The incidence of spinal diseases has been steadily increasing in recent years, with affected populations becoming progressively younger. With the rapid development of artificial intelligence and medical image processing technologies, automated intelligent diagnostic algorithms based on spinal X-ray images have been continuously developed. However, current deep learning–based diagnostic methods for spinal diseases face limitations such as the limited availability of labeled data and subtle inter-class feature differences. To address these challenges in spinal X-ray image diagnosis, this study proposes a novel Siamese Swin Transformer model based on transfer learning, within the Internet of Medical Things (IoMT) framework—referred to as the SpineSiamSwin model. The model adopts a Siamese network structure and leverages metric learning loss functions to maximize the distance between different classes in the embedding space, thereby enhancing the model’s sensitivity to subtle structural differences between disease categories. Extensive experiments on real-world spinal X-ray datasets demonstrate the effectiveness and practical applicability of the SpineSiamSwin model.

In recent years, the rapid advancement and application of deep learning in medical imaging have demonstrated its effectiveness in reducing physicians’ workload and lowering the risk of misdiagnosis in pathological spine diagnosis. Nevertheless, deep learning–based models for pathological spine diagnosis have not yet matured to the level required for clinical deployment. Several challenges contribute to this limitation. First, the availability of spinal X-ray images for training is limited, and the class distribution of samples is often imbalanced. Second, conventional deep learning models rely on convolutional kernels that primarily capture local features in X-ray images, while overlooking the global morphological characteristics of the spine. To address these issues, we propose ViTST, a Vision Transformer (ViT)–based model with a self-supervised learning task for scoliosis classification. ViTST incorporates a masked strategy–based self-supervised pretext task to mitigate the challenges posed by limited training data and leverages the ViT architecture to capture global structural features of spinal X-ray images. This design enables more effective modeling of inter-regional relationships and variations within the spine. Moreover, by jointly optimizing reconstruction loss and cross-entropy loss, ViTST learns robust image representations even from relatively small datasets. In addition, we introduce a healthcare Internet of Medical Things (IoMT) architecture to enable the practical deployment of ViTST in clinical environments. Through this IoMT platform, clinicians can monitor patients’ conditions in real time and adapt treatment plans dynamically, thereby enhancing clinical decision-making and accelerating patient recovery. Finally, we conducted extensive experiments on a real-world pathological spine image dataset to validate the effectiveness of the proposed model. Experimental results demonstrate that ViTST achieved a Precision of 0.975, an Accuracy of 0.979, and an F1-score of 0.975, confirming its strong potential for application in clinical practice.

In this work, we introduce Wonder3D++, a novel method for efficiently generating high-fidelity textured meshes from single-view images. Recent methods based on Score Distillation Sampling (SDS) have shown the potential to recover 3D geometry from 2D diffusion priors, but they typically suffer from time-consuming per-shape optimization and inconsistent geometry. In contrast, certain works directly produce 3D information via fast network inferences, but their results are often of low quality and lack geometric details. To holistically improve the quality, consistency, and efficiency of single-view reconstruction tasks, we propose a cross-domain diffusion model that generates multi-view normal maps and the corresponding color images. To ensure the consistency of generation, we employ a multi-view cross-domain attention mechanism that facilitates information exchange across views and modalities. Lastly, we introduce a cascaded 3D mesh extraction algorithm that drives high-quality surfaces from the multi-view 2D representations in only about 3 minute in a coarse-to-fine manner. Our extensive evaluations demonstrate that our method achieves high-quality reconstruction results, robust generalization, and good efficiency compared to prior works.

Deep neural network (DNN)-based video denoising has demonstrated significant performance, but deploying state-of the-art models on edge devices remains challenging due to stringent real-time and energy efficiency requirements. Computing in-Memory (CIM) chips offer a promising solution by integrating computation within memory cells, enabling rapid matrix-vector multiplication (MVM). However, existing DNN models are often designed without considering CIM architectural constraints, thus limiting their acceleration potential during inference. To address this, we propose a hardware-algorithm co-design framework incorporating two innovations: (1) a CIM-Aware Architecture, CIM-NET, optimized for large receptive field operation and CIM's crossbar-based MVM acceleration; and (2) a pseudo convolutional operator, CIM-CONV, used within CIM-NET to integrate slide-based processing with fully connected transformations for high-quality feature extraction and reconstruction. This framework reduces the number of MVM operations, improving inference speed on CIM chips while maintaining competitive performance. Experimental results indicate that, compared to the conventional lightweight model FastDVDnet, our improved variant, CIM-NET, built upon it substantially reduces MVM operations with a slight decrease in denoising performance. With a stride value of 8, CIM-NET reduces MVM operations to 1/77th of the original, while maintaining competitive PSNR (35.03 dB vs. 35.57 dB of FastDVDnet).

Clothes-Changing person re-identification algorithm (Re-ID) is the task of retrieving the query person in the case of the change of pedestrian clothing. Changes in pedestrian clothing lead to an offset in clothing features, resulting in a decrease in identification performance. Simply removing clothing may lead to the loss of contour information. Furthermore, algorithms based on feature decoupling cannot guarantee the accuracy of the positional relationship between clothing features and other features due to the lack of groundtruth. To address these issues, we propose a novel clothes-changing person re-identification algorithm based on causal feature supervision decoupling. Utilizing a multi-scale feature fusion module to extract fine-grained features of clothing and add supervised information labels. This enables the dual-branch network to separately approach overall and clothing features, promoting the extraction of effective identity information by the causal decoupling module, and obtaining unbiased estimations of pedestrians. Experimental results show that the proposed algorithm achieves the highest mAP and Top-1 accuracy on the LTCC and PRCC datasets. The source code is available at https://github.com/zhihu250/CISupNet.

In the edge learning process of B5G Internet of Things (IoT) systems, the allocation strategy of edge sensors will directly affect the learning results of the system. This article proposes multiple methods to allocate edge devices that can learn and predict the usage of spectrum data, aiming to improve the efficiency and fairness of edge learning. We design an efficient edge device allocation strategy to enhance the edge learning efficiency and propose a metric called ineffective transmission parameter (ITP) to evaluate its performance. To solve the optimization problem, mathematical analysis is performed and closed-form expressions are obtained. The scenarios considered include: devices with different learning performance and the same learning performance on the same band. We propose three edge device allocation methods: 1) iterative hierarchical Hungarian allocation; 2) bow allocation; and 3) category-divided allocation to ensure the fairness of edge learning among sub-bands. The fairness of the system is measured by evaluating the lowest learning performance in the sub-band. To adapt to the actual scenario, we enhance fairness by introducing band attribute parameters (considering the priority, anti-interference ability, and congestion level of the main users of the sub-band). Simulation results show that the proposed strategy significantly improves the ITP of edge learning in IoT systems, especially in the case of varying band utilization. The fairness scheme improves the overall edge learning fairness of the system.

In this paper, we propose a novel dual index pulse position modulation differential chaotic shift keying (DI-PPM-DCSK) system for wireless covert communication. Unlike conventional PPM schemes, at the transmitter, we propose to modulate index bits with the positions of both the reference and information-bearing signals, thereby incorporating more index bits into each symbol. Thus both energy efficiency and data rate can be improved. Besides, the information-bearing modulated signals are hidden with the artificial noise to enhance the covertness of communications. At the receiver, we propose to demodulate the information bits by calculating the coherence of adjacent symbols. Moreover, we derive analytical expressions for the bit error rate (BER) and covert performance of the system, which are validated through numerical simulations. The results demonstrate that the proposed system achieves superior data rate and BER performance compared to existing counterparts over fading channels. Furthermore, the covertness is validated with both theoretical analysis and simulation results.

The computer-aided diagnostic method based on deep learning can automatically identify whether X-ray images exhibit symptoms of osteoarthritis, thereby improving the quality and speed of early osteoarthritis diagnosis. However, osteoarthritis involves changes in structures such as soft tissue and joint spaces, and X-ray images of osteoarthritis have low contrast and high noise. These factors can blur the boundaries of osteoarthritic lesion areas, making feature extraction from X-ray images of osteoarthritis relatively difficult. In addition, the max-pooling operation in previous models tends to lose details and positional information in X-ray images, reducing the model's sensitivity to osteoarthritic structure features. To address the two issues in the diagnosis model mentioned above, we propose a deep neural network based on a masked strategy and fuzzy pooling (DNNMSFP) to automatically diagnose bone joint X-ray images to observe whether the image exhibits arthritis symptoms. The DNNMSFP model integrates the advantages of masked autoencoders and fuzzy pooling to address the aforementioned issues. First, the DNNMSFP model uses a self-supervised feature learning module designed using a masked strategy to learn effective features from noisy X-ray images. Furthermore, the DNNMSFP model replaces the max-pooling operation of the original feature learner with fuzzy pooling. Fuzzy pooling is a variant of traditional pooling operations that performs Gaussian blurring on pixel values in feature maps to extract features and reduce dimensionality. Comprehensive experiments on a real bone joint X-ray image dataset validate the superiority of the DNNMSFP model. Specifically, the DNNMSFP model achieves an excellent prediction performance on the real dataset, with precision, recall, and accuracy of 0.974, 0.948, and 0.965, respectively.

Multi-camera setups find widespread use across various applications, such as autonomous driving, as they greatly expand sensing capabilities. Despite the fast development of Neural radiance field (NeRF) techniques and their wide applications in both indoor and outdoor scenes, applying NeRF to multi-camera systems remains very challenging. This is primarily due to the inherent under-calibration issues in multi-camera setup, including inconsistent imaging effects stemming from separately calibrated image signal processing units in diverse cameras, and system errors arising from mechanical vibrations during driving that affect relative camera poses. In this paper, we present UC-NeRF, a novel method tailored for novel view synthesis in under-calibrated multi-view camera systems. Firstly, we propose a layer-based color correction to rectify the color inconsistency in different image regions. Second, we propose virtual warping to generate more viewpoint-diverse but color-consistent virtual views for color correction and 3D recovery. Finally, a spatiotemporally constrained pose refinement is designed for more robust and accurate pose calibration in multi-camera systems. Our method not only achieves state-of-the-art performance of novel view synthesis in multi-camera setups, but also effectively facilitates depth estimation in large-scale outdoor scenes with the synthesized novel views.

Automatic modulation recognition techniques enable fast spectrum access. In recent years, deep learning methods have received much attention in the field of modulation recognition. However, its performance requires a large number of data samples as support. Data augmentation methods are regarded as one of the effective ways to solve the insufficient data samples. To this end, this paper proposes a data augmentation algorithm based on the conditional diffusion model to solve the problem that the model cannot be adequately trained in the case of insufficient data. In the proposed algorithm, the noise observation model acquires the noise in the input signal at different time steps. Then, the input is continuously denoised by removing the noise observed by the noise observation model during the inverse process of the conditional diffusion model to generate the corresponding modulated signal. We put the modulation modes with high confidence in the generated signals into the training set for data augmentation. Experimental results show that the data augmentation algorithm based on the conditional diffusion model proposed in this paper can effectively improve the classification performance of the model.

In order to meet increasing demands of higher energy efficiency and better reliability performance for the multiuser Internet of things (IoT) secure communications, in this paper, we design a reliable and energy efficient superposition modulation (SM) and singular value decomposition (SVD) detection based multi-user orthogonal frequency division modulation-based differential chaos shift keying (SM-MU-OFDM-DCSK) transceiver for IoT devices. At the transmitter, we propose to superimpose and overlap the information-bearing chaotic signals for higher energy efficiency. Since the chaotic modulated signals of each user share identical reference chaotic signals, the transmitted superimposed signal matrices have the low-rank property. Then at the receiver, we apply the SVD-aided detection to recover these low-rank signals, which can suppress the noise to attain better reliability performance. Moreover, we prove that the proposed design can achieve the maximum likelihood detection performances. Furthermore, we derive the theoretical bit rate, energy efficiency and bit error rate expressions over additive white Gaussian noise (AWGN) channel. Simulation results are then provided to validate the theoretical derivations. Subsequently, the simulated performances over AWGN and fading channels are investigated to show that higher energy efficiency and better reliability performances than benchmark schemes can be achieved in industrial IoT scenarios.

Hyperspectral anomaly detection (HAD) is of great value in both practical and theoretical terms. However, due to the lack of available semantic labels, previous works mainly relied on unsupervised or semi-supervised methods to construct learning models, which inevitably lacked semantic guidance and led to limited anomaly detection (AD) effectiveness. Besides, few previous methods jointly mine spectral and spatial global dependencies, which limits their effectiveness in practical scenarios. To address the above problems, we design a novel self-supervised HAD method, named the Self-Supervised Hyperspectral Anomaly Detection method based on the Finite Spatial-wise Attention. The core of proposed method is the designed Self-Supervised Hyperspectral Anomaly Detection transFormer (SSHADFormer). It explores the specific spectral attributes of hyperspectral images (HSIs) to reconstruct background HSI from a given RGB image, which solves the difficulty of acquiring semantic information and enhances the agility of AD models. In addition, we propose a Finite Spatial-wise Attention mechanism. The mechanism mines the cluster structure of the background spectrum in a data-driven manner, enhancing the discriminative ability between background and anomalous targets while avoiding anomalous targets interference during training. Extensive experiments on six public datasets demonstrate the effectiveness and agility of the proposed method.

Haze can reduce the visibility of the captured image, making it hard to accurately distinguish the details of each object in the captured image scene. Aiming at the problem of detail loss in existing dehazing models, this paper proposes a lightweight end-to-end image dehazing framework called DFE-GAN (Detail Feature Enhancement-GAN). The missing detail contours in the haze image can be predicted by employing a densely connected detail feature prediction network. Supplemented with a patch discriminator and an improved loss function, the restoration of details in the dehazing image is enhanced to improve image quality. We apply inverse residual modules to extract and fuse multi-scale features from images, which can ensure the real-time processing capability of the model. Compared with previous state-of-the-art approaches, solid experimental results on various benchmark datasets validate the robustness and effectiveness of our model.

Due to the limited availability of anomalous samples for training, video anomaly detection is commonly viewed as a one-class classification problem. Many prevalent methods investigate the reconstruction difference produced by AutoEncoders (AEs) under the assumption that the AEs would reconstruct the normal data well while reconstructing anomalies poorly. However, even with only normal data training, the AEs often reconstruct anomalies well, which depletes their anomaly detection performance. To alleviate this issue, we propose a simple yet efficient framework for video anomaly detection. The pseudo anomaly samples are introduced, which are synthesized from only normal data by embedding random mask tokens without extra data processing. We also propose a normalcy consistency training strategy that encourages the AEs to better learn the regular knowledge from normal and corresponding pseudo anomaly data. This way, the AEs learn more distinct reconstruction boundaries between normal and abnormal data, resulting in superior anomaly discrimination capability. Experimental results demonstrate the effectiveness of the proposed method.

Video anomaly detection is an essential but challenging task. The prevalent methods mainly investigate the reconstruction difference between normal and abnormal patterns but ignore the semantics consistency between appearance and motion information of behavior patterns, making the results highly dependent on the local context of frame sequences and lacking the understanding of behavior semantics. To address this issue, we propose a framework of Appearance-Motion Semantics Representation Consistency that uses the gap of appearance and motion semantic representation consistency between normal and abnormal data. The two-stream structure is designed to encode the appearance and motion information representation of normal samples, and a novel consistency loss is proposed to enhance the consistency of feature semantics so that anomalies with low consistency can be identified. Moreover, the lower consistency features of anomalies can be used to deteriorate the quality of the predicted frame, which makes anomalies easier to spot. Experimental results demonstrate the effectiveness of the proposed method.

Frame prediction based on AutoEncoder plays a significant role in unsupervised video anomaly detection. Ideally, the models trained on the normal data could generate larger prediction errors of anomalies. However, the correlation between appearance and motion information is underutilized, which makes the models lack an understanding of normal patterns. Moreover, the models do not work well due to the uncontrollable generalizability of deep AutoEncoder. To tackle these problems, we propose a multi-level memory-augmented appearance-motion correspondence framework. The latent correspondence between appearance and motion is explored via appearance-motion semantics alignment and semantics replacement training. Besides, we also introduce a Memory-Guided Suppression Module, which utilizes the difference from normal prototype features to suppress the reconstruction capacity caused by skip-connection, achieving the tradeoff between the good reconstruction of normal data and the poor reconstruction of abnormal data. Experimental results show that our framework outperforms the state-of-the-art methods, achieving AUCs of 99.6%, 93.8%, and 76.3% on UCSD Ped2, CUHK Avenue, and ShanghaiTech datasets.

Radio frequency watermarking systems embed the secret watermark bits into host bits for secure transmissions. However, malicious users may initiate falsifying attacks via broadcasting wireless channels. Meanwhile, the interferences of watermark signals to host signals will degrade the bit error rate (BER) performances. With the aim to enhance both the security and the reliability performances, we propose to utilize the Hash function to generate the unique digital digest of host signals for the integrity check. Then the lightweight encryption and spreading are conducted to compose the watermark signals. At the receiver, reverse operations are performed to recover both host signals and watermark signals. Then the retrieved digest is used to check the integrity and validity of host signals to combat falsifying attacks. Subsequently, we derive the bit error rate (BER) and the information leakage expressions. Simulation results are then provided to validate the proposed design.

Abstract Unsupervised anomaly detection refers to the discovery of unconventional images that are globally or locally different from the training set. Recently, reconstruction‐based anomaly detection methods have made great progress. However, most of the existing methods take reconstructing the original image as the goal of latent feature learning. Due to lack of effective semantic guidance, latent features have intrinsic characteristics which retain redundant details of spatial structure. Such information is too general and cause over‐expression problem. To solve this problem, in this paper, dual transformation‐aware embeddings are coined which aims to achieve a stable model to learn high‐level latent features in a self‐supervised manner. To be more specific, the authors try to extract transformation‐detectable feature embeddings for both structure and content views which explore the regular pattern under different transformations in normal situations. In addition, the relationship between the original feature and the transformed feature is established. Based on such relationship, the latent feature of generated image to predict transformation parameter is extracted. Then, a transformation‐consistency regularization is proposed to constrain decoder to generate high‐quality image with high‐level consistency and achieve a more stable model. Experiments on MVTec‐AD and CIFAR10 datasets prove the effectiveness and robustness of the proposed method.

In this paper, we propose a pre-coded multi-carrier$M$-ary chaotic vector cyclic shift keying (PC-MC-$M$-CVCSK) transceiver for reliable information transmissions. In this design, we exploit the principle of diagonalization to combat noises and interferences. At the PC-MC-$M$-CVCSK transmitter, we propose to precode the$M$-ary modulated symbols based on the discrete Fourier transform (DFT) and the reverse matrix of an eigenvalue matrix. Meanwhile, the chaotic generator outputs chaotic sequences, which are then cyclically shifted and used to modulate precoded symbols. At the receiver, reverse operations are conducted to recover the data. Moreover, we prove that a circulant matrix can be constructed by selecting the cyclic shift length, while the DFT aided processing will diagonalize the circulant matrix. Thanks to the diagonalization design, the inner product of information-bearing chaotic vectors becomes zero, thus signals can be orthogonalized to suppress interferences. Furthermore, theoretical performances of the proposed transceiver, including the bit error rate (BER), the symbol rate, the energy efficiency and the spectrum efficiency, and the computational complexity are analyzed. Simulation results validate the exact analysis. Furthermore, the results demonstrate that the proposed PC-MC-$M$-CVCSK system outperforms the counterpart schemes, which can provide$M$-ary high symbol rate transmission services with enhanced reliability performances.

In this paper, we propose a Sparse Code Spreading-aided Multi-Carrier Differential Chaos Shift Keying (SCS-MC-DCSK) transceiver to improve the efficiency and reliability performances for multiuser transmissions. With the aim to improve the spectrum efficiency and enhance the reliability, we construct a multi-dimension codebook based on a factor graph matrix to spread information bits in a non orthogonal way, thus the signals could overlap and multiple users are allowed to share all sub-carrier resources via the non orthogonal spreading. The information bits using the same codebook are regarded as a layer, while each user could choose to use one or multiple layers. Then the resultant symbols are spread by the reference chaotic sequence and its quadrature version generated by Hilbert transform in the time domain. At the receiver, reverse operations are conducted, and we propose the Minimum Distance (MD) detection method to retrieve the estimates. Furthermore, theoretical performances, including the Symbol Error Rate (SER), capacity, energy efficiency, spectrum efficiency and computational complexity, are analyzed and compared among the proposed design and benchmarks. Simulation results are then provided to validate the theoretical analysis. Moreover, the Bit Error Rate (BER) performances under different channel conditions demonstrate the superior reliability and efficiency to benchmarks.

With the increasing popularity of digital video communication, video data hiding has become an active research topic in covert communication and privacy protection. Traditional video data hiding methods often use quantized discrete cosine transform (QDCT) coefficients to carry a sufficient payload. However, since QDCT coefficients expose texture features and motion characteristics of the present video frame heavily, data embedding with QDCT coefficients may lead to significant intra-frame distortion and inter-frame distortion drift. To avoid obvious visual artifacts and keep bit-rate within a satisfactory level of the marked video, data embedding in QDCT coefficients should take into account both the intra-frame and inter-frame distortion impacts. It motivates the authors to propose an efficient cost assignment-based video data hiding method in this paper. The proposed cost assignment method aims to accurately evaluate the data embedding distortion. Specifically, the proposed scheme considers intra-frame changes and intra-frame distortion drift, for which the texture and motion changes of frames can be measured. The frame position is also used to reflect a cumulative distortion difference of multiple frames. For data embedding, syndrome-trellis code (STC) is adopted to minimize the overall distortion. Experimental results show that the proposed method significantly outperforms existing works in terms of payload-distortion performance.

Narrow band Internet of Things (NB-IoT) systems support massive devices to connect with the wide area network with the low energy consumption. However, due to the broadcasting property and variant conditions of wireless channels, both the security and the reliability performances are required to be improved for NB-IoT terminals. In this paper, we propose a multidimensional orthogonal code (MOC) aided radio frequency (RF) watermark scheme to enhance both the security and the reliability performances. In our design, we exploit the repeat transmission mechanism in NB-IoT systems, and propose to use the repeated signal as the host signal to carry multiple low-power watermark signals, wherein different orthogonal codewords are utilized as the embedding coefficients to hide the host signals. Thanks to the orthogonality of MOC watermarks, the mutual interferences among the transmitted signals can be effectively suppressed, thus the reliability performances could be improved. Furthermore, we derive the theoretical bit error rate (BER) and analyze the efficiency as well as the covertness. Simulation results validate the theoretical analysis. Moreover the results demonstrate that using the proposed MOC aided RF watermark design, NB-IoT systems can achieve better reliability and covertness performances as well as higher security performances than counterpart schemes.

In this paper, we propose a novel two-dimensional frequency and time diversity aided orthogonal frequency division multiplexing based differential chaos shift keying (OFDM-DCSK) system. Our aim is to provide secure and robust transmissions for practical wireless systems, where perfect channel state information (CSI) may not be available at the receiver, by exploiting the natural high security of chaotic sequences and frequency diversity gains brought by the frequency hopping (FH). In our design, the information bits are firstly modulated by chaotic chips, then non-repetitive FH operations are performed on both reference chips and chaotic modulated symbols. After the inverse fast Fourier transform (IFFT), the non-repetitive reference chips and chaotic modulated symbols are respectively transmitted over different subcarriers. Subsequently, the receiver recovers the information using the received reference chaotic chips which naturally embed the channel frequency response (CFR) of all subcarriers. We then analyze the energy and spectral efficiencies, derive the bit error rate (BER) and information leakage expressions, and provide a detailed complexity analysis of the proposed scheme. Simulation results verify the effectiveness of our derivations and demonstrate that the proposed system achieves better BER and security performances compared with the benchmark system, especially when the CSI is imperfect or unknown.

In this paper, we present a frequency hopping (FH) orthogonal frequency division multiplexing-aided differential chaos shift keying (OFDM-DCSK) modulation system for cognitive radio (CR) systems operating over non contiguous bands. Our aim is to provide reliable transmissions for CR transceivers which may hardly get the exact and perfect channel state information (CSI). In our design, we utilize the natural high security of chaotic sequences and the frequency diversity brought by the FH. The information bits are firstly modulated by chaotic chips, then both reference chips and chaotic modulated symbols are carried out FH operations and hopped non-repetitively. After performing the inverse fast Fourier transform (IFFT) over available non-contiguous spectrum bands, the resultant OFDM symbols are transmitted and the non-repetitive reference chips are respectively transmitted over different subcarriers. Subsequently, the receiver would retrieve the information using the received reference chaotic chips which naturally embed the channel frequency response (CFR) of all subcarriers. The bit error rate (BER) expressions are derived and simulations results verify the effectiveness of our derivations, which demonstrate that the presented system can achieve better BER than counterpart systems, especially when the CSI is not exact or unknown.

In this paper, we proposed a novel carrier interferometry (CI) code index modulation aided orthogonal frequency division multiplex (OFDM) differential chaotic shift keying (DCSK) communication system. In our design, we utilize the orthogonality property of CI codes matrix, and propose to transmit more information bits using the index of the CI codes matrix. The input information stream is divided into two subsets. One subset is modulated by the chaotic sequence and then spread by the information-bearing CI codes matrix which carries another information subset. At the receiver, the non-coherent chaotic demodulation and the maximum likelihood detection are respectively performed on these two subsets to retrieve the information. Since more bits can be transmitted while not requiring extra chaotic sequences and remaining the same overall energy, the energy efficiency is improved. Additionally, thanks to the spreading in the frequency domain achieved by the usage of CI codes, the peak to average power ratio (PAPR) can be suppressed. Moreover, theoretical performances including the energy efficiency, the bit error rate (BER) expressions are analyzed. Simulation results are provided to validate the theoretical analysis, and demonstrate the satisfactory PAPR and BER performances achieved by our design.

In this paper, we present a general iterative receiver for multi-carrier differential chaos shift keying (MC-DCSK) systems. The imperfect channels conditions may induce transmission errors which will degrade the reliability performances. In order to address this issue, we propose an iterative demodulation structure to improve bit error rate (BER) performances by improving the signal to noise ratio (SNR) of reference chaotic signals. At the presented iterative receiver, we propose to evaluate correlation coefficients between the reference chaotic signals and each information-bearing chaotic modulated signal. Then they are used to update and feedback the reference chaotic signal for the next iteration. With the aid of the renewed reference signals, a similar procedure is carried out until the iteration stopping criteria is reached and the threshold decisions are provided. The presented general iterative receiver can be applied to any MC-DCSK systems without introducing any additional modules at transmitters. Theoretical analysis is provided to prove the iteration convergence and to derive the BER performances. Simulation results verify the effectiveness of the theoretical analysis, which demonstrates that better reliability performances can be achieved over additive white Gaussian noise (AWGN) and fading channels compared with counterpart systems.

In this paper, we present a novel noise resistent diagonal iteration-based carrier interferometry (DICI) codes for spreading optical orthogonal frequency-division multiplexing (OOFDM) symbols, and thereby suppressing the peak to average power ratio (PAPR) for high data rate OOFDM based systems. Considering that the high date rate transmission requires the subcarrier number to be large, and that the large subcarrier number may induce smaller intervals between the symbols and higher PAPR and thus leading to the performance degradation, we propose to use DICI codes to enlarge the intervals and suppress the noises including the signal- dependent noises in intensity modulated system and the phase noises in coherent modulated systems to improve the performances. More explicitly, we perform the diagonal iteration on the carrier interferometry (CI) codes by calculating the Kronecker product of identity matrix and CI code matrix, thus the matrix extension is achieved by the DICI codes. Moreover, we prove that the presented DICI codes are orthogonal and invertible. Simulations are performed and the results demonstrate the outstanding bit error rate (BER) and PAPR performances of the proposed DICI-aided spreading OOFDM systems.

In this paper, we propose a noise suppressing chaos generator to improve the bit error rate (BER) performances for the high-order differential chaos shift keying (DCSK) systems. The presented chaos generator outputs the overlapping chaotic sequences for the information modulation, which use a buffer for temporary storage, and the stored chaotic sequence is used to modulate the information bit while the second element of the chaotic sequence is fed back to serve as the initial value to generate chaotic sequence for the modulation of the next information bit. Namely, the different information bit may share the same chaotic segments of the whole chaotic sequence. Thus, the repetitive transmission of the same reference chaotic segments suppress the channel noise, and the BER performance of high-order DCSK systems are improved. We derive the theoretical BER expression for the proposed scheme over additive white Gaussian noise (AWGN) channel. The simulation results match the theoretical BER, and demonstrate that the BER performances have been improved with the use of noise-suppressing chaos generator.

MOTIVATION: Previously, we developed a computational model to identify genomic co-occurrence networks that was applied to capture the coevolution patterns within genomes of influenza viruses. To facilitate easy public use of this model, an R package 'cooccurNet' is presented here. RESULTS: 'cooccurNet' includes functionalities of construction and analysis of residues (e.g. nucleotides, amino acids and SNPs) co-occurrence network. In addition, a new method for measuring residues coevolution, defined as residue co-occurrence score (RCOS), is proposed and implemented in 'cooccurNet' based on the co-occurrence network. AVAILABILITY AND IMPLEMENTATION: 'cooccurNet' is publicly available on CRAN repositories under the GPL-3 Open Source License ( http://cran.r-project.org/package=cooccurNet ). CONTACT: [email protected] or [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

In this paper, a software defined radio (SDR) based waveform generator platform with spectrally compliant cognitive capability for cognitive radio (CR) and dynamic spectrum access (DSA) network is demonstrated. In our previous work, an overlay CR has been demonstrated via a spectrally modulated spectrally encoded (SMSE) framework to autonomously stitch unused spectrum fragments and transmit over non-contiguous frequency bands in a dynamically changing environment. Such an overlay CR can coexist with primary users (PUs) and other secondary users (SUs) without harmful interference by turning off the subcarriers which are shared with active PUs and other SUs on the band. To further enhance spectrum usage, we extend it and implement a hybrid overlay/underlay waveform generator via a soft-decision SMSE (SD-SMSE) framework, where both unused and under-used spectra are explored. Specifically, the hybrid overlay/underlay waveform can transmit over not only unused spectrum bands, but also under-used spectrum and share the spectrum with active PUs and other SUs by underlaying them, since PU/SU has certain capability to tolerant interference as long as the underlay transmission power is lower than such a interference tolerance level (ITL). On the other hand, if any active SU on the band is not a friendly transmission, the hybrid waveform can easily increase the transmission power underlaying with the SU much higher than the ITL to fully disrupt the unfriendly transmission. Moreover, the proposed platform is capable of generating conventional single-carrier waveform with different modulation schemes and radio frequency (RF) parameters. Therefore, the platform implements many desirable functions, realizes coexistence with PUs and other SUs or unfriendly transmission disruption, and maximizes the spectrum efficiency, as well as provides a testbed for cognitive radio and cognitive RF research and development.

FD-MC-CDMA is an attractive frequency domain CDMA system that provides high performance in multi-path fading channels by exploiting both diversity gain and multi-user detection (MUD) gain. In FD-MC-CDMA, by decomposing the entire subcarrier set into multiple non-contiguous subcarrier sets, the number of interfering users within each subcarrier set is significantly reduced. As a direct result, an optimal maximum likelihood MUD receiver can be implemented at low complexity. In this paper, we first revisit previously developed phase rotation spreading code design to bring signal space diversity to conventional MC-CDMA systems. Similarly, due to the binary nature of the spreading codes, full diversity is not always exploited in FD-MC-CDMA system. Then we combine the phase rotated spreading code design scheme with FD-MC-CDMA system to exploit full diversity. With the phase rotated spreading codes and the exploitation of full diversity, the new FD-MC-CDMA system offers significant performance enhancement compared with the original FD-MC-CDMA system along with conventional MC-CDMA system. Simulation results over multi-path fading channels confirm the performance gain of the proposed scheme.

In this paper, we apply advanced encryption standard (AES) to improve the security of chaotic cognitive radio (CCR) system. More specifically, we propose to encrypt the information with AES, then the AES-encrypted information is modulated by frequency domain based chaos sequences and transmitted over the non continuous subcarriers sensed by the CCR. Both the transmitter and the receiver share the secret of AES encryption, while the eavesdroppers could not retrieve the information without any information of the secret. The simulation results verify that the AES-assisted CCR system offers a high security. The information effectively hides and the power spectral density of the `Lena' image is smoothed with AES encryption. Furthermore, the bit error rate analysis demonstrate that the malicious users can not retrieve the original information due to the high bit error rate without AES secret key.

To allow users to access networks and services ubiquitously, more and more user equipments (UEs) are equipped with multiple radio transceivers. However, due to the proximity of transmit and receive frequencies of in-device wireless technologies, the transmitter (TX) of one in-device wireless technology can interfere with the reception of another wireless technology within the same device, and the spurious emissions of the aggressor TX may result in unacceptable interference at the victim receiver which is co-located in-device. Hence, it is crucial to detect the in-device interference and adapt the strategies to avoid harm. In this paper, we consider the scenario of LTE coexisting with WiFi. Different from traditional spectrum sensing in cognitive radio which detects signal in the background of Gaussian noise, the WiFi leakage detection problem in this paper aims to detect WiFi leakage in the background of LTE signal and Gaussian noise. This makes the problem challenging. By identifying unique features of the WiFi leakage, we investigate this problem thoroughly and propose two detection algorithms, including entropy ratio based and cyclostationary based algorithms. Simulation results under different scenarios are presented to illustrate the effectiveness and efficiency of the proposed detection techniques.

In this paper, we propose a new method for blind modulation detection based on detailed second order cyclostationary features for underwater acoustic communication. Due to the severe and fast varying Doppler shift and phase noise in the underwater acoustic communication channel, much shorter signal length can be used to detection the modulation scheme, leading to less accuracy in the modulation detection performance. Here we propose a brand new method to perform modulation detection: by analyzing the zoomed-in spectral coherence function (SOF) of short time signal, we observe distinctive detailed features for different modulations. Taking advantage of such detailed features in short time SOF, we design a novel blind modulation detection algorithm for underwater acoustic communication system. To the best of our knowledge, our paper is the first to employ such detailed, in some sense hidden, features of cyclostationary analysis to conduct modulation detection. Coupled with our previous work on blind carrier frequency estimation and symbol rate estimation algorithms, the proposed modulation detection algorithm enjoys very high detection accuracy, high speed, and low complexity. Real experimental data collected at sea are used to validate the effectiveness of the proposed algorithm. It is also important to note that the proposed method does not assume any a priori knowledge of the target signal.

Target detection is a surveillance problem of practical importance that is well suited to wireless sensor networks. In this paper, we study the target search via track-before-detect process for moving targets in large area utilizing a network of distributed sensor nodes. Specifically, we extend the track-before-detect process to heterogeneous sensors with imperfect communication links between the sensor and fusion center. We consider two different models: (1) heterogeneous sensors with different probability of detection and perfect communication links; and (2) heterogeneous sensors with imperfect communication links. To analyze the performance of searching process for the system, we derive the expressions for the probabilities of both successful search and false search. We also investigate the strategy to optimize the system by obtaining the minimal false search when meeting the requirement of the successful search performance.

Orthogonal Frequency Division Multiplexing (OFDM) has been considered as a strong candidate for next generation high speed aerial vehicle communication systems. However, OFDM systems suffers severe performance degradation due to inter-carrier interference (ICI) in high mobility channel, if no ICI cancellation is performed. Traditionally, training symbols have been employed in one packet to help the OFDM receiver to estimate the multi-path channel and the carrier frequency offset (CFO) between the transmitter local oscillator and the receiver local oscillator. However, in aerial vehicle communication, the relative transmitter-receiver speed changes so rapidly that it is unreasonable to assume a constant speed (and CFO) during the entire packet transmission. Hence, to accurately estimate the CFO, training symbols need to be transmitted for every OFDM symbol. Obviously, this significantly reduces OFDM throughput while adding complexity due to repeated CFO estimation. In this paper, we extend our previous work to propose a joint channel/CFO estimation and ICI cancellation algorithm. Specifically, in our previous work, we have proposed a total ICI cancellation algorithm using parallel processing for OFDM system which offers the excellent ICI cancellation and BER performance. However, in this work, perfect channel information was assumed. In this paper, we combine the channel estimation with the ICI cancellation together. The proposed general total ICI cancellation algorithm has the ability to jointly estimate the carrier frequency offset and channel information, and improve the performance significantly. Meanwhile, a serial processing is proposed to reduce the computation complexity. Simulation results in different scenarios confirm the performance of the proposed scheme in multipath fading channels for high speed aerial vehicle communication.

Orthogonal Frequency Division Multiplexing (OFDM) has been considered as a strong candidate for next generation wireless communication systems. Compared to traditional OFDM, Single Carrier OFDM (SC-OFDM) has demonstrated excellent bit error rate (BER) performance, as well as low peak to average power ratio (PAPR). Similar to other multi-carrier transmission technologies, SC-OFDM suffers significant performance degradation resulting from intercarrier interference (ICI) in high mobility environments. Existing techniques for OFDM can be directly adopted in SC-OFDM to improve performance, however, this improved performance comes at costs such as decreased throughput. In this paper, we analyze the effect of ICI on an SC-OFDM system and propose a novel modulation scheme. The proposed Magnitude-Keyed Modulation (MKM) modulation provides SC-OFDM system immunity to ICI and with an easy implementation it significantly outperforms OFDM, SC-OFDM and MC-CDMA systems with Phase Shift Keying (PSK) modulation and Quadrature Amplitude Modulation (QAM) in severe ICI environment. Analysis also illustrates the proposed SC-OFDM system with MKM modulation maintains low PAPR compared to traditional OFDM and SC-OFDM systems with PSK and QAM modulations. Simulation results for different modulation schemes in various ICI environments confirm the effectiveness of the proposed system.

Cyclostationary analysis such as spectral correlation function (SCF) and spectral coherence function (SOF) has been accepted as an important tool in signal detection and radio frequency (RF) parameter estimation in cognitive radios. However, cyclostationary analysis requires extremely high resolution to fully observe the cyclic frequency features, leading to very high computational complexity and difficulty in real time implementation, especially on software defined radios where computational capability is limited. In this paper, we implement and demonstrate a real time SCF/SOF RF signal analyzer by adopting previously proposed two-stage dynamic resolution SCF/SOF estimation method using software defined radios. Specifically, high resolution SCF/SOF calculation is only performed near the expected cyclic feature locations obtained through a low complexity coarse estimation employing spectral analysis. This cyclostationary analysis based RF signal analyzer is capable of sensing the existence of primary users and estimating the carrier frequency and symbol rate accurately. Using Universal Software Radio Peripheral (USRP) software defined radio platform and GNU radio software, we implement and demonstrate such a cyclostationary analysis based RF signal analyzer in real time and validate the performance in realistic wireless communication channels.

In recently developed cognitive radio network (CRN), the spectrum sharing leads to many uncertainties associated with the aggregate interference in the network. It is highly desired to build an interference model for such cognitive radio networks to express such uncertainties to quantify the effect of the interference on the primary network. However, existing interference models have not account for lognormal shadowing due to the difficulty to estimate the entire lognormal sum distribution. In this paper, we propose to utilize the Scaled Student's t distribution to approximate the shadowing effect and improve existing interference models in CRN. Closed form probability density function (PDF), cumulative distribution function (CDF) and characteristic function (CF) of the interference including shadowing effects are derived. Simulation results of CDF, complementary CDF (CCDF), CF and bit error rate (BER) performance in various scenarios confirm the effectiveness of the proposed approximation method.

In this paper, we propose a new method for blind carrier frequency and symbol rate estimation based on second order cyclostationary features for underwater acoustic communication. Followed by a coarse carrier frequency and symbol rate estimation via spectral analysis, a cyclostationary based estimation algorithm is developed to provide high resolution estimation of both parameters. Specifically, a short time varying resolution spectrum correlation function and spectrum coherence function algorithm is developed to obtain spectral lines in cyclic frequency domain to accurately estimate (1) varying carrier frequency and Doppler shift caused by relative motion and (2) symbol rate for single carrier communication. Simulations confirm the effectiveness, fast speed and low complexity of the proposed algorithm. The proposed method does not assume any a priori knowledge of the target signal.

In cognitive radio (CR) and dynamic spectrum access (DSA) network research, most of current work on spectrum sensing focuses on the detection of existence of the spectrum holes for secondary user (SU) to harness. However, in a more sophisticated CR, the SU needs to detect more than just the existence of primary users (PUs) and spectrum holes: e.g., the transmission power and location of the primary users. In our previous work, we combined the spectrum sensing and PU power/localization detection together, and developed a joint PU detection and power/localization detection algorithm via compressed sensing (CS). By employing compressed sensing, the measurement ratio for the spectrum sensors is significantly reduced. However, if the measurement ratio is too low, the compressed sensing algorithm will not provide accurate estimates. Since the sparsity in the frequency domain is dynamically changing, it is unfeasible to set a predetermined measurement ratio. In this paper, we extend our previous work to employ the Bayesian Compressed Sensing (BCS) to improve the reconstruction results and dynamically determine the measurement ratio. Specifically, the BCS algorithm provides an "error bar" along with the reconstruction of the target vector. This "error bar" can then be used to determine if the current measurement ratio is sufficient. When the spectrum environment changes, the "error bar" will change accordingly, giving us direction to increase or decrease the measurement ratio. Simulation results including the measurement ratio, the miss detection probability (MDP), false alarm probability (FAP) and reconstruction probability (RP) confirm the effectiveness and robustness of the proposed method.

In wideband cognitive radio (CR) networks, spectrum sensing is one of the key issues that enable the whole network functionality. Collaborative spectrum sensing among the cognitive radio nodes can greatly improve the sensing performance, and is also able to obtain the location information of primary radios (PRs). Most existing work merely studies the cognitive radio networks with static PRs, yet how to deal with the situations for mobile PRs remains less addressed. In this paper, we propose a collaborative compressive sensing based approach to estimate both the power spectrum and locations of the PRs by exploiting the sparsity facts: the relative narrow band nature of the transmitted signals compared with the broad bandwidth of available spectrum and the mobile PRs located sparsely in the operational space. To effectively track mobile PRs, we implement a Kalman filter using the current estimations to update the location information. To handle dynamics in spectrum usage, a dynamic compressive spectrum sensing algorithm is proposed. Joint consideration of the above two techniques is also investigated. Simulation results validate the effectiveness and robustness of the proposed approach.

In this paper, we provide a fundamental channel model to characterize the interference effect inherent in cognitive radio systems. Mutual information rates of our proposed probabilistic block interference channels for both primary and secondary users are derived without assuming that receivers have knowledge on channel interference states. Novel constrained optimization problems are then put forward with a constraint on the performance loss margin tolerated by the primary user. Furthermore, we investigate some special cases where conditions are provided to justify the optimality of adopting Neyman-Pearson rule. Also presented are some scenarios in which randomized decision without using sensing measurement is needed to balance the rateloss for PU and throughput gain for SD.

A spectrally modulated spectrally encoded (SMSE) based overlay cognitive radio has been implemented and demonstrated in [1] via GNU software define radio (SDR). However, like most of the current cognitive radio implementations and demonstrations, this work does not consider the mobility between cognitive radio nodes. In a high mobility environment, the frequency offset introduced by Doppler shift leads to loss of the orthogonality among subcarriers. As a direct result, severe inter-carrier interference (ICI) and performance degradation is observed. In our previous work, we have proposed a new ICI cancellation method (namely Total ICI Cancellation) for OFDM [2] and MC-CDMA [3] mobile communication systems, which eliminates the ICI without lowering the transmission rate nor reducing the bandwidth efficiency. In this paper, we apply the total ICI cancellation algorithm onto the SMSE base overlay cognitive radio to demonstrate a high performance cognitive radio in high mobility environment. Specifically, we demonstrate an SMSE based overlay cognitive radio that is capable of detecting primary users in real time and adaptively adjusting its transmission parameters to avoid interference to (and from) primary users. When the primary user transmission changes, the cognitive radio dynamically adjusts its transmission accordingly. Additionally, this cognitive radio maintains seamless real time video transmission between the cognitive radio pair even when large frequency offset is introduced by mobility between CR transmitter and receiver.

One of the major challenging issues in wireless communication is spectrum scarcity. In order to better utilize the licensed spectrum, the concept of cognitive radio has been introduced in which unlicensed users (secondary users) sense the spectrum and use the available bandwidth for their own transmission. One of the methods for detecting licensed users is through cyclostationary processing, which is based on the estimation of the spectral correlation function of the received signal. In this paper a new method for the detection of licensed users is proposed. The proposed Hybrid Smoothing Method (HSM) combines pre-existing time smoothing and frequency smoothing algorithms in cyclostationary processing in a cascading format. HSM estimates the SCF of the received signal and then sets a threshold for its decision. The threshold to switch from frequency smoothing to time smoothing in HSM is set by Neyman-Pearson lemma. Simulation results show that HSM not only works in a noisy environment but also outperforms a standalone time or frequency smoothing algorithm in terms of probability of signal detection.

With the proliferation of wireless devices being used, the RF spectrum's capacity continues to dwindle. In recent years, a new technology called Cognitive Radio has been advocated to solve the impending spectral drought. The premise of Cognitive Radio is that it can modify its signal to either avoid currently occupied frequency bands or alter its transmission parameters so as to cohabit the frequency band without interfering with the primary user. However, if the widespread use of Cognitive Radios and Dynamic Access Networks becomes a reality, it would enable multiple users to occupy the same frequency band There have yet to be any works published regarding how to classify the signals of multiple users, a barrier which will have great implications in the future use of Cognitive Radio. In addition to future commercial applications for multiuser signal classification, there is currently a need for this technology in the military. Military communication devices are used in scenarios where the RF spectrum is filled with jamming and interference from enemies. A method to detect and classify what signals are being used to jam and interfere would solve a significant roadblock for the military. Cyclic spectral analysis has proven to be a key tool in Cognitive Radios, giving them the ability to determine the parameters of the present signal, thus being able to modify its own transmission accordingly. Using this analysis as a foundation, we revisit the signal classification problem and propose a novel multi-user signal classification scheme using spectral correlation.

For orthogonal frequency division multiplexing (OFDM) communication systems, the orthogonality among subcarriers is lost in mobile radio channels due to the frequency offsets caused by mobility. As a direct result, intercarrier interference (ICI) is observed on each and every subcarrier, leading to significant performance degradation. Many ICI cancellation methods such as windowing and frequency domain coding have been proposed in the literature to cancel ICI and improve the BER performance of OFDM in mobile channel. However, the performance improvement achieved by all the existing ICI cancellation methods is far from enough: the BER performance after ICI cancellation is still much worse than the BER performance of original OFDM without ICI. Moreover, popular ICI cancellation methods like ICI self-cancellation reduce ICI at the price of lowering the transmission rate and reducing the bandwidth efficiency. Other frequency-domain coding methods do not reduce the data rate, but produce less reduction in ICI as well. In this paper, we propose a novel ICI cancellation scheme which can eliminate the ICI entirely and offer a OFDM mobile system with the same BER performance of a OFDM system without ICI. More importantly, the proposed ICI cancellation scheme, namely Total ICI cancellation, does not lower the transmission rate or reduce the bandwidth efficiency. Specifically, the total ICI cancellation scheme takes advantage of the orthogonality of the ICI matrix and offers perfect ICI cancellation and significant BER improvement at linearly growing cost. Simulation results in AWGN channel and multi-path fading channel confirm the superb performance of the proposed total ICI cancellation scheme in the presence of frequency offset or time variations in the channel, outperforming all the existing ICI cancellation methods.

Multi-carrier code division multiple access (MC-CDMA)has been considered as a strong candidate for next generation wireless communication system due to its excellent performance in multi-path fading channel and simple receiver structure. However, like all the multi-carrier transmission technologies such as OFDM, the inter-carrier interference (ICI) produced by the frequency offset between the transmitter and receiver local oscillators or by Doppler shift due to high mobility causes significant BER (bit error rate) performance degradation in MC-CDMA system. Many ICI cancellation methods such as windowing and frequency domain coding have been proposed in the literature to cancel ICI and improve the BER performance for multi-carrier transmission technologies. However, existing ICI cancellation methods do not cancel ICI entirely and the BER performance after ICI cancellation is still much worse than the BER performance of original system without ICI. Moreover, popular ICI cancellation methods like ICI self-cancellation reduce ICI at the price of lowering the transmission rate and reducing the bandwidth efficiency. Other frequency-domain coding methods do not reduce the data rate, but produce less reduction in ICI as well. In this paper, we propose a novel ICI cancellation scheme that can eliminate the ICI entirely and offer a MC-CDMA mobile system with the same BER performance of a MC-CDMA system without ICI. More importantly, the proposed ICI cancellation scheme (namely Total ICI Cancellation) does not lower the transmission rate or reduce the bandwidth efficiency. Specifically, by exploiting frequency offset quantization, the proposed scheme takes advantage of the orthogonality of the ICI matrix and offers perfect ICI cancellation and significant BER improvement at linearly growing cost. Simulation results in AWGN channel and multi-path fading channel confirm the excellent performance of the proposed Total ICI Cancellation scheme in the presence of frequency offset or time variations in the channel, outperforming existing ICI cancellation methods.

The resource allocation optimization for multicast service in orthogonal frequency division multiple access (OFDMA) systems is an important research topic. It is known that in traditional multicast schemes the rate in each multicast group is severely limited by the user who has the minimum channel gain (MCG) on each subchannel. To counteract this effect, in this paper, we introduce the multiple description coding (MDC) into the multiple-group multicast service optimization for the downlink OFDMA, and study the weighted sum rate (WSR) maximization methods under various system constraints and assuming discrete-rate modulation. Simulation results show that the proposed method provides a significant performance enhancement than the MCG scheme and an MDC-based near-optimal iterative bit loading (IBL) approach. Besides this, our method converges very fast and enjoys a low complexity that is linear in the numbers of users, subchannels, and modulation bit levels.

For orthogonal frequency division multiplexing (OFDM) communication systems, the orthogonality among subcarriers is lost in mobile applications due to frequency offset resulting from either transmitter-receiver local oscillator differences or Doppler shift caused by mobility. As a direct result, inter-carrier interference (ICI) is observed on each and every subcarrier, leading to significant performance degradation. There are a lot of OFDM carrier frequency offset (CFO) estimation schemes classified as data aided estimation and blind estimation. Due to the system power and high bandwidth efficiencies, blind estimators have received a lot of attention recently. Many blind CFO schemes were proposed for OFDM systems, some of which are based on power spectrum smoothing, kurtosis-type cost functions and minimum output variance. In this paper, we propose a novel blind CFO estimator based on Minimum Reconstruction Error (MRE). In contrast to other blind CFO estimators, the proposed technique can be used for any constellation schemes, does not require a large number of blocks to reach acceptable estimation error and provides reliable estimation performance with very low mean square error (MSE). Simulation results in AWGN and multi-path fading channels confirm that performance of the proposed highly accurate blind CFO estimator is superior when frequency offset or time variation occurs in the channel - the proposed technique outperforms most existing blind CFO estimation methods.

Applicability of Spectrally Modulated, Spectrally Encoded (SMSE) waveform design has been expanded for future Cognitive Radio (CR)-based Software Defined Radio (SDR) applications. As previously demonstrated, the SMSE waveform design process can exploit statistical knowledge of PU spectral and temporal behavior to maximize SMSE system throughput (bits/second) while adhering to SMSE and Primary User (PU) spectral constraints. The capacity of SMSE systems is extended here using spectral partitioning with carrier-interferometry (CI) coding to increase SMSE waveform agility in the presence of a spectrally diverse transmission channel. By adaptively varying the modulation order and optimally allocating power within each spectral partition, inherent SMSE flexibility is more fully exploited and substantially increases system throughput while meeting Power Spectral Density (PSD) constraints. A coexistent scenario is provided in which the analytic optimization of the SMSE waveform is demonstrated while meeting spectral mask requirements. Results show that spectrally partitioned CI-SMSE waveforms have a significantly greater ability to adapt to varying spectral requirements.

In this paper, we implement and demonstrate an adaptive interference avoidance TDCS (Transform-domain Communication System) based cognitive radio via software defined radio implementation. By dynamically notching the occupied bands prior to applying IDFT, the designed cognitive radio communicates without interference to primary users and from primary users, as well as provides coexistence between primary users and secondary users. The designed system applies GNU software radio as the platform, and USRP (Universal Software Radio Peripheral) as the hardware solution. This cognitive radio is capable of detecting primary users in real time and adaptively adjusting its transmission parameters to avoid interference to primary users. Additionally, we have demonstrated that when the primary user transmission changes, the cognitive radio dynamically adjusts its transmission accordingly. We have demonstrated seamless real time video transmission between two cognitive radio nodes, while avoiding interference from primary users and interference to primary users operating in the same spectrum.

The impact of channel estimation error is investigated for Spectrally Modulated, Spectrally Encoded (SMSE) waveform designs in a coexistent environment containing multiple 802.11 Primary User (PU) systems. As previously demonstrated, the SMSE waveform design process can exploit statistical knowledge of PU spectral and temporal behavior to maximize SMSE system throughput (bits/second). This can be done by enforcing SMSE and PU bit error rate constraints while limiting mutual coexistent interference limited to manageable levels. Since maximum system performance requires accurate channel state knowledge at the SMSE transmitter, the presence of channel estimation error decreases the ability to design spectrally agile signals that optimally exploit coexistent spectral regions. Relative to a spectrally-only adapted system, the spectrally-temporally adapted SMSE system provides significant performance improvement by leveraging knowledge of PU temporal statistics to design temporally agile signals while maintaining desired performance levels for each system. Superiority of spectrally-temporally adapted signals is demonstrated here in terms of increased SMSE throughput (bits/symbol) and greater tolerance to increased channel estimation error.

Interest in Cognitive Radio (CR) remains strong as the communications community strives to solve the spectrum congestion problem. In conventional CR implementations, interference to primary users is minimized using either overlay waveforms that exploit unused (white) spectrum holes or underlay waveforms that spread their power spectrum density over an ultra-wide bandwidth. In Part I, we proposed a novel hybrid overlay/underlay waveform that realizes benefits of both waveforms and demonstrated its performance in an AWGN channel. This was done by extending the original Spectrally Modulated Spectrally Encoded (SMSE) framework to enable soft decision CR implementations that exploit both unused (white) and underused (gray) spectral areas. In Part II, we analyze and evaluate performance of the proposed hybrid overlay/underlay waveform in frequency selective fading channels. A simulated performance analysis of overlay, underlay and hybrid overlay/ underlay waveforms in frequency selective fading channels is presented and benefits discussed.

In this paper, we study the coexistence and optimization of a multicell cognitive radio network (CRN) which is overlaid with a multicell primary radio network (PRN). We propose a PRN-willingness-based design framework for coexistence and subchannel sharing, and a Lagrange duality based technique to optimize the weighted sum rate (WSR) of secondary users (SUs) over multiple cells. First, to avoid unacceptable SU interference to primary users (PUs), the PRN determines its interference margin based on its target performance metric and channel conditions, and broadcasts this information to the CRN. Second, each CRN cell optimizes its WSR and implements intercell iterative waterfilling (IC-IWF) to control the intercell interference. To account for the interference and transmit power limits at SUs, multilevel waterfilling (M-WF) and direct-power truncation (DPT) duality schemes are developed. Third, we develop a serial dual update technique which enables low-complexity and fast-convergence of the proposed duality schemes. Numerical results demonstrate the effects of multiple parameters, such as the number of SUs per cell, subchannel occupancy probability (SOP), and outage probability of the PUs. Our results show that the proposed duality schemes provide a large performance enhancement than the channel-greedy and access-fairness based resource allocation schemes.

Abstract In this paper, we present a software‐defined radio (SDR) based cognitive radio (CR) implementation and demonstration over the frequency modulation (FM) band. Using GNU Radio as the software platform and USRP (Universal Software Radio Peripheral) SDR boards as the hardware solution, we implement and demonstrate a CR that autonomously senses the entire FM band, detects all the active FM stations (occupied bands) as well as the spectrum holes (non‐occupied bands), exploits the available spectrum holes to transmit secondary users' data without interference with the existing FM transmission. Moreover, we demonstrate that when a primary user's transmission returns to its allocated spectrum band after a period of absence, the CR automatically adapts its transmission and hands off to a different spectrum hole that is available to avoid interference with the returning primary user. Moreover, we have proposed, implemented, and demonstrated a frequency hopping scheme over multiple spectrum holes to support multiple secondary users while attaining the minimum interference among the users. Copyright © 2009 John Wiley & Sons, Ltd.

SC-OFDM (single carrier orthogonal frequency division multiplexing) has demonstrated excellent performance in multipath fading channels with very low peak to average power ratio (PAPR). Similar to other multi-carrier transmission technologies such as MC-CDMA and OFDM, SC-OFDM also suffers significant performance degradation to inter-carrier interference (ICI) when there is a frequency offset between the transmitter and receiver or in a high mobility environment. In this paper, we analyze the effect of ICI on SC-OFDM system and propose a novel modulation scheme called Magnitude Shift Keying (MSK) for SC-OFDM system. The MSK modulation provides SC-OFDM system immunity to ICI and significantly outperforms SC-OFDM system and OFDM system with Phase Shift Keying (PSK) modulations in severe ICI environment. Simulation results over AWGN channel and multi-path fading channels with different ICI confirm the effectiveness of the proposed system.

This paper proposes a novel multi-carrier CDMA scheme, namely FD-CI/MC-CDMA (frequency division carrier interferometry multi-carrier code division multiple access), to combine the benefits of previously developed FD-MC-CDMA with CI/MC-CDMA together to provide higher BER performance in multipath fading channels. FD-MC-CDMA is capable of exploiting the available frequency diversity benefits in multipath fading channels while reducing MAI. Specifically, instead of transmitting all users' information bits over all carriers, FD-MC-CDMA employs a subset of non-contiguous carriers to support a subset of users (while maintaining the same overall system capacity and throughput as in MC-CDMA). However, since the length of Hadamard-Walsh codes only exists for certain integers, FD-MC-CDMA lacks the flexibility to accurately divide all subcarriers into subsets according to the channel condition. On the other hand, CI/MC-CDMA offers good BER performance while providing any length of orthogonal spreading codes. By combining these two systems together, the proposed FD-CI/MC-CDMA system significantly outperforms FD-MC-CDMA (as well as MC-CDMA) in multipath fading channel at similar complexity.

In this paper, we present a software defined radio (SDR) based cognitive radio implementation and demonstration. Using GNU Radio and USRP SDR boards, we implement and demonstrate a cognitive radio that detects spectrum holes in the FM band and exploits the available spectrum holes to transmit digital and analog data without interfering the existing FM transmission. Moreover, we demonstrate that when a primary user's transmission starts over the original spectrum holes, the cognitive radio automatically adapts its transmission to avoid interference to the newly coming primary user. Additionally, we have implemented and demonstrated a frequency hopping over multiple spectrum holes to support multiple secondary users with minimum interference to each other.

MC-CDMA (Multi-carrier Code Division Multiple Access) remains a strong candidate for next generation wireless communication systems. Due to its capability of exploiting frequency diversity, MC-CDMA provides high BER performance in multi-path fading channels. In our previous work, we proposed CI/MC-CDMA (Carrier Interferometry MC-CDMA) and demonstrated better performance relative to traditional MC-CDMA using novel polyphase CI spreading codes. In this work, we evaluate the BER performance of MC-CDMA and CI/MC-CDMA systems in the presence of narrowband interference (NBI). Specifically, theoretical analysis of BER performance for both systems in a multi-path fading channel is derived. We show that the CI/MC-CDMA system provides better NBI suppression capability than MC-CDMA and offers better BER. Simulation results over frequency selective fading channels confirm the validity of the theoretical analysis.

MC-CDMA (multiple carrier code division multiple access) has been considered a strong candidate for cognitive radio and dynamic spectrum access network, due to its high spectral efficiency, easy implementation and the capability of operating over non-contiguous frequency bands. However, like other multi-carrier transmission technologies such as OFDM (orthogonal frequency division multiplexing), the orthogonality among subcarriers in MC-CDMA system is lost in a mobile environment, leading to inter-carrier interference (ICI) and performance degradation. In this paper, we apply the ICI self-cancellation scheme previously proposed for OFDM system to orthogonal MC-CDMA system with Hadamard-Walsh codes to diminish ICI and improve BER performance. We show that the ICI self-cancellation scheme for MC-CDMA is equivalent to employing a subset of the Hadamard code matrix. As a direct result, the proposed ICI self cancellation method does not increase the complexity at both transmitter and receiver. To compensate for the network capacity (in terms of the number of users) loss in the proposed ICI self-cancellation method, a second set of orthogonal Hadamard-Walsh codes on the quadrature subcarriers are introduced. The proposed system offers significant BER performance gain than original MC-CDMA with the presence of ICI and same network capacity when BPSK modulation is employed. Simulation results over multi-path fading channel confirm the effectiveness of the proposed scheme, without increasing any complexity.

Channel allocation is an important area of research in open spectrum networks which asserts a significant impact on the spectrum utilization and the fairness among users. This paper studies the optimization of channel allocation, considering multiple objectives. For each objective, a binary programming model is described. Then a new optimization objective called fairness constrained maximum throughput is proposed. To achieve this optimization objective, a unified binary linear programming (UBLP) model is constructed which is then solved by the simplex method and branch-and-bound search. The solution to this model satisfies a bandwidth requirement for each user, e.g., the bandwidth for each user is equal to or larger than a per-user bandwidth minimum, and the solution also maximizes the network throughput. We prove that given different per-user bandwidth minimum, the optimal solution to the UBLP model achieves specific optimization objectives, such as the maximum network throughput and the max-min fairness. For the proportional fairness objective, the solution to the UBLP model proves to be within a bound of the optimal solution.

Sums of lognormal random variables occur in many important problems in wireless communications. However, the lognormal sum distribution is known to have no close-form and is difficult to compute numerically. Several approximation methods have already been proposed to approximate the lognormal sum distribution. However, these approximation methods all have their drawbacks: some widely used approximation methods are not very accurate at the lower region, some other approximation methods require the CDF curve from Monte Carlo simulation first. In this paper, we propose a novel approximation method, namely the Log Skew Normal (LSN) approximation, to model and approximate the sum of M lognormal distributed random variables. The proposed LSN approximation method has very high accuracy in most of the region, especially in the lower region. Furthermore, this approximation method does not require the CDF curve from Monte Carlo simulation first. The closed-form probability density function (PDF) of the resulting LSN random variable is presented and its parameters are derived from those of the M individual lognormal random variables by using an moment matching technique. Simulation results on the cumulative distribution function (CDF) of sum of M lognormal random variables in different conditions are used as reference curves to compare various approximation techniques. LSN approximation is found to provide better accuracy over a wide CDF range over other approximation methods.

Recent studies suggest that spectrum congestion is primarily due to inefficient spectrum usage rather than spectrum availability. Dynamic spectrum access (DSA) and cognitive radio (CR) are two techniques being considered to improve spectrum efficiency and utilization. The advent of CR has created a paradigm shift in wireless communications and instigated a change in FCC policy towards spectrum regulations. Within the hierarchical DSA model, spectrum overlay and underlay techniques are employed to enable primary and secondary users to coexist while improving overall spectrum efficiency. As employed here, spectrum overlay exploits unused (white) spectral regions while spectrum underlay exploits underused (gray) spectral regions. In general, underlay approaches use more spectrum than overlay approaches and operate below the noise floor of primary users. Spectrally modulated, spectrally encoded (SMSE) signals, to include orthogonal frequency domain multiplexing (OFDM) and multi-carrier code division multiple access (MC-CDMA), are candidate CR waveforms. The SMSE structure supports and is well suited for CR-based software defined radio (SDR) applications. This paper provides a general soft decision SMSE (SDSMSE) framework that extends the original SMSE framework to achieve synergistic CR benefits of overlay and underlay techniques. This extended framework provides considerable flexibility to design overlay, underlay and hybrid overlay/underlay waveforms that are scenario dependent. Overlay/underlay framework flexibility is demonstrated herein for a family of SMSE signals, including OFDM and MC-CDMA. Analytic derivation of CR error probability for overlay and underlay applications is presented. Simulated performance analysis of overlay, underlay and hybrid overlay/underlay waveforms is also presented and benefits discussed, to include improved spectrum efficiency and channel capacity maximization. Performance analysis of overlay/underlay CR waveform in fading channels will be discussed in Part II of the paper.

In this paper, we propose a novel signal classification method using cyclic spectral analysis and neural networks for multipath fading channels. The proposed system provides excellent classification performance in realistic multipath fading channels at low SNR, while assuming no a priori knowledge of the signal statistics, including carrier frequency, phase offset, or symbol rate. Due to its insensitivity to these statistics and its robustness to multipath fading channels, the spectral coherence function (SOF) is employed in the proposed system to produce a highly reliable classifier. Additionally, by employing a multiple-antenna based system, even greater advantages are achieved by exploiting spatial diversity. Numerical results demonstrate the classifier performance under a variety of channel conditions.

In this paper, we propose a novel signal classification method using cyclic spectral analysis and neural network for cognitive radio applications. In cogni- tive radio, it is desirable to have an accurate and reliable signal classification algorithm which can operate at low signal to noise ratio and without knowledge of the carrier frequency and bandwidth of the target signal. Cyclic spectral analysis has been proven to be a powerful tool for classifying signals. However, the amount of data introduced by spectral analysis is too large for any classifier to employ. Hence, a spectral analysis based feature extraction has to be performed to drastically reduce the data. Specifically, we propose to use both the α profile and the frequency profile of the Spectral Coherence Function (SOF) as the feature. Numerical results show significant performance improvement compared to those of using only the α profile feature.

In this article we propose a novel Dynamic Spectrum Access (DSA) paradigm to combine the benefits of UWB transmission and cognitive radio technology to optimize the efficiency of wireless spectrum usage and maximize the channel capacity. Specifically, this work presents a multi-carrier platform not only transmits over unused spectrum "holes", but transmits over underused spectrum blocks as well, while minimizing interference to existing primary narrowband transmissions. It is also shown that current cognitive radio and UWB transmission are two special cases of the proposed general paradigm. As a direct result, this paradigm offers a high degree of flexibility of spectrum use for next generation wireless communication systems.

Orthogonal frequency-division multiplexing (OFDM) has gained a great deal of attention lately and is considered as a strong candidate for many next-generation wireless communication systems. However, OFDM (in its current implementation) does not demonstrate robustness to narrowband interference. In our earlier work, we showed how carrier interferometry (CI) spreading codes may be applied to spread OFDM symbols over all N subcarriers - this allows OFDM to exploit frequency diversity and improve performance (without loss in throughput). In this paper, we show that, by spreading OFDM symbols over all N subcarriers via CI spreading codes, the resulting carrier interferometry OFDM (CI/OFDM) system is also capable of suppressing narrowband interference. Simulation results for OFDM, coded OFDM (COFDM), CI/OFDM, and coded CI/OFDM over additive white Gaussian noise and multipath fading channels confirm CI/OFDM's robustness to narrowband interference.

OFDM (orthogonal frequency division multiplexing) has gained a great deal of attention of late and is considered a strong candidate for many next generation wireless communication systems. However, OFDM (in its current implementation) does not demonstrate robustness to narrowband interference. In our earlier work, we showed how carrier interferometry (CI) spreading codes may be applied to spread OFDM symbols over all N subcarriers. This allows OFDM to exploit frequency diversity and improve performance (without loss in throughput). In this paper, we show that, by spreading OFDM symbols over all N subcarriers via CI spreading codes, the resulting CI/OFDM system is also capable of suppressing narrowband interference. Simulation results over AWGN and multi-path fading channels confirm CI/OFDM's robustness in the presence of narrowband interference.

DS-CDMA is a dominant player in the world of wireless telecommunications, emerging as the standard of choice in 3G UMTS (Universal mobile telecommunications system). Here, DS-CDMA is also referred to as WCDMA (wideband CDMA). In this work, we introduce a novel multicarrier chip-shaping filter at the transmitter, replacing the usual root-raised cosine chip shape filter in WCDMA systems. At the receiver side, a corresponding frequency-based processing is proposed, where chips are separated into their N carrier components, and then recombined in order to minimize interference and noise while exploiting frequency diversity gains. Our performance analysis assumes system parameters and channel models consistent with UMTS WCDMA standards, and results show that the frequency-based WCDMA physical layer offers a 5 dB-performance gain at P(/spl epsiv/)=10/sup -2/ (uncoded transmission) over a traditional time-based WCDMA system with RAKE reception.

In this work, we propose a new multicarrier platform to optimize the efficiency of wireless operators' licensed bands and to enable flexible sharing of licensed and unlicensed bands (in different spectral regions). This research supports a recent FCC proposal which suggests innovative spectrum management regulations to improve spectral efficiency. Specifically, this work presents a multicarrier platform capable of (1) achieving high spectral efficiency by application of "narrow" orthogonal carriers; and (2) enables flexible spectral sharing across different licensed and unlicensed bands via operator borrowing/ lending.

In this letter, a large set of spreading codes that doubles capacity in multicarrier code-division multiple-access (MC-CDMA) systems without any cost in bandwidth and with negligible cost in performance is introduced. This large set is comprised of: 1) complex spreading codes instead of conventional real-valued spreading codes and 2) two sets, each made up of orthogonal complex spreading codes, with minimum cross correlation between sets. Simulations performed over Rayleigh fading channels demonstrate 100% gains in terms of MC-CDMA capacity with negligible loss in performance.

This paper introduces a novel merger of OFDM systems, MIMO systems, and carrier interferometry (CI) spreading codes. Specifically, previous works have illustrated the performance benefits of (1) applying CI spreading codes to OFDM and (2) merging MIMO and OFDM systems. In this paper, OFDM systems are combined with both a MIMO system and CI spreading codes, resulting in the so-called MIMO-CI/OFDM system, capable of very reliable communication at SNRs previously possible only with channel coding.

Tapped delay line RAKE receivers are commonly used to achieve path diversity gain in DS-CDMA systems: Here, a bank of correlators is used to separate the multipath components and a combiner coherently recombines the multipath energies. In this paper, the optimal combining techniques for DS-CDMA RAKE receivers (under different criteria) are introduced. Specifically, based on the maximum likelihood criteria, optimal combining techniques are derived and discussed for the following scenarios: (1) channel-fade-information is not available at the receiver; (2) a statistical characterization of the channel fades is available at the receiver, but the actual, instantaneous fade information is not; and (3) the instantaneous channel fade information is available at the receiver. This paper shows that maximum ratio combining (MRC), typically considered the optimal combining scheme when fade parameters are perfectly tracked, is sub-optimal. We present the optimal alternative to traditional MRC.

In this work, we propose an innovative high performance, high throughput direct sequence spreading (DSS) ultra wideband (UWB) system. Our proposed system employs a novel multi-carrier pulse waveform at the UWB transmit side: At the receiver side, the received DSS UWB pulse is decomposed into its subcarriers and recombined to (1) exploit diversity in the frequency domain and (2) provide resistance to inter symbol interference (ISI) and/or multi-access interference (MAI). As a direct result, the proposed frequency-based UWB DSS system is shown to significantly outperform time-based DSS UWB systems, offering significant gain in throughput (up to 32 fold) without performance degradation, or, alternatively, avoiding error floors due to MAI that limit the performance of time-based systems.

In the author's earlier work, we demonstrated how performance degradation and PAPR concerns in OFDM can be overcome by application of carrier interferometry (CI) spreading codes. In this work, the authors employ RF test equipment and analyze the practical performance of OFDM (orthogonal frequency division multiplexing) based IEEE 802.11a WLAN vs. their proposed carrier interferometry (CI) OFDM based WLAN. Specifically, RF test results (in a typical indoor office environment) are used to analyze the proposed CI/OFDM and CI/COFDM technologies. It is shown that in a typical office environment, at a bit error rate of 10/sup -3/, the CI technology gains 5-7 dB over current OFDM.

The paper introduces to orthogonal frequency-division multiplexing (OFDM) systems a novel pseudo-orthogonal carrier interferometry spreading code which spreads each parallel data stream over all the OFDM carriers. Pseudo-orthogonal carrier interferometry (PO-CI) spreading codes are carefully selected to introduce the following benefits to OFDM: up to 2N parallel data streams can be coded onto N carriers, with little degradation in performance; when rate 1/2 channel coding is applied in addition to PO-CI spreading codes, the resulting binary phase-shift keying OFDM systems demonstrate the performance of coded OFDM and the throughput of uncoded OFDM; PO-CI codes are carefully selected to spread in a manner which eliminates the peak-to-average power ratio problems characteristic of traditional OFDM.

This paper introduces a novel chip shaping scheme increasing the network capacity in DS-CDMA systems without any increase in. bandwidth or chip rate, and without performance degradation. The chip shape corresponds to an interferometry pattern created by the superposition of N carriers. Two sets of orthogonal chip shapes with minimum inter-chip interference are positioned pseudo-orthogonally (in time) within a single symbol duration, allowing the novel system to support many more chips in the same symbol duration and bandwidth. Simulations performed over Rayleigh fading channels indicate the new chip shaping (and corresponding detection strategy) enables 100% gains in DS-CDMA network capacity without any loss in performance. With the exception of chip shape and receiver design, this new system retains all the features of a conventional DS-CDMA system.

This paper proposes a novel scheme which doubles capacity in MC-CDMA systems without any cost in bandwidth and with negligible cost in performance. Specifically: (1) complex spreading codes are used instead of conventional real value spreading codes; and (2) two groups of orthogonal complex spreading codes are used simultaneously, where minimum cross-correlation exists between the two groups. Simulations performed over Rayleigh fading channels using these novel codes demonstrate that the proposed scheme enables gain of 100% in terms of MC-CDMA capacity with negligible performance losses.

This paper proposes a novel maximum likelihood combining (MLC) scheme for MC-CDMA systems. Currently, minimized mean square error combining (MMSEC) is widely considered the best combining scheme for MC-CDMA. The MLC proposed in this paper is optimal from the standpoint of minimizing the probability of error performance. It is shown that MLC is very close to MMSEC in a fully loaded system, justifying the selection of MMSEC over other combining schemes. Moreover, when the load is small (as measured by number of users), MLC is shown to outperform MMSEC (and, other combining schemes).

Abstract Multi‐carrier technologies in general, and OFDM and MC‐CDMA in particular, are quickly becoming an integral part of the wireless landscape. In this first of a two‐part survey, the authors present the innovative transmit/receive multi‐carrier implementation of TDMA and DS‐CDMA systems. Specifically, at the transmit side, the pulse shape (in TDMA) and the chip shape (in DS‐CDMA) corresponds to a linear combining of in‐phase harmonics (called a CI signal). At the receiver side, traditional time‐domain processing (equalization in TDMA and RAKE reception in DS‐CDMA) is replaced by innovative frequency based processing. Here, receivers decompose pulse (or chip) shapes into carrier subcomponents and recombine these in a manner achieving both high frequency diversity gain and low MAI. The resulting system outperforms traditional TDMA and DS‐CDMA systems by 10–14 dB at typical BERs, and, by application of pseudo‐orthogonal pulse shapes (chip shapes), is able to double system throughput while maintaining performance gains of up to 8 dB. Copyright © 2002 John Wiley & Sons, Ltd.

Abstract Multi‐carrier technologies in general, and OFDM and MC‐CDMA in particular, are an integral part of the wireless landscape. In this second part of a two‐part survey, the authors present an innovative set of spreading codes known as CI codes, and demonstrate how these significantly increase performance and capacity in OFDM and MC‐CDMA systems, all the while eliminating PAPR concerns. Regarding OFDM: the spreading of each symbol over all N carriers using CI spreading codes (replacing the current one symbol per carrier strategy) are presented. CI codes are ideally suited for spreading OFDM since, when compared to traditional OFDM, CI‐based OFDM systems achieve the performance of coded OFDM (COFDM) while maintaining the throughput of uncoded OFDM, and, at the same time, eliminate PAPR concerns. When applied to MC‐CDMA, CI codes provide a simple means of supporting 2N users on N carriers while maintaining the performance of an N‐user Hadamard Walsh code MC‐CDMA system, i.e., CI codes double MC‐CDMA network capacity without loss in performance. The CI codes used in OFDM and MC‐CDMA systems are directly related to the CI pulse (chip) shapes used to enhance TDMA and DS‐CDMA (see part 1): hence, the CI approach provides a common hardware platform for today's multi‐carrier/multiple‐access technologies, enabling software radio applications. Copyright © 2002 John Wiley & Sons, Ltd.

OFDM (orthogonal frequency division multiplexing) is susceptible to high peak-to-average power due to an unstable envelope. Many solutions have been utilized in order to decrease the high peaks that are possible, but in these cases complexity is also added to the system architecture. In our earlier work, we introduced CI codes as a powerful tool to increase OFDM performance. This paper shows how carrier interferometry phase coding eliminates peaks in the signal envelope and in effect the problems associated with large PAPR.

One important requirement for next generation wireless communication systems is the ability to support a wide range of high rate data services. Despite advances in efficient modulation and multiple access technologies, very high data rate transfer will still require ultra-wide frequency bands. Traditional DS-CDMA systems operate over one single continuous bandwidth, and with the limited spectrum available from the FCC auctioned off in small blocks, it will not be possible for DS-CDMA to operate over a contiguous ultra-wide frequency band. This paper demonstrates how a novel chip shaping scheme, designed specifically for DS-CDMA systems, enables DS-CDMA to operate over non-adjacent frequency bands in a way that is identical to its operation if all these bands are made contiguous. The chip shape corresponds to an interferometry pattern created by the superposition of N carriers (adjacent or non-adjacent in the frequency domain). Besides the ability to operate over non-adjacent bands, this novel chip shaping enables DS-CDMA to achieve very high performance by exploiting frequency diversity (instead of path diversity) over the combined bands. With the exception of chip shape and receiver design for operation over non-contiguous bands, this new system retains all the features of a traditional DS-CDMA system.

Airport surface operations (aircraft servicing and maintenance, gate scheduling, and taxi operations) significantly affect the efficiency of airline flight schedules. Therefore, an optimized wireless communication system that supports surface operations can promote airline efficiency. This work presents multipath propagation measurements at airport ramp areas at 5.8 GHz. Statistical characterizations of the wireless channel, from these measurements, indicate large delay spreads (on average 14.78 /spl mu/s) and small coherence bandwidths (0.5 coherence bandwidth on the order of 12.25 Hz). These findings suggest that the radio environment at an airport offers large diversity gains that can be exploited in future spread-spectrum systems.