In blockchain, proof-of-work (PoW) is a popular consensus mechanism in which block publishers secure block contents through competitive computation. This competition has led to the emergence of specialized computing devices, such as application-specific integrated circuits (ASICs). Consequently, block publishing has become monopolized by a small group of top publishers equipped with ASICs and benefiting from economy of scale. This monopoly undermines immutability and security that are derived from the decentralized structure of blockchains. In this paper, we introduce a type of blockchain consensus algorithm named error-correction code verifiable computation consensus (ECCVCC), which includes conventional PoW. After that, we propose a novel ECCVCC utilizing a syndrome decoding problem as its crypto puzzle. The ECCVCC algorithm suppresses the development of efficient ASICs by utilizing time-varying cryptographic puzzles. As a result, the decentralization of a blockchain with ECCVCC can be improved compared to the blockchains with other consensus algorithms. Our analysis and simulation demonstrate that ECCVCC achieves robust control over block-generation time and difficulty under practical scenarios. Finally, we discuss that ASIC-resistant consensus algorithms, such as ECCVCC, sustain a blockchain network decentralized for a significantly longer period compared to conventional hash-PoW.

Drug combination therapy is crucial in cancer treatment, but accurately predicting drug synergy remains a challenge due to the complexity of drug combinations. Machine learning and deep learning models have shown promise in drug combination prediction, but they suffer from issues such as gradient vanishing, overfitting, and parameter tuning. To address these problems, the deep drug synergy prediction network, named as EDNet is proposed that leverages a modified triangular mutation-based differential evolution algorithm. This algorithm evolves the initial connection weights and architecture-related attributes of the deep bidirectional mixture density network, improving its performance and addressing the aforementioned issues. EDNet automatically extracts relevant features and provides conditional probability distributions of output attributes. The performance of EDNet is evaluated over two well-known drug synergy datasets, NCI-ALMANAC and deep-synergy. The results demonstrate that EDNet outperforms the competing models. EDNet facilitates efficient drug interactions, enhancing the overall effectiveness of drug combinations for improved cancer treatment outcomes.

The rapid growth in popularity of online social networks provides new opportunities in computer science, sociology, math, information studies, biology, business, and more. Social network analysis (SNA) is a paramount technique supporting understanding social relationships and networks. Accordingly, certain studies and reviews have been presented focusing on information dissemination, influence analysis, link prediction, and more. However, the ultimate aim is for social network background knowledge and analysis to solve real-world social network problems. SNA still has several research challenges in this context, including users’ privacy in online social networks. Inspired by these facts, we have presented a survey on social network analysis techniques, visualization, structure, privacy, and applications. This detailed study has started with the basics of network representation, structure, and measures. Our primary focus is on SNA applications with state-of-the-art techniques. We further provide a comparative analysis of recent developments on SNA problems in the sequel. The privacy preservation with SNA is also surveyed. In the end, research challenges and future directions are discussed to suggest to researchers a starting point for their research.

One of the leading causes of cancer-related deaths among women is cervical cancer. Early diagnosis and treatment can minimize the complications of this cancer. Recently, researchers have designed and implemented many deep learning-based automated cervical cancer diagnosis models. However, the majority of these models suffer from over-fitting, parameter tuning, and gradient vanishing problems. To overcome these problems, in this paper a metaheuristics-based lightweight deep learning network (MLNet) is proposed. Initially, the hyper-parameters tuning problem of convolutional neural network (CNN) is defined as a multi-objective problem. Particle swarm optimization (PSO) is used to optimally define the CNN architecture. Thereafter, Dynamically hybrid niching differential evolution (DHDE) is utilized to optimize the hyper-parameters of CNN layers. Each particle of PSO and solution of DHDE together represent the possible CNN configuration. F-score is used as a fitness function. The proposed MLNet is trained and validated on three benchmark cervical cancer datasets. On the Herlev dataset, MLNet outperforms the existing models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 1.6254%, 1.5178%, 1.5780%, 1.7145%, and 1.4890%, respectively. Also, on the SIPaKMeD dataset, MLNet achieves better performance than the existing models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 2.1250%, 2.2455%, 1.9074%, 1.9258%, and 1.8975%, respectively. Finally, on the Mendeley LBC dataset, MLNet achieves better performance than the competitive models in terms of accuracy, f-measure, sensitivity, specificity, and precision by 1.4680%, 1.5845%, 1.3582%, 1.3926%, and 1.4125%, respectively.

With the advancement in artificial intelligence (AI) based E-healthcare applications, the role of automated diagnosis of various diseases has increased at a rapid rate. However, most of the existing diagnosis models provide results in a binary fashion such as whether the patient is infected with a specific disease or not. But there are many cases where it is required to provide suitable explanatory information such as the patient being infected from a particular disease along with the infection rate. Therefore, in this paper, to provide explanatory information to the doctors and patients, an efficient deep ensemble medical image captioning network (DCNet) is proposed. DCNet ensembles three well-known pre-trained models such as VGG16, ResNet152V2, and DenseNet201. Ensembling of these models achieves better results by preventing an over-fitting problem. However, DCNet is sensitive to its control parameters. Thus, to tune the control parameters, an evolving DCNet (EDC-Net) was proposed. Evolution process is achieved using the self-adaptive parameter control-based differential evolution (SAPCDE). Experimental results show that EDC-Net can efficiently extract the potential features of biomedical images. Comparative analysis shows that on the Open-i dataset, EDC-Net outperforms the existing models in terms of BLUE-1, BLUE-2, BLUE-3, BLUE-4, and kappa statistics (KS) by 1.258%, 1.185%, 1.289%, 1.098%, and 1.548%, respectively.

Abstract Crop image segmentation is widely used for the analysis of crops. A wide variety of crops are present in the agriculture field, which varies in intensity and complex backgrounds. The thresholding method based on entropy is quite popular for the segmentation of an image. Among all, minimum cross entropy has been widely used. However, the complexity of computation increases when it is used for multilevel thresholding (MLT). Recursive minimum cross entropy is used to resolve the complexity of computation, and cuckoo search (CS) using Levy flight is used to find the optimal threshold for this objective function. Because real‐time applications require less processing time while maintaining high performance, which is validated by the CS algorithm using recursive minimum cross entropy (R‐MCE‐CS) without constraint. The proposed method uses one constraint based on the structural similarity index (SSIM), which leads to an increment in the accuracy for a higher level of thresholding. The accuracy of the proposed method has been tested over 10 crop images with complex backgrounds and high dimensions of colour intensity space. The outcome of the proposed technique has been compared with five algorithms such as wind‐driven optimisation (WDO), bacterial foraging optimisation (BFO), differential evolution (DE), artificial bee colony (ABC), and firefly algorithm (FFA). The result shows that the proposed method gives the most promising result, and the accuracy is also improved.

Remote sensing images taken during poor environmental conditions are degraded by the scattering of atmospheric particles, which affects the performance of many imaging systems. Hence, an efficient visibility restoration model is required to remove haze from distorted images. However, the design of visibility restoration models is an ill-posed problem as the physical information, such as depth information and attenuation model, is usually unknown. The physical parameters computed using existing models, such as dark channel prior and gradient channel prior, are not accurate, especially for images with large haze gradients. Therefore, in this article, an evolving visibility restoration model is proposed for remote sensing images. Initially, the fusion-based transmission map is computed from the foreground and sky regions. The transmission map is further improved by designing a hybrid constraint-based variational model. Finally, a dynamic differential evolution is utilized to optimize the control parameters of the proposed model. The proposed model is validated on 50 synthetic benchmarks and 50 real-life remote sensing images. For comparative analysis, ten well-known restoration models are also considered. The comparative analysis demonstrates that the proposed model outperforms the existing restoration models.

A noniterative fractional-order (FO) two-dimensional (2-D) adaptive filtering mechanism is proposed in this article. Quartiles-based adaptive textural segmentation is employed for the calculation of texture-dependent FO. Statistically, the quartiles/quintiles/quantiles are cut points for dividing the range of a probability distribution into continuous intervals with equal probabilities. For the purpose of texture-based isolation of the spatial regions, a 2-D textural map is framed by evaluating spatial entropy by considering a pixel-wise local circular neighborhood. A novel end-to-end framework is proposed for FO texture-dependent image sharpening in an independent manner without influencing the other classes of textural regions. A novel inclusion of texture-wise adaptive gamma correction is also proposed in this article by drafting a mechanism where different kinds of textural regions can be separately processed in an independent manner. A novel quintiles-based multiscale Retinex (MSR) inspired approach for reflectance computation is coined in this article for suppressing environmental artifacts and unbalanced/nonuniform illumination. In this context, various scales required for MSR are themselves computed through quartiles-based intensity levels. The proposed model is highly modular. So, it can also be pipelined in a parallel manner, along with any well-established state-of-the-art contrast enhancement approach. Also, this approach is noniterative and highly robust. It can be proposed as an add-on for several possible (as well as pre-existing) image processing procedures. Rigorous comparative evaluations are performed so that the excellence of the proposed approach can be underlined.

This paper presents a new efficient method for implementing the Hilbert transform using an all-pass filter, based on fractional derivatives (FDs) and swarm optimization. In the proposed method, the squared error difference between the desired and designed responses of a filter is minimized. FDs are introduced to achieve higher accuracy at the reference frequency (ω0), which helps to reduce the overall phase error. In this paper, two approaches are used for finding the appropriate values of the FDs and reference frequencies. In the first approach, these values are estimated from a series of experiments, which require more computation time but produce less accurate results. These experiments, however, justify the behavior of the error function, with respect to the FD and ω0, as a multimodal and nonconvex problem. In the second approach, a variant of the swarm-intelligence-based multimodal search space technique, known as the constraint-factor particle swarm optimization, is exploited for finding the suitable values for the FD and ω0. The performance of the proposed FD-based method is measured in terms of fidelity aspects, such as the maximum phase error, total squared phase error, maximum group delay error, and total squared group delay error. The FD-based approach is found to reduce the total phase error by 57% by exploiting only two FDs.

Cooperation among sensors in a wireless sensor network, deployed for industrial monitoring in an indoor scenario, is a topic of interest in the smart factory and smart city research. The indoor wireless communication channel is very harsh and the observations of all the sensors cannot be sent reliably to the base station. Failure to transmit correct sensing results to the base station may result in false alarms or missed detection of events. Therefore, we propose a cooperation scheme for the wireless sensors to send the data reliably to the base station. Our aim is to increase the reliability of the received information, reduce the probability of error, lower the overall power consumption, and keep the latency to an acceptable low level. We propose a reliability factor feedback algorithm to adjust the weight of unreliable sensors in the decision‐making process. The proposed scheme is analyzed based on its latency, power consumption, and packet delivery ratio. Our results show significant improvement in the reliability of the received data, improved packet delivery, and reduced false alarm ratio for full repetition and cluster head‐based cooperation. The power consumption and latency in data transmission are also kept to an acceptable low level.

Sensor networks have been widely used traditionally in monitoring the state of heavy machinery and large factories whose condition is critical to the operation of machine as well as the safety of people around them. Recently, industrial wireless sensor networks (IWSNs) are getting popular for environment monitoring in such conditions to help us make a decision on the state of machines in a certain area of interest. However, the wireless communication channels, which these sensors must operate in, are not always reliable, and observations of some sensors cannot be reported successfully to the base station. In order to deal with this problem, we propose a dual-hop cooperative WSN scheme, which uses in-network data aggregation mechanism in order to reduce the overall latency as well as improve the reliability of the received information. We also devise a protocol for the organization and operation of the proposed sensor network. The proposed scheme effectively increases the probability of correct decision about the state of the machines, and reduces the probability of false alarms at a given signal level.

Industrial wireless sensor networks are getting popular for indoor monitoring of heavy machinery and large factories to make a reliable decision on the state of machines in a certain area of interest. However, the indoor wireless communication channel is not always reliable, and observations of some sensors cannot be reported successfully to the base station. In order to deal with this problem, we propose a cooperative WSN scheme by introducing a novel cooperation mechanism and a medium access control protocol. The proposed scheme effectively increases the probability of correct decision about the state of the machine, reduces the probability of false alarms at a given signal level, and reduces the overall energy consumption as compared to noncooperative schemes. We also present a closed-form expression for the symbol-error rate analysis of the proposed scheme, which shows that our proposed scheme achieves full diversity order offered by the cooperation scheme.

In this paper, we study a support set reconstruction problem for multiple measurement vectors (MMV) with different sensing matrices, where the signals of interest are assumed to be jointly sparse and each signal is sampled by its own sensing matrix in the presence of noise. Using mathematical tools, we develop upper and lower bounds of the failure probability of the support set reconstruction in terms of the sparsity, the ambient dimension, the minimum signal-to-noise ratio, the number of measurement vectors, and the number of measurements. These bounds can be used to provide guidelines for determining the system parameters for various compressed sensing applications with noisy MMV with different sensing matrices. Based on the bounds, we develop necessary and sufficient conditions for reliable support set reconstruction. We interpret these conditions to provide theoretical explanations regarding the benefits of taking more measurement vectors. We then compare our sufficient condition with the existing results for noisy MMV with the same sensing matrix. As a result, we show that noisy MMV with different sensing matrices may require fewer measurements for reliable support set reconstruction, under a sublinear sparsity regime in a low noise-level scenario.

Channel sharing in TV whitespace (TVWS) is challenging because of signal propagation characteristics and diversity in network technologies employed by secondary networks coexisting in TVWS. In this paper, the TVWS sharing problem is modeled as a multiobjective optimization problem, where each objective function tackles an important coexisting requirement, such as interference and disparity in network technologies. We propose an evolutionary algorithm that shares the TVWS among coexisting networks taking care of their channel occupancy requirements. In this paper, the channel occupancy is defined as the time duration; a network desires to radiate on a channel to achieve its desired duty cycle. Simulation results show that the proposed algorithm outperforms existing TVWS sharing algorithms regarding allocation fairness and a fraction of channel occupancy requirements of the coexisting networks.

A coexistence decision-making (CDM) system for efficient TV whitespace (TVWS) sharing among whitespace objects (WSOs) is introduced in this paper. The proposed system is considered versatile in functionality as it jointly takes care of three distinct channel allocation features: (a) optimizing system quality of service (QoS) performance metrics, (b) improving TVWS utility, and (c) satisfying WSO channel demands. Regarding system QoS performance metrics, the TVWS sharing problem is defined as an optimization problem with an aim to maximize the system throughput and minimize unfairness in allocation. Supporting the WSOs channel demands in a TVWS sharing problem is a multifold task which requires elaborate consideration in different aspects of the system performance. To this end, the variations of the SNR of wireless frequency channels which result in variable throughput gain of the WSOs are also taken care of in the proposed CDM system. A fast channel allocation algorithm is then designed that implements the TVWS sharing mechanism in a reasonable amount of time. Additionally, the proposed algorithm improves the TVWS utility by promoting a novel frequency reuse method by exploiting the inter-WSO interference information. Simulation results show the superiority of the proposed algorithm over existing TVWS sharing algorithms.

Underwater environment monitoring is an important application of wireless sensor networks (WSNs). However, WSNs face challenges, such as erroneous communication, ensuring the lifetime and robustness of the network, and cost constraints. The underwater acoustic channel (UAC) is highly frequency-selective, and the channel response changes over time because of variations in the channel conditions. Therefore, designing a cooperative coded orthogonal frequency division multiplexing (COFDM) system that is suitable for the doubly selective UAC and has reduced power consumption is very challenging. We propose a cooperative spatial-domain coding scheme combined with the low-density parity-check-coded OFDM system, called spatially concatenated channel-network code, for underwater acoustic WSNs. The designed underwater acoustic WSN exhibits a significant advantage regarding the required number of sensors, bit error rate (BER), and power consumption over the non-cooperative COFDM communication system. We also analyze sensor deployment schemes and find out the area in which our proposed scheme can be beneficial in terms of reduced power consumption and enhanced BER.

In this paper, we investigate a market equilibrium in multichannel sharing cognitive radio networks (CRNs): it is assumed that every subchannel is orthogonally licensed to a single primary user (PU), and can be shared with multiple secondary users (SUs). We model this sharing as a spectrum market where PUs offer SUs their subchannels with limiting the interference from SUs; the SUs purchase the right to transmit over the subchannels while observing the inference limits set by the PUs and their budget constraints. Moreover, we consider each SU limits the total interference that can be invoked from all other SUs, and assume that every transmitting SU marks the interference charges to other transmitting SUs. The utility function of SU is defined as least achievable transmission rate, and that of PU is given by the net profit. We define a market equilibrium in the context of extended Fisher model, and show that the equilibrium is yielded by solving an optimization problem, Eisenberg-Gale convex program. To make the solutions of the convex program meet the market equilibrium, we apply monotone-transformation to the utility function of each SU. Furthermore, we develop a distributed algorithm that yields the stationary solutions asymptotically equivalent to the solutions given by the convex program.

In this paper, we consider a cooperative wireless network in which there are multiple sources and multiple relays. Owing to unreliable wireless channels, the quality of network links between nodes can vary. This results in the failure of intermediate nodes that generate linear combinations of incoming messages in network coding schemes. We propose an analytical framework to evaluate the recovery performance of source messages at the base station. To this end, we consider a random transmission matrix in which each element of the transmission matrix is processed a random variable, where its distribution is a function of the outage probability. We derive an upper bound for the reconstruction performance, i.e., decoding failure probability and nullity. The proposed framework provides an evaluation tool that enables us to investigate the impact of a large number of sources and relays, as well as the field size of the network codes on system performance.

Advanced communication techniques exploiting network coding and cooperative schemes have attracted considerable attention as ways to improve power efficiency in wireless transmission as well as to achieve high throughput and spectral efficiency. In this paper, we consider a cooperative wireless network with two nodes and one base station, and investigate the impact of the outage performance on using non-binary network coding. We derive the exact and general outage probability in our network coding schemes, and obtain the asymptotic expression of the outage probability. We compare outage probabilities between exact and asymptotic results. We discuss the problem of optimal power allocation.

In this study, the authors investigate the expected complexity of increasing radii algorithm (IRA) in an independent and identified distributed Rayleigh fading multiple‐input–multiple‐output channel with additive Gaussian noise and then present its upper bound result. IRA employs several radii to yield significant complexity reduction over sphere decoding, whereas performing a near‐maximum‐likelihood detection. In contrast to the previous expected complexity presented by Gowaikar and Hassibi (2007), where the radius schedule was hypothetically fixed for analytic convenience, a new analytical result is obtained by considering the usage of multiple radius schedules. The authors analysis reflects the effect of the random variation in the radius schedule and thus provides a more reliable complexity estimation. The numerical results support their arguments, and the analytical results show good agreement with the simulation results.

In this paper, we consider a data collection network (DCN) system where sensors take samples and transmit them to a Fusion Center (FC). Signal correlation is modeled with signal sparseness. The number of compressed measurements which allows correct signal recovery at FC is investigated. This is done by studying the probability of signal recovery failure. The joint typical decoder (JT decoder) similar to the one proposed by Akcakaya and Tarokh is used to avoid dependence on particular choice of recovery routines. The following interesting results have been obtained: 1) The detection failure probability linearly converges to zero as the number of sensors increases. 2) The number of compressed measurements per sensor (PSM) needed for successful recovery converges to sparsity as the number of sensors increases.

In this study, joint source–channel decoding for non-binary source samples is conducted. The non-binary source samples can be modelled as the output of a multi-state Markov chain (MC). As the source samples are directly transmitted after channel coding without source compression, the transmitted signals can be highly correlated. At the receiver, the multi-state MC module can be designed to exploit the statistical correlation of source samples to improve the error correcting performance. However, as the number of states is increased, the multi-state MC module requires high computational complexity. To alleviate this problem, a simplified MC module is proposed. In the simplified MC module, the multi-state MC is replaced with multiple number of two-state MCs each of which exploits bit-level correlation of samples. Simulation results demonstrate that the simplified MC module can lead to competitive reduction in the required signal-to-noise ratio in comparison with the multi-state MC module with reduced computational complexity.

In this letter, we aim to present a near-maximum-likelihood (ML) decoding algorithm with low-complexity for wider ranges of SNR and system dimension in multiple-input-multiple-output (MIMO) systems. Based on the proposed radius design criterion, we introduce the effective radius (ER) which is determined using the statistics of path metric under correct and incorrect decoding cases. Since the constraint established by the ER maintains tightness during most search procedure, the proposed scheme further improves the complexity, and its performance loss is still negligible by properly selecting design probabilities.

Driven by advances in signal processing and multiuser detection (MUD) technologies, it has become possible for a wireless node to simultaneously receive multiple signals from other transmitters. In order to take full advantage of MUD in multi-packet reception (MPR) capable wireless networks, it is highly desirable to make the compounded signal from multiple transmitters more separable on its constellation at the receiver by coordinating the carrier phase offsets of the transmitters. In this paper, we propose a feedback-based carrier phase adjustment scheme that estimates the carrier phase offset for each user's received signal, computes the optimal phase shift to maximize the minimum Euclidean distance between the constellation points, and finally feeds the phase shift information back to the transmitters. We then evaluate the performance of the proposed carrier phase adjustment scheme and compare it to that of the no carrier phase adjustment case for QPSK and 8PSK with 2-4 users, and subsequently show that the proposed scheme significantly reduces the error probability in a multiuser communication system having MPR capability.

Postural synergies of the hand have been widely proposed in the literature, but only a few attempts were made to visualize temporal postural synergies, i.e., profiles of postural synergies varying over time. This paper aims to derive temporal postural synergies from kinematic synergies extracted from joint angular velocity profiles of rapid grasping movements. The rapid movements constrain the kinematic synergies to combine instantaneously, and thus, the movements can be approximated by a weighted summation of synchronous synergies. After being extracted by using singular value decomposition, the synchronous kinematic synergies were translated into temporal postural synergies, which revealed strategies of enslaving, metacarpal flexion for larger movements, and hierarchical recruitment of joints, adapted by subjects while grasping.

This paper addresses the power control problem in wireless multiple access communication systems with a multi-packet reception (MPR) channel. Driven by advances in signal processing and multiuser detection (MUD) technologies such as UWB and MIMO, it has become possible for a wireless node to simultaneously receive several packets from other transmitters. In this case, the receiver node can determine the set of transmitters from which it wants to receive data and instruct them to adjust their transmit power levels in order to maximize the overall network capacity by exploiting the MPR capability. We first show that the efficiency of the MPR channel utilization can be maximized by equalizing the transmission durations of simultaneously transmitting nodes, and then propose a power control that increases/decreases the transmit power of the nodes to equalize the transmission durations. Under the proposed power control, a node with a long transmission duration is allowed to use a higher transmit power if the next higher transmission rate is achievable with the increased transmit power. Through extensive simulations, we show that the proposed power control significantly improves the overall network capacity in multiple access.

This paper proposes an efficient method to acquire sources of tremor in patients with movement disorders based on blind source separation of convolutive mixtures. The extracted sources indicated neural activities that might be generated in the central nervous system. Four patients with essential tremor were tested in a set of movement tasks. Subjects wore a data glove that measured finger movements of the hand. The experimental data were then fed to a convolutive-mixture model, which revealed sources that imbibed in them the tremor frequency components of 2--8 Hz. Time--frequency analysis of these sources might be of potential help to clinicians to devise tasks that can manifest visible tremor from patients.

In this paper, we derive closed form upper bounds on the error probability of low-density parity-check (LDPC) coded modulation schemes operating on quasi-static fading channels. The bounds are obtained from the so-called Fano-Gallager's tight bounding techniques, and can be readily calculated when the distance spectrum of the code is available. In deriving the bounds for multiple-input multiple-output (MIMO) systems, we assume the LDPC code is concatenated with the orthogonal space-time block code as an inner code. We obtain an equivalent single-input single-output (SISO) channel model for this concatenated coded-modulation system. The upper bounds derived here indicate good matches with simulation results of a complete transceiver system over Rayleigh and Rician MIMO fading channels in which the iterative detection and decoding algorithm is employed at the receiver.

Low-density generator-matrix (LDGM) codes are of particular interest not only because of their satisfying performance while maintaining low encoding complexity, but also because of promising applications such as network coding in cooperative multiple-access networks and joint source-channel coding where the LDGM codes are suitable. We investigate the performance of regular LDGM codes under the majority-rule based (MB) iterative decoding algorithm. Thethresholdanderror-floorbehaviors of the code can be extracted. A lower-bound expression for the error floors is obtained. The optimalmajorityweightfor the MB decoding algorithm to achieve the lowest error floor is found.

We consider a low-density parity-check (LDPC)-coded modulation scheme in multi-input multi-output (MIMO) multiple-access systems. The receiver can be regarded as a serially concatenated iterative detection and decoding scheme, where the LDPC decoders perform the role of outer decoder and the multiuser demapper does that of the inner decoder. In this paper, we investigate the performance of the scheme with simulation results and bounds. Union upper bounds are derived, which can be used as additional means to evaluate the performance of the MIMO multiple access system.

An assumption of random block codes is made for the closed-form derivation of union upper bounds. The random code works as the outer code for concatenation with the inner space-time modulation over the multi-input multi-output (MIMO) fading channels. From the union bound expression, we further obtain the closed-form expressions of error exponent and cut-off rate for MIMO channels. The error exponent is compared with Gallager's classical error exponent which is generally evaluated in the Monte Carlo method. The obtained bounds can be useful to serve as space-time code design tools or as performance prediction measures for the MIMO systems which employ turbo-like block codes as the outer code to drive the inner space-time code.

A performance evaluation framework based on the maximum-likelihood (ML) union bound is proposed for the concatenated coding scheme where the outer linear block code, low-density parity-check (LDPC) or Turbo code, drives a sequence of inner space-time codes. The outer code can be partitioned in a combinatorial manner such that all codewords in one subset/subcode are associated with an identical pairwise error probability. The union bound is therefore the summation of all distinct pairwise error probabilities, each of which is weighted by the cardinality of the corresponding subset. The cardinality is computable by making use of the statistical property of an ensemble of codes, and an approach of polynomial expansion is proposed for efficient evaluation of the closed-form union bound. Numerical results are presented for different channel scenarios and modulation schemes, which indicate that the ML union bounds are useful to benchmark the performance of practical iterative detection and decoding algorithms.

Under the assumption of random coding, we derive a closed-form union bound on the error performance of MIMO systems. The distance spectrum of random block codes, instead of that of LDPC or turbo codes, is used in this paper for the purpose of closed-form derivation. As compared with the simulation results, the bound is useful as a performance benchmark for a concatenated coding scheme over MIMO systems in which the LDPC or turbo code serves as the outer code to drive an inner space-time modulation block. In addition, the derived closed-form error exponent and cutoff rate are consistent with Gallager's random coding exponent and the information-theoretic channel capacity in a variety of modulation and channel scenarios

Closed-form union bounds are derived for the error performance of low-density parity-check (LDPC) coded Q-ary modulation systems over fast fading multi-input multi-output (MIMO) channels. The LDPC codewords are mapped onto space-time modulation blocks, referred to as associated space-time (AST) codewords, in one-to-one correspondence. Based on this relationship, the distance spectrum of the AST code is identified in a combinatorial manner, and each AST codeword possessing the same distance metric renders the identical pairwise error probability. The union bound is obtained in a closed form and can be efficiently evaluated by the method of polynomial expansion. In addition, the analysis is extended to the system with random coding which contributes to a concise expression of the error exponent. Numerical results are presented for different channel scenarios and modulation schemes, which indicate that the bounds are useful to benchmark the practical performance of iterative detection and decoding algorithms

In this paper, we derive closed-form upper bounds on the error probability of coded modulation schemes used for multi-input multi-output (MIMO) quasi-static fading channels. The bounds are obtained from new tight union-bound techniques, and can be readily calculated when the distance spectrum is given. In deriving the bounds for the MIMO system, we assume the use of orthogonal space-time block code (OSTBC) as the inner code. The outer code is the block code. We obtain an equivalent single-input single-output (SISO) channel model for this concatenated coded-modulation system. The upper bounds derived here indicate good match with the simulation results of the complete transceiver the system in which the turbo-iterative decoding algorithm is employed at the receiver.

A novel suboptimal low-complexity equalization and turbo-iterative decoding scheme is proposed in this paper. The scheme is developed for multiple transmit- and multiple receive-antenna systems operating over severe frequency-selective fading intersymbol interference channels. The signal-processing complexity may be of a concern for such systems. The complexity of a full-search equalization grows in a power-law manner O(M/sup NtL/), where M denotes for M-ary channel symbols, N/sub t/ the number of transmit-antennas, and L the number of delay channel taps. A low-complexity solution can be obtained by pruning an equalizer tree. The two main operations include a sphere list detection and a threshold-based tree-search. In the operation of extracting extrinsic messages from the pruned tree, a set of explored paths with different survival lengths poses a fairness problem: a longer-lived path naturally builds a larger discrepancy-metric than a shorter lived path does. A novel survival-length compensation-rule is devised so that all explored paths with different survival lengths are utilized fairly in generating the output message. Simulation results are obtained for multi-input and multi-output systems equipped with four transmit and four receive antennas. They indicate the performance of the receiver is very robust.

Mapping has a significant impact on multilevel modulation schemes. These schemes for conventional codes with iterative demapping and decoding on static channel and Rayleigh fading channel have been studied previously. In this paper, we first investigate the mapping influence on LDPC-code based multilevel modulations on static and Rayleigh fading channels. Then we extend our work to multiple-input multiple-output (MIMO) fading channels and MIMO multiple access channels (MAC). A conclusion can be made as that the Gray map is the best with both iterations and super-iterations for all these channels, which is different from the results of conventional codes.

We consider the equalization of a (N/sub t/, N/sub r/) MIMO, L + 1-tap fading channel. We first evaluate the performance of a full complexity, vector MAP equalizer which runs the forward/backward algorithm on a trellis which has M(N/sub t//spl times/L) states with M(N/sub t/) transitions from each state when an M-ary constellation is used. This MAP equalizer achieves the N/sub r//spl times/(L+1) diversity benefit while realizing N/sub t//spl times/log/sub 2/(M) bits/sec/Hz. We then propose a novel iterative per-antenna MAP(PAMAP) approach which can be used (1) to reduce the complexity and (2) to achieve the full diversity order N/sub t//spl times/N/sub r//spl times/(L+1) at the rate of log/sub 2/(M) bits/sec/Hz. The proposed equalizer consists of a probabilistic "signal separator" and a bank of N/sub t/ PAMAPs each having M/sup L/ states with M transitions from each state. The signal separator and the PAMAPs exchange extrinsic information during iterations. Simulation results indicate that the proposed receiver closely achieves the performance of the full complexity MAP within 2 to 3 iterations. The proposed scheme saves a significant amount of complexity in uncoded systems with a large number of transmit antennas and a high modulation order. In coded systems, the PAMAP scheme becomes more beneficial when iterative equalization and decoding is used.

We propose a spectrum efficient and robust space-time-frequency code system for a (N/sub t/, N/sub r/) MIMO, (L+1)-tap, fading channel. The LDPC (low-density parity-check) code is employed at the transmitter and iterative maximum a posteriori (MAP) equalizers are used at the receiver. We propose a novel, reduced-complexity iterative decoding and equalization scheme in which we combine the message-passing decoder, on the bipartite graph for the LDPC code, with an iterative MAP equalizer in a turbo-like manner. First, we consider a full-complexity vector MAP equalizer which runs the forward/backward algorithm on the trellis which has M/sup Nt/spl times/L/ states with M/sup Nt/ transitions from each state when an M-ary constellation is used. Second, we replace the full MAP with the bank of N/sub t/ per-antenna MAP (PAMAP) equalizers - each works on an M/sup L/ state trellis - and evaluate the performance loss due to the use of the reduced complexity scheme. The simulation results show that the performance of the PAMAP-bipartite equalization/decoding scheme is very close to the full complexity vector MAP-bipartite. In addition, in the fast fading case, the performance of the proposed space-time-frequeney system with N/sub t/ = 2, N/sub r/ = 2 and L = 2 is very close, within 0.5 dB, to the results of the LDPC code on AWGN channels.

We propose a novel cross layer design concept that could improve the network throughput significantly for mobile ad hoc networks. The channel reservation control packets employed at the MAC layer can be utilized at the physical layer for exchanging timely channel estimation information to enable an adaptive selection of a spectrally efficient transmission rate. In particular, the size of a digital constellation can be varied dynamically based on the channel condition estimated at the receiver which can be relayed to the transmitter via the control packets. In addition, this channel adaptive information gathered at the MAC layer can be communicated to the routing layer via different routing metrics for optimal route selection. We have examined the performance improvement at the network layer due to cross layer communications. For this, we present a simple cross layer design implemented with minor modifications on the IEEE 802.11 standard and the dynamic source routing (DSR) protocol. We demonstrate that the network throughput is significantly increased, as much as 50% to 100%, in low mobility scenarios simulated using ns-2.

We provide an analytical and network-systematic framework to characterize the self-similar, fractal scaling phenomenon which is believed to be ubiquitously present in modern high speed data network traffic. We show that the self-similar network traffic is due mainly to the use of closed-loop flow control at the transport layers, such as the use of the classes of protocols from the TCP family. For in-depth investigation on the subject, we inject synthetically generated application-level traffic which is completely short-range traffic, into a very simple network simulated with NS2.0, and examine the influence of different parameters of a TCP algorithm on a variety of different fractal scaling behaviors observed at the packet-level traffic traversing a link in the simulated network. We provide a very simple - but intuitive - mathematical explanation of the observed phenomenon using the shot-noise processes. Specifically, different kernel filters of the shot-noise are constructed to model the behavior of the window process cwnd in different stages of the congestion avoidance algorithms employed in a TCP algorithm. With the use of exponential-law shot-noise process, for example, we indicate that the cwnd process in the slow start phase results in a unique scaling behavior from RTT to a finer time scale, having a scaling slope of /spl gamma/ = 2. From RTT to a coarse time-scale, the more conventional fractal scaling behavior with the Hurst parameter less than 1.0 is observed; we compare this with the rectangular-Pareto shot noise process.

This paper extends previous works in matched filter bounds and provides a theoretical calculation of detection probability of q-ary QAM signals transmitted over the diversity reception and multipath fading ISI channels. The matched filter bounds represent the best attainable detection performance of a particular system, which may or may not be realized with a practical system. While an exact analytical expression of detection performance of a transceiver system is difficult to obtain for multipath fading ISI channels, the matched filter bounds are relatively easy to obtain, provide much useful information, and give a good comparison with the simulation results of realistic systems.

We examine adaptive equalization and diversity combining methods for fast Rayleigh-fading frequency selective channels. We assume a block adaptive receiver in which the receiver coefficients are obtained from feedforward channel estimation. For the feedforward channel estimation, we propose a novel reduced dimension channel estimation procedure, where the number of unknown parameters are reduced using a priori information of the transmit shaping filter's impulse response. Fewer unknown parameters require a shorter training sequence. We obtain least-squares, maximum-likelihood, and maximum a posteriori (MAP) estimators for the reduced dimension channel estimation problem. For symbol detection, we propose the use of a matched filtered diversity combining decision feedback equalizer (DFE) instead of a straightforward diversity combining DFE. The matched filter form has lower computational complexity and provides a well-conditioned matrix inversion. To cope with fast time-varying channels, we introduce a new DFE coefficient computation algorithm which is obtained by incorporating the channel variation during the decision delay into the minimum mean square error (MMSE) criterion. We refer to this as the non-Toeplitz DFE (NT-DFE). We also show the feasibility of a suboptimal receiver which has a lower complexity than a recursive least squares adaptation, with performance close to the optimal NT-DFE.