This paper examines a reconfigurable intelligent surface (RIS)-assisted single-input multiple-output (SIMO) frequency-modulated continuous-wave (FMCW) radar system for detecting vital signs, particularly breath rates, of multiple targets. The proposed method employs manifold optimization (MO) to design RIS phase shifts, aiming to maximize the signal-to-interference-plus-noise ratio (SINR) of the received radar signal. The estimation of signal parameters via rotational invariance technique (ESPRIT) algorithm is then applied to estimate the targets' vital signs. Simulation results show that the proposed scheme outperforms both the no-RIS scenario with an added singular value decomposition (SVD)-based processing at the radar receiver and a semidefinite relaxation-based RIS design approach, all while maintaining lower computational complexity.

The simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) enhances millimeterwave (mmWave) communication by enabling full-space coverage, making it ideal for vehicle-to-everything (V2X) applications. This paper explores a multiuser multiple-input single-output (MU-MISO) mmWave non-orthogonal multiple access (NOMA) downlink vehicular environment aided by STAR-RIS, aiming to maximize the sum rate of the infrastructure-to-vehicle (I2V) links through the design of base station (BS) beamforming, power allocation, and STAR-RIS phase shifts under coupled phase constraints. We propose an unsupervised learning model for STAR-RIS phase design, complemented by analytical approaches for BS beamforming and power allocation. Simulations using the simulation of urban mobility (SUMO) software confirm the superior performance of the proposed scheme in various scenarios while meeting STAR-RIS and NOMA requirements.

Robustness of reconfigurable intelligent surface (RIS) has been a concern due to potential hardware impairments (HWI) and imperfect channel state information (CSI) measurements caused by the numerous passive elements on board. Recent studies observe that the impairments not only introduce mis-alignment in phase adjustments but also affect the amplitude of reflected signals, which further complicates the issue. To address this issue, we introduce a novel deep reinforcement learning (DRL)-based discrete optimization framework aimed at mitigating various HWI and CSI imperfections in RIS-assisted millimeter-wave (mmWave) multi-input-single-output (MU-MISO) systems. Employing proximal policy optimization (PPO), our method discretely addresses HWI and CSI challenges without continuous relaxation. Simulation results demonstrate the superiority of our approach over the traditional optimal beamforming baseline in minimizing the worst-case mean squared error (MSE) of the signal received by the users. The code has been made open-source on GitHub, serving as a valuable reference for further research and application in RIS-assisted communication systems.

Precise vehicle positioning is the key foundation for advancing vehicle automation technology beyond level three. However, the conventional global positioning system (GPS) is susceptible to inaccuracies caused by environmental interference. Existing works for improving positioning accuracy either require fundamental infrastructure modification to replace GPS or utilize prior knowledge of environmental information to reduce interfer-ence, where both are impractical in the real world. To improve the GPS-based vehicle positioning system to provide more accurate coordinate estimates without prior knowledge of environmental information, we propose a self-supervised learning architecture composed of four learning methods: hierarchical density-based spatial clustering (HDBSCAN), graph convolution network (GCN), domain-adversarial neural network (DANN), and long short-term memory (LSTM). The proposed framework utilizes both spatial and temporal information in vehicle positioning. The simulation results within our proposed comprehensive framework demonstrate a significant improvement in the accuracy of vehicle coordinate estimates, with the estimation error mean decreasing by 46 % and the error standard deviation decreasing by 34 % compared to the baseline.

Vehicle-to-everything (V2X) communication is an essential technology for future vehicular applications. It is challenging to simultaneously achieve vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications, given the shared spectrum. Deep reinforcement learning (DRL)-based algorithms have been proposed for resource allocation in V2I and V2V designs. Existing DRL designs focus on the objectives of high-capacity V2I and high-reliability V2V links. In this study, a multi-agent DRL algorithm is proposed to maximize the sum capacity of V2I links while ensuring capacity fairness among the V2V links. The simulation results demonstrate the balance between the V2I–V2V objectives achieved by the proposed algorithm.

Frequency-modulation continuous wave (FMCW) radar has been employed to implement a non-contact vital sign monitoring system for future healthcare applications. This paper proposes a multi-target vital sign (heart rate and breath rate) detection scheme with limited channel information for single-input multiple-output (SIMO)-FMCW radar systems in multipath propagation environments. In the proposed method, multipath effect mitigation is first achieved by the decomposition of vital sign signals from self- and mutual-multipath interferences using the multichannel singular spectrum analysis (MSSA) algorithm. Then, the desired vital signs are estimated via the estimation of signal parameters via rotational invariance technique (ESPRIT). Simulation shows that the proposed scheme achieves superior performance in terms of the estimation error in multipath propagation environments.

In recent years, spectrum congestion has become a significant issue. Thus, significant attention has been paid to spectrum-sharing. The dual-function radar-communication (DFRC) system is an attractive solution for the spectrum-sharing problem. Existing studies primarily focus on transmitted beamforming at the base station (BS). In this study, we designed both the transmitted and received beamformers of a DFRC BS to perform multiple-input multiple-output (MIMO) radar sensing and multi-user multiple-input single-output (MU-MISO) communication employing hybrid beamforming. To address the difficulty of the primal problem, we recast the nonconvex design problem into a convex form and subsequently derive suboptimal solutions. Simulation results demonstrate satisfactory performance in terms of the sum-rate, interference mitigation, desired signal enhancement, and complexity of the proposed scheme.

Designing a signal-processing algorithm for frequency-modulated continuous-wave (FMCW) radar applications with advanced functionality remains a challenging problem. Specifically, traditional algorithms improve detection resolution by increasing bandwidth and contribute to inefficient spectral use and maximum range reduction. To strike a balance between the maximum detection range and range resolution, we propose a resolution improvement algorithm based on super-resolution techniques. The low-resolution detection results were used to infer the high-resolution data by emphasizing the hidden spatial correlations. Simulation results confirmed that the proposed algorithm possesses an outstanding ability to achieve high-resolution detection while employing reduced bandwidth, leading to two advantages: spectral efficiency and maximum detectable range.

Vehicle positioning is a key component of autonomous driving. The global positioning system (GPS) is the most commonly used vehicle positioning system currently. However, its accuracy will be affected by environmental differences and thus fails to meet the requirements of meter-level accuracy. We consider a coordinate neighboring vehicle positioning sys-tem (CNVPS) based on GPS, omnidirectional radar, and V2V communication ability to obtain additional information from neighboring vehicles to improve the GPS positioning accuracy of vehicles in various environments. We further use the concept of transfer learning (TL) wherein an adversarial mechanism is designed to eliminate the deviation of multiple environments to optimize vehicle positioning accuracy in multiple environments using one model. The simulation results show that, compared with the existing methods, the proposed system architecture not only improves the performance but also effectively reduces the amount of data required for training.

Millimeter wave (mmWave) is a key technology for fifth-generation (5G) and beyond communications. Hybrid beamforming has been proposed for large-scale antenna systems in mmWave communications. Existing hybrid beamforming designs based on infinite-resolution phase shifters (PSs) are impractical due to hardware cost and power consumption. In this paper, we propose an unsupervised-learning-based scheme to jointly design the analog precoder and combiner with low-resolution PSs for multiuser multiple-input multiple-output (MU-MIMO) systems. We transform the analog precoder and combiner design problem into a phase classification problem and propose a generic neural network architecture, termed the phase classification network (PCNet), capable of producing solutions of various PS resolutions. Simulation results demonstrate the superior sum-rate and complexity performance of the proposed scheme, as compared to state-of-the-art hybrid beamforming designs for the most commonly used low-resolution PS configurations.

Remote vital sign monitoring systems have attracted attention from researchers as a non-contact solution for the detection of health issues. This assumes significance during the epidemic outbreak, where infections can be transmitted through contact. This paper proposes a deep learning (DL)-aided weighted scheme, where observations are fused, for implementing a vital sign system for fast acquisition and accurate estimation by considering spatial correlations. The specially designed weighted technique properly fuses spatial features to reduce the required size of the observation signal, thereby accomplishing the goal of fast acquisition. Moreover, a convolutional neural network (CNN) is employed to extract hidden information for accurate vital sign detection by performing two-dimensional convolution operations, which effectively utilizes spatial diversity to improve detection performance. Experimental results show that the proposed data-fusion-based scheme achieves satisfactory performance with limited observations for fast acquisition. Furthermore, its performance is comparable to that of conventional contact equipment; the absolute error of 90% breathing measurements is less than 3 respirations per minute (rpm), and the absolute error of 75% heartbeat measurements is less than 3 beats per minute (bpm), thereby confirming the potential of the proposed scheme.

Designing hybrid beamforming transceivers in millimeter wave (mmWave) MIMO-OFDM systems with satisfactory performance and acceptable complexity is a challenging problem. The well-known weighted minimum mean square error manifold optimization (WMMSE-MO) algorithm offers desired performance but has high computational complexity. In this paper, we propose to apply the deep unfolding technique to the WMMSE-MO algorithm. The proposed deep unfolding model yields faster convergence to better solutions as compared to the original algorithm. Simulation results demonstrate remarkable spectral efficiency performance with reduced computational time complexity for the proposed scheme, under different hardware (RF chains) and algorithmic (inner/outer iterations) settings for a massive MIMO-OFDM system.

Beam-based wireless power transfer and Fog/edge computing are promising dual technologies for realizing wireless powered Fog computing networks to support the upcoming B5G/6G IoT applications, which require latency-aware and intensive computing, with a limited energy supply. In such systems, IoT devices can either offload their computing tasks to the proximal Fog nodes or execute local computing with replenishing energy from the dedicated beamforming. However, effective integration of these techniques is still challenging, where two new issues arise: energy-aware task offloading and signal interferences from spillovers of wireless beamforming. In this paper, we observe that the beam-ripple phenomenon, which takes advantage of beamformer defects to transfer energy to IoT devices, is the key to jointly addressing these two issues. Different from traditional SWIPT technology, as in our approach the stream is not separately divided into data/energy streams, but target IoT devices can potentially harvest the whole stream. Inspired by this phenomenon, we treat the collaborative energy beamforming and edge computing design as a strongly$\mathcal {NP}$-hard optimization problem. The proposed solution is an iterative algorithm to cascadingly integrate a polynomial-time$\left({1 - \frac {1}{e}}\right)$-approximation algorithm, which achieves the theoretical upper bound in approximation ratio unless$\mathcal {P} = \mathcal {NP}$, and an optimal dynamic programming algorithm. The numerical results show that the energy minimization goal among IoT devices can achieve, and the developed harvest-when-interfered protocol is practical in the wireless powered Fog computing networks.

Beamforming is a key technology in communication systems of the fifth generation and beyond. However, traditional optimization-based algorithms are often computationally prohibited from performing in a real-time manner. On the other hand, the performance of existing deep learning (DL)-based algorithms can be further improved. As an alternative, we propose an unsupervised ResNet-inspired beamforming (RI-BF) algorithm in this paper that inherits the advantages of both pure optimization-based and DL-based beamforming for efficiency. In particular, a deep unfolding technique is introduced to reference the optimization process of the gradient ascent beamforming algorithm for the design of our neural network (NN) architecture. Moreover, the proposed RI-BF has three features. First, unlike the existing DL-based beamforming method, which employs a regularization term for the loss function or an output scaling mechanism to satisfy system power constraints, a novel NN architecture is introduced in RI-BF to generate initial beamforming with a promising performance. Second, inspired by the success of residual neural network (ResNet)-based DL models, a deep unfolding module is constructed to mimic the residual block of the ResNet-based model, further improving the performance of RI-BF based on the initial beamforming. Third, the entire RI-BF is trained in an unsupervised manner; as a result, labelling efforts are unnecessary. The simulation results demonstrate that the performance and computational complexity of our RI-BF improves significantly compared to the existing DL-based and optimization-based algorithms.

Recently, non-contact vital sign estimating devices, which are used for health monitoring, have gradually gained interest among researchers. However, most of these devices have the disadvantages of high power consumption and high cost, which limit their practicality. Therefore, a less-expensive radar-based system is suggested for long-term health monitoring. Existing radar-based vital sign estimating schemes introduce unacceptable estimating errors. In order to improve the precision and stability, we employ Newtonized Orthogonal Matching Pursuit (NOMP) algorithm. NOMP provides better estimating results compared to existing schemes in vital sign estimation tasks. However, the performance of NOMP deteriorates severely under conditions of low signal-to-noise ratio, which causes poor power efficiency. In this study, we propose deep learning (DL)-aided NOMP schemes to tackle the aforementioned issue. Our simulation results and over the air measurements suggest that DL-aided NOMP schemes are superior to existing schemes.

By knowing the future price range, which is the difference between the closing price and the opening price, we can calculate the long or short positions in advance. This paper presents a Recurrent Neural Network (RNN) based approach to forecast the price range. Compared to other methods based on machine learning, our method puts greater focus on the characteristics of the stock data, such as the zero-crossing rate (ZCR), which represents the ratio where the sign of the data changes within a time interval. We propose a decision-making method based on an estimate of the ZCR to enhance the ability to predict the stock price range, and apply our method to the Standard & Poors 500 (S&P500) stock index. The results indicate that our method can achieve better outcomes than other methods.

In Asian countries, seals are widely used for authenticating the identity of a person or organization. Therefore, the ability to efficiently verify whether a seal is either genuine or forged is important. We propose an effective method of verification based on Hough transformation to approximate the imprint borders and the four vertexes, and use geometric transformation to align the perspective of the detected imprint image with the genuine imprint. After the edge-difference images between the original image and the detected image are created, distance transformation and connected-component labeling are applied. Finally, the number of edges in the connected component and the distance to the closest point in the edge of original image are used to calculate the input vector for the SVM (support vector machine). The imprint is then determined to be either genuine or forged. The experimental results show the effectiveness of the proposed verification approach.

The Society of Automotive Engineers (SAE) emphasizes that radars have become a critical technology. With the consideration of application scenarios and cost, increasing importance has been attributed to millimeter Wave (mmWave) radars. Because of the unique features of simultaneous detection and communication, Orthogonal Frequency- Division Modulation (OFDM) radars have been discussed frequently in the literature. In this paper, under the architecture of an OFDM radar, we propose a novel protocol which divides different radar users (i.e., different vehicles) into different logical channels by time and frequency. Following the rules of the protocol, a non- cooperative interference avoidance logical channel selection method is also proposed to choose a logical channel with the least inter-carrier interferences (ICIs). Simulation results show that the proposed logical channel selection method can choose a nearly optimal channel in a short time with a high probability.

The well-known cell-averaging constant false alarm rate (CA-CFAR) scheme and its variants suffer from masking effect in multi-target scenarios. Although order-statistic CFAR (OS-CFAR) scheme performs well in such scenarios, it is compromised with high computational complexity. To handle masking effects with a lower computational cost, in this paper, we propose a deep-learning based CFAR (DL- CFAR) scheme. DL-CFAR is the first attempt to improve the noise estimation process in CFAR based on deep learning. Simulation results demonstrate that DL-CFAR outperforms conventional CFAR schemes in the presence of masking effects. Furthermore, it can outperform conventional CFAR schemes significantly under various signal-to-noise ratio conditions. We hope that this work will encourage other researchers to introduce advanced machine learning technique into the field of target detection.

Fog computing, evolves from the cloud and migrates the computing to the edge, is a promising solution to meet the increasing demand for ultra-low latency services in wireless networks. Via the forward-looking perspective, we advocate a Fog Radio Access Network (F-RAN) model, which leverages the existing infrastructure such as small cells with limited computing power, to achieve the ultra-low latency by joint edge computing and near-range communications across multiple Fog groups. We formulate the low latency design as an NP-hard optimization problem, which demonstrates the tradeoff between communication and computing in the time domain. Due to each F-RAN node's potential as each user's master F-RAN node with 1) different self computing power; and 2) different cooperative power of assisted F-RAN nodes, we first tackle globally optimized master F-RAN node selection for each user and propose a latency-driven cooperative Fog algorithm with dynamic programming solution for simultaneous selection of the F-RAN nodes to serve proper heterogeneous Fog resource allocation for multi-Fog groups. Considering the limited heterogeneous Fog resources shared among all users, we propose the one-for-all strategy for every user putting him/herself into others' shoes and reaching a “win-win” outcome. The numerical results show that the low latency services can be accomplished by F-RAN via latency-driven Fog cooperation approach.

In NB-IoT systems, UEs with poor signal quality employ more repetitions to compensate for additional signal attenuation. Excessively high CE levels and repetitions of UEs lead to wastage of valuable wireless resources, whereas inadequate CE levels and repetitions result in data retrieval failure at the receiving end. Therefore, a machine learning-based adaptive repetition scheme for a 3GPP NB-IoT system is proposed in this work to effectively improve overall network transmission efficiency. The results of simulation show the effect of the discount factor? on the convergence behavior of the proposed scheme, with a lower discount factor value denoting the myopic behavior of the proposed scheme, which results from the fact that it places more emphasis on immediate rewards. And the propose scheme is capable of effectively improving the average spectral efficiency.

In this paper, an energy harvesting cognitive network with two-way overlay relaying is discussed, whose secondary users harvest the radio frequency energy from ambient primary information-bearing signals while obtain the spectrum sharing opportunities by relaying those signals. In addition, an adaptive power and time usage (APTUS) energy harvesting relay protocol is proposed as a flexible method that absorbs the essence and discards the dross of the existing schemes for the comprehensive applications. The outage probabilities of primary and secondary users with the APTUS protocol are also derived, and then verified by computer simulations under Nakagami-m frequency-selective fading channels.

Cyber-physical systems, such as smart grids, have received lots of attention recently. Unfortunately, security breaches in cyber-physical systems can result in catastrophic consequences, thus needing to be carefully monitored. For example, abnormal voltage quality events, which are more likely to happen because of unstable renewable energy sources in smart grids, harm delicate electronic devices. We thus focus on the quickest classification, or multi-hypothesis quickest change detection, which jointly detects and classifies multiple abnormal events. Both the classification delay and misclassification probability need to be low. Multiple smart meters are adopted, where each meter transmits its local decision to a fusion center for making the final decision. For energy saving, the bandwidth (link capacity) between each meter and the fusion center is limited to be one bit. Moreover, some meters may be faulty and mislead the final decision. To combat these faulty meters under the limited bandwidth, a code-based framework for quickest classification is proposed. Our contribution is two-fold. First, a new local decision rule based on the stochastic ordering theory is proposed. Compared with existing matrix-cumulative-sums algorithm, the newly proposed local decision rule has lower complexity and comparable performance. Second, a new fusion method based on codebook switching and minimum Hamming distance rule is developed. Compared with existing fault-tolerant methods, the newly-developed method can significantly lower the misclassification probabilities.

The goal of the smart grid is to develop a more reliable, secure, and environmentally friendly power grid. Unfortunately, power quality (PQ) events are more likely to happen due to unstable renewable energy sources in smart grids. We thus focus on the quickest classification, or multihypothesis quickest change detection, which jointly detects and classifies multiple abnormal PQ events. Both the classification delay and misclassification probability are aimed to be minimized. Multiple smart meters in the grid are used, where each meter transmits its local decision to a fusion center for making final decisions. For energy saving, the capacity between each meter and the fusion center is limited to be one bit. Moreover, some meters may be faulty and misleading the final decision. To combat these faulty meters under limited link capacity, a code- based framework for quickest classification is proposed. Our contribution is twofold. First, new local decision rule based on stochastic ordering theory is proposed, which has lower complexity and competing performance compared with existing matrix Cumulative Sums (CUSUM). Second, a new fusion method based on codebook switching and minimum Hamming distance rule is developed, which can significantly lower the misclassification probability.

In this work, we propose a recursive semiblind channel estimation algorithm to mitigate the ill effects of pilot contamination in massive multi- user multiple-input multiple output (MU-MIMO) systems. In the proposed approach, we first project the received signals onto the subspace with minimal interference, where a low-complexity modified power method is employed to recursively determine the bases of this subspace. Specifically, a few pilot symbols are applied to generate initial channel estimate of the projected channel coefficients. Finally, a recursive algorithm is introduced to alternatively detect the data symbols and update the channel estimates. Notably, the proposed scheme requires neither cell cooperation nor pilot scheduling. Simulation results demonstrate the performance superiority compared with the existing works.

Fog computing is emerging as one promising solution to meet the increasing demand for ultra-low latency services in wireless networks. Taking a forward-looking perspective, we propose a Fog-Radio Access Network (F-RAN) model, which utilizes the existing infrastructure, e.g., small cells and macro base stations, to achieve the ultra-low latency by joint computing across multiple F-RAN nodes and near-range communications at the edge. We treat the low latency design as an optimization problem, which characterizes the tradeoff between communication and computing across multiple F-RAN nodes. Since this problem is NP-hard, we propose a latency-driven cooperative task computing algorithm with one-for-all concept for simultaneous selection of the F-RAN nodes to serve with proper heterogeneous resource allocation for multi-user services. Considering the limited heterogeneous resources shared among all users, we advocate the one-for-all strategy for every user taking other's situation into consideration and seek for a "win-win" solution. The numerical results show that the low latency services can be achieved by F-RAN via latency-driven cooperative task computing.

Massive multiple-input multiple-output (massive MIMO) has been studied to improve the throughput in cellular communication systems via spatial degrees of freedom and array gain. However, in massive MIMO systems, due to the hardware cost considerations, it is infeasible to install a radio frequency (RF) chain for each antenna. To deploy massive MIMO within reasonable hardware cost, hybrid precoding is proposed in which the signal is processed by a low-dimensional digital baseband precoder and a high-dimensional analog RF precoder. On the other hand, cooperative transmission is another way to enhance system throughput, in which base stations (BSs) can jointly transmit to a user. In this paper, we first investigate the hybrid precoder design for cooperative multiuser massive MIMO systems. We propose hybrid precoding algorithms for two performance metrics: weighted sum rate and max-min fairness. We also propose a low complexity user pairing for both precoder designs. Numerical results show that the proposed hybrid precoding algorithms outperform existing hybrid precoding algorithms for non-cooperative transmissions, and perform closely compared with existing digital precoding algorithms for cooperative transmissions, when the number of users does not exceed the total number of RF chains of the BSs in cooperative (i.e., the largest possible channel rank). In summary, this paper provides a hybrid precoder design guideline for existing and developing cellular communication systems, including Long Term Evolution-Advanced and the fifth generation cellular systems.

In this paper, we propose a novel transmission approach, namely, analog precoding-aided virtual space modulation (APAVSM), and its corresponding multimode hybrid precoder designs in the multiple-input multiple-output system. We consider the system equipped with a single radio frequency chain and a phase shift network. Our proposed designs can attain benefits of both beamforming and spatial modulation, and therefore enable the significant signal-to-noise power ratio enhancement with efficient degrees of freedom utilization and low cost. We elaborate APAVSM and formulate the multimode hybrid precoder design problem. To solve the problem, we impose restrictions on the number of candidate modes for selection, and propose the hybrid precoder design approach in which two non-convex max-min problems are solved sequentially by iterative approaches. To trade off between error rate and complexity, we propose alternative designs by reducing the dimensionality and/or relaxing constraints. Moreover, by the concept of beamspace, heuristic algorithms are proposed with extremely low complexities. We offer complexity analyses for proposed design approaches. Simulations in Ralyigh fading and millimeter-wave (mmWave) channels are used to evaluate the proposed designs. Results show that our designs can outperform the existing SM-MIMO systems, and provide the feasible operation in mmWave channels with severe path loss. Moreover, the superiority of proposed designs to the conventional precoding-aided MIMO systems is numerically demonstrated.

Massive multi-user multiple-input multiple-output (MU-MIMO) systems are a promising solution for achieving high throughput and robust transmission in next generation mobile communications. Achieving the optimal transceiver design in such systems requires an accurate knowledge of the channel state information. However, in massive MU-MIMO systems, the quality of the channel estimates is often degraded by pilot contamination. In this paper, we propose a low-complexity semiblind channel estimation algorithm to mitigate the ill effects of pilot contamination. In the proposed approach, the received signals are first projected onto the subspace with minimal interference, where the bases of this subspace are determined recursively via a low-complexity modified power method. An initial estimate of the projected channel coefficients is then made based on a small number of pilot symbols. Finally, data symbols are detected and the channel estimation is refined alternatively. Compared with existing channel estimation methods, the proposed algorithm has lower complexity due to the subspace projection and innovation process. An asymptotic analysis reveals that the mean square error of the channel estimates is inversely proportional to the length of the data symbols. Simulation results demonstrate that the proposed algorithm outperforms the existing works and alleviates the pilot contamination effects effectively.

Technologies in millimeter wave (mmWave) communications have gained increasing attention because of its potential to provide substantially extra bandwidth. The main challenges of mmWave communications include high propagation attenuation and sparse scattering propagation channel. A promising technique to cope with propagation attenuation is to adopt antenna array, and use the hybrid transceiver architecture to exploit the array gain for lower hardware costs. On the other hand, the sparse scattering channel causes large fluctuations in link quality. Cooperative transmission is an effective approach for the link quality fluctuation problem, in which multiple Txs can transmit to the same Rx of poor receiving quality. The design of precoder for cooperative transmission in hybrid precoding architecture is crucial in improving system performance. In this paper, we propose the two-stage hybrid precoder design for cooperative multiple input single output (MISO) systems where the design goal is to ensure every Rx has good receiving quality. Because the number of Rx supported by a Tx is limited, we also propose a low computational complexity Tx-Rx pairing algorithm. Numerical results show that performances of the proposed algorithms are near-optimal, and outperform existing hybrid precoding algorithms.

With the increasing demand for ultra-low latency services in 5G cellular networks, fog with edge computing is one of promising solutions which migrate the computing from the cloud to the edge of the network. Rather than relying on the distant cloud or additional servers, we propose the Fog-Radio Access Network (F-RAN), which leverages the current infrastructures in the radio access network, such as small cells and macro base stations, to pursue the ultra-low latency by joint powerful computing of multiple F-RAN nodes and near-range communications at the edge. The optimization problem is firstly formulated to tackle the tradeoff between communication and computing resources into time domain within distributed computing scenario, and then we propose a cooperative task computing operation algorithm to simultaneously decide how many F-RAN nodes should be selected with proper communication resource allocation and computing task assignment. The numerical results show that the ultra low-latency services can be achieved by F-RAN via cooperative task computing.

The distinct ability of spatial modulation (SM) to effectively utilizing spatial degrees of freedom with only a single radio frequency chain renders it promising for millimeter wave (mmWave) communications. In this work we investigate the transmitter design employing the SM concept in mmWave multiple-input multiple-output (MIMO) systems. To introduce beamforming gain while maintaining the advantages of SM, we apply the virtual antenna concept to SM and construct spatial signatures with the aid of analog beamforming. For a given configuration of the space-signal constellation, we formulate an optimization problem for designing the analog beamforming matrix and propose two approaches via exploiting characteristics of mmWave channel and transmitter architecture to acquire effective solutions with low complexity. To further improve the performance, we optimize the configuration of the spacesignal constellation by selecting the optimal sizes of spatial and signal constellations while guaranteeing the transmission rate requirement. The overhead and design complexity of proposed design approaches are analyzed. Besides, we exploit simulations to evaluate the proposed designs, and briefly compare between analog beamforming aided SM-MIMO and conventional MIMO to show advantages of our designs.

The demand for electrical power is growing tremendously, which poses grand challenges to modern energy systems. The telecommunications industry takes a significant part in the global power consumption. In this work we introduce the multi-beam zooming concept as a new perspective to the energy-efficient 5G network, where the multi-beam zooming technique incorporates the beam zooming and multi-beam beamforming methods. This approach enables multiple beams to be casted on different users with adjustable amplitudes of individual beams based on user locations obtained using PRS in LTE. We developed an LTE simulation framework to quantitatively evaluate the advantages of the multi-beam zooming approach in terms of the power savings. Using the realistic mobility traces, we analyze the effect of crucial system setting of antenna tapering method and its effect on the potential power savings achievable at the base stations.

In this paper, we investigate the bit error rate (BER) for multicast multiple-input multiple-output (MIMO) systems, employing spatial modulation (SM) and its variants, called multicast SM-type MIMO systems, in Rayleigh fading channels. The system BER, here, is derived by first attaining the BER of the worst receiver of each channel realization set, and then averaging over all possible sets. We first consider the uncorrelated channels. By exploiting the system statistics, a tight BER upper bound is proposed and the diversity is discussed for the systems. We then perform the asymptotic analysis, and show that the BER of the multicast SM-type MIMO system can be alternatively analyzed by analyzing the simple point-to-point SM-type MIMO system with Weibull fading channels. Through this property, a closed-form asymptotic BER upper bound is provided and the impact of the receiver number on BER is analyzed. Subsequently, our investigation is extended to correlated channels where all the receivers share the same correlation statistics. The BER analysis is performed again through the framework similar to the uncorrelated case. In the analysis, the tight upper bound is derived and the effect of correlations is analyzed. Moreover, we provide an explicit expression for the SNR degradation caused by the receive correlation through the analysis. Finally, simulations are exploited to evaluate the BER of the multicast SM-type MIMO systems and validate the analyses.

In this paper, we present a fast Fourier transform algorithm over extension binary fields, where the polynomial is represented in a non-standard basis. The proposed Fourier-like transform requires O(h lg(h)) field operations, where h is the number of evaluation points. Based on the proposed Fourier-like algorithm, we then develop the encoding/decoding algorithms for (n = 2m, k) Reed-Solomon erasure codes. The proposed encoding/erasure decoding algorithm requires O(n lg(n)), in both additive and multiplicative complexities. As the complexity leading factor is small, the proposed algorithms are advantageous in practical applications. Finally, the approaches to convert the basis between the monomial basis and the new basis are proposed.

The use of nuclear energy to generate electric power is crucial to meet the high energy demand of a modern economy. In newly constructed nuclear power plants (NPPs), the trend among control systems is to replace the obsolete analog hard-wired systems with the contemporary digital and cyber-based systems. Therefore, cyberphysical security as well as dependability are critical issues in safety critical NPPs. In this paper, we present different levels/layers of protection to manage cyber/physical security. We also discuss the interrelationship between cyber and physical attacks. We adopt generalized stochastic Petri nets to quantitatively evaluate the intrusion probability. We then propose a new cyberframework and show that the proposed framework not only prevents cyberattacks but also conforms to cybersecurity regulations. We also propose a physical framework to prevent potential physical attacks. Finally, we discuss dependability through three metrics, i.e., reliability, maintainability, and availability. A case study is presented to demonstrate that the proposed cyberframework is highly dependable through analyzing steady-state probabilities.

In this paper, we propose coding-aided K-means clustering (CKMC) blind transceiver for space shift keying (SSK) multiple-input multiple-output (MIMO) systems, where the training of channel state information (CSI) is not required for detection. For the scenario where transmitter is with limited processing capability and limited power such as Internet Of Things (IOT) and wireless sensor network (WSN), SSK is preferable to typical MIMO due to its simplicity and improved energy efficiency. The proposed CKMC blind communication trades off receiver complexity for training overhead, which provides better spectral efficiency compared to training-based transceiver. In CKMC, the blind communication problem is converted to the problems of clustering and permutation; for the clustering problem, we propose K-means clustering (KMC) detector to reduce detection complexity; for the permutation problem, we propose to perform depermutation with the aid of channel decoding. The analysis of CKMC blind transceiver is conducted, and the verification of performance of CKMC is presented in the simulations section. The results show that the performance of CKMC blind communication can closely approach the performance of optimal receiver with perfect CSI under certain scenarios.

In this paper, we consider the network coding (NC) enabled three-phase protocol for information exchange between two users in a wireless two-way (bidirectional) relay network. Modulo-based (nonbinary) and XOR-based (binary) NC schemes are considered as information mixture schemes at the relay while all transmissions adopt pulse amplitude modulation (PAM). We first obtain the optimal constellation mapping at the relay that maximizes the decoding performance at the users for each NC scheme. Then, we compare the two NC schemes, each in conjunction with the optimal constellation mapping at the relay, in different conditions. Our results demonstrate that, in the low SNR regime, binary NC outperforms nonbinary NC with 4-PAM, while they have mixed performance with 8-PAM. This observation applies to quadrature amplitude modulation (QAM) composed of two parallel PAMs.

In this paper, we investigate the configuration (i.e., both mode and antenna) selection and precoder design to improve the multicast multiple-input multiple-output systems employing spatial modulation. We first elaborate the advantages of configuration selection and then propose two selection schemes to maximize minimum Euclidean distance with low complexity. By configuring the selection problem as a tree search problem and adopting tree pruning technique, the first selection scheme attains low complexity while obtaining the optimal solution. In the second scheme, by pruning the less dominant nodes and approximating the signal constellation, the Euclidean distance computation is converted to a simple table look-up operation. Combining this with the first scheme, the second scheme attains even lower complexity without optimality guarantee. Besides configuration selection, the precoder design is investigated, and a precoder design approach is proposed to improve the system. Finally, simulation results demonstrate the efficacy of the proposed approaches in the bit error rate improvement and complexity reduction.

In this paper, the distributed channel access schemes for ALOHA-based cognitive radio networks are considered. In the considered system, time is divided into frames, which are further divided into sensing phase and transmission phase. We derive channel sensing policy in the sensing phase, and channel access policy in the transmission phase for a secondary user (SU) to maximize its throughput. To mitigate high complexities of the above scheme, we propose a threshold-based channel access scheme. In this scheme, an appropriate threshold is set based on channel occupancy information and channel state information, and only channels providing potentially high throughput will be sensed and accessed. The proposed schemes are fully distributed, i.e., no extra information exchange is needed among SUs, which is a highly desired and beneficial property. Simulation results confirm that the proposed schemes outperform prior random access schemes.

In this paper, we propose two generalized precoder designs to enhance the bit error rates for the general category of spatial modulation (SM) in multiple-input multiple-output (MIMO) systems with channel state information at the transmitter (CSIT). We investigate typical SM-MIMO systems and propose two optimization formulations for designing precoders. Our design rationale for the first formulation is to maximize the minimum Euclidean distance among codewords; for the second formulation, it is to minimize the total signal power for the lower-bounded Euclidean distances among codewords. Since both formulations are non-convex and their optimal solutions are generally intractable, we propose an algorithm that acquires effective solutions by iteratively solving the alternative convex problem linearized and approximated from the original non-convex problem. Discussions on complexity analysis, performance comparisons, design challenges, and robustness in imperfect CSIT are then provided. By generalizing the design formulations, the proposed precoder designs can be extended to generalized SM, which completes the investigation for virtually all SM-type systems. Simulation results show that the proposed designs improve the performance of SM-/GSM-MIMO systems and outperform existing precoding methods with a potentially higher complexity cost.

Regenerating codes represent a class of block codes applicable for distributed storage systems. The [n, k, d] regenerating code has data recovery capability while possessing arbitrary k out of n code fragments, and supports the capability for code fragment regeneration through the use of other arbitrary d fragments, for k ≤ d ≤ n - 1. Minimum storage regenerating (MSR) codes are a subset of regenerating codes containing the minimal size of each code fragment. The first explicit construction of MSR codes that can perform exact regeneration (named exact-MSR codes) for d ≥ 2k - 2 has been presented via a product-matrix framework. This paper addresses some of the practical issues on the construction of exact-MSR codes. The major contributions of this paper include as follows. A new product-matrix framework is proposed to directly include all feasible exact-MSR codes for d ≥ 2k - 2. The mechanism for a systematic version of exact-MSR code is proposed to minimize the computational complexities for the process of message-symbol remapping. Two practical forms of encoding matrices are presented to reduce the size of the finite field.

We propose the application of cosine windowing for the orthogonal frequency-division multiplexing (OFDM) systems to self-cancel intercarrier interference (ICI) in fast time-varying channels prior to receptions. With a time-domain cosine window immediately after the inverse discrete Fourier transform (IDFT) unit in OFDM transmitters, the ICI fractions from adjacent subcarriers significantly cancel one another at the expense of the orthogonality violation among subcarriers in the main lobe. As a result, the frequency-domain channel matrix reshaped by the cosine windowing can be closely approximated to a strictly banded matrix. In the complex exponential basis expansion model (CE-BEM), we present the estimation of the channel matrix with the assistance of the pilot clusters. Simulation results show that the receivers implementing the CE-BEM channel estimation and the low-complexity block turbo minimum mean square error (MMSE) equalization perform with considerably lower bit error rates (BER) even in very fast time-varying channels.

In this paper, we present a new basis of polynomial over finite fields of characteristic two and then apply it to the encoding/decoding of Reed-Solomon erasure codes. The proposed polynomial basis allows that h-point polynomial evaluation can be computed in O(hlog2(h)) finite field operations with small leading constant. As compared with the canonical polynomial basis, the proposed basis improves the arithmetic complexity of addition, multiplication, and the determination of polynomial degree from O(hlog2(h)log2log2(h)) to O(hlog2(h)). Based on this basis, we then develop the encoding and erasure decoding algorithms for the (n=2r, k) Reed-Solomon codes. Thanks to the efficiency of transform based on the polynomial basis, the encoding can be completed in O(nlog2(k)) finite field operations, and the erasure decoding in O(nlog2(n)) finite field operations. To the best of our knowledge, this is the first approach supporting Reed-Solomon erasure codes over characteristic-2 finite fields while achieving a complexity of O(nlog2(n)), in both additive and multiplicative complexities. As the complexity leading factor is small, the algorithms are advantageous in practical applications.

Due to safety reasons, using mobile phones while driving is prohibited in many countries. Research has also shown that motorcycle riders are 20 times more likely to be killed in a crash than vehicle occupants. Therefore, it is more critical to restrict the use of mobile phones of motorcycle drivers than car drivers. There are some studies that focus on how to distinguish phone use between a driver and other passengers in a car. The techniques used for cars, however, are not always applicable to motorcycles. In this paper, we propose a way to detect phone use of motorcycle drivers. By using two low-cost Bluetooth emitters, mobile phones of the driver and the passenger can measure the signal strengths and decide their locations. We have conducted extensive experiments with various smartphones. The results show that on average we can achieve 96% accuracy.

We investigate the detector design in generalized space shift keying (GSSK) modulation for large MIMO systems. An orthogonal matching pursuit (OMP) based detector design is adopted due to its low complexity compared with the maximum likelihood (ML) detector. To improve the performance of the OMP algorithm, our first design is to propose an equalizer at the receiver in order to orthogonalize the equivalent channel matrix; our second design is to propose an equalizer whose columns pursue orthonormality to the columns of the channel matrix. We obtain closed-form expressions of the equalizers under the two design objectives. Simulation results demonstrate the performance superiority compared with the standard OMP algorithm.

In multiple-input multiple-output (MIMO) systems adopting space shift keying (SSK), the use of adaptive precoder on transmitter offers the opportunity to improve its performance significantly. One major challenge in precoder operation is the difficulty in obtaining the full channel state information on transmitter (CSIT). In this work, we investigate the precoder design for SSK-MIMO systems with limited feedback through using the codebook for the precoding. We formulate a distortion metric to evaluate the quality of a codebook based on the maximum minimum Euclidean distance criterion and propose a codebook design criterion accordingly. Two effective codebook design algorithms are proposed based on thorough analysis of the criterion. Simulation results show the performance improvement of the proposed codebook-based precoding, and also support our analyses on the codebook design.

This paper studies spectrum trading in cognitive radio networks in which multiple service providers (SPs) sell unused spectrum to multiple unlicensed secondary users (SUs). Motivated by the nature of the problem with new considerations, spectrum trading is modeled as a multi-leader multi-follower expected Stackelberg game with two levels of competition. The SPs as leaders compete in offering subscription prices (upper-level subgame) and the SUs as followers compete in selecting service from the SPs (lower-level subgame). The lower-level subgame incorporates the time-varying spectrum availability as the external state so that the proposed scheme does not require knowledge of dynamic spectrum availability. To achieve self-organized network operation, we propose decentralized, stochastic learning-based algorithms for the game. The convergence properties of the proposed algorithms toward the Nash equilibrium (NE) are theoretically and numerically studied. The proposed scheme demonstrates good utility performance for the SUs as compared to other service selection schemes.

The performance of smart grid applications such as advanced metering infrastructure (AMI) and demand response management (DRM) can be improved by exploiting wireless communication technologies. The communication data loading and data losses in smart grid impact the system. In this work, we propose the routing protocol which adopts the cooperative transmission architecture in smart grid communications. Our proposed scheme enables the cooperative nodes to encode and forward the packets through the distributed packet-level erasure coding. The experimental results indicate that our proposed erasure code embedded routing protocol can obtain the better throughput than the conventional routing protocols.

This paper considers a multi-way relay network in which multiple users intend to achieve full information exchange with one another with the aid of a single relay. A general method for designing the transmission protocol with binary physical- layer network coding (PNC) is developed based on a tree representation. Different transmission schemes are analytically and numerically examined in terms of the decoding strategy, throughput performance, and energy consumption. It is shown that distributing the load of transmissions unevenly among users may achieve a better network throughput performance although some users will consume remarkably more energy than others. A systematic approach to designing the transmission scheme such that minimum error probability is achieved while some specified energy constraint is satisfied is also proposed.

This work investigates the configuration selection in IA-aided UL CoMP systems which pursues the maximal achievable sum rate. The configuration is defined by the number of data streams transmitted in each user. Intuitively, the solution can be found by exhaustively calculating the achievable sum rate for all the possible configurations, and select the configuration leading to the maximal achievable sum rate. The exhaustive search method is infeasible due to its high complexity. Based on the characteristics of IA-aided UL CoMP systems, an algorithm is proposed to obtain the configuration with extremely low complexity, and the proposed approach achieves comparable performance as in exhaustive method.

How to effectively exchange information between parties in a disaster management system is one of the fundamental challenges to support timely and efficient disaster response and relief. Specifically, the timeliness, scalability, and availability are three desirable features for information exchange. We call the framework to support information exchange with the three features an Open Information Gateway (OIGY). In this paper, we present and experiment the mechanisms to recover communicaiton service during and after disasters for Open Information Gateway. The designed mechanisms adopt long distance radio to support tele-communication and computer networks, dynamic power adjustment for radio stations to maximize the radio recovery with minimal interference, and dynamic MESH routers to connect devices located in different networks. We experimented the proposed mechanims on a physical communication testbeds to measure the delay for service recovery and reliability of the proposed mechanism.

The authors investigate linear transceiver design in uplink (UL) coordinated multipoint transmission and reception (CoMP) multiple‐input multiple‐output (MIMO) systems with joint detections. A two‐stage design algorithm is proposed by exploiting the technique of interference alignment, optimising the structure of the effective channel and employing power loading, with the goal to achieve high throughput and convergence performance. In contrast to conventional CoMP transceiver design, which is investigated under a predefined number of data streams transmitted by each user, the authors further investigate the selection of the number of data streams, called the configuration selection, and propose a corresponding low‐complexity algorithm. By combining the proposed linear transceiver algorithm and low‐complexity configuration selection algorithm, this work presents a new practical framework for linear transceiver design in UL CoMP MIMO systems. The simulation results confirm that the proposed algorithms achieve higher sum‐rate performance than prior linear transceivers used in UL CoMP MIMO systems. Furthermore, the proposed transceiver offers comparable performance to existing IA‐aided transceivers with significantly faster convergence.

The$(n,k,d)$regenerating code is a class of$(n,k)$erasure codes with the capability to recover a lost code fragment from other$d$existing code fragments. In this paper, we focus on the design of exact regenerating codes at minimum bandwidth regenerating (MBR) points. For$d=n-1$, a class of$(n,k,d=n-1)$exact-MBR codes, termed as repair-by-transfer codes, have been developed in prior work to avoid arithmetic operations in node repairing process. The first result of this paper presents a new class of repair-by-transfer codes via congruent transformations. As compared with prior works, the advantages of proposed codes include: i) the minimum of field size is significantly reduced from$n \atopwithdelims ()2$to$n$; ii) the encoding complexity is decreased from$n^4$to$n^3$. Our simulation results show that the proposed code achieves faster operations than the prior approach does under large$n$. The second result of this paper presents a new form of coding matrix for product-matrix exact-MBR codes. The proposed coding matrix includes the following advantages: i) the minimum of finite field size is reduced from$n-k+d$to$n$; ii) the fast Reed-Solomon erasure coding algorithms can be applied on the proposed exact-MBR codes to reduce the time complexities.

In this paper, we study the iterative decoding of distributed channel encoded fixed-length codes for power-efficient cooperative transmissions. By exploiting the technique of iterative source channel decoding (ISCD), the iterative decoding for cooperative transmissions exploits two types of the redundant information: the residual redundancy inherently remaining in the source codes, and the artificial redundant information provided by the distributed channel coder. To avoid the information loss on the bit-to-symbol conversion, we adopt symbol-level ISCD scheme consisting of the symbol-level channel decoder and the symbol-level source decoder. Our proposed iterative decoding can improve parameter signal-to-noise ratio (PSNR) performances by iteratively refining the extrinsic information for the source and the distributed channel decoders. Furthermore, the proposed source decoding algorithm based on the symbol-level BCJR algorithm enables the iterative decoding to efficiently exploit the extrinsic information conveyed by relay and source nodes. Simulation results show that the proposed iterative decoding scheme with the proposed source decoder and the symbol-level BCJR channel decoder delivers the robust power-efficient performance, and outperforms conventional decoding schemes.

This paper presents a new class of Blockwise-Lattice-Reduction (BLR) aided Tomlinson-Harashima precoders (BLR-THPs) for multiuser multiple-input-multiple-output (MU-MIMO) downlink communications with clusters of correlated channels. The proposed BLR-THPs take the clustering information into account by first decoupling the overall downlink channel into multiple subchannels (one for each cluster) before applying the lattice-reduction technique to each subchannel. In comparison to the conventional LR-THPs, the proposed BLR-THPs require much lower complexity as the computationally intensive LR procedure in the LR-THPs is now effectively approximated by multiple LR procedures with much lower dimensionality. We present a number of new precoders that belong to this class and then compared them with the existing THPs with or without LR through extensive computer simulations.

Due to the dynamic interruptions from the primary users (PUs), the secondary user (SU) (i.e., cognitive radio) network is highly dynamic with fast varying radio resources. Once a PU requests to occupy the licensed spectrum, all of affected SUs (i.e., those SUs who opportunistically access the licensed spectrum) are mandated to immediately terminate the spectrum usage and switch their data transmissions to other unoccupied spectra to avoid interference with PUs. During the spectrum transition phase, the severe throughput degradation may occur in SU networks. In this paper, we proposed a local rerouting and channel recovery (LRCR) scheme to dynamically search the rerouting paths and available channels for those affected SUs. When a channel is occupied due to the request of a PU, the SUs who use the same spectrum as the PU instantly perform the proposed LRCR scheme to find an unoccupied path for data rerouting. The proposed LRCR scheme can be combined with the existing robust topology control approach. Simulation results demonstrate that the proposed scheme can be effectively applied to the existing robust topology control approach and efficiently enhance robustness and network throughput.

An efficient interference alignment (IA) aided transceiver design algorithm for uplink (UL) coordinated multipoint (CoMP) systems is proposed to mitigate interference. We use the block QR decomposition (BQRD) to resolve the interdependency of precoders among user equipment (UE) through the successive interference cancellation (SIC) technique. To further improve the efficiency, an additional constraint is employed by a projection operation. Simulation results show that the proposed algorithm substantially reduces iterations and obtain comparable performance as in conventional approaches.

Regenerating code represents a class of erasure codes applicable to distributed storage systems. An [n, k, d] regenerating code admits the recovery of the message from any k out of the n encoded fragments, and the recovery of an erasure fragment from other arbitrary d valid fragments, for k ≤ d ≤ n - 1. Minimum Storage Regenerating (MSR) code is the regenerating code attaining the minimal storage requirement. By product-matrix framework, the [n, k, d ≥ 2k-2] exact-MSR code is presented by Rashmi et al. This paper presents a novel version of exact-MSR codes for the same set of parameters. As compared with previous works, the major contributions of work include: i). An approach is proposed to directly include the d > 2k - 2 Exact-MSR codes in the product-matrix representation; ii). The required size of the finite field is reduced to n from n(d - k +1). The proposed coding techniques further improve the practicability of Exact-MSR codes.

The signal-to-leakage-and-noise ratio (SLNR) based precoding is a promising linear precoding scheme in multiuser (MU) downlink multiple-input-multiple-output (MIMO) communications. It gains its popularity recently as it nicely decouples the design problem and allows for a closed-form solution. In this paper, we extend the idea to nonlinear precoding and propose a closed-form SLNR- based Tomlinson-Harashima precoder. The proposed design is derived from a more general form of the simultaneous diagonalization conditions, allowing the the effective SINR among received streams of the same user to be more balanced. A new user-ordering algorithm is also developed for the proposed design based on the max-min SINR criterion to further improve the average error rate performance and the fairness among users. The performance advantages of the proposed design are quantified through extensive computer simulations.

This paper proposes a clustering-based blind detector for multiple-input multiple-output system using space shift keying modulation. First, we convert the blind detection problem to a clustering problem while considering block fading channel. Second, we use the well-known k-means clustering algorithm to design the blind detector. Third, the proposed k-means clustering detector for a blind receiver can provide comparable performance to that of the optimal receiver with perfect channel state information under the conditions of sufficient channel coherent time and sufficient random initializations of the k-means clustering algorithm. Simulations are conducted to demonstrate the performance of the proposed detector.

We consider an amplify-and-forward multiple-input multiple-output relay system with a direct source- destination link. We adopt the minimum-mean-square- error (MMSE) criterion at the destination. The problem of interest is to jointly design source and relay precoders so as to minimize mean square error of transmitted symbols under total power constraints at the source and relay nodes. We propose a method which diagonalizes the MSE matrix using singular value decomposition (SVD) and generalized SVD techniques. The proposed approach based on this diagonalized MSE matrix is suboptimal and aims to reduce the design complexity of the precoders. The solution can be obtained via an iterative water- filling technique. Simulations results show the performance advantages of the proposed approach.

In this paper, we consider employing a multi-element antenna (MEA) with close spacing to tackle the challenging channel estimation (CE) in highly mobile OFDM systems. Instead of large spacing for diversity, we propose to place the adjacent elements with one symbol distance in the moving direction to observe the quasi-duplicated channels in temporal difference of one symbol period. In exploiting the quasi-duplicated channels, we developed a novel CE-symbol detection (SD) iteration that cooperates with the standardized comb-type pilots to track fast varying channels. From simulation results, we show the proposed system outperforms the conventional receiver of two antennas with spatial diversity in highly mobile channels as long as the mutual coupling effects with the close-spaced elements are restricted.

Virtual multiple-input multiple-output (VMIMO) enables the implementation of conventional MIMO on mobile devices equipped with insufficient numbers of antennas via cooperation. This paper considers a spectral efficient broadcasting network in which selected mobile devices form a VMIMO system to relay the broadcasted data to help other devices decode the source data more reliably. In particular, the relay selection problem, a fundamental issue in the construction of VMIMO, is examined. We first review existing selection schemes for users operating in the amplify-and-forward (AF) mode. We then propose a distributed selection scheme based on post-processing SNR. In the proposed scheme, each user individually finds the most favorable candidates for VMIMO construction and then all users obtain a joint decision through a voting process. Simulation results show that the proposed distributed scheme outperforms existing distributed selection schemes and achieves a near-optimal performance with lower complexity compared to the centralized scheme.

In cognitive radio (CR) networks, upon the request of a primary user (PU) to utilize the licensed spectrum, all the secondary users (SUs) using the same licensed spectrum must terminate the spectrum usage immediately and switch their data transmissions to unoccupied spectra to avoid interference with the PU. The spectrum switching may lead to severe throughput degradation in SU networks. To mitigate the impact of spectrum usage termination and switching, Minimum Interference Robust Topology Construction (MIRTC) is a critical problem. In this paper, we formulate the problem as an integer programming problem and propose a genetic-algorithm-based channel assignment (GACA) scheme to construct a robust CR topology while minimizing interference. Our proposed formulation maintains the connectivity of each source-destination pair under the interruption of any single channel. A Bisearch algorithm is further proposed to approach the optimal solution of the problem. Simulation results demonstrate that the proposed scheme can reduce network interference and enhance network throughput efficiently.

The (n,k) information dispersal algorithm (IDA) is a coding technique converting a digital source file into n small digital files (shadows), and the receipt of any k out of the n shadows can losslessly reconstruct the source file. This paper presents an encoding and two decoding algorithms of (n,k) IDA via the fast Fermat number transform (FNT). The proposed encoding algorithm requires O(nlog k) arithmetic operations, and the two decoding algorithms have complexities O(nlog k) and O(klog2k) for a reasonably large file. As compared with existing work, the proposed algorithms generate significant improvement in the throughput in the low code rate k/n ≤ 1/2 settings.

In this paper, we consider a two-way relay network in which two users exchange messages through a single relay using a physical-layer network coding (PNC) based protocol. The protocol comprises two phases of communication. In the multiple access (MA) phase, two users transmit their modulated signals concurrently to the relay, and in the broadcast (BC) phase, the relay broadcasts a network-coded (denoised) signal to both users. Nonbinary and binary network codes are considered for uniform and nonuniform pulse amplitude modulation (PAM) adopted in the MA phase, respectively. We examine the effect of different choices of symbol mapping (i.e., mapping from the denoised signal to the modulation symbols at the relay) and bit mapping (i.e., mapping from the modulation symbols to the source bits at the user) on the system error-rate performance. A general optimization framework is proposed to determine the optimal symbol/bit mappings with joint consideration of noisy transmissions in both communication phases. Complexity-reduction techniques are developed for solving the optimization problems. It is shown that the optimal symbol/bit mappings depend on the signal-to-noise ratio (SNR) of the channel and the modulation scheme. A general strategy for choosing good symbol/bit mappings is also presented based on a high-SNR analysis, which suggests using a symbol mapping that aligns the error patterns in both communication phases and Gray and binary bit mappings for uniform and nonuniform PAM, respectively.

Detecting the existence of the transmitter emitting signals is an important mechanism in many applications, e.g., the spectrum sensing in the cognitive radio. In conventional detection schemes, the predefined number of samples is taken for detection and the statistics of the signals are assumed to be available in the signal model. However, under the ubiquitous fading effects and the non-cooperation of the targets, the signal statistics are not accurately obtainable at the detector. In this paper, we propose a sequential detector operating on the signal model described by the autoregressive moving average (ARMA) process without assuming known coefficients. The sequential detector for the ARMA model is derived by using the likelihood ratio test framework and the predictive distributions of the ARMA process. The novelties the proposed sequential detector include: 1) performing detection without requiring complete knowledge of the signal; 2) using smaller number of samples to reach the decision on average; and 3) allowing user-specified probabilities of detection and false alarm. We derive the approximate average number of samples required to reach the decision. The energy detector and sequential energy detector are compared with the proposed sequential detector by simulations. The results show the sequential detector uses the smaller average number of samples than the energy detector and sequential energy detector to termination.

A new efficient algorithm using the compressed domain features of H.264 INTRA frames is proposed for moving object extraction on huge video surveillance archives. To achieve searching efficiency, we propose to locate moving objects by scrutinizing only the INTRA frames in video surveillance archives in H.264 compressed domain with short GOP length. In the proposed structure, a modified codebook algorithm is designed to build the block-based background models from the INTRA coding features. Through the subtraction with the background codebook models, the foreground energy frame is filtered and normalized for detecting the existence of moving objects. To overcome the over-segmentation problem and enable the unsupervised searching, a new structure of hysteresis thresholding, where the thresholds are obtained automatically by an efficient algorithm, is adopted to extract foreground blocks. At the final step, the connected components labeling (CCL) and morphological filters are employed to obtain the list of moving objects. As shown in the experimental results, the proposed algorithm outperforms representative existing works.

How to exchange information between parties in a mega-scale disaster management system is one of the fundamental challenges to support timely and efficient disaster response and relief. Specifically, the timeliness, scalability, and availability are three desirable features for information exchange. We call the framework to support information exchange with the three features an open information gateway, OIGY in short. In this paper, we present the challenges of information gateway, and the design of the communication protocols and the fundamental components and algorithms to support the aforementioned features. The efforts of this work will be divided into two major components: one is the distributed Truthful Real-time Information Publishing and Subscribing (TRIPS), and the other one is Heterogeneous And Plug-n-PlaY networks (HAPPY). The two components in OIGY collaborate to provide reliable and timely information publish and subscription service. TRIPS is responsible for logical information exchange management. Compared to modern real-time publish and subscription services, TRIPS is aimed at information responsiveness and distributed content-based filtering in an un-reliable network. To achieve better responsiveness, TRIPS will take advantage of the run-time service composition of SOA framework to select information sources. To enhance the success rate, TRIPS relies the information routing information provided by HAPPY. HAPPY will integrate heterogeneous communication networks including 3G/WiMAX telecommunication network and mesh mobile network into a coherent communication network and discovers the routes with probabilistic bandwidth guarantee.

The effective call admission control (CAC) scheme provides preferential treatments on call requests to achieve efficient utilization of system resources. The Markov Decision Process (MDP) is one of the optimization schemes used to derive the optimal CAC policy for pre-determined goals. However, the MDP-based approaches suffer from the Curse of Dimensionality problem, where the size of state space grows exponentially with the increase of the number of call types and the number of channels. The Curse of Dimensionality problem makes the MDP-based approach difficult to be implemented in practice. In this paper, we consider a heterogeneous telecommunication system with multiple service priority classes. We propose a suboptimal MDP-based CAC scheme, designed based on a two-phase reduced dimension structure to substantially reduce the overall computational complexity from the order of O(C12) to the order of O(C4), where C denotes number of system capacity. Our proposed MDP-based CAC scheme is evaluated via an event driven simulator and the results are compared with the conventional complete sharing scheme and different guard channel schemes under various traffic loads.

The detection problem of the space shift keying (SSK) signaling and its generalized form (namely, generalized SSK or GSSK) in the emerging large-scale multiple-input multiple-output (MIMO) systems is discussed in this paper. First, we explicitly formulate the tree search and column search detection schemes achieving optimal maximum likelihood (ML) performance, and discuss their pros and cons in the context of large MIMO systems where the size of the GSSK modulation alphabet increases significantly. Secondly, we propose two useful suboptimal detection methods for large MIMO systems and large-alphabet GSSK signaling based on convex relaxation, which induce an approximately 2-4 dB performance penalty as shown through experimental results.

This paper proposes a space-time shift keying (STSK) modulation scheme, which maps the information onto two dimensions, i.e., the temporal and spatial dimensions. For the proposed modulation, a detector with reduced complexity is also proposed. The complexity of proposed detector is reduced with the slight degradation of performance. The simulation results show that the proposed system achieves good performance. Furthermore, since the STSK utilizes two resources to modulate information bits, the STSK obtains different error rates for the information modulated by different resources. Therefore, the potential of the proposed STSK to be applied in the unequal error protection (UEP) is also discussed.

In this paper, two critical issues, i.e., energy efficiency and throughput, are jointly considered for non-infrastructure based cognitive radio networks. We formulated a Mixed Integer Non-linear Programming (MINP) Problem to determine the power control and channel assignment so as to minimize energy consumption and simultaneously maximize the network throughput. Due to the NP-hard property of the MINP, the MINP is decomposed into |N| problems so that each problem can be distributedly and locally solved at each node. Furthermore, a Lagrangian Relaxation based Heuristic (LRH) was proposed to approximate the solution of each problem. In simulations, we perform LRH on five randomly generated networks to observe the influence of transmission power on the network throughput.

In wireless multicast communications, the mitigation of distributed errors in different users is crucial. The corrupted packets in the transmission of multicast streaming can be recovered by retransmitting another redundancy version of the same packets or by transmitting a new parity packet generated by packet-level erasure encoders. In this paper, a hybrid automatic repeat request (HARQ) scheme is proposed for efficient multicast communications. The proposed scheme estimates the contributions of each candidate packet to the marginal recovery on the erroneous packets and then transmits the packet which generates the most marginal recovery. The length of coded bits in the redundancy versions can also be adjusted to further save the radio resources. Simulations are conducted to verify the throughput efficiency of the proposed scheme, which outperforms the conventional schemes in all the range of packet error rates.

In this paper, we jointly consider power control, channel assignment, and routing to minimize network energy consumption while maintaining data rate requirements on each radio link in the multi-hop cognitive radio network. This problem is formulated as a Mixed Integer Non-linear Programming (MINP). Due to the NP-hard property of the MINP, the MINP is decomposed into |N| (the number of nodes in the network) problems so that each problem can be distributedly and locally solved at each node. Furthermore, a Lagrangean Relaxation based Heuristic (LRH) was proposed to approximate the solution of each problem. Through the technique of Lagrangean relaxation, each problem is transformed into its relaxed form and solved iteratively. Numerical results demonstrate that the proposed scheme can efficiently save network energy consumption up to 90%.

We investigate the MIMO antenna selection using space shift keying (SSK) modulation and amplitude/phase modulation (APM). In the joint SSK and APM, both the constellation of APM and the antenna indexes of SSK convey information. The multiple-input multiple-output (MIMO) system increases the capacity and data rates at the cost of the multiple RF chains, which can be reduced by antenna selection techniques. In this paper, the antenna selection techniques are jointly designed with the SSK-based MIMO systems, and the decoding scheme achieving maximum-likelihood (ML) criterion is explicitly described. The proposed antenna selection criteria pursue the best antenna configuration by utilizing channel state information. The simulations demonstrate significant performance improvements of SSK-based MIMO systems over conventional systems.

A new multiple-input multiple-output (MIMO) detection scheme combining minimum-mean-square-error (MMSE) detection and the K-best detection algorithm is proposed. The proposed scheme leverages the MMSE detection results to ease the demand of a large K in the conventional K-best algorithm to achieve satisfactory performance. The post-detection SNR obtained after MMSE detection is consulted to determine the symbols upon which a reduced-dimension K-best algorithm (h-best algorithm) is performed to obtain final detection results. Parameters associated with the proposed scheme are empirically chosen to make a fair comparison with the conventional K-best algorithm. Extensive Monte Carlo simulation demonstrates that the hybrid approach exhibits significant performance gain over both MMSE and K-best detection schemes.

In this paper, we investigate resource block assignment in femtocell networks. A resource block assignment algorithm is designed to avoid co-channel intercell interference and ensure service quality for femtocell networks with dense and random femto deployments. We first formulate the optimization problem as the integer linear programming (ILP) on resource block assignment. The goal of the optimization formulation is to maximize the overall utilization of resource blocks with quality of service (QoS) constraints. We propose an efficient and simple algorithm termed interference-aware resource block assignment (IARBA). By considering conditions of resource blocks, the proposed approach achieves better resource block efficiency and assignment within QoS requirements. Our analytical and simulation results show that IARBA not only provides interference-free resource block assignment but also outperforms existing schemes in terms of average throughput with comparable complexities.

In highly mobile OFDM systems, the carrier frequency offsets (CFO) with Doppler spreads for downlink detection can be considerably large, which degrades the frequency alignment for uplink transmission, particularly in employing directional antennas for inter-carrier interference (ICI) reduction. In prior works, the directional antenna was investigated with appropriate frequency alignment in receiver's local oscillator to efficiently reduce ICI in fast time varying OFDM systems. To resolve the optimal frequency offsets problem with Doppler spreads, this paper develops a simple scheme to capture instant Doppler power spectrum density (PSD) through moving directional antennas with arbitrary gain patterns. Thus, the optimal aligning frequency is derived as the center of gravity of the Doppler PSD. Simulations show our approach acquires the highest carrier to interference (C/I) ratio and the lowest bit error rates (BER) compared with other approaches.

We consider the data detection in a multiple-input multiple-output (MIMO) broadcasting system where the source simultaneously transmits multiple data streams to single-antenna receivers. To detect multiple data streams, conventional MIMO techniques require the receivers to have multiple receive antennas, the number of which is greater than or equal to the number of transmit antennas. However, user cooperation can be applied to combat this limitation. In this paper, we propose a user decode- and-forward (DF) cooperation scheme and the corresponding detection method. The transmission consists of two phases: source broadcasting and the user cooperation. During the user cooperation phase, a selected leader in the cooperation group collects the detections from the other users. The leader then obtains its final detection by using maximum likelihood (ML) criteria and broadcasts the results to all the other users in the cooperation group. Through simulations we show that the average symbol error performance of the proposed cooperation scheme are affected by several factors including the cooperation group size, the number of users activated for transmission and the length of the cooperation phase. With moderately large group size, the proposed system can potentially outperform the conventional system whose number of receive antennas are equal to the number of transmit antennas.

The ad hoc network is composed of multiple sensor nodes to serve various applications, such as data collection or environmental monitoring. In many applications, the sensor nodes near the boundary of the deployment region provide biased or low-quality information because they have limited number of neighboring nodes and only partial information is available. Hence, the boundary recognition is an important issue in the ad hoc networks. By the statistical approach in high node density networks, Fekete's pioneer work identified the boundary node by number of neighboring nodes and using a specific threshold. By exploiting the number of nodes in the two-hop region, our proposed algorithm has significant improvement of boundary recognition contrasted with Fekete's algorithm in the low-density network. Given the information topology and the cost function, the analyses provide a framework to obtain the optimal threshold for boundary recognition. Besides, the simulation results reveal the proposed algorithm has greater than 90% detection rate and lower than 10% false alarm rate.

In this paper, a MIMO detection scheme is proposed based on a combination of Monte Carlo technique and list detection. Specifically, a list of Gaussian samples are first generated to determine the search range of constellation points in which the transmitted symbol is most likely to locate. Linear equalizations are then applied to equalize the effect caused by the channel mixing, and a list detector is used to search within the determined search range. By varying the parameters in the Monte Carlo method, different symbol error rate (SER) versus complexity tradeoff can be obtained to account for different system design requirements. Simulation results also show that near-ML SER performance with considerably less computational complexity can be achieved by the proposed scheme compared to the exhaustive search.

This paper presents a category of detection schemes for Multiple-Input Multiple-Output (MIMO) system called Parallel Sphere Decoder (PSD). Compared to the conventional depth-first sphere decoder with Schnorr-Euchner enumeration (SE-SD), the proposed PSD algorithms use parallel computations and achieve approximately 50% searching time reductions under the same amount of computations. Namely, in hardware implementation, the proposed work provides trade-off between computational time and computing units. Simulations of the proposed algorithms in 4×4 16-QAM and 3×3 64-QAM MIMO systems show the searching time reductions of the proposed algorithms while maintaining ML performances.

This paper proposes cooperative opportunistic routing (COR), a throughput improvement scheme for the cooperative opportunistic routing in multi-hop wireless mesh networks (WMNs). We investigate the two major issues in opportunistic routing, the selection and the prioritization metric for the candidate set. The COR is presented to select and prioritize the candidate node with minimum expected cost. This candidate selection with low expected cost on each transmission constructs a throughput efficient routing path. The COR's robust packet handling strategy is also proposed to avoid duplicated transmission without forwarding list. With more efficient candidate set and packet handling, the average throughput improves by 76% and the end-to-end delay is reduced by 15% in our simulation results.

The usage of wireless sensor networks (WSNs) to monitor a region is an important functionality in defense and security applications. In these applications, a fundamental issue is to determine the minimum degree of coverage in the concerned region. The past researches focus on the binary disk sensing model, where sensors are assumed to be accurate in detecting targets within their sensing ranges. In this paper, we investigate the coverage problem under a more realistic model, the probabilistic sensing model, in which the probability of detection by a sensor decays with the distances. We generalize the coverage problem to the probabilistic sensing model and propose an algorithm to calculate the minimum degree of coverage. The accuracy of the proposed algorithm is verified via simulations.

Energy-efficient communication using a class of spatial modulation (SM) that encodes the source information entirely in the antenna indices is considered in this paper. The energy-efficient modulation design is formulated as a convex optimization problem, where minimum achievable average symbol power consumption is derived with rate, performance, and hardware constraints. The theoretical result bounds any modulation scheme of this class, and encompasses the existing space shift keying (SSK), generalized SSK (GSSK), and Hamming code-aided SSK (HSSK) schemes as special cases. The theoretical optimum is achieved by the proposed practical energy-efficient HSSK (EE-HSSK) scheme that incorporates a novel use of the Hamming code and Huffman code techniques in the alphabet and bit-mapping designs. Experimental studies demonstrate that EE-HSSK significantly outperforms existing schemes in achieving near-optimal energy efficiency. An analytical exposition of key properties of the existing GSSK (including SSK) modulation that motivates a fundamental consideration for the proposed energy-efficient modulation design is also provided.

Multimedia Broadcast/Multicast Service (MBMS) is a bandwidth efficient broadcast scheme for multimedia communications. To support prioritized transmissions, the unequal error protection (UEP) for multi-resolution multimedia sources can be realized through MBMS. Nevertheless, the enhancement on the transmission fidelity in base layer typically sacrifices the fidelity of enhancement layers. Herein, a novel dual diversity space-time coding (DDSTC) is proposed to exploit the intrinsic UEP capability of space-time codes by utilizing a constellation mapping duo for two consecutive transmission periods in multiple-input multiple-output (MIMO) systems. As compared with Alamouti coding, the DDSTC achieves coding gains on the transmission error rates of base layer without significant degradations on the enhancement layers. At the transmission rates of base and enhancement layers equal to 2 bits per transmission, the DDSTC obtains 1.3 dB and 3.0 dB coding gains for base layer in 2 × 2 and 2 × 3 MIMO systems respectively. Besides, analytical analysis on symbol error probabilities verifies that 6 dB asymptotic coding gain is reachable in rich transmit diversity scenarios. While attaining the considerable improvements on error rates, the DDSTC avoids the high decoding complexity by adopting our proposed decoding schemes. Simulation results show that DDSTC outperforms conventional UEP schemes based on hierarchical modulations or power allocations.

The (t, n) visual cryptography (VC) is a secret sharing scheme where a secret image is encoded into n transparencies, and the stacking of any t out of n transparencies reveals the secret image. The stacking of t - 1 or fewer transparencies is unable to extract any information about the secret. We discuss the additions and deletions of users in a dynamic user group. To reduce the overhead of generating and distributing transparencies in user changes, this paper proposes a (t, n) VC scheme with unlimited n based on the probabilistic model. The proposed scheme allows n to change dynamically in order to include new transparencies without regenerating and redistributing the original transparencies. Specifically, an extended VC scheme based on basis matrices and a probabilistic model is proposed. An equation is derived from the fundamental definitions of the (t, n) VC scheme, and then the (t, ∞) VC scheme achieving maximal contrast can be designed by using the derived equation. The maximal contrasts with t = 2 to 6 are explicitly solved in this paper.

Reversible information-embedding (RIE) is a technique transforming host signals and the message into the stego-signals, and the stego-signals can be losslessly reversed to the host signals and the message. We consider the conditions: 1) the host signals are composed of gray-scale independent and identically distributed (i.i.d.) samples; 2) the mean squared error is adopted as the measure of distortion; and 3) the procedure is a scalar approach, i.e., the encoder only reads a host signal and then outputs the corresponding stego-signal in each iteration. In this paper, we propose an iterative algorithm to calculate the signal transition probabilities approximating the optimal rate-distortion bound. Then we propose an explicit implementation to embed a message in an i.i.d. host sequence. The experiments show that the proposed method closely approaches the expected rate-distortions in i.i.d. gray-scale signals. By the image prediction model, the proposed method can be applied to gray-scale images.

The tree representation of the multiple-input multiple-output (MIMO) detection problem is illuminating for the development, interpretation, and classification of various detection methods. Best-first detection based on Dijkstra's algorithm pursues tree search according to a sorted list of tree nodes. In the first part of the paper, a new probabilistic sorting scheme is developed and incorporated in a modified Dijkstra's algorithm for MIMO detection. The proposed sorting exploits the statistics of the problem and yields effective tree exploration and truncation in the proposed algorithm. The second part of the paper generalizes the results in the first part and removes some limitations. A generalized Dijkstra's algorithm is developed as a unified tree-search detection framework. The proposed framework incorporates a parameter triplet that allow the configuration of the memory usage, detection complexity, and sorting dynamic associated with the tree-search algorithm. By tuning different parameters, desired performance-complexity tradeoffs are attained and a fixed-complexity version can be produced. Simulation results and analytical discussions demonstrate that the proposed generalized Dijkstra's algorithm shows abilities to achieve highly favorable performance-complexity tradeoffs.

A unified tree-search detection scheme based on Dijkstra's algorithm is developed for MIMO systems. The proposed framework generalizes the original Dijkstra's algorithm by allowing the memory usage, detection complexity, and sorting dynamic associated with the algorithm to be customized. By tuning different parameters, desired performance- complexity tradeoffs are attained and a fixed- complexity version can be produced to facilitate hardware implementation. Simulation results demonstrate that the proposed algorithm shows abilities to achieve highly favorable performance- complexity tradeoffs.

Linear detectors such as zero-forcing (ZF) and minimum mean square error (MMSE) require only a small fraction of computational complexity compared to maximum likelihood (ML) detector. However, linear detections suffer from severe performance degradation. In this paper, we propose a novel detection scheme which obtains the initial symbol detection by MMSE detector and then perform symbol ordering by signal-to-interference-and-noise ratio (SINR). The MMSE detected symbols with higher SINR are retained as part of final solution and cancelled from the original received signals. The remaining symbols with lower SINR are detected by K-best algorithm, which selects K best nodes in each layer of the partial tree search. The small value of K is sufficient to achieve good performances, and therefore the extra computational complexity is minimal. Simulation results show the performance superiority of the proposed method compared to the conventional MMSE detection. Moreover, at the similar symbol error rates, the total number of nodes visited in the proposed approach is much smaller than the conventional K-best detection scheme.

The tree representation of the MIMO detection problem is illuminating for the development, interpretation, and classification of various detection methods. One method, based on the Dijkstra's search algorithm, pursues tree exploration according to a bounded, sorted list of tree nodes. Since sorting directly affects tree exploration and truncation in this tree-search method, it is critical to the performance. Motivated by the observation that sorting according to nodes' path metric, as in the conventional algorithm, does not adequately represent the "goodness" of nodes, a new probabilistic sorting rule is developed by innovatively exploiting the statistical properties of the path metric to yield more effective sorting. The relationship between the probabilistic sorting and the conventional one is established, and new features of the probabilistic sorting are presented. The effectiveness of the proposed method is demonstrated by computer simulation, where the new method outperforms the previous tree-search method in achieving near-ML detection performance, and meanwhile offers significant complexity reduction compared to the previous tree-search method.

A low-complexity, effective detection method for multiple-input multiple-output (MIMO) systems based on eigenspace search and linear equalization-based detection schemes is proposed in this paper. Based on the observation that solutions yielded by linear detectors are corrupted by color noise, the proposed method introduces a new constellation search procedure to augment linear detectors. Specifically, calibrated search is conducted around the initial solution yielded by linear detectors, in the directions guided by the eigenvectors corresponding to the dominant eigenvalues of the covariance matrix of the color noise to identify improved solutions. Complexity analysis is performed to understand the cost of this search procedure. Simulation results demonstrate that the proposed scheme yields an approximate 5 dB gain over linear equalization-based detectors in terms of symbol error rate (SER), at moderate additional computational cost.

Zigbee, formalized by the IEEE 802.15.4 standard, is a specification for wireless personal area networks with low power, low cost, and a low data rate. In Zigbee, tree topology is commonly practiced to form wireless sensor networks and perform data delivery applications. In Zigbee wireless applications, data de livery failures occur constantly due to the node movements and topology changes of networks. To tackle the topology changes, conventional route reconstruction often involves huge resource consumption. In this paper, we utilize the regularity of mobility patterns to reduce the frequency of route reconstructions and achieve higher efficiency in sending data to mobile nodes. To increase the data delivery ratio, we introduce the metric of mobility-robustness in a tree topology, and propose tree construction with an objective to maximize the mobility-robustness of the constructed tree. We develop an efficient algorithm for effective tree construction. The effectiveness of network topologies constructed using this mobility-robustness metric is demonstrated by NS2 simulations against a real-world scenario.

We propose a novel joint source-channel coding (JSCC) framework that jointly optimizes encoding modes of macroblocks and unequal error protection (UEP) of packets for error-resilient video transmission, and address the problem of source packet loss due to bit errors incurred in error-prone channels. In the proposed framework, we consider the source packet loss probability as a function of the encoding configuration of a slice. The task of optimization is complicated by the inherent interdependency between macroblocks in the same slice, since the encoding mode optimization for each macroblock requires the encoding configuration of entire slice to be known in advance. We resolve such interdependency via an iterative method that alternates between the optimization of the slice size and the associated encoding modes, and we also utilize adaptive quantization for coding residual for source rate control in different channel conditions. Simulation results show that our framework outperforms existing approaches.

Spatial multiplexing is an efficient transmission technique for video streaming in multiple-input multiple-output (MIMO) wireless communication links due to its capability to support the high transmit rate. Nevertheless, the video quality is sensitive to packet losses resulting from channel fading and co-interference between transmit antennas in spatial multiplexing systems. Antenna selection techniques have been investigated to balance the spatial multiplexing gain and the link reliability. Herein, a novel cross-layer framework using dynamic antenna selection is proposed to control the multiplexing-diversity tradeoff with joint error-resilient source and channel coding. The cross-layer optimization is performed to optimize the physical, link, and application layers, and to minimize the end-to-end video distortion.

The use of nuclear energy to generate electric power is crucial in meeting the high energy demand of modern economy. The dependability analysis of nuclear power plants has been a critical issue and the reactor containment is the most important safety structure acting as a barrier against the release of radioactive material to the environment. In this paper, we analyze the dependability of the reactor containment. We also propose a tool for design, implementation, and V&V to enhance the dependability of reactor containment through an integrated leakage rate test. Our practical experiences in the on-site tests are also discussed.

This work proposes an adaptive intercell interference avoidance scheme for the self-organization in Orthogonal Frequency Division Multiple Access (OFDMA) femtocell networks. Due to the expected large number of user-deployed cells, the femtocell networks suffers from the intercell interference problem. In this paper, we define the self-organizing resource allocation problem to maximize resource efficiency with OFDMA architecture. The proposed problem formulation supports the desired Quality of Service (QoS) criteria while satisfying reliability constraints. We develop an autonomous resource allocation algorithm to pursue the most efficient frequency allocation. From the simulation results, the proposed approaches can increase the system throughput by over 13%, while the femtocell interference can be avoided completely.

The use of nuclear energy to generate electric power is crucial in meeting the high energy demand of modern economy. The dependability of nuclear power plants has been a critical issue and the reactor containment is the most important safety structure acting as a barrier against the release of radioactive material to the environment. In this paper, we propose a practical framework for design, implementation, and V&V to enhance the dependability of reactor containment through an integrated leakage rate test.

The ad hoc sensor network has been widely applied to various applications, such as environmental data collection and surveillance. To enable these applications, the accurate localization of sensor nodes is crucial. The DV-Hop provides a basic scheme to retrieve the localization information without GPS. The DV-Hop scheme requires the anchor nodes to be localized in advance, and the locations of the anchor nodes are used to localize other unknown nodes. The hop count between two anchor nodes can be exchanged through multi-hop routing. Using the hop counts and locations of other anchor nodes, the anchor nodes obtain the average distances per hop among one another. The distance estimation errors are caused by the uncertainties in per-hop distance estimation and the communication ranges. We propose a scheme where a node ranks the orders of its neighbor nodes through exchanging neighbor information locally. The neighbor information with orders provides useful information for node localization. Besides, the analysis reveals the probability among the order distance and the node density. Therefore, the distance estimation is improved by using the order information of neighbor nodes. The simulation results show more than 22% error reduction compared with the DV-Hop.

A modified sphere decoding (SD) scheme is proposed for multiple-input multiple-output (MIMO) communication systems in this paper. The conventional SD goes from the lower dimension to the higher dimension to examine whether a lattice point lies inside the sphere of some radius, which remains fixed for all dimensions. Since the sphere radius directly affects the search range and thus the complexity, it is an important parameter to design. The proposed scheme employs a set of dimension-dependent sphere radii, which performs more aggressive search-space reduction in low dimensions. The proposed method is shown by computer simulation to offer substantial complexity benefits with little symbol error rate (SER) performance loss (0.5 dB), compared to the optimal maximum likelihood (ML) decoding. The contribution of this paper includes the complexity advantage yielded by the proposed scheme as well as the introduction of a systematic approach to sphere radius design and control.

Time-varying channel causes intercarrier interference (ICI) and impairs the orthogonality among subcarriers in wireless mobile Orthogonal Frequency Division Multiplexing (OFDM) systems. The ICI and loss of carrier orthogonality degrade the bit error rate (BER), and thus results in intolerable loss of quality especially in applying multimedia data transmission. In this paper, we propose an unequal density of pilot placement for OFDM systems in time-varying channel, which provides Unequal Error Protection (UEP) for important multimedia transmission. A dynamic image data assignment (DIDA) scheme for UEP utilizing visual saliency is presented to reallocate image data in OFDM symbol for the UEP transmission. Simulation results show substantial improvements of quality in the region of interest in the image by using proposed method.

Sequences with specific autocorrelation (AC) and cross-correlation (CC) properties are crucial components in radar and wireless communications. In this paper, we derive the theoretical bounds on the AC and CC for OFDM sequences with constraints of spectral nulls, e.g., the mandatory nulls on the DC sub-carrier and guardbands in OFDM systems. The bounds and trade-off limits are provided for the properties of sequences, including the peak AC and CC levels, the cardinality of the sequence set, the sequence length, and the temporal length of the low correlation zone. We also investigate the trade-offs of correlations among sequence sets. The presented trade-off limits can serve as guidelines for applications where the performance measures or design criteria are related to the peak AC and CC levels.

Compressed video data is very sensitive to channel-induced errors and network losses. Most conventional unequal error protection techniques involve forward error correction codes of different rates or asymmetric signal constellations for data of different priorities. It is yet unknown if combining the above two techniques can further improve performance. In this paper, we investigate rate-compatible punctured convolutional (RCPC) codes concatenated with hierarchical QAM for H.264 encoded video sequences. We investigate system constraints and propose an optimization formulation to compute the optimal parameters of the proposed system under the given source significance information. An upper bound to the bit error rate of the proposed system is derived as a function of system parameters, including the code rate and geometry of the constellation. The example shown demonstrates system design for H.264 video, where PSNR improvement is observed.

Diversity techniques for various communication and MIMO systems exploit the spatial and temporal diversity attributes to mitigate the ill effects of the fading channels. To evaluate these techniques, a method to generate multiple correlated fading channels is crucial. We propose a unified approach capable of generating correlated flat-fading envelope processes with the desired auto-correlation functions, cross-correlation functions, and probability density functions (pdfs). The proposed approach utilizes the Gaussian vector autoregressive process and the inverse transform sampling techniques. Comparing to the past research focusing on generating fading channels of the same family, the novelty of the proposed approach is its capability to generate fading processes of heterogeneous pdfs. Three examples are demonstrated. In the first example, the autocorrelated Nakagami channel is generated. The second example is designed to generate correlated 2x2 MIMO Rayleigh channels. In the third example, the proposed approach generates three correlated channels having Nakagami, Rician, and Rayleigh pdfs. The settings of the first two examples are adopted from previously published results, which are intended to verify the effectiveness of our proposed approach to tackle previously known problems. The third example demonstrates the novelty of the proposed approach to generate correlated channels of heterogeneous pdfs.

Fading phenomena impact the performance of wireless communication systems. We propose the modified hidden semi-Markov model (MHSMM) for modeling the flat fading envelope process. The properties of the envelope process are dominated by the physical fading processes and speeds of the mobile terminal. Thus, the statistics of the fading process may be non-stationary, due to different fading conditions over some time durations. The MHSMM incorporate these time-variant statistics of the envelope process in a single model, which facilitates computations of the envelope probability density function and the autocorrelation function. We provide a parameter estimation scheme to estimate the MHSMM parameters. We demonstrate the proposed parameter estimation scheme by using simulated and experimental data. The simulation is based on the GSM system parameters often encountered in practical conditions. We conducted two experiments, including cooperating and noncooperating channel disturbances. Based on these data, the MHSMM is compared with the amplitude-based finite-state Markov chains model and the hidden Markov model. The results verify the advantages of the MHSMM and the effectiveness of the associated parameter estimation schemes.