The performance analysis of maritime communication networks is frequently constrained by the widespread yet unrealistic assumption of a uniform spatial distribution of vessels. This paper presents a unified stochastic geometry framework that integrates high-fidelity physical channel models with nonhomogeneous vessel topologies to provide a more accurate analytical foundation. We first systematically demonstrate that the macroscopic geographical distribution of vessels is a dominant factor governing overall network performance, with significant performance variations observed across different deployment scenarios. Subsequently, to analyze the spatial origins of this performance, we introduce the coverage probability density function (Cpdf) as a novel metric. The Cpdf enables a finegrained spatial decomposition of the total coverage probability, thereby identifying key geographical areas that contribute most to network performance and quantifying the precise spatial impact of physical phenomena, such as multipath fading nulls. Our results affirm the primacy of realistic spatial modeling and provide a new analytical tool for the design and optimization of next-generation maritime networks.

To ensure future high-quality and reliable massive communication during 6G uplink transmissions, we propose a conditional diffusion model-driven massive MIMO detector, which can iteratively estimate channels and detect data for the uplink multiuser access. This approach utilizes a generative diffusion model to learn the score function of the joint posterior by integrating the prior distribution with the likelihood derived from the transmission model. The prior distribution is obtained either by learning from channel statistics or through analytical derivation from the symbol constellation. By employing noise matching initialization and an asynchronous annealed Langevin dynamics (ALD) sampling scheme, the receiver alternates efficiently between score-based channel estimation and data detection, thus avoiding traps of local minima. Simulation results demonstrate that this iterative diffusion process outperforms Bayesian-based and existing synchronous ALD channel estimation and data detection schemes in multiuser uplink scenarios.

This paper addresses the challenge of energy-constrained maritime monitoring networks by proposing an unmanned aerial vehicle (UAV)-enabled integrated sensing, communication, powering and backhaul transmission scheme with a tailored time-division duplex frame structure. Within each time slot, the UAV sequentially implements sensing, wireless charging and uplink receiving with buoys, and lastly forwards part of collected data to the central ship via backhaul links. Considering the tight coupling among these functions, we jointly optimize time allocation, UAV trajectory, UAV-buoy association, and power scheduling to maximize the performance of data collection, with the practical consideration of sea clutter effects during UAV sensing. A novel optimization framework combining alternating optimization, quadratic transform and augmented first-order Taylor approximation is developed, which demonstrates good convergence behavior and robustness. Simulation results show that under sensing quality-of-service constraint, buoys are able to achieve an average data rate over 22 bps/Hz using around 2 mW harvested power per active time slot, validating the scheme’s effectiveness for open-sea monitoring. Additionally, it is found that under the influence of sea clutters, the optimal UAV trajectory always keeps a certain distance with buoys to strike a balance between sensing and other multi-functional transmissions.

Multiple-input multiple-output (MIMO) technology in conjunction with orthogonal frequency division multiplexing (OFDM) transmission is widely adopted in fifth-generation mobile networks to support multiple users. However, in these mobile communication systems, high power amplifiers (HPAs) at user terminals’ transmitters are driven into their saturation regions, which makes the multiuser frequency-selective MIMO-OFDM uplink channel nonlinear and renders the standard multiuser detection (MUD) at the base station (BS) ineffective. In this paper machine learning is employed to combat the distortions in the uplink of this multiuser frequency-selective MIMO-OFDM communication system. More specifically, a powerful complex-valued B-spline neural network (BSNN) based design is developed to simultaneously identify the system’s channel impulse response (CIR) matrix and the BSNN model for the nonlinear transmitters’ HPA together with the BSNN inversion for the nonlinear HPA at transmitters. This enables the BS to effectively implement MUD by utilizing the estimated MIMO-OFDM CIR matrix as well as to compensate for the transmitter HPAs’ saturation distortions using the estimated BSNN inversion. A simulation study is included to evaluate the effectiveness of this novel BSNN assisted design in combating multiuser and dispersive channel interference as well as nonlinear distortions for multiuser MIMO-OFDM nonlinear uplink.

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.

Massive random access is an important technology for achieving ultra-massive connectivity in next-generation wireless communication systems. It aims to address key challenges during the initial access phase, including active user detection (AUD), channel estimation (CE), and data detection (DD). This paper examines massive access in massive multiple-input multiple-output (MIMO) systems, where deep learning is used to tackle the challenging AUD, CE, and DD functions. First, we introduce a Transformer-AUD scheme tailored for variable pilot-length access. This approach integrates pilot length information and a spatial correlation module into a Transformer-based detector, enabling a single model to generalize across various pilot lengths and antenna numbers. Next, we propose a generative diffusion model (GDM)-driven iterative CE and DD framework. The GDM employs a score function to capture the posterior distributions of massive MIMO channels and data symbols. Part of the score function is learned from the channel dataset via neural networks, while the remaining score component is derived in a closed form by applying the symbol prior constellation distribution and known transmission model. Utilizing these posterior scores, we design an asynchronous alternating CE and DD framework that employs a predictor-corrector sampling technique to iteratively generate channel estimation and data detection results during the reverse diffusion process. Simulation results demonstrate that our proposed approaches significantly outperform baseline methods with respect to AUD, CE, and DD.

Massive multi-input multi-output (MIMO) signal processing algorithms heavily rely on high-dimension matrix operations, which impose excessively high computational complexity. Moreover, in the post-Moore era, the performance of the classical von Neumann computing architecture is facing severe limitations. The in-memory computing (IMC) technique holds the potential to break the memory wall and enhance the circuit’s energy efficiency. In this paper, we present an memristor crossbar based IMC circuit design for performing the classical least square (LS) channel estimation with high computation parallelism. Simulation results demonstrate that even when considering the writing and reading errors, the mean square error (MSE) of the proposed circuit with 7-bit memristor is almost the same as that achieved by the digital computer. Moreover, the proposed circuit achieves the same level of computing performance as the NVIDIA RTX 6000 Ada Generation, but with about 1/18 times as low computation time and about 25 times as high energy efficiency, as this benchmark commercial processor.

The integrated sensing and communication (ISAC) technology has emerged as an enabler that promises to transform the traditional mobile communication networks into the multifunctional perceptive mobile networks (PMNs), where precise positioning and motion state estimation of network nodes can be achieved relying on wireless communications within the network itself. However, in a practical PMN, multiple types of individually estimated parameters corresponding to multiple sensing targets are not naturally associated with each specific target, which may cause severe obstacles to subsequent signal processing tasks, such as positioning and motion state estimation. To address this challenge, a high-performance low-complexity sensing parameter association algorithm is proposed in this paper. Different from previous works, we first develop a novel spatial filter by exploiting the convolutional beamspace based beamformer to separate paths with different directions of arrival (DOA), and then leverage a low-complexity correlation-based algorithm to associate the DOA estimates with the corresponding paired range-velocity estimates. Extensive simulation results are provided to validate the superior performance of the proposed parameter association algorithm over state-of-the-art schemes.

The growing demand for reliable and extensive connectivity has made low Earth orbit (LEO) satellites aided Internet of Things (IoT) systems a critical area of research. However, current theoretical studies on IoT-over-LEO satellite systems often rely on unrealistic assumptions, such as infinite terrestrial areas and omnidirectional satellite coverage, leaving significant gaps in theoretical analysis for more realistic operational constraints. These constraints involve finite terrestrial area, limited satellite coverage, Earth curvature effect, integral uplink and downlink analysis, and link-dependent interference. To address these gaps, this paper proposes a novel stochastic geometry based model to rigorously analyze the performance of IoT-over-LEO satellite systems. By adopting a binomial point process (BPP) instead of the conventional Poisson point process (PPP), our model accurately characterizes the geographical distribution of a fixed number of IoT devices in a finite terrestrial region. This modeling framework enables the derivation of distance distribution functions for both the links from the terrestrial IoT devices to the satellites (T-S) and from the satellites to the Earth station (S-ES), while also accounting for limited satellite coverage and Earth curvature effects. To realistically represent channel conditions, the Nakagami fading model is employed for the T-S links to characterize diverse small-scale fading environments, while the shadowed-Rician fading model is used for the S-ES links to capture the combined effects of shadowing and dominant line-of-sight paths. Furthermore, the analysis incorporates uplink and downlink interference, ensuring a comprehensive evaluation of system performance. The accuracy and effectiveness of our theoretical framework are validated through extensive Monte Carlo simulations. These results provide insights into key performance metrics, such as coverage probability and average ergodic rate, for both individual links and the overall system. Our study also offers an important analytical tool for optimizing the design and performance of IoT-over-LEO satellite systems with the operational constraints that are more realistic.

The ever-evolving Internet of Things (IoT) has ushered in a new era of intelligent manufacturing across multiple industries. However, the security and privacy of real-time data transmitted over the public channel of the Industrial IoT (IIoT) remain formidable challenges. Existing lightweight protocols often omit one or more critical security features, such as anonymity and untraceability, and are susceptible to threats like desynchronization attacks. Additionally, they struggle to achieve an optimal balance between robust security and performance efficiency. To bridge these gaps, we introduce a new lightweight key agreement security scheme that guarantees secure access to the IIoT-enabled flexible manufacturing system (FMS). The strength of our scheme lies in its utilization of the authenticated encryption with associative data (AEAD) primitive, AEGIS, along with hash functions and physical unclonable functions, which secure the IIoT ecosystem. Additionally, our scheme offers flexibility in the form of the addition of new machines, password updates, and revocation in cases of theft or loss. A comprehensive security analysis demonstrates the efficacy of the proposed scheme in thwarting various attacks. The formal analysis, based on the Real-or-Random (RoR) model, ensures session key indistinguishability, while the informal analysis highlights its resilience against known attacks. The comparative assessment demonstrates that the proposed scheme consistently outperforms the benchmark schemes across multiple dimensions, including security and functionality features, computational and communication overheads, and runtime efficiency. Specifically, the proposed scheme achieves peak performance enhancements of 77.55%, 44.73%, and 69.6% in computational overhead, runtime overhead, and communication overhead, respectively, underscoring its substantial performance advantages.

With line-of-sight mode deployment and fast response, unmanned aerial vehicle (UAV), equipped with the cutting-edge integrated sensing and communication (ISAC) technique, is poised to deliver high-quality communication and sensing services in maritime emergency scenarios. In practice, however, the real-time transmission of ISAC signals at the UAV side cannot be realized unless the reliable wireless fronthaul link between the terrestrial base station and UAV are available. This paper proposes a multicarrier-division duplex based joint fronthaul-access scheme, where mutually orthogonal subcarrier sets are leveraged to simultaneously support four types of fronthaul/access transmissions. In order to maximize the end-to-end communication rate while maintaining an adequate sensing quality-of-service (QoS) in such a complex scheme, the UAV trajectory, subcarrier assignment and power allocation are jointly optimized. The overall optimization process is designed in two stages. As the emergency area is usually far away from the coast, the optimal initial operating position for the UAV is first found. Once the UAV passes the initial operating position, the UAV’s trajectory and resource allocation are optimized during the mission period to maximize the end-to-end communication rate under the constraint of minimum sensing QoS. Simulation results demonstrate the effectiveness of the proposed scheme in dealing with the joint fronthaul-access optimization problem in maritime ISAC networks, offering the advantages over benchmark schemes.

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 this paper, we propose a novel translation model, UniTranslator, for transforming representations between visually distinct domains under conditions of limited training data and significant visual differences. The main idea behind our approach is leveraging the domain-neutral capabilities of CLIP as a bridging mechanism, while utilizing a separate module to extract abstract, domain-agnostic semantics from the embeddings of both the source and target realms. Fusing these abstract semantics with target-specific semantics results in a transformed embedding within the CLIP space. To bridge the gap between the disparate worlds of CLIP and StyleGAN, we introduce a new non-linear mapper, the CLIP2P mapper. Utilizing CLIP embeddings, this module is tailored to approximate the latent distribution in the StyleGAN's latent space, effectively acting as a connector between these two spaces. The proposed UniTranslator is versatile and capable of performing various tasks, including style mixing, stylization, and translations, even in visually challenging scenarios across different visual domains. Notably, UniTranslator generates high-quality translations that showcase domain relevance, diversity, and improved image quality. UniTranslator surpasses the performance of existing general-purpose models and performs well against specialized models in representative tasks.

In harsh environments, such as mountainous terrain, dense vegetation and urban landscapes, a single type of unmanned aerial vehicles (UAVs) may encounter challenges like flight restrictions, difficulty in task execution or increased risk. Therefore, employing multiple types of UAVs to collaborate along with satellite assistance, becomes essential in such scenarios. In this context, we present a stochastic geometry based approach for modeling the heterogeneous non-terrestrial networks (NTNs) by using the classical binomial point process and introducing a novel point process, called Matérn hard-core cluster process (MHCCP) which possesses both properties of exclusivity and clustering. Through simulations, MHCCP has been validated as a more suitable model for UAV groups composed of multiple clusters, compared with traditional point processes such as Poisson point process, binomial point process, and Poisson cluster process. This is because MHCCP ensures inter-cluster repulsion while effectively capturing the clustered distribution observed in practical scenarios. Then, taking into account the influence of terrain shadows on the aerial-satellite links in low-altitude harsh environments, we derive closed-form expressions of the outage probability and average ergodic rate for the aerial-to-satellite uplink of heterogeneous NTNs. Unlike existing studies, our analysis adopts an advanced system configuration that combines beamforming with frequency division multiple access and incorporates a shadowed-Rician fading model to accurately capture signal fading under complex environmental conditions. Furthermore, we investigate link performance in the presence of co-channel interference. Monte Carlo simulations validate that the derived closed-form solutions of the outage probability and the average ergodic rate provide a precise quantitative tool for evaluating the reliability and transmission efficiency of the aerial-satellite links, offering deeper insights into system performance in complex environments.

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.

Learning-based Multi-View Stereo (MVS) methods, typically reliant on cascaded cost volume formulations, perform well on small-scale scenes. However, as the depth range of captured images becomes broader and more varied, the coarse-to-fine depth sampling process, which depends solely on feature matching, is increasingly prone to local optima. Despite recent advancements in feature representation, depth sampling patterns, and cost aggregation techniques, challenges related to model generalization and computational efficiency persist. In this paper, we propose SR-MVSNet, a novel framework that integrates multi-view feature matching and RGB-D cross-modal structural consistency learning to achieve high-quality 3D reconstruction. Our approach begins with the construction of Low-Resolution (LR) cost volumes for initial LR depth estimation, which are then enhanced to full-resolution via a tailored uncertainty-aware guided depth super-resolution module. To ensure cross-view consistency, the depth maps undergo further refinement through multi-view feature matching. By avoiding high-resolution cost volume processing, our framework improves depth estimation robustness and efficiency. Additionally, we introduce an iterative depth fusion post-processing strategy during inference to improve reconstruction in ambiguous matching regions, a critical challenge for MVS methods. Experiments show that our method achieves top-3 performance on the DTU and Tanks & Temples datasets and ranks first on the ETH3D dataset. Furthermore, it uses significantly fewer GPU resources than most high performing methods, offering a favorable trade-off between reconstruction quality and computational efficiency.

Despite recent advances in stereo matching, the extension to intricate underwater settings remains unexplored, primarily owing to: 1) the reduced visibility, low contrast, and other adverse effects of underwater images; 2) the difficulty in obtaining ground truth data for training deep learning models, i.e. simultaneously capturing an image and estimating its corresponding pixel-wise depth information in underwater environments. To enable further advance in underwater stereo matching, we introduce a large synthetic dataset called UWStereo. Our dataset includes 29,568 synthetic stereo image pairs with dense and accurate disparity annotations for left view. We design four distinct underwater scenes filled with diverse objects such as corals, ships and robots. We also induce additional variations in camera model, lighting, and environmental effects. In comparison with existing underwater datasets, UWStereo is superior in terms of scale, variation, annotation, and photo-realistic image quality. To substantiate the efficacy of the UWStereo dataset, we undertake a comprehensive evaluation compared with eleven state-of-the-art algorithms as benchmarks. The results indicate that current models still struggle to generalize to new domains. Hence, we design a new strategy that learns to reconstruct cross domain masked images before stereo matching training and integrate a cross view attention enhancement module that aggregates long-range content information to enhance the generalization ability.

Source-Free unsupervised Domain Adaptation (SFDA) aims to classify target samples by only accessing a pre-trained source model and unlabelled target samples. Since no source data is available, transferring the knowledge from the source domain to the target domain is challenging. Existing methods normally exploit the pair-wise relation among target samples and attempt to discover their correlations by clustering these samples based on semantic features. The drawbacks of these methods include: 1) the pair-wise relation is limited to exposing the underlying correlations of two more samples, hindering the exploration of the structural information embedded in the target domain; and 2) the clustering process only relies on the semantic feature, while overlooking the critical effect of domain shift, i.e., the distribution differences between the source and target domains. To address these issues, we propose a new SFDA method that exploits the high-order neighborhood relation and explicitly takes the domain shift effect into account. Specifically, we formulate the SFDA as a hypergraph learning problem and construct hyperedges to explore the deep structural and context information among multiple samples. Moreover, we integrate a self-loop strategy into the constructed hypergraph to elegantly introduce the domain uncertainty of each sample. By clustering these samples based on hyperedges, both the semantic feature and domain shift effects are considered. We then describe an adaptive relation-based objective to tune the model with soft attention levels for all samples. Extensive experiments are conducted on Office-31, Office-Home, VisDA, DomainNet-126 and PointDA-10 datasets. The results demonstrate the superiority of our method over state-of-the-art counterparts. Our code is avaliable at https://github.com/OUC-POVA/HG-SFDA.

Encrypted sharing of Internet of Medical Things (IoMT) data is essential for facilitating collaboration, safeguarding patient privacy, and advancing clinical research. However, existing encryption schemes face numerous challenges in multi-user environments. Traditional proxy re-encryption requires exclusive ciphertext for each user, which is evidently unsuitable for IoMT's multi-user scenarios. Meanwhile, attribute-based encryption provides flexible data access control, but its complex computations and high resource demands limit its use in large-scale IoMT environments. Additionally, challenges like single-point failure and redundant backups emerge in ciphertext storage. To address these challenges, we propose a dual-cryptography mechanism integrating enhanced proxy re-encryption and attribute-based encryption. Our scheme enables unified ciphertext access for authorized users while applying attribute encryption exclusively to small data keys. To mitigate potential data loss from storage server failures, we propose a decentralized ciphertext storage and recovery mechanism with verifiable secret sharing. Furthermore, we implement decentralized ciphertext storage using verifiable secret sharing, ensuring recoverability from server failures. Formal analysis proves confidentiality under the random oracle model. Experimental results demonstrate high security strength, computational efficiency, and robustness. The solution prevents single-point failures, resists collusion attacks, and maintains traceability through blockchain-integrated audit trails.

Future mobile wireless networks will catalyze substantial demand for precise distributed cooperative positioning (DCP), especially when the global navigation satellite systems are unavailable. However, conventional message passing based DCP methods may suffer considerable performance degradation due to message approximation and sparsity/mobility of nodes. In this paper, we first present a high-accuracy parametric message approximation method, which achieves closed-form representations of all types of messages involved and reduces the computational complexity of message passing procedures. Using these representations, we propose a model- and data-driven hybrid inference approach, dubbed graph neural network enhanced spatio-temporal message passing (GNN-STMP), which fine-tunes parametric messages passed on factor graph and obtains more accuratea posterioridistribution of nodes’ positions by exploiting GNN-generated messages. Furthermore, we develop a universal framework for the parametric message passing based DCP problem, by integrating GNN-STMP with the extend Kalman filter based node’s state prediction and refinement. This framework significantly reduces the positioning ambiguity caused by insufficient spatial ranging measurements from neighbor nodes. Simulation results and analyses demonstrate that, compared with state-of-the-art methods, our proposed approaches achieve the best and near-best positioning accuracy when insufficient and sufficient spatial ranging measurements are available, respectively, while incurring modest computational complexity.

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.

The memristive crossbar array (MCA) has been successfully applied to accelerate matrix computations of signal detection in massive multiple-input multiple-output (MIMO) systems. However, the unique property of massive MIMO channel matrix makes the detection performance of existing MCA-based detectors sensitive to conductance deviations of memristive devices, and the conductance deviations are difficult to be avoided. In this paper, we propose an MCA-based detector circuit, which is robust to conductance deviations, to compute massive MIMO zero forcing and minimum mean-square error algorithms. The proposed detector circuit comprises an MCA-based matrix computing module, utilized for processing the small-scale fading coefficient matrix, and amplifier circuits based on operational amplifiers (OAs), utilized for processing the large-scale fading coefficient matrix. We investigate the impacts of the open-loop gain of OAs, conductance mapping scheme, and conductance deviation level on detection performance and demonstrate the performance superiority of the proposed detector circuit over the conventional MCA-based detector circuit. The energy efficiency of the proposed detector circuit surpasses that of a traditional digital processor by several tens to several hundreds of times.

The emerging analog matrix computing technology based on memristive crossbar array (MCA) constitutes a revolutionary new computational paradigm applicable to a wide range of domains. Despite the proven applicability of MCA for massive multiple-input multiple-output (MIMO) detection, existing schemes do not take into account the unique characteristics of massive MIMO channel matrix. This oversight makes their computational accuracy highly sensitive to conductance errors of memristive devices, which is unacceptable for massive MIMO receivers. In this paper, we propose an MCA-based circuit design for massive MIMO zero forcing and minimum mean-square error detectors. Unlike the existing MCA-based detectors, we decompose the channel matrix into the product of small-scale and large-scale fading coefficient matrices, thus employing an MCA-based matrix computing module and amplifier circuits to process the two matrices separately. We present two conductance mapping schemes which are crucial but have been overlooked in all prior studies on MCA-based detector circuits. The proposed detector circuit exhibits significantly superior performance to the conventional MCA-based detector circuit, while only incurring negligible additional power consumption. Our proposed detector circuit maintains its advantage in energy efficiency over traditional digital approach by tens to hundreds of times.

In harsh environments such as mountainous terrain, dense vegetation areas, or urban landscapes, a single type of unmanned aerial vehicles (UAVs) may encounter challenges like flight restrictions, difficulty in task execution, or increased risk. Therefore, employing multiple types of UAVs, along with satellite assistance, to collaborate becomes essential in such scenarios. In this context, we present a stochastic geometry based approach for modeling the heterogeneous non-terrestrial networks (NTNs) by using the classical nbinomial point process and introducing a novel point process, called Matérn hard-core cluster process (MHCCP). Our MHCCP possesses both the exclusivity and the clustering properties, thus it can better model the aircraft group composed of multiple clusters. Then, we derive closed-form expressions of the outage probability (OP) for the uplink (aerial-to-satellite) of heterogeneous NTNs. Unlike existing studies, our analysis relies on a more advanced system configuration, where the integration of beamforming and frequency division multiple access, and the shadowed-Rician (SR) fading model for interference power, are considered. The accuracy of our theoretical derivation is confirmed by Monte Carlo simulations. Our research offers fundamental insights into the system-level performance optimization of NTNs.

In this paper, we propose a stochastic geometry based innovative model to characterize the impact of the limited-size distribution region of terrestrial terminals in terrestrial-to-aerial networks by jointly using a binomial point process (BPP) and a type-II Matérn hard-core point process (MHCPP). Then, we analyze the relationship between the spatial distribution of the coverage areas of aerial nodes and the limited-size distribution region of terrestrial terminals, thereby deriving the distance distribution of the terrestrial-aerial (T-A) links. Furthermore, we consider the stochastic nature of the spatial distributions of terrestrial terminals and unmanned aerial vehicles (UAVs), and conduct a thorough analysis of the coverage probability of the T-A links under Nakagami fading. Finally, the accuracy of our theoretical derivations are confirmed by Monte Carlo simulations. Our research offers fundamental insights into the system-level performance optimization for the realistic terrestrial-to-aerial networks involving nomadic aerial base-stations and terrestrial terminals confined in a limited-size region.

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.

The envisioned sixth-generation (6G) of wireless communications is expected to give rise to the necessity of connecting very large quantities of heterogeneous wireless devices, which requires advanced system capabilities far beyond existing network architectures. In particular, such massive communication has been recognized as a prime driver that can empower the 6G vision of future ubiquitous connectivity, supporting Internet of Human-Machine-Things (IoHMT) for which massive access is critical. This article surveys the most recent advances toward massive access in both academic and industrial communities, focusing primarily on the promising compressive sensing (CS)-based grant-free massive access (GFMA) paradigm. We first specify the limitations of existing random access schemes and reveal that the practical implementation of massive communication relies on a dramatically different random access paradigm from the current ones mainly designed for human-centric communications. Then, a CS-based GFMA roadmap is presented, where the evolutions from single-antenna to large-scale antenna array-based base stations, from single-station to cooperative massive multiple-input-multiple-output (MIMO) systems, and from unsourced to sourced random access scenarios are detailed. Finally, we discuss key challenges and open issues to indicate potential future research directions in GFMA.

With the increasing demands from passengers for data-intensive services, millimeter-wave (mmWave) communication is considered as an effective technique to release the transmission pressure on high speed train (HST) networks. However, mmWave signals encounter severe losses when passing through the carriage, which decreases the quality of services on board. In this paper, we investigate an intelligent refracting surface (IRS)-assisted HST communication system. Herein, an IRS is deployed on the train window to dynamically reconfigure the propagation environment, and a hybrid time division multiple access-nonorthogonal multiple access scheme is leveraged for interference mitigation. We aim to maximize the overall throughput while taking into account the constraints imposed by base station beamforming, IRS discrete phase shifts and transmit power. To obtain a practical solution, we employ an alternating optimization method and propose a two-stage algorithm. In the first stage, the successive convex approximation method and branch and bound algorithm are leveraged for IRS phase shift design. In the second stage, the Lagrangian multiplier method is utilized for power allocation. Simulation results demonstrate the benefits of IRS adoption and power allocation for throughput improvement in mmWave HST networks.

The emerging drone swarms are capable of carrying out sophisticated tasks in support of demanding Internet-of-Things (IoT) applications by synergistically working together. However, the target area may be out of the coverage of the ground station and it may be impractical to deploy a large number of drones in the target area due to cost, electromagnetic interference and flight-safety regulations. By exploiting the innate agility and mobility of unmanned aerial vehicles (UAVs), we conceive a mobile relaying-assisted drone swarm network architecture, which is capable of extending the coverage of the ground station and enhancing the effective end-to-end throughput. Explicitly, a swarm of drones forms a data-collecting drone swarm (DCDS) designed for sensing and collecting data with the aid of their mounted cameras and/or sensors, and a powerful relay-UAV (RUAV) acts as a mobile relay for conveying data between the DCDS and a ground station (GS). Given a time period, in order to maximize the data delivered whilst minimizing the delay imposed, we harness an -multiple objective genetic algorithm (-MOGA) assisted Pareto-optimization scheme. Our simulation results demonstrate that the proposed mobile relaying is capable of delivering more data. As specific examples investigated in our simulations, our mobile relaying-assisted drone swarm network is capable of delivering 45.38% more data than the benchmark solutions, when a stationary relay is available, and it is capable of delivering 26.86% more data than the benchmark solutions when no stationary relay is available.

Cooperative satellite-aerial-terrestrial networks (CSATNs), where unmanned aerial vehicles (UAVs) are utilized as nomadic aerial relays (A), are highly valuable for many important applications, such as post-disaster urban reconstruction. In this scenario, direct communication between terrestrial terminals (T) and satellites (S) is often unavailable due to poor propagation conditions for satellite signals, and users tend to congregate in regions of finite size. There is a current dearth in the open literature regarding the uplink performance analysis of CSATN operating under the above constraints, and the few contributions on the uplink model terrestrial terminals by a Poisson point process (PPP) relying on the unrealistic assumption of an infinite area. This paper aims to fill the above research gap. First, we propose a stochastic geometry based innovative model to characterize the impact of the finite-size distribution region of terrestrial terminals in the CSATN by jointly using a binomial point process (BPP) and a type-II Matérn hard-core point process (MHCPP). Then, we analyze the relationship between the spatial distribution of the coverage areas of aerial nodes and the finite-size distribution region of terrestrial terminals, thereby deriving the distance distribution of the T-A links. Furthermore, we consider the stochastic nature of the spatial distributions of terrestrial terminals and UAVs, and conduct a thorough analysis of the coverage probability and average ergodic rate of the T-A links under Nakagami fading and the A-S links under shadowed-Rician fading. Finally, the accuracy of our theoretical derivations are confirmed by Monte Carlo simulations. Our research offers fundamental insights into the system-level performance optimization for the realistic CSATNs involving nomadic aerial relays and terrestrial terminals confined in a finite-size region.

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.

Collaborative resource scheduling between edge terminals and cloud centers is regarded as a promising means of effectively completing computing tasks and enhancing quality of service. In this paper, to further improve the achievable performance, the edge cloud resource scheduling (ECRS) problem is transformed into a multi-objective Markov decision process based on task dependency and features extraction. A multi-objective ECRS model is proposed by considering the task completion time, cost, energy consumption and system reliability as the four objectives. Furthermore, a hybrid approach based on deep reinforcement learning (DRL) and multi-objective optimization are employed in our work. Specifically, DRL preprocesses the workflow, and a multi-objective optimization method strives to find the Pareto-optimal workflow scheduling decision. Various experiments are performed on three real data sets with different numbers of tasks. The results obtained demonstrate that the proposed hybrid DRL and multi-objective optimization design outperforms existing design approaches.

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.

To glean the benefits offered by massive multi-input multi-output (MIMO) systems, channel state information must be accurately acquired. Despite the high accuracy, the computational complexity of classical linear minimum mean squared error (MMSE) estimator becomes prohibitively high in the context of massive MIMO, while the other low-complexity methods degrade the estimation accuracy seriously. In this paper, we develop a novel rank-1 subspace channel estimator to approximate the maximum likelihood (ML) estimator, which outperforms the linear MMSE estimator, but incurs a surprisingly low computational complexity. Our method first acquires the highly accurate angle-of-arrival (AoA) information via a constructed space-embedding matrix and the rank-1 subspace method. Then, it adopts thepost-receptionbeamforming to acquire the unbiased estimate of channel gains. Furthermore, a fast method is designed to implement our new estimator. Theoretical analysis shows that the extra gain achieved by our method over the linear MMSE estimator grows according to the rule of O(log10M), while its computational complexity islinearlyscalable to the number of antennasM. Numerical simulations also validate the theoretical results. Our new method substantially extends the accuracy-complexity region and constitutes a promising channel estimation solution to the emerging massive MIMO communications.

Deep networks have made remarkable progress in Multi-View Stereo (MVS) task in recent years. However, the problem of finding accurate correspondences across different views under ill-posed matching situations remains unresolved and crucial. To address this issue, this paper proposes a Geometry-enhanced Attentive Multi-View Stereo (GA-MVS) network, which can access multi-view consistent feature representation and achieve accurate depth estimation in challenging situations. Specifically, we propose a geometry-enhanced feature extractor to explore illumination-invariant geometric features and incorporate them with common texture features to improve matching accuracy when dealing with view-dependent photometric effects, such as shadow and specularity. Then, we design a novel attentive learning framework to explore per-pixel adaptive supervision, effectively improving the depth estimation performance of textureless regions. The experimental results on the DTU and Tanks & Temples benchmarks demonstrate that our method achieves state-of-the-art results compared to other advanced MVS models.

Although attribute-based signcryption (ABSC) offers a promising technology to ensure the security of IoT data sharing, it faces a two-fold challenge in practical implementation, namely, the linearly increasing computation and communication costs and the heavy load of single authority based key management. To this end, we propose a Secure and Lightweight Multi-authority ABSC scheme called SLIM in this paper. The signcryption and de-signcryption costs of devices are reduced to a small constant by offloading most of the computation to the edge server. To minimize communication and storage costs, a short and constant-size ciphertext is designed. Moreover, we adopt a hierarchical multi-authority architecture, setting up multiple attribute authorities that manage keys independently to prevent the bottleneck. Rigorous security analysis proves that the SLIM scheme can resist adaptive chosen ciphertext attacks and adaptive chosen message attacks under the standard model. Simulation experiments demonstrate the correctness of our theoretical derivations and the cost reduction of the SLIM scheme in computation, communication and storage.

We propose a distributed artificial noise-assisted precoding scheme for secure communications over wiretap multi-input multi-output (MIMO) interference channels, where K legitimate transmitter-receiver pairs communicate in the presence of a sophisticated eavesdropper having more receive-antennas than the legitimate user. Realistic constraints are considered by imposing statistical error bounds for the channel state information of both the eavesdropping and interference channels. Based on the asynchronous distributed pricing model, the proposed scheme maximizes the total utility of all the users, where each user’s utility function is defined as the secrecy rate minus the interference cost imposed on other users. Using the weighted minimum mean square error, Schur complement and sign-definiteness techniques, the original non-concave optimization problem is approximated with high accuracy as a quasi-concave problem, which can be solved by the alternating convex search method. Simulation results consolidate our theoretical analysis and show that the proposed scheme outperforms the artificial noise-assisted interference alignment and minimum total mean-square error-based schemes.

Recent DeepFake detection methods have shown excellent performance on public datasets but are significantly degraded on new forgeries. Solving this problem is important, as new forgeries emerge daily with the continuously evolving generative techniques. Many efforts have been made for this issue by seeking the commonly existing traces empirically on data level. In this paper, we rethink this problem and propose a new solution from the unsupervised domain adaptation perspective. Our solution, called DomainForensics, aims to transfer the forgery knowledge from known forgeries (fully labeled source domain) to new forgeries (label-free target domain). Unlike recent efforts, our solution does not focus on data view but on learning strategies of DeepFake detectors to capture the knowledge of new forgeries through the alignment of domain discrepancies. In particular, unlike the general domain adaptation methods which consider the knowledge transfer in the semantic class category, thus having limited application, our approach captures the subtle forgery traces. We describe a new bi-directional adaptation strategy dedicated to capturing the forgery knowledge across domains. Specifically, our strategy considers both forward and backward adaptation, to transfer the forgery knowledge from the source domain to the target domain in forward adaptation and then reverse the adaptation from the target domain to the source domain in backward adaptation. In forward adaptation, we perform supervised training for the DeepFake detector in the source domain and jointly employ adversarial feature adaptation to transfer the ability to detect manipulated faces from known forgeries to new forgeries. In backward adaptation, we further improve the knowledge transfer by coupling adversarial adaptation with self-distillation on new forgeries. This enables the detector to expose new forgery features from unlabeled data and avoid forgetting the known knowledge of known forgery. Extensive experiments demonstrate that our method is surprisingly effective in exposing new forgeries, and can be plug-and-play on other DeepFake detection architectures.

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.

As an emerging learning paradigm, lifelong learning solves multiple consecutive tasks based upon previously accumulated knowledge. When facing with a new task, existing lifelong learning approaches need both input and desired output data to construct task models before knowledge transfer can succeed. However, labeling each task requires extensive labors and time, which can be prohibitive for real-world lifelong regression problems. To reduce this burden, we propose to incorporate unsupervised feature into lifelong regression via coupled dictionary learning, enabling to learn new tasks without target output data. Specifically, the input data for each task is encoded as unsupervised feature while both input and output data are used to construct task predictor. The unsupervised feature is linked with task predictor through two dictionaries that are coupled by a joint sparse representation. Because of the learned coupling between the two spaces, the task predictor for the new coming task can be recovered given only the input data. We further incorporate active task selection into this framework, enabling actively choosing tasks to learn in a task-efficient manner. Three case studies are used to evaluate the effectiveness of our method, in comparison with existing lifelong learning approaches. Results show that our method is able to accurately predict new tasks through unsupervised transfer, eliminating the need to label tasks before constructing the predictor.

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.

With the widespread popularity of mobile terminals in the Internet of Things (IoT), the demand for cross-domain access of mobile terminals between different regions has also increased significantly. The nature of wireless communication media makes mobile terminals vulnerable to security threats in cross-domain access. Identity authentication is a prerequisite for secure data transmission in the cross-domain, and it is also the first step to guarantee the credibility of data sources. Most existing authentication schemes are based on bilinear pairing or public-key encryption and decryption with high computation overhead, which are not suitable for the resource-limited mobile IoT terminals. Moreover, these schemes have some security drawbacks and cannot meet the security requirements of cross-domain access. In this article, we propose a lightweight cross-domain mutual identity authentication (LCDMA) for the mobile IoT environment. LCDMA uses a symmetric polynomial instead of high-complexity bilinear pairing in the traditional schemes. We theoretically analyze the security performance under the random oracle model. Our results show that LCDMA not only resists common attacks but also preserves secure traceability while guaranteeing anonymity. Performance evaluation further demonstrates that our scheme has better performance in terms of computation and communication overhead, compared with other existing representative schemes.

Low altitude platform (LAP) unmanned aerial vehicles (UAVs), also called drones, are currently being exploited by Edge computing (EC) systems to execute complex resource-hungry use cases, such as virtual reality, smart cities, autonomous vehicles, etc., by attaching portable edge devices on them. However, a typical drone has limited flight time, coupled with the resource-constrained attached edge device, which can jeopardize aerial computing missions if they are not holistically taking into consideration. Moreover, the fundamental challenge is how to co-schedule multi-drone among multi-location where EC services are needed, such that drones are scheduled to maximize the utility from the activities while meeting computing resource and flight time constraints. Therefore, for a given fleet of drones and tasks across disjointed target locations in a city, we derive a machine learning (ML) linear regression model that estimates these tasks resource requirement and execution time. Leveraging this estimation values, we jointly consider each drone’s flight time availability and its attached edge device resource capacity, and formulate a novel Multi-Location Capacitated Mission Scheduling Problem (MLCMSP) that selects suitable drones and co-schedules their flight routes with the least total distance to visit and execute tasks at the target locations. Then, we show that faster scheduling and execution of complex tasks at each location, while considering the inter-task dependencies is important to achieve effective solution for our MLCMSP. Hence, we further propose EdgeDrones, a variant bin-packing optimization approach through gang-scheduling of inter-dependent tasks that co-schedules and co-locates tasks tightly so as to achieve faster execution time, as well as to fully utilize available resources. Extensive experiments on Alibaba cluster trace with information on task dependencies (about 12,207,703 dependencies) show that EdgeDrones achieves up to 73% higher resource utilization, up to 17.6 times faster executions, and up to 2.87 times faster flight travel time compared to the baseline approaches.

Internet of Vehicles (IoV) enables a wealth of modern vehicular applications, such as pedestrian detection, real-time video analytics, etc., that can help to improve traffic efficiency and driving safety. However, these applications impose significant resource demands on the in-vehicle resource-constrained Edge Computing (EC) device installation. In this article, we study the problem of resource-aware offloading of these computation-intensive applications to the Closest roadside units (RSUs) or telecommunication base stations (BSs), where on-site EC devices with larger resource capacities are deployed, and mobility of vehicles are considered at the same time. Specifically, we propose an Integrated EC framework, which can keep edge resources running across various in-vehicles, RSUs and BSs in a single pool, such that these resources can be holistically monitored from a single control plane (CP). Through the CP, individual in-vehicle, RSU or BS edge resource availability can be obtained, hence applications can be offloaded concerning their resource demands. This approach can avoid execution delays due to resource unavailability or insufficient resource availability at any EC deployment. This research further extends the state-of-the-art by providing intelligent multi-task scheduling, by considering both task dependencies and heterogeneous resource demands at the same time. To achieve this, we propose FedEdge, a variant Bin-Packing optimization approach through Gang-Scheduling of multi-dependent tasks that co-schedules and co-locates multi-task tightly on nodes to fully utilize available resources. Extensive experiments on real-world data trace from the recent Alibaba cluster trace, with information on task dependencies and resource demands, show the effectiveness, faster executions, and resource efficiency of our approach compared to the existing approaches.

Low earth orbit (LEO) satellite constellation-enabled communication networks are expected to be an important part of many Internet of Things (IoT) deployments due to their unique advantage of providing seamless global coverage. In this paper, we investigate the random access problem in massive multiple-input multiple-output-based LEO satellite systems, where the multi-satellite cooperative processing mechanism is considered. Specifically, at edge satellite nodes, we conceive a training sequence padded multi-carrier system to overcome the issue of imperfect synchronization, where the training sequence is utilized to detect the devices’ activity and estimate their channels. Considering the inherent sparsity of terrestrial-satellite links and the sporadic traffic feature of IoT terminals, we utilize the orthogonal approximate message passing-multiple measurement vector algorithm to estimate the delay coefficients and user terminal activity. To further utilize the structure of the receive array, a two-dimensional estimation of signal parameters via rotational invariance technique is performed for enhancing channel estimation. Finally, at the central server node, we propose a majority voting scheme to enhance activity detection by aggregating backhaul information from multiple satellites. Moreover, multi-satellite cooperative linear data detection and multi-satellite cooperative Bayesian dequantization data detection are proposed to cope with perfect and quantized backhaul, respectively. Simulation results verify the effectiveness of our proposed schemes in terms of channel estimation, activity detection, and data detection for quasi-synchronous random access in satellite systems.

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.

In many core problems of signal processing and wireless communications, Karush-Kuhn-Tucker (KKT) conditions based optimization plays a fundamental role. Hence we investigate the KKT conditions in the context of optimizing positive semidefinite matrix variables under nonconvex rank constraints. More explicitly, based on the properties of KKT conditions, we optimize a reconfigurable intelligent surface (RIS) aided multi-user multi-input multi-output (MU-MIMO) network. Specifically, we consider the capacity maximization and sum mean square error (MSE) minimization problems of both the RIS-aided MU-MIMO uplink (UL) and downlink (DL) under multiple weighted power constraints and rank constraints. As for the RIS-aided MU-MIMO UL, the optimal structures of the signal covariance matrices are derived based on the KKT conditions. Furthermore, an efficient procedure is designed for solving the capacity maximization and sum mean square error (MSE) minimization problems. Then the UL-DL dualities are exploited for solving the capacity maximization and MSE minimization problems of the RIS-aided MU-MIMO DL based on the results of the UL optimization. Hence in the proposed framework, the phase shifting matrix of the RIS is jointly optimized with the signal covariance matrices for both the UL and DL. Our simulation results demonstrate the performance advantages of the proposed framework.

The main challenge for industrial predictive models is how to effectively deal with big data from high-dimensional processes with nonstationary characteristics. Although deep networks, such as the stacked autoencoder (SAE), can learn useful features from massive data with multilevel architecture, it is difficult to adapt them online to track fast time-varying process dynamics. To integrate feature learning and online adaptation, this article proposes a deep cascade gradient radial basis function (GRBF) network for online modeling and prediction of nonlinear and nonstationary processes. The proposed deep learning method consists of three modules. First, a preliminary prediction result is generated by a GRBF weak predictor, which is further combined with raw input data for feature extraction. By incorporating the prior weak prediction information, deep output-relevant features are extracted using a SAE. Online prediction is finally produced upon the extracted features with a GRBF predictor, whose weights and structure are updated online to capture fast time-varying process characteristics. Three real-world industrial case studies demonstrate that the proposed deep cascade GRBF network outperforms existing state-of-the-art online modeling approaches as well as deep networks, in terms of both online prediction accuracy and computational complexity.

Multioutput regression of nonlinear and nonstationary data is largely understudied in both machine learning and control communities. This article develops an adaptive multioutput gradient radial basis function (MGRBF) tracker for online modeling of multioutput nonlinear and nonstationary processes. Specifically, a compact MGRBF network is first constructed with a new two-step training procedure to produce excellent predictive capacity. To improve its tracking ability in fast time-varying scenarios, an adaptive MGRBF (AMGRBF) tracker is proposed, which updates the MGRBF network structure online by replacing the worst performing node with a new node that automatically encodes the newly emerging system state and acts as a perfect local multioutput predictor for the current system state. Extensive experimental results confirm that the proposed AMGRBF tracker significantly outperforms existing state-of-the-art online multioutput regression methods as well as deep-learning-based models, in terms of adaptive modeling accuracy and online computational complexity.

The next-generation Internet of vehicles (IoVs) seamlessly connects humans, vehicles, roadside units (RSUs), and service platforms, to improve road safety, enhance transit efficiency, and deliver comfort while conserving the environment. Currently, numerous entities communicate in the IoVs environment via insecure public channels that are susceptible to a variety of security assaults and threats. To address these security challenges, we design an anonymous authenticated key exchange mechanism for the IoVs in smart transportation supported by blockchain, referred to as AAKE-BIVT. AAKE-BIVT securely transmits traffic information to a cluster head, before heading to a nearby RSU utilizing the established secret session keys via mutual authentication and key agreement. A cloud server (CS) then securely aggregates data from related RSUs and generates transactions. The CS combines the transactions into blocks in a peer-to-peer network of CSs, and the blocks are confirmed and added to the blockchain via a voting-based consensus method. By means of rigorous informal security studies and formal security analysis through the random oracle model, we reveal that the proposed AAKE-BIVT is resistant to a broad range of potential security assaults in the IoVs environment. Furthermore, a comparative study reveals that AAKE-BIVT outperforms existing state-of-the-art techniques, in terms of security and functionality while being more efficient in terms of communication and computation. Additionally, the blockchain simulation validates the implementation viability of our proposed AAKE-BIVT.

In-vehicle network security plays a vital role in ensuring the secure information transfer between vehicle and Internet. The existing research is still facing great difficulties in balancing the conflicting factors for the in-vehicle network security and hence to improve intrusion detection performance. To challenge this issue, we construct a many-objective intrusion detection model by including information entropy, accuracy, false positive rate and response time of anomaly detection as the four objectives, which represent the key factors influencing intrusion detection performance. We then design an improved intrusion detection algorithm based on many-objective optimization to optimize the detection model parameters. The designed algorithm has double evolutionary selections. Specifically, an improved differential evolutionary operator produces new offspring of the internal population, and a spherical pruning mechanism selects the excellent internal solutions to form the selected pool of the external archive. The second evolutionary selection then produces new offspring of the archive, and an archive selection mechanism of the external archive selects and stores the optimal solutions in the whole detection process. An experiment is performed using a real-world in-vehicle network data set to verify the performance of our proposed model and algorithm. Experimental results obtained demonstrate that our algorithm can respond quickly to attacks and achieve high entropy and detection accuracy as well as very low false positive rate with a good trade-off in the conflicting objective landscape.

With the development of wireless communication, people have put forward higher requirements for train-ground communications in the high-speed railway (HSR) scenarios. With the help of mobile relays (MRs) installed on the roof of the train, the application of Millimeter-Wave (mm-wave) communication which has rich spectrum resources to the train-ground communication system can realize high data rate, so as to meet users’ increasing demand for broad-band multimedia access. Also, full-duplex (FD) technology can theoretically double the spectral efficiency. In this paper, we formulate the user association and transmission scheduling problem in the mm-wave train-ground communication system with MR operating in the FD mode as a nonlinear programming problem. In order to maximize the system throughput and the number of users meeting quality of service (QoS) requirements, we propose an algorithm based on coalition game to solve the challenging NP-hard problem, and also prove the convergence and Nash-stable structure of the proposed algorithm. Extensive simulation results demonstrate that the proposed coalition game based algorithm can effectively improve the system throughput and meet the QoS requirements of as many users as possible, so that the communication system has a certain QoS awareness.

Sea subsurface temperature, an essential component of aquatic wildlife, underwater dynamics, and heat transfer with the sea surface, is affected by global warming in climate change. Existing research is commonly based on either physics-based numerical models or data-based models. Physical modeling and machine learning are traditionally considered as two unrelated fields for the sea subsurface temperature prediction task, with very different scientific paradigms (physics-driven and data-driven). However, we believe that both methods are complementary to each other. Physical modeling methods can offer the potential for extrapolation beyond observational conditions, while data-driven methods are flexible in adapting to data and are capable of detecting unexpected patterns. The combination of both approaches is very attractive and offers potential performance improvement. In this article, we propose a novel framework based on a generative adversarial network (GAN) combined with a numerical model to predict sea subsurface temperature. First, a GAN-based model is used to learn the simplified physics between the surface temperature and the target subsurface temperature in the numerical model. Then, observation data are used to calibrate the GAN-based model parameters to obtain a better prediction. We evaluate the proposed framework by predicting daily sea subsurface temperature in the South China Sea. Extensive experiments demonstrate the effectiveness of the proposed framework compared to existing state-of-the-art methods.

Integrated sensing and communication (ISAC) has opened up numerous game-changing opportunities for realizing future wireless systems. In this paper, we propose an ISAC processing framework relying on millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems. Specifically, we provide a compressed sampling (CS) perspective to facilitate ISAC processing, which can not only recover the high-dimensional channel state information or/and radar imaging information, but also significantly reduce pilot overhead. First, an energy-efficient widely spaced array (WSA) architecture is tailored for the radar receiver, which enhances the angular resolution of radar sensing at the cost of angular ambiguity. Then, we propose an ISAC frame structure for time-varying ISAC systems considering different timescales. The pilot waveforms are judiciously designed by taking into account both CS theories and hardware constraints induced by hybrid beamforming (HBF) architecture. Next, we design the dedicated dictionary for WSA that serves as a building block for formulating the ISAC processing as sparse signal recovery problems. The orthogonal matching pursuit with support refinement (OMP-SR) algorithm is proposed to effectively solve the problems in the existence of the angular ambiguity. We also provide a framework for estimating the Doppler frequencies during payload data transmission to guarantee communication performances. Simulation results demonstrate the good performances of both communications and radar sensing under the proposed ISAC framework.

Interactive single-image segmentation is ubiquitous in the scientific and commercial imaging software. Lightweight neural network is one practical and effective way to accomplish the single-image segmentation task. This work focuses on the single-image segmentation problem only with some seeds such as scribbles. Inspired by the dynamic receptive field in the human being’s visual system, we propose the Gaussian dynamic convolution (GDC) to fast and efficiently aggregate the contextual information for neural networks. The core idea is randomly selecting the spatial sampling area according to the Gaussian distribution offsets. Our GDC can be easily used as a module to build lightweight or complex segmentation networks. We adopt the proposed GDC to address the typical single-image segmentation tasks. Furthermore, we also build a Gaussian dynamic pyramid Pooling to show its potential and generality in common semantic segmentation. Experiments demonstrate that the GDC outperforms other existing convolutions on three benchmark segmentation datasets including Pascal-Context, Pascal-VOC 2012, and Cityscapes. Additional experiments are also conducted to illustrate that the GDC can produce richer and more vivid features compared with other convolutions. In general, our GDC is conducive to the convolutional neural networks to form an overall impression of the image.

Real-world multilabel data are high dimensional, and directly using them for label distribution learning (LDL) will incur extensive computational costs. We propose a multilabel distribution learning algorithm based on multioutput regression through manifold learning, referred to as MDLRML. By exploiting smooth, similar spaces' information provided by the samples' manifold learning and LDL, we link the two spaces' manifolds. This facilitates using the topological relationship of the manifolds in the feature space to guide the manifold construction of the label space. The smoothest regression function is used to fit the manifold data, and a locally constrained multioutput regression is designed to improve the data's local fitting. Based on the regression results, we enhance the logical labels into the label distributions, thereby mining and revealing the label's hidden information regarding importance or significance. Extensive experimental results using real-world multilabel datasets show that the proposed MDLRML algorithm significantly improves the multilabel distribution learning accuracy and efficiency over several existing state-of-the-art schemes.

This paper investigates the power allocation problem for secure multiple-input single-output transmission with the injection of artificial noise (AN), in the presence of an unknown eavesdropper (Eve). Two power allocation schemes, the optimal adaptive power allocation (OAPA) and suboptimal fixed power allocation (SFPA) schemes, are proposed to enhance the physical layer security of the considered system. Since the noise power at Eve is unknown, both power allocation schemes are designed for the worst-case scenario in which the noise power at Eve is assumed to be zero, aiming to minimize the secrecy outage probability (SOP). To characterize the performance of the proposed power allocation schemes, approximate closed-form expressions for average SOP under a preset noise power level are derived by applying Gauss-Chebyshev quadrature. We also address the worst-case secrecy outage performance for the proposed OAPA and SFPA schemes. Our analytical and numerical results show that, compared with the exhaustive search method that requires Eve’s prior information, the proposed OAPA scheme exhibits comparable secrecy outage performance without Eve’s prior information. Additionally, the SFPA scheme, also without Eve’s prior information, is capable of achieving almost the same worst-case SOP as the OAPA scheme, with a much lower implementation complexity.

Existing deep-network based texture synthesis approaches all focus on fine-grained control of texture generation by synthesizing images from exemplars. Since the networks employed by most of these methods are always tied to individual exemplar textures, a large number of individual networks have to be trained when modeling various textures. In this paper, we propose to generate textures directly from coarse-grained control or high-level guidance, such as texture categories, perceptual attributes and semantic descriptions. We fulfill the task by parsing the generation process of a texture into the three-level Bayesian hierarchical model. A coarse-grained signal first determines a distribution over Markov random fields. Then a Markov random field is used to model the distribution of the final output textures. Finally, an output texture is generated from the sampled Markov random field distribution. At the bottom level of the Bayesian hierarchy, the isotropic and ergodic characteristics of the textures favor a construction that consists of a fully convolutional network. The proposed method integrates texture creation and texture synthesis into one pipeline for real-time texture generation, and enables users to readily obtain diverse textures with arbitrary scales from high-level guidance only. Extensive experiments demonstrate that the proposed method is capable of generating plausible textures that are faithful to user-defined control, and achieving impressive texture metamorphosis by interpolation in the learned texture manifold.

We propose a novel probabilistic label enhancement algorithm, called PLEA, to solve challenging label distribution learning (LDL) for multi-label classification problems. We adopt the well-known maximum entropy model based label distribution learner. However, unlike the existing LDL algorithms based on the maximum entropy model, we propose to use manifold learning to enhance the label distribution learner. Specifically, the supervised information in the label manifold is utilized in the feature manifold space construction to improve the accuracy of feature extraction, while dramatically reducing the feature dimension. Then the robust linear regression is employed to estimate the label distributions associated with the extracted reduced-dimension features. Using the enhanced reduced-dimension features and their associated estimated label distributions in the maximum entropy model, the unknown true label distributions can be estimated more accurately, while imposing considerably lower computational complexity. We evaluate the proposed PLEA method on a wide-range artificial and high-dimensional real-world datasets. Experimental results obtained demonstrate that our proposed PLEA method has advantages in LDL accuracy and runtime performance, compared to the latest multi-label LDL approaches. The results also show that our PLEA compares favourably with the state-of-the-arts multi-label learning algorithms for classification tasks.

A key characteristic of biological systems is the ability to update the memory by learning new knowledge and removing out-of-date knowledge so that intelligent decision can be made based on the relevant knowledge acquired in the memory. Inspired by this fundamental biological principle, this article proposes a multi-output selective ensemble regression (SER) for online identification of multi-output nonlinear time-varying industrial processes. Specifically, an adaptive local learning approach is developed to automatically identify and encode a newly emerging process state by fitting a local multi-output linear model based on the multi-output hypothesis testing. This growth strategy ensures a highly diverse and independent local model set. The online modeling is constructed as a multi-output SER predictor by optimizing the combining weights of the selected local multi-output models based on a probability metric. An effective pruning strategy is also developed to remove the unwanted out-of-date local multi-output linear models in order to achieve low online computational complexity without scarifying the prediction accuracy. A simulated two-output process and two real-world identification problems are used to demonstrate the effectiveness of the proposed multi-output SER over a range of benchmark schemes for real-time identification of multi-output nonlinear and nonstationary processes, in terms of both online identification accuracy and computational complexity.

The modern computing applications are presently adapting to the convenient availability of huge and diverse data for making their pattern recognition methods smarter. Identification of dominant emotion solely based on the text data generated by humans is essential for the modern human–computer interaction. This work presents a multimodal text-keystrokes dataset and associated learning methods for the identification of human emotions hidden in small text. For this, a text-keystrokes data of 69 participants is collected in multiple scenarios. Stimuli are induced through videos in a controlled environment. After the stimuli induction, participants write their reviews about the given scenario in an unguided manner. Afterward, keystroke and in-text features are extracted from the dataset. These are used with an assortment of learning methods to identify emotion hidden in the short text. An accuracy of 86.95% is achieved by fusing text and keystroke features. Whereas, 100% accuracy is obtained for pleasure-displeasure classes of emotions using the fusion of keystroke/text features, tree-based feature selection method, and support vector machine classifier. The present work is also compared with four state-of-the-art techniques for the same task, where the results suggest that the present proposal performs better in terms of accuracy.

Recent research on edge computing (EC) has proposed federated or collaborative learning technique, where machine learning models are shared among participating edge deployments, thereby benefiting from all available datasets without exchanging them. In addition, EC systems are currently exploiting attaching portable edge devices on drones for data processing close to the sources, to achieve high performance, fast response times and real-time insights. Existing researches lack the potential to federate edge resources and manage corresponding service entities running across multiple drones, thus resulting to sub-optimal performance. Therefore, we introduce Aerial Edge, a federated learning-based orchestration framework for a federated aerial EC system. We propose a federated multi-output linear regression model to estimate multi-task resource requirements and execution time, to select the closest drone deployment having congruent resource availability and flight time to execute ready tasks at any given time. For better utilization of resources, we propose a variant bin-packing optimization approach through gang-scheduling of multi-dependent containerized tasks that co-schedules and co-locates tasks tightly on nodes to fully utilize available resources. Extensive experiments on real-world data-trace from Alibaba cluster trace with information on task dependencies show the effectiveness, fast executions, and resource efficiency of our approach.

A popular technique of designing multiple-input multiple-output (MIMO) communication systems relies on optimizing the positive semidefinite covariance matrix at the source. In this paper, a unified MIMO optimization framework based on the Karush-Kuhn-Tucker (KKT) conditions is proposed. In this framework, with the aid of matrix optimization theory,Theorem 1presents a generic optimal transmit covariance matrix for MIMO systems with diverse objective functions subject to various power constraints and different levels of channel state information (CSI). Specifically,Theorem 1fundamentally reveals that for a diverse family of MIMO systems, the optimal transmit covariance matrices associated with different objective functions under various power constraints can be derived in a unified generic water-filling-like form. When applyingTheorem 1to the case of multiple general power constraints, we firstly equivalently transform multiple power constraints into a single counterpart by introducing multiple weighting factors based on Pareto optimization theory. The optimal weighting factors can be found by the proposed modified subgradient method. On the other hand, for the imperfect MIMO system with statistical CSI errors, we firstly address the non-convexity of the robust optimization problem by following the idea of alternating optimization. Finally, our numerical results verify the optimal solution structure inTheorem 1and the global optimality of the proposed modified subgradient method, as well as demonstrate the performance advantages of the proposed alternating optimization algorithm.

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.

The secrecy capacity based on the assumption of having continuous distributions for the input signals constitutes one of the fundamental metrics for the existing physical layer security (PHYS) solutions. However, the input signals of real-world communication systems obey discrete distributions. Furthermore, apart from the capacity, another ultimate performance metric of a communication system is its symbol error ratio (SER). In this article, we pursue a radically new approach to PHYS by considering rigorous direct SER optimization exploiting the discrete nature of practical modulated signals. Specifically, we propose a secure precoding technique based on a multi-objective SER criterion, which aims for minimizing the confidential messages' SER at their legitimate user, while maximizing the SER of the confidential messages leaked to the illegitimate user. The key to this challenging multi-objective optimization problem is to introduce a priority factor that controls the priority of directly minimizing the SER of the legitimate user against directly maximizing the SER of the leaked confidential messages. Furthermore, we define a new metric termed as the security-level, which is related to the conditional symbol error probability of the confidential messages leaked to the illegitimate user. Additionally, we also introduce the secure discrete-input continuous-output memoryless channel (DCMC) capacity referred to as secure-DCMC-capacity, which serves as a classical security metric of the confidential messages, given a specific discrete modulation scheme. The impacts of both the channel's Rician factor and the correlation factor of antennas on the security-level and the secure-DCMC-capacity are investigated. Our simulation results demonstrate that the proposed priority-aware secure precoding based on the direct SER metric is capable of securing transmissions, even in the challenging scenario, where the eavesdropper has three receive antennas, while the legitimate user only has a single one.

Photometric stereo recovers three-dimensional (3D) object surface normal from multiple images under different illumination directions. Traditional photometric stereo methods suffer from the problem of non-Lambertian surfaces with general reflectance. By leveraging deep neural networks, learning-based methods are capable of improving the surface normal estimation under general non-Lambertian surfaces. These state-of-the-art learning-based methods however do not associate surface normal with reconstructed images and, therefore, they cannot explore the beneficial effect of such association on the estimation of the surface normal. In this paper, we specifically exploit the positive impact of this association and propose a novel dual regression network for both fine surface normals and arbitrary reconstructed images in calibrated photometric stereo. Our work unifies the 3D reconstruction and rendering tasks in a deep learning framework, with the explorations including: 1. generating specified reconstructed images under arbitrary illumination directions, which provides more intuitive perception of the reflectance and is extremely useful for visual applications, such as virtual reality, and 2. our dual regression scheme introduces an additional constraint on observed images and reconstructed images, which forms a closed-loop to provide additional supervision. Experiments show that our proposed method achieves accurate reconstructed images under arbitrarily specified illumination directions and it significantly outperforms the state-of-the-art learning-based single regression methods in calibrated photometric stereo.

In this article, we propose a knowledge distillation approach with two teachers for facial age estimation. Due to the nonstationary patterns of the facial-aging process, the relative order of age labels provides more reliable information than exact age values for facial age estimation. Thus, the first teacher is a novel ranking method capturing the ordinal relation among age labels. Especially, it formulates the ordinal relation learning as a task of recovering the original ordered sequences from shuffled ones. The second teacher adopts the same model as the student that treats facial age estimation as a multiclass classification task. The proposed method leverages the intermediate representations learned by the first teacher and the softened outputs of the second teacher as supervisory signals to improve the training procedure and final performance of the compact student for facial age estimation. Hence, the proposed knowledge distillation approach is capable of distilling the ordinal knowledge from the ranking model and the dark knowledge from the multiclass classification model into a compact student, which facilitates the implementation of facial age estimation on platforms with limited memory and computation resources, such as mobile and embedded devices. Extensive experiments involving several famous data sets for age estimation have demonstrated the superior performance of our proposed method over several existing state-of-the-art methods.

Multi-hop communication with the aid of large-scale antenna arrays will play a vital role in future emergence communication systems. In this paper, we investigate amplify-and-forward based and multiple-input multiple-output assisted multi-hop communication, in which all nodes employ hybrid transceivers. Moreover, channel errors are taken into account in our hybrid transceiver design. Based on the matrix-monotonic optimization framework, the optimal structures of the robust hybrid transceivers are derived. By utilizing these optimal structures, the optimizations of analog transceivers and digital transceivers can be separated without loss of optimality. This fact greatly simplifies the joint optimization of analog and digital transceivers. Since the optimization of analog transceivers under unit-modulus constraints is nonconvex, a projection type algorithm is proposed for analog transceiver optimization to overcome this difficulty. Based on the derived analog transceivers, the optimal digital transceivers can then be derived using matrix-monotonic optimization. Numerical results obtained demonstrate the performance advantages of the proposed hybrid transceiver designs over other existing solutions.

Understanding and characterizing the connectivity of vehicular networks has become increasingly important because of their wide applications and fast development. To address the dynamical links in vehicular networks, time-varying graph (TVG) is one of the most important models. Nowadays, due to the fact that lots of vehicular applications can tolerate a small amount of latency in communication, opportunistic reachability graph (ORG) characterizes the connectivity better by introducing delay tolerance to the model. However, people still do not have a high-level summarization, i.e., the topology, of the vehicular network on how nodes are clustered and isolated. In this paper, based on ORG model, we analyze the opportunistic topology of taxi networks in urban mobility environment by mainly focusing on the number, location and evolution of connected components and the size of the largest components to reveal the unique properties of the taxi networks instead of just links and hops. Our analysis is based on the real taxi traces of big cities and reflects the real urban mobility environment. We find that the opportunistic topology of the networks with delay tolerance is substantially different from the instantaneous topology without considering the delay. Moreover, we unveil the fundamental relationships and trade-offs between the dynamical topology and the key network parameters related to mobility, e.g., delay tolerance, transmission distance, etc. To the best of our knowledge, our study is the first work to reveal the characteristics of opportunistic topology models in the large-scale urban mobility environment with real traces.

For the multiple-input multiple-output (MIMO) uplink employing high-order quadrature amplitude modulation (QAM) signaling and with nonlinear high power amplifiers (HPAs) at mobile users' transmitters, the existing multiuser detection methods can no longer be applied. We propose a novel nonlinear multiuser detection scheme for the nonlinear MIMO uplink. Specifically, we adopt an effective B-spline parameterization of the nonlinear transmit HPAs and derive an efficient and accurate algorithm to identify the nonlinear MIMO uplink channel, including the nonlinear B-spline model of the nonlinear transmit HPAs and the estimate of the linear MIMO channel matrix. Moreover, as the direct result of this nonlinear MIMO channel identification, the B-spline inverse model of nonlinear transmit HPAs can readily be identified. The nonlinear multiuser detection can be effectively implemented by the zero-forcing linear detection based on the estimated linear MIMO channel and followed by compensating the nonlinear distortion of the nonlinear transmit HPAs based on the estimated B-spline inverse model. An extensive simulation investigation is performed to demonstrate the effectiveness of our proposed nonlinear multiuser detection scheme for nonlinear MIMO uplink with high-order QAM signaling.

Channel estimation is conceived for hybrid multiple-input multiple-output (MIMO) communication systems. Both mean square error minimization and mutual information maximization are used as our performance metrics and a pair of low-complexity channel estimation schemes are proposed. In each scheme, the training sequence and the analog matrices of the transmitter and receiver are jointly optimized. We commence by designing the optimal training sequences and analog matrices for the first scheme. Upon relying on the resultant optimal structures, the training optimization problems are substantially simplified and the nonconvexity resulting from the analog matrices can be overcome. In the second scheme, the channel estimation and data transmission share the same analog matrices, which beneficially reduces the overhead of optimizing the associated analog matrices. Therefore, a composite channel matrix is estimated instead of the true channel matrix. By exploiting the statistical optimization framework advocated, the analog matrices can be designed independently of the training sequence. Based on the resultant analog matrices, the training sequence can then be efficiently designed according to diverse channel statistics and performance metrics. Finally, we conclude by quantifying the performance benefits of the proposed estimation schemes.

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.

General traffic analysis based on Deep Packet Inspection (DPI) techniques at the gateways or access points cannot grasp the detailed knowledge of network applications going among internal nodes, and the statistics-based reports of routers are also lack of flow-level recognition of the traffic in the form of only five tuple. Therefore, network-wise accurate flow-awareness by packet sampling is highly desired for fine-grained quality of service guarantee, internal network management, traffic engineering, and security analysis and so on. In this paper, we propose a Spatio-Temporal Collaborative Sampling (STCS) problem based on the Software-Defined Networking (SDN) technique. The goal of STCS is to maximize the network-wise sampling accuracy of both elephant and mice flows, which considers both of the comprehensive influences of nodes and the effect on sampling accuracy imposed by the collaborative strategy among nodes in the time dimension. We present a approach to calculate the near optimal solution of STCS in two steps: 1) Top-K nodes selection by iterative comprehensive influence, and 2) spatio-temporal cosampling solution based on the local value maximization strategy. We evaluate the proposed approach by a realistic large-scale topology, and the results show that the sampling accuracy can be effectively improved by the method, especially for mice flows, and the redundant ratio of sampled packets is reduced by 34.4%.

This paper proposes a multiple local model learning approach for nonlinear and nonstationary microwave heating process (MHP). The proposed local learning framework performs model adaption at two levels: (1) adaptation of the local linear model set, which adaptively partitions the process's data into multiple process states, each fitted with a local linear model; (2) online adaptation of model prediction, which selects a subset of candidate local linear models and linearly combines them to produce the model prediction. Adaptive process state partition and fitting a new local linear model to the newly emerging process state is based on statistical hypothesis testing, and the optimal combining coefficients of the selected subset linear models are obtained by minimizing the mean square error with the constraint that the sum of these coefficients is unity. A case study involving a real-world industrial MHP is used to demonstrate the superior performance of the proposed multiple local model learning approach, in terms of online modeling accuracy and computational efficiency.

Understanding the patterns of mobile data consumption is extremely valuable to reveal human activities and ecology in urban areas. This task is nontrivial in terms of three challenges: the complexity of mobile data consumption in large urban environment, the disturbance of abnormal events, and lack of prior knowledge for urban traffic patterns. We propose a novel approach to design a powerful system that consists of three subsystems: time series decomposing of mobile traffic data, extracting patterns from different components of the original traffic, and detecting anomalous events from noises. Our investigation involving the mobile traffic records of 6,400 cellular towers in Shanghai reveals three important observations. First, among all the 6,400 cellular towers, we identify five daily patterns corresponding to different human daily activity patterns. Second, we find that two natural patterns can be extracted from the weekly trend of mobile traffic consumption, which reflects modes of human activities. Last but not least, besides the regular patterns, we investigate how irregular activities affect mobile traffic consumption, and exploit this knowledge to successfully detect unusual events like concerts and soccer matches. Our proposed methodology therefore will aid a comprehensive understanding of large-scale mobile traffic consumption in urban areas.

This paper proposes a closed-loop sparse channel estimation (CE) scheme for wideband millimeter-wave hybrid full-dimensional multiple-input multiple-output and time division duplexing based systems, which exploits the channel sparsity in both angle and delay domains. At the downlink CE stage, random transmit precoding matrix is designed at base station (BS) for channel sounding, and receive combining matrices at user devices (UDs) are designed whereby the hybrid array is visualized as a low-dimensional digital array for facilitating the multi-dimensional unitary ESPRIT (MDU-ESPRIT) algorithm to estimate respective angle-of-arrivals (AoAs). At the uplink CE stage, the estimated downlink AoAs, namely, uplink angle-of-departures (AoDs), are exploited to design multi-beam transmit precoding matrices at UDs to enable BS to estimate the uplink AoAs, i.e., the downlink AoDs, and delays of different UDs, whereby the MDU-ESPRIT algorithm is used based on the designed receive combining matrix at BS. Furthermore, a maximum likelihood approach is proposed to pair the channel parameters acquired at the two stages, and the path gains are then obtained using least squares estimator. According to spectrum estimation theory, our solution can acquire the super-resolution estimations of the AoAs/AoDs and delays of sparse multipath components with low training overhead. Simulation results verify the better CE performance and lower computational complexity of our solution over state-of-the-art approaches.

Multi-class channel coded layered asymmetrically clipped optical orthogonal frequency-division multiplexing (LACO-OFDM) is proposed, where the achievable rate of the system is derived based on our mutual information analysis. We conceive a multi-class channel encoding scheme integrated with the layered transmitter. At the receiver, both the coded and uncoded likelihood ratios are extracted for inter-layer interference cancellation and symbol detection, respectively. Simulations are conducted, and the results show that our design approaches the achievable rate within 1.1 dB for 16-QAM fourlayer LACO-OFDM with the aid of a half-rate eight-iteration turbo code at BER = 10-3, outperforming its conventional counterpart by about 3.6 dB.

We investigate the energy efficiency (EE) of multiple-input-multiple-output (MIMO) amplify-and-forward relaying networks relying on the realistic imperfect channel state information (CSI). Specifically, the relay jointly optimizes the source covariance and relay beamforming matrices by maximizing the EE under additive or multiplicative relay-destination CSI errors. The optimal channel-diagonalizing structure is derived for the source covariance and relay beamforming matrices under the spectral-norm constrained additive or multiplicative CSI error. Then, the existence of a saddle point is proved, which shows that the channel-diagonalizing transmission strategy is optimal in the robust EE maximization under these two types of CSI errors, and the original matrix-valued fractional robust EE problem is transformed into a scalar fractional problem. We propose the Dinkelbach method-based alternating optimization scheme for this transformed robust EE problem, which is capable of finding a locally optimal solution of the original robust EE problem efficiently, and show that the semi-closed-form solution to each of the two associated subproblems can be obtained. We then prove that the channel-diagonalizing transmission strategy remains optimal when the statistically imperfect source-relay channel is additionally imposed. We also extend our work into multi-hop MIMO relaying scenarios and prove that the channel-diagonalizing structure is optimal for the source covariance matrix and the multiple relay beamforming matrices.

This paper investigates how to enhance the energy and spectral efficiency (ESE) performance of large-scale cellular networks by offloading mobile traffic with the aid of device-to-device (D2D) communication. By appropriately exploiting the D2D-based mobile-traffic offloading mechanism, the users' behaviors and the specific network operating conditions, we develop an ESE evaluation framework for large-scale D2D-enabled cellular networks. This framework enables us to characterize the explicit relationship between the network's ESE and the offloading parameters as well as to quantify the influence of the users' behavior. Explicitly, we quantify the effects of the mobile-traffic intensity, the users' quality of service requirements as well as the base station density and other cellular system parameters on the achievable ESE. Tractable closed-form ESE-expressions are derived for a pair of spectrum sharing schemes, namely, D2D overlay and underlay in-band modes. Furthermore, we apply the analytical results to derive an optimal D2D-enabled mobile-traffic offloading scheme for the D2D overlay cellular networks to maximize the network's ESE under a specific maximal cellular user outage and D2D transmitter power constraint. The numerical and simulation results are provided to verify our modeling accuracy and to demonstrate the impact of the system parameters on the achievable ESE.

Wireless body sensor networks (WBSNs), which efficiently and intelligently sense the physiological signals of the medical patients to support various medial applications, have allured tremendous attention from various research communities. For energy and resource constrained WBSNs, the important issues include: 1)~dynamic channel characteristics due to mobility and postural dynamics; 2) high energy efficiency owing to limited battery power; 3) high quality-of- service (QoS) requirement due to critical physiological data. To address the above issues, a cost-effective heuristic packet scheduling scheme is designed to provide the high network throughput and fair QoS to WBSNs. Unlike most of the existing works, we also consider the optimal delay- constraint in order to achieve the optimized packet transmission delay and to manage the heavy traffic load optimally. Specifically, we consider the critical factors of WBSNs to prioritize the data packets among access points, e.g., medical emergent patients have the higher priority to send their data packets than the normal patients. We formulate the proposed scheme mathematically. Simulation results are presented to demonstrate the effectiveness of the proposed heuristic packet scheduling scheme over other existing state-of- the-art solutions, in terms of packet transmission delay, cost and network throughput.

In a device-to-device (D2D) communications underlaying cellular network, any user is a potential eavesdropper for the transmissions of others that occupy the same spectrum. The physical-layer security mechanism of theoretical secure capacity, which maximizes the rate of reliable communication from the source user to the legitimate receiver and ensure unauthorized users learn as little as information as possible, is typically employed to guarantee secure communications. As hand-held devices are carried by human beings, we may leverage their social trust to decrease the number of potential eavesdroppers. Aiming to establish a new paradigm for solving the challenging problem of security and efficiency tradeoff, we propose a social trust-aware D2D communication architecture that exploits the social-domain trust for securing the physical-domain communication. In order to understand the impact of social trust on the security of transmissions, we analyze the system ergodic rate of social trust aided communications via stochastic geometry, and our result based on a real data set shows that the proposed social trust aided D2D communication increases the system secrecy rate by about 63% compared with the scheme without considering social trust relation. Furthermore, in order to provide implementation mechanism, we utilize matching theory to implement efficient resource allocation among multiple users. Numerical results show that our proposed mechanism increases the system secrecy rate by 28% with fast convergence over the social oblivious approach.

We propose a regularized zero-forcing transmit precoding (RZF-TPC)-aided and distance-based adaptive coding and modulation (ACM) scheme to support aeronautical communication applications, by exploiting the high spectral efficiency of the large-scale antenna arrays and link adaption. Our RZF-TPC-aided and distance-based ACM scheme switches its mode according to the distance between the communicating aircraft. We derive the closed-form asymptotic signal-to-interference-plus-noise ratio (SINR) expression of the RZF-TPC for the aeronautical channel, which is Rician, relying on a non-centered channel matrix that is dominated by the deterministic line-of-sight component. The effects of both realistic channel estimation errors and of the co-channel interference are considered in the derivation of this approximate closed-form SINR formula. Furthermore, we derive the analytical expression of the optimal regularization parameter that minimizes the mean square detection error. The achievable throughput expression based on our asymptotic approximate SINR formula is then utilized as the design metric for the proposed RZF-TPC-aided and distance-based ACM scheme. Monte-Carlo simulation results are presented for validating our theoretical analysis as well as for investigating the impact of the key system parameters. The simulation results closely match the theoretical results. In the specific example that two communicating aircrafts fly at a typical cruising speed of 920km/h, heading in opposite direction over the distance up to 740km taking a period of about 24 min, the RZF-TPC-aided and distance-based ACM is capable of transmitting a total of 77 GB of data with the aid of 64 transmit antennas and four receive antennas, which is significantly higher than that of our previous eigen-beamforming transmit precoding-aided and distance-based ACM benchmark.

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.

Many industrial processes contain both linear and nonlinear parts, and kernel principal component analysis (KPCA), widely used in nonlinear process monitoring, may not offer the most effective means for dealing with these nonlinear processes. This paper proposes a new hybrid linear-nonlinear statistical modeling approach for nonlinear process monitoring by closely integrating linear principal component analysis (PCA) and nonlinear KPCA using a serial model structure, which we refer to as serial PCA (SPCA). Specifically, PCA is first applied to extract PCs as linear features, and to decompose the data into the PC subspace and residual subspace (RS). Then, KPCA is performed in the RS to extract the nonlinear PCs as nonlinear features. Two monitoring statistics are constructed for fault detection, based on both the linear and nonlinear features extracted by the proposed SPCA. To effectively perform fault identification after a fault is detected, an SPCA similarity factor method is built for fault recognition, which fuses both the linear and nonlinear features. Unlike PCA and KPCA, the proposed method takes into account both linear and nonlinear PCs simultaneously, and therefore, it can better exploit the underlying process's structure to enhance fault diagnosis performance. Two case studies involving a simulated nonlinear process and the benchmark Tennessee Eastman process demonstrate that the proposed SPCA approach is more effective than the existing state-of-the-art approach based on KPCA alone, in terms of nonlinear process fault detection and identification.

With the popularity of watching mobile videos, a major form of multimedia contents, many works focus on the geographic features of user viewing behaviors, but few study them in the context of an entire metropolitan city. Different regions of a large city have different intensity of economy activities with respect to their different distances to the downtown, and how this will influence video popularity and similarity is still unclear. To quantitatively study the spatial popularity and similarity of watching videos in a large urban environment, we collect a dataset with two-month video view requests from the largest network provider in Shanghai, containing the top six content providers, and study the spatial features of video access in regions of different scales. We find that: 1) video popularity and similarity exist at different scales of city division; 2) the concentration of video popularity becomes higher as the region is closer to downtown; and 3) when comparing the regions of same scale, the similarity of popular videos becomes lower as the region is farther away from the downtown. Finally, we correlate our findings with cache deployment, advertising, and video recommendation to illustrate the implications.

Physical layer security (PLS) is a promising technology in device-to-device (D2D) communications by exploiting reciprocity and randomness of wireless channels, which attracts considerable research attention in the D2D communications community. In this paper, we investigated PLS for secure key generation rate (SKGR) in D2D communications based on cooperative trusted and non-trusted relays. By leveraging social ties, we exploit three social phenomena for secure communications, i.e., trusted scenario (social trust), non-trusted scenario (social reciprocity), and partially trusted scenario (mixed social trust and social reciprocity). The coalition game theory is further utilized to select the optimal relay pairs for improving SKGR. On the basis of social ties, we develop an algorithm for SKGR that protects the keys secret from both eavesdropper and non-trusted selected relays. We incorporate secure relays selection and system wide security for D2D communications. The stability and convergence of the proposed algorithm are also proved in this paper. Both numerical and analytical results verify effectiveness and consistency of our proposed scheme, which ensures better SKGR performance in D2D communications.

In order to meet the demands of “Internet above the clouds,” we propose a multiple-antenna aided adaptive coding and modulation (ACM) for aeronautical communications. The proposed ACM scheme switches its coding and modulation mode according to the distance between the communicating aircraft, which is readily available with the aid of the airborne radar or the global positioning system. We derive an asymptotic closed-form expression of the signal-to-interference-plus-noise ratio (SINR) as the number of transmitting antennas tends to infinity, in the presence of realistic co-channel interference and channel estimation errors. The achievable transmission rates and the corresponding mode-switching distance-thresholds are readily obtained based on this closed-form SINR formula. Monte-Carlo simulation results are used to validate our theoretical analysis. For the specific example of 32 transmit antennas and four receive antennas communicating at a 5-GHz carrier frequency and using 6-MHz bandwidth, which are reused by multiple other pairs of communicating aircraft, the proposed distance-based ACM is capable of providing as high as 65.928-Mb/s data rate when the communication distance is less than 25 km.

Principal component analysis (PCA) and kernel PCA (KPCA) are the state-of-art machine learning methods widely used in industrial process monitoring and fault detection field. However, these methods build shallow statistical models based on single layer of features and may not achieve the best monitoring performance. In order to sufficiently mine the intrinsic data features, a deep learning based nonlinear PCA method, referred to as deep PCA (DePCA), is proposed in this paper. Motivated by the idea of deep learning, a layer-wise statistical model structure is designed to extract multilayer data features, including both linear and nonlinear principal components. At each layer, two monitoring statistics are constructed to monitor the feature changes. For integrating the monitoring statistics of all feature layers, a Bayesian inference strategy is applied to convert the monitoring statistics into fault probabilities, which are weighted to form two probability-based comprehensive monitoring statistics for process fault detection. A case study using the benchmark Tennessee Eastman process demonstrates the superior performance of the proposed DePCA method over the traditional PCA and KPCA methods.

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.

Device-to-device (D2D) communication, which enables direct transmissions between mobile devices to improve spectrum efficiency, is one of the preferable candidate technologies for the next generation cellular network. Network coding, on the other hand, is widely used to improve throughput in ad hoc networks. Thus, the performance of D2D communications in cellular networks can potentially benefit from network coding. Aiming to improve the achievable capacity of D2D communications, we propose a system with inter-session network coding enabled to assist D2D transmissions. We formulate the joint problem of relay selection and resource allocation in network coding assisted D2D communications, and obtain the overall capacity of the network under complex interference conditions as a function of the relay selection and resource allocation. To solve the formulated problem, we propose a two-level decentralized approach termed NC-D2D, which solves the relay selection and resource allocation problems alternatively to obtain stable solutions for these two problems. Specifically, a coalition formation game associates relays with D2D pairs to enable network coding aided transmissions, and a greedy algorithm based game allocates limited cellular resources to D2D pairs and relays in NC-D2D, respectively. The performances of the proposed scheme is evaluated through extensive simulations to prove its superiority.

A kernel independent component analysis (KICA) is widely regarded as an effective approach for nonlinear and non-Gaussian process monitoring. However, the KICA-based monitoring methods treat every KIC equally and cannot highlight the useful KICs associated with fault information. Consequently, fault information may not be explored effectively, which may result in degraded fault detection performance. To overcome this problem, we propose a new nonlinear and non-Gaussian process monitoring method using Gaussian mixture model (GMM)-based weighted KICA (WKICA). In particular, in WKICA, GMM is first adopted to estimate the probabilities of the KICs extracted by KICA. The significant KICs embodying the dominant process variation are then discriminated based on the estimated probabilities and assigned with larger weights to capture the significant information during online fault detection. A nonlinear contribution plots method is also developed based on the idea of a sensitivity analysis to help identifying the fault variables after a fault is detected. Simulation studies conducted on a simple four-variable nonlinear system and the Tennessee Eastman benchmark process demonstrate the superiority of the proposed method over the conventional KICA-based method.

Complex-valued (CV) B-spline neural network approach offers a highly effective means for identifying and inverting practical Hammerstein systems. Compared with its conventional CV polynomial-based counterpart, a CV B-spline neural network has superior performance in identifying and inverting CV Hammerstein systems, while imposing a similar complexity. This paper reviews the optimality of the CV B-spline neural network approach. Advantages of B-spline neural network approach as compared with the polynomial based modeling approach are extensively discussed, and the effectiveness of the CV neural network-based approach is demonstrated in a real-world application. More specifically, we evaluate the comparative performance of the CV B-spline and polynomial-based approaches for the nonlinear iterative frequency-domain decision feedback equalization (NIFDDFE) of single-carrier Hammerstein channels. Our results confirm the superior performance of the CV B-spline-based NIFDDFE over its CV polynomial-based counterpart.

We propose a nonlinear hybrid decision feedback equalizer (NHDFE) for single-carrier (SC) block transmission systems with nonlinear transmit high power amplifier (HPA), which significantly outperforms our previous nonlinear SC frequency-domain equalization (NFDE) design. To obtain the coefficients of the channel impulse response (CIR) as well as to estimate the nonlinear mapping and the inverse nonlinear mapping of the HPA, we adopt a complex-valued (CV) B-spline neural network approach. Specifically, we use a CV B-spline neural network to model the nonlinear HPA, and we develop an efficient alternating least squares scheme for estimating the parameters of the Hammerstein channel, including both the CIR coefficients and the parameters of the CV B-spline model. We also adopt another CV B-spline neural network to model the inversion of the nonlinear HPA, and the parameters of this inverting B-spline model can be estimated using the least squares algorithm based on the pseudo training data obtained as a natural byproduct of the Hammerstein channel identification. The effectiveness of our NHDFE design is demonstrated in a simulation study, which shows that the NHDFE achieves a signal-to-noise ratio gain of 4dB over the NFDE at the bit error rate level of 10-4.

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.

In this paper, the energy-spectral efficiency (ESE) benefiting from the joint optimization of coordinated multi-point (CoMP) transmission and base station (BS) deployment is evaluated in the context of dense large-scale cellular network. We first derive a closed-form network ESE expression for a large-scale CoMP-enhanced network, which allows us to quantify the influence of key network parameters on the achievable network ESE, including the BS density and the cooperation activation probability, characterized by a CoMP activation factor as well as the users' behaviors, such as their geographical mobile-traffic intensity and average user rate. With the aid of this tractable ESE expression and for a given BS density, we next formulate a cellular-scenario-aware CoMP activation optimization problem while considering the users' outage probability as constraints to maximize the network's ESE. We then jointly optimize the CoMP activation factor and the BS density to maximize the network ESE, again under the constraint of the users' outage probability. Our simulation results confirm the accuracy of our analysis and verify the impact of several key parameters on the network ESE. Finally, the ESE improvement of our proposed strategies is evaluated under diverse scenarios, which provides valuable insight into the joint CoMP and BS deployment optimization in dense large-scale cellular networks.

With the popularity of watching mobile videos, many works focus on the geographic features of user viewing behaviors, but few study them in the context of an entire metropolitan city. Different regions of a large city have different intensity of economy activities with respect to their different distances to the downtown, and how this will influence video popularity and similarity is still unclear. To quantitatively study the spatial popularity and similarity of watching videos in a large urban environment, we collect a dataset with two-month video view requests from the largest network provider in Shanghai, containing top six content providers, and study the spatial features of video access in regions of different scales. We find that 1) video popularity and similarity exist at different scales of city division; 2) the concentration of video popularity becomes higher as the region is closer to downtown; 3) when comparing the regions of same scale, the similarity of popular videos becomes lower as the region is farther away from the downtown. Finally, we correlate our findings with cache deployment, advertising and video recommendation to illustrate the implications.

Complex-valued (CV) B-spline neural network approach offers a highly effective means for identification and inversion of Hammerstein systems. Compared to its conventional CV polynomial based counterpart, CV B-spline neural network has superior performance in identifying and inverting CV Hammerstein systems, while imposing a similar complexity. In this paper, we review the optimality of CV B-spline neural network approach and demonstrate its excellent approximation capability for a real-world application. More specifically, we develop a CV B-spline neural network based approach for the nonlinear iterative frequency-domain decision feedback equalization (NIFDDFE) of single-carrier Hammerstein channels. Advantages of B-spline neural network approach as compared to polynomial based modeling approach are extensively discussed, and the effectiveness of CV neural network based NIFDDFE is demonstrated in a simulation study.

For mobile networks diverse usage scenarios have different capability requirements on connection density and user experienced data rate, and modeling such capability diversity is crucial to the strategy evaluation in addressing the problem of high traffic load and scalability of network resources. Therefore, it is necessary to build a capability model in two dimensions of connection density and user experienced data rate. This paper aims at addressing this challenge based on an investigation of network capability in large-scale urban environment. First, our statistical study shows that the spatial distribution of these two parameters can be accurately fitted by log-normal mixture model. Second, we find that only six basic capability patterns exist among the 9,000 cellular base stations. Their connections with the urban functions of geographical locations are also explored in our work. Based on these two discoveries, we build a network capability model which can generate synthetic base stations with diverse connection density and user experienced data rate. We believe that this flexible and powerful model can help telecommunication operators to design and standardize mobile network in the future.

This paper describes a novel on-line learning approach for radial basis function (RBF) neural network. Based on an RBF network with individually tunable nodes and a fixed small model size, the weight vector is adjusted using the multi-innovation recursive least square algorithm on-line. When the residual error of the RBF network becomes large despite of the weight adaptation, an insignificant node with little contribution to the overall system is replaced by a new node. Structural parameters of the new node are optimized by proposed fast algorithms in order to significantly improve the modeling performance. The proposed scheme describes a novel, flexible, and fast way for on-line system identification problems. Simulation results show that the proposed approach can significantly outperform existing ones for nonstationary systems in particular.

Recently, cloud computing technology has been utilized to make vehicles on roads smarter and to offer better driving experience. Consequently, the concept of mobile vehicular cloudlet (MVC) was born, where nearby smart vehicles were connected to provide cloud computing services locally. Existing researches focus on MVC system models and architectures, and no work to date addresses the critical question of what is the potential, i.e., level of local cloud computing service, achievable by MVCs in real-world large-scale urban environments. This issue is fundamental to the practical implementation of MVC technology. Answering this question is also challenging because MVCs operate in highly complicated and dynamic environments. In this paper, we directly address this challenging issue and we introduce the concept of serviceability to measure the ability of an MVC to provide cloud computing service. In particular, we evaluate this measure in practical environments through a real-world vehicular mobility trace of Beijing. Using the time-varying graph model for mobile cloud computing under different scenarios, we find that the serviceability has a relationship with the delay tolerance of the undertaken computational task, which can be described by two characteristic parameters. The evolution of serviceability through a day and the influence of network congestion are also analyzed. We also portray the spatial distribution of the serviceability and analyze the influence of connectivity and mobility in both MVC and vehicle levels. Our observations are valuable to assist designing vehicular cloud computing systems and applications, as well as to help make offloading decisions.

Roadside units (RSUs), which enable vehicles-to-infrastructure communications, are deployed along roadsides to handle the ever-growing communication demands caused by explosive increase of vehicular traffics. How to efficiently utilize them to enhance the vehicular delay tolerant network (VDTN) performance are the important problems in designing RSU-aided VDTNs. In this work, we implement an extensive experiment involving tens of thousands of operational vehicles in Beijing city. Based on this newly collected Beijing trace and the existing Shanghai trace, we obtain some invariant properties for communication contacts of large scale RSU-aided VDTNs. Specifically, we find that the contact time between RSUs and vehicles obeys an exponential distribution, while the contact rate between them follows a Poisson distribution. According to these observations, we investigate the problem of communication contact-aware mobile data replication for RSU-aided VDTNs by considering the mobile data dissemination system that transmits data from the Internet to vehicles via RSUs through opportunistic communications. In particular, we formulate the communication contact-aware RSU-aided vehicular mobile data dissemination problem as an optimization problem with realistic VDTN settings, and we provide an efficient heuristic solution for this NP-hard problem. By carrying out extensive simulation using realistic vehicular traces, we demonstrate the effectiveness of our proposed heuristic contact-aware data replication scheme, in comparison with the optimal solution and other existing schemes.

This paper considers the problem of pilot contamination (PC) in large-scale multi-cell multiple-input multiple-output-aided orthogonal frequency division multiplexing systems. We propose an efficient scheme relying on an optimal pilot design conceived for time-domain channel estimation, which can either completely eliminate PC or significantly reduce it, depending on the channel's coherence time. This is achieved by designing an optimal pilot set allowing us to beneficially group the users in all the cells and to assign a time-shifted pilot transmission to the different groups. Unlike the existing PC elimination schemes, which require an excessively long channel coherence time, our proposed scheme is capable of completely eliminating PC under a much shorter coherence time. Moreover, the existing PC elimination schemes can no longer be used if the channel coherent time is insufficiently large. By contrast, even for extremely short channel coherent time, our scheme can still be implemented to significantly reduce PC. This is particularly beneficial for high velocity scenarios. Our simulation results demonstrate the efficiency of the proposed scheme.

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.

An efficient data based-modeling algorithm for nonlinear system identification is introduced for radial basis function (RBF) neural networks with the aim of maximizing generalization capability based on the concept of leave-one-out (LOO) cross validation. Each of the RBF kernels has its own kernel width parameter and the basic idea is to optimize the multiple pairs of regularization parameters and kernel widths, each of which is associated with a kernel, one at a time within the orthogonal forward regression (OFR) procedure. Thus, each OFR step consists of one model term selection based on the LOO mean square error (LOOMSE), followed by the optimization of the associated kernel width and regularization parameter, also based on the LOOMSE. Since like our previous state-of-the-art local regularization assisted orthogonal least squares (LROLS) algorithm, the same LOOMSE is adopted for model selection, our proposed new OFR algorithm is also capable of producing a very sparse RBF model with excellent generalization performance. Unlike our previous LROLS algorithm which requires an additional iterative loop to optimize the regularization parameters as well as an additional procedure to optimize the kernel width, the proposed new OFR algorithm optimizes both the kernel widths and regularization parameters within the single OFR procedure, and consequently the required computational complexity is dramatically reduced. Nonlinear system identification examples are included to demonstrate the effectiveness of this new approach in comparison to the well-known approaches of support vector machine and least absolute shrinkage and selection operator as well as the LROLS algorithm.

A new sparse kernel density estimator is introduced based on the minimum integrated square error criterion for the finite mixture model. Since the constraint on the mixing coefficients of the finite mixture model is on the multinomial manifold, we use the well-known Riemannian trust-region (RTR) algorithm for solving this problem. The first- and second-order Riemannian geometry of the multinomial manifold are derived and utilized in the RTR algorithm. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with an accuracy competitive with those of existing kernel density estimators.

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.

We propose a norm-based joint transmit and receive antenna selection (NBJTRAS) aided near-capacity multiple-input-multiple-output (MIMO) system relying on the assistance of a novel two-tier channel estimation scheme. Specifically, a rough estimate of the full MIMO channel is first generated using a low-complexity, low-training-overhead minimum mean square error based channel estimator, which relies on reusing a modest number of radio frequency (RF) chains. NBJTRAS is then carried out based on this initial full MIMO channel estimate. The NBJTRAS aided MIMO system is capable of significantly outperforming conventional MIMO systems equipped with the same modest number of RF chains while dispensing with the idealized simplifying assumption of having perfectly known channel state information (CSI). Moreover, the initial subset channel estimate associated with the selected subset MIMO channel matrix is then used for activating a powerful semi-blind joint channel estimation and turbo detector-decoder, in which the channel estimate is refined by a novel block-of-bits selection based soft-decision aided channel estimator (BBSB-SDACE) embedded in the iterative detection and decoding process. The joint channel estimation and turbo detection-decoding scheme operating with the aid of the proposed BBSB-SDACE channel estimator is capable of approaching the performance of the near-capacity maximum-likelihood (ML) turbo transceiver associated with perfect CSI. This is achieved without increasing the complexity of the ML turbo detection and decoding process.

We propose a simple yet effective norm-based joint transmit and receive antenna selection (NBJTRAS) assisted and two-tier channel estimation (TTCE) aided space-time shift keying (STSK) system, which is capable of significantly outperforming the conventional STSK system, while efficiently utilising available radio frequency (RF) chains. Specifically, the NBJTRAS carries out antenna selection based on the channel estimation (CE) generated using a low-complexity training based least square channel estimator by reusing RF chains. The selected sub-channel matrix is further refined by an efficient semi-blind CE and data detection scheme. Our simulation results show that only a few iterations are sufficient for the TTCE scheme to approach the optimal maximum-likelihood detection performance associated with perfectly channel state information.

We develop an on-line Gaussian mixture density estimator (OGMDE) in the complex-valued domain to facilitate adaptive minimum bit-error-rate (MBER) beamforming receiver for multiple antenna based space-division multiple-access systems. Specifically, the novel OGMDE is proposed to adaptively model the probability density function of the beamformer's output by tracking the incoming data sample by sample. With the aid of the proposed OGMDE, our adaptive beamformer is capable of updating the beamformer's weights sample by sample to directly minimize the achievable bit error rate (BER). We show that this OGMDE based MBER beam-former outperforms the existing on-line MBER beamformer, known as the least BER beamformer, in terms of both the convergence speed and the achievable BER.

A practical single-carrier (SC) block transmission with frequency domain equalisation (FDE) system can generally be modelled by the Hammerstein system that includes the nonlinear distortion effects of the high power amplifier (HPA) at transmitter. For such nonlinear Hammerstein channels, the standard SC-FDE scheme no longer works. In this paper, we propose a novel B-spline neural network based nonlinear SC-FDE scheme for Hammerstein channels. In particular, We model the nonlinear HPA, which represents the complex-valued static nonlinearity of the Hammerstein channel, by two real-valued B-spline neural networks, one for modelling the nonlinear amplitude response of the HPA and the other for the nonlinear phase response of the HPA. We then develop an efficient alternating least squares algorithm for estimating the parameters of the Hammerstein channel, including the channel impulse response coefficients and the parameters of the two B-spline models. Moreover, we also use another real-valued B-spline neural network to model the inversion of the HPA's nonlinear amplitude response, and the parameters of this inverting B-spline model can easily be estimated using the standard least squares algorithm based on the pseudo training data obtained as a byproduct of the Hammerstein channel identification. Equalisation of the SC Hammerstein channel can then be accomplished by the usual one-tap linear equalisation in frequency domain as well as the inverse B-spline neural network model obtained in time domain. The effectiveness of our nonlinear SC-FDE scheme for Hammerstein channels is demonstrated in a simulation study.

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.

Many communication signal processing applications involve modeling and inverting complex-valued (CV) Hammerstein systems. We develop a new CV B-spline neural network approach for efficient identification of the CV Hammerstein system and effective inversion of the estimated CV Hammerstein model. In particular, the CV nonlinear static function in the Hammerstein system is represented using the tensor product from two univariate B-spline neural networks. An efficient alternating least squares estimation method is adopted for identifying the CV linear dynamic model's coefficients and the CV B-spline neural network's weights, which yields the closed-form solutions for both the linear dynamic model's coefficients and the B-spline neural network's weights, and this estimation process is guaranteed to converge very fast to a unique minimum solution. Furthermore, an accurate inversion of the CV Hammerstein system can readily be obtained using the estimated model. In particular, the inversion of the CV nonlinear static function in the Hammerstein system can be calculated effectively using a Gaussian-Newton algorithm, which naturally incorporates the efficient De Boor algorithm with both the B-spline curve and first-order derivative recursions. The effectiveness of our approach is demonstrated using the application to equalization of Hammerstein channels.

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.

Joint channel estimation (CE) and turbo multiuser detection (MUD)/decoding for space-division multiple-access based orthogonal frequency-division multiplexing communication has to consider both the decision-directed CE optimisation on a continuous search space and the MUD optimisation on a discrete search space, and it iteratively exchanges the estimated channel information and the detected data between the channel estimator and the turbo MUD/decoder to gradually improve the accuracy of both the CE and the MUD. We evaluate the capabilities of a group of evolutionary algorithms (EAs) to achieve optimal or near optimal solutions with affordable complexity in this challenging application. Our study confirms that the EA assisted joint CE and turbo MUD/decoder is capable of approaching both the Cramér-Rao lower bound of the optimal channel estimation and the bit error ratio performance of the idealised optimal turbo maximum likelihood (ML) MUD/decoder associated with the perfect channel state information, respectively, despite only imposing a fraction of the complexity of the idealised turbo ML-MUD/decoder.

Opportunistic contact between moving vehicles is one of the key features in vehicular delay tolerant networks (VDTNs) that critically influences the design of routing schemes and the network throughput. Due to prohibitive costs to collect enough realistic contact recodes, to the best of our knowledge, little experiment work has been conducted to study the opportunistic contact patterns in large scale urban vehicular mobility environment. 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 some invariant characteristics of the opportunistic contacts for large scale urban VDTN. Specifically, in terms of contact duration, we find that there exists a characteristic time point, up to which and including at least 80% of the distribution, the contact duration obeys an exponential 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. In terms of contact interval, we find that its distribution can be modelled by a three-segmented distribution, and there exists a characteristic time point, up to which the contact interval obeys a power law distribution, while beyond which it decays as an exponential one. Our observations thus reveal fundamental patterns for large scale vehicular mobility, and further provide useful guidelines for the design of new urban VDTN' routing protocols and their performance evaluation.

We propose a novel reduced-complexity detector for the orthogonal frequency division multiple access (OFDMA)/single-carrier frequency division multiple access (SC-FDMA)-aided space-time shift keying (STSK) architecture. STSK employing OFDMA/SC-FDMA has recently been shown to be beneficial in dispersive multiuser downlink/uplink scenarios. These schemes exhibit excellent performance at a considerably reduced decoding complexity. In this paper, we propose a new detector, which is capable of further reducing the decoding complexity. The proposed detector is particularly suitable for STSK-based transmission over frequency-selective multiple-input multiple-output (MIMO) channels employing frequency-domain equalization (FDE). The complexity of the proposed scheme is quantified and it is observed that the scheme maintains its superior performance at a significantly reduced complexity.

In this paper, we propose multi-carrier code division multiple access (MC-CDMA)-aided space-time shift keying (STSK) for mitigating the performance erosion of the classic STSK scheme in dispersive channels, while supporting multiple users. The codewords generated by the STSK scheme are appropriately spread in frequency-domain (FD) and transmitted over a number of parallel frequency-flat subchannels. We propose a new receiver architecture amalgamating the single-stream maximum-likelihood (ML) detector of the STSK system and the multiuser detector (MUD) of the MC-CDMA system. The performance of the proposed scheme is evaluated for transmission over frequency-selective channels in both uncoded and channel-coded scenarios. The results of our simulations demonstrate that the proposed scheme overcomes the channel impairments imposed by wideband channels and exhibits near-capacity performance in a channel-coded scenario.

We propose a novel joint channel estimation and three-stage iterative detection/decoding scheme for near-capacity MIMO systems. In our scheme, as usual, the detected soft information is first exchanged a number of times within the inner turbo loop between the unity-rate-code (URC) decoder and the MIMO soft-demapper, and the information gleaned from the inner URC decoder is then iteratively exchanged with the outer decoder in the outer turbo loop. Our channel estimator however exploits the a posteriori information produced by the MIMO soft-demapper to select a sufficient blocks of high-quality detected soft bits, and it is naturally embedded into the original iterative three-stage detection/decoding process, without introducing the costly iterative loop between the decision-directed channel estimator and the three-stage turbo detector/decoder. Hence, the computational complexity of our joint channel estimation and three-stage turbo detection is similar to that of the three-stage turbo detection/decoding scheme associated with the perfect CSI. Moreover, our reduced-complexity semi-blind scheme is capable of achieving the optimal maximum-likelihood turbo detection performance attained under the perfect CSI, with the same number of turbo iterations.

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.

Joint Timing and Channel Estimation (JTCE) for bandlimited long-code-aided Multi-Carrier Direct-Sequence Code Division Multiple Access (MC-DS-CDMA) systems is investigated. We establish the optimal multiuser timing and channel estimates for the uplink MC-DS-CDMA receiver by minimising a weighted least squares cost function with respect to K independent parameters, where K is the number of active users. A guided random search procedure known as Repeated Weighted Boosting Search (RWBS) is invoked for numerically solving this challenging multivariate optimisation problem, and thereby for producing near-optimal timing and channel estimates. The Cramer-Rao Lower Bound (CRLB) for the JTCE problem of interest is derived to benchmark the performance of the proposed RWBS based estimator. Quantitatively, for the scenario of K=10 users, E_b/N_0≥3 dB where E_b is the energy per bit and N_0 the single-sided noise power spectral density, and for a near-far ratio of 10 dB, the RWBS based estimator using an observation window of 20 symbols is shown to approach the CRLB at a complexity 10 orders of magnitude lower in comparison to its full maximum likelihood search based counterpart. The proposed algorithm does not require the transmission of known pilots, yet it is capable of handling time-variant channel states.

We propose a low-complexity joint channel estimation (CE) and three-stage iterative demapping-decoding scheme for near-capacity coherent space-time shift keying (CSTSK) based multiple-input multiple-output (MIMO) systems. In the proposed scheme, only a minimum number of space-time shift keying training blocks are employed for generating an initial least square channel estimate, which is then used for initial data detection. As usual, the detected soft information is first exchanged a number of times within the inner turbo loop between the unity-rate-code (URC) decoder and the CSTSK soft-demapper, and the information gleaned from the inner URC decoder is then iteratively exchanged with the outer decoder in the outer turbo loop. Our CE scheme is embedded into the outer turbo loop, which exploits the a posteriori information produced by the CSTSK soft-demapper to select a sufficient number of high-quality decisions only for CE. Since the CE is embedded into the iterative three-stage demapping-decoding process, no additional iterative loop is required for exchanging information between the decision-directed channel estimator and the three-stage turbo detector. Hence, the computational complexity of the proposed joint CE and three-stage turbo detection remains similar to that of the three-stage turbo detection-decoding scheme with the given channel estimate. Moreover, our proposed low-complexity semi-blind scheme is capable of approaching the optimal maximum likelihood turbo detection performance attained with the aid of perfect channel state information, with the same low number of turbo iterations as the latter, as confirmed by our extensive simulation results.

A two-stage linear-in-the-parameter model construction algorithm is proposed aimed at noisy two-class classification problems. The purpose of the first stage is to produce a prefiltered signal that is used as the desired output for the second stage which constructs a sparse linear-in-the-parameter classifier. The prefiltering stage is a two-level process aimed at maximizing a model's generalization capability, in which a new elastic-net model identification algorithm using singular value decomposition is employed at the lower level, and then, two regularization parameters are optimized using a particle-swarm-optimization algorithm at the upper level by minimizing the leave-one-out (LOO) misclassification rate. It is shown that the LOO misclassification rate based on the resultant prefiltered signal can be analytically computed without splitting the data set, and the associated computational cost is minimal due to orthogonality. The second stage of sparse classifier construction is based on orthogonal forward regression with the D-optimality algorithm. Extensive simulations of this approach for noisy data sets illustrate the competitiveness of this approach to classification of noisy data problems.

A novel probability density function (PDF) estimation based over-sampling approach is proposed for two-class imbalanced classification problems. The Parzen-window kernel function is applied to estimate the PDF of the positive class, from which synthetic instances are generated as additional training data to re-balance the class distribution. Utilising the re-balanced over-sampled training data, a radial basis function (RBF) classifier is constructed by applying an orthogonal forward regression, in which the classifier's structure and the parameters of RBF kernels are determined using a particle swarm optimisation algorithm based on the criterion of minimising the leave-one-out misclassification rate. The effectiveness of the proposed approach is demonstrated by an empirical study on several imbalanced data sets.

We develop a complex-valued (CV) B-spline neural network approach for efficient identification and inversion of CV Wiener systems. The CV nonlinear static function in the Wiener system is represented using the tensor product of two univariate B-spline neural networks. With the aid of a least squares parameter initialisation, the Gauss-Newton algorithm effectively estimates the model parameters that include the CV linear dynamic model coefficients and B-spline neural network weights. The identification algorithm naturally incorporates the efficient De Boor algorithm with both the B-spline curve and first order derivative recursions. An accurate inverse of the CV Wiener system is then obtained, in which the inverse of the CV nonlinear static function of the Wiener system is calculated efficiently using the Gaussian-Newton algorithm based on the estimated B-spline neural network model, with the aid of the De Boor recursions. The effectiveness of our approach for identification and inversion of CV Wiener systems is demonstrated using the application of digital predistorter design for high power amplifiers with memory.

A Differential Evolution (DE) algorithm assisted Minimum Symbol Error Ratio (MSER) Multi-User Detection (MUD) scheme is proposed for multi-user Multiple-Input Multiple-Output (MIMO) aided Orthogonal Frequency-Division Multiplexing / Space Division Multiple Access (OFDM/SDMA) systems. Quadrature Amplitude Modulation (QAM) is employed in most wireless standards by virtue of providing a high throughput. The MSER Cost Function (CF) may be deemed to be the most relevant one for QAM, but finding its minimum is challenging. Hence we propose a sophisticated DE assisted MSER-MUD scheme, which directly minimizes the SER CF of multi-user OFDM/SDMA systems employing QAM. Furthermore, the effects of the DE assisted MSER-MUD's algorithmic parameters, namely those of the population size Ps, of the scaling factor λ and of the crossover probability Cron the number of DE generations required for attaining convergence were investigated in our simulations. This allowed us to directly quantify their complexity. The simulation results also demonstrate that the proposed DE assisted MSER-MUD scheme significantly outperforms the conventional MMSE-MUD in term of the system's overall BER and it is capable of narrowing its BER performance discrepancy with respect to the optimal Maximum Likelihood (ML) MUD to about 4dB, while requiring about 200 times less CF evaluations compared to the optimal ML-MUD scheme.

Stochastic optimization assisted joint Channel Estimation (CE) and Multi-User Detection (MUD) were conceived and compared in the context of multi-user Multiple-Input Multiple-Output (MIMO) aided Orthogonal Frequency-Division Multiplexing/Space Division Multiple Access (OFDM/SDMA) systems. The development of stochastic optimization algorithms, such as Genetic Algorithms (GA), Repeated Weighted Boosting Search (RWBS), Particle Swarm Optimization (PSO) and Differential Evolution (DE) has stimulated wide interests in the signal processing and communication research community. However, the quantitative performance versus complexity comparison of GA, RWBS, PSO and DE techniques applied to joint CE and MUD is a challenging open issue at the time of writing, which has to consider both the continuous-valued CE optimization problem and the discrete-valued MUD optimization problem. In this study we fill this gap in the open literature. Our simulation results demonstrated that stochastic optimization assisted joint CE and MUD is capable of approaching both the Cramer-Rao Lower Bound (CRLB) and the Bit Error Ratio (BER) performance of the optimal ML-MUD, respectively, despite the fact that its computational complexity is only a fraction of the optimal ML complexity.

We considers the beamforming assisted multiple-antenna receiver for space-division multiple-access based multiuser systems that employ high-throughput quadrature amplitude modulation (QAM) signalling. The bit error ratio (BER) expression as the function of the beamformer's weight vector is derived, and the minimum BER (MBER) beamforming receiver is then obtained as the solution of the resulting optimisation problem that minimises the MBER criterion. A simplified conjugate gradient algorithm, which has previously demonstrated its effectiveness in solving the minimum symbol error ratio (MSER) optimisation problem, is employed to solve this MBER optimisation. For high-order QAM, although the bit decision is an inherently more complicated procedure than making a symbol decision, it turns out that the computational complexity of computing the MBER solution is similar to that of computing the MSER solution. As expected, our simulation results show that both the MBER and MSER systems achieve the same BER performance, and they significantly outperform the standard minimum mean squares error based solution.

A differential evolution (DE) algorithm aided iterative channel estimation and turbo multi-user detection (MUD) scheme is proposed for multi-user multi-input multiple-output aided orthogonal frequency-division multiplexing / space-division multiple-access (OFDM/SDMA) systems. The proposed scheme iteratively exchanges the estimated channel information and the detected data between the channel estimator and MUD employing a turbo technique, which gradually improves the accuracy of the channel estimation and the MUD, especially for the first iteration. Quadrature amplitude modulation (QAM) is employed in most wireless standards by virtue of providing a high throughput. However, the optimal maximum likelihood (ML)-MUD becomes extremely complex for employment in QAM-aided multi-user systems. Hence, two different DE aided MUD schemes, the DE aided minimum symbol error rate (MSER)-MUD as well as the discrete DE aided ML-MUD, were developed, and their achievable performance versus complexity was characterized. The simulation results demonstrate that the proposed DE aided channel estimator is capable of approaching the Cramer-Rao lower bound with just two or three iterations. The ultimate bit error rate lower-bound of the single-user additive white Gaussian noise scenario has been approached in the range of Eb/ N0≥ 10 dB and Eb/ N0≥ 6 dB for the DE aided MSER-MUD and the discrete DE aided ML-MUD, respectively.

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.

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.

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.

A joint channel estimation and Multi-User Detection (MUD) scheme is proposed for multi-user Multiple-Input Multiple-Output (MIMO) Space Division Multiple Access / Orthogonal Frequency-Division Multiplexing (SDMA/OFDM) systems. We design a Dual Repeated Weighted Boosting Search (DRWBS) scheme for joint channel estimation and MUD, which is capable of providing 'soft' outputs, directly fed to the Forward Error Correction (FEC) decoder. The proposed scheme reduces the complexity of the receiver, since it integrates the channel estimation and MUD into a single module and it forwards the Log-Likelihood Ratios (LLRs) to the channel decoder. It also provides an effective solution to the multi-user MIMO channel estimation and MUD problem in ``rank-deficient'' scenarios, when the number of users is higher than the number of receiver antennas. The simulation results demonstrate that the proposed scheme is capable of attaining a BER performace close to the ideal scenario of the Maximum Likelihood (ML) MUD associated with perfect channel knowledge.

The combination of the synthetic minority oversampling technique (SMOTE) and the radial basis function (RBF) classifier is proposed to deal with classification for imbalanced two-class data. In order to enhance the significance of the small and specific region belonging to the positive class in the decision region, the SMOTE is applied to generate synthetic instances for the positive class to balance the training data set. Based on the over-sampled training data, the RBF classifier is constructed by applying the orthogonal forward selection procedure, in which the classifier structure and the parameters of RBF kernels are determined using a particle swarm optimization algorithm based on the criterion of minimizing the leave-one-out misclassification rate. The experimental results on both simulated and real imbalanced data sets are presented to demonstrate the effectiveness of our proposed algorithm.

In this paper a new nonlinear digital baseband predistorter design is introduced based on direct learning, together with a new Wiener system modeling approach for the high power amplifiers (HPA) based on the B-spline neural network. The contribution is twofold. Firstly, by assuming that the nonlinearity in the HPA is mainly dependent on the input signal amplitude the complex valued nonlinear static function is represented by two real valued B-spline neural networks, one for the amplitude distortion and another for the phase shift. The Gauss-Newton algorithm is applied for the parameter estimation, in which the De Boor recursion is employed to calculate both the B-spline curve and the first order derivatives. Secondly, we derive the predistorter algorithm calculating the inverse of the complex valued nonlinear static function according to B-spline neural network based Wiener models. The inverse of the amplitude and phase shift distortion are then computed and compensated using the identified phase shift model. Numerical examples have been employed to demonstrate the efficacy of the proposed approaches.

An adaptive semi-blind space-time equaliser (STE) has recently been proposed based on a concurrent gradient Newton constant modulus algorithm and soft decision-directed scheme for dispersive multiple-input multiple-output (MIMO) systems that employ high-throughput quadrature amplitude modulation signalling. We investigate the performance of this adaptive semi-blind STE operating in Rayleigh fading MIMO systems. Our results obtained show that the tracking performance of this semi-blind adaptive algorithm is close to that of the training-based recursive least squares algorithm. This study, therefore, demonstrates that the proposed semi-blind algorithm offers a practical means to adapt a STE in the hostile dispersive Rayleigh fading MIMO environment.

We develop a semi-blind adaptive space-time shift keying (STSK) based multiple-input multiple-output system using a low-complexity iterative channel estimation and data detection scheme. We first employ the minimum number of STSK training blocks, which is related to the number of transmitter antennas, to obtain a rough least square channel estimate (LSCE). Low-complexity single-stream maximum likelihood (ML) data detection is then carried out based on the initial LSCE and the detected data are utilised to refine the decision-directed LSCE. We show that a few iterations are sufficient to approach the optimal ML detection performance obtained with the aid of perfect channel state information.

Motivated by the recent concept of Space-Time Shift Keying (STSK), we propose a novel cooperative STSK family, which is capable of achieving a flexible rate-diversity tradeoff, in the context of cooperative space-time transmissions. More specifically, we first propose a Coherent cooperative STSK (CSTSK) scheme, where each Relay Node (RN) activates Decode and-Forward (DF) transmissions, depending on the success or failure of Cyclic Redundancy Checking (CRC). We invoke a bit to-STSK mapping rule, where according to the input bits, one of the Q pre-assigned dispersion vectors is activated to implicitly convey log2Q bits, which are transmitted in combination with the classic log2L-bit modulated symbol. Additionally, we introduce a beneficial dispersion vector design, which enables us to dispense with symbol-level Inter-Relay Synchronization (IRS). Further more, the Destination Node (DN) is capable of jointly detecting the signals received from the source-destination and relay destination links, using a low-complexity single-stream-based Maximum Likelihood (ML) detector, which is an explicit benefit of our Inter-Element Interference (IEI)-free system model. More importantly, as a benefit of its design flexibility, our cooperative CSTSK arrangement enables us to adapt the number of the RNs, the transmission rate as well as the achievable diversity order. Moreover, we also propose a Differentially-encoded cooperative STSK (DSTSK) arrangement, which dispenses with CSI estimation at any of the nodes, while retaining the fundamental benefits of the cooperative CSTSK scheme.

In this brief, a new complex-valued B-spline neural network is introduced in order to model the complex-valued Wiener system using observational input/output data. The complex-valued nonlinear static function in the Wiener system is represented using the tensor product from two univariate B-spline neural networks, using the real and imaginary parts of the system input. Following the use of a simple least squares parameter initialization scheme, the Gauss-Newton algorithm is applied for the parameter estimation, which incorporates the De Boor algorithm, including both the B-spline curve and the first-order derivatives recursion. Numerical examples, including a nonlinear high-power amplifier model in communication systems, are used to demonstrate the efficacy of the proposed approaches.

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.

In this paper, motivated by the recent concept of Spatial Modulation (SM), we propose a novel Generalized Space-Time Shift Keying (G-STSK) architecture, which acts as a unified Multiple-Input Multiple-Output (MIMO) framework. More specifically, our G-STSK scheme is based on the rationale that P out of Q dispersion matrices are selected and linearly combined in conjunction with the classic PSK/QAM modulation, where activating P out of Q dispersion matrices provides an implicit means of conveying information bits in addition to the classic modem. Due to its substantial flexibility, our G-STSK framework includes diverse MIMO arrangements, such as SM, Space-Shift Keying (SSK), Linear Dispersion Codes (LDCs), Space-Time Block Codes (STBCs) and Bell Lab's Layered Space-Time (BLAST) scheme. Hence it has the potential of subsuming all of them, when flexibly adapting a set of system parameters. Moreover, we also derive the Discrete-input Continuous-output Memoryless Channel (DCMC) capacity for our G-STSK scheme, which serves as the unified capacity limit, hence quantifying the capacity of the class of MIMO arrangements. Furthermore, EXtrinsic Information Transfer (EXIT) chart analysis is used for designing our G-STSK scheme and for characterizing its iterative decoding convergence.

Motivated by the recent concept of Space-Time Shift Keying (STSK), we propose a novel cooperative STSK scheme, which is capable of achieving a flexible rate-diversity tradeoff, in the context of cooperative space-time transmissions. More specifically, in our cooperative STSK scheme each Relay Node (RN) activates Decode-and-Forward (DF) transmissions, depending on the success or failure of Cyclic Redundancy Checking (CRC). We propose a novel bit-to STSK mapping rule, where according to the input bits, one of the Q pre-assigned dispersion vectors is activated to implicitly convey log2 Q bits, which are transmitted in combination with the classic log2(L)-bit modulated symbol. Additionally, we introduce a beneficial dispersion vector design, which enables us to dispense with symbol-level Inter-Relay Synchronization (IRS). Furthermore, the Destination Node (DN) is capable of jointly detecting the signals received from the source-destination and relay-destination links, using a low-complexity single-stream-based Maximum Likelihood (ML) detector, which is an explicit benefit of our Inter-Element Interference (IEI)-free system model. More importantly, as a benefit of its design flexibility, our cooperative STSK arrangement enables us to adapt the number of the RNs, the transmission rate as well as the achievable diversity order.

In this paper, we propose a novel Space-Time Shift Keying (STSK) modulation scheme for MIMO communication systems, where the concept of spatial modulation is extended to include both the space and time dimensions, in order to provide a general shift-keying framework. More specifically, in the proposed STSK scheme one out of Q dispersion matrices is activated during each transmitted block, which enables us to strike a flexible diversity and multiplexing tradeoff. This is achieved by optimizing both the space-time block duration as well as the number of the dispersion matrices in addition to the number of transmit and receive antennas. We will demonstrate that the resultant equivalent system model does not impose any inter-channel interference, and hence the employment of single-stream maximum likelihood detection becomes realistic at a low-complexity. Furthermore, we propose a Differential STSK (DSTSK) scheme, assisted by the Cayley unitary transform, which does not require any Channel State Information (CSI) at the receiver. Naturally, dispensing with CSI is achieved at the cost of the usual error-doubling in comparison to Coherent STSK (CSTSK). Additionally, we introduce an enhanced CSTSK scheme, which avoids the requirement of inter-antenna synchronization between the RF chains associated with the transmit antenna elements by imposing a certain constraint on the dispersion matrix design.

In this paper, we propose a differential Space-Time Coding (STC) scheme designed for asynchronous cooperative networks, where neither channel estimation nor symbol-level synchronization is required at the cooperating nodes. More specifically, our system employs differential encoding during the broadcast phase and a Space-Time Spreading (STS)-based amplify-and-forward scheme during the cooperative phase in conjunction with interference rejection Direct Sequence (DS) spreading codes, namely Loosely Synchronized (LS) codes. The LS codes exhibit a so-called Interference Free Window (IFW), where both the autocorrelation and cross-correlation values of the codes become zero. The IFW allows us to eliminate both the Multi-User Interference (MUI) as well as the potential dispersion-induced orthogonality degradation of the cooperative space-time codeword and the interference imposed by the asynchronous transmissions of the relay nodes. Furthermore, the destination node can beneficially combine both the directly transmitted and the relayed symbols using low-complexity correlation operations combined with a hard-decision detector. Our simulation results demonstrate that the proposed Cooperative Differential STS (CDSTS) scheme is capable of combating the effects of asynchronous uplink transmissions without any Channel State Information (CSI), provided that the maximum synchronization delay of the relay nodes is within the width of IFW. It will be demonstrated that in the frequency-selective environment considered our CDSTS arrangement is capable of exploiting both space-time diversity and multi-path diversity with the aid of a RAKE combiner.

We develop a particle swarm optimisation (PSO) aided orthogonal forward regression (OFR) approach for constructing radial basis function (RBF) classifiers with tunable nodes. At each stage of the OFR construction process, the centre vector and diagonal covariance matrix of one RBF node is determined efficiently by minimising the leave-one-out (LOO) misclassification rate (MR) using a PSO algorithm. Compared with the state-of-the-art regularisation assisted orthogonal least square algorithm based on the LOO MR for selecting fixed-node RBF classifiers, the proposed PSO aided OFR algorithm for constructing tunable-node RBF classifiers offers significant advantages in terms of better generalisation performance and smaller model size as well as imposes lower computational complexity in classifier construction process. Moreover, the proposed algorithm does not have any hyperparameter that requires costly tuning based on cross validation.

A sparse kernel density estimator is derived based on the zero-norm constraint, in which the zero-norm of the kernel weights is incorporated to enhance model sparsity. The classical Parzen window estimate is adopted as the desired response for density estimation, and an approximate function of the zero-norm is used for achieving mathematical tractability and algorithmic efficiency. Under the mild condition of the positive definite design matrix, the kernel weights of the proposed density estimator based on the zero-norm approximation can be obtained using the multiplicative nonnegative quadratic programming algorithm. Using the D-optimality based selection algorithm as the preprocessing to select a small significant subset design matrix, the proposed zero-norm based approach offers an effective means for constructing very sparse kernel density estimates with excellent generalisation performance.

In this paper, we propose a generalized multiple-input multiple-output (MIMO) transmit preprocessing system, where both the channel coding and the linear MIMO transmit precoding components exploit the knowledge of the channel. Moreover, we also propose a novel technique, hereby referred to as pilot symbol assisted rateless (PSAR) coding, where a predetermined fraction of binary pilot symbols is interspersed with the channel-coded bits at the channel coding stage, instead of multiplexing the pilots with the data symbols at the modulation stage, as in classic pilot symbol assisted modulation (PSAM). We will subsequently demonstrate that the PSAR code-aided transmit preprocessing scheme succeeds in gleaning more beneficial knowledge from the inserted pilots, because the pilot bits are not only useful for estimating the channel at the receiver, but they are also beneficial in terms of significantly reducing the computational complexity of the rateless channel decoder.

In this paper, motivated by the recent concept of Spatial Modulation (SM), we propose a novel Generalized Space-Time Shift Keying (G-STSK) architecture, which acts as a unified Multiple-Input Multiple-Output (MIMO) framework. More specifically, our G-STSK scheme is based on the rationale that P out of Q dispersion matrices are selected and linearly combined in conjunction with the classic PSK/QAM modulation, where activating P out of Q dispersion matrices provides an implicit means of conveying information bits in addition to the classic modem. Due to its substantial flexibility, our G-STSK framework includes diverse MIMO arrangements, such as SM, Space-Shift Keying (SSK), Linear Dispersion Codes (LDCs), Space-Time Block Codes (STBCs) and Bell Lab's Layered Space-Time (BLAST) scheme. Hence it has the potential of subsuming all of them, when flexibly adapting a set of system parameters. Moreover, we also derive the Discrete-input Continueousoutput Memoryless Channel (DCMC) capacity for our G-STSK scheme, which serves as the unified capacity limit, hence quantifying the capacity of diverse MIMO arrangements. Furthermore, EXtrinsic Information Transfer (EXIT) chart analysis is used for designing our G-STSK scheme and for characterizing its iterative decoding convergence.

This paper proposes a cooperative space-time coding (STC) protocol, amalgamating the concepts of asynchronous cooperation, non-coherent detection as well as Distributed Turbo Coding (DTC), where neither symbol-level time synchronization nor CSI estimation is required at any of the cooperating nodes, while attaining a high performance even at low SNRs. More specifically, a practical cooperative differential space-time spreading (CDSTS) scheme is designed with the aid of interference rejection spreading codes, in order to eliminate the effect of synchronization errors between the relay nodes without the assistance of channel estimation or equalization. Furthermore, a set of space-time codewords are constructed based on Differential Linear Dispersion Codes (DLDC), which allows our CDSTS system to support an arbitrary number of relay nodes operating at a high transmission rate due to its flexible design. Rather than using conventional single-relay-assisted DTCs, novel multi-relay-assisted DTCs and a three-stage iteratively-decoded destination receiver structure are developed. In our simulations the system parameters are designed with the aid of EXIT chart analysis, followed by the characterization of the achievable BER performance for various synchronization delay values as well as for various diversity-multiplexing relationships in frequency-selective fast and/or quasi-static Rayleigh fading environments.

A generalised vector precoding (VP) design based on the minimum bit error rate (MBER) criterion is proposed for multiuser transmission in the downlink of a multiuser system where the base station (BS) equipped with multiple transmit antennas communicates with single-receive-antenna mobile station (MS) receivers each having a modulo detection device. Our transmit preprocessing scheme generates the effective symbol vector based on the MBER criterion, given the knowledge of the channel state information and the current information symbol vector to be transmitted. The proposed MBER based generalised VP algorithm is shown to outperform even the powerful minimum mean-square-error VP benchmark, particularly for rank-deficient systems where the number of BS's transmit antennas is smaller than the number MSs supported.

A low-complexity semi-blind joint channel estimation and data detection scheme is proposed for space-time shift keying (STSK) based multiple-input multiple-output systems. The minimum number of STSK training blocks, which is related to the number of transmitter antennas, is first utilized to provide a rough initial least square channel estimate (LSCE). Then low-complexity single-stream maximum likelihood (ML) data detection is carried out based on the initial LSCE and the detected data are employed to refine the decision-directed LSCE. It is demonstrated that a few iterations are sufficient to approach the optimal ML detection performance obtained with the perfect channel state information.

This letter proposes a differential Space-Time Coding (STC) scheme designed for asynchronous cooperative networks, where neither channel estimation nor symbol-level synchronization is required at the cooperating nodes. More specifically, our system employs differential encoding during the broadcast phase and a Space-Time Spreading (STS)-based amplify-and-forward scheme during the cooperative phase in conjunction with interference rejection direct sequence spreading codes, namely Loosely Synchronized (LS) codes. Our simulation results demonstrate that the proposed Cooperative Differential STS (CDSTS) scheme is capable of combating the effects of asynchronous uplink transmissions without any channel state information.

Motivated by the recent concept of Spatial Modulation (SM), we propose a novel Space-Time Shift Keying (STSK) modulation scheme for Multiple-Input Multiple-Output (MIMO) communication systems, where the concept of SM is extended to include both the space and time dimensions, in order to provide a general shift-keying framework. More specifically, in the proposed STSK scheme one out of Q dispersion matrices is activated during each transmitted block, which enables us to strike a flexible diversity and multiplexing tradeoff. This is achieved by optimizing both the space-time block duration as well as the number of the dispersion matrices in addition to the number of transmit and receive antennas. We will demonstrate that the resultant equivalent system model does not impose any Inter-Channel Interference (ICI), and hence the employment of single-stream Maximum Likelihood (ML) detection becomes realistic at a low-complexity. Furthermore, we propose a Differential STSK (DSTSK) scheme, assisted by the Cayley unitary transform, which does not require any Channel State Information (CSI) at the receiver. {Here, the usual error-doubling, caused by the differential decoding, gives rise to 3-dB performance penalty in comparison to Coherent STSK (CSTSK).} Additionally, we introduce an enhanced CSTSK scheme, which avoids the requirement of Inter-Antenna Synchronization (IAS) between the RF chains associated with the transmit {Antenna Elements (AEs)} by imposing a certain constraint on the dispersion matrix design{, where each column of the dispersion matrices includes only a single non-zero component}. Moreover, according to the turbo-coding principle, the proposed CSTSK and DSTSK schemes are combined with multiple serially concatenated codes and an iterative bit-to-symbol soft-demapper{. More specifically,} the associated STSK parameters are optimized with the aid of EXtrinsic Information Transfer (EXIT) charts{, for the sake of achieving a near-capacity performance}.

We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density function estimation. A particle swarm optimization (PSO) aided orthogonal forward regression (OFR) algorithm based on leave-one-out (LOO) criteria is developed to construct parsimonious radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines the center vector and diagonal covariance matrix of one RBF node by minimizing the LOO statistics. For regression applications, the LOO criterion is chosen to be the LOO mean square error, while the LOO misclassification rate is adopted in two-class classification applications. By adopting the Parzen window estimate as the desired response, the unsupervised density estimation problem is transformed into a constrained regression problem. This PSO aided OFR algorithm for tunable-node RBF networks is capable of constructing very parsimonious RBF models that generalize well, and our analysis and experimental results demonstrate that the algorithm is computationally even simpler than the efficient regularization assisted orthogonal least square algorithm based on LOO criteria for selecting fixed-node RBF models. Another significant advantage of the proposed learning procedure is that it does not have learning hyperparameters that have to be tuned using costly cross validation. The effectiveness of the proposed PSO aided OFR construction procedure is illustrated using several examples taken from regression and classification, as well as density estimation applications.

A generalized or tunable-kernel model is proposed for probability density function estimation based on an orthogonal forward regression procedure. Each stage of the density estimation process determines a tunable kernel, namely, its center vector and diagonal covariance matrix, by minimizing a leave-one-out test criterion. The kernel mixing weights of the constructed sparse density estimate are finally updated using the multiplicative nonnegative quadratic programming algorithm to ensure the nonnegative and unity constraints, and this weight-updating process additionally has the desired ability to further reduce the model size. The proposed tunable-kernel model has advantages, in terms of model generalization capability and model sparsity, over the standard fixed-kernel model that restricts kernel centers to the training data points and employs a single common kernel variance for every kernel. On the other hand, it does not optimize all the model parameters together and thus avoids the problems of high-dimensional ill-conditioned nonlinear optimization associated with the conventional finite mixture model. Several examples are included to demonstrate the ability of the proposed novel tunable-kernel model to effectively construct a very compact density estimate accurately.

In this paper, we propose a generalized multipleinput multiple-output (MIMO) transmit preprocessing system, where both the channel coding and the linear MIMO transmit precoding components exploit the knowledge of the channel. This was achieved by exploiting the inherently flexible nature of a specific family of rateless codes that are capable of modifying their code-rate as well as their degree distribution based on the channel state information (CSI), in an attempt to adapt to the time-varying nature of the channel. Moreover, we also propose a novel technique, hereby referred to as pilot symbol assisted rateless (PSAR) coding, where a predetermined fraction of binary pilot symbols is interspersed with the channel-coded bits at the channel coding stage, instead of multiplexing the pilots with the data symbols at the modulation stage, as in classic pilot symbol assisted modulation (PSAM). We will subsequently demonstrate that the PSAR code-aided transmit preprocessing scheme succeeds in gleaning more beneficial knowledge from the inserted pilots, because the pilot bits are not only useful for estimating the channel at the receiver, but they are also beneficial in terms of significantly reducing the computational complexity of the rateless channel decoder. Our results suggest that more than a 30% reduction in the decoder's computational complexity can be attained by the proposed system, when compared to a corresponding benchmarker scheme having the same pilot overhead but using the PSAM technique.

Semi-blind space-time equalisation is considered for dispersive multiple-input multiple-output systems that employ high-throughput quadrature amplitude modulation signalling. A minimum number of training symbols, approximately equal to the dimension of the space-time equaliser (STE), are first utilised to provide a rough initial least squares estimate of the STE's weight vector. A gradient-Newton-type concurrent constant modulus algorithm and soft decision-directed scheme is then applied to adapt the STE. The proposed semi-blind adaptive STE is capable of converging fast and accurately to the optimal minimum mean square error STE solution.

In this paper we consider the low-complexity detection in hybrid direct-sequence time-hopping ultrawide bandwidth (DS-TH UWB) systems. A reduced-rank adaptive LBER detector is proposed, which is operated in the least bit error-rate (LBER) principles within a detection subspace obtained with the aid of the principal component analysis (PCA)-assisted reduced-rank technique. Our reduced-rank adaptive LBER detector is free from channel estimation and does not require the knowledge about the number of resolvable multipaths as well as that about the multipaths' strength. In this paper the bit error-rate (BER) performance of the hybrid DS-TH UWB system is investigated, when communicating over the UWB channels modelled by the Saleh-Valenzuela (S-V) channel model. Our study and simulation results show that this reduced-rank adaptive LBER detector constitutes a feasible detection scheme for deployment in practical pulse-based UWB systems.

The powerful tool of EXtrinsic Information Transfer (EXIT) charts is used to design a new serially concatenated Irregular Variable Length Coded (IrVLC) and unity-rate precoded Time-Hopping (TH) Pulse Position Modulation (PPM) aided Ultra-Wide Bandwidth (UWB) Spread-Spectrum (SS) impulse radio for near-capacity operation in Nakagami-m fading channels contaminated by Partial Band Noise Jamming (PBNJ). The benefits of the 3-stage concatenation of the TH-UWB detector, the unity-rate decoder and the outer IrVLC decoder are quantified. A number of novel Variable Length Coding (VLC) codebooks having different coding rates are utilized by the IrVLC scheme for encoding specific fractions of the input source symbol stream. More explicitly, EXIT charts are employed to appropriately select these input stream fractions to shape the inverted EXIT curve of the IrVLC in order to match that of the inner decoder and hence to achieve an infinitesimally low Bit Error Ratio (BER) at near-capacity SNR values.

In this paper, we introduce a novel Markov chain (MC) representation aided minimum bit error rate (MBER) detection method that is applicable to an M-QAM modulated SDM/SDMA uplink system. Compared to the conventional MBER scheme, the proposed MC-MBER scheme is capable of reducing the complexity imposed with the aid of its efficient detection candidate set generation assisted by the Markov chain process. Our performance results demonstrate that the MC-MBER multi-user detection (MUD) is capable of reducing the computational complexity by a factor of eight in comparison to the conventional MBER MUD in a rank-deficient system transmitting four 4-QAM uplink substream with the aid of two receive antennas at the base station (BS), while achieving a BER performance comparable to that of the MBER MUD.

We consider the downlink of multiuser system from a transmitter equipped with multiple antennas to multiple non-cooperative single-antenna mobile receivers. Particle swarm optimisation (PSO) algorithm is invoked to solve the constrained nonlinear optimisation problem for the minimum bit error rate (MBER) multiuser transmission (MUT). The proposed PSO aided MBER-MUT scheme provides much better performance over the conventional minimum mean-square-error MUT scheme, and it achieves a much lower complexity compared to the state-of-the-art sequential quadratic programming based MBER MUT.

We propose novel reconfigurable rateless codes, that are capable of not only varying the block length but also adaptively modify their encoding strategy by incrementally adjusting their degree distribution according to the prevalent channel conditions without the availability of the channel state information at the transmitter. In particular, we characterize a reconfigurable rateless code designed for the transmission of 9,500 information bits that achieves a performance, which is approximately 1 dB away from the discrete-input continuous-output memoryless channel's (DCMC) capacity over a diverse range of channel signal-to-noise (SNR) ratios.

Irregular Variable Length Coding Design for serial concatenated and iteratively decoded Time-Hopping (TH) Pulse Position Modulation (PPM) UltraWideBand (UWB) Spread-Spectrum (SS) Impulse radio system is considered. The proposed design is capable of low Signal-to-Noise Ratio (SNR) operation in Nakagami-m fading channel amalgamated with joint source and channel coding schemes. A number of component Variable Length Coding (VLC) codebooks with different coding rates are being utilized by IVLC scheme for encoding specific fractions of the input source symbol stream. The EXtrinsic Information Transfer (EXIT) charts are used to select these fractions in order to shape inverted EXIT curve of IVLC according to EXIT curve of the inner decoder match. The proposed scheme can achieve near-zero bit error ratio at low SNR values. This IVLC based scheme provides a gain of up to 0.45 dB over the identical-rate single class VLC based scheme.

A near-capacity time hopping (TH) pulse position modulation (PPM) based ultrawideband (UWB) impulse radio (IR) system is proposed, which invokes iteratively detected self-concatenated convolutional codes (SeCCC) and employs powerful design technique of extrinsic information transfer (EXIT) charts. Orthogonal prolate spheroidal wave function (OPSWF) based signalling pulse shapes are used for the sake of minimizing the multi-user interference (MUI) and intersymbol interference (ISI). Recursive systematic convolutional (RSC) codes are employed as constituent codes combined with an interleaver for randomising the extrinsic information exchange between the constituent codes. Furthermore, a puncturer assists us in increasing the achievable bandwidth efficiency. Iterative decoding is invoked for exchanging extrinsic information between the hypothetical decoder components at the receiver end. The convergence behaviour of the decoder is analysed with the aid of bit-based EXIT charts. Finally, we propose a novel THPPM-UWB-IR-SeCCC system configuration, which is capable of operating within about 0.9 dB of the information-theoretic limits.

This paper investigates the effect of different array geometries on the performance of the turbo coding assisted beamforming uplink. More specifically, we focus on the maximum achievable rate as a measure of the system performance, which is calculated with the aid of EXIT chart analysis. Our performance results recorded for K = 4 uplink receiver antenna elements at the Base Station (BS) supporting M = 4 or M = 7 users demonstrated that the Hexagonal Array (HA) slightly outperforms the Uniform Linear Array (ULA) and Uniform Circular Array (UCA), when we have a low angular spread ¿ for the Direction-Of-Arrival (DOA) of each user. It is also demonstrated that the performance difference becomes smaller upon increasing the angular spread ¿.

A novel vector precoding scheme is proposed for the downlink of a multiuser system equipped with multiple antennas transmitting to single-antenna aided mobile receivers. Our transmit preprocessing scheme first invokes a regularized channel inversion and then superimposes a perturbation vector to directly minimize the bit error ratio (BER) of the system as an improvement to the well-known minimum mean-square-error (MMSE) vector precoding scheme. Our simulation results demonstrate that the proposed vector precoding scheme achieves the same BER performance as the MMSE vector precoding at the same complexity, when only discrete vector perturbations are allowed. However, the performance of the proposed vector precoding scheme can be further improved based on the MBER criterion, when continuous-valued vector perturbations are carried out.

A novel Markov chain (MC) representation aided minimum bit error rate (MBER) detection method is proposed for anM-QAM modulated SDM/SDMA uplink system. Compared to the conventional MBER scheme, the proposed MC-MBER scheme is capable of reducing the complexity imposed with the aid of its efficient detection candidate set generation assisted by the Markov chain process. Our performance results demonstrate that the MC-MBER multiuser detection (MUD) is capable of reducing the computational complexity by a factor of eight in comparison to the conventional MBER MUD in a rank-deficient system transmitting four 4-QAM substreams with the aid of two receive antennas, while achieving a BER performance comparable to that of the MBER MUD.

An orthogonal forward selection (OFS) algorithm based on leave-one-out (LOO) criteria is proposed for the construction of radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines an RBF node, namely, its center vector and diagonal covariance matrix, by minimizing the LOO statistics. For regression application, the LOO criterion is chosen to be the LOO mean-square error, while the LOO misclassification rate is adopted in two-class classification application. This OFS-LOO algorithm is computationally efficient, and it is capable of constructing parsimonious RBF networks that generalize well. Moreover, the proposed algorithm is fully automatic, and the user does not need to specify a termination criterion for the construction process. The effectiveness of the proposed RBF network construction procedure is demonstrated using examples taken from both regression and classification applications.

We present a novel topology of the radial basis function (RBF) neural network, referred to as the boundary value constraints (BVC)-RBF, which is able to automatically satisfy a set of BVC. Unlike most existing neural networks whereby the model is identified via learning from observational data only, the proposed BVC-RBF offers a generic framework by taking into account both the deterministic prior knowledge and the stochastic data in an intelligent manner. Like a conventional RBF, the proposed BVC-RBF has a linear-in-the-parameter structure, such that it is advantageous that many of the existing algorithms for linear-in-the-parameters models are directly applicable. The BVC satisfaction properties of the proposed BVC-RBF are discussed. Finally, numerical examples based on the combined D-optimality-based orthogonal least squares algorithm are utilized to illustrate the performance of the proposed BVC-RBF for completeness.

We propose novel reconfigurable rateless codes, that are capable of not only varying the block length but also adaptively modify their encoding strategy by incrementally adjusting their degree distribution according to the prevalent channel conditions without the availability of the channel state information at the transmitter. In particular, we characterize a reconfigurable rateless code designed for the transmission of 9,500 information bits that achieves a performance, which is approximately 1 dB away from the discrete-input continuous-output memoryless channel's (DCMC) capacity over a diverse range of channel signal-to-noise (SNR) ratios.

A novel semi-blind space-time equaliser (STE) is proposed for dispersive multiple-input multiple-output systems that employ high-throughput quadrature amplitude modulation signalling. A minimum number of training symbols, approximately equal to the dimension of the STE, are first utilised to provide a rough initial least squares estimate of the STE's weight vector. A concurrent gradient-Newton constant modulus algorithm and soft decision-directed scheme is then applied to adapt the STE. The proposed semi-blind adaptive STE is capable of converging fast to the minimum mean square error STE solution. Simulation results confirms that the convergence speed of this semi-blind adaptive algorithm is very close to that of the training-based recursive least squares algorithm.

We consider the downlink of a multiuser system equipped with multiple antennas transmitting to multiple single-antenna mobile receivers. Particle swarm optimisation (PSO) is invoked to solve the constrained nonlinear optimisation problem for the minimum bit error rate (MBER) multiuser transmitter (MUT). The proposed PSO aided symbol-specific MBER-MUT and average MBER-MUT schemes provide improved performance in comparison to the conventional minimum mean-square-error MUT scheme, while imposing a reduced complexity compared to the state-of-the-art sequential quadratic programming based symbol-specific MBER-MUT and average MBER-MUT schemes, respectively.

Low-density parity-check (LDPC) codes are typically characterized by a relatively high-complexity description, since a considerable amount of memory is required in order to store their code description, which can be represented either by the connections of the edges in their Tanner graph or by the non-zero entries in their parity-check matrix (PCM). This problem becomes more pronounced for pseudo-random LDPC codes, where literally each non-zero entry of their PCM has to be enumerated, and stored in a look-up table. Therefore, they become inadequate for employment in memory- constrained transceivers. Motivated by this, we are proposing a novel family of structured LDPC codes, termed as Multilevel Structured (MLS) LDPC codes, which benefit from reduced storage requirements, hardware-friendly implementations as well as from low-complexity encoding and decoding. Our simulation results demonstrate that these advantages accrue without any compromise in their attainable Bit Error Ratio (BER) performance, when compared to their previously proposed more complex counterparts of the same code-length. In particular, we characterize a half-rate quasi-cyclic (QC) MLS LDPC code having a block length of 8064 that can be uniquely and unambiguously described by as few as 144 edges, despite exhibiting an identical BER performance over both Additive White Gaussian Noise (AWGN) and uncorrelated Rayleigh (UR) channels, when compared to a pseudorandom construction, which requires the enumeration of a significantly higher number of 24,192 edges.

A nonlinear beamforming aided detector is proposed for multiple-antenna assisted quadrature phase shift keying systems. By exploiting the inherent symmetry of the optimal Bayesian detection solution, a symmetric radial basis function (SRBF) detector is developed which is capable of approaching the optimal Bayesian performance using channel-impaired training data. In the uplink case, adaptive nonlinear beamforming can be implemented effectively by estimating the channel matrix based on the least squares channel estimate. Adaptive implementation of nonlinear beamforming in the downlink case by contrast is much more challenging, and we adopt a cluster-variation enhanced clustering algorithm to directly identify the SRBF centre vectors required for realising the optimal Bayesian detector.

Semi-blind spatial equalisation is considered for multiple-input multiple-output (MIMO) systems that employ high-throughput quadrature amplitude modulation scheme. A minimum number of training symbols, equal to the number of transmitters, are first utilised to provide a rough least squares channel estimate of the system's MIMO channel matrix for the initialisation of the spatial equalisers' weight vectors. A constant modulus algorithm aided soft decision-directed blind algorithm is then employed to adapt the spatial equalisers. This semi- blind scheme has a very-low computational complexity, and it converges fast to the minimum mean-square-error spatial equalisation solution as demonstrated in our simulation study.

Based on an Extrinsic Information Transfer (EXIT) chart-assisted receiver design, a low-complexity near-Maximum A Posteriori (MAP) detector is constructed for high-throughput systems. A high throughput is achieved by invoking high-order modulation schemes or multiple transmit antennas, while employing a novel sphere detector (SD) termed as a center-shifting SD scheme. The center-shifting SD is assisted by the MMSE soft- interference-cancellation (SIC-MMSE) algorithm. The resultant scheme is capable of attaining a considerable complexity reduction over the conventional SD-aided iterative benchmark receiver. For example, the SIC-MMSE center-shifting scheme may enable the iterative receiver to achieve a near-MAP performance in the challenging scenario of an (8 x 4)-element rank-deficient 4-QAM SDM/OFDM system. This near-MAP performance is achieved, despite imposing a reduced detection-candidate-list-generation- related complexity, which is about an order of magnitude lower than that exhibited by the list-SD dispensing with the proposed center-shifting scheme. As a further benefit, the computational complexity associated with the extrinsic LLR calculation was reduced by a factor of about 64. The associated memory requirements were also reduced by a factor of 64.

A novel iteratively detected Sphere Packing (SP) modulation aided Orthogonal Frequency Division Multiplexing (OFDM) scheme is proposed, which we refer to as the SP- OFDM arrangement. The advocated SP-OFDM system outperforms conventional OFDM dispensing with SP. As its upper- bound performance limit, the Discrete Input Continuous Output Memoryless Channel (DCMC) capacity of this system is formulated. In order to maximize the DCMC channel capacity, a SP-to- OFDM-sub-carrier mapper (SPTSCM) is contrived, which is capable of providing an approximately 0.3 bit/s/Hz DCMC capacity benefit, by employing a variety of different SPTSCM arrangements. Additionally, the proposed SP-OFDM scheme exhibits a DCMC capacity advantage of 0.8 bit/s/Hz over the conventional equivalent-throughput Gray Mapping (GM) and Anti-Gray Mapping (AGM) based QPSK-OFDM schemes. Furthermore, the performance of the SP-OFDM system can be improved by serially concatenated convolutional coding relying on iterative extrinsic information exchange between the SP-symbol-to-bit demapper and the channel decoder. Explicitly, the proposed turbo-detected SP-OFDM scheme exhibits an approximately Eb/N0= 3 dB and Eb/N0= 4.5 dB gain at a Bit Error Ratio (BER) of 10-4over the equivalent- throughput turbo-detected GM based QPSK-OFDM scheme and AGM QPSK-OFDM scheme, respectively. EXtrinsic Information Transfer (EXIT) charts are employed for analyzing the achievable convergence behaviour.

In this paper, we propose the novel concept of Repeat Accumulate Code Division Multiple Access (RA-CDMA), which employs unique, user-specific channel code generator matrices (interleavers) for differentiating the users. We also suggest a Multilevel Structured (MLS) interleaver generation technique for the sake of reducing the memory storage requirements of our system and compare its correlation properties to those of the conventional random interleaver. A novel hybrid detector is designed for our system with the aid of Extrinsic Information Transfer (EXIT) charts. Furthermore, we also design a low-complexity unequal power allocation scheme, assigning a different power to each user of this near-capacity transmission scheme. It is shown that when a sufficiently high interleaver length is employed, the RA-CDMA system is capable of approaching the Gaussian channel's capacity.

A novel sparse kernel density estimator is derived based on a regression approach, which selects a very small subset of significant kernels by means of the D-optimality experimental design criterion using an orthogonal forward selection procedure. The weights of the resulting sparse kernel model are calculated using the multiplicative nonnegative quadratic programming algorithm. The proposed method is computationally attractive, in comparison with many existing kernel density estimation algorithms. Our numerical results also show that the proposed method compares favourably with other existing methods, in terms of both test accuracy and model sparsity, for constructing kernel density estimates.

We consider a fully complex-valued radial basis function (RBF) network for regression application. The locally regularised orthogonal least squares (LROLS) algorithm with the D-optimality experimental design, originally derived for constructing parsimonious real-valued RBF network models, is extended to the fully complex-valued RBF network. Like its real-valued counterpart, the proposed algorithm aims to achieve maximised model robustness and sparsity by combining two effective and complementary approaches. The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalisation performance while the D-optimality design criterion further enhances the model efficiency and robustness. By specifying an appropriate weighting for the D-optimality cost in the combined model selecting criterion, the entire model construction procedure becomes automatic. An example of identifying a complex-valued nonlinear channel is used to illustrate the regression application of the proposed fully complex-valued RBF network.

We propose a complex-valued symmetric radial basis function (CV-SRBF) network for nonlinear beamforming in multiple-antenna aided communication systems that employ the complex-valued quadrature phase shift keying modulation scheme. The proposed CV-SRBF classifier explicitly exploits the inherent symmetry property of the underlying data generating mechanism, and this significantly enhances the detection accuracy. An orthogonal forward selection (OFS) algorithm based on the multi-class (four-class) Fisher ratio of class separability measure (FRCSM) is derived for constructing parsimonious CV-SRBF classifiers from noisy training data. Effectiveness of the proposed approach is illustrated using simulation, and the results obtained demonstrate that the sparse CV-SRBF classifier constructed by the multi-class FRCSM-based OFS achieves excellent beamforming detection bit error rate performance.

In this contribution, we investigate the attainable performance of quasi-cyclic (QC) protograph low-density parity-check (LDPC) codes for transmission over both Additive White Gaussian noise (AWGN) and uncorrelated Rayleigh channels. The codes presented are constructed using the Vandermonde matrix and benefit from both low- complexity encoding and decoding, low memory requirements as well as hardware-friendly implementations. Our simulation results demonstrate that the advantages offered by this family of QC protograph LDPC codes accrue without any compromise in the attainable bit error ratio (BER) and block error ratio (BER) performance. In fact, it is also shown that despite their implementational benefits, the proposed codes exhibit slight BER/BLER gains when compared to some of their more complex counterparts of the same length.

In this paper, we introduce and outline the concept of channel code division multiple access (CCDMA) using a design example based on the proposed multilevel structured (MLS) LDPC codes. We succeeded in making the memory requirements of the multi-user transceiver to become practically independent of the total number of users supported by the system as well as ascertain that each user benefits from the same quality of service (QoS). Finally, we demonstrate that despite their beneficial compact structure, the proposed MLS LDPC codes do not suffer from any bit error ratio (BER) or block error ratio (BLER) performance degradation, when compared to an otherwise identical benchmarker scheme using significantly more complex LDPC codes having pseudo-random parity-check matrices.

We propose a semi-blind adaptive beamforming approach for wireless systems that employ high-throughput quadrature amplitude modulation signalling schemes. A minimum number of training symbols, equal to the number of receive antenna-array's elements, are first utilised to provide a rough initial least squares estimate of the beamformer's weight vector. A concurrent constant modulus algorithm and soft decision- directed scheme, originally developed for single-user blind channel equalisation, is then applied to adapt the beamformer. It is demonstrated that this semi-blind adaptive beamforming scheme is capable of converging fast to the minimum mean-square-error beamforming solution.

This paper presents a novel ultrawideband (UWB), direct sequence code division multiple access (DS-CDMA) aided system designed for the IEEE 802.15.3a UWB channel specifications. Substantial performance improvements can be attained by serially concatenated channel encoding combined with a unity rate code (URC). We compare the performance of the iterative aided decoding correlation (Corr) and minimum mean square error (MMSE) detectors exchanging extrinsic information between the URC's decoder as well as the outer recursive systematic convolutional (RSC) code's decoder. Moreover, the iterative decoding convergence analysis of the proposed system is carried out with the aid of extrinsic information transfer (EXIT) charts. As expected, the iteratively decoded fully-loaded system employing MMSE detection outperforms its counterpart employing the equivalent Corr detection. Explicitly, UWB DS-CDMA using the MMSE detector attains a BER of 10-5at Eb/No=2 dB when supporting U=32 users employing i=12 decoding iterations, while the same system using the Corr detector operates at BER-10-1.

This paper presents a novel analysis on the decoding convergence of Time Hopping (TH) and Direct Sequence (DS) Code-Division Multiple-Access (CDMA) Ultrawide Bandwidth (UWB) systems when communicating over multipath Nakagami channels. The analysis is based on the Extrinsic Information Transfer (EXIT) chart where the UWB systems are serially concatenated pulse-position modulated TH and code-synchronous DS-CDMA. It is shown from an EXIT chart analysis that the multipath diversity can yield a larger area under the EXIT curve of the inner detector. This area is related to the achievable rate of the system and it can be exploited with the aid of iterative detection. Simulation results of the iteratively detected TH and DS-CDMA UWB schemes verify the EXIT chart analysis.

Based on an extrinsic information transfer (EXIT) chart assisted receiver design, a low-complexity near-maximum a posteriori (MAP) detector is constructed for high-throughput MIMO systems. A high throughput is achieved by invoking high-order modulation schemes and/or multiple transmit antennas, while employing a novel sphere detector (SD) termed as a center-shifting SD scheme, which updates the SD's search center during its consecutive iterations with the aid of channel decoder. Two low-complexity iterative center-shifting SD aided receiver architectures are investigated, namely the direct-hard-decision center-shifting (DHDC) and the direct-soft-decision center-shifting (DSDC) schemes. Both of them are capable of attaining a considerable memory and complexity reduction over the conventional SD-aided iterative benchmark receiver. For example, the DSDC scheme reduces the candidate-list-generation-related and extrinsic-LLR-calculation related complexity by a factor of 3.5 and 16, respectively. As a further benefit, the associated memory requirements were also reduced by a factor of 16.

When the maximum number of best candidates retained at each tree search level of the K-Best Sphere Detection (SD) is kept low for the sake of maintaining a low memory requirement and computational complexity, the SD may result in a considerable performance degradation in comparison to the full-search based Maximum Likelihood (ML) detector. In order to circumvent this problem, in this contribution we propose a novel complexity-reduction scheme, referred to as the Apriori-LLR- Threshold (ALT) based technique for the A'-best SD, which was based on the exploitation of the a priori LLRs provided by the outer channel decoder in the context of iterative detection aided channel coded systems. For example, given a BER of 10-5, a near- ML performance is achieved in an (8 times 4)-element rank-deficient 4-QAM system, despite imposing a factor two reduced detection candidate list generation related complexity and a factor eight reduced extrinsic LLR calculation related complexity, when compared to the conventional SD-aided iterative benchmark receiver. The associated memory requirements were also reduced by a factor of eight.

With the advent of extrinsic information transfer (EXIT) charts, we are capable of analyzing, predicting and visually comparing the convergence behaviours of different turbo multi-user detectector (MUD)s. The different MUDs have diverse EXIT characteristics and hence their superposition allows us to create a combined EXIT curve, which closely matches that of the channel decoder. Hence a near-capacity operation is facilitated by combining the benefits of different MUDs and therefore to create a superior MUD. Thus in this contribution, we propose a novel hybrid MUD combining scheme, which combines the advantages of a high performance and low complexity in form of an advanced hybrid MUD solution. The transmitted bits are unknown at the receiver, hence it is not feasible to directly evaluate the mutual information gain of the iterative MUD in consecutive iterations, hence we propose a realistic algorithm for estimating this mutual information gain, which is then used for activating the most appropriate constituent MUD as and when it is necessary. The constituent MUDs are the matched filter (MF) based soft interference cancellation (SoIC) and the optimum Bayesian MUDs, which are invoked in the scenario of frequency-domain-spread chip- interleaved (FDSCI) multiple carrier code division multiple access (MC-CDMA). The resultant hybrid MUD is capable of outperforming both the MF-SoIC and Bayesian turbo MUDs in the terms of the attainable complexity and bit-error-rate (BER) performance.

This paper studies a novel iterative soft interference cancellation (SIC) aided beamforming receiver designed for high-throughput quadrature amplitude modulation systems communicating over additive white Gaussian noise channels. The proposed linear SIC aided minimum symbol error rate (MSER) multiuser detection scheme guarantees the direct and explicit minimisation of the symbol error rate at the output of the detector. Based on the extrinsic information transfer (EXIT) chart technique, we compare the EXIT characteristics of an iterative MSER multiuser detector (MUD) with those of the conventional minimum mean squared error (MMSE) detector. As expected, the proposed SIC- MSER MUD outperforms the SIC aided MMSE MUD.

Iterative if-best sphere detection (SD) and channel decoding is appealing, since it is capable of achieving a near- maximum-a-posteriori (MAP) performance at a low complexity. However, a potentially excessive computational cost is imposed, especially in a high-throughput Spatial Division Multiplexing (SDM) aided OFDM system employing a large number of transmit antennas and/or high-order modulation schemes. This problem is further aggravated, when the number of transmit antennas exceeds that of the receive antennas, namely in the challenging scenario of rank-deficient systems. In order to further reduce the complexity imposed, we propose a unity-rate-code-aided (URC) three-stage concatenated transceiver constituted by an inner and outer channel encoder/decoder pair as well as the SDM aided OFDM transmitter and K-best SD. We have demonstrated that our proposed three-stage scheme is capable of achieving a substantial performance gain over the classic two-stage scheme. For example, given a target Bit Error Ratio (BER) of 10 5, the three-stage SD-aided receiver is capable of achieving a performance gain of 4 dB over its two-stage counterpart in an (8 x 4)-element SDM/OFDM system. Furthermore, we have also investigated the achievable performance of our novel center- shifting aided SD in the context of this three-stage scheme. Consequently, an additional 0.5 dB performance gain can be attained. Finally, the convergence behavior of the proposed schemes were studied with the aid of 3D Extrinsic Information Transfer (EXIT) charts and their 2D projections.

Semi-blind joint maximum likelihood (ML) channel estimation and data detection is proposed for multiple-input multiple-output (MIMO) systems. The joint ML optimization over channel and data is decomposed into an iterative two-level optimization loop. An efficient optimization search algorithm referred to as the repeated weighted boosting search (RWBS) is employed at the upper level to identify the unknown MIMO channel while an enhanced ML sphere detector termed as the optimized hierarchy reduced search algorithm is used at the lower level to perform ML detection of the transmitted data. Only a minimum pilot overhead is required to aid the RWBS channel estimator's initial operation, which not only speeds up convergence but also avoids ambiguities inherent in blind joint estimation of both the channel and data.

A novel iterative soft interference cancellation (SIC) aided beamforming receiver is developed for high-throughput quadrature amplitude modulation systems. The proposed SIC- based minimum symbol error rate (MSER) multiuser detection scheme guarantees the direct and explicit minimization of the symbol error rate at the output of the detector. Adopting the extrinsic information transfer (EXIT) chart technique, we compare the EXIT characteristics of an iterative MSER multiuser detector (MUD) with those of the conventional minimum mean-squared error (MMSE) detector. As expected, the proposed SIC-MSER MUD outperforms the SIC-MMSE MUD.

A nonlinear beamforming assisted detector is proposed for multiple-antenna-aided wireless systems employing complex-valued quadrature phase shift-keying modulation. By exploiting the inherent symmetry of the optimal Bayesian detection solution, a novel complex-valued symmetric radial basis function (SRBF)-network-based detector is developed, which is capable of approaching the optimal Bayesian performance using channel-impaired training data. In the uplink case, adaptive nonlinear beamforming can be efficiently implemented by estimating the system's channel matrix based on the least squares channel estimate. Adaptive implementation of nonlinear beamforming in the downlink case by contrast is much more challenging, and we adopt a cluster-variation enhanced clustering algorithm to directly identify the SRBF center vectors required for realizing the optimal Bayesian detector. A simulation example is included to demonstrate the achievable performance improvement by the proposed adaptive nonlinear beamforming solution over the theoretical linear minimum bit error rate beamforming benchmark.

In this paper, we propose a powerful symmetric radial basis function (RBF) classifier for nonlinear detection in the so-called "overloaded" multiple-antenna-aided communication systems. By exploiting the inherent symmetry property of the optimal Bayesian detector, the proposed symmetric RBF classifier is capable of approaching the optimal classification performance using noisy training data. The classifier construction process is robust to the choice of the RBF width and is computationally efficient. The proposed solution is capable of providing a signal-to-noise ratio (SNR) gain in excess of 8 dB against the powerful linear minimum bit error rate (BER) benchmark, when supporting four users with the aid of two receive antennas or seven users with four receive antenna elements.

Using the classical Parzen window (PW) estimate as the target function, the sparse kernel density estimator is constructed in a forward-constrained regression (FCR) manner. The proposed algorithm selects significant kernels one at a time, while the leave-one-out (LOO) test score is minimized subject to a simple positivity constraint in each forward stage. The model parameter estimation in each forward stage is simply the solution of jackknife parameter estimator for a single parameter, subject to the same positivity constraint check. For each selected kernels, the associated kernel width is updated via the Gauss-Newton method with the model parameter estimate fixed. The proposed approach is simple to implement and the associated computational cost is very low. Numerical examples are employed to demonstrate the efficacy of the proposed approach.

In this brief, we propose an orthogonal forward regression (OFR) algorithm based on the principles of the branch and bound (BB) and A-optimality experimental design. At each forward regression step, each candidate from a pool of candidate regressors, referred to as S, is evaluated in turn with three possible decisions: 1) one of these is selected and included into the model; 2) some of these remain in S for evaluation in the next forward regression step; and 3) the rest are permanently eliminated from S . Based on the BB principle in combination with an A-optimality composite cost function for model structure determination, a simple adaptive diagnostics test is proposed to determine the decision boundary between 2) and 3). As such the proposed algorithm can significantly reduce the computational cost in the A-optimality OFR algorithm. Numerical examples are used to demonstrate the effectiveness of the proposed algorithm.

We consider beamforming assisted detection for multiple antenna aided multiuser systems that employ the bandwidth efficient quadrature amplitude modulation scheme. A minimum symbol error rate (MSER) design is proposed for the beamforming assisted receiver, and it is shown that this MSER design provides significant performance enhancement, in terms of achievable symbol error rate, over the standard minimum mean square error (MMSE) design. A sample-by-sample adaptive algorithm, referred to as the least symbol error rate, is derived for adaptive implementation of the MSER beamforming solution. The proposed adaptive MSER scheme is evaluated in simulation using Rayleigh fading channels, in comparison with the adaptive MMSE benchmarker.

This contribution investigates semi-blind adaptive spatial filtering or equalisation for multiple-input multiple-output (MIMO) systems that employ high-throughput quadrature amplitude modulation (QAM) signalling. A minimum number of training symbols, equal to the number of receivers (we assume that the number of transmitters is no more than that of receivers), are first utilized to provide a rough least squares channel estimate of the system is MIMO channel matrix for the initialization of the spatial equalizer is weight vectors. A constant modulus algorithm aided soft decision-directed blind algorithm, originally derived for blind equalization of single-input single-output and single-input multiple-output systems employing high-order QAM signalling, is then extended to adapt the spatial equalizers for MIMO systems. This semi-blind scheme has a low computational complexity, and our simulation results demonstrate that it converges fast to the minimum mean-square-error spatial equalization solution.

This paper studies the mutual information transfer characteristics of a novel iterative soft interference cancellation (SIC) aided beamforming receiver communicating over both additive white Gaussian noise (AWGN) and multipath slow fading channels. Based on the extrinsic information transfer (EXIT) chart technique, we investigate the convergence behavior of an iterative minimum bit error rate (MBER) multiuser detection (MUD) scheme as a function of both the system parameters and channel conditions in comparison to the SIC aided minimum mean square error (SIC-MMSE) MUD. Our simulation results show that the EXIT chart analysis is sufficiently accurate for the MBER MUD. Quantitatively, a two-antenna system was capable of supporting up to K=6 users at Eb/Na=3dB, even when their angular separation was relatively low, potentially below 20deg.

In this paper, the mutual information transfer characteristics of several novel turbo multiuser detectors (MUD) employed in space division multiple access (SDMA) aided orthogonal frequency division multiplexing (OFDM) systems are investigated with extrinsic information transfer (EXIT) charts. These novel schemes are the Bayesian, the Soft Interference cancellation aided minimum bit error rate (SIC-MBER) and the reduced-complexity minimum Bit Error Rate (RMBER) turbo MUDs. In order to increase the effective throughput of the system, a powerful MUD has to be employed in the so- called "rank-deficient" scenario, namely when the number of transmit antennas exceeds that of the receiver antennas. The classic minimum mean square error (MMSE) solutions exhibit a low complexity. However, they are overwhelmed in rank-deficient scenarios. In these situations powerful non-linear MUDs are required, which are designed in this treatise.

Blind and semiblind adaptive schemes are proposed for joint maximum likelihood (ML) channel estimation and data detection for multiple-input multiple-output (MIMO) systems. The joint ML optimisation over channel and data is decomposed into an iterative two-level optimisation loop. An efficient global optimisation search algorithm called the repeated weighted boosting search is employed at the upper level to identify the unknown MIMO channel model while an enhanced ML sphere detector called the optimised hierarchy reduced search algorithm aided ML detector is used at the lower level to perform the ML detection of the transmitted data. A simulation example is included to demonstrate the effectiveness of these two schemes.

We consider nonlinear detection in rank-deficient multiple-antenna assisted beamforming systems. By exploiting the inherent symmetry of the underlying optimal Bayesian detection solution, a symmetric radial basis function (RBF) detector is proposed and two adaptive algorithms are developed for training the proposed RBF detector. The first adaptive algorithm, referred to as the nonlinear least bit error, is a stochastic approximation to the Parzen window estimation of the detector output's probability density function while the second algorithm is based on a clustering. The proposed adaptive solutions are capable of providing a signal to noise ratio gain in excess of 8 dB against the theoretical linear minimum bit error rate benchmarker, when supporting four users with the aid of two receive antennas or five users employing three antenna elements.

The ever-increasing demand for mobile communication capacity has motivated the development of adaptive antenna array assisted spatial processing techniques for bandwidth efficiency, high-throughput quadrature amplitude modulation (QAM) systems. We evaluate performance of adaptive beam- forming assisted detection for QAM systems in Rayleigh fading environments. An adaptive minimum symbol error rate design, referred to as the least symbol error rate, is shown to be capable of successfully operating in fast fading conditions and to consistently outperform the conventional adaptive beamforming benchmarker based on the least mean square algorithm.

Using the classical Parzen window estimate as the target function, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density estimates. The proposed algorithm incrementally minimises a leave-one-out test error score to select a sparse kernel model, and a local regularisation method is incorporated into the density construction process to further enforce sparsity. The kernel weights are finally updated using the multiplicative nonnegative quadratic programming algorithm, which has the ability to reduce the model size further. Except for the kernel width, the proposed algorithm has no other parameters that need tuning, and the user is not required to specify any additional criterion to terminate the density construction procedure. Two examples are used to demonstrate the ability of this regression-based approach to effectively construct a sparse kernel density estimate with comparable accuracy to that of the full-sample optimised Parzen window density estimate.

We propose a powerful symmetric kernel classifier for nonlinear detection in challenging rank-deficient multiple-antenna aided communication systems. By exploiting the inherent odd symmetry of the optimal Bayesian detector, the proposed symmetric kernel classifier is capable of approaching the optimal classification performance using noisy training data. The classifier construction process is robust to the choice of the kernel width and is computationally efficient. The proposed solution is capable of providing a signal-to-noise ratio gain in excess of 8 dB against the powerfull linear minimum bit error rate benchmarker, when supporting five users with the aid of three receive antennas.

This constribution considers nonlinear space-time equalisation (STE) designed for single-input multiple-output (SIMO) systems. By exploiting the inherent symmetry of the underlying optimal Bayesian STE solution, a novel symmetric radial basis function (RBF) based STE scheme is proposed, which is capable of achieving the optimal Bayesian equalisation performance. The adaptive adjustment of the STE taps of this symmetric RBF (SRBF) based STE can be achieved by estimating the SIMO channel encountered using the classic least mean square channel estimator and computing the optimal RBF centres from the resultant SIMO channel matrix estimate. Our simulation results demonstrate that the performance of this SRBF based STE is robust with respect to the choice of the algorithmic parameters.

This paper studies the mutual information transfer characteristics of a novel iterative soft interference cancellation (SIC) aided beamforming receiver designed for quadrature phase shift keying (QPSK) modulated systems communicating over additive white Gaussian noise (AWGN) channels. Based on the extrinsic information transfer (EXIT) chart technique, we investigate the convergence behaviour of an iterative minimum bit error rate (MBER) multiuser detection scheme as a function of the system parameters and channel conditions. We also compare the achievable performance and convergence behaviour of different multiuser detectors (MUD) and channel decoders. Our simulation results show that the EXIT chart analysis is sufficiently accurate for reliably predicting the performance of the MBER MUD, despite its potentially non-Gaussian output distribution because we invoke the histogram-based approximation of the true distribution. As expected, the proposed SIC- MBER MUD outperforms the SIC aided minimum mean square error (SIC-MMSE) MUD.

This paper studies the mutual information transfer characteristics of a novel low-complexity Bayesian Multiuser Detector (MUD) proposed for employment in Space Division Multiple Access (SDMA) aided Orthogonal Frequency Division Multiplexing (OFDM) systems. The design of the Bayesian MUD advocated is based on extending the optimum single-user Bayesian design to multiuser OFDM signals modeled by a Gaussian mixture, rather than by a single Gaussian distribution, when characterizing the conditional PDF of the received signal. In order to reduce the complexity of the Bayesian MUD, we introduce an a priori information threshold and then discard the low- probability terms during the calculation of the extrinsic information generated . The achievable complexity reduction as a function of different threshold values is analyzed and the best tradeoff values are derived with the aid of simulation. Both non-systematic and recursive systematic convolutional codes are used for exchanging extrinsic information with the MUD for the sake of achieving a turbo-detection aided iteration gain. The convergence behavior of the proposed low-complexity Bayesian turbo MUD is investigated using Extrinsic Information Transfer (EXIT) chart analysis and compared to that of Soft Interference Cancellation aided Minimum Mean Square Error (SIC-MMSE) MUD schemes. As expected, the simulation results show that the proposed low-complexity Bayesian Turbo MUD outperforms the SIC-MMSE MUDs. A substantial benefit of the proposed MUD is that it is potentially capable of supporting up to three times higher number of users than the number of receiver antennas. In this challenging multiuser scenario, the resultant channel- matrix becomes rank-deficient, resulting in a linearly non-separable detector output phasor constellation, when classic linear receivers tend to exhibit a poor performance.

A minimum bit error rate (MBER) decision feedback equaliser (DFE) designed for single-input multiple-output (SIMO) systems employing a quadrature phase shift keying (QPSK) modulation scheme is proposed. It is shown that this MBER design is superior over the standard minimum mean square error DFE in the SIMO scenario considered, in terms of the achievable system bit error rate. A sample-by-sample adaptive implementation of this MBER DFE is derived, which is referred to as the least bit error rate (LBER) algorithm. It is shown that for SIMO systems using a QPSK scheme, the LBER algorithm has a similar computational complexity as the simple least mean square (LMS) algorithm. Simulation results demonstrate that the proposed adaptive LBER-based DFE outperforms the adaptive LMS-based DFE, in both stationary and fading cases.

We propose a clustering-based symmetric radial basis function (SRBF) detector for multiple-antenna assisted beamforming systems. By exploiting the inherent symmetry of the underlying optimal Bayesian detection solution, this SRBF detector is capable of realizing the optimal Bayesian performance by clustering noisy observation data using an enhanced K-means clustering algorithm. The proposed adaptive solution provides a signal-to-noise ratio gain in excess of 8 dB against the theoretical linear minimum bit error rate benchmark, when supporting five users with the aid of three receive antennas.

Many kernel classifier construction algorithms adopt classification accuracy as performance metrics in model evaluation. Moreover, equal weighting is often applied to each data sample in parameter estimation. These modeling practices often become problematic if the data sets are imbalanced. We present a kernel classifier construction algorithm using orthogonal forward selection (OFS) in order to optimize the model generalization for imbalanced two-class data sets. This kernel classifier identification algorithm is based on a new regularized orthogonal weighted least squares (ROWLS) estimator and the model selection criterion of maximal leave-one-out area under curve (LOO-AUC) of the receiver operating characteristics (ROCs). It is shown that, owing to the orthogonalization procedure, the LOO-AUC can be calculated via an analytic formula based on the new regularized orthogonal weighted least squares parameter estimator, without actually splitting the estimation data set. The proposed algorithm can achieve minimal computational expense via a set of forward recursive updating formula in searching model terms with maximal incremental LOO-AUC value. Numerical examples are used to demonstrate the efficacy of the algorithm.

In this paper we propose a novel space division multiplexing (SDM) detection method. The proposed technique constitutes a list search method and may be regarded as an advanced extension of the sphere decoder (SD). Our method may be employed in the so-called over-loaded scenario, where the number of transmit antenna elements exceeds that of the receive antenna elements. Furthermore, it is suitable for high-throughput, non-constant modulus modulation schemes, such as 16 and 64-QAM. We introduce a series of optimization rules which facilitate a substantial reduction in computational complexity. More specifically, we demonstrate that the method proposed, which we refer to as the soft-output optimized hierarchy (SOPHIE)-aided SDM detector exhibits the near-optimum performance of log-MAP SDM detector in all considered scenarios. The associated computational complexity, which we control using two complexity-control parameters, is substantially lower than that imposed by all previously proposed methods

A novel decision-feedback (DF) aided reduced complexity maximum likelihood (ML) space-time equalizer (STE) designed for a single-carrier system is introduced. Two different methods of incorporating DF into the recursive tree search based receiver structure are proposed for allowing detection at a moderate computational cost. Additionally, a further complexity reduction scheme is proposed, which exploits the specific characteristics of both the wide-band channel and the proposed DF-STE. In comparison to the DF-STE not benefiting from this complexity reduction, the proposed detector is capable of reducing the complexity by several orders of magnitude. More quantitatively, for the specific rank-deficient system considered, which detected the signal transmitted from four transmit antennas with the aid of two receive antennas, the complexity might be reduced by a factor of 100 at low Signal-to-noise ratios (SNRs) without noticeable performance degradation. By contrast, at higher SNRs a complexity reduction of a factor of 10 might be achieved, depending on the tolerable performance degradation.

A novel parallel interference cancellation (PIC) based turbo space time equalizer (STE) structure designed for multiple antenna assisted uplink receivers is introduced. The proposed receiver structure allows the employment of non-linear type of detectors such as the Bayesian decision feedback (DF) assisted turbo STE or the maximum aposteriori (MAP) STE, while operating at a moderate computational cost. Receivers based on the proposed structure outperform the linear turbo detector benchmarker based on the minimum mean-squared error (MMSE) criterion, even if the latter aims for jointly detecting all transmitters' signals. Additionally the PIC based receiver is capable of equalizing non-linear binary pre-coded channels. The performance difference between the presented algorithms is discussed using extrinsic information transfer-function (EXIT) charts

An orthogonal forward selection (OFS) algorithm based on leave-one-out (LOO) misclassification rate is proposed for the construction of radial basis function (RBF) classifiers with tunable units. Each stage of the construction process determines a RBF unit, namely its centre vector and diagonal covariance matrix as well as weight, by minimising the LOO statistics. This OFS-LOO algorithm is computationally efficient and it is capable of constructing parsimonious RBF classifiers that generalise well. Moreover, the proposed algorithm is fully automatic and the user does not need to specify a termination criterion for the construction process. The effectiveness of the proposed RBF classifier construction procedure is demonstrated using three classification benchmark examples.

An adaptive beamforming assisted receiver is proposed for multiple antenna aided multiuser systems that employ bandwidth efficient quadrature amplitude modulation (QAM). A novel minimum symbol error rate (MSER) design is proposed for the beamforming assisted receiver, where the system's symbol error rate is directly optimized. Hence the MSER approach provides a significant symbol error ratio performance enhancement over the classic minimum mean square error design. A sample-by-sample adaptive algorithm, referred to as the least symbol error rate (LBER) technique, is derived for allowing the adaptive implementation of the system to arrive from its initial beamforming weight solution to MSER beamforming solution

In this contribution we propose a space-time decision feedback equalization (ST-DFE) assisted multiuser detection (MUD) scheme for multiple antenna aided space division multiple access systems. A minimum bit error rate (MBER) design is invoked for the MUD, which is shown to be capable of improving the achievable bit error rate performance over that of the minimum mean square error (MMSE) design. An adaptive MBER ST-DFE-MUD is proposed using the least bit error rate algorithm, which is demonstrated to consistently outperform the least mean square (LMS) algorithm, while achieving a lower computational complexity than the LMS algorithm for the binary signalling scheme. Simulation results demonstrate that the MBER ST-DFE-MUD is more robust to channel estimation errors as well as to error propagation imposed by decision feedback errors, compared to the MMSE ST-DFE-MUD

Iterative multiuser receivers constitute an effective solution for transmission over multiple access interference (MAI) infested channels, when invoking a combined multiuser detector and channel decoder. Most reduced-complexity methods in this area use the complex-valued minimum mean squared error (CMMSE) multiuser detector (MUD). Since the desired output of BPSK systems is real-valued, minimizing the mean square error (MSE) between the beamformer's desired output and the real part of the beamformer output has the potential of significantly improving the attainable bit error rate (BER) performance. We refer to this MMSE design as the real-valued MMSE (RMMSE) receiver. In this paper, we explore a new soft-input soft-output (SISO) interference cancellation multiuser detection algorithm based on the novel minimum BER (MBER) criterion. We demonstrate that the MBER turbo receiver outperforms both the CMMSE and the RMMSE algorithms, particularly in so-called 'overloaded' beamforming systems, where the number of receiver antennas is lower than the number of users supported

The paper considers adaptive beamforming assisted receiver for multiple antenna aided multiuser systems that employ binary phase shift keying (BPSK) modulation. The standard minimum mean square error (MMSE) design is based on the principle of minimising the mean square error (MSE) between the beamformer's desired output and complex-valued beamformer output. Since the desired output for BPSK systems is real-valued, minimising the MSE between the beamformer's desired output and real-part of the beamformer output can significantly improve the bit error rate (BER) performance, and we refer to this alternative MMSE design as the real-valued MMSE (RV-MMSE) to contrast to the standard complex-valued MMSE (CV-MMSE). The minimum BER (MBER) design however still outperforms the RV-MMSE solution, particularly for overloaded systems where degree of freedom of the antenna array is smaller than the number of BPSK users. Adaptive implementation of this RV-MMSE design is realised using a least mean square (LMS) type adaptive algorithm, which we refer to as the RV-LMS, in comparison to the standard CV-LMS algorithm. The RV-LMS adaptive beamformer has the same computational complexity as the adaptive least bit error (LBER) algorithm, imposing half of the computational requirements of the CV-LMS algorithm

A novel decision-feedback (DF) aided reduced complexity maximum likelihood (ML) space-time equalizer (STE) designed for single-carrier multiple antenna assisted receivers is introduced. The proposed receiver structure is based on a recursive tree search, which is capable of achieving ML performance at a moderate computational cost and substantially outperforms the linear benchmarker based on the minimum mean-squared error (MMSE) criterion. Additionally a further complexity reduction scheme is proposed, which exploits the specific characteristics of both the wide-band channel and the proposed DF-STE

A novel iterative multiuser detector (MUD) is proposed for employment in space division multiple access (SDMA) aided orthogonal frequency division multiplexing (OFDM) systems, where the uplink transmissions of the users share the same bandwidth. The individual users' signals are differentiated with the aid of their unique user-specific channel impulse responses (CIRs). The MUD commences its operation on a subcarrier by subcarrier basis from the minimum mean square error (MMSE) MUD antenna array weight vector and invokes the conjugate gradient (CG) algorithm for the sake of iteratively adjusting the array weight vector in the direction of the MUD's true minimum bit error rate (MBER) solution. Recursive systematic convolutional (RSC) codes are used for enhancing the system's attainable BER performance, which exchange extrinsic information with the MBER MUD for the sake of achieving a turbo-detection aided iteration gain, resulting in the creation of a powerful turbo MBER MUD for employment in multiuser SDMA OFDM systems. The novel benefit of the proposed system is that it is capable of supporting up to a factor two higher number of users than the number of receiver antennas. Explicitly, the technique advocated outperforms other SDMA MUDs with the advent of its MBER optimization criterion and turbo detection structure. Up to 2 dBs of iteration gains are attained

In this letter, a novel iterative multiple-input multiple-output (MIM)O detector reminiscent of sphere decoding (SD) is presented. Its main benefit is that in contrast to other soft-input soft-output (SISO) SD algorithms presented in the literature, the proposed iterative detector is capable of performing well in so-called overloaded systems, where the number of transmit antennas is significantly higher than the number of receive antennas, and hence, the channel's covariance matrix is rank-deficient. Furthermore, the proposed detector is guaranteed to achieve the performance of the max-log detector without the necessity of choosing a specific SD radius or an SD candidate list for generating soft-outputs. For example, when receiving signals from four transmit antennas with the aid of only two receive antennas, the proposed detector is capable of approaching the channel capacity limit within about 2 dBs

A novel minimum bit-error rate (MBER) space-time-equalization (STE)-based multiuser detector (MUD) is proposed for multiple-receive-antenna-assisted space-division multiple-access systems. It is shown that the MBER-STE-aided MUD significantly outperforms the standard minimum mean-square error design in terms of the achievable bit-error rate (BER). Adaptive implementations of the MBER STE are considered, and both the block-data-based and sample-by-sample adaptive MBER algorithms are proposed. The latter, referred to as the least BER (LBER) algorithm, is compared with the most popular adaptive algorithm,known as the least mean square (LMS) algorithm. It is shown that in case of binary phase-shift keying, the computational complexity of the LBER-STE is about half of that required by the classic LMS-STE. Simulation results demonstrate that the LBER algorithm performs consistently better than the classic LM Salgorithm, both in terms of its convergence speed and steady-state BER performance.

A greedy technique is proposed to construct parsimonious kernel classifiers using the orthogonal forward selection method and boosting based on Fisher ratio for class separability measure. Unlike most kernel classification methods, which restrict kernel means to the training input data and use a fixed common variance for all the kernel terms, the proposed technique can tune both the mean vector and diagonal covariance matrix of individual kernel by incrementally maximizing Fisher ratio for class separability measure. An efficient weighted optimization method is developed based on boosting to append kernels one by one in an orthogonal forward selection procedure. Experimental results obtained using this construction technique demonstrate that it offers a viable alternative to the existing state-of-the-art kernel modeling methods for constructing sparse Gaussian radial basis function network classifiers that generalize well.

A novel technique is presented to construct sparse generalized Gaussian kernel regression models. The proposed method appends regressors in an incremental modeling by tuning the mean vector and diagonal covariance matrix of an individual Gaussian regressor to best fit the training data, based on a correlation criterion. It is shown that this is identical to incrementally minimizing the modeling mean square error (MSE). The optimization at each regression stage is carried out with a simple search algorithm re-enforced by boosting. Experimental results obtained using this technique demonstrate that it offers a viable alternative to the existing state-of-the-art kernel modeling methods for constructing parsimonious models.

Many signal processing applications pose optimization problems with multimodal and nonsmooth cost functions. Gradient methods are ineffective in these situations, and optimization methods that require no gradient and can achieve a global optimal solution are highly desired to tackle these difficult problems. The paper proposes a guided global search optimization technique, referred to as the repeated weighted boosting search. The proposed optimization algorithm is extremely simple and easy to implement, involving a minimum programming effort. Heuristic explanation is given for the global search capability of this technique. Comparison is made with the two better known and widely used guided global search techniques, known as the genetic algorithm and adaptive simulated annealing, in terms of the requirements for algorithmic parameter tuning. The effectiveness of the proposed algorithm as a global optimizer are investigated through several application examples.

This correspondence introduces a new orthogonal forward regression (OFR) model identification algorithm using D-optimality for model structure selection and is based on an M-estimators of parameter estimates. M-estimator is a classical robust parameter estimation technique to tackle bad data conditions such as outliers. Computationally, The M-estimator can be derived using an iterative reweighted least squares (IRLS) algorithm. D-optimality is a model structure robustness criterion in experimental design to tackle ill-conditioning in model structure. The orthogonal forward regression (OFR), often based on the modified Gram-Schmidt procedure, is an efficient method incorporating structure selection and parameter estimation simultaneously. The basic idea of the proposed approach is to incorporate an IRLS inner loop into the modified Gram-Schmidt procedure. In this manner, the OFR algorithm for parsimonious model structure determination is extended to bad data conditions with improved performance via the derivation of parameter M-estimators with inherent robustness to outliers. Numerical examples are included to demonstrate the effectiveness of the proposed algorithm.

An adaptive beamforming technique is proposed based on directly minimizing the bit-error rate (BER). It is demonstrated that this minimum BER (MBER) approach utilizes the antenna array elements more intelligently than the standard minimum mean square error (MMSE) approach. Consequently, MBER beamforming is capable of providing significant performance gains in terms of a reduced BER over MMSE beamforming. A block-data adaptive implementation of the MBER beamforming solution is developed based on the Parzen window estimate of probability density function. Furthermore, a sample-by-sample adaptive implementation is considered, and a stochastic gradient algorithm, referred to as the least bit error rate, is derived. The proposed adaptive MBER beamforming technique provides an extension to the existing work for adaptive MBER equalization and multiuser detection.

The best measure quantifying FWL (finite word length) stability is the one that bases on the largest stable perturbation hypercube. But the computing of this FWL stability measure has not been solved. For second order digital systems, this paper develops an analytic computing method. Through solving 12 linear equations and 12 quadratic equations, the measure value can be obtained exactly.

A novel adaptive beamforming technique is proposed for wireless communication with quadrature phase shift keying signalling based on the minimum bit error rate (MBER) criterion. It is shown that the MBER approach provides significant performance gain in terms of smaller bit error rate over the standard minimum mean square error approach. Using the classical Parzen window estimate of probability density function, both the block-data and sample-by-sample adaptive implementations of the MBER solution are developed.

The paper proposes a concurrent constant modulus algorithm (CMA) and soft decision-directed (SDD) scheme for low-complexity blind equalization of high-order quadrature amplitude modulation channels. Simulation using a fractionally spaced equalization setting is used to compare the proposed scheme with the recently introduced state-of-art concurrent CMA and decision-directed (DD) scheme. The proposed CMA+SDD blind equalizer is shown to have simpler computational complexity per weight update, faster convergence speed, and slightly improved steady-state equalization performance, compared with the CMA+DD blind equalizer.

This paper introduces an automatic robust nonlinear identification algorithm using the leave-one-out test score also known as the PRESS (Predicted REsidual Sums of Squares) statistic and regularised orthogonal least squares. The proposed algorithm aims to achieve maximised model robustness via two effective and complementary approaches, parameter regularisation via ridge regression and model optimal generalisation structure selection. The major contributions are to derive the PRESS error in a regularised orthogonal weight model, develop an efficient recursive computation formula for PRESS errors in the regularised orthogonal least squares forward regression framework and hence construct a model with a good generalisation property. Based on the properties of the PRESS statistic the proposed algorithm can achieve a fully automated model construction procedure without resort to any other validation data set for model evaluation.

This letter shows that the wireless communication system capacity is greatly enhanced by employing nonlinear beamforming and that the optimal Bayesian beamformer outperforms the standard linear beamformer significantly in terms of a reduced bit error rate, at a cost of increased complexity. A block-data adaptive implementation of the Bayesian beamformer is realized based on an orthogonal forward selection procedure with the Fisher ratio for class separability measure.

This paper presents an efficient construction algorithm for obtaining sparse kernel density estimates based on a regression approach that directly optimizes model generalization capability. Computational efficiency of the density construction is ensured using an orthogonal forward regression, and the algorithm incrementally minimizes the leave-one-out test score. A local regularization method is incorporated naturally into the density construction process to further enforce sparsity. An additional advantage of the proposed algorithm is that it is fully automatic and the user is not required to specify any criterion to terminate the density construction procedure. This is in contrast to an existing state-of-art kernel density estimation method using the support vector machine (SVM), where the user is required to specify some critical algorithm parameter. Several examples are included to demonstrate the ability of the proposed algorithm to effectively construct a very sparse kernel density estimate with comparable accuracy to that of the full sample optimized Parzen window density estimate. Our experimental results also demonstrate that the proposed algorithm compares favorably with the SVM method, in terms of both test accuracy and sparsity, for constructing kernel density estimates.

The paper introduces an efficient construction algorithm for obtaining sparse linear-in-the-weights regression models based on an approach of directly optimizing model generalization capability. This is achieved by utilizing the delete-1 cross validation concept and the associated leave-one-out test error also known as the predicted residual sums of squares (PRESS) statistic, without resorting to any other validation data set for model evaluation in the model construction process. Computational efficiency is ensured using an orthogonal forward regression, but the algorithm incrementally minimizes the PRESS statistic instead of the usual sum of the squared training errors. A local regularization method can naturally be incorporated into the model selection procedure to further enforce model sparsity. The proposed algorithm is fully automatic, and the user is not required to specify any criterion to terminate the model construction procedure. Comparisons with some of the existing state-of-art modeling methods are given, and several examples are included to demonstrate the ability of the proposed algorithm to effectively construct sparse models that generalize well.

A new robust neurofuzzy model construction algorithm has been introduced for the modeling of a priori unknown dynamical systems from observed finite data sets in the form of a set of fuzzy rules. Based on a Takagi-Sugeno (T-S) inference mechanism a one to one mapping between a fuzzy rule base and a model matrix feature subspace is established. This link enables rule based knowledge to be extracted from matrix subspace to enhance model transparency. In order to achieve maximized model robustness and sparsity, a new robust extended Gram-Schmidt (G-S) method has been introduced via two effective and complementary approaches of regularization and D-optimality experimental design. Model rule bases are decomposed into orthogonal subspaces, so as to enhance model transparency with the capability of interpreting the derived rule base energy level. A locally regularized orthogonal least squares algorithm, combined with a D-optimality used for subspace based rule selection, has been extended for fuzzy rule regularization and subspace based information extraction. By using a weighting for the D-optimality cost function, the entire model construction procedure becomes automatic. Numerical examples are included to demonstrate the effectiveness of the proposed new algorithm.

A novel adaptive beamforming technique is proposed for wireless communication application based on the minimum bit error rate (MBER) criterion. It is shown that the MBER approach provides significant performance gain in terms of smaller bit error rate (BER) over the standard minimum mean square error (MMSE) approach. Using the classical Parzen window estimate of probability density function (p.d.f.), both the block-data and sample-by-sample adaptive implementations of the MBER solution are developed.

In this correspondence new robust nonlinear model construction algorithms for a large class of linear-in-the-parameters models are introduced to enhance model robustness via combined parameter regularization and new robust structural selective criteria. In parallel to parameter regularization, we use two classes of robust model selection criteria based on either experimental design criteria that optimizes model adequacy, or the predicted residual sums of squares (PRESS) statistic that optimizes model generalization capability, respectively. Three robust identification algorithms are introduced, i.e., combined A- and D-optimality with regularized orthogonal least squares algorithm, respectively; and combined PRESS statistic with regularized orthogonal least squares algorithm. A common characteristic of these algorithms is that the inherent computation efficiency associated with the orthogonalization scheme in orthogonal least squares or regularized orthogonal least squares has been extended such that the new algorithms are computationally efficient. Numerical examples are included to demonstrate effectiveness of the algorithms.

The paper derives a stochastic-gradient minimum symbol-error-rate (MSER) algorithm, called the least symbol error rate (LSER), for training the linear equalizer and linear-combiner decision feedback equalizer (DFE) with M -PAM signalling. This LSER algorithm has some performance advantages, in terms of faster convergence rate and smaller steady-state symbol error rate (SER) misadjustment, over an existing simpler stochastic-gradient adaptive MSER algorithm called the approximate MSER (AMSER).

The paper investigates the application of a recently introduced learning technique, referred to as the relevance vector machine (RVM), to construct a block-adaptive kernel-based nonlinear multiuser detector (MUD) for direct-sequence code-division multiple-access (DS-CDMA) signals transmitted through multipath channels. It is demonstrated that the RVM MUD is capable of closely matching the performance of the optimal Bayesian one-shot detector, with the aid of a significantly more sparse kernel representation than that required by the state-of-the-art support vector machine (SVM) technique.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

An adaptive minimum bit error rate (MBER) linear multiuser detector (MUD) is proposed for DS-CDMA systems. Based on the approach of kernel density estimation for approximating the bit error rate (BER) from training data, a least mean squares (LMS) style adaptive algorithm is developed for training linear MUDs. Computer simulation results show that this adaptive MBER linear MUD outperforms two existing LMS-style adaptive MBER algorithms.

A detector based on multiple-hyperplane partitioning of the signal space is derived for realizing the optimal Bayesian decision feedback equaliser (DFE). It is known that the optimal Bayesian decision boundary separating any two neighbouring signal classes is asymptotically piecewise linear and consists of several hyperplanes, when the signal to noise ratio (SNR) tends to infinity. The proposed technique determines these hyperplanes and uses them to partition the observation space. The resulting detector can closely approximate the optimal Bayesian detector, at an advantage of considerably reduced decision complexity.

A decision-feedback equalizer scheme is derived based on multiple-hyperplane partitioning of signal space for detecting M-ary pulse amplitude modulation symbols transmitted through a noisy intersymbol interference channel. The proposed scheme is based on the fact that the optimal Bayesian decision boundary separating two neighboring signal classes is asymptotically piecewise linear and consists of several hyperplanes, when the signal-to-noise ratio tends to infinity. An algorithm is developed to determine these hyperplanes, which are then used to partition the observation signal space. The resulting detector can closely approximate the optimal Bayesian detector, at an advantage of considerably reduced detector complexity.

A postprocessing technique is developed for image quality enhancement. In this method, a distortion-recovery model extracts multiresolution edge features from the decompressed image and uses these visual features as input to estimate the difference image between the original uncompressed image and the decompressed image. Coding distortions are compensated by adding the model output to the decompressed image. Unlike many existing postprocessing methods, which smooth blocking artifacts and are designed specifically for transform coding or vector quantization, the proposed technique is generic and can be applied to all of the main coding methods. Experimental results involving postprocessing four coding systems show that the proposed technique achieves significant improvements on the quality of reconstructed images, both in terms of the objective distortion measure and subjective visual assessment.

The relevance vector machine (RVM) technique is applied to communication channel equalization. It is demonstrated that the RVM equalizer can closely match the optimal performance of the Bayesian equalizer, with a much sparser kernel representation than that is achievable by the state-of-art support vector machine (SVM) technique.

The problem of constructing an adaptive multiuser detector (MUD) is considered for direct sequence code division multiple access (DS-CDMA) signals transmitted through multipath channels. The emerging learning technique, called support vector machines (SVM), is proposed as a method of obtaining a nonlinear MUD from a relatively small training data block. Computer simulation is used to study this SVM MUD, and the results show that it can closely match the performance of the optimal Bayesian one-shot detector. Comparisons with an adaptive radial basis function (RBF) MUD trained by an unsupervised clustering algorithm are discussed.

The conventional decision feedback equalizer (DFE) separates the different signal classes using a single hyperplane. It is well known that the popular minimum mean square error (MMSE) design is generally not the optimal minimum bit error rate (MBER) solution. We propose a method of designing the separating hyperplane for the conventional DFE based on support vector machines (SVMs). The SVM design achieves asymptotically the MBER solution and can be computed efficiently.

The paper presents a two-level learning method for radial basis function (RBF) networks. A regularized orthogonal least squares (ROLS) algorithm is employed at the lower level to construct RBF networks while the two key learning parameters, the regularization parameter and the RBF width, are optimized using a genetic algorithm (GA) at the upper level. Nonlinear time series modeling and prediction is used as an example to demonstrate the effectiveness of this hierarchical learning approach.

An important family of blind equalization algorithms identify a communication channel model based on fitting higher order cumulants, which poses a nonlinear optimization problem. Since higher order cumulant-based criteria are multimodal, conventional gradient search techniques require a good initial estimate to avoid converging to local minima. We present a novel scheme which uses genetic algorithms to optimize the cumulant fitting cost function. A microgenetic algorithm implementation is adopted to further enhance computational efficiency. As is demonstrated in computer simulation, this scheme is robust and accurate and has a fast convergence performance.

We present a method of modifying the structure of radial basis function (RBF) network to work with nonstationary series that exhibit homogeneous nonstationary behavior. In the original RBF network, the hidden node's function is to sense the trajectory of the time series and to respond when there is a strong correlation between the input pattern and the hidden node's center. This type of response, however, is highly sensitive to changes in the level and trend of the time series. To counter these effects, the hidden node's function is modified to one which detects and reacts to the gradient of the series. We call this new network the gradient RBF (GRBF) model. Single and multistep predictive performance for the Mackey-Glass chaotic time series were evaluated using the classical RBF and GRBF models. The simulation results for the series without and with a tine-varying mean confirm the superior performance of the GRBF predictor over the RBF predictor.

The paper investigates adaptive equalization of time-dispersive mobile radio fading channels and develops a robust high performance Bayesian decision feedback equalizer (DFE). The characteristics and implementation aspects of this Bayesian DFE are analyzed, and its performance is compared with those of the conventional symbol or fractional spaced DFE and the maximum likelihood sequence estimator (MLSE). In terms of computational complexity, the adaptive Bayesian DFE is slightly more complex than the conventional DFE but is much simpler than the adaptive MLSE. In terms of error rate in symbol detection, the adaptive Bayesian DFE outperforms the conventional DFE dramatically. Moreover, for severely fading multipath channels, the adaptive MLSE exhibits significant degradation from the theoretical optimal performance and becomes inferior to the adaptive Bayesian DFE.>

A multi-stage blind clustering algorithm is proposed for equalisation of multi-level quadrature amplitude modulation (M-QAM) channels. A hierarchical decomposition divides the task of equalising a high-order QAM channel into much simpler sub-tasks. Each sub-task can be accomplished fast and reliably using a blind clustering algorithm derived originally for 4-QAM signals. The constant modulus algorithm (CMA) is used as a benchmark to assess this multi-stage blind equaliser. It is demonstrated that the new blind algorithm achieves much faster convergence and is very robust when input symbols are not sufficiently white. This multi-stage clustering equaliser only requires slightly more computations than the CMA and, like the latter, its computational complexity does not increase as the levels of digital symbols increase.>

The orthogonal, least squares (OLS) algorithm is an efficient implementation of the forward regression procedure for subset model selection. The ability to find good subset parameters with only linear increase in computational complexity makes this method attractive for practical implementations. We examine the computation requirement of the OLS algorithm to reduce a model of K terms to a subset model of R terms when the number of training data available is N. We show that in the case where N/spl Gt/K, we can reduce the computation requirement by introducing an unitary transformation on the problem.>

The application of a radial basis function network to digital communications channel equalization is examined. It is shown that the radial basis function network has an identical structure to the optimal Bayesian symbol-decision equalizer solution and, therefore, can be employed to implement the Bayesian equalizer. The training of a radial basis function network to realize the Bayesian equalization solution can be achieved efficiently using a simple and robust supervised clustering algorithm. During data transmission a decision-directed version of the clustering algorithm enables the radial basis function network to track a slowly time-varying environment. Moreover, the clustering scheme provides an automatic compensation for nonlinear channel and equipment distortion. Computer simulations are included to illustrate the analytical results.