As a legacy from conventional wireless services, illegal eavesdropping is regarded as one of the critical security challenges in Connected and Autonomous Vehicles (CAVs) network. Our work considers the use of Distributed Kalman Filtering (DKF) and Deep Reinforcement Learning (DRL) techniques to improve anti-eavesdropping communication capacity and mitigate jamming interference. Aiming to improve the security performance against smart eavesdropper and jammer, we first develop a DKF algorithm that is capable of tracking the attacker more accurately by sharing state estimates among adjacent nodes. Then, a design problem for controlling transmission power and selecting communication channel is established while ensuring communication quality requirements of the authorized vehicular user. Since the eavesdropping and jamming model is uncertain and dynamic, a hierarchical Deep Q-Network (DQN)-based architecture is developed to design the anti-eavesdropping power control and possibly channel selection policy. Specifically, the optimal power control scheme without prior information of the eavesdropping behavior can be quickly achieved first. Based on the system secrecy rate assessment, the channel selection process is then performed when necessary. Simulation results confirm that our jamming and eavesdropping defense technique enhances the secrecy rate as well as achievable communication rate compared with currently available techniques.

A main challenge for radar-aided millimeter wave (mmWave) vehicle-to-infrastructure (V2I) communication application, is that it requires mitigating the mutual interference between vehicular radar and communication operating at same frequency bands. This paper considers the joint design of the multiple-input multiple-output (MIMO) transmit waveform and receive filter bank for road side unit (RSU)-mounted radar in a spectrally crowded V2I communication environment. With the criterion of maximizing the average signal-to-interference-plus-noise ratio (SINR), a non-convex problem, which involves the weighted-sum waveform energy over the overlayed space-frequency bands, energy and similarity constraints, is formulated. An iterative algorithm is proposed to solve the joint optimization problem. At each iteration, the transmit waveform is optimized based on the alternating direction method of multipliers (ADMM) method with a significantly lower computational complexity. As a consequence, accurate location information derived from the optimized radar is used to reduce the beam training overhead of V2I communication links. Finally, simulation results display the effectiveness of the devised method in finding feasible and enhanced solutions, importantly outperforming several counterparts.

Vehicle-to-vehicle (V2V) communication applications face significant challenges to security and privacy since all types of possible breaches are common in connected and autonomous vehicles (CAVs) networks. As an inheritance from conventional wireless services, potential eavesdropping is one of the main threats to V2V communications. In our work, the anti-eavesdropping scheme in CAVs networks is developed through the use of cognitive risk control (CRC)-based vehicular joint radar-communication (JRC) system. In particular, the supplement of off-board measurements acquired using V2V links to the perceptual information has presented the potential to enhance the traffic target positioning precision. Then, transmission power control is performed utilizing reinforcement learning, the result of which is determined by a task switcher. Based on the threat evaluation, a multiple armed bandit problem is designed to implement the secret key switching procedure when it is needed. Through constant perception-execution loops (PELs), the security and confidentiality is improved for the authorized vehicles in their behavioral interactions with the illegal eavesdropper. Numerical experiments have presented that the developed approach has anticipated performance in terms of some risk assessment indicators.

Vehicle-to-vehicle (V2V) communication applications face significant challenges to security and privacy since all types of possible breaches are common in connected and autonomous vehicles (CAVs) networks. As an inheritance from conventional wireless services, illegal eavesdropping is one of the main threats to Vehicle-to-vehicle (V2V) communications. In our work, the anti-eavesdropping scheme in CAVs networks is developed through the use of cognitive risk control (CRC)-based vehicular joint radar-communication (JRC) system. In particular, the supplement of off-board measurements acquired using V2V links to the perceptual information has presented the potential to enhance the traffic target positioning precision. Then, transmission power control is performed utilizing reinforcement learning, the result of which is determined by a task switcher. Based on the threat evaluation, a multi-armed bandit (MAB) problem is designed to implement the secret key selection procedure when it is needed. Numerical experiments have presented that the developed approach has anticipated performance in terms of some risk assessment indicators.

Multiple-input multiple-output (MIMO) radar design for extended targets in a spectrally crowded environment is a challenge owing to the high sensitivity of the target impulse response (TIR) and the increasing requests for spectrum. Assuming unknown TIR, this paper proposes a joint design method to optimize the transmit waveforms and receive filter bank in MIMO structure ensuring spectral compatibility with the overlayed radiators. A priori information is used to impose a spectral constraint on the waveforms, which is the result of a non-convex optimization problem aimed at enhancing the average signal-to-interference-plus-noise-ratio (SINR) over a finite uncertainty set for the TIR. The new method realizes an improved spectral cohabitation with the surrounding radiators through an appropriate modulation of the transmitted energy. In addition, we develop an iterative optimization algorithm which successively enhances the average SINR. Each iteration of the algorithm involves a hidden convex problem, which can be solved resorting to the rank-one decomposition procedure. Finally, the performance is assessed by studying the trade-off among the achieved SINR and spectral shape. The reported results are presented to analyze the performance of the devised method against several counterparts in terms of the SINR value and robustness.

We develop a novel method for high-resolution inverse synthetic aperture radar (ISAR) imaging by exploring the block-sparse structure inherent in the ISAR image. First, the Laplacian scale mixture (LSM) prior is utilized to encode the sparsity of the ISAR image. Then, a 2-D pattern-coupled scale prior for sparse coefficients that encourage dependencies among neighboring coefficients is utilized to capture the block-sparse structure of the target scene. Finally, the Laplace approximation-based variational Bayesian inference (VBI) is employed to infer the posterior of the ISAR image along with hyperparameters. Numerical results based on simulated and measured data are provided to validate the effectiveness of the proposed algorithm.

Connected devices generate large amount of data for IoT applicatons. Assisted by edge computing, caching IoT data at the edge nodes is considered as a promising technique for its advantage in reducing network traffic and service delay of cloud platform. However, the IoT data is characterized by transient lifetime and cache capacity that is limited by the edge nodes. As a consequence, caching policy should consider both data transiency and storage capacity of edge nodes. Inspired by the success of deep reinforcement learning (DRL) in deal with Markov Decision Process (MDP) problem in unknown environment, A DRL-based algorithm for edge caching problem is proposed in this paper. The proposed Advantage Actor Critic (A2C)-based algorithm is aimed at maximizing the long-term energy saving without knowledge of the IoT data popularity profiles. Simulation results demonstrate that the proposed DRL-based algorithm can achieve higher energy saving and cache hit ratio compared with the baseline algorithms.

The model-based power allocation has been investigated for decades, but this approach requires mathematical models to be analytically tractable and it has high computational complexity. Recently, the data-driven model-free approaches have been rapidly developed to achieve near-optimal performance with affordable computational complexity, and deep reinforcement learning (DRL) is regarded as one such approach having great potential for future intelligent networks. In this paper, a dynamic downlink power control problem is considered for maximizing the sum-rate in a multi-user wireless cellular network. Using cross-cell coordinations, the proposed multi-agent DRL framework includes off-line and on-line centralized training and distributed execution, and a mathematical analysis is presented for the top-level design of the near-static problem. Policy-based REINFORCE, value-based deep Q-learning (DQL), actor-critic deep deterministic policy gradient (DDPG) algorithms are proposed for this sum-rate problem. Simulation results show that the data-driven approaches outperform the state-of-art model-based methods on sum-rate performance. Furthermore, the DDPG outperforms the REINFORCE and DQL in terms of both sum-rate performance and robustness.

The model-driven power allocation (PA) algorithms in the wireless cellular networks with interfering multiple-access channel (IMAC) have been investigated for decades. Nowadays, the data-driven model-free machine learning-based approaches are rapidly developing in this field, and among them the deep reinforcement learning (DRL) is proved to be of great potential. Different from supervised learning, the DRL takes advantages of exploration and exploitation to maximize the objective function under certain constraints. In our paper, we propose a two-step training framework. First, with the off-line learning in simulated environment, a deep Q network (DQN) is trained with deep Q learning (DQL) algorithm, which is well-designed to be in consistent with this PA issue. Second, the DQN will be further fine-tuned with real data in on-line training procedure. The simulation results show that the proposed DQN achieves the highest averaged sum-rate, comparing to the ones with present standard DQL training. With different user densities, our DQN outperforms benchmark algorithms and thus a good generalization ability is verified.

Abstract Learning English grammar is a very challenging task for many students especially for nonnative English speakers. To learn English well, it is important to understand the concepts of the English grammar with lots of practise on exercise questions. Previous recommendation systems for learning English mainly focused on recommending reading materials and vocabulary. Different from reading material and vocabulary recommendations, grammar question recommendation should recommend questions that have similar grammatical structure and usage to the question of interest. The content similarity calculation methods used in existing recommendation methods cannot represent the similarity between grammar questions effectively. In this paper, we propose a content‐based approach for personalized grammar question recommendation, which recommends similar grammatical structure and usage questions for further practising. Specifically, we propose a novel structure namedparse‐key treeto capture the grammatical structure and usage of grammar questions. We then propose 3 measures to compute the similarity between the question query and database questions for grammar question recommendation. Additionally, we incorporated the proposed recommendation method into a Web‐based English grammar learning system and presented its performance evaluation in this paper. The experimental results have shown that the proposed approach outperforms other classical and state‐of‐the‐art methods in recommending relevant grammar questions.

To meet the spectrum requirement in the future wireless communication system, the m ‐ary phase position shift keying as a type of the spectrally efficient modulation has been considered in this study. However, the inter‐symbol interference (ISI), the waveform distortion and the non‐white noise are introduced by the band‐pass infinite impulse response filters, which is adopted to limit the signal bandwidth and eliminate the out‐band interference. The traditional demodulation methods based on impacting filter or matched filter cannot work well in these scenarios. Therefore, a neural network (NN)‐based demodulator is proposed to solve the problem of waveform distortion. Additionally, a novel NN‐based iterative decision feedback (IDF) demodulator is also proposed to further reduce the ISI iteratively by using the previous estimated symbol information. Simulation results show that both the NN‐based demodulator and the NN‐based IDF demodulator can greatly outperform the traditional demodulation methods in the band‐limited communication system, and the NN‐based IDF demodulator achieves better performance than the normal NN‐based demodulator.

With the popularity of online educational platforms, English learners can learn and practice no matter where they are and what they do. English grammar is one of the important components in learning English. To learn English grammar effectively, it requires students to practice questions containing focused grammar knowledge. In this paper, we study a novel problem of retrieving English grammar questions with similar grammatical focus. Since the grammatical focus similarity is different from textual similarity or sentence syntactic similarity, existing approaches cannot be applied directly to our problem. To address this problem, we propose a syntactic based approach for English grammar question retrieval which can retrieve related grammar questions with similar grammatical focus effectively. In the proposed syntactic based approach, we first propose a new syntactic tree, namely parse-key tree, to capture English grammar questions’ grammatical focus. Next, we propose two kernel functions , namely relaxed tree kernel and part-of-speech order kernel, to compute the similarity between two parse-key trees of the query and grammar questions in the collection. Then, the retrieved grammar questions are ranked according to the similarity between the parse-key trees. In addition, if a query is submitted together with answer choices, conceptual similarity and textual similarity are also incorporated to further improve the retrieval accuracy . The performance results have shown that our proposed approach outperforms the state-of-the-art methods based on statistical analysis and syntactic analysis.

Prior asymptotic performance analyses are based on the series expansion of the moment-generating function (MGF) or the probability density function (PDF) of channel coefficients. However, these techniques fail for lognormal fading channels because the Taylor series of the PDF of a lognormal random variable is zero at the origin and the MGF does not have an explicit form. Although lognormal fading model has been widely applied in wireless communications and free-space optical communications, few analytical tools are available to provide elegant performance expressions for correlated lognormal channels. In this paper, we propose a novel framework to analyze the asymptotic outage probabilities of selection combining (SC), equal-gain combining (EGC), and maximum-ratio combining (MRC) over equally correlated lognormal fading channels. Based on these closed-form results, we show: 1) the outage probability of EGC or MRC becomes an infinitely small quantity compared to that of SC at high signal-to-noise ratio (SNR); 2) channel correlation can cause an infinite performance loss at high SNR; and 3) negatively correlated lognormal channels can outperform the independent lognormal channels. The analyses reveal insights into the long-standing problem of asymptotic performance analyses over correlated lognormal channels, and circumvent the time-consuming Monte Carlo simulation and numerical integration.

In vehicular ad hoc network (VANET), the accuracy of the channel estimation plays a key role toward obtaining considerable improvement to the physical layer performance. In this study, least square (LS) and minimum mean square error (MMSE) estimation techniques are discussed and their performances in different VANET transmission scenarios are thoroughly investigated. Furthermore, a modified scheme of these techniques to improve the performance of the system and reduce the complexity in such a way that suits VANET communications is developed. Discreet Fourier transform and singular value decomposition techniques are companied with LS and MMSE to develop a channel estimation scheme which suits VANET channel conditions and ensures performance improvement and remarkable complexity reduction. Comprehensive simulations results show that the developed scheme greatly improves the performance of the system compared with LS and substantially reduces the complexity of MMSE.

Prior work made a comparison between the diversity orders of adaptive decode-and-forward (ADF) and improved adaptive decode-and-forward (IADF) over free- space optical (FSO) Gamma-Gamma fading channels. However, the diversity orders of FSO lognormal channels are infinite, and thus the prior method of comparison becomes invalid for FSO lognormal fading channels. In this work, we compare the performance of ADF and IADF over lognormal fading channels based on asymptotic relative diversity order. We analytically prove that the frame error rate of IADF is much smaller than that of ADF when the signal-to-noise ratio is sufficiently large.

The modulation and demodulation problems for efficient multi‐carrier communications are considered in this study. To maintain compact spectrum and realise dense multi‐carrier transmission, a novel modulation method based on random hopping position, namely time hopping m ‐ary phase position shift keying (TH‐MPPSK), is proposed to remove the discrete spectrum. Then, a multi‐carrier scheme without guard interval among subcarriers is provided to reduce complexity in the practical communication systems. Moreover, a novel demodulation method is proposed to improve the efficient communication performance. Simulation results demonstrate that the TH‐MPPSK can significantly remove the discrete spectrum and the proposed demodulation method can satisfactory performance.

Prior results on exact error rates and outage probabilities of diversity receptions over arbitrarily correlated fading channels involve intractable nested integrals, which makes the performance analyses challenging to carry out. In this paper, we derive asymptotic expressions and asymptotically tight bounds for error rates and outage probabilities of diversity receptions (including maximal-ratio combining, selection combining, and equal-gain combining) over arbitrarily correlated Beckmann fading channels. These new asymptotic performance expressions reveal insights into the diversity systems over arbitrarily correlated Beckmann fading channels, and the accuracy of the asymptotic performance expressions is explicitly quantified by the performance bounds. Using these analytical tools, one can assess the performance of the diversity reception systems over arbitrarily correlated Beckmann fading channels without resorting to time-consuming Monte Carlo simulation or numerical multifold integration.

Prior performance analyses of diversity receptions over arbitrarily correlated Nakagami-m fading channels use power correlation matrix to define correlation among branches. However, the power correlation matrix cannot uniquely determine the joint probability density function of the branch amplitudes as well as the system performance. In this paper, we derive asymptotically tight and closed-form upper and lower bounds for the error rates and the outage probabilities of maximum-ratio combining, equal-gain combining, and selection combining over arbitrarily correlated Nakagami-m fading channels. The correlation between the Nakagami-m random variables is defined through the correlation matrix of the accompanying Gaussian random variables, which can uniquely determine the joint probability density function of the Nakagami-m variables. Using the analytical results and Monte Carlo simulation, we show that the performance of diversity reception systems over Nakagami-m fading channels cannot be uniquely determined by the power correlation matrix of the branches. Furthermore, we also study the factors that determine the asymptotic performance of the diversity receptions.

Exact error rate expressions for equal-gain combining over arbitrarily correlated Rician channels involve intractable integrals. Thus, asymptotic error rate expression was derived to provide accurate approximation in large signal-to-noise ratio region. However, the existing asymptotic analysis cannot evaluate the accuracy of the asymptotic error rate expression for equal-gain combining. In this work, we derive closed-form and asymptotically tight upper and lower bounds for the error rate of equal-gain combining over Rician fading channels with arbitrary correlation. These bounds can be used to evaluate the accuracy of the asymptotic error rate expression for equal-gain combining at high signal-to-noise ratio without using the time-consuming Monte Carlo simulation.

The sparse channel estimation which sufficiently exploits the inherent sparsity of wireless channels, is capable of improving the channel estimation performance with less pilot overhead. To reduce the pilot overhead in massive MIMO systems, sparse channel estimation exploring the joint channel sparsity is first proposed, where the channel estimation is modeled as a joint sparse recovery problem. Then the block coherence of MIMO channels is analyzed for the proposed model, which shows that as the number of antennas at the base station grows, the probability of joint recovery of the positions of nonzero channel entries will increase. Furthermore, an improved algorithm named block optimized orthogonal matching pursuit (BOOMP) is also proposed to obtain an accurate channel estimate for the model. Simulation results verify our analysis and show that the proposed scheme exploring joint channel sparsity substantially outperforms the existing methods using individual sparse channel estimation.

Prior work on exact error rate of diversity receptions over arbitrarily correlated Rician channels involves intractable integrals. Although asymptotic error rates provide closed-form approximations which are accurate at high signal-to-noise ratio, it is still unknown at what signal-to-noise ratio the approximation becomes accurate. In this paper, asymptotically tight closed-form upper and lower bounds are derived for maximal-ratio combining and selection combining over arbitrarily correlated Rician channels. Using these bounds, we can show at what signal-to-noise ratio that the asymptotic error rates are accurate without resorting to time-consuming Monte Carlo simulation.

An improved M‐ary phase position shift keying (MPPSK) modulation, aiming to increase the spectral and energy efficiencies of existing MPPSK schemes while preserving their low demodulation complexity and error performance, is proposed in this study. Specifically, an improved MPPSK with orthogonal signals called OI‐MPPSK is presented. For the proposed OI‐MPPSK, the authors develop two demodulation schemes that employ a special impact filter (SIF): (i) the orthogonal SIF correlator (OSC) scheme, which consists of the orthogonal transmission, the SIF and a correlator; (ii) the orthogonal SIF position (OSP) scheme, which is the same as the OSC scheme except that the coherent demodulator is replaced by exploiting position‐related information. As a result of exploiting the position‐related information in the proposed OI‐MPPSK scheme, the SER performance of the OSP‐based demodulator is superior to that of the OSC‐based demodulator.

Optimal relay placement is studied for free-space optical multihop communication with link obstacles and infeasible regions. An optimal relay placement scheme is proposed to achieve the lowest outage probability, enable the links to bypass obstacles of various geometric shapes, and place the relay nodes in specified available regions. When the number of relay nodes is large, the searching space can grow exponentially, and thus, a grouping optimization technique is proposed to reduce the searching time. We numerically demonstrate that the grouping optimization can provide suboptimal solutions close to the optimal solutions, but the average searching time linearly grows with the number of relay nodes. Two useful theorems are presented to reveal insights into the optimal relay locations. Simulation results show that our proposed optimization framework can effectively provide desirable solution to the problem of optimal relay nodes placement.

We study the performance of multi-branch hierarchical combining diversity systems. Closed-form expressions are derived for asymptotic error rate and asymptotic outage probability of hierarchical selection-combining maximal-ratio combining (SC-MRC) and selection-combining equal-gain combining over Rician channels with arbitrary correlation. Comparison is made between hybrid-selection maximal-ratio combining (HS/MRC) and hierarchical SC-MRC, and it is shown that SC-MRC suffers at most 0.6 dB signal-to-noise ratio (SNR) loss with respect to HS/MRC for practical number of antennas. However, SC-MRC incurs much smaller insertion loss. Numerical results show that the analytical solutions can provide accurate estimation of error rate and outage probability in large SNR region.

Optimal relay placement is studied for free-space optical multi-hop communication when obstacles and infeasible regions exist. We propose an optimization framework to find the optimal relay locations in the specified available regions and make laser links bypass obstacles of various shapes. A grouping optimization technique is proposed to reduce the optimization time when the number of relays is large, and we numerically demonstrate that this technique can provide solutions close to the optimal solutions, and its average searching time grows linearly with the number of relays. Simulation results show that our proposed optimization framework can effectively provide good solution to the problem of optimal relay placement in limited available regions, and enable the laser links to bypass obstacles in order to achieve minimum end-to-end outage probability.

Owing to the characteristic of single frequency network (SFN) and non-line of sight (NLOS) environment, base station (BS) identification becomes a prominent problem in SFN positioning systems. In this work, a universal BS identification algorithm is proposed for any DTV standard. The basic idea is to formulate the base station identification problem as a data classification which is then solved by the least distance classifier. The state prediction model is utilized to estimate kinematics parameters and thus decrease the system hardware requirement. In order to obtain accurate state prediction for BS identification, an interacting multiple model (IMM) method is adopted to mitigate the NLOS effect for performance improvement. Because of close relation between the position estimation and BS identification, the proposed method is more effective as compared to some existing methods. Simulation results show that the proposed algorithm can perform well in both unfixed BS set and NLOS environments.

In this paper, a wavelet-based underwater acoustic (UWA) communication system is proposed. The convolutional structure of the UWA channel is exploited and the pilot assisted channel estimation is formulated as a sparse recovery problem. Then the restricted isometry property (RIP) of the measurement matrix is investigated via eigenvalue analysis and Gersgorin circle theorem. It's proved that the sparse recovery of the wavelet-based UWA channel satisfies the RIP. With the above setup, comparisons of channel estimation for OFDM-based and wavelet-based UWA communication systems are deployed. Simulation results show that the wavelet-based system achieves more accurate channel estimation performance than the OFDM-based system under the same conditions of bandwidth, duration, data rate and channel profile.

Underwater acoustic (UWA) channel is typically sparse. In this paper, a complex Homotopy algorithm is presented and then applied for UWA OFDM channel estimation. Two enhancements that exploit UWA channel temporal correlation for the compressed-sensing(CS)-based channel estimators are proposed. The first one is based on a first-order Gauss-Markov (GM) model which uses the previous channel estimate to assist current one. The other is to use the recursive least-squares (RLS) algorithm together with the CS algorithms to track the time-varying UWA channel. Simulation results show that the Homotopy algorithm offers faster and more accurate UWA channel estimation performance than other sparse recovery methods, and the proposed enhancements offer further performance improvement.

A base station (BS) identification algorithm which does not modify the transmitter hardware and digital terrestrial television broadcasting signal framework is proposed for single-frequency networks positioning system. Similar to the outlier detection in multi-object tracking environments, the BS identification is formulated as a data validation problem which is solved with gate construction. Compared with traditional gate algorithm, an additional mode probability parameter is introduced to describe the wireless channel condition and overcome its drawback in non-stationary wireless channel environments by the interacting multiple model approach. In the proposed algorithm, two parallel innovation statistical distribution deduction modes with line-of-sight and non-line-of-sight condition are employed to estimate the corresponding parameters, respectively. Then a combination scheme is utilised to mix the two estimated statistical parameters by the predicted mode probabilities and obtains the final innovation distribution. At last, gate parameter is calculated like traditional gate algorithm and the BS identification is achieved. Simulation results demonstrate its robustness and high performance in different scenarios.

Due to multipath delay spread and relatively high sampling rate in OFDM systems, the channel estimation is formulated as a sparse recovery problem, where a hybrid compressed sensing algorithm as subspace orthogonal matching pursuit (SOMP) is proposed. SOMP first identifies the channel sparsity and then iteratively refines the sparse recovery result, which essentially combines the advantages of orthogonal matching pursuit (OMP) and subspace pursuit (SP). Since SOMP still belongs to greedy algorithms, its computational complexity is in the same order as OMP. With frequency orthogonal random pilot placement, the technique is also ex tend to MIMO OFDM systems. Simulation results based on 3GPP spatial channel model (SCM) demonstrate that SOMP performs better than OMP, SP and interpolated least square (LS) in terms of normalized mean square error (NMSE).

In order to reduce the pilot number and improve the spectral efficiency, recently emerged compressed sensing (CS) technique is applied for digital broadcast channel estimation. According to the six channel profiles of the ETSI digital radio mondiale (DRM) standard, the subspace pursuit (SP) algorithm is employed for the delay spread and attenuation estimation of each path in the case where the channel profile is identified and the multipath number is known beforehand. The stop condition for SP is that the estimated sparsity equals the multipath number. For the case where the multipath number is unknown, the orthogonal matching pursuit (OMP) algorithm is employed for channel estimation, while the stop condition is that the estimation satisfies the level of the noise variance. Simulation results show that with the same number of pilots, CS algorithms with randomly placed pilots outperform traditional cubic-spline-interpolation-based least square (LS) channel estimation. SP is also demonstrated to be better than OMP when the multipath number is known as a priori.

The authors investigate the performance of the probabilistic data association (PDA) detector in a cooperative system that consists of a source, a relay and a destination, which are all multiantenna nodes. Taking advantage of the soft information provided by the PDA detector at the relay, the authors present a relaying scheme in light of which the relay can just transmit signals to the destination under the condition of having high detection reliability. Characterised by the means on how to exploit the information provided by both the source and the relay at the destination, two detectors named as combing PDA (C-PDA) and iterative PDA (I-PDA) are developed, respectively. C-PDA adopts one PDA detector to retrieve the transmit signals by combining the received signals from the source and the relay. I-PDA employs two concatenated PDA detectors to perform detection with respect to the received signals from the source and the relay, respectively. Just as in turbo decoding, soft information is iteratively exchanged between these two concatenated PDA detectors to enhance the performance of I-PDA. Simulation results have shown that significant performance gains can be achieved by both C-PDA and I-PDA over the conventional zero-forcing detector.

Compressed sensing (CS) has recently been applied for pilot-aided sparse channel estimation. However, the design of the pilot placement has not been considered. In this letter, we propose a scheme using the modified discrete stochastic approximation to optimize the pilot placement in OFDM systems. The channel data is employed to offline search the near-optimal pilot placement before the transmission. Meanwhile we also get a criterion to select CS algorithms based on the mean squared error (MSE) minimization. Simulations using a sparse wireless channel model have validated the effectiveness of the proposed scheme, which is demonstrated to be much faster convergent and more efficient than the exhaustive search. It has been shown that substantial performance improvement can be achieved for OMP and YALL1 based channel estimation, where YALL1 is preferred.

The separable low complexity 2D HMM proposed in [1] builds on the assumption of conditional independence in the relationship between adjacent blocks. The authors view this assumption as the key assumption to reduce the complexity. In this communication, we show that this key assumption is entirely unnecessary.

For original paper by Wiegandt et al., see IEEE Trans. Commun., vol.51, no.7, p.1123-34 (July 2003).

Mumford-Shah piecewise smooth functional is a variational PDE model widely used in image segmentation and smoothing. An analogous discrete model which models image as an MRF has also been built. In this paper, we propose another discrete Mumford-Shah piecewise smooth model on lattice from a different perspective. We present a discrete objective functional, as well as the method to find the solution. Only two simple and deterministic optimization techniques, that is, derivation and greedy algorithm are used in the model to seek the solution. Compared with traditional continuous model, the model in this paper is much simpler and the approach is much easier and faster

Facial feature detection is very important in the face recognition literature. We propose a facial boundary extraction method, which includes two steps. Firstly, the facial geometry feature, such as eye, mouth, nose is detected by the eigenfeature method. Secondly, under the help of the geometry feature, the face boundary can be extracted by the statistical constraint active contour model (SCACM) and repeating initialization. The experiment shows the proposed method is effective.