determined multiple-input multiple-output (UD-MIMO) systems with N transmit antennas and M <; N receive antennas. The proposed algorithm utilizes the unique structure of UD-MIMO systems by dividing the N detection layers into two groups. Group 1 contains layers 1 to M that have similar structures as a symmetric MIMO system; while Group 2 contains layers M + 1 to N that contribute to the rank deficiency of the channel Gram matrix. Tree search algorithms are used for both groups, but with different search radii. A new method is proposed to adaptively adjust the tree search radius of Group 2 based on the statistical properties of the received signals. The employment of the adaptive tree search can significantly reduce the computational complexity. Simulation results show that the proposed algorithm can reduce the complexity by one orders of magnitude with less than 0.01 dB degradation in the Bit-Error-Rate (BER) performance.

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

A polar decomposition of mutual information between a complex-valued channel's input and output is proposed for an input whose amplitude and phase are independent of each other. The mutual information is symmetrically decomposed into three terms: 1) an amplitude term; 2) a phase term; and 3) a cross term, where the cross term is negligible. Theoretical bounds of the amplitude and phase terms are derived for additive white Gaussian noise channels with Gaussian inputs. This decomposition is applied to amplitude phase shift keying with product constellation (product-APSK) design and analysis. It facilitates the product-APSK to achieve a considerable shaping gain at some interesting rates over conventional quadrature amplitude modulation with a similar low complexity.

Key challenges in vehicular transportation and communication systems are understanding vehicular mobility and utilizing mobility prediction, which are vital for both solving the congestion problem and helping to build efficient vehicular communication networking. Most of the existing works mainly focus on designing algorithms for mobility prediction and exploring utilization of these algorithms. However, the crucial questions of how much the mobility is predictable and how the mobility predictability can be used to enhance the system performance are still the open and unsolved problems. In this paper, we consider the fundamental problem of the predictability limits of vehicular mobility. By using two large-scale urban city vehicular traces, we propose an intuitive but effective model of areas transition to describe the vehicular mobility among the areas divided by the city intersections. Based on this model, we examine the predictability limits of large-scale urban vehicular networks and obtain the maximal predictability based on the methodology of entropy theory. Our study finds that about 78%-99% of the location and above 70% of the staying time, respectively, are predicable. Our findings thus reveal that there is strong regularity in the daily vehicular mobility, which can be exploited in practical prediction algorithm design.

To cope with explosive vehicular traffic and ever-increasing application demands in the vehicular cellular network, opportunistic vehicular networks are used to disseminate mobile data by high-capacity device-to-device communication, which offloads significant traffic from the cellular network. In the current opportunistic vehicular data transmission, coding-based schemes are proposed to address the challenge of opportunistic contact. However, whether coding techniques can be beneficial in the context of vehicular mobile data offloading is still an open question. In this paper, we establish a mathematical framework to study the problem of coding-based mobile data offloading under realistic network assumptions, where 1) mobile data items are heterogeneous in terms of size; 2) mobile users have different interests to different data; and 3) the storage of offloading participants is limited. We formulate the problem as a users' interest satisfaction maximization problem with multiple linear constraints of limited storage. Then, we propose an efficient scheme to solve the problem, by providing a solution that decides when the coding should be used and how to allocate the network resources in terms of contact rate and offloading helpers' storage. Finally, we show the effectiveness of our algorithm through extensive simulations using two real vehicular traces.

Vehicular networks have been attracting increasing attention recently from both the industry and research communities. One of the challenges in this area is understanding vehicular mobility, which is vital for developing accurate and realistic mobility models to aid the vehicular communication and network design and evaluation. Most of the existing works mainly focus on designing microscopic level models that describe the individual mobility behaviors. In this paper, we explore the use of Markov jump process to model the macroscopic level vehicular mobility. Our proposed simple model can accurately describe the vehicular mobility and, moreover, it can predict various measures of network-level performance, such as the vehicular distribution, and vehicular-level performance, such as average sojourn time in each area and the number of sojourned areas in the networks. Model validation based on two large scale urban city vehicular motion traces confirms that this simple model can accurately predict a number of system metrics crucial for vehicular network performance evaluation. Furthermore, we propose two applications to illustrate that the proposed model is effective in analysis of system-level performance and dimensioning for vehicular networks.

To cope with explosive traffic demands on current cellular networks of limited capacity, Disruption Tolerant Networking (DTN) is used to offload traffic from cellular networks to high capacity and free device-to-device networks. Current DTN-based mobile data offloading models are based on simple and unrealistic network assumptions which do not take into account the heterogeneity of mobile data and mobile users. We establish a mathematical framework to study the problem of multiple-type mobile data offloading under realistic assumptions, where (i) mobile data are heterogeneous in terms of size and lifetime; (ii) mobile users have different data subscribing interests; and (iii) the storages of offloading helpers are limited. We formulate the objective of achieving maximum mobile data offloading as a submodular function maximization problem with multiple linear constraints of limited storage, and propose three algorithms, suitable for the generic and more specific offloading scenarios, respectively, to solve this challenging optimization problem. We show that the designed algorithms effectively offload data to the DTN by using both the theoretical analysis and simulation investigations which employ both real human and vehicular mobility traces.

With the emerging demands for local area services of popular content downloading, device-to-device (D2D) communication is conceived as a vital technological component for next-generation cellular communication networking to increase the spectral efficiency and to enhance the system capacity. Targeting the application of mobile content downloading, we investigate the fundamental problems of how D2D communication improves the system performance of cellular networks and what is the potential effect of D2D communication, with the aid of the optimal solutions for the system resource allocation and mode selection obtained under the realistic user and mobility conditions. Specifically, by formulating a max-flow optimization problem that maximizes the content downloading flows from all the cellular base stations to the content downloaders through any possible ways of transmission, we obtain the theoretical upper bound to system content-downloading performance. Using realistic mobility model and trace driven simulations, we evaluate the effects of the different system settings on the performance of the mobile content-downloading system, and reveal the fundamental influence of D2D communication.

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%.

In this paper, we propose an improved preamble structure based on distance detection using a pair of training sequences in frequency domain, whereby self-adaptive algorithm is introduced. Guard interval with cyclic prefix (CP) property is used to achieve better signalling transmission performance under frequency selective fading channel. Moreover, delay and differential operation is applied to eliminate continuous wave (CW) interference. Theoretical analyses and simulation results show that the proposed scheme has superior signalling demodulation and synchronization performances to the existing methods.

Physical-layer security could efficiently prevent wiretapping for wireless communication from the information-theoretic perspective. However, most related works in this area are based on the assumption of Gaussian inputs, which are impractical for a practical communication system. In this paper, we study the physical-layer security with finite-alphabet inputs, wherein amplitude phase shift keying (APSK) constellation is considered to improve the achievable secrecy rate performance. It is concluded that the secrecy rate can be improved by using APSK, comparing to its quadrature amplitude modulation (QAM) counterpart, which is verified by both the minimum mean square error (MMSE) theoretic derivation and simulation results. A specific strategy of selecting constellation orders for secure communication is also presented.

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.

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

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

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

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

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

Contact duration between moving vehicles is one of the key metrics in vehicular ad hoc networks (VANETs), that critically influences the design of routing schemes and network throughput. Due to prohibitive costs to collect enough realistic contact records, little experimental work has been conducted to study the contact duration in urban VANETs. In this work, we carry out an extensive experiment involving tens of thousands of operational taxis in Beijing city. Based on studying this newly collected Beijing trace and the existing Shanghai trace, we find an invariant characteristic that there exists a characteristic time point, up to which the contact duration obeys an exponential distribution that includes at least 80% of the whole distribution, while beyond which it decays as a power law one. This property is in sharp contrast to the recent empirical data studies based on human mobility, where the contact duration exhibits a power law distribution. Our observations thus provide fundamental guidelines for the design of new urban VANETs' routing protocols and their performance evaluation.

A two-stage list sphere decoding (LSD) algorithm is proposed for under-determined multiple-input multiple-output (UD-MIMO) systems that employ N transmit antennas and M<;N receive antennas. The two-stage LSD algorithm exploits the unique structure of UD-MIMO systems by dividing the N detection layers into two groups. Group 1 contains layers 1 to M that have similar structures as a symmetric MIMO system; while Group 2 contains layers M+1 to N that contribute to the rank deficiency of the channel Gram matrix. Tree search algorithms are used for both groups, but with different search radii. A new method is proposed to adaptively adjust the tree search radius of Group 2 based on the statistical properties of the received signals. The employment of the adaptive tree search can significantly reduce the computation complexity. We also propose a modified channel Gram matrix to combat the rank deficiency problem, and it provides better performance than the generalized Gram matrix used in the Generalized Sphere-Decoding (GSD) algorithm. Simulation results show that the proposed two-stage LSD algorithm can reduce the complexity by one to two orders of magnitude with less than 0.1 dB degradation in the Bit-Error-Rate (BER) performance.

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

A modified FSD algorithm is proposed for under-determined (UD) multiple-input multiple-output (MIMO) systems with N transmit antennas and M < N receive antennas. This paper focuses on the low-complexity detection of coded UD-MIMO systems with iterative turbo detection, where a soft-input soft-output (SISO) MIMO detector exchanges soft information with a SISO decoder. In the first iteration, a modified fixed complexity sphere decoding (FSD) method is developed by utilizing the structure of a UD-MIMO system. The modified FSD employs a new detection ordering scheme that has a lower complexity but a better performance compared to the conventional ordering scheme. From the second iteration and beyond, the MIMO detector is implemented with a generalized serial interference cancelation (GSIC) scheme and a block decision feedback equalizer (BDFE) to further reduce the complexity. Simulation results show that the newly proposed FSD-GSIC-BDFE structure can achieve significant performance gains over existing schemes, especially for systems with high level modulations.

This paper proposes a "column extension" design method for rate-compatible quasi-cyclic (QC) low-density parity-check (LDPC) codes, which can provide efficient multi-rate coding schemes for communication systems with flexible spectrum efficiency. The optimization of degree distributions among multiple code rates makes the designed QC-LDPC codes achieve excellent performance at each code rate, while the nested parity-check matrix structure of the designed codes facilitates the implementation of multi-rate encoder/decoder and offers the advantage of low-complexity. A comparison with the multi-rate LDPC codes in DVB-S2 specification is addressed. Extrinsic information transfer (EXIT) chart analysis predicts the superiorities of the optimized degree distributions in Eb/N0thresholds, and the bit error rate (BER) simulation results show that the designed codes perform 0.03-0.1 dB better than the DVB-S2 64K codes at each typical code rate, with even shorter code length.

This paper presents a decision directed frequency and clock tracking scheme to improve the spectrum efficiency of burst mode OFDM transmission with frequency selective channel. OFDM divides one broadband channel to parallel narrowband subchannels of different channel characteristics. In this scheme, the subcarriers with better attenuation are selected out to do the decision-directed based clock and frequency tracking. a novel phase error detection method is proposed which keeps the hard decision working well when tracking is performed. Furthermore, Particular algorithm is designed to ensure the carrier frequency offset and sampling frequency offset could be solved out from detected phase error. Simulations illustrate that the new scheme can work well in typical system.

There has been an explosion in mobile data traffic, but cellular networks alone could not support the fast growing demand on data transmission. This dilemma is caused mainly by the reason that the same content is repeatedly transmitted in the network, since many people are interested in the same content. Broadcast networks, however, could alleviate this problem by delivering popular content to multiple clients simultaneously. This paper presents a content dissemination system combining broadcast network with cellular network. Based on the model of Markov Decision Process, we propose an online optimal scheme to maximize the expected number of clients receiving content. The clients' interest and queuing length at broadcast and cellular base stations are two important elements that are considered in our scheme. Simulation results demonstrate that, compared with random scheme and single network scheme, our scheme performs the best in terms of lowering packet loss rate at base stations and enhancing the average number of clients who receive their interested content.

Wireless traffic has two features: people have common interests in the hottest contents, and a large amount of traffic belongs to non-realtime services. Exploiting these two features, we propose a content dissemination system, integrating broadcast network and opportunistic network. In the proposed system, popular contents are broadcasted through broadcast network and then disseminated to users through opportunistic network. Such system can significantly reduces the same contents repeated transmission and efficiently delivers contents to their clients. Using a Markov model, we theoretically analyze content dissemination delay of the proposed system.

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

We study the performance of collaborative vehicular content dissemination, where the content is distributed within the network by vehicle-to-vehicle opportunistic communications and the vehicle nodes are equipped with directional antennas. Through analysing a large real-world vehicle trace, we adopt an accurate mobility model of Levy-walk to set up the realistic vehicular network simulation environment. Using a fluid approximation, we derive a theoretical model to depict the system performance of content dissemination time. The accuracy of the proposed analysis is confirmed by simulation results, which also show that the directional antenna performs better than the omni-directional antenna in our considered scenario, especially when the antenna beam is well scheduled with small beamwidth and high beam steering rate.

Amplitude phase shift keying (APSK) constellation is capable of outperforming quadrature amplitude modulation (QAM) in terms of both the mutual information and the error-control performance. However, the lack of simplified demapper prevents its potential applications due to the inherent high computation complexity. In this paper, the concept of APSK using product constellation labeling is introduced, and two simplified demappers are proposed. Theoretical analysis and simulations show that the performance degradation caused by these simplified demappers is negligible, while the complexity is significantly reduced, compared to the traditional Max-Log-MAP demapper.

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

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

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

A novel preamble design is proposed for orthogonal frequency division multiplexing systems, which exploits the variable distance between a pair of training sequences for the transmission parameter signalling. Compared to the existing P1-symbol based preamble for the second generation digital terrestrial television broadcasting standard, the proposed design maintains the high performance and robustness in timing and carrier frequency offset estimation while significantly reducing the signalling detection complexity. Simulation results demonstrate that the proposed novel preamble achieves a better signalling detection performance than the P1 symbol design.

In Delay Tolerant Networks (DTNs), there exists only intermittent connectivity between communication sources and destinations. In order to provide successful communication services for these challenged networks, a variety of relaying and routing algorithms have been proposed with the assumption that nodes are homogeneous in terms of contact rate and delivery cost. However, various applications of DTN have shown that mobile nodes can be divided into different classes in terms of their energy requirements and communication ability, and real application data have revealed the heterogeneous contact rates between node pairs. In this paper, we design an optimal relaying scheme for DTNs, which takes into account nodes' heterogeneous contact rates and delivery costs when selecting relays to minimise the delivery cost while satisfying the required message delivery probability. Extensive results based on real traces demonstrate that our relaying scheme requires the least delivery cost and achieves the largest maximum delivery probability, compared with the schemes that neglect nodes' heterogeneity.

In order to accommodate the different applications in wireless transmission environment, communication systems usually provide several configurable transmission modes. Therefore, transmission parameter signalling (TPS) is critical for the receiver to recognize the signal transmission modes. In this paper, the pseudo-noise (PN) training sequence with embedded signalling in the time domain synchronous block transmission (TDS-BT) system is investigated, where the TPS could be conveyed through the transformation of the PN sequences in the frame head. The proposed signalling-embedded PN sequences could provide precise synchronization as well as robust signalling performance without extra transmission resource consumption. Computer simulations are also presented to verify the effectiveness of our proposed methods.

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

Signal space diversity (SSD) is a well-known power- and bandwidth-efficient diversity technique with constellation rotation and coordinate interleaving. This paper investigates issues of combining SSD with coded modulation systems (CMSs), e.g., bit-interleaved coded modulation (BICM) and its iterative version, BICM-ID. It demonstrates that the average mutual information (AMI) between the signal after rotated constellation mapping and the signal before or after the soft demapper varies with the rotation angle. A new criterion for determining the optimal rotation angle by maximizing such AMI is therefore proposed. Specific considerations of combining SSD with BICM (BICM-SSD) and BICM-ID (BICM-ID-SSD) are addressed. The demapper's extrinsic information transfer (EXIT) curve in traditional BICM-ID systems exhibits different slopes under different channels, which consequently prevents traditional BICM-ID systems from having simultaneously excellent performance under different channels. It is shown that such a different-slope problem can be simply mitigated by a proper use of SSD. Analysis and simulation show that the proposed BICM-ID-SSD systems hold a near-capacity performance under both additive white Gaussian noise (AWGN) and Rayleigh fading channels simultaneously.

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

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

Signal space diversity (SSD) is a well-known technique providing excellent performance gain over fading channels. This paper investigates the system design of bit-interleaved coded modulation with iterative demodulation and SSD (BICM-ID-SSD). It demonstrates that within SSD the average mutual information (AMI) between the signal after rotated constellation mapping and that before the soft demapper varies with the rotation angle. Therefore, a new criterion for searching the optimal rotation angle by maximizing such AMI is proposed. Based on this criterion it is shown that the optimal rotation angle is not relevant to the labeling, and 45-degree is found to be the optimal or near optimal rotation angle for square quadrature amplitude modulation (QAM) at low to moderate code rates. Furthermore, the procedure of BICM-ID-SSD system design could be divided into two independent steps: 1) determining the optimal rotation angle based on the above criterion, and 2) choosing well-fitted labeling and outer channel code with the aid of extrinsic information transfer (EXIT) charts. Analysis and simulation results show that the proposed system exhibits a near-capacity performance, meanwhile being robust over both additive white Gaussian noise (AWGN) and Rayleigh fading channels.

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.

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

To improve the system capacity of conventional orthogonal frequency division multiplexing (OFDM) systems, the layered modulation OFDM scheme is proposed. The modulated OFDM symbols are separated into two layers as differential modulated symbols and coherent symbols. The differential symbols are used as pilots, which are combined with the coherent modulated data and sent out by the transmitter. At the receiver side, the data in pilot position will be recovered after the differential demodulation. The recovered differential symbols will be used for channel estimation to demodulate the coherent symbols. Furthermore, the differential modulated data can employ low reception threshold and low complexity demodulator, which will facilitate the usage in mobile TV transmission. Simulations show the two layers' reception can be compatible with each other.

Cochannel interference has been the bottleneck to the capacity of cellular network. To solve this issue, a virtual multiple-input multiple-output (MIMO) system can be constructed for the uplink OFDMA cellular system. However, if multiple cochannel MIMO clusters are used, they will still interfere with each other. Capacity of this virtual MIMO system in cellular networks with cochannel interference is derived and simulated. The influence of some parameters to the system capacity are studied, including path loss component, transmit power, the radius of cell and the number of antennas in base stations. Simulation results show that if only the first 1 neighbor layer of cochannel cells are considered, the virtual MIMO capacity will be greatly larger than that of the single-input single-output (SISO) system with universal frequency reuse. However, the cochannel interference can greatly reduce the virtual MIMO capacity in the uplink cellular system compared with the interference free environment. Thus interference cancellation techniques are required to be developed in the future.

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.

While a significant amount of work has been carried out on digital receivers to support flexible and adaptive high data rate processing for multiple systems, small interest has been shown on the RF part and especially the advantages of using a broadband RF front end. This paper presents a challenge raised in the design of a single flexible multimode and multiband RF receiver. It proposes a new direct down conversion technique and its advantages using six-port techniques. A study of six-port based direct down conversion receiver is first presented. Then the functionality of this direct conversion receiver concept is then verified for a multi-carrier system based on HIPERLAN/2 parameters and a single-carrier system based on UMTS-FDD parameters. This new direct down conversion receiver technique is suitable for multi-system platforms and software defined radios.