Visual tracking has seen remarkable advancements, largely driven by the availability of large-scale training datasets that have enabled the development of highly accurate and robust algorithms. While significant progress has been made in tracking general objects, research on more challenging scenarios, such as tracking camouflaged objects, remains limited. Camouflaged objects, which blend seamlessly with their surroundings or other objects, present unique challenges for detection and tracking in complex environments. In critical fields like military, security, agriculture, and marine monitoring, accurately tracking camouflaged objects is essential. To address this gap, we introduce the Camouflaged Object Tracking Dataset (COTD), a specialized benchmark designed specifically for evaluating camouflaged object tracking methods. The COTD dataset comprises 200 sequences and approximately 80,000 frames, each annotated with detailed bounding boxes. Our evaluation of 20 existing tracking algorithms reveals significant deficiencies in their performance with camouflaged objects. To address these issues, we propose a novel tracking framework, HIPTrack-MLS, which demonstrates promising results in improving tracking performance for camouflaged objects. COTD and code are avialable at https://github.com/openat25/HIPTrack-MLS.

Data imbalance is an important issue in data-driven deep-learning methodologies. In sound event detection (SED), there are two types of data imbalance issues caused by the diverse time duration of sound events: the data imbalance between sound event classes (inter-class imbalance) and the active/inactive imbalance within the class (intra-class imbalance). In this paper, we propose a unified loss function (ULF), which adeptly addresses both the inter-class imbalance and intra-class imbalance simultaneously. Evaluation experiments substantiate that the ULF consistently yields superior and more stable performance compared to existing loss functions that singularly address either type of imbalance. Furthermore, the ULF loss also enhances the model's capacity to detect hard-to-detect sound events.

As an alternative to traditional meteorological methods, rain attenuation in satellite-to-Earth microwave communication signals has been used for rainfall reconstruction in recent years. In this article, the existing 2-D rain field reconstruction problem is extended to a 3-D scenario by leveraging the low Earth orbit satellite system. A compressive sensing approach is further proposed to solve the 3-D rain field reconstruction problem. The Starlink system is used as a reference, and two synthetic rain events near the Great Barrier Reef in Australia, which are generated from the weather research and forecasting model, are used to evaluate the reconstruction performance. Simulation results show that the compressive sensing approach performs better than both the traditional least squares and the least absolute shrinkage and selection operator approaches.

A differential approach is proposed for tomographic rain field reconstruction using the estimated signal-to-noise ratio of microwave signals from low earth orbit satellites at the ground receivers, with the unknown baseline values eliminated before using least squares to reconstruct the attenuation field. Simulations are done when the baseline is modelled by an autoregressive process and when the baseline is assumed fixed. Comparisons between the reconstruction results for the differential and non-differential approaches suggest that the differential approach performs better in both scenarios. For high correlation coefficient and low model noise in the autoregressive process, the differential approach surpasses the non-differential approach significantly.

The attenuation of microwave signals can be used for meteorological observations. For example, the received signal level (RSL) of backhaul links of cellular systems, which usually has a quantization error of 0.1 dB or more for commercial systems, has been used to measure rainfall. In this work, through the mean square error (MSE) analysis of an ideal RSL estimator, it is found that the estimation error can be lower than 0.01 dB for high signal-to-noise ratio (SNR), thereby making it feasible to measure other meteorological variables such as water vapor and clouds. However, the RSL-based estimator has poor performance in low SNR. To improve the performance, we propose a new path attenuation measurement method based on SNR estimation. Although the performance of the SNR-based estimator is better than the RSL based one for low SNR, it becomes worse in high SNR when the path attenuation is small. To solve the problem, another method is proposed based on estimating the signal power (SP) only. Both MSE analysis and simulation results show that the SP-based method is superior to both RSL and SNR based estimators for most scenarios.

Sound event detection (SED) has been widely applied in real world applications. Convolutional recurrent neural network based SED approaches have achieved state-of-the-art performance. However, the convolution process is typically performed by using a fixed sized kernel, which adversely affects the detection accuracy especially when the acoustic features of different event classes are characterized by high variations. To deal with this, this article proposes a sound event detection technique using a convolutional recurrent neural network framework with multiple convolutional kernels of different sizes. The top performing kernels are selected from a kernel pool based on the unsupervised clustering errors and the accuracies of the temporarily trained models. Afterwards, the selected kernels are fed to multiple convolution layers to deal with the acoustic feature variations. Experimental results on different subsets of AudioSet, namely the DCASE Challenge 2017 Task 4 and DCASE Challenge 2018 Task 4, demonstrate the performance of the proposed approach compared to state-of-the-art systems.

Acoustic event detection deals with the acoustic signals to determine the sound type and to estimate the audio event boundaries. Multi-label classification based approaches are commonly used to detect the frame wise event types with a median filter applied to determine the happening acoustic events. However, the multi-label classifiers are trained only on the acoustic event types ignoring the frame position within the audio events. To deal with this, this paper proposes to construct a joint learning based multi-task system. The first task performs the acoustic event type detection and the second task is to predict the frame position information. By sharing representations between the two tasks, we can enable the acoustic models to generalize better than the original classifier by averaging respective noise patterns to be implicitly regularized. Experimental results on the monophonic UPC-TALP and the polyphonic TUT Sound Event datasets demonstrate the superior performance of the joint learning method by achieving lower error rate and higher F-score compared to the baseline AED system.

A case study is presented for teaching the first course in communication technologies in intensive mode. Several strategies, including using workshops and support sessions extensively and requiring students to take online quizzes before lectures, have been implemented to facilitate flipped learning and active learning. With data collated for the past seven years, intensive mode teaching and traditional mode teaching are compared. From the case study, it can be concluded that it is possible to use intensive mode teaching to deliver a mathematically intensive course for a relatively large number of students, with improved students' performance and satisfaction.

Cognitive radio (CR) is a promising technology to address the challenge of spectrum scarcity due to the massive number of objects in the Internet of Things (IoT). Equipping IoT objects with CR capability can also alleviate interference situations and achieve seamless connectivity in IoT. This paper deals with CR spectrum sensing and proposes a new eigenvalue-based detector by exploiting the summation of multiple large eigenvalues of the covariance matrix of received signals. By analyzing the distribution of the sum of the dependent large eigenvalues, we derive an approximate but explicit expression for the theoretical performance of the proposed detector. The theoretical analysis of the proposed detector is validated and its superior performance is demonstrated with real world signals. It is shown that the proposed detector outperforms the existing eigenvalue-based detectors and is more robust against noise uncertainty.

In this paper, we propose a novel approach for 3-D rain field reconstruction using satellite signals. It uses the estimated signal-to-noise ratio (SNR) at the ground receivers for low-earth orbit (LEO) satellites and, thus, indirectly estimates the path-integrated rain attenuation of the microwave communication links. A least-squares algorithm is employed to perform the 3-D tomographic reconstruction of the rain field. The proposed system model consists of an LEO satellite with a realistic overpass trajectory and multiple ground receivers with SNR estimators. Two synthetic rain events near the Great Barrier Reef in Australia are used to test the reconstruction outcome. Simulation results suggest that the reconstructed rain field has close agreement with the synthetic rain field.

In acoustic event detection, the training data size of some acoustic events is often small and imbalanced. To deal with this, this paper proposes generating the virtual training data categorically using the auxiliary classifier generative adversarial networks. Soft labels of acoustic events are first calculated to represent the acoustic event localization information. The closer the current frame is to the middle of the manually labeled acoustic event, the higher the soft label will be, which makes the soft labels positively correlated with the acoustic event localization. Then, the acoustic event class and the quantized soft labels are used as the input condition to the auxiliary classifier generative adversarial networks to generate an arbitrary number of training samples. Experimental results on the TUT Sound Event 2016 under the home environment and TUT Sound Event 2017 under the street environment demonstrate the improved performance of the proposed technique compared to existing acoustic event detection systems.

Acoustic event detection, the determination of the acoustic event type and the localisation of the event, has been widely applied in many real-world applications. Many works adopt the multi-label classification technique to perform the polyphonic acoustic event detection with a global threshold to detect the active acoustic events. However, the manually labeled boundaries are error-prone and cannot always be accurate, especially when the frame length is too short to be accurately labeled by human annotators. To deal with this, a confidence is assigned to each frame and acoustic event detection is performed using a multi-variable regression approach in this paper. Experimental results on the latest TUT sound event 2017 database of polyphonic events demonstrate the superior performance of the proposed approach compared to the multi-label classification based AED method.

In this paper, we propose to reconstruct the vertical plane of a rainfall field using rain attenuation of satellite signal links. With received signals at multiple ground stations form multiple satellites, linear least squares is employed to reconstruct the the rain attenuation, thereby the rainfall maps. We use a synthetic super-cell event as our reconstruction target and firstly propose a simple model to simulate satellite link attenuation caused by the rain event. Simulation results suggest that the reconstruction is successful when two satellite-receiver pairs are employed.

In this paper, we propose to use the microwave communication links of commercial low earth orbit (LEO) satellites to carry out rainfall field tomographic reconstruction. A modified radial basis function (MRBF) model is used to accommodate the attributes of LEO satellite links, and successfully transforms the reconstruction process into a supervised learning problem with global convergence property. It will be shown that a rainfall map reconstructed by the proposed technique using received signal level (RSL) records of an LEO satellite has close agreement with the Global Rainfall Map in Near Real Time (GSMaP_NRT) product covering the same area available from Japan Aerospace Exploration Agency (JAXA) Global Rainfall Watch.

This paper presents a modified MMSE channel estimation for turbo equalization in uplink massive multiple-input multiple-output (MIMO) systems with pilot contamination. Unlike other work on channel estimation in massive MIMO systems where perfect knowledge of inter-cell large scale channel coefficients is assumed, a modified method for data-aided MMSE estimation is proposed without requiring the knowledge of intra-cell large scale channel coefficients. Simulation results show that the proposed channel method outperforms the existing pilot-based channel estimators and has little performance loss compared to the existing data-aided estimator requiring the perfect knowledge of inter-cell large scale fading.

This paper deals with random forest regression based acoustic event detection (AED) by combining acoustic features with bottleneck features (BN). The bottleneck features have a good reputation of being inherently discriminative in acoustic signal processing. To deal with the unstructured and complex real-world acoustic events, an acoustic event detection system is constructed using bottleneck features combined with acoustic features. Evaluations were carried out on the UPC-TALP and ITC-Irst databases which consist of highly variable acoustic events. Experimental results demonstrate the usefulness of the low-dimensional and discriminative bottleneck features with relative 5.33% and 5.51% decreases in error rates respectively.

This paper deals with the acoustic event detection (AED) to improve the detection accuracy of acoustic events. Acoustic event detection task is performed by a regression via classification (RvC) based approach along with the random forest technique. A discretization process is used to convert the continuous frame positions within acoustic events into event duration class labels. Outputs of the category-specific random forest classifiers are then reversed back to the event boundary information. Evaluations on the UPC-TALP database which consists of highly variable acoustic events demonstrate the efficiency of the proposed approaches with improvements in detection error rate compared to the best baseline system.

Acoustic event detection, the determination of the acoustic event type and the localisation of the event, has been widely applied in many real-world applications. Many works adopt multi-label classification techniques to perform the polyphonic acoustic event detection with a global threshold to detect the active acoustic events. However, the global threshold has to be set manually and is highly dependent on the database being tested. To deal with this, we replaced the fixed threshold method with a frame-wise dynamic threshold approach in this paper. Two novel approaches, namely contour and regressor based dynamic threshold approaches are proposed in this work. Experimental results on the popular TUT Acoustic Scenes 2016 database of polyphonic events demonstrated the superior performance of the proposed approaches.

In this study, the authors propose an l 0 ‐norm penalised shrinkage linear least mean squares ( l 0 ‐SH‐LMS) algorithm and an l 0 ‐norm penalised shrinkage widely linear least mean squares ( l 0 ‐SH‐WL‐LMS) algorithm for sparse system identification. The proposed algorithms exploit the priori and the posteriori errors to calculate the varying step‐size, thus they can adapt to the time‐varying channel. Meanwhile, in the cost function they introduce a penalty term that favours sparsity to enable the applicability for sparse condition. Moreover, the l 0 ‐SH‐WL‐LMS algorithm also makes full use of the non‐circular properties of the signals of interest to improve the tracking capability and estimation performance. Quantitative analysis of the convergence behaviour for the l 0 ‐SH‐WL‐LMS algorithm verifies the capabilities of the proposed algorithms. Simulation results show that compared with the existing least mean squares‐type algorithms, the proposed algorithms perform better in the sparse channels with a faster convergence rate and a lower steady‐state error. When channel changes suddenly, a filter with the proposed algorithms can adapt to the variation of the channel quickly.

In this paper, we address the design of message-passing receiver for massive multiple-input multiple-output orthogonal frequency division multiplex (MIMO-OFDM) systems. With the aid of the central limit argument and Taylor-series approximation, a computationally efficient receiver that performs joint channel estimation and decoding is devised by the framework of expectation propagation. In particular, the local belief defined at the channel transition function is expanded up to the second order with Wirtinger calculus, to transform the messages sent by the channel transition function to a tractable form. As a result, the channel impulse response between each pair of antennas is estimated by Gaussian message passing. In addition, a variational expectation-maximization-based method is derived to learn the channel power-delay profiles. The proposed scheme is assessed in 3D massive MIMO-OFDM systems with spatially correlated channels, and the empirical results corroborate its superiority in terms of performance and complexity.

Single carrier frequency domain equalization (SC-FDE) is an alternative technology to orthogonal frequency division multiplexing to deal with the frequency selective channel fading effect in broadband wireless communication systems. In this paper, we consider a robust SC-FDE design with imperfect channel knowledge at a receiver due to the channel estimation error. Based on a statistical model for channel estimation, the optimal equalization coefficients are derived under the criterion of minimizing the mean square error conditioned on a given channel estimate. The bit error rate is further analyzed and a tight performance approximation is proposed. We also propose two robust FDE schemes in coded systems, where feedback from the channel decoder is utilized to improve the equalization and/or channel estimation performance. Simulation results show that the proposed robust FDE schemes achieve significant performance improvement over the conventional FDE schemes.

In MIMO applications, the minimum mean square error parallel interference cancellation (MMSE-PIC) based Soft-Input Soft-Output (SISO) detector has been widely adopted because of its low complexity and good bit error rate (BER) performance. In this paper, we firstly propose to use a Gaussian model based MMSE detection algorithm to implement MMSE-PIC with low complexity. This algorithm, which can detect a length-Nrreceived data block by a single Hermitian matrix (sized Nt× Nt) inversion, is especially preferable in Massive MIMO up-link applications where the number of transmit antennas Ntfrom each end terminal is much less than the number of receive antennas Nrin the Base Station. Then we derive a new method to calculate the matrix inversion by a linear combination of two matrices, which reduces the complexity from O(Nt3) to O(Nt2). At last, in order to improve the system performance for the first pass when there is no a priori information available, a self-iteration method is proposed and thus a system performance gain of 1dB to 2dB is achieved at the cost of modest complexity increase.

Power-line communications (PLC) commonly employs orthogonal frequency-division multiplexing (OFDM) as the modulation technique, and impulsive noise has a significant negative impact on its performance. Using the property of null subcarriers in OFDM and the fact that impulsive noise is sparse, we formulate a minimization problem to detect and estimate the impulsive noise. In previous works, ℓ1-norm minimization was employed to achieve this task. In this paper, we propose to use a smoothed ℓ0-norm minimization algorithm for impulsive noise detection instead. Simulation results show that this approach is promising as it achieves comparable performance as ℓ1-norm minimization but with much lower complexity.

In this paper, we propose a new blind learning algorithm, namely, the Benveniste-Goursat input-output decision (BG-IOD), to enhance the convergence performance of neural network-based equalizers for nonlinear channel equalization. In contrast to conventional blind learning algorithms, where only the output of the equalizer is employed for updating system parameters, the BG-IOD exploits a new type of extra information, the input decision information obtained from the input of the equalizer, to mitigate the influence of the nonlinear equalizer structure on parameters learning, thereby leading to improved convergence performance. We prove that, with the input decision information, a desirable convergence capability that the output symbol error rate (SER) is always less than the input SER if the input SER is below a threshold, can be achieved. Then, the BG soft-switching technique is employed to combine the merits of both input and output decision information, where the former is used to guarantee SER convergence and the latter is to improve SER performance. Simulation results show that the proposed algorithm outperforms conventional blind learning algorithms, such as stochastic quadratic distance and dual mode constant modulus algorithm, in terms of both convergence performance and SER performance, for nonlinear equalization.

We propose a message-passing algorithm of joint channel estimation and decoding for OFDM systems, where expectation propagation is exploited to deal with channel estimation. Specially, the message updating is formulated into a recursive form. As a result, for system with K subcarriers and L channel taps, only O(K + L) messages need to be tracked, and meanwhile they can be efficiently calculated using FFT with complexity O(K|A| + K log2K), where |A| denotes the constellation size. Numerical experiments show that our algorithm achieves BER performance within 0.5 dB of the known-channel bound.

This paper considers the coded OFDM system and instead of discarding the cyclic prefix (CP) at the receiver, we utilize the CP observation for joint detection, decoding and channel estimation. In particular, detection and decoding are performed iteratively between an equalizer and a soft-input soft-output (SISO) decoder based on the turbo principle, and the expectation-maximization (EM) algorithm is employed in the equalizer for joint detection and channel estimation via message passing. Models for the CP observation, non-CP observation and the time correlation of the time-varying channel are presented in Forney-style factor graphs (FFGs), and a scheduling scheme is proposed to pass messages between the graphs. Simulation results show that with unknown channel impulse response (CIR), the performance of the proposed algorithm approaches the case where CIR is perfectly known and through proper exploitation of the CP, the proposed algorithm outperforms the conventional algorithm (i.e. CP is discarded) with known CIR, as well as the alternative algorithm in the literature (where CP is exploited) with unknown CIR.

For the spatially correlated multiuser MIMO-OFDM channels, the conventional iterative MMSE-SIC detection suffers from a considerable performance loss. In this paper, we use the factor graph framework to design robust detection algorithms by clustering a group of symbols to combat the spatial correlation and using the principle of expectation propagation to improve message passing. Furthermore, as the complexity of detection becomes one of the issues in the design of large-scale multiuser MIMO-OFDM systems, we propose a low-complexity approximate message-passing algorithm by opening the channel transition node, which eliminates the expensive matrix inversions involved in the MMSE-SIC based algorithms. Finally, numerical results are presented to verify the proposed algorithms.

The downlink throughput of opportunistic‐feedback multiuser wireless systems with capture effects employing maximal ratio transmission (MRT) in a Rayleigh fading environment is studied. To the best of the authors’ knowledge, MRT has not been employed to improve multiuser system throughput with capture effects. Employing MRT means that the channel fading gains between the base station (BS) and users are a chi‐square distribution with 2 L degrees of freedom, where L is the diversity order, as opposed to Rayleigh distribution which has been examined in detail in the literature. Thus, the main challenge is to obtain a new and compact channel capacity expression for the system in which the roles of BS and users are reversed to make it practically sound. The new downlink throughput expression is derived as a function of L , fading‐gain threshold η , capture ratio z and other parameters. For large η , the capture effects are not as effective as when η is small. Effects of the capture ratio z on system throughput are also obtained. The new findings are validated using available results in the open literature.

A contention‐based opportunistic feedback protocol has been proposed in the literature, to achieve multiuser diversity for the downlink transmission of a multiuser wireless system, with the assumption that the incoming data for the active users are infinitely backlogged. However, this assumption is not valid if the authors consider the finite‐length queuing effect in practical systems. By taking not only the channel states, but also the queuing states into account, the authors propose an improved opportunistic feedback protocol with multiuser diversity, and analyse its performance with adaptive modulation and coding. Additionally, instead of solving a system of equations directly, a simple iterative method is proposed to obtain the stationary distribution of the system, where the system behaviour is modelled by a two‐dimensional finite‐state Markov chain. Numerical results demonstrate that the authors proposed protocol is superior to the protocol in the literature in terms of the average throughput, the average spectral efficiency and the average packet delay, when considering the queuing dynamics.

Impulsive noise is one of the main channel impairments in orthogonal frequency division multiplexing (OFDM)-based power line communications (PLC). Clipping nonlinearites such as conventional clipping, blanking, joint blanking/clipping, and deep clipping have been proposed to mitigate the impulsive noise. However, OFDM systems result in high peak amplitude after inverse discrete Fourier transform (IDFT) at the transmitter, which leads to large peak-to-average power ratio (PAPR). High peak amplitude can be falsely clipped by the impulsive noise clipper at the receiver leading to poor system performance. In this paper we propose a joint peak amplitude clipping and impulsive noise clipping nonlinearities to improve the bit error rate (BER) performance.

This paper investigates the MMSE-based frequency domain equalization (FDE) algorithms in turbo equalization systems. As opposed to the conventional FDE systems where the cyclic prefix (CP) is discarded at the receiver, we take advantage of the redundancy and make use of all the observed signals for equalization purpose. First, we interpret the conventional frequency domain equalizer as a Forney-style factor graph (FFG), and accordingly an equalization algorithm is derived based on the Gaussian message passing (GMP) technique. Second, the normally discarded CP part is presented similarly using an FFG, and an algorithm that integrates both of the FFGs is proposed. As a result, two extrinsic messages about the data symbols are obtained rather than one, and they are merged together based on a symbol-wise combination. Third, with approximations made on two covariance matrices, the complexity of the proposed equalization algorithm is maintained at the same order as that of the conventional FDE algorithm, i.e. O(Nlog2N) per block per iteration. Simulations results verify that, a gain of around 0.7dB is achieved compared with the conventional algorithm at 1/4 CP ratio, for both 16QAM and 64QAM system with Gray mapping over AWGN or ISI channels.

An iterative frequency domain equalization approach for coded single-carrier block transmissions over frequency selective channels is developed by using the recently proposed generalized approximate message passing (GAMP) algorithm. Compared with the low-complexity iterative frequency domain linear minimum mean square error (FD-LMMSE) equalization, the proposed approach can achieve significant performance gain with slight complexity increase.

In this paper, a novel technique is proposed to address the joint sampling timing acquisition for baseband and broadband power-line communication (BB-PLC) systems using Orthogonal-Frequency-Division-Multiplexing (OFDM), including the sampling phase offset (SPO) and the sampling clock offset (SCO). Under pairwise correlation and joint Gaussian assumption of received signals in frequency domain, an approximated form of the log-likelihood function is derived. Instead of a high complexity two-dimension grid-search on the likelihood function, a five-step method is employed for accurate estimations. Several variants are presented in the same framework with different complexities. Unlike conventional pilot-assisted schemes using the extra phase rotations within one OFDM block, the proposed technique turns to the phase rotations between adjacent OFDM blocks. Analytical expressions of the variances and biases are derived. Extensive simulation results indicate significant performance improvements over conventional schemes. Additionally, effects of several noise models including non-Gaussianity, cyclo-stationarity, and temporal correlation are analyzed and simulated. Robustness of the proposed technique against violation of the joint Gaussian assumption is also verified by simulations.

In OFDM systems, cyclic prefix (CP) insertion and removal enables the use of a set of computationally efficient single-tap equalizers at the receiver. Due to the extra transmission time and energy, the CP causes a loss in both spectrum efficiency and power efficiency. On the other hand, as a repetition of part of the data, the CP brings extra information and can be exploited for detection. Therefore, instead of discarding the CP observation as in the conventional OFDM system, we utilize all the received signals in a soft-input soft-output equalizer of a turbo equalization OFDM system. First, the models for both the CP part and the non-CP part of observation are presented in a Forney-style factor graph (FFG). Then based on the computation rules of the FFG and the Gaussian message passing (GMP) technique, we develop an equalization algorithm. With proper approximation, the complexity of the proposed algorithm is reduced to \changedmathcal O(2RNlog2N+4RGlog2G+2RG) per data block for R iterations, where N is the length of the data block and G is equal to P+L-1 with P the length of the CP and L the maximum delay spread of the channel. To justify the performance improvement, SNR analysis is provided. Simulation results show that the proposed approach achieves a significant gain over the conventional approach and the turbo equalization system converges within two iterations.

This paper presents a theoretical boundedness and convergence analysis of online gradient method for the training of two-layer feedforward neural networks. The well-known linear difference equation is extended to apply to the general case of linear or nonlinear activation functions. Based on this extended difference equation, we investigate the boundedness and convergence of the parameter sequence of concern, which is trained by finite training samples with a constant learning rate. We show that the uniform upper bound of the parameter sequence, which is very important in the training procedure, is the solution of an inequality regarding the bound. It is further verified that, for the case of linear activation function, a solution always exists and, moreover, the parameter sequence can be uniformly upper bounded, while for the case of nonlinear activation function, some simple adjustment methods on the training set or the activation function can be derived to improve the boundedness property. Then, for the convergence analysis, it is shown that the parameter sequence can converge into a zone around an optimal solution at which the error function attains its global minimum, where the size of the zone is associated with the learning rate. Particularly, for the case of perfect modeling, a strong global convergence result, where the parameter sequence can always converge to an optimal solution, is proved.

In a wireless network, multi-user diversity can be employed to improve system throughput performance by scheduling the channel to the user with the best instantaneous channel state information (CSI). However, the overhead induced by polling CSIs of a large number of users can overshadow the multi-user diversity gain. In our previous work, a user identification approach (UIDA) was proposed to reduce the system overhead. In this paper, by allowing a small degree of outage to occur, we simplify the UIDA to reduce the overhead further, and present the throughput and outage analysis of the simplified UIDA over Rayleigh fading channels. Computer simulations based on IEEE 802.11a systems show that the simplified UIDA achieves considerable throughput improvement.

In this paper, frame error rate (FER) performance of enhanced verification-based decoding algorithm (EVA) is investigated for packet-based low-density parity-check (LDPC) codes over binary symmetric channel (BSC). By taking the false verification into account, a recursive statistical model is proposed to analyze the FER performance based on the packet-level analysis. Simulation results demonstrate that the proposed model works for various packet sizes and channel parameters of practical interest with different verification constraints.

In this paper, we first develop a state-space system model for channel estimation, then propose a recursive channel estimation approach using the Gaussian message passing (GMP) technique, which is equivalent to the LS (least squares) approach. By introducing two different approximations to a GMP updating rule, the computational complexity of this recursive approach is significantly reduced, leading to two low-complexity unbiased FFT (fast Fourier transform)-based channel estimators. The proposed estimators are very suitable for turbo equalization, where the estimates of the data symbols available from the decoder are exploited as a virtual training sequence for channel estimation (In contrast, a direct use of the LS estimator incurs high complexity since the matrix inversion involved needs to be calculated online). We apply the proposed channel estimators in turbo equalization of quasi-static and time-varying frequency selective channels, and simulation results demonstrate their effectiveness.

We present a graph-based channel estimation approach for SC-IFDE (single-carrier transmissions with iterative frequency domain equalization) without CP (cyclic prefix) over doubly selective channels using the recently developed Gaussian message passing (GMP) technique. A direct application of the GMP updating rules in the FFG (Forney-style factor graph) of the SC-IFDE system model incurs high complexity. Approximate updating rules are therefore developed to overcome this problem. The proposed GMP-based channel estimation approach has similar complexity as the low-complexity Kalman-filtering based frequency domain channel estimation approach in the literature, but significantly outperforms the latter due to its enhanced capability in capturing the time correlation information of doubly selective channels through bidirectional processing.

In this paper, we propose a statistical model to analyze the performance of verification-based algorithm (VA) for packet-based low-density parity-check (LDPC) codes over binary symmetric channel (BSC). In contrast to the analysis of VA in the literature, we propose to take the false verification into consideration. For a given ensemble of LDPC codes and channel parameters, the proposed analysis model gives an efficient way to find the average performance of packet-based LDPC codes with verification-based decoding. Through numerical results, we find that the proposed method can provide a close estimation of frame error rate (FER) for packet-based LDPC codes with verification- based decoding over BSC for all crossover probabilities of practical interests.

In this paper, we propose a low-complexity iterative joint channel estimation, detection and decoding technique for doubly selective channels. The key to the proposed technique is a segment-by-segment processing strategy under the assumption that the channel is approximately static within a short segment of a data block. Through a virtual zero-padding technique, the proposed segment-by-segment equalization approach inherits the low-complexity advantage of the conventional frequency domain equalization (FDE), but does not need the assistance of guard interval (for cyclic-prefixing or zero-padding), thereby avoiding the spectral and power overheads. Furthermore, we develop a low-complexity bidirectional channel estimator, where the Gaussian message passing (GMP) technique is used to exploit the channel correlation information, and the intermediate channel estimation results in the iterative process are employed to perform inter-tap interference cancellation. Simulation results demonstrate the effectiveness of the proposed detection and channel estimation algorithms.

In this paper, we apply the enhanced verification-based decoding algorithm (EVA) for packet-based LDPC codes over wireless channels. Compared with the verification algorithm (VA) in the literature, the EVA algorithm enhances the verification condition thereby reducing the likelihood of false verification. The wireless channels in simulations are modeled by binary symmetric channel (BSC) and Gilbert-Elliott (GE) channel. The numerical results indicate the proposed algorithm gives a superior performance but with only moderate computation increase compared with VA. For example, when the bit error probability is less than 10-3in bad state of GE channel, the EVA reduces the frame error rate (FER) by one order with less than 35% complexity increase compared with VA.

With the aid of rate adaptation, multiuser diversity can be exploited in wireless networks by allowing the mobile user with the best channel to use the channel. However, polling mobile stations to obtain channel state information in large networks can result in large overhead, outweighing the multiuser diversity gain. Multiuser Diversity with Capture (MDC) is a wireless medium access control protocol that explicitly employs the capture effect to overcome the overhead problem. In this paper, we analyse the goodput performance of MDC over Rayleigh fading channels and compare it with the Medium Access Diversity (MAD) scheme proposed in the literature. Our results show that MDC is effective in networks with radio receivers possessing reasonably good capture properties and in networks where the number of mobile stations is reasonably large.

In a wireless network, with the aid of rate adaptation, multiuser diversity can be exploited by allowing the mobile user with the best channel to use the channel. However, the overhead that results from polling mobile stations to obtain channel state information (CSI) in large networks can outweigh the multiuser diversity gain. In this paper, we propose a wireless medium access control protocol, namely multiuser diversity with capture (MDC), which explicitly employs the capture effect to obviate the overhead problem. We analyze the good put performance of MDC and compare it with the medium access diversity (MAD) scheme proposed in the literature. Our results show that MDC is effective in networks with radio receivers possessing reasonably good capture properties and in networks where the number of mobile stations is reasonably large.

Multiuser diversity refers to the inherent diversity in channel quality across several mobile users in a wireless communication network. Multiuser diversity can be exploited through rate adaptation to increase system goodput, however the overhead incurred in polling channel state information in large networks can overshadow the multiuser diversity gain. In this paper, we propose a novel wireless medium access control protocol, multiuser diversity with capture (MDC), that uses the capture effect to avoid the large overhead incurred in polling channel state information. We simulate the goodput performance of MDC and compare it with the medium access diversity (MAD) scheme in the literature. Our results show that the proposed MDC outperforms MAD when the number of mobile stations in the network is reasonably large.

Multi-user diversity can be employed in wireless communications to significantly improve system performance by scheduling a channel to the user with the best instantaneous channel condition. Conventional multi-user diversity schemes require all mobile terminals' channel state information to be available at the access point, thereby inducing huge system overhead. In this paper, we propose a user identification approach to significantly reduce system overhead for wireless network with multi-user diversity. The proposed user identification approach is then applied to OFDM systems. We shows that the network throughput upper limit can be significantly improved by using the user identification approach compared with the user polling approach in the literature.

Conventionally, subcarrier based beamforming was used in an OFDM (orthogonal frequency division multiplexing) system under MIMO (multiple-input and multiple-output) channels to achieve optimal performance, where many DFT/IDFT (discrete Fourier transform/inverse DFT) blocks, each corresponding to one antenna, are required. To reduce the complexity induced by the DFT/IDFT processing, symbol based beamforming was recently proposed with only one IDFT block at the transmitter and one DFT block at the receiver. However, this approach results in a significant performance loss especially when the number of distinct paths in the channel is large. In this paper, we propose a multi-stage beamforming (MSB) scheme for MIMO-OFDM systems employing both the principles of subcarrier level beamforming and symbol level beamforming to effectively tradeoff system performance and complexity. Using the proposed MSB scheme and with a little complexity increase compared with the symbol based beamforming, system performance can be significantly improved. Under some channel conditions, the proposed MSB scheme can achieve a performance close to the optimal subcarrier based beamforming scheme but with much lower complexity. For the MSB scheme, we propose an iterative algorithm to jointly optimize the symbol level and subcarrier level weighting coefficients. To reduce the complexity of the joint weighting coefficients calculation algorithm, we propose a reduced complexity algorithm to first obtain the symbol level weighting coefficients and then the subcarrier level weighting coefficients. Simulation results show that good performance can be achieved using the proposed weighting coefficients calculation algorithms with reasonable complexity

Subcarrier based space processing was conventionally employed in Orthogonal Frequency Division Multiplexing (OFDM) systems under Multiple-Input and Multiple-Output (MIMO) channels to achieve optimal performance. At the receiver of such systems, multiple Discrete Fourier Transform (DFT) blocks, each corresponding to one receive antenna, are required to be used. This induces considerable complexity. In this paper, we propose a pre-DFT processing scheme for the receiver of MIMO-OFDM systems with space-time-frequency coding. With the proposed scheme, the number of DFT blocks at the receiver can be any number from one to the number of receive antennas, thus enabling effective complexity and performance tradeoff. Using the pre-DFT processing scheme, the number of input signals to the space-time-frequency decoder can be reduced compared with the subcarrier based space processing. Therefore, a high dimensional MIMO system can be shrunk into an equivalently low dimension one. Due to the dimension reduction, both the complexity of the decoder and the complexity of channel estimation can be reduced. In general, the weighting coefficients calculation for the pre-DFT processing scheme should be relevant to the specific space-time-frequency code employed. In this paper, we propose a simple universal weighting coefficients calculation algorithm that can be used to achieve excellent performance for most practical space-time-frequency coding schemes. This makes the design of the pre-DFT processing scheme independent of the optimization of the space-time-frequency coding, which is desirable for multiplatform systems.

Space-time-frequency coding is an important technology for OFDM (orthogonal frequency division multiplexing) under MIMO (multiple-input and multiple-output) channels to achieve high speed wireless multimedia transmission. Traditionally, multiple DFT (discrete Fourier transform) blocks, each corresponding to one receive antenna, are employed at the receiver of a MIMO-OFDM system with space-time- frequency coding, which induces huge complexity. In this paper, we propose a pre-DFT processing scheme for such a system, where the number of DFT blocks employed can be adjusted, thus enabling effective complexity and performance tradeoff. Simulation results will show that, under some channel conditions, the performance of the proposed scheme can be almost as good as the traditional optimal scheme but with much lower complexity.

Carrier frequency offset (CFO) in orthogonal frequency-division multiplexing(OFDM) systems, which can induce the loss of orthogonality among subcarriersand result in significant performance degradation, is critical to be estimatedand compensated for. In this paper, a suboptimal scheme is proposed to estimatethe CFO using null subcarriers. In the proposed scheme, the CFO is dividedinto the fractional part and the integer part, with the fractional CFO estimatedfollowed by the integer CFO estimation. Compared with previous work usingnull subcarriers and exhaustive search, the implementation complexity of theproposed scheme is significantly lower due to the fact that the main componentrequired is a simple correlator. Furthermore, with only one trainingOFDM symbol and proper null subcarrier allocation, the estimation range ofthe proposed scheme can be the inverse of the sampling duration. In contrast,conventional schemes with the same estimation range and complexity require two OFDM symbols. To achieve good performance,it will be shown that the null subcarrier allocation should be based uponsome specific binary sequences such as the proposed extended m-sequences. It willalso be shown by simulations that the same null subcarrier allocation criterionshould be employed for optimal maximum-likelihood CFO estimation.

Carrier frequency offset (CFO) in OFDM systems, which induces the loss of orthogonality among OFDM subcarriers, can result in significant performance degradation. As a result, it needs to be estimated and compensated for. In this paper, we present a general CFO estimator based on the maximum likelihood (ML) estimation criterion, with which CFO can be obtained using training OFDM symbols, pilot tones, null subcarriers, or a combination of them. Using the proposed CFO estimator, the performance of CFO estimation can be significantly improved by taking advantage of the channel side information. In particular, using the channel statistics information, such performance improvement can be achieved for low SNR values and all SNR values over Rayleigh fading channels and Ricean fading channels, respectively. When the complete channel impulse response (CIR) information is available, simulation results will show that the performance improvement can be more than 6 dB. To further demonstrate the capability of the proposed CFO estimator, we will consider an OFDM system using the signal structure of the IEEE WLAN standard 802.11a. Compared with previous work using null sub-carriers alone, we will show that by taking advantage of the pilot tones, null sub-carriers, and channel statistics, the performance of CFO estimation can be improved by about 2 dB

In this paper, we presented a joint time-frequency beamforming scheme for an OFDM system with MIMO antennas, where both frequency-domain and time-domain beamforming are employed. Using the proposed scheme, better performance can be achieved by using more DFT/IDFT blocks at the receiver and/or the transmitter. However, the margin of the performance improvement decreases along with the increase of the number of DFT/IDFT blocks. Therefore, the proposed scheme can be used to effectively trade off system performance and complexity. Furthermore, to calculate the weighting coefficients for the proposed scheme, we propose an iterative algorithm to achieve good performance with reasonable complexity.

Recently, orthogonal frequency-division multiplexing (OFDM), with clusters of subcarriers allocated to different subscribers (often referred to as OFDMA), has gained much attention for its ability in enabling multiple-access wireless multimedia communications. In such systems, carrier frequency offsets (CFOs) can destroy the orthogonality among subcarriers. As a result, multiuser interference (MUI) along with significant performance degradation can be induced. In this paper, we present a scheme to compensate for the CFOs at the base station of an OFDMA system. In the proposed scheme, circular convolutions are employed to generate the interference after the discrete Fourier transform processing, which is then removed from the original received signal to increase the signal-to-interference power ratio (SIR). Both SIR analysis and simulation results will show that the proposed scheme can significantly improve system performance.

Orthogonal frequency-division multiplexing (OFDM) is a promising technology in broadband wireless communications, with its ability to transform a frequency-selective fading channel into multiple flat-fading channels. However, the time-varying characteristics of wireless channels induce the loss of orthogonality among OFDM subcarriers, which was generally considered harmful to system performance. In this paper, we propose a bit-interleaved time-frequency coded modulation (BITFCM) scheme for OFDM to achieve both the time and frequency diversity inherent in broadband time-varying channels. We will show that the time-varying characteristics of the channel are beneficial to system performance. Using the BITFCM scheme, and for relatively low maximum normalized Doppler frequency, a reduced-complexity maximum-likelihood decoding approach is proposed to achieve good performance with low complexity. For high maximum normalized Doppler frequency, the intercarrier interference (ICI) can be large, and an error floor will be induced. To solve this problem, we propose two ICI-mitigation schemes by taking advantage of the second-order channel statistics and the complete channel information, respectively. It will be shown that both schemes can reduce the ICI significantly.

In broadband wireless communications, extensive research has been devoted to orthogonal frequency division multiplexing (OFDM) due to its ability in mitigating the multipath effects. In OFDM, however, carrier frequency offsets (CFO) induce the loss of orthogonality among the sub-carriers and this results in significant performance degradation. In this paper, we propose a reduced complexity algorithm for CFO estimation with the aid of null sub-carriers. Using only one training OFDM symbol with N sub-carriers, the estimation range of the proposed scheme can be N/2 times larger than conventional CFO estimation schemes As a result, the precision of the oscillators can be significantly-reduced, thus, enabling low cost implementation. This paper will also investigate null sub-carrier allocations for the proposed scheme. It will be shown that the allocations should be based upon some specific binary sequences such as the extended m-sequences proposed in this paper.

In broadband wireless communications, coded orthogonal frequency-division multiplexing (OFDM) can be used with multiple receive antennas to achieve both frequency diversity and space diversity. In this scenario, the optimal approach is subcarrier-based space combining. However, such an approach is quite complex, because multiple discrete Fourier transform (DFT) blocks, each per receive antenna, are used. We propose a pre-DFT processing scheme based upon eigenanalysis. In the proposed scheme, the received signals are weighted and combined both before and after the DFT processing. As a result, the required number of DFT blocks can be significantly reduced. With perfect weighting coefficients, the margin of the performance improvement decreases along with the increase of the number of DFT blocks, thus enabling effective performance and complexity tradeoff. To achieve a maximum average pairwise codeword distance, it will be shown that the maximum number of DFT blocks required is equal to the minimum of the number of receive antennas and the number of distinct paths in the channel. When the number of distinct paths is larger than the number of receive antennas and with a smaller number of DFT blocks, extensive simulation results will also show that near-optimal performance can still be achieved for most channels. Finally, in an OFDM system with differential modulation, we use a signal covariance matrix to obtain the weighting coefficients before the DFT processing. In this case, simulation results will demonstrate that the performance of the proposed scheme can be better than subcarrier-based space combining, but with much lower complexity.

The authors present a coded orthogonal frequency division multiplexing (COFDM) system with multiple-input/multiple-output (multiple transmit and/or multiple receive) antennas for high-rate wireless data transmission. A symbol-based space diversity technique, which can take advantage of the inherent space diversity, is proposed. In contrast to conventional subcarrier-based space diversity, it is shown that the proposed technique can be implemented using only one discrete Fourier transform block and the same weighting coefficients for the whole OFDM symbol. This significantly reduces the system complexity while achieving almost the same diversity order as that of the traditional space diversity approach. They also propose an iterative algorithm to obtain the antenna weighting coefficients. Simulation results show that the proposed algorithm converges fast and approaches the global optimal solution for most channel realizations. It is also shown that, when the proposed technique is employed in a time division duplex scenario, where the uplink and downlink channels are reciprocal, the system complexity can be further reduced.

In wireless communications, multiple receive antennas can be used with orthogonal frequency division multiplexing (OFDM) to significantly improve system capacity and performance. However, the complexity of such combination can be very large because multiple discrete Fourier transform (DFT) blocks, each for one receive antenna, are required to fully take advantage of the space diversity. In this paper, based on orthogonal designs, we present a general receiver architecture for an OFDM system employing multiple receive antennas. Using the proposed architecture, the number of DFT blocks, the number of receive antennas and system performance can be effectively traded off. As a result, with a little performance degradation, one can reduce system complexity and consequently reduce the power consumption in the receivers. The proposed receivers can also be featured as a performance enhancing scheme for an existing OFDM system where performance improvement is achieved through the use of software radio and without major modifications to the existing system hardware. For example, with only one DFT block, the performance can be improved by 5.1 dB using one more antenna. Finally, it is shown that the proposed scheme exhibits superior performance to that of selection diversity especially when the channels are rich in frequency diversity.

OFDM (orthogonal frequency division multiplexing) is an enabling technology for multimedia wireless communications due to its ability in mitigating the effects of multi-path propagation. However, OFDM is sensitive to the carrier frequency offset (CFO), which destroys the orthogonality among sub-carriers and induces inter-carrier interference (ICl). When OFDM is employed in a multiple access scenario, with clusters of sub-carriers allocated to different users (often referred to as OFDMA). CFOs induced both ICI and MUI (multi-user interference), which result in significant performance degradation. In this paper, the CFU estimation methodologies for OFDM/OFDMA are first reviewed. We then present a scheme to compensate for the CFOs in OFDMA systems. In the proposed scheme, circular convolutions are employed to reduce the interference after the DFI (discrete Fourier transform) processing. The interference is then removed from the original received signal to reduce the MUl. Simulation results show that the proposed scheme can significantly improve system performance.

Coded orthogonal division multiplexing (OFDM) is an enabling technology for broadband wireless communications. Multiple receive antennas can be employed in OFDM systems to achieve space diversity and thus significantly improve system performance. Conventionally, sub-carrier based space combining is used in such systems and the complexity can be huge because multiple DFT blocks, each for one receive antenna, are needed. In this paper, a pre-DFT processing technique based upon eigen-analysis, which can be employed in coded OFDM systems to effectively trade off system performance and complexity, is presented. For coherent modulation and without performance loss compared with the conventional scheme, it is shown that the number of DFT blocks required is the minimum of the number of receive antennas and the number of distinct paths in the channel. For differential modulation and with a smaller number of DFT blocks, simulation results show that the performance can even be better than that of the conventional scheme.

Orthogonal frequency division multiplexing (OFDM) has been recently receiving significant attention because of its spectrum efficiency and ability in providing reliable wideband wireless data access. Multiple transmit and/or multiple receive (MIMO) antennas can be employed with OFDM to better trade off frequency efficiency and power efficiency. In this paper, we study a coded OFDM (COFDM) system with MIMO antennas for high-rate wireless data transmission. To take advantage of the inherent space diversity, we propose a symbol-based space combining technique, which can be implemented using only one fast Fourier transform (FFT) block and the same weighting coefficients for the whole OFDM symbol. Thus, in contrast to conventional sub-carrier based space combining, the system complexity is significantly reduced while almost the same diversity order is achieved. Furthermore, it is shown that the proposed technique is not sensitive to channel variations when used in reciprocal channels such as that in a time division duplexing (TDD) scenario.