Currently, balancing low bitrate coding with speech quality is a highly debated topic in the research community. At very low bitrates, existing methods often fail to maintain speech naturalness, intelligibility, and personalization. To address this issue, we introduce an innovative ultra-low bitrate semantic speech coding approach, termed Semantic Speech Coding (SSC). Specifically, the multi-level feature extraction and compression mechanism sequentially extracts and compresses speech features at different levels, ensuring speech quality at ultra-low bit rates. Using a semantic vector quantization codec to fuse spectral and pitch features to extract essential semantic information, achieving more efficient compression while enhancing intelligibility and naturalness. The low-data-overhead speaker feature encoder captures time-invariant speaker characteristics, enabling personalized speech synthesis without additional data overhead, ensuring the synthesized speech retains personalization and naturalness. The diffusion loss mechanism employs a conditional diffusion model to progressively restore details, mitigating the detail loss typically seen in conventional codecs, further enhancing the naturalness and realism of the synthesized speech. We achieved significant improvements in speech quality at an ultra-low bitrate of 106 bps, which approaches the theoretical upper limit of information rate.

Airway mucus plugs are a key clinical feature of respiratory diseases. Their precise identification and evaluation significantly impact asthma management, aiding diagnosis, treatment planning, and outcome prediction. Current clinical detection based on manual CT scan inspection remains time consuming and prone to subjective bias. Although deep learning methods have shown promise in mucus plug detection, existing solutions still face performance limitations. These challenges primarily stem from the similarity of mucus plugs to vascular structures and airways, combined with their morphological heterogeneity and minuscule target sizes. To overcome these limitations, we propose a novel detection model called the Two-Stream Mucus Plug Detector (TSMPD) based on Faster R-CNN architecture. Our model employs two parallel stream: The first stream conducts inter-slice differential analysis through bivariant attention to highlight CT feature transition zones, particularly capturing both intensity gradients and morphological alterations like boundary thickening and blurring. The second stream performs intra-slice grayscale analysis by leveraging density variations for rapid mucus region of interest masking, significantly narrowing the candidate search space. Experimental results show that the proposed method outperforms previous detection methods, with the potential for clinical implementation in CT imaging systems.

Analyzing interactions among multiple medical time-series variables is essential for biomarker discovery and clinical decision-making; however, traditional statistical methods struggle with the diverse, nonlinear, and temporal nature of such data, and deep learning techniques often lack interpretable time-series relationships. To address these limitations, we propose Covariance-Variational Autoencoder (Cov-VAE), which extends the Variational Autoencoder (VAE) framework by relaxing the independent latent distribution assumption, incorporating covariance estimation to compute mutual information among variables, and embedding a variable-level attention mechanism within the encoder–decoder architecture to reveal complex interactions. On a real-world medical time-series dataset, Cov-VAE outperforms the standard VAE and other dimensionality reduction models by achieving notable improvements in mean absolute error (MAE), mean squared error (MSE), and Kullback–Leibler (KL) divergence. Furthermore, by constructing a multivariate mutual information–based variable relationship graph, our method identifies and validates critical combinations of medical variables, outperforming existing approaches. This work delivers a powerful, interpretable method for complex time-series multivariable analysis in healthcare.

Affine frequency division multiplexing (AFDM) has emerged as a promising waveform for high-mobility communication systems, whose performance in doubly dispersive channels relies on accurate channel estimation. However, conventional AFDM channel estimation schemes exhibit significant limitations. Therefore, we propose a low-complexity channel estimation scheme which adopts different pilot designs for integer and fractional Doppler cases. Specifically, the generalized approximate message passing (GAMP) algorithm is employed to replace the expectation step of the sparse Bayesian learning algorithm based on expectation maximization (EM), thereby reducing computational complexity. Extensive simulation results demonstrate that, compared with conventional methods, the proposed scheme not only offers advantages in pilot power consumption and overhead, but also achieves excellent performance and low complexity in various Doppler cases.

Automatic segmentation of Organs-At-Risk (OARs) and Clinical Target Volume(CTV) is crucial for the radiotherapy treatment planning of cervical cancer. This task is challenging due to the variation in sizes, shapes, and positions as well as the similar textures among the OARs and CTV. In this paper, we propose a manifold learning-based method based on U-Net. Firstly, the weight matrix of each convolutional layer is constrained to the Stiefel manifold. This constraint enhances the model’s ability to preserve the consistency of the learned feature from CT images. Secondly, we transform the optimization in Euclidean space into Riemannian optimization. This enables the model to optimize the segmentation performance on the manifold space, allowing the model to adapt to the irregular shapes of CTV and OARs. Our experimental results demonstrate that the proposed manifold learning-based method achieves superior performance in segmenting OARs and CTV for cervical cancer as compared to other SOTA methods. Overall, our proposed method demonstrates the potential of manifold learning techniques to improve the segmentation performance of medical images.

Major adverse cardiovascular events (MACE) encompass pivotal cardiovascular outcomes such as myocardial infarction, unstable angina, and cardiovascular-related mortality. Patients undergoing peritoneal dialysis (PD) exhibit specific cardiovascular risk factors during the treatment, which can escalate the likelihood of cardiovascular events. Hence, the prediction and key factor analysis of MACE have assumed paramount significance for peritoneal dialysis patients. Current pathological methodologies for prognosis prediction are not only costly but also cumbersome in effectively processing electronic health records (EHRs) data with high dimensionality, heterogeneity, and time series. Therefore in this study, we propose the CVEformer, an attention-based neural network designed to predict MACE and analyze risk factors. CVEformer leverages the self-attention mechanism to capture temporal correlations among time series variables, allowing for weighted integration of variables and estimation of the probability of MACE. CVEformer first captures the correlations among heterogeneous variables through attention scores. Then, it analyzes the correlations within the time series data to identify key risk variables and predict the probability of MACE. When trained and evaluated on data from a large cohort of peritoneal dialysis patients across multiple centers, CVEformer outperforms existing models in terms of predictive performance.

Generating food images from recipe and ingredient information can be applied to many tasks such as food recommendation, recipe development, and health management. For the characteristics of food images, this paper proposes ML-CookGAN, a novel CGAN. This network enables the generation of food images based on recipe and ingredient labels. The generator of ML-CookGAN, Multi-Label Fusion Generator, converts recipe and ingredient labels into different granularity features and generates corresponding food images. The discriminator of ML-CookGAN, Multi-Branch Discriminator, implements discrimination and classification with a multi-branch structure. In addition, we propose two training strategies, Region-Wise Pooling and Image Style Distillation, to better the network performance. Region-Wise Pooling handles region-wise features with the discriminator. Image Style Distillation aims at extracting image latent features to assist image generation by an unsupervised method. The experiments conducted on VIREO Food-172 databases validate the proposed method to generate high-quality Chinese food images. And Region-Wise Pooling and Image Style Distillation are proven to enhance the diversity and realism of generated food images.

We analyze the prediction confidence heat map of current state-of-the-art models in medical image segmentation. We find that low confidence usually comes with a high probability of error. Based on this observation, we propose a novel Recurrent Recalibration Network (R2Net) for medical images segmentation. Specifically, the decoder will forward multiple times in a recurrent manner while the encoder forward only once to extract features during training time. Except for the first time, each decoder input features at time t will be recalibrated by the confidence heat maps which were output from the decoder at previous time t −1. As a result, R2Net can learn to focus on the regions which are error-prone to segment. The recurrent manner makes the feature reusable and saves the parameters. We validate the proposed models on two benchmark datasets: multiorgan segmentation and cardiac organ segmentation datasets. The experimental results show that our Recurrent Recalibration scheme consistently improves the segmentation performance of various models while preserving computational efficiency.

In this article, we investigate a two-hop decode-and-forward (DF) multicasting multiple-input multiple-output (MIMO) wireless relay communication system. Different to conventional systems, the radio frequency (RF) energy from the source node is harvested at the relay node and used for forwarding signals to a group of receivers. Considering the structure of the energy harvesting (EH) relay node, we present a power splitting (PS) based protocol and a novel time switching (TS) based protocol by introducing two additional TS factors. For both protocols, we maximize the system mutual information (MI) of the multicasting MIMO relay system by jointly optimizing the source and relay covariance matrices under the constraints of the source energy and the relay harvested energy. In addition, a practical nonlinear EH model is adopted, where the energy harvested by the relay node is bounded as the incident RF signal power increases, and the harvested power is zero when the input power is below the minimum power for harvesting. For the TS based protocol, we also consider peak transmission power constraints at both the source and relay nodes. The performance of the proposed algorithms is verified via numerical simulations. The results demonstrate that the novel TS based protocol achieves a larger MI than the conventional TS protocol. The PS and TS based protocols achieve tradeoffs at different source power levels. In particular, compared with the PS based protocol, the proposed novel TS based protocol can reach a higher system MI when the EH bound is not reached, while the former protocol reaches a higher MI when the EH circuit is saturated. We show that the peak harvested energy constraint plays an important role in selecting the optimal location of the relay node.

Targeting at a better design of the analogue network coding (ANC)-assisted two-way amplify-and-forward (AF) multiple-input multiple-output (MIMO) multi-relay communication systems, we bring in the nonlinear minimal mean-squared error (MMSE)-decision feedback equalization (DFE) receiving technique to jointly optimize the source precoding, relay amplifying, feed-forward and feedback matrices. Under the transmission power constraints at both source nodes and each relay node, the two-way sum mean-squared error (MSE) of the signal waveform estimation of all data streams is minimized. To solve the complicated nonconvex optimization problem with four groups of system parameters, this paper develops an iterative block coordinate descent (BCD) algorithm, which converges to at least a Nash point. On the basis of it, for mitigating the error propagation in MMSE-DFE receivers, a group of permutation matrix variables, determining the detection orders of all data streams, are further introduced in our system optimization. Moreover, in case there is no sufficiently precise channel state information (CSI), we also make an extension of the developed algorithms, yielding a robust design scheme, to handle the channel uncertainties. Numerical simulation results show that, compared with the existing linear MMSE receiving-based algorithm, our proposed nonlinear ones provide improved MSE and bit-error-rate (BER) performance as well as good robustness against the imperfect CSI, indicating a promising application prospect of this research.

Impulsive noise can greatly affect the performance of underwater acoustic (UA) orthogonal frequency-division multiplexing (OFDM) systems. In this paper, by utilizing the sparsity of the UA channel impulse response and impulsive noise, we first propose a novel sparse Bayesian learning (SBL) based expectation maximization (EM) algorithm for joint channel estimation and impulsive noise mitigation in UA OFDM systems. Secondly, considering that the UA channel and impulsive noise are fast time-varying, we develop a new approach which combines the SBL with the forward-backward Kalman filtering to track the UA channel and impulsive noise. To further improve the system performance, we utilize the information available on data subcarriers for joint time-varying channel estimation and data detection, based on the SBL algorithm and the Kalman filter. The performance of our proposed algorithms is verified through both numerical simulations and by data collected during a UA communication experiment conducted in the estuary of the Swan River, Perth, Australia. The results demonstrate that compared with existing approaches, the proposed algorithms achieve a better system bit-error-rate and frame-error-rate performance.

In vehicular ad-hoc network (VANET), due to its unique characteristics such as high nodes' speed, dynamic network topology and variable nodes' density, end-to-end data transmission faces many challenges. To address these challenges, many routing protocols specially for VANET have been proposed. But a chief part of them are designed only in network layer and independently of other layers. Besides, few of them consider the influence of interference. In this paper, we propose a new cross-layer routing with interference constraint for VANET which combines technologies of network layer and MAC layer. First, we adopt a location-based resource allocation algorithm to constrain interference between vehicles. Then on the basis of it, relay is selected depending on their geographical zones. In addition, we also use the relative distance among vehicles and the reception Signal to Interference plus Noise Ratio (SINR) of hello messages to optimize the relay selection scheme. By simulation, we compare it to other protocols and experimental results show that our routing protocol outperforms existing solutions in terms of packet delivery ratio (PDR).

The orthogonal frequency-division multiplexing (OFDM) technology receives increasing attention in underwater acoustic (UA) communications. This paper presents a real-time OFDM-based UA communication system, implemented using the National Instruments CompactDAQ device and the LabVIEW software. The system design including both the transmitter and receiver is discussed. The performance of this real-time system is verified through a UA communication experiment conducted recently in a tank. Compared with conventional digital signal processor (DSP)-based design, the proposed implementation simplifies the prototype design and reduces the software development time.

Impulsive noise occurs frequently in underwater acoustic (UA) channels and can significantly degrade the performance of UA orthogonal frequency-division multiplexing (OFDM) systems. In this paper, we propose two novel compressed sensing based algorithms for joint channel estimation and impulsive noise mitigation in UA OFDM systems. The first algorithm jointly estimates the channel impulse response and the impulsive noise by utilizing pilot subcarriers. The estimated impulsive noise is then converted to the time domain and removed from the received signals. We show that this algorithm reduces the system bit-error-rate through improved channel estimation and impulsive noise mitigation. In the second proposed algorithm, a joint estimation of the channel impulse response and the impulsive noise is performed by exploiting the initially detected data. Then, the estimated impulsive noise is removed from the received signals. The proposed algorithms are evaluated and compared with existing methods through numerical simulations and on real data collected during a UA communication experiment conducted in the estuary of the Swan River, WA, Australia, during December 2015. The results show that the proposed approaches consistently improve the accuracy of channel estimation and the performance of impulsive noise mitigation in UA OFDM communication systems.

In orthogonal frequency-division multiplexing access (OFDMA) system, a distributed Bayesian compressive sensing (DBCS) based blind carrier frequency offset (CFO) estimator has been proposed, which offers a significant improvement on the performance of multiple-parameter estimation, compared with the existing subspace theory based method. However, the analysis for theoretical performance and computational complexity is absent. In this paper, we conduct a further study on this method, and derive the Cramer-Rao Bound to evaluate the performance. Then we highlight our work on the complexity reduction, for which a multi-layer DBCS based algorithm is presented. Additionally, we analyze the computational complexity of this new method, and derive an effective solution for determining the optimal layer number. Simulation results demonstrate our analysis, and show that our proposed algorithm can reduce the complexity by approximate two orders of magnitude with only a slight loss in performance at low signal-to-noise ratio (SNR).

In this paper, we propose statistically robust design for multiuser multiple-input multiple-output (MIMO) relay systems with direct source-destination links and imperfect channel state information (CSI). The minimum mean-squared error (MMSE) of the signal waveform estimation at the destination node is adopted as the design criterion. We develop two iterative methods to solve the nonconvex joint source, relay, and receiver optimization problem. Simulation results demonstrate the improved robustness of the proposed algorithms against CSI errors.

Spatially coupled LDPC codes have attracted much attention due to the promising performance. Recursive encoding with low delay and low complexity has been proposed in the literature for selected node degrees. To realize the recursive encoding of spatially coupled LDPC codes with arbitrary rates, we propose in this paper a modified structure of the parity-check matrix and implement the encoding using a shift-register regardless of the node degrees. By rearranging the edge connections, the parity bits at each coupling position can be jointly determined by the information bits at the current position and the encoded bits at former positions. Performance analysis in terms of design rate and density evolution has been provided. It can be observed that the modified code structure leads to a better belief-propagation threshold. Finite-length simulation results are provided, which verify the theoretical analysis.

Polar codes are proven to achieve the rate distortion bound of Gaussian sources under the condition that the size of reconstruction alphabet and code length grow to infinity. However, this condition cannot be satisfied in practice. In this paper, we propose a practical source coding scheme based on multilevel polar codes, called polar coded quantization (PCQ). In this scheme, extended reconstruction alphabet, set-partition (SP) labeling and successive cancellation (SC) (or its improved) encoding algorithm are combined to approach the rate distortion bound. Furthermore, an efficient upper bound of encoding rate for a given distortion is derived to guide the practical design of PCQ. Simulation results show that PCQ provides a flexible and constructive framework to efficiently approximate the rate distortion bound of Gaussian sources.

In this letter, the modified least mean squares (MLMS) algorithm proposed by Kretschmer is generalized to fractional order α (0≤ 1). Such generalization is achieved by replacing the first order difference of the weight updating equation with a fractional one. The convergence speed, weight noise and implementation issue of the generalized MLMS (GMLMS) algorithm are examined. It is shown that for smaller step size, the fractional order α functions the same as the step size, which means that a smaller α will give smaller weight noise while a bigger α will give faster convergence speed.

In this paper, we propose statistically robust design for multiple-input multiple-output (MIMO) relay systems with the direct source-destination link and imperfect channel state information (CSI). The minimum mean-squared error (MMSE) of the signal waveform estimation at the destination node is adopted as the design criterion. We develop two iterative methods to solve the nonconvex joint source, relay, and receiver optimization problem. In particular, we derive the structure of the optimal relay precoding matrix and show the effect of CSI mismatch on the structure of the optimal robust source and relay matrices. The proposed algorithms generalize the transceiver design of MIMO relay systems with the direct link to the practical scenario of imperfect CSI knowledge. Simulation results demonstrate an improved performance of the proposed algorithms with respect to the conventional methods at various levels of CSI mismatch.

A hybrid automatic repeat request scheme with Chase combing (HARQ-CC) of polar codes is proposed. The existing analysis tools of the underlying rate-compatible punctured polar (RCPP) codes for additive white Gaussian noise (AWGN) channels are extended to Rayleigh fading channels. Then, an approximation bound of the throughput efficiency for the polar coded HARQ-CC scheme is derived. Utilizing this bound, the parameter configurations of the proposed scheme can be optimized. Simulation results show that, the proposed HARQ-CC scheme under a low-complexity SC decoding is only about 1.0dB away from the existing schemes with incremental redundancy (HARQ-IR). Compared with the polar coded HARQ-IR scheme, the proposed HARQ-CC scheme requires less retransmissions and has the advantage of good compatibility to other communication techniques.

The distance spectrum is used to estimate the maximum likelihood (ML) performance of block codes. A practical method which can run on a memory-constrained computer is proposed to calculate the distance spectrum of polar codes. Utilizing the distance spectrum, the frame error rate (FER) and bit error rate (BER) performances of non-systematic polar codes (NSPCs) and systematic polar codes (SPCs) are analyzed. The union bounds as well as the simulation results illustrate that, under ML decoding, SPCs are superior to NSPCs in BER performance while NSPCs and SPCs have the same FER performance.

Recent work in the area of compressed sensing mainly focuses on the perfect recovery of the entire support for sparse signals. However, partial support recovery, where a part of the signal support is correctly recovered, may be adequate in many practical scenarios. In this study, in the high‐dimensional and noisy setting, the authors develop the probability of partial support recovery of the optimal maximum‐likelihood (ML) algorithm. When a large part of the support is available, the asymptotic mean‐square‐error (MSE) of the reconstructed signal is further developed. The simulation results characterise the asymptotic performance of the ML algorithm for partial support recovery, and show that there exists a signal‐to‐noise ratio (SNR) threshold, beyond which the increase of SNR cannot bring any obvious MSE gain.

Scheduling with single carrier property restriction has a significant impact on system performance in Long Term Evolution (LTE) uplink (UL). Due to the unavoidable delay of control signaling, the scheduling decisions have great influence on the variation of inter-cell interference (ICI) which affects the performance of adaptive modulation and coding (AMC). Many studies have been carried out on the topic of resource allocation in single-carrier frequency division multiple access (SC-FDMA) system. Some of them deal with the ICI variation with inter-cell measurements or coordination. In this paper, we present the problem of frequency domain packet scheduling (FDPS), analyze the negative effects of ICI variation and develop our improved proportional fair (PF) scheduling algorithm based on the traditional one for LTE UL. Our proposed algorithm decreases the ICI variation and improves AMC accuracy without any inter-cell coordination. Compared with the traditional way, system level performance shows that at least 29% gain can be obtained at both the cell user average spectral efficiency and the cell edge user spectral efficiency with comparable proportional fairness in typical interference-limited scenario.

Wavelet transform has proved to be an attractive tool in terms of analyzing singularities and irregular structures, which can characterize irregular edges of signals. Thus, it is well motivated to apply the wavelet transform approach to wideband spectrum sensing. But the existing wavelet-based spectrum sensing schemes work under the assumption that the frequency response of the analog signal input at the sensing receiver is real. To make this method work for more types of signals, this paper develops a novel wavelet-based approach to compressive wide-band spectrum sensing. In the proposed scheme, the wide-band time domain signal is fed into a number of filters and the sub-Nyquist sampled outputs are utilized to detect the occupancy of spectrum via a wavelet-based edge detector. The filters' outputs are real and nonnegative, regardless of the style of the analog signal. Furthermore, through simply adding the measurement vectors at the fusion center, this scheme can be applied to the case of multiple cognitive radios (CRs), reducing the complexity compared with traditional joint recovery algorithms.

We propose an opportunistic scheduling method to achieve DoF (degrees of freedom) gain by BIA (blind interference alignment) in block fading K-user 2 × 1 MISO broadcast channel. The optimal scheduling method is obtained by solving a general model of linear integer program. All the users are divided into user pairs to form a 2-user 2 × 1 BIA. Each pair has the same opportunity to be scheduled. When K ≥ 10, the expectation of the achieved DoF can be very close to4/3.

We study a repeated auction for the resource allocation problem in OFDM-based cognitive radio networks (CRNs), in which secondary users (SUs) share the primary spectrum under the interference constraints of primary users (PUs). With the inter-cell interference and mutual interference between PUs and SUs, the resource allocation problem is formulated as a non-convex optimization problem. Auction performs well in solving non-convex problems, therefore the interference auction with cooperative bidding is proposed. Moreover, with the theoretical analysis of equilibrium, an implementation algorithm for the auction is developed and the convergence is proved. Simulation results show that the interference auction obtains a good spectrum efficiency improvement and a rapid convergence rate.

As a substantial means for improving throughtput, network coding has recently attracted much attention to apply in wireless multicast services. To guarantee the reliable multicast, packet retransmission schemes are employed. With one source, network coding has been well studied for the reliable multicast. But for the multicast cell with two sources, the presented schemes are not good enough to approach an impressive effect with the multiple receivers, demands and link situations. In this paper, for the 2-1-(D1,D2) multicast cell, which has two sources, one relay and two groups users with the receivers numbers of D1and D2, we propose three schemes to reduce the number of retransmission, which combine the lost packets and retransmit them with network coding, and obtains the least number of retransmissions. Some theoretical results are derived on the bandwidth efficiency of the traditional automatic repeat-request scheme (ARQ), network coding with ARQ scheme (XOR-ARQ) and the improved network coding with ARQ scheme (iXOR-ARQ). Compared with ARQ and XOR-ARQ, iXOR-ARQ is more advantageous on the bandwidth efficiency and the coding gain, which has been confirmed by both simulations and theoretical analysis.

In this paper, we propose a distributed call admission control scheme for multiservice OFDMA system. In our proposed call admission control (CAC) scheme, a cell dynamically adjusts optimal new call and handoff acceptance ratios by three steps. First, exchange numbers of calls in handoff area with its adjacent cells. Then, dynamically allocate resource for different types of traffic. Finally, use a Markov queue model and a bidirectional iterative search method (BIS) to calculate the new call and handoff acceptance ratios to obtain a near-maximal network utility which depends on both packets delay and the number of ongoing calls. Simulation results show the performance gain of the proposed scheme.

This paper is concerned with a simplified hard decision feedback equalizer (S-HDFE) for single carrier modulation with cyclic prefix. In this paper, we focus on the coded system in which the feedback symbols are reproduced from hard decisions of channel decoding results. Cholesky decomposition is the key technique for simplifying the feedback filter calculation of S-HDFE. With the same matched filter bound (MFB), the maximum number of the feedback filter taps in S-HDFE is smaller than that in the existing hard decision feedback equalizer with noise prediction (DFE-NP). Therefore, the computational complexity of S-HDFE is lower than DFE-NP with the same MFB. Furthermore, the simulation result shows that if the feedback filter tap number is the same for both equalizers, the BER performance of S-HDFE is better than DFE-NP.

This paper investigates the energy-efficient transmission under hybrid spectrum sharing scenario, where secondary users (SUs) can select a proper spectrum sharing method based on the state of primary users (PUs). Firstly, an optimization model to evaluate the energy-efficiency, as measured by the "throughput per Joule" metric, is proposed. Then, we show that there exists a unique globally optimal transmission power scheme for SU to achieve the maximum energy-efficiency by decomposing the optimization problem into two sub-problems. It's usually difficult to directly solve the optimal problem which is equivalent to solve joint nonlinear equations. So we propose a one-dimension low-complexity search algorithm, considering the unimodal characteristic of energy-efficiency function. Our simulation results show that the proposed transmission scheme can greatly improve energy savings with energy-efficiency maximization objective.

We investigate a decode and forward (DF) scheme of Asymmetric modulation suited for two-way relay (TWR) with network coding. The considering network coding consists of two time-slots: two users transmit wireless signal to the relay in time slot 1, and the processed signal is broadcasted in time-slot 2 by the relay. With the received asymmetric modulated signals by relay in time-slot 1, the judging, coding and power allocating problems are investigated. A judging region (JR) scheme is proposed to solve the issues and the symbol error ratio (SER) is derived. We also give the optimized power allocation method of JR scheme according to different asymmetric modulations. The performance evaluations show correctness of SER expression and efficiency of power allocation method.

Distributed video coding (DVC) has recently been proposed to reduce the complexity of the encoder, whereas it suffers from the sampling cost of huge amount of image data. To relax such sampling burden, this paper develops a novel sub-sampling distributed video coding (SuDVC) by utilizing compressive sensing (CS) technique. Due to the inherent sparsity in video sources, the video frames are compressively sampled at the encoder. On the other hand, by exploiting the correlation between CS measurements and side information and by performing sparsity recovery, the video frames are recovered at the decoder. When compared with the traditional fully-sampling equivalence, SuDVC enjoys the reduction of transmission rate, the reduction of implementation complexity and the robustness to channel losses, which are verified in the simulations.

Channel estimation is important for coherent detection in orthogonal frequency-division multiplexing (OFDM) systems. Current frequency-domain Kalman filtering (FDKF) channel tracking method requires a large number of pilots, which reduces the spectral efficiency of the system and increases the complexity. In this paper, in order to solve this problem, a new channel estimation method based on the recent methodology of distributed compressed sensing (DCS) and FDKF is proposed. By exploiting the sparse attribute of OFDM channels and introducing DCS, the number of pilots could be reduced greatly, which means more resources are saved for data transmission. Moreover, simulations indicate the proposed method achieves a better performance than conventional FDKF and least square (LS) method.

In this paper, we develop a distributed sub-carrier allocation algorithm with a low complexity in OFDMA multi-cell system , which is decomposed into two sub-problems: inter-cell and intra-cell sub-carrier allocation. During inter-cell process, based on time-variant characteristics and performance differ-ences among cells of sub-carriers, each base station applies Ant Colony Optimization (ACO) to choose available sub-carriers dynamically, which contributes to reduce co-channel interference. According to pheromone associated with channel capacity, the probability of choosing sub-carrier with better capability is higher. Then during intra-cell process, each base station sufficiently uses multi-user diversity to satisfy all users' QoS requirements and greatly increase system throughput. Simulation results show that this algorithm exhibits substantial gains over existing frequency reuse schemes.

We study in this paper a sub-optimal beamforming algorithm for the MISO interference channel based on interference pricing, defined as user's marginal decrease in its utility due to interference. An iterative approach is considered given a set of interference prices and beams. Combining the interference price from other users and channel state information, the transmitter can update the beams to maximize its pure utility, which is defined as its own utility minus loss from other users' utility caused by its interference. Meanwhile, the receiver can update its interference price according to the total received interference. Our results from comprehensive simulations show that this algorithm is vastly superior to the existing beamforming algorithms, e.g. the maximum-ratio transmission (MRT) beamforming scheme, the zero-forcing (ZF) beamforming scheme and the algorithm in in terms of efficiency, convergence and robustness, which can get close to the Pareto edge of the available rate region in.

This letter considers the relay-assisted multiuser downlink transmission with limited feedback in a wireless cellular network. Taking into account the channel quantization errors, we propose two robust linear beamforming schemes at the relay station based on zero forcing (ZF) criterion and minimum mean square error (MMSE) criterion, respectively. It is noted that the channel direction information (CDI) feedback is sufficient for determining the beamforming vectors in our proposed schemes, which reduces the feedback load. Simulation results show that compared with the conventional ZF beamforming and MMSE beamforming in limited feedback systems, the proposed precoding schemes achieve an improvement in the average bit error rate (BER) performance of the system.

We present the end-to-end performance of dual-hop wireless communication systems with non-regenerative fixed gain relays operating over independent not necessarily identically generalized-K (KG) fading channels. New closed-form expressions are derived for the moments of the end-to-end signal-to-noise ratio (SNR), while the corresponding moment- generating function (MGF) is accurately approximated with the aid of Pade approximants theory. Useful performance criteria are studied; the average end-to-end SNR and the amount of fading, which are expressed in closed form, the average bit-error probability for several coherent, noncoherent, and multilevel modulation schemes, and the outage probability, which are both accurately approximated using the well-known MGF approach. Furthermore, novel closed-form expression is obtained for the gain of semi-blind relays over KGfading channels. The proposed mathematical analysis is complemented by various performance evaluation results, which demonstrate the accuracy of the theoretical approach.

In this work we consider the rate control issue for network coding based multicast among multiple sessions, which can be formulated as a network utility maximization problem. To solve the problem we propose a distributed optimization decomposition approach, which is different from the previous work in the literature in that (1) it is a hierarchical decomposition approach where the primal problem is decomposed recursively, until an independent rate control module is obtained and the decomposed subproblems can be solved by the distributed max-flow algorithm and we emphasize the layered functionality allocation of decomposed subproblems following the framework of "Layering as Optimization Decompositions"; (2) we first separate the primal problem by relaxing the capacity constraint among sessions; (3) to implement end-to-end control, we separate the independent rate control module at end node from the operation in the interior of the network. In this work we intend to propose not only a rate control algorithm but also a possible choice of network architecture for network coding based communication with rate control capability.

End-to-end performance of multihop wireless communication systems with non-regenerative channel state information (CSI)-assisted relays operating over independent not necessarily identically Weibull fading channels is presented. With the aid of the inequality between harmonic and geometric means, the end-to-end signal-to-noise ratio (SNR) is bounded. By considering the product of rational powers of N Weibull random variables, novel expressions are derived for the moment-generating, probability density, and cumulative distribution functions in closed form. Using these results, convenient closed-form bounds are obtained for the average end-to-end SNR, the channel capacity and the average bit-error probability of multihop wireless communication systems. The tightness of the proposed bounds is verified by performing comparisons between numerical evaluation and computer simulations results.

In spite of the significant performance gain that multiple-input multiple-output (MIMO) transmission can provide in wireless communication, the use of MIMO in realistic systems will lead to large control signaling overhead. Performance comparison of different transmission schemes should consider the control channel (CC) constraints. In this paper, we first build the model of CC for downlink MIMO transmission, which is an extension of previous work in, and then we introduce the concept of CC operation point and propose a principle that performance comparison should be with same CC operation point. Three transmission schemes defined in 3GPP long term evolution (LTE), 1 × 2 maximum ratio combining (MRC), 2 × 2 open-loop transmit diversity (OLTD), and 2 × 2 closed-loop spatial multiplexing (CLSM), are compared through system level simulations, and results show that to keep CC operation point constant, the requirements on CC resource are different, which will further influence the spectral efficiency of the transmission schemes.

In radio resource management of multimedia broadcast/multicast service (MBMS), user equipment (UE) feedback can be used by evolved Node B (eNB) to improve the radio spectral efficiency. As UE feedback is costly we here suggest schemes to reduce the uplink feedback while maintaining the spectral efficiency. For the 3 schemes analyzed in this paper we utilize the special characteristic of a broadcast channel, that spectral efficiency is mainly determined by the UE with lowest channel quality. The performance of the suggested schemes are evaluated with long term evolution (LTE) system simulation and we conclude that using geometry for selecting feedback UEs is the optimal solution and 4 feedback UEs are enough to meet the coverage requirement.

Multimedia Broadcast/Multicast Service (MBMS) provides efficient solution for multimedia broadcasting services by distributing resources among a group of subscribers. Different from previous broadcasting services, MBMS subscribers will send feedback information which can be used to improve system performance by adapting resource allocation to channel state. In this paper in LTE frameworks we propose a frequency domain packet scheduling (FDPS) algorithm for MBMS, in which resource blocks are dynamically allocated to MBMS based on knowledge of instantaneous channel condition. The objective is to optimize system throughput performance while guaranteeing system coverage. This paper examines the algorithm in details with different (channel quality indicator) CQI reporting modes, alterable frequency bandwidth for MBMS and different numbers of MBMS subscribers per sector. Simulations show that comparing to a simple blind FDPS algorithm, the proposed algorithm improves system throughput without sacrificing coverage.

Multimedia Broadcast/Multicast Service (MBMS) based on point-to-multipoint (p-t-m) radio transmission was introduced by 3GPP to deliver broadcast service to a group of user equipments (UEs) in a cellular system. As a broadcast system with uplink feedback, its performance is limited by UEs in poor connections. To utilize the limited radio resource more efficiently, a simple but effective mechanism is proposed to improve the system throughput by sacrificing those UEs that are expensive to cover, which means the cost of this improvement is coverage degradation. We set a threshold for the SINR of UEs, and uplink feedback from UEs with G-factor lower than the threshold will be discarded in packet scheduling and link adaptation. Compared with other methods to increase the throughput, this mechanism has two advantages. First, the coverage can be exactly controlled by adjusting the threshold. Second, much higher throughput gain can be achieved for the same cost. Simulation results show that throughput will be increased by 22% with reasonable coverage degradation when there are 10 UEs per sector. Larger gains can be achieved with more UEs per sector.

In this paper, we develop an analytical model based on Markov chains for studying the handoff performance of the Integrated Cellular Ad hoc Relaying (iCAR) system. We verify the analytical model via simulations. Then, based on the model, we analyze the call blocking and dropping probabilities of iCAR system with and without call admission control (CAC) schemes. The fractional guard channel scheme is deployed as the CAC scheme in the iCAR system. From the analytical and simulation results, we find that although the iCAR system has much lower call dropping probability than cellular networks, when the traffic load is high, it still cannot guarantee a low call dropping probability without CAC schemes. It is anticipated that the analytical model reported in this paper will serve as a guideline for handoff performance evaluation and CAC scheme design of the iCAR system.

Discrete Fourier transform (DFT)-based least square (LS) channel estimation with phase shifted pilots (PSP) is a practical method in multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. In this paper, a real number pilot sequence is presented to replace PSP in time-domain LS channel estimation algorithm, which can reduce computation and save memory while keeping the precision. Furthermore, using discrete Hartley transform (DHT) in LS channel estimation algorithm, a simpler estimator is derived. The simulation results demonstrate the effectiveness of the proposed pilot sequence and the DHT-based channel estimation algorithm.

Based on interference temperature model, the problem of secondary spectrum sharing can be formulated as a power optimization problem at physical layer. In this paper, we consider decentralized cognitive radio networks, and we especially focus on the spectrum sharing scenario where multiple measurement points are located in the licensed system. Game theory is used to investigate the distributed power control for providing the maximum throughput in cognitive radio networks. There are two aspects that should be considered in the design of the payoff function for each player, one is counteracting negative externalities in cognitive radio networks, and the other is satisfying all the interference temperature limits from measurement points. A tax-based power control game algorithm is introduced to implement power allocation optimization in a distributed mode, and guarantees the convergence to globally optimal power allocations.

Conventional heterogeneous-traffic scheduling schemes utilize zero-delay constraint for real-time services, which aims to minimize the average packet delay among real-time users. However, in light or moderate load networks this strategy is unnecessary and leads to low data throughput for non-real-time users. In this paper, we propose a heuristic scheduling scheme to solve this problem. The scheme measures and assigns scheduling priorities to both real-time and non-real-time users, and schedules the radio resources for the two user classes simultaneously. Simulation results show that the proposed scheme efficiently handles the heterogeneous-traffic scheduling with diverse QoS requirements and alleviates the unfairness between real-time and non-real-time services under various traffic loads.

In this paper, we proposed a revenue-based low-delay and efficient (R-LDE) algorithm for downlink packet scheduling in OFDMA systems. In actual operation of the network, telecommunication operators always expect the most revenue. Therefore, a simple revenue model is introduced and integrated into our proposed packet scheduling algorithm. The significances of R-LDE algorithm are threefold: firstly, it can guarantee the delay requirement of certain traffic; secondly, it can keep the efficiency of the system; thirdly, it considers the revenue of the telecommunication operators. The performance of R-LDE is evaluated in terms of packet delay, throughput and revenue with actual traffic models. It is then compared with conventional multi-carrier proportional fairness (MC-PF) algorithm and max SNR algorithm with mixed traffics such as FTP and video traffics. Simulation results show that the R-LDE algorithm can achieve the most scheduling revenue and performs much better than the algorithms stated before in terms of packet delay and throughput with video traffic at the cost of slight deterioration of FTP traffic performance.

To meet the challenges in sensing the spectrum in a reliable and timely manner, an approach of multi-antenna spectrum sensing based on spectral correlation property is proposed in this paper. Without any prior information of the licensed system, it extracts the frequency-domain channel information from the spectral correlation functions (SCF) of multiple antenna signals, and then generates the SCF of multi-antenna combining (SCF-MAC) over the frequency-cycle frequency plane. The expressions for the detection and false alarm probabilities of a decision based on SCF-MAC are derived for frequency selective channel, which indicate the proposed approach is capable of achieving full spatial diversity in spectrum sensing. Simulation results corroborate the theoretical analysis and show the significant performance improvement.

In this paper, a fair and simple resource allocation scheme is proposed for orthogonal frequency-division multiple-access distributed wireless communication systems (OFDMA-DWCS). Instead of finding global optimal solution, we hierarchically decouple the subcarrier and power allocation problem into two subproblems. A distributed scheduling is employed on two levels: access-point-level (AP-level) and user-level. In AP-level scheduling, the subcarriers are roughly allocated by the central unit (CU) to different APs. In user-level scheduling, a further subcarrier and power allocation is performed by each AP for its underlying users. We adopt Nash bargain solution fairness criterion in our scheme, which ensures a good tradeoff between fairness and efficiency on both the AP-level and the user-level. Simulation results demonstrate that the distributed scheme can achieve comparable overall system rate to that of centralized algorithms with much reduced complexity.

A novel resource allocation problem for min-rate guaranteed services is studied in orthogonal frequency division multiplexing (OFDM) distributed antenna systems (DAS) by this paper. In addition to multiuser diversity as well as conventional space diversity (such as multiple-input multiple-output (MIMO) -OFDM), distributed multiantenna diversity and time diversity are also exploited by the proposed algorithm. The proposed allocation algorithm iteratively assigns subcarriers to users to maximize rate-sum capacity subject to min-rate constraints. Simulated results show the proposed algorithm can fully utilize all degrees of freedom including antenna, user, time, and frequency to ensure users' min-rate transmission. Consequently, OFDM DAS outperforms OFDM co-located antenna systems (CAS) in terms of rate-sum capacity due to distributed antenna deployment.

Multiuser downlink beamforming methods have been studied recently. As known, the MIMO broadcast channel capacity can be achieved by dirty paper coding (DPC). However, the high complexity of DPC is hard to implement. Linear zero-forcing beamforming strategy can achieve the same asymptotic sum-rate as that of DPC, when the number of users goes to infinity. For the linear zero-forcing system, (D. Bartolome et al., 2004) gives several power allocation methods among users for different objectives. The above power allocation has an assumption of sum power constraint (SPC), however, recently some researcher study a more practical power constraint name per-antenna power constraint (PAPC). The optimum power allocation aims for maximize the sum-rate under the PAPC is given in (F. Boccardi et al., 2006). In this paper, we find that if the transmit antenna number M is larger than the user number K, a multi-level zero-forcing method can be adopted, and a multi-layer structure is proposed for realizing the method. A sub-optimal power allocation algorithm is also proposed to find the power allocation matrix. Simulation result shows that the proposed multi-level zero-forcing method can achieve a higher sum-capacity than that of (F. Boccardi et al., 2006) under PAPC.

A simplified layered scheduling scheme is presented, which is based on utility-based scheduling, such as Max-Delay- Utility (MDU) scheduling in this paper. In this scheme, the scheduling is divided into two steps-- macro and micro scheduling. In macro step, the utility functions of traffics are defined, according to which the scheduling order of various services is determined. Then in micro step, the scheduling is among all users of the traffic type which is determined in macro step. By simulation in a multi-user orthogonal frequency division multiplexing (OFDM) network, it is demonstrated that the simplified layered MDU scheduling can effectively handle multiple traffic types with diverse QoS requirements and achieve almost the same performance from the view of utility, delay, throughput and fairness as MDU does. Moreover, the simplified layered MDU scheduling has much lower computational complexity than the MDU scheduling and almost the same utility values.

We propose a new connection admission control algorithm called DAC for real-time services in packet-switched OFDM wireless cellular networks. The main idea of DAC is to use maximum acceptance ratio for real-time services according to the desired packet-level and call-level QoS requirements. The control mechanism is periodical. At the beginning of the control period, the admission controller gathers the loading information from its own and neighboring cells. Then, according to the information, the maximum acceptance ratio is calculated by using a Markovian queueing model and time-dependent distribution of the number of connections. Finally the admission controller uses the computed ratio to admit connection requests in this control period. Extensive simulation results show that our algorithm satisfies packet-level and call-level QoS requirements for real-time services while maintaining high bandwidth utilization.

This paper deals with the problem of downlink scheduling in SDMA packet access networks. The performance of the SDMA system is predominantly affected by the mutual interference between users. In order to ensure the quality of service (QoS) and enhance the resource utilization, the scheduler needs to jointly consider the constraints from the physical layer (spatial separability for instance) and the data link layer (such as QoS requirements). In this paper, we propose a simple cross-layer downlink scheduler for SDMA based systems. As an example, it is implemented on an OFDMA/SDMA system. Analysis and simulations are given to show the effectiveness of our proposal.

This paper formulates resource allocation problem in orthogonal frequency division multiplexing (OFDM) distributed antenna systems (DAS) by extending conventional one in OFDM co-located antenna systems (CAS). The algorithm to achieve maximum rate-sum capacity is derived, and the achievable capacity of a single user is also given by theoretical analysis. Simulated and theoretical results show that OFDM DAS can achieve much more rate-sum capacity than OFDM CAS, and the performance gain increases with the number of antennas in systems.

In this paper, a new utility-based subcarrier and power allocation scheme in uplink OFDMA systems is proposed. The utility function considered here measures the average achieved utility per joule of energy consumed on each subcarrier. The goal is to maximize the system utility in the uplink. For the purpose, we formulate an optimization problem subject to the subcarrier and power constraints. It is shown that the system utility is maximized when the subcarrier is allocated to the user with the best "effective channel gain". The numerical results show that the proposals outperform the existing schemes.

In this paper, we propose a joint admission control and bandwidth allocation scheme for packet-switched multiservices cellular networks. The main idea of the scheme is to adjust the acceptance ratios and the bandwidth allocated for different types of services in order to maintain maximum bandwidth utilization while guaranteeing quality of service (QoS) requirements for users. First, we define a total user utility function as the measure of how efficiently the bandwidth resource is used. Then, by using a Markovian queueing model, we obtain the optimal connection acceptance ratios and bandwidth allocation strategy that maximize total user utility. Simulation results demonstrate that using our scheme, bandwidth resource is most efficiently utilized and at the same time, the call-level and packet-level QoS requirements for both voice and data users are guaranteed.

In this paper, list detection based on lattice reduction (LDLR) in MIMO systems is proposed. By generating a list of candidate transmit symbol vectors we can improve the performance of linear detection based on lattice reduction and successive interference cancellation based on lattice reduction significantly. The performance improvement is demonstrated by computer simulations, which also show that a list with two entries is enough for the MMSE based detection to approach the maximum likelihood (ML) detection at high signal-to-noise ratio (SNR) in case of (4,4) MIMO

Iterative list sphere decoding (LSD) can achieve near capacity on a multiple-antenna channel, however, with a rather high complexity. In order to reduce the complexity, in this paper, linear detection-based iterative soft interference cancellation (ISIC) instead of LSD is applied at the second and later iterations. We propose to exploit extrinsic information transfer (EXIT) chart analysis to select a specific ISIC to alleviate large performance degradation. Here MF-based and MMSE-base iterative soft interference cancellations (ISIC) are selected to be combined with the LSD. Simulation shows that the iterative combined detections have little signal-to-noise ratio (SNR) loss and a much lower complexity in comparison with iterative LSD

MIMO systems can offer high multiplexing and diversity gain, but the increased complexity hampers the practical applications. Through antenna selection, the complexity and cost can be greatly reduced while the gain of MIMO systems can be reserved. In this paper, a fast sub-optimal antenna selection algorithm based on norm and correlation is proposed for maximizing the channel capacity. Compared with the near-optimal selection algorithm of M. Gharavi-Alkhansari and A. Greshman (2004), our sub-optimal algorithm can capture most of the capacity property while the complexity and memory requirement are greatly decreased. Simulation results show that our algorithm is very effective when SNR is high and the number of the selected antennas is relatively small. Especially for a rank deficient channel matrix, the sub-optimal algorithm outperforms the norm-based selection algorithm. Our algorithm also shows capacity advantages when compared with the correlation based method (CBM) algorithm of Y.S. Choi et al. (2003)