Recognition of radio signals on collaborative satellites is a prerequisite part of spectrum monitoring onboard, which can overcome the influence of terrestrial obstacles and the limitation of resources on a single satellite. In this paper, in order to efficiently implement the recognition of radio signals onboard based on Deep Neural Network (DNN) inference, we divide the DNN inference tasks into partitions, deploy them on the selected collaborative low earth orbit (LEO) satellites and offer them with suitable resources for processing. Furthermore, a problem for assignment of inference tasks is formulated with jointly considering latency, energy and memory. To tackle this challenge, we propose an algorithm based on proximal policy optimization (PPO) and design a conditional immediate reward function, which balances the optimization objectives while respecting the privacy of collected radio signals and the constraints of resources on the satellites. The numerical results demonstrate that the proposed method can reliably and effectively solve the assignment problem of inference tasks, which performs a remarkable improvement in the aspects of total delay, energy consumption and balance in memory utilization for the signal recognition on the collaborative satellites.

Considering progressively more flexible satellite payloads and increasingly dynamic and heterogeneous traffic requirements, it is essential to allocate the limited and multidimensional communication resources efficiently. In order to match system throughput and users’ demands, an algorithm based on multi-agent reinforcement learning for multi-beam satellite systems is proposed in this paper, which takes two degrees of freedom, power and frequency, into account. To that end, a downlink model is established firstly. Then a joint frequency assignment and power allocation optimization problem is formulated and a multi-agent deep reinforcement learning algorithm using centralized training and distributed execution is presented, where each beam is modeled as one agent to avoid the curse of dimensionality in the action space. Moreover, a converting multi-discrete actions to discrete (CMD) trick is raised to handle the hierarchical multi-discrete actions. Simulation results show that the proposed approach has better performance compared to the existing algorithms and can achieve real-time frequency assignment and power allocation intelligently.

In this paper, a linear precoding scheme is proposed to maximize the total throughput of a broadband geostationary earth orbit (GEO) satellite system based on traffic demands. The weighted max-min fair (WMMF) problem under the per-antenna power constraints and the traffic demands constraints is investigated to achieve this purpose. Semidefinite relaxation (SDR) approach and bisection search method are jointly adopted to solve the corresponding related problem, and then Gaussian randomization and a power control problem are proposed to get a suboptimal solution of the WMMF problem. Different traffic demands scenarios and weighted values selection schemes are considered to analyze the impact of them on the precoding design. Simulation results demonstrate that the total performance of the proposed scheme has been improved significantly over the traditional schemes and is better than the rate balance scheme.

In this study, a new scheme called adaptive hierarchical coding and modulation has been proposed, based on Adaptive Coding and Modulation (ACM) and hierarchical modulation (HM). The primary contribution of this study is summarised as the following aspects. Firstly, 16‐ to 64‐APSK constellations with size and labelling optimisation method are proposed for HM, which can be adapted to transmitting multiple hierarchies with different QoS streams and to the non‐linear satellite channels due to a lower peak‐to‐average power ratio compared to traditional hierarchical QAM modulation. Bit channel capacity and Chernoff error bound are introduced to optimise size and labelling of the constellation with interior‐point algorithm and modified binary switch algorithm for a specific channel model to get a better performance. Secondly, combining with optimised constellations, coding and modulation modes set is obtained from using constellation design and optimisation method, with different modulation orders and code rates. And over satellite channels with rain and location attenuation, the proposed scheme is able to adapt itself to a changing channel condition and increase the transmission rate compared with ACM schemes of DVB‐S2 and DVB‐S2X standard.

Wireless networks are growing in the value of application in many areas, in which accurate clock synchronization is required when tasks are performed in a collaborative fashion among nodes. Especially, cooperative synchronization techniques lead to significant performance improvement compared with traditional methods. However, the correlation among agents renders the performance analysis of cooperative network synchronization difficult. In this paper, we introduce the concept of information coupling intensity to the analysis of interaction between agents. Our approach enables us to derive closed-form asymptotic expressions under specific network topologies, and relate them to various network parameters.

Tomlinson-Harashima precoding (THP) is a popular nonlinear interference mitigation technique employed at the transmitter side which can provide better performance in comparison with linear precoding technique with a slight increase in complexity. It can be considered as a dual to the successive interference cancellation (SIC). In this paper, we focus on a new THP scheme for the downlink of multiuser multiple-input multiple-output (MIMO) systems with decentralized receivers. In contrast to previous approaches, the proposed algorithm allows the provision of transmitting different types of information simultaneously subjected to different Quality of Service (QoS) requirements of each active user. This algorithm is based on minimum mean square error (MMSE) criterion and the closed-form solution of the processing matrices is found by applying Cholesky factorization with symmetric permutation. For this THP algorithm, by suitably ordering the rows of channel matrix we can obtain better performance. Moreover, it significantly improves the bit error rate (BER) performance as well as the achievable rate as compared with zero-forcing (ZF) THP available in the literature, which can be shown from simulation results.

The rapid development of mobile communications requires even higher spectral efficiency. Non-orthogonal multiple access (NOMA) has emerged as a promising technology to further increase the access efficiency of wireless networks. Among several NOMA schemes, it has been shown that sparse code multiple access (SCMA) is able to achieve better performance. In this paper, we consider a downlink MIMO-SCMA system over frequency selective fading channels. For optimal detection, the complexity increases exponentially with the product of the number of users, the number of antennas and the channel length. To tackle this challenge, we propose near optimal low-complexity iterative receivers based on factor graph. By introducing auxiliary variables, a stretched factor graph is constructed and a hybrid belief propagation (BP) and expectation propagation (EP) receiver, named stretch-BP-EP, is proposed. Considering the convergence problem of BP algorithm on loopy factor graph, we convexify the Bethe free energy and propose a convergence-guaranteed BP-EP receiver, named conv-BP-EP. We further consider cooperative network and propose two distributed cooperative detection schemes to exploit the diversity gain, namely, belief consensus-based algorithm and the Bregman alternative direction method of multipliers (ADMM)-based method. Simulation results verify the superior performance of the proposed conv-BP-EP receiver compared with other methods. The two proposed distributed cooperative detection schemes can improve the bit error rate performance by exploiting the diversity gain. Moreover, Bregman ADMM method outperforms the belief consensus-based algorithm in noisy inter-user links.

In this paper, a code-aided maximum-likelihood and moment-based joint SNR estimator is proposed for M- ary amplitude phase shift keying (APSK) signals over AWGN channels. The proposed estimator significantly improves the performance at low SNRs by utilizing the syndrome in the LDPC codes to act as a reference measurement of estimation performance. Moreover, a methodology to measure the performance of the estimation by the syndrome of the LDPC codes is derived and the simulation results reveal that the number of 0s in the syndrome presents a positive correlation with the SNR. Compared with code-aided maximum-likelihood (ML) estimators and moment-based estimators for M-APSK signals, it is validated that the proposed joint SNR estimator has integrated the advantages of the classical approaches and simulation results also show that the proposed estimator exploiting the decision metric of the selector performs better at the SNR estimation range.

As the mobile communications develop rapidly, ever higher spectral efficiency is required. The sparse code multiple access (SCMA) has been recognized as a promising technology to further increase the access efficiency of wireless networks. In this paper, we consider the receiver design problem for SCMA system over frequency selective channels. The conventional minimum mean squared error (MMSE) detection method suffers from huge complexity due to the the multi-user and inter-symbol interferences. To this end, we propose a near optimal low complexity message passing receiver. By approximating the discrete log-likelihood ratio as Gaussian random variable, all messages on factor graph can be obtained as Gaussian distributions. Furthermore, we propose to introduce auxiliary variables to the factor graph and develop a novel hybrid belief propagation (BP) and expectation propagation (EP) receiver. Simulation results show that the proposed hybrid BP-EP method performs close to the MMSE-based receiver with reduced complexity. Also, compared to the orthogonal multiple access scheme, the considered SCMA system with the proposed receiver is able to support 50% more users.

We propose a novel method for joint two-dimensional (2D) direction-of-arrival (DOA) and channel estimation with data detection for uniform rectangular arrays (URAs) for the massive multiple-input multiple-output (MIMO) systems. The conventional DOA estimation algorithms usually assume that the channel impulse responses are known exactly. However, the large number of antennas in a massive MIMO system can lead to a challenge in estimating accurate corresponding channel impulse responses. In contrast, a joint DOA and channel estimation scheme is proposed, which first estimates the channel impulse responses for the links between the transmitters and antenna elements using training sequences. After that, the DOAs of the waves are estimated based on a unitary ESPRIT algorithm using previous channel impulse response estimates instead of accurate channel impulse responses and then, the enhanced channel impulse response estimates can be obtained. The proposed estimator enjoys closed-form expressions, and thus it bypasses the search and pairing processes. In addition, a low-complexity approach toward data detection is presented by reducing the dimension of the inversion matrix in massive MIMO systems. Different cases for the proposed method are analyzed by changing the number of antennas. Experimental results demonstrate the validity of the proposed method.

Location-aware wireless networks can provide precise location information in harsh environments, however, which is only possible when all nodes are well-functioning. In this paper, we propose algorithms and analyze the performance limits for both non-cooperative and cooperative localization networks in the presence of ranging outliers. Especially, we show that the localization performance can be boosted by using the cooperative outlier detection scheme. An algorithm based on expectation-maximization is proposed for non-cooperative localization networks, while a variational message passing-based algorithm is proposed for the cooperative counterparts. Performance limits are investigated using Cramér-Rao lower bound. Further inspection on the performance limits confirms the performance gain from the cooperative detection scheme. Stochastic geometric analysis is also carried out to account for the stochastic nature of wireless networks, as well as to provide simpler expressions and additional insights. Simulation results corroborate the analytical results, and show that both of the proposed algorithms are capable of attaining the corresponding performance limits at a significantly reduced computational cost compared with existing algorithms.

In this paper, we present a conditional restricted Boltzmann machine (CRBM) based speech bandwidth extension (BWE) method. A CRBM is employed to obtain time information and model deep non-linear relationships between the spectral envelope features of low frequency (LF) and high frequency (HF). Two exclusive CRBMs are adopted to model the distribution of LF's and HF's spectral envelope features. respectively. A neural network (NN) is then used to model the joint distribution of hidden variables extracted from the two CRBMs. The proposed method takes advantage of the strong ability of CRBM in discovering the temporal correlation between adjacent frames and modeling deep non-linear relationships between input and output. Both the objective and subjective evaluations indicate that our proposed method outperforms the conventional Gaussian mixture model based methods and other NN based methods.

Owing to the wireless signal power received by the energy harvesting (EH) node is generally higher than that of the information decoding (ID) node in simultaneous wireless information and power transfer (SWIPT) system, the confidential information becomes vulnerable to be wiretapped. Motivated, in this paper, we aim at realizing secure communications for two-user MIMO interference channel with SWIPT. Unfortunately, the formulated secrecy rate maximization problem is non-convex with respect to the covariance matrices of two transmitters, thus an alternative algorithm is proposed to solve the nonconvex optimization problem. Firstly, the orthogonal-projection-based optimization algorithm is performed to completely suppress the interference to ID receiver, then by applying the Taylor series expansion, the covariance matrix of information transmitter is optimized based on the dual optimization. Finally, numerical experiments are conducted to validate the security performance of the proposed algorithm.

Modern mobile communication applications raise the requirement of high quality support for high mobility users. In this paper, we present a Bayesian graphical model based frequency domain equalization method for faster-than-Nyquist (FTN) signaling in doubly selective channels. The conventional frequency domain minimum mean squared error (FD-MMSE) equalizer suffers high complexity due to the interferences induced by adjacent frequency symbols. To tackle this problem, a low complexity iterative message passing method namely, belief propagation is employed on the Bayesian graphical model to detect the FTN symbols. Compared to the low complexity variational inference method, the proposed algorithm considers the conditional dependencies between symbols and therefore can improve the performance. Simulation results show that the proposed equalization method has similar performance of the MMSE equalizer and outperforms the variational inference method.

Obtaining the location of a “passive” target in wireless sensor networks has attracted numerous interest in recent years. This paper considers the passive localization based on time-of-arrival measurements in an asynchronous sensor network where the receivers are with both clock skew and offset. Based on the factor graph model, the beliefs (approximated marginal) of target location and clock parameters can be obtained by executing iterative message passing algorithms. To reduce the huge complexity of particle based method, we propose two approximate approaches to determine parametric Gaussian message passing. Simulation results show that the proposed low complexity algorithm performs close to the particle-based method and attain the Cramer-Rao bound.

A maximum likelihood-based code-aided joint carrier phase estimation and ambiguity resolution algorithm is proposed for coded amplitude and phase shift keying (APSK) signals. The proposed estimator iteratively uses the a posteriori probability of coded bits obtained from the channel decoder to improve the performance of phase estimation and ambiguity resolution. Two initialization schemes are employed for systems with and without pilot symbols, which reduce the number of initial phase values required to bootstrap the iterative estimation algorithm. Compared with the existing estimators, simulation results demonstrate the performance improvement of the proposed algorithm in both mean-squared estimation error and bit error rate with lower computational complexity.

Thanks to gigahertz unlicensed spectrum, the millimeter wave (mmWave) communication becomes an important enabling technology to meet the increasing data rate demands of future communication systems. It is also well-known that wireless communications are susceptible to security threatening, especially for wireless two-way relaying networks. Hence, in this work, we propose two secrecy beamforming schemes for large mmWave two-way relaying networks. Firstly, the secrecy rate maximization problem is considered with the constraint of relay power. Then the relay transmit power is optimized to satisfy the secrecy rate requirement of network. Owing to the nonconvexity of original optimization problem, the null-space beamforming is utilized to transform both problems into the standard SOCP problem, which can be solved effectively with the convex optimization technique. Finally, numerical experiments are conducted to show the superior security performance and high energy efficiency of the two proposed secrecy beamforming schemes, respectively.

In this paper, we present a new speech bandwidth extension method (BWE) using recurrent temporal restricted Boltzmann machine (RTRBM). The conventional Gaussian mixture model (GMM)-based and deep neural networks (DNNs)-based BWE methods perform stably and effectively. However, the mapping function of GMM-based methods is a piecewise linear transformation, which is insufficient to model the complex nonlinear mapping relationship between the spectral envelope features of low frequency (LF) and high frequency (HF). In the conventional DNNs methods, temporal correlations across speech frames are ignored, resulting in spectral detail loss of the reconstructed speech by BWE. To counter these issues, a multilayer DNN which is composed of two RTRBMs and a feedforward neural network (NN) is employed to obtain time information and model deep nonlinear relationships between the spectral envelope features of LF and HF. The proposed method takes advantage of the strong ability of RTRBM in discovering the temporal correlation in the high-order space and modeling deep nonlinear relationships between input and output. Both the objective and subjective evaluations indicate that our proposed method outperforms the conventional GMM-based methods and other NN-based methods.

This work deals with frequency-domain equalization for faster-than-Nyquist (FTN) signaling in doubly selective channels (DSCs). To handle the interference of frequency-domain symbols, the minimum mean square error (MMSE) equalizer involves high complexity in DSCs. To overcome the problem, we propose low-complexity receivers based on two variational methods, i.e., mean field (MF) and Bethe approximations. Compared with the MF method, the Bethe approximation takes into account the conditional dependencies of pairwise symbols. By only considering a small set of the frequency-domain symbols that have strong interference to each other, the complexity of the proposed algorithms increases linearly with the block length. Simulation results demonstrate that the proposed algorithms for FTN signaling are able to perform close to the MMSE equalizer in DSCs while with significantly reduced computational complexity.

In this paper, we investigate a rateless code design for a two-time slot two-way relay channel (TWRC), where network coding operation is employed at the relay. Rateless codes are used at both users and the relay nodes as they can cope with various channel conditions. We consider a general TWRC framework, where the two users may use different rateless code generator matrices, thus, the relay cannot directly recover the physical-layer network coded (PNC) message of the two users. We propose a hybrid estimate and forward (EF)/decode and forward (DF) protocol with rateless codes for the TWRC. In the proposed scheme, the relay uses a joint belief propagation decoding process to decode the messages of two users. Depending on whether the relay recovers both users' messages, only one user's message or no users' messages, the relay will generate three different types of signals, namely, NC, soft NC, and soft PNC, respectively. We design the degree distributions of the rateless codes for the three nodes of the TWRC, to make them suitable not only for decoding at two users at high signal-to-noise ratio (SNR), but also for decoding at the relay at low SNR. We show that the proposed hybrid EF/DF protocol with the designed rateless codes outperforms other schemes significantly in terms of bit error rate performance.

In this paper, we study the time of arrival (TOA)-based distributed passive localization in asynchronous wireless network. Performing synchronization between receivers before target localization is possible but costs extra energy and bandwidth. To this end, We propose an expectation maximization (EM) algorithm to locate the passive target in the presence of receivers' clock offsets. To improve the robustness of the proposed algorithm, we employ the average consensus scheme to obtain the location of target at each receiver in a distributed way. A quadratic polynomial approximation is proposed to reduce the communication overhead and computational complexity. To evaluate the performance of the proposed algorithm, the Cramer-Rao bound (CRB) of the target's position estimation is derived. Simulation results show that the proposed distributed EM algorithm performs close to the centralized counterpart. It outperforms the conventional two step estimation method and the one based on time difference of arrival. Moreover, the proposed algorithm can attain the derived CRB, which demonstrates the effectiveness of the algorithm.

In wireless sensor networks, acquiring accurate timing information is a crucial requirement for time-based sensor localization. Utilizing a joint localization and synchronization method in sensor networks can improve positioning speed and accuracy. In this paper, we present a unified factor graph framework based on time of arrival (TOA) measurements to solve the problem of joint localization and time synchronization. A novel distributed cooperative joint estimation method based on belief propagation (BP) is proposed. We linearize the nonlinear terms in messages on factor graph in order to obtain a closed Gaussian form solution of message update. Accordingly, only the means and variances have to be updated and transmitted, which significantly reduce the communication overhead and computational complexity. To further reduce the communication overhead, we propose a message passing schedule. Simulation results show that the proposed BP method reach close performance to particle-based approaches with lower complexity.

This paper proposes a new speech bandwidth expansion method, which uses Deep Neural Networks (DNNs) to build high-order eigenspaces between the low frequency components and the high frequency components of the speech signal. A four-layer DNN is trained layer-by-layer from a cascade of Neural Networks (NNs) and two Gaussian-Bernoulli Restricted Boltzmann Machines (GBRBMs). The GBRBMs are adopted to model the distribution of spectral envelopes of the low frequency and the high frequency respectively. The NNs are used to model the joint distribution of hidden variables extracted from the two GBRBMs. The proposed method takes advantage of the strong modeling ability of GBRBMs in modeling the distribution of the spectral envelopes. And both the objective and subjective test results show that the proposed method outperforms the conventional GMM based method.

We analyze the achievable rate of the uplink of a single-cell multi-user distributed massive multiple-input-multiple-output (MIMO) system. Each user is equipped with single antenna and the base station (BS) is equipped with a large number of distributed antennas. We derive an analytical expression for the asymptotic ergodic achievable rate of the system under zero-forcing (ZF) detector. In particular, we consider circular antenna array, where the distributed BS antennas are located evenly on a circle, and derive an analytical expression and closed-form bounds for the achievable rate of an arbitrarily located user. Subsequently, closed-form bounds on the average achievable rate per user are obtained under the assumption that the users are uniformly located. Based on the bounds, we can understand the behavior of the system rate with respect to different parameters and find the optimal location of the circular BS antenna array that maximizes the average rate. Numerical results are provided to assess our analytical results and examine the impact of the number and the location of the BS antennas, the transmit power, and the path-loss exponent on system performance. Simulations on multi-cell networks are also demonstrated. Our work shows that circularly distributed massive MIMO system largely outperforms centralized massive MIMO system.

In this paper, the conditional codebook mapping (CCM) method is proposed for super-wideband extension (SWB) of speech signals to achieve a wider bandwidth up to 12.8 kHz. As we know, the performance of bandwidth extension by codebook mapping (CM) is restricted by the codebook size. The proposed CCM-based BWE method reduces the restriction while introducing a conditional codebook and is applied in AMR-WB at 19.85 kbps. Both the Objective test results in the term of Log-spectral distortion (LSD) and Perceptual Evaluation of Speech Quality (PESQ) and the subjective test results show that the CCM-based SWB extension method achieves higher speech quality compared with the CM method.

Speech synthesizer is commonly used in human-computer interaction. In many applicational cases, the computing resource is limited while real-time synthesis is demanded. The HMM-based speech synthesis technique allows creating a natural voice quality with small footprint, but current synthesizers require the concatenation of sentence level acoustic units, which is not applicable in real-time mode. In this paper, we propose a blocked parameter generation algorithm for low latency speech synthesis which can work real-time in resource limited applications. Phonetic units at various time spans are used as blocks. The objective and subjective evaluations suggest that the proposed system produce promising voice quality with a low demand for the computing resource.

The evaluation of Cramer-Rao Bounds (CRBs) for phase estimation of coded linearly modulated signals are difficult due to the intractable expectations of the likelihood function with respect to coded symbols. In this paper, we propose two methods towards this end. The first one is a semi-analytical method for coded QPSK signals. Based on Gaussian approximation of extrinsic information, the expression of CRB is derived in terms of signal-to-noise ratio (SNR) and the mean of extrinsic information in closed form. For high-order modulations, e.g., 16QAM signal, we propose a numerical method based on multidimensional Gauss-Hermite Quadrature (GHQ). It is shown that, without suffering from the linearization error, the results of numerical method by GHQ outperform the semi-analytical results, and the former are consistent with that of the Monte Carlo simulations for systems with different codes and numbers of decoding iterations.

Localization in wireless sensor networks has become an attractive research field in recent years. Most studies focus on the mitigation of measurement noise by assuming the positions of anchors are perfectly known, which may become impractical due to some inevitable errors in the observations of anchors' positions. This paper addresses the problem by taking into account the a priori position information of inaccurate anchors. Considering that the maximum likelihood (ML) algorithm suffers from the intractable integrals involved, we resort to expectation maximization (EM) algorithm to solve this problem iteratively. The a posteriori probability of the anchor position is approximated by circularly symmetric Gaussian distribution, with parameters optimized by minimizing Kullback-Leibler divergence of the two distributions. Building on this approximation, we are able to derive the expectation step in closed form. Particle swarm optimization is then followed to perform the maximization step. Numerical results demonstrate that the proposed EM estimator is less sensitive to the anchors' uncertainties and it significantly outperforms the traditional ML estimator which ignores the prior information of anchors.

The authors propose a low complexity list successive cancellation (LCLSC) decoding algorithm, where the advantages of the successive cancellation (SC) decoding and the list successive cancellation (LSC) decoding are both considered. In the proposed decoding, SC decoding instead of LSC decoding is implemented when all information bits from bad subchannels are received reliably. While the reliability of each information bit is estimated by its likelihood ratio (LR), the bit channel quality is measured via its Bhattacharyya parameter. To achieve this goal, the authors introduce two thresholds: LR threshold and Bhattacharyya parameter threshold. Also, the methods to determine them are both elaborated. The numerical results suggest that the complexity of LCLSC decoding is much lower than LSC decoding and can be close to that of SC decoding, while the error performance is almost equal to that of LSC decoding. Especially, when the code rate is in low region, the advantage of our decoding is more obvious.

In this paper, the authors investigate the distributed power allocation for the multicell orthogonal frequency division multiple access networks by taking both the energy efficiency and the intercell interference (ICI) mitigation into account. A performance metric termed as throughput contribution is exploited to measure how the ICI is effectively coordinated. To achieve a distributed power allocation scheme for each base station (BS), the throughput contribution of each BS to the network is first given based on a pricing mechanism. Different from the existing works, a biobjective problem is formulated based on the multiobjective optimisation theory, which aims at maximising the throughput contribution of the BS to the network and minimising its total power consumption at the same time. By using the method of the Pascoletti and Serafini scalarisation, the relationship between the varying parameters and the minimal solutions is revealed. Furthermore, to exploit the relationship an algorithm is proposed based on which all the solutions on the boundary of the efficient set can be achieved by adaptively adjusting the involved parameters. With the obtained solution set, the decision maker has more choices in the power allocation schemes in terms of both the energy consumption and the throughput. Finally, the performance of the algorithm is assessed by the simulation results.

Localisation in wireless sensor networks (WSNs) has received much attention, where most studies focus on mitigating the effects of measurement noise under the assumption of accurate anchors’ positions. However, anchors’ positions could be inaccurate for the inevitable errors in practical observations. This paper studies the sensor localisation with both inaccurate anchors’ positions and noisy range measurements in WSNs. To solve the intractable integrals in likelihood function, the authors propose to use expectation‐maximisation (EM) algorithm to obtain the maximum likelihood (ML) estimation iteratively. The ‘a posteriori’ distribution of the anchor's position uncertainty is approximated to a circularly symmetric Gaussian distribution by minimising the Kullback‐Leibler divergence between them. Building on this, the authors derive the expectation step in a closed‐form expression. In the maximisation step, based on the Taylor expansion of the confluent hypergeometric function of the first kind presented in the expectation step, analytical solutions are obtained. Simulation results show that the proposed EM estimator significantly outperforms the approximated ML estimator. The performance gain by using the EM estimator becomes larger as the increase of anchors’ position uncertainties. Moreover, the performance of the EM estimator is close to that of the Monte Carlo‐based estimator with much less computational complexities.

This paper proposes a novel multichannel audio signal compression method based on tensor decomposition. The multichannel audio tensor space is established with three factors (channel, time, and frequency) and is decomposed into the core tensor and three factor matrices based on tucker model. Only the truncated core tensor is transmitted to the decoder which is multiplied by the factor matrices trained before processing. The performance of the proposed method is evaluated with approximation errors, compression degree and listening tests. When the core tensor is smaller, the compression degree will be higher. A very noticeable compression capability will be achieved with an acceptable retrieved quality. The novelty of the proposed method is that it enables both high compression capability and backward compatibility with little signal distortion to the hearing.

A code-aided (CA) iterative signal-to-noise ratio (SNR) estimator based on Expectation Maximization (EM) algorithm is proposed for M-ary amplitude phase shift keying (APSK) signals. The estimation algorithm utilizes a posteriori probabilities of coded bits obtained from channel decoder to improve estimation precision at low SNRs. Furthermore, Cramer-Rao bound (CRB) of the proposed CA iterative SNR estimator for M-APSK is derived and simulated numerically. Compared with the non-data-aided (NDA) EM-based estimator and moments-based estimators for M-APSK signals, computer simulation results show that the proposed estimator exploiting a posteriori information has more excellent performance, especially at low SNRs. It is also demon-strated that the performances of the proposed CA SNR estimator for 16- and 32-APSK signals are very close to the derived CRB.

This paper presents a message passing approach to joint channel estimation, data detection and decoding over time-selective Rayleigh fading channel with residual carrier frequency offset (CFO). The proposed algorithm utilizes the sum product algorithm (SPA) implemented on a factor graph (FG) representing the joint a posteriori probability distribution of the unknown CFO, information bits and channel coefficients vector given the channel output. A combination of particle filtering and Gaussian parameterization is employed to approximate the exact probability density function in message passing for CFO and channel estimation. Computer simulations demonstrate the effectiveness of the proposed algorithm in combating the CFO over unknown Rayleigh fading channels.

In this study, a unified linear minimum mean‐square‐error (LMMSE) transceiver design framework is investigated, which is suitable for a wide range of wireless systems. The unified design is based on an elegant and powerful mathematical programming technology termed as quadratic matrix programming (QMP). Based on QMP it can be observed that for different wireless systems, there are certain common characteristics which can be exploited to design LMMSE transceivers, for example, the quadratic forms. It is also discovered that evolving from a point‐to‐point multiple‐input–multiple‐output (MIMO) system to various advanced wireless systems such as multi‐cell coordinated systems, multi‐user MIMO systems, MIMO cognitive radio systems, amplify‐and‐forward MIMO relaying systems and so on, the quadratic nature is always kept and the LMMSE transceiver designs can always be carried out via iteratively solving a number of QMP problems. A comprehensive framework on how to solve QMP problems is also given. The work presented in this study is likely to be the first shot for the transceiver design for the future ever‐changing wireless systems.

In this letter, we investigate multiple-input multiple-output (MIMO) communications under energy harvesting (EH) constraints. In our considered EH system, there is one information transmitting (ITx) node, one traditional information receiving (IRx) node and multiple EH nodes. EH nodes can transform the received electromagnetic waves into energy to enlarge the network operation life. When the ITx node sends signals to the destination, it should also optimize the beamforming/precoder matrix to charge the EH nodes efficiently simultaneously. Additionally, the charged energy should be larger than a predefined threshold. Under the EH constraints, in our work both minimum mean-square-error (MMSE) and mutual information are taken as the performance metrics for the beamforming designs at the ITx node. In order to make the proposed algorithms suitable for practical implementation and have affordable overhead, our work focuses on the beamforming designs with partial CSI and this is the distinct contribution of our work. Finally, numerical results are given to show the performance advantages of the proposed algorithms.

Code-aided (CA) iterative carrier phase synchronizer can improve the phase estimation performance significantly. However, due to the coupling involved between phase recovery and decoding, most studies depend on extensive simulations rather than on theoretical analysis to evaluate the performance of CA phase recovery. In this paper, we propose analytical methods to fill this void. The first step is to model the cross-talks caused by phase offset as an additional Gaussian noise at low signal-to-noise ratios (SNRs). Then, a semi-analytical method is proposed to express the distribution of extrinsic information from channel decoder as a function of phase offset. Building on this model, both the open-loop and closed-loop performance of CA iterative hard/soft decision-directed phase synchronizers are derived in closed-form. The analytical results explicitly reveal how extrinsic information contributes to the performance improvement of carrier phase estimation. Monte Carlo simulation results corroborate that the proposed methods are able to accurately characterize the performance of CA iterative carrier phase recovery for systems with different channel codes.

Digital baseband predistortion technology can effectively reduce the system nonlinear distortion. However, the predistortion algorithms based on indirect structure are sensitive to measurement noise and the traditional loop-delay estimation algorithms are vulnerable to the amplifier's nonlinear distortion especially in the amplifier saturation region. In this paper, we first present an adaptive nonlinear predistorter based on direct learning structure and then propose a new loop-delay estimation algorithm which applies to PA nonlinear distortion conditions by analyzing the relationship between the system loop-delay misalignment and the amplitude of HPA model identification error output. Simulation results show that the power spectral density (PSD) after the predistortion is very close to that of the ideal signal and a good NMSE performance is achieved.

In this paper, we consider robust distributed filter-and-forward beamforming design for two-way relaying networks over the frequency selective fading channels, in which two users exchange information with the aid of multiple single-antenna relay nodes. In contrast to the prior works in which the channel state information (CSI) is available, in our work CSI for all relevant links between the users and relay nodes is not perfectly known with stochastic channel errors. Under stochastic channel errors, a robust beamforming design aiming at maximizing the total system signal-to-interference-plus-noise-ratio (SINR) under individual relay power constraints is proposed. Simulation results show that the proposed robust beamformer reduces the sensitivity of the two-way relaying systems to channel estimation errors, and performs better than the algorithm using estimated channels only.

In this paper, we propose a novel signal-to-noise ratio (SNR) estimation method for linear modulations on additive white Gaussian noise (AWGN) channel. It estimates noise power directly without estimating the received total power. The estimation mean and mean square error (MSE) are analyzed theoretically and verified by simulations. Results show that the proposed SNR estimator performs better at low SNR than the conventional estimators while it suffers a little degradation at high SNR. Moreover, the proposed estimator can be implemented with less real multiplications than the conventional ones.

In this paper, we propose an improved iterative receiver scheme for low density parity check (LDPC) codes transmitted over unknown channels affected by a strong phase noise. For achieving joint channel parameter estimation, data detection and decoding, the proposed algorithm utilizes the sum product algorithm (SPA) implemented on the factor graph (FG) that represents the joint a posteriori probability of information symbols and channel parameters given the channel output. Through the iterative use of the soft information on coded symbols from channel decoder, the proposed algorithm employs forward-backward recursions for message passing on the graph. Numerical results for binary LDPC codes show that, the proposed algorithm can be able to cope with a strong phase noise under unknown channel information and achieve nearly the same performance as the optimal coherent receiver under DVB-S2 compliant ESA phase noise model, and only a slightly decrease under strong Wiener model.

In this paper, we investigate maximum mutual information design for multi-hop amplify-and-forward (AF) multiple-input multiple-out (MIMO) relaying systems with imperfect channel state information, i.e., Gaussian distributed channel estimation errors. The robust design is formulated as a matrix-variate optimization problem. Exploiting the elegant properties of Majorization theory and matrix-variate functions, the optimal structures of the forwarding matrices at the relays and precoding matrix at the source are derived. Based on the derived structures, a water-filling solution is proposed to solve the remaining unknown variables.

Code-aided (CA) iterative carrier phase synchroniser can greatly improve the accuracy of phase estimation in turbo receivers. However, because of the iteration involved between phase recovery and decoding, most existing studies depend on extensive simulations rather than on theoretical analysis to evaluate the performance improvement of phase recovery by exploiting the coding constraints. In this study, the authors propose analytical methods to fill this void. The first step is to approximate the cross-talks caused by phase offset as Gaussian noise at low signal-to-noise ratios. Then, a semi-analytical method is proposed to express the distribution of extrinsic information from channel decoder as a function of phase offset. Building on this model, both the open-loop and closed-loop performances of CA iterative phase synchronisers are derived in closed-form. The analytical results explicitly reveal how extrinsic information contributes to the performance of carrier phase estimation. Monte Carlo simulation results corroborate that the proposed methods are able to accurately characterise the performance of CA iterative carrier phase recovery for systems with different channel codes.

In this paper, joint transceiver design for dual-hop amplify-and-forward (AF) MIMO relay systems with Gaussian distributed channel estimation errors in both two hops is investigated. Due to the fact that various linear transceiver designs can be transformed to a weighted linear minimum mean-square-error (LMMSE) transceiver design with specific weighting matrices, weighted mean square error (MSE) is chosen as the performance metric. Precoder matrix at source, forwarding matrix at relay and equalizer matrix at destination are jointly designed with channel estimation errors taken care of by Bayesian philosophy. Several existing algorithms are found to be special cases of the proposed solution. The performance advantage of the proposed robust design is demonstrated by the simulation results.

The determination of the optimal fractional Fourier transform (FrFT) order is a crucial issue for FrFT. This paper introduces a novel algorithm is proposed for the estimation of FrFT order. We use the information of pitches, harmonies and formants in the correlogram of Gammatone filterbanks to get a few candidates of the transform order. The proposed method reduces the computation complexity in the searching of optimal transform order. We apply this method for speech processing such as Mel-frequency cepstral coefficients (MFCC) extraction and speech enhancement. The experiment of MFCC extraction shows that the proposed method is superior to the traditional method based on Fourier Transform in the sense of Fisher distance improvement. The experimental results of speech enhancement also show the improvement in sense of SNR and the Itakura-Saito distance of LPC coefficients.

In this paper, we propose two spectrally efficient adaptive partial decode-and-forward (DF) cooperative communication schemes with quantized feedback: adaptive partial repetition DF scheme and adaptive partial coded cooperation DF scheme. We assume that the relay node only has partial channel-state information, which is obtained via an quantized feedback link. We use the so-called mutual information (MI) model to adaptively optimize the amount of message transmitted by the relay node under a given block-error-rate constraint. We develop adaptive algorithms and implementation details for both schemes. Simulation results show that with the quantized feedback, the MI model can predict well the amount of message that needs to be transmitted by the relay node, and that the two proposed schemes can substantially increase the spectral efficiency of the system.

In this paper, we focus on a two-hop amplify-and-forward (AF) cooperative network, in which one source node equipped with multiple antennas communicates with a single-antenna destination, relying on a single-antenna relay. In order to implement beamforming, channel state information (CSI) is feeded back to the source from relay using random vector quantization (RVQ) codebook. Furthermore, one relay with the best CSI is selected in each communication. An upper bound of the outage probability of the whole system is derived and the simulation results show that this bound is very tight.

This study studies the feedforward (FF) non-data-aided (NDA) carrier phase estimation of the amplitude and phase shift keying (APSK) signals. The true Cramer–Rao bounds (CRBs) for NDA phase estimation of APSK signals are derived and evaluated numerically using Gauss–Hermite quadrature. The jitter variance of the FF Viterbi–Viterbi (V&V) algorithm is analysed assuming the absence of data pattern noise. It is proved that, when the design parameter μ=2, the jitter variance is able to converge asymptotically to the modified CRB (MCRB) at high signal-to-noise ratios (SNRs). For practical application, the parameter μ is also optimised for 16/32/64-APSK signals at different SNRs. The analytical results of the jitter variance are verified by Monte-Carlo evaluations. It is shown that, for 32/64-APSK signals, the plain V&V algorithm cannot approach the CRBs due to the data pattern noise. A modified V&V algorithm based on the constellation partition and a linear combination of the sub-estimators is proposed to eliminate the divergence. Simulation results show that the jitter variance of the proposed estimator is very close to the CRB at the SNRs of interest and converges to the MCRB at high SNRs.

We analyze the performance of a code-aided (CA) decision-directed (DD) timing synchronizer, which can exploit the dependence structure across coded symbols to improve the timing recovery accuracy. Due to the inherent coupling between timing recovery and decoding, most existing studies rely on extensive simulation rather than on analytical methods to evaluate performance of timing recovery for coded systems. We propose analytical methods in this paper towards this end. A first key step is to approximate timing-offset-induced inter-symbol interference (ISI) as an additive Gaussian noise, since in the low signal-to-noise ratio (SNR) regime the background noise is large enough to mask the ISI. Then, we derive semi-analytical expressions for the mean and variance of extrinsic information as functions of timing offset, building on which we characterize both open-loop and closed-loop performance of decision-directed timing synchronizers. Monte Carlo simulation results corroborate that the proposed method accurately characterizes the performance of CA DD timing recovery, for systems with a wide range of channel bandwidth and different channel codes.

In this paper, we consider the effect of different rules of symbol decision on the performance of decision-directed synchronizers for LDPC-coded systems. Different from the conventional hard symbol decision based on the Maximum-A- Posteriori (MAP) criterion, soft symbol decision can be considered as the Minimum-Mean-Square-Error (MMSE) estimation of the transmitted symbol. By whether or not the coding constraints are taken into account, soft symbol decision is derived in non-code-aided (NCA) mode and code-aided (CA) mode, respectively. The performance of the soft decision-directed (SDD) synchronizer is compared with that of the hard decision-directed (HDD) counterpart in both NCA and CA mode. Simulation results show that, without much implementation complexity increase, the jitter performance of the synchronizer in CA mode significantly outperforms that in NCA mode. It is also observed that, in the scenario of this paper, SDD synchronizer works only slightly better than HDD synchronizer.

In this paper, based on analyzing some existing phase lock detectors for quadratic phase-shift keying (QPSK),we propose a novel method of estimating the signal-to-noise ratio (SNR) operating with the lock metric value of classical Mth-power (M=4) phase lock detector. The proposed lock detector-based SNR estimator can perform better than the conventional SNR estimator in terms of mean estimated value (MEV) at medium to high SNR when phase recovery loop is in-lock status. And its normalized mean squared error (NMSE) can reach Cramer-Rao bounds (CRBs) at that SNR. Moreover, the computational complexity of new estimator operating in phase recovery loop is analyzed with less additional multiplications than that of conventional one. This estimation method can also be applied to other similar phase lock detectors.

An improved data-aided (DA) frequency error detector (FED) and a frequency lock detector are proposed under large frequency offset for Digital Video Broadcasting Satellite Second Generation (DVB-S2) system. Computer simulations results show that the proposed error detector can increase the frequency acquisition range and decrease the acquisition time without complexity increase. Its closed-loop normalized frequency root mean square error (RMSE) improves at least 1.5 dB compared with that of conventional error detector. The proposed lock detector shows good lock indication. Its modified version can save more symbols to indicate the locking status.

For the symbol timing recovery techniques which are susceptible to carrier phase offset, clockless phase recovery is necessary in advance. In this paper, we propose a clockless nondata-aided (NDA) feedback phase error detector (PED) for M-ary phase-shift keying (MPSK) signal. Its derivation is given based on maximum likelihood (ML) criterion. The clockless NDA (Mthpower)PED can also be generalized with a design parameter 0≤m≤M to improve performance at low SNR. The openloop S-curves and closed-loop phase error variances of these PEDs are given with extensive simulations. Results show that the S-curve of clockless Mth-power PED is robust to signal-tonoise ratio (SNR). Its phase error variance is investigated with excellent performance under different shaping roll-off factors and parameter m. We also analyze the clockless decision-directed (DD) PED which may not be a good choice for over-sampled signals due to its unstable equilibrium point of S-curve.

Data-aided (DA) signal-to-noise ratio (SNR) estimation is required especially at low SNR. The conventional maximum likelihood (ML) DA SNR estimator requires perfect carrier phase estimation and frequency recovery. In this paper, we propose a novel carrier frequency robust DA SNR estimator with its improved variant using autocorrelation of received MPSK symbols. Computer simulations are used to examine their performance in terms of mean estimation value (MEV) and normalized mean square error (NMSE). For the example system in simulations, the MEV of proposed estimator is accurate enough with normalized frequency error on the order of symbol rate. However, its NMSE can not reach DA normalized Cramer-Rao bound (NCRB) even with large observatory length, whereas its NMSE may perform a little worse at high SNR for short pilot symbols. On the other hand, fortunately the its improved variant can reach NCRB with enough pilot symbols. What's more, the proposed DA SNR estimators can operate under large frequency errors or before the frequency recovery unit with baud rate. The implementation complexity is also analyzed.

The partially repeated cooperation system employing Low Density Parity-Check (LDPC) codes achieves space diversity and extra coding gain at the same time. This paper focuses on the power constrained system, in which the total power of the source node and the relay node remains constant. Thus different power allocation schemes result in different repetition lengths at the relay node, and it is interesting to determine the optimal in terms of performance. A modified Gaussian Approximation algorithm is proposed to search for the feasible decoding threshold of the partially repeated regular LDPC codes, with which the outage probability is obtained and used to show the power efficiency of different power allocation schemes. The numerical results show that the partially repeated scheme can achieve a higher power efficiency than the all repeated one. However, the efficiency of the power allocation scheme strongly depends on the channel realizations of the system.

In this study, the authors propose an exact maximum likelihood (ML) signal-to-noise ratio (SNR) estimator for coded linearly modulated signals. The estimator is expressed in terms of the marginal a posteriori probabilities (APPs) of the coded symbols, which can be obtained efficiently by the Bahl–Cocke–Jelinek–Raviv (BCJR) algorithm for codes defined on trellises. Simulation results show that the proposed ML code-aided (CA) SNR estimator significantly outperforms the non-data-aided (NDA) estimators in the low SNR regime. The Cramer–Rao bound (CRB) for CA SNR estimator is also derived and evaluated numerically. It is shown that the proposed ML-CA estimator performs very close to the derived bound. Comparisons of the CRBs for CA and NDA scenarios with different linearly modulated signals further illustrate the intrinsic performance improvement by exploiting the channel coding constraints.

In the above-referenced paper, equations (18) and (19) in page 691 are incorrectly printed. They are corrected herein.

Forward error control (FEC) and multiple description coding (MDC) are two classical techniques to resist packet loss for voice over IP (VoIP). AMR-WB codec has been standardized for wideband speech conversational applications and has widely potential applications in the migration of wireless or wired networks toward a single converged IP network. However, how to choose the optimal FEC or MDC for AMR-WB in different loss rate conditions is an unexplored option. In this paper, we compare the performance of different FEC and MDC techniques for AMR-WB codec both analytically and experimentally. Based on the comparison results, some practical configurations of FEC and MDC for AMR-WB codec are obtained.

In this paper, an algorithm for removing cycles in quasi-cyclic(QC) LDPC codes is presented. This algorithm can ensure that the code after cycle removal process preserves the quasi-cyclic structure and significantly improves the flexibility in parameter selection (such as the length of the code) of algebraic constructions of QC-LDPC codes. Besides, it has far lower computational complexity than the existing cycle removal algorithm. Experimental results show that this algorithm is very effective in improving the performance of the QC-LDPC codes and can construct code which has better performance than the corresponding binary LDPC code based on IEEE 802.16e standard.

In this paper, we present three algebraic methods for constructing structured nonbinary LDPC codes over finite fields, among which the first method is used to construct quasi-cyclic codes with girth at least 6 based on the automorphisms of finite fields, the second method gives a class of (4,ρ) quasi-cyclic codes with girth at least 8, the third method gives a class of codes with cycles limited. Simulation results show that the constructed codes perform very well over AWGN channel, and they have better performances or far lower computational complexities than the corresponding random Mackay codes or codes algebraically constructed by Lin.

An accurate and simple link quality model is crucial in system evaluation of different link adaptation (LA) solutions, for it acts as a link-to-system (L2S) interface which provides the block-error rate (BLER) performance of a certain modulation coding scheme (MCS) corresponding to the current SINR states. Our previous work in the work of Chen et al. (2007) has verified the good accuracy of the MI-based model for rate compatible LDPC coded OFDM system. In this paper, adaptive modulation and coding (AMC) strategy and two hybrid ARQ schemes of variable retransmission length, partial chase combining and full incremental redundancy, have been designed to check the accuracy of the look-up tables (LUTs) obtained by the MI-based model. The predicted results are quite in accord with the simulation ones, which confirms that the MI- based model provides accurate and simple LUTs for LA design of LDPC codes.

In this paper, we present three algebraic methods for constructing structured nonbinary LDPC codes over finite fields, among which the first method is based on the multiplicative inverses of nonzero elements in finite fields and gives a class of quasi-cyclic codes with girth 6, the second method gives a class of (3, rho) quasi-cyclic codes with girth 8, the third method gives a class of structured codes with cycles limited. The codes given in examples perform well over AWGN channel and have better performances or far lower computational complexities than the corresponding random Mackay codes or codes algebraically constructed by Shu Lin.

Robust and flexible speech codecs are required more and more by speech communication over unreliable and heterogeneous channels such as the Internet. In this paper, a novel multiple description scalable codec is proposed, which is based on the sinusoidal model and equivalent rectangular bandwidth (ERB) noise model. It combines the advantages of scalable coding and multiple descriptions coding (MDC), and provides relatively high transmission reliability and rate adaptation. The lost packet does not affect the state recovery of this state-less codec. The performance of the proposed codec was evaluated under memory and memoryless channels. Analytical and experimental results show that it is suitable to unreliable and band limited channel such as Internet.

Eigen-beamforming technique is introduced in two typical multi-antenna systems: space-time block coding and VBALST. When the channel covariance information is known by the transmitter, optimal power allocation algorithm for eigen-beamforming is proposed based on minimizing the transmission errors. Also, it is indicated in this paper that this algorithm can be simplified when the channel is independent - identically distributed (I.I.D.) channel. By simulation and analysis, eigen-beamforming can greatly improve the performance of multi-antenna system when this power allocation algorithm is adopted.

System evaluation of different resource allocation, power control and link adaptation (LA) solutions require the multi-cell system simulations, which provide important reference to the standardization and system design. Such a system-level simulator is expected to be reliable and quick enough, which requires a very accurate and simple link quality model. The exponential effective-SNR mapping (EESM) model [1, 2] and mutual information-based (MI-based) model [3] were proposed recently, which have been proved to be pretty accurate and independent of fading for 3 GPP Turbo-coded orthogonal frequency division multiplexing (OFDM) system [3, 4]. This paper investigates the accuracies of the above two models for the low-density parity-check (LDPC) codes, in terms of veracity and complexity for different coding rates, block lengths, mobile speeds and modulation schemes. Simulation results verify that both methods show quite good performance in different multi-state channels. The simpler MI-based model performs even better. Especially its coding model is independent of modulation schemes.

IEEE P802.16e standard specifies the construction of low-density parity-check (LDPC) codes with four different code rates, i.e. 1/2, 2/3, 3/4, 5/6, and totally six parity check matrices. However, such a coarse coding rate granularity cannot provide comparable link adaptation performance comparing to turbo codes in HSDPA. In another aspect, more mother codes lead to much higher complexity of encoder implementation due to individual parity check matrices. To reach a balance between the implementation complexity and the fine granularity in link adaptation, this paper investigates the performance of the rate-compatible (RC) scheme, with targeted coding rate varying from 0.1 to 0.9, based on different sets of mother LDPC codes. As a summary, this paper proposes an instruction of mother codes selection and RC schemes for different targeted coding rates, which makes the link adaptation of LDPC codes very convenient for WiMAX systems.

A modified M&M frequency estimation algorithm for DVB-S2 system is proposed in this paper. This estimator provides a larger estimation range compared to the well known Fitz and L&R methods and achieves CRB in the whole SNR operation range of DVB-S2, with a little increase in computational complexity. The enlarged estimation range will reduce the acquisition time of the coarse frequency synchronizer in a two-step carrier frequency recovery scheme. Minimum accumulation lengths to achieve a certain RMS frequency estimation error for the L&R and modified M&M estimator at different SNR are presented. A variable accumulation length scheme based on SNR estimation or coding/modulation scheme employed in system is proposed to minimize the acquisition time in the fine frequency recovery.

In this paper we present M-TORA, a new multi-path routing protocol based on temporally-ordered routing algorithm (TORA) for mobile ad hoc networks (MANETs). Although TORA is a highly adaptive distributed routing algorithm, it can not make the most use of multiple paths which are already built and maintained in the process of route creation. We designed M-TORA for unleashing the potential power of multiple paths and optimizing performance of classical TORA. According to the modification of Internet MANET encapsulation protocol (IMEP), a node can spread its MAC layer information to its neighbors. The MAC information and the route hop count will be used in the calculation of route selection probability when a node has multiple paths to the destination. By using the probability routing algorithm, M-TORA can spread the network overload to multiple routing paths, which will lead to automatic load balancing. In simulation based on OPNET we show that M-TORA can outperform classical TORA in decreasing packet end-to-end delay, improving network packet delivery ratio and achieving fair node's local energy consumption.

In this paper, a data-driven temporal processing method based on Independent Component Analysis (ICA) is proposed for obtaining a more robust speech representation. Two different schemes of dominant temporal filters based on ICA are investigated. The one is the perceptuallybased filter which always focuses on the modulation frequency range between 1 and 16 Hz and the other is the most independent component discovered by ICA algorithm. Detailed comparative analysis between the proposed ICA-derived temporal filters and the previous statistical methods including Linear Discriminant Analysis (LDA) and Principle Component Analysis (PCA) is presented. The preliminary experiments show that the performance of the ICA based temporal filtering is much better in comparison with the LDA and PCA based methods in noisy environment.

This paper introduced a system employing hybrid DS/FH spread spectrum (SS) coupled with RS forward error-control coding. By the flexible redefinition of frequency-occupation and frequency-collision event, the frequency-occupation probability of the DS/FH SS network was analyzed. This probability was based on the simultaneous transmission number threshold and discussed in both synchronous and asynchronous circumstances, respectively. The network throughput based on the packet correct reception probability was analyzed. Two models which had finite and infinite population respectively was discussed. Finally, simulation results were given.

Distributed speech recognition (DSR) based on client-server mode means that the recognizer front-end is located in the terminal and is connected over a data network to a remote back-end recognition server. In this paper, we evaluate the distributed speech system in the client-server framework based on the ETSI AURORA platform for Chinese digit recognition and compare the performance of client-server mode with the server-only mode where speech is transmitted by using the GSM AMR encoder and all recognition processing is done at the server side. The recognition result shows that without consideration of channel error the DSR system based on client-server mode can achieve satisfactory recognition accuracy and the lowest hit rate in comparison with sever-only mode.

A non-uniform signal constellation can be used to obtain shaping gain. One typical way to design non-uniform constellation is make the output signal follow a Gaussian distribution by using equally like signals with unequal spacing. However, for binary turbo coded modulation (BTCM) with M-QAM system, this non-uniform constellation design criteria can be modified based on different impacts of various turbo encoded hits sequence on BER performance. In this paper, author points out that shaping gain for M-QAM with BTCM can be achieved integrated with optimization of mapping rule, performance of the proposed scheme outperforms over BTCM with uniform constellation, especially under low and moderate SNR region. Meanwhile, the effects of code rate, order of QAM constellation on shaping gain are investigated. Numeral results are provided to support the proposed scheme and analysis. The proposed scheme can be applied to high rate data service with hybrid ARQ, such as HSDPA system.

High speed downlink packet access (HSPDA) is proposed as the evolution of WCDMA recently, aiming at providing higher downlink transmission rate with peak rate about 8-10 Mbps. Turbo code combined with high order modulation can provide bandwidth efficient transmission with substantial coding gain over traditional trellis coded modulation (TCM). In this paper, author evaluates the feasibility of the application of binary turbo coded modulation (BTCM) with 16QAM in HSPDA. Some improvements of interleaver design according to label mapping are proposed to enhance the performance. And the impact of rate matching functionality on the performance of BTCM is analyzed. Simulation results show that the improved BTCM scheme with 16QAM can be implemented with low code rate and outperform the conventional scheme.

An analysis of "unequal power control (UPC)" for the general integrated services is presented based on our former discussion about UPC for voice/data integrated services. This optimal control problem is clarified first, and then an approximate solution is derived by linear programming. For practical application, we propose an implementation scheme based on simulation. Numerical results show that unequal power control can expand the "allowable load area" and provide a significant improvement in system capacity comparing with equal power control.