This paper presents a robust yet efficient Grey Wolf Optimizer-Support Vector Machine algorithm, termed K-GWO-SVM, for the analysis of ECG signals in smart healthcare systems, aiming to improve classification accuracy and computational efficiency. The proposed model introduces three main contributions: (1) the use of GWO to automatically search for the optimal hyperparameters of SVM tailored to each dataset, (2) a mini-batch strategy guided by K-means clustering to improve the efficiency and convergence of GWO by selecting representative subsets of data, and (3) an enhanced regulation function integrated into GWO that prevents premature convergence by improving the balance between exploration and exploitation. A convergence study is conducted to demonstrate the influence of mini-batch size on both classification accuracy and computational efficiency, showing that using mini-batches as small as 10% of the training data significantly improves computational efficiency without compromising classification accuracy. The K-GWO-SVM framework is evaluated on two benchmark datasets: WESAD for emotion recognition and MIT-BIH Arrhythmia for cardiac classification. The proposed model achieves 99.02% accuracy on WESAD with over a 90% reduction in computational time (10% mini-batch), and 100% accuracy on MIT-BIH with over a 50% reduction in computational time (50% mini-batch), validating its effectiveness, robustness, and suitability for deployment in resource-constrained smart healthcare environments. • Robust yet efficient K-GWO-SVM algorithm is presented for ECG signal analysis. • A novel mini-batch technique is introduced to reduce computational complexity. • K-means defines centroids to form mini-batches for faster GWO convergence. • Convergence study demonstrates mini-batch size effects on accuracy and efficiency. • Dual validation on WESAD and MIT-BIH datasets proves clinical applicability.

The Linear Quadratic Regulator (LQR) problem for systems with uncertainty is challenging: model-based design loses optimality, while prevailing reinforcement learning methods demand prohibitive data and computation. This paper introduces a Hybrid-Driven Recurrent Learning (HDRL) framework that bridges this gap by synergizing model-based optimal control with data-driven learning in a novel way. The core of HDRL is a hybrid training strategy: the policy is evaluated by rolling out on the actual uncertain system (forward pass), while the policy gradient is computed by backpropagating through a deterministic nominal model (backward pass). This approach creates a low-variance, model-guided policy gradient, a fundamental departure from high-variance model-free estimators. Architecturally, HDRL employs a recurrent neural network-like structure, repurposing the LQR cost as a self-supervised loss. A key enabler is our method to convert both additive and structured uncertainties into an additive signal, making training feasible without knowledge of the perturbed dynamics. Simulations demonstrate that HDRL provides a robust, sample-efficient, and practical solution for optimal control under uncertainty.

This paper investigates the finite-blocklength performance of reconfigurable holographic surfaces (RHS) for ultra-reliable low-latency communication (URLLC). A physics-consistent RHS model is established, and the information-theoretic dispersion is derived in closed form using the Mellin transform method, we derive a closed-form probability density function of the RHS-induced channel gain. Then, we apply second-order Taylor expansion and saddle point approximation to obtain analytical expressions for the mutual information and unconditional information variance, which explicitly capture the amplitude-induced anisotropic variance. Finally, leveraging the Berry–Esseen theorem, the closed-form achievability and converse bounds are established which quantify the impact of RHS design parameters, blocklengthn, and average error probability ϵ on the rate. Analytical and simulation results demonstrate that RHS reduces the variance of the effective channel power by 35–50% in required blocklength compared with reconfigurable intelligent surfaces (RIS) under identical resource budgets. The findings identify RHS as a physically scalable and mathematically tractable architecture for short-packet 6G communication.

The integration of the Internet of Things (IoT) with Federated Learning (FL) offers a transformative approach to addressing the challenges of massive data processing and privacy preservation in distributed systems. As a decentralized machine learning paradigm, FL enables model training on distributed datasets while safeguarding data privacy, making it well-suited for IoT applications. However, the performance of wireless FL systems is often constrained by limited communication resources and the mobility of participating clients, which can disrupt efficient model training and convergence. In this paper, we propose a novel mobility-aware FL scheduling strategy that leverages interpretable machine learning to enhance resource allocation in wireless networks. A more effective and fair resource allocation strategy can be achieved by dynamically adjusting the weight of the model quality and the communication quality of the training participants. We evaluate the proposed strategy against traditional scheduling methods in both single and multi-base station scenarios. Simulation results reveal that our approach significantly enhances overall learning efficiency by prioritizing high-value local models. Furthermore, for mobile clients, we identify an optimal range of average speed and participant numbers that maximizes the performance of wireless FL systems, offering practical insights for real-world deployments.

Reconfigurable intelligent surface (RIS) has emerged as a promising technology for future 6G wireless communications. However, the passive nature of RIS and the high-dimensional cascaded channels pose significant challenges for channel estimation (CE), particularly in practical scenarios where decomposition dictionaries cannot be predefined. This paper proposes a novel three-stage joint CE and positioning (JCEP) framework for RIS-assisted communication systems. It first performs the initial CE based on a predefined row dictionary that exploits the structural properties of cascaded channels, and then conducts positioning based on the initial CE results. Finally, it refines the CE results by incorporating the positioning output to construct customized column dictionaries. The framework employs a unitary approximate message passing sparse Bayesian learning (UAMP-SBL) based channel estimator that adapts to both initial and CE refinement stages. For positioning, we design a graph attention network (GAT) to achieve robust positioning performance in dynamic environments. Furthermore, in the CE refinement, we introduce a location-aware dictionary design that leverages position priors to reduce computational overhead. Additionally, we employ meta-learning to enable rapid adaptation to new environments. Extensive simulations show that our framework achieves superior performance in CE and positioning accuracy with low complexity.

In this paper, we investigate a cell-free massive multiple-input and multiple-output (MIMO)-enabled integration communication, computation, and sensing (ICCS) system, aiming to minimize the maximum overall latency to guarantee the stringent sensing requirements. We consider a two-tier offloading framework, where each multi-antenna terminal can optionally offload its local tasks to either multiple mobile-edge servers for distributed computation or the cloud server for centralized computation. The above offloading problem is formulated as a mixed-integer programming and non-convex problem, which can be decomposed into three sub-problems, namely, distributed offloading decision, beamforming design, and execution scheduling mechanism. First, the continuous relaxation and penalty-based techniques are applied to tackle the distributed offloading strategy. Then, the weighted minimum mean square error (WMMSE) and successive convex approximation (SCA)-based lower bound are utilized to design the integrated communication and sensing (ISAC) beamforming. Finally, the other resources can be judiciously scheduled to minimize the maximum latency. A rigorous convergence analysis and numerical results substantiate the effectiveness of our method. Furthermore, simulation results demonstrate the benefits of multi-point cooperation in cell-free massive MIMO-enabled ICCS and reveal the trade-off between the number of involved APs and the resulting latency, highlighting the inherent interplay among communication, sensing, and computation.

Reconfigurable intelligent surface (RIS) has emerged as a promising wireless communication technology in the 6G era. Its ability to adaptively reflect signals offers improved coverage and low energy consumption. Existing channel estimation methods for RIS primarily rely on sparse signal recovery techniques with large overcomplete dictionaries, which results in prohibitive computational complexity. To address this issue, we employ the vision transformer (ViT) model for adaptive user positioning and propose a novel user position based dictionary design approach, to effectively reduce dictionary size and solve the off-grid problem. This design approach is incorporated into a unified framework, where user positioning and channel estimation are performed jointly for integrated sensing and communications. A modified unitary approximate message passing sparse Bayesian learning algorithm with an early stopping scheme is proposed to address potential overfitting issues in channel estimation. Extensive simulation results demonstrate the effectiveness and robustness of our proposed framework.

Atrial fibrillation (AF) is a prevalent cardiac arrhythmia that poses a serious threat to patients' cardiovascular health. Deep learning-based single-lead wearable ECG devices have been widely studied and shown satisfactory performance in early AF detection. However, most existing models increase complexity by employing deep networks and feature fusion techniques to enhance robustness and generalization. Despite several model compression techniques also have been applied to reduce model parameters by compromising certain aspects of performance. In this study, to strike a balance between model performance and complexity, we proposed a Piecewise Aggregate Approximation(PAA)-based lightweight feature fusion schema in the proposed KAN-based Network, with an improved connected layer from Kolmogorov-Arnold Network(KAN) to replace the fully connected layer. Our innovation leveraged the learnable nonlinear activation functions of the KAN Layer to enhance the complex high-dimensional representations at our network's output. Additionally, to reduce the number of parameters generated by feature fusion and the KAN Layer, we applied large-scale dimensionality reduction to the ECG signals using PAA as the secondary feature, allowing the lowdimensional features to represent part of the high-dimensional features through sequence-level fusion. Testing on the PhysioNet2017 dataset demonstrates that our method outperformed the benchmark, achieving a$\mathbf{2. 2 \%}$improvement in the F1 score for AF detection while reducing parameters by$13 \%$compared to traditional feature fusion.

This paper investigates the maximal achievable rate for a given maximal error probability, and blocklength for the reconfigurable intelligent surface (RIS) assisted multiple-input and multiple-output (MIMO) system. The result consists of a finite blocklength and finite alphabet constraints channel coding achievability and converse bounds based on the Berry-Esseen theorem, the Mellin transform and the closed-form expression of the mutual information and the unconditional variance. The numerical evaluation shows a fast speed of convergence to the maximal achievable rate as the blocklength increases and also proves that the channel variance is a sound measurement of the backoff from the maximal achievable rate due to finite blocklength.

In this paper, we propose a transmission mechanism for fluid antennas (FAs) enabled multiple-input multiple-output (MIMO) communication systems based on index modulation (IM), named FA-IM, which incorporates the principle of IM into FAs-assisted MIMO system to improve the spectral efficiency (SE) without increasing the hardware complexity. In FA-IM, the information bits are mapped not only to the modulation symbols, but also the index of FA position patterns. Additionally, the FA position pattern codebook is carefully designed to further enhance the system performance by maximizing the effective channel gains. Then, a low-complexity detector, referred to efficient sparse Bayesian detector, is proposed by exploiting the inherent sparsity of the transmitted FA-IM signal vectors. Finally, a closed-form expression for the upper bound on the average bit error probability (ABEP) is derived under the finite-path and infinite-path channel condition. Simulation results show that the proposed scheme is capable of improving the SE performance compared to the existing FAs-assisted MIMO and the fixed position antennas (FPAs)-assisted MIMO systems while obviating any additional hardware costs. It has also been shown that the proposed scheme outperforms the conventional FA-assisted MIMO scheme in terms of error performance under the same transmission rate.

This paper examines the energy efficiency optimization problem of Intelligent Reflective Surface (IRS)assisted multi-user Rate-Splitting Multiple Access (RSMA) under terahertz propagation. Comparing Salp Swarm Algorithm (SSA) and Successive Convex Approximation (SCA), it is found that SCA requires multiple iterations to solve non-convex resource allocation problems. At the same time, SSA can consume less time to improve energy efficiency.

This paper investigates a deep learning-based algorithm to optimize the unmanned aerial vehicle (UAV) trajectory and reconfigurable intelligent surface (RIS) reflection coefficients in UAV-RIS-aided cell-free (CF) hybrid non-orthogonal multiple-access (NOMA)/orthogonal multiple-access (OMA) networks. The practical RIS reflection model and user grouping optimization are considered in the proposed network. A double cascade correlation network (DCCN) is proposed to optimize the RIS reflection coefficients, and based on the results from DCCN, an inverse-variance deep reinforcement learning (IV-DRL) algorithm is introduced to address the UAV trajectory optimization problem. Simulation results show that the proposed algorithms significantly improve the performance in UAV-RIS-assisted CF networks.

This article analyzes the maximal achievable rate for a given blocklength and maximal error probability over a multiple-antenna ambient backscatter channel. The result consists of a finite blocklength channel coding achievability bound and a converse bound for the legacy system with finite alphabet constraints and multiple-input-multiple-output based on the Neyman–Pearson test, the Berry–Esseen theorem, and the Mellin transform. Then, we derive the closed-form expression of the mutual information and the information variance to reduce the complexity of the computation. By applying the low-complexity maximum-likelihood detection, the relation between the maximal error probability of the RF source signal and the average error probability of the tag symbol with respect to the blocklength is proposed. Finally, numerical evaluation of these bounds shows fast convergence to the maximal achievable rate as the blocklength increases and also proves that the information variance is an accurate measure of the backoff from the maximal achievable rate due to finite blocklength.

Emotion recognition or detection is broadly utilized in patient–doctor interactions for diseases, such as schizophrenia and autism and the most typical techniques are speech detection and facial recognition. However, features extracted from these behavior-based emotion recognitions are not reliable since humans can disguise their emotions. Recording voices or tracking facial expressions for a long term is also not efficient. Therefore, our aim is to find a reliable and efficient emotion recognition scheme, which can be used for nonbehavior-based emotion recognition in real time. This can be solved by implementing a single-channel electrocardiogram (ECG)-based emotion recognition scheme in a lightweight embedded system. However, existing schemes have relatively low accuracy. For instance, the accuracy is about 82.78% by using a least squares support vector machine (SVM). Therefore, we propose a reliable and efficient emotion recognition scheme—exploitative and explorative gray wolf optimizer-based SVM (X-GWO-SVM) for ECG-based emotion recognition. Two data sets, one raw self-collected iRealcare data set, and the widely used benchmark WESAD data set are used in the X-GWO-SVM algorithm for emotion recognition. Leave-single-subject-out cross-validation yields a mean accuracy of 93.37% for the iRealcare data set and a mean accuracy of 95.93% for the WESAD data set. This work demonstrates that the X-GWO-SVM algorithm can be used for emotion recognition and the algorithm exhibits superior performance in reliability compared to the use of other supervised machine learning methods in earlier works. It can be implemented in a lightweight embedded system, which is much more efficient than existing solutions based on deep neural networks.

The rateless multiple access (RMA) scheme is a promising distributed multiple access scheme to achieve simultaneous high reliability, low latency and massive connectivity. In this paper, we investigate the maximum likelihood (ML) decoding performance of the RMA scheme in an Additive white Gaussian noise (AWGN) channel with binary phase-shift keying (BPSK) modulation. For the first time, this paper derives the ensemble weight distribution of the RMA scheme. We derive an upper bound on the decoding error performance of the RMA scheme under ML decoding in an AWGN channel with BPSK modulation. Using the derived bound as the fitness function, we adopt the continuous genetic algorithm to optimize the parameters of the RMA scheme. Simulation results show the tightness of the derived bound and the superiority of the optimized degree distribution over the conventional degree distributions.

This article investigates a wireless-powered intelligent radio environment, where a fractional nonlinear energy harvesting (NLEH) is proposed to enable an intelligent reflecting surface (IRS)-assisted wireless-powered Internet of Things (WP IoT) network. The IRS engages in downlink wireless energy transfer (WET) and uplink wireless information transfer (WIT). We aim to improve the overall performance of the considered network, and the approach is to maximize its sum throughput subject to constraints of two different types of IRS beam patterns and time durations. To solve the formulated problem, we first consider the Lagrange dual method and Karush–Kuhn–Tucker (KKT) conditions to optimally design the time durations in closed form. Then, a quadratic transformation (QT) is proposed to iteratively transform the fractional NLEH model into the subtractive form, where the IRS phase shifts are optimally derived by the complex circle manifold (CCM) method in each iteration. Finally, numerical results are demonstrated to promote the proposed scheme in comparison to the benchmark schemes, where the benefits are induced by the IRS compared with the benchmark schemes.

The paper proposed a novel intelligent transmission surface (ITS) aided transmitter in an intelligent reflection surface (IRS) assisted multiuser multiple-input multiple-output (MIMO) network. The ITS deployed in the transmitter architecture can reduce the power consumption in signal beamforming at the base station (BS), and the IRS can help the information transfer from the ITS-aided transmitter to the users. We first maximize the weighted sum rate (WSR) of the users by jointly designing the beamforming vector at the BS and the phase shifts of ITS and IRS. To solve this non-convex optimization problem, we propose an effective algorithm in which the Lagrangian dual transform, the alternative optimization (AO) algorithm and the quadratic transform (QT) method are adopted to simplify the objective function. Then, the bisection search and the alternating direction method of multipliers (ADMM) algorithm are considered to design the optimal beamforming vector and phase shifts of ITS and IRS, respectively. Furthermore, the paper explores the energy efficiency (EE) maximization problem to emphasize the value of the ITS-assisted transmitter in terms of power savings. Finally, we compare the simulation results to various state-of-the-art techniques to see how much better the proposed algorithm is in terms of WSR and EE.

Micro-vibration, a ubiquitous nature phenomenon, can be seen as a characteristic feature on the objects, these vibrations always have tiny amplitudes which are much less than the wavelengths of the sensing systems, thus these motions information can only be reflected in the phase item of echo. Normally the conventional radar system can detect these micro vibrations through the time frequency analyzing, but these vibration characteristics can only be reflected by time-frequency spectrum, the spatial distribution of these micro vibrations can not be reconstructed precisely. Ghost imaging (GI), a novel imaging method also known as Coincidence Imaging that originated in the quantum and optical fields, can reconstruct unknown images using computational methods. To reconstruct the spatial distribution of micro vibrations, this paper proposes a new method based on a coincidence imaging system. A detailed model of target micro-vibration is created first, taking into account two categories: discrete and continuous targets. We use the first-order field correlation feature to obtain objective different micro vibration distribution based on the complex target models and time-frequency analysis in this work.

In this paper, we theoretically analyse the uplink (UL) and downlink (DL) performance of massive multiple-input and multiple-output (mMIMO) networks, in term of coverage probability, cell spectral efficiency and network area spectral efficiency, using stochastic geometry. A sophisticated but yet practical system model is considered, taking into account a path loss model differentiating line-of-sight and non-line-of-sight transmissions, an idle mode capability at the base stations and a finite user density. Our analysis pays particular attention to the existence of a finite number of UL pilots for channel estimation and the effect of pilot contamination. We study for the first time the joint impact of the number of UL pilot sequences, the user density, and the base-station density on the pilot contamination issue in a mMIMO network, which in turn characterizes the DL and UL network performance. Moreover, using the proposed framework, we investigate two different scheduling problems—UE and pilot scheduling—, to find the optimal simultaneously scheduled UE density per time-frequency resource as well as the optimal UL pilot number to maximise the spectral efficiency.

Publishing datasets plays an essential role in open data research and promoting transparency of government agencies. However, such data publication might reveal users' private information. One of the most sensitive sources of data is spatiotemporal trajectory datasets. Unfortunately, merely removing unique identifiers cannot preserve the privacy of users. Adversaries may know parts of the trajectories or be able to link the published dataset to other sources for the purpose of user identification. Therefore, it is crucial to apply privacy preserving techniques before the publication of spatiotemporal trajectory datasets. In this paper, we propose a robust framework for the anonymization of spatiotemporal trajectory datasets termed as machine learning based anonymization (MLA). By introducing a new formulation of the problem, we are able to apply machine learning algorithms for clustering the trajectories and propose to use k-means algorithm for this purpose. A variation of k-means algorithm is also proposed to preserve the privacy in overly sensitive datasets. Moreover, we improve the alignment process by considering multiple sequence alignment as part of the MLA. The framework and all the proposed algorithms are applied to T-Drive, Geolife, and Gowalla location datasets. The experimental results indicate a significantly higher utility of datasets by anonymization based on MLA framework.

We discover a new capacity scaling law in ultra-dense networks under practical system assumptions, such as a general multi-piece path loss model, a non-zero base station to user equipment antenna height difference, and a finite user equipment density. The intuition and implication of this new capacity scaling law are completely different from those found in the year 2011. That law indicated that the increase of the interference power caused by a denser network would be exactly compensated by the increase of the signal power due to the reduced distance between transmitters and receivers, and thus, network capacity should grow linearly with network densification. However, we find that both the signal and interference powers become bounded in practical ultra-dense networks, which leads to a constant capacity scaling law. Moreover, our new discovery on the constant capacity scaling law indicates three network optimization problems respectively for base station deployment, user equipment scheduling and base station coordination. These three optimization problems are justified and solved in this paper, shedding new light on the deployment and optimization of ultra-dense networks.

Recent years have seen rising needs for location-based services in our everyday life. Aside from the many advantages provided by these services, they have caused serious concerns regarding the location privacy of users. Adversaries can monitor the queried locations by users to infer sensitive information, such as home addresses and shopping habits. To address this issue, dummy-based algorithms have been developed to increase the anonymity of users, and thus, protecting their privacy. Unfortunately, the existing algorithms only assume a limited amount of side information known by adversaries, which may face more severe challenges in practice. In this paper, we develop an attack model termed as Viterbi attack, which represents a realistic privacy threat on user trajectories. Moreover, we propose a metric called transition entropy that enables the evaluation of dummy-based algorithms, followed by developing a robust algorithm that can defend users against the Viterbi attack while maintaining significantly high performance in terms of the traditional metrics. We compare and evaluate our proposed algorithm and metric on a publicly available dataset published by Microsoft, i.e., Geolife dataset.

The ambient backscatter communication (AmBC) system enables radio-frequency (RF) powered devices (e.g., tags, sensors) to transmit their information bits to readers by backscattering and modulating the ambient RF signal. Different from traditional radio-frequency identification (RFID) systems, an AmBC system does not require a reader to transmit excitation signals to the tag and there is no additional carrier emitters required. Therefore, AmBC systems exhibit low-cost and high energy efficiency. The existing AmBC systems utilize an energy detector or a Minimum Mean Square Error (MMSE) detector to detect tag signals which suffers from high bit error rate (BER). In this paper, a machine learning based detection method is proposed to detect the tag signals for an AmBC system by transforming the detection problem into a classification problem. In more detail, the proposed method classifies the received signals into two groups based on the energy features of the received signals. Our simulation results show that the proposed machine learning based detection method outperforms the traditional detection methods, especially in the low SNR regime.

In this paper, a multiple-input multiple-output (MIMO) amplify-and-forward (AF) relay system with direct link is investigated, where simultaneous wireless information and power transfer (SWIPT) technique is applied at the multiple-antenna relay node which splits the received signal into two parts, one portion is used for information decoding (ID) and the other portion is adopted for energy harvesting (EH). By considering power splitting (PS) scheme of SWIPT, the relay node consumes the harvested energy to forward the ID signal to the destination. We design the precoding matrixes of the source and relay nodes to derive the achievable rate. Finally, the analytical results are validated by the simulations.

In this paper, a multiple-input multiple-output (MIMO) two-way amplify-and-forward (AF) relay system is investigated, where simultaneous wireless information and power transfer (SWIPT) technique is applied at the massive-antenna relay node which splits the received signal into two parts, one portion is used for information decoding (ID) and the other portion is adopted for energy harvesting (EH). By considering power splitting (PS) scheme of SWIPT, the relay node consumes the harvested energy to forward the ID signal to two users. When the number of relay antennas approaches to infinity, the deterministic sum-rate of the relay system is analyzed. The close-form expression of optimal PS factor is derived. Finally, the analytical results are validated by the simulations.

Caching the most popular contents at the wireless network edge such as small-cell base stations (SBSs) is a smart way of reducing duplicated content transmissions and offloading the mobile data traffic in the network backhaul. Currently, most small-cell caching strategies are conceived, designed, and optimized based on the global content request probability (GCRP), with very limited consideration of the individual content request probability (ICRP) reflecting personal preferences. To enable more efficient wireless caching, in this paper, we propose a novel localized deterministic caching framework, drawing upon the recent advances in recommendation systems based on machine learning techniques. By introducing the concept of the rating matrix, we first propose a new Bayesian learning method to predict personal preferences and estimate the ICRP. This crucial information is then incorporated into our caching strategy for maximizing the system throughput, or equivalently, minimizing the download latency, where a deterministic caching algorithm based on reinforcement learning is proposed to optimize the content placement. To this end, we extend the framework to enable device-to-device (D2D) connections to further reduce the download delay, and also design a feedback mechanism to improve the accuracy in the ICRP estimation. Our simulation results verified that with the estimated ICRP and the proposed caching strategy, the proposed framework can significantly outperform the existing methods in terms of hit rate and system throughput.

Very recent studies showed that the area spectral efficiency (ASE) of downlink cellular networks will continuously decrease and finally crash to zero as the base station (BS) density increases toward infinity if the absolute height difference between BS antenna and user equipment antenna is larger than zero. Such a phenomenon is referred to as the ASE crash. We revisit this issue by considering optimizing the BS antenna downtilt in cellular networks. It is common to adjust antenna pattern to tune the direction of the vertical beamforming and thus increasing received signal power and/or reducing inter-cell interference power to improve network performance. This paper focuses on investigating the relationship between the BS antenna downtilt and the downlink network performance in terms of the coverage probability and the ASE. Our results reveal an interesting find that there exists an optimal antenna downtilt to achieve the maximum coverage probability for each BS density. Numerically solvable expressions are derived for such optimal antenna downtilt, which is a function of the BS density. Our numerical results show that after applying the optimal antenna downtilt, the network performance can be significantly improved, and hence the ASE crash can be delayed by nearly one order of magnitude in terms of the BS density. Our results also give guidance on setting the optimum downtilt angle to maximize network performance given a fixed BS density.

Reliable command and control channels to unmanned aerial vehicles (UAVs) are needed to allow beyond visual line of sight (LoS) operations. Cellular networks, with their almost ubiquitous coverage, are an obvious candidate to provide such conditions. However, up to which extent the current networks designed for ground users can support UAV communications is an open question. In this paper, we provide a comprehensive theoretical analysis, using stochastic geometry, of the performance that operators could expect from traditional cellular networks with omnidirectional antennas when supporting UAV downlink command and control channels. Our study employs the latest UAV height-dependent path loss model defined by the 3GPP, with LoS and non-LoS transmissions and a probabilistic model to switch between them. We derive analytical expressions for the coverage probability and area spectral efficiency, while accounting for base stations with idle mode capabilities, a practical finite UAV density, and different UAV heights. Results show that networks based on base stations with omnidirectional coverage can support low-height UAVs but will struggle with high-height ones. Network densification helps to provide a better performance.

The aggressive spatial spectrum reuse (SSR) by network densification using smaller cells has successfully driven the wireless communication industry onward in the past decades. In our future journey toward ultra-dense networks (UDNs), a fundamental question needs to be answered. Is there a limit to SSR? In other words, when we deploy thousands or millions of small cell base stations (BSs) per square kilometer, is activating all BSs on the same time/frequency resource the best strategy? In this paper, we present theoretical analyses to answer such question. In particular, we find that both the signal and interference powers become bounded in practical UDNs with a non-zero BS-to-UE antenna height difference and a finite UE density, which leads to a constant capacity scaling law. As a result, there exists an optimal SSR density that can maximize the network capacity. Hence, the limit to SSR should be considered in the operation of future UDNs.

This paper studies the impact of the base station (BS) idle mode capability (IMC) on the network performance of multi-tier and dense heterogeneous cellular networks (HCNs). Different from most existing works that investigated network scenarios with an infinite number of user equipments (UEs), we consider a more practical setup with a finite number of UEs in our analysis. More specifically, we derive the probability of which BS tier a typical UE should associate to and the expression of the activated BS density in each tier. Based on such results, analytical expressions for the coverage probability and the area spectral efficiency (ASE) in each tier are also obtained. The impact of the IMC on the performance of all BS tiers is shown to be significant. In particular, there will be a surplus of BSs when the BS density in each tier exceeds the UE density, and the overall coverage probability as well as the ASE continuously increase when the BS IMC is applied. Such finding is distinctively different from that in existing work. Thus, our result sheds new light on the design and deployment of the future 5G HCNs.

Millimeter wave is a promising technology for the next generation of wireless systems. As it is well-known for its high path loss, the systems working in this spectrum tend to exploit the shorter wavelength to equip the transceivers with a large number of antennas to overcome the path loss issue. The large number of antennas leads to large channel matrices and consequently a challenging channel estimation problem. The channel estimation algorithms that have been proposed so far either neglect the probability of estimation error or require a high feedback overload from receivers to ensure the target probability of estimation error. In this paper, we propose a multi-stage adaptive channel estimation algorithm called robust adaptive multi-feedback (RAF). The algorithm is based on using the estimated channel coefficient to predict a lower bound for the required number of measurements. Our simulations demonstrate that compared with existing algorithms, RAF can achieve the desired probability of estimation error while on average reducing the feedback overhead by 75.5% and the total channel estimation time by 14%.

In this paper, we conduct performance analysis for uplink (UL) massive multiple input and multiple output (mMI-MO) networks using stochastic geometry. With the consideration of practical system assumptions, such as sophisticated path loss model incorporating both line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions and a finite user equipment (UE) density, we derive the coverage probability and the area spectral efficiency (ASE) performance. In particular, we adopt a practical user association strategy (UAS) based on the smallest pathloss since we differentiate LoS and NLoS transmissions, and we consider the correlation among the positions of UEs and base stations (BSs) in realistic networks. From our simulation and analytical results, we find that the performance impacts of the probabilistic LoS/NLoS transmissions and a finite UE density on UL mMIMO networks are significant. More specifically, the coverage probability performance suffers from a moderate decrease or even a severe degradation when the UE density becomes large in sparse mMIMO networks. Moreover, our results indicate that there exists an optimal BS density to maximize the sum spectral efficiency per BS. However, the ASE performance keeps growing with network densification.

A social network is a social structure made up of a set of social actors and a set of dyadic ties between these actors. The actors form a number of communities. In communities, some actors want to transmit their information to all other actors. Each actor corresponds to a user equipment (UE). The UE of the actor which has information to be transmitted is also called the source, and the UEs of all other actors are called destinations. The information is transmitted from the source to destinations with the assistance of helpers, which can be small cell base stations (SCBSs). A novel information dissemination approach, namely expanded network coding with a finite buffer size (ENCFB), is proposed for the case when the buffer size of helpers is limited. The performance comparison of the uncoded information dissemination approach, the network coded approach and the ENCFB approach is conducted. Comparison results show that the ENCFB approach can significantly improve the performance of information dissemination when the buffer size is limited.

This paper develops and evaluates an accumulate-then-transmit framework for multiuser scheduling in a full-duplex (FD) wireless-powered Internet-of-Things (IoT) system, consisting of multiple energy harvesting (EH) IoT devices (IoDs) and one FD hybrid access point (HAP). All IoDs have no embedded energy supply and thus need to perform EH before transmitting their data to the HAP. Thanks to its FD capability, the HAP can simultaneously receive data uplink and broadcast energy-bearing signals downlink to charge IoDs. The instantaneous channel information is assumed unavailable throughout this paper. To maximize the system average throughput, we design a new throughput-oriented scheduling scheme, in which a single IoD with the maximum weighted residual energy is selected to transmit information to the HAP, while the other IoDs harvest and accumulate energy from the signals broadcast by the HAP. However, similar to most of the existing throughput-oriented schemes, the proposed throughout-oriented scheme also leads to unfair interuser throughput because IoDs with better channel performance will be granted more transmission opportunities. To strike a balance between the system throughput and user fairness, we then propose a fairness-oriented scheduling scheme based on the normalized accumulated energy. To evaluate the system performance, we model the dynamic charging/discharging processes of each IoD as a finite-state Markov chain. Analytical expressions of the system outage probability and average throughput are derived over Rician fading channels for both proposed schemes. Simulation results validate the performance analysis and demonstrate the performance superiority of both proposed schemes over the existing schemes.

In this paper, we propose a tractable semi-analytical approach for the network performance analysis of uplink (UL) cellular networks, which is based on a deterministic network analysis using a Gaussian approximation (DNA-GA). The key contribution of this paper is to investigate the UL signal-to-interference ratio (SIR) performance using the DNA-GA analysis. In particular, the SIR is modeled as a ratio of two random variables (RVs), representing the signal power and the aggregate interference power, respectively. The signal power is further characterized by a product of two RVs, i.e., a lognormal RV and an RV with an arbitrary distribution. The former RV comes from a common assumption of lognormal shadow fading, and the latter one takes the rest of random factors into account, such as random user positions, arbitrary types of multi-path fading, and so on. The aggregate interference power is approximated by an RV with a power lognormal distribution. The proposed DNA-GA analysis has several desirable features: 1) it naturally considers lognormal shadow fading; 2) it can treat arbitrary shape and/or size of cell coverage areas; 3) it can handle non-uniform user distributions; 4) it can cope with any type of multi-path fading; and 5) it can be applied to multi-antenna base stations. These features make the DNA-GA analysis very useful for the network performance analysis of the 5th generation systems with general cell deployment and user distribution.

In this paper, we study the impact of the base station (BS) idle mode capacity (IMC) on the network performance of multi-tier and dense heterogeneous cellular networks (HCNs) with both line-of-sight (LoS) and non-line-of-sight transmissions. Different from most existing works that investigated network scenarios with an infinite number of user equipments (UEs), we consider a more practical set-up with a finite number of UEs in our analysis. Moreover, in our model, the small BSs (SBSs) apply a positive power bias in the cell association procedure, so that macrocell UEs are actively encouraged to use the more lightly loaded SBSs. In addition, to address the severe interference that these cell range expanded UEs may suffer, the macro BSs (MBSs) apply enhanced inter-cell interference coordination, in the form of almost blank subframe (ABS) mechanism. For this model, we derive the coverage probability and the rate of a typical UE in the whole network or a certain tier. The impact of the IMC on the performance of the network is shown to be significant. In particular, it is important to note that there will be a surplus of BSs when the BS density exceeds the UE density, and thus a large number of BSs switch off. As a result, the overall coverage probability, as well as the area spectral efficiency, will continuously increase with the BS density, addressing the network outage that occurs when all BSs are active and the interference becomes LoS dominated. Finally, the optimal ABS factors are investigated in different BS density regions. One of major findings is that MBSs should give up all resources in favor of the SBSs when the small cell networks go ultra-dense. This reinforces the need for orthogonal deployments, shedding new light on the design and deployment of the future 5G dense HCNs.

Ghost imaging (GI) is a novel imaging method originated from quantum and optical areas. Due to its nonlocal and nonscanning features, the GI has drawn much attention from different research communities and been adopted into microwave imaging scenarios recently. However, most of the current microwave GI schemes reported in the literature are mainly theoretical approaches. On the one hand, this is because conventional microwave GI systems require a large number of antennas transmitting random modulated signals which is quite challenging in practice. On the other hand, unlike optical GI where charge-coupled devices are used to collect the spatial information of background light fields, current microwave GI schemes can only rely on the estimation of the background microwave fields. Consequently, the reconstruction of objects will suffer from extra perturbations due to estimation errors. Therefore, in order to reduce the complexity and difficulty in the practical implementation of microwave GI, we proposed a novel microwave GI scheme based on nonrandom electromagnetic (EM) fields in this paper. By applying purposely designed EM fields to illuminate the imaging scenario, both the requirement of randomness and the involvement of background microwave field estimations in the framework of current microwave GI have been removed. Thus, its mathematical imaging model has been simplified from a total-least-square (LS) problem to an ordinary-LS one. In addition, by the employing binary orthogonal matrix as the reference for generating the background EM fields, the reconstructed image can be obtained directly without iterative refinement. Numerical simulations show that the proposed nonrandom microwave GI can effectively reconstruct objects with different profiles under different signal-to-noise-ratio conditions. It also shows that both the reconstruction performance and complexity of the proposed method are superior to conventional microwave GI. Besides, similar comparison results can be observed when compressive samplings are applied.

In this paper, we theoretically study the proportional fair (PF) scheduler in the context of ultra-dense networks (UDNs). Analytical results are obtained for the coverage probability and the area spectral efficiency (ASE) performance of dense small cell networks (SCNs) with the PF scheduler employed at base stations (BSs). The key point of our analysis is that the typical user is no longer a random user as assumed in most studies in the literature. Instead, a user with the maximum PF metric is chosen by its serving BS as the typical user. By comparing the previous results of the round-robin (RR) scheduler with our new results of the PF scheduler, we quantify the loss of the multi-user diversity of the PF scheduler with the network densification, which casts a new look at the role of the PF scheduler in UDNs. Our conclusion is that the RR scheduler should be used in UDNs to simplify the radio resource management (RRM).

Small cell networks (SCNs) are envisioned to embrace dynamic time division duplexing (TDD) in order to tailor downlink (DL)/uplink (UL) subframe resources to quick variations and burstiness of DL/UL traffic. The study of dynamic TDD is particularly important because it provides valuable insights on the full duplex transmission technology, which has been identified as one of the candidate technologies for the 5th-generation (5G) networks. Up to now, the existing works on dynamic TDD have shown that the UL of dynamic TDD suffers from severe performance degradation due to the strong DL-to-UL interference in the physical (PHY) layer. This conclusion raises a fundamental question: Despite such obvious technology disadvantage, what is the true value of dynamic TDD? In this paper, we answer this question from a media access control (MAC) layer viewpoint and present analytical results on the DL/UL time resource utilization (TRU) of synchronous dynamic TDD, which has been widely adopted in the existing 4th-generation (4G) systems. Our analytical results shed new light on the dynamic TDD in future synchronous 5G networks.

In this paper, we analyze the coverage probability and the area spectral efficiency (ASE) for the uplink (UL) of dense small cell networks (SCNs) considering a practical path loss model incorporating both line-of-sight (LoS) and non-line-ofsight (NLoS) transmissions. Compared with the existing work, we adopt the following novel approaches in this paper: 1) we assume a practical user association strategy (UAS) based on the smallest path loss, or equivalently the strongest received signal strength; 2) we model the positions of both base stations (BSs) and the user equipments (UEs) as two independent homogeneous Poisson point processes; and 3) the correlation of BSs' and UEs' positions is considered, thus making our analytical results more accurate. The performance impact of LoS and NLoS transmissions on the ASE for the UL of dense SCNs is shown to be significant, both quantitatively and qualitatively, compared with existing work that does not differentiate LoS and NLoS transmissions. In particular, existing work predicted that a larger UL power compensation factor would always result in a better ASE in the practical range of BS density, i.e., 101~ 103BSs/km2. However, our results show that a smaller UL power compensation factor can greatly boost the ASE in the UL of dense SCNs, i.e., 102~ 103BSs/km2, while a larger UL power compensation factor is more suitable for sparse SCNs, i.e., 101 ~ 102 BSs/km2.

In this paper, we study the impact of the base station (BS) idle mode capability (IMC) on the network performance in dense small cell networks (SCNs). Different from existing works, we consider a sophisticated path loss model incorporating both line-of-sight (LoS) and non- line-of-sight (NLoS) transmissions. Analytical results are obtained for the coverage probability and the area spectral efficiency (ASE) performance for SCNs with IMCs at the BSs. The upper bound, the lower bound and the approximate expression of the activated BS density are also derived. The performance impact of the IMC is shown to be significant. As the BS density surpasses the UE density, thus creating a surplus of BSs, the coverage probability will continuously increase toward one. For the practical regime of the BS density, the results derived from our analysis are distinctively different from existing results, and thus shed new light on the deployment and the operation of future dense SCNs.

© 2002-2012 IEEE. In this paper, we analyze the coverage probability and the area spectral efficiency (ASE) for the uplink (UL) of dense small cell networks (SCNs) considering a practical path loss model incorporating both line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions. Compared with the existing work, we adopt the following novel approaches in this paper: 1) we assume a practical user association strategy (UAS) based on the smallest path loss, or equivalently the strongest received signal strength; 2) we model the positions of both base stations (BSs) and the user equipments (UEs) as two independent homogeneous Poisson point processes; and 3) the correlation of BSs' and UEs' positions is considered, thus making our analytical results more accurate. The performance impact of LoS and NLoS transmissions on the ASE for the UL of dense SCNs is shown to be significant, both quantitatively and qualitatively, compared with existing work that does not differentiate LoS and NLoS transmissions. In particular, existing work predicted that a larger UL power compensation factor would always result in a better ASE in the practical range of BS density, i.e., 10-1∼ 10-3 BSs/km2. However, our results show that a smaller UL power compensation factor can greatly boost the ASE in the UL of dense SCNs, i.e., 10-2∼ 10-3 BSs/km2 , while a larger UL power compensation factor is more suitable for sparse SCNs, i.e., 10-1∼ 10-2,BSs/km-2.

In this paper, we propose a new approach of network performance analysis, which is based on our previous works on the deterministic network analysis using the Gaussian approximation (DNA-GA). First, we extend our previous works to a signal-to-interference ratio (SIR) analysis, which makes our DNA-GA analysis a formal microscopic analysis tool. Second, we show two approaches for upgrading the DNA-GA analysis to a macroscopic analysis tool. Finally, we perform a comparison between the proposed DNA-GA analysis and the existing macroscopic analysis based on stochastic geometry. Our results show that the DNA-GA analysis possesses a few special features: (i) shadow fading is naturally considered in the DNA-GA analysis; (ii) the DNA-GA analysis can handle non-uniform user distributions and any type of multi-path fading; (iii) the shape and/or the size of cell coverage areas in the DNA-GA analysis can be made arbitrary for the treatment of hotspot network scenarios. Thus, DNA-GA analysis is very useful for the network performance analysis of the 5th generation (5G) systems with general cell deployment and user distribution, both on a microscopic level and on a macroscopic level.

In this paper, a novel distributed caching strategy in mobile social networks based on device-to-device communications is proposed. The proposed approach combines the characters of social networks to handle some practical issues, e.g., the selfishness of users. In order to maximize the throughput of the whole system, a fast convergence learning automaton, called the discrete generalized pursuit algorithm is utilized. Incorporating with social characters, the algorithm not only optimizes the content placement problems in caching theory, but also satisfies the physical and social constraints appropriately. Simulation results show that, compared with other investigated caching strategies, the proposed algorithm has higher convergence speed and at the same time, it can reduce the transmission delay and improve the system throughput. Moreover, the proposed algorithm can get a better performance in higher density district.

Cooperative inter-working of the long-term evolution (LTE) and the wireless fidelity (Wi-Fi) networks have drawn much attention recently, and several strategies have been proposed to enhance their network capacity. In this paper, we propose a new framework to analyze the network performance of several inter-working strategies for the LTE and the Wi-Fi. The proposed framework considers both the LTE and the Wi-Fi systems, both the downlink (DL) and the uplink (UL) transmissions, and the generated interference in both the time and the spatial domains. Based on such a framework, we theoretically analyze for the first time the performance of a Wi-Fi network, taking into account the intra-cell time efficiency and the signal and inter-cell interference with spatial randomness. Moreover, we study the performance of: 1) a coexisting architecture where Wi-Fi coexists with an ideal carrier sense multiple access (CSMA) duplex system, which represents an upper bound performance for the LTE Release 14 licensed assisted access network and 2) a brand-new architecture that allows UL on LTE and DL on Wi-Fi, referred to as the Boost architecture. We derive analytical results for both the DL and the UL network performances in terms of the signal quality distribution and the total area system throughput (AST) in these two architectures, and quantify their performance gain compared with the traditional disjoint LTE Wi-Fi architecture. Simulation results validate our analysis results, and show that, in a typical outdoor scenario, the coexisting architecture and the Boost architecture can, respectively, increase the total AST up to 11% and 25%, compared with the traditional disjoint LTE Wi-Fi.

Evidence indicates that downloading on-demand videos accounts for a dramatic increase in data traffic over cellular networks. Caching popular videos in the storage of small-cell base stations (SBS), namely, small-cell caching, is an efficient technology for reducing the transmission latency while mitigating the redundant transmissions of popular videos over back-haul channels. In this paper, we consider a commercialized small-cell caching system consisting of a network service provider (NSP), several video retailers (VRs), and mobile users (MUs). The NSP leases its SBSs to the VRs for the purpose of making profits, and the VRs, after storing popular videos in the rented SBSs, can provide faster local video transmissions to the MUs, thereby gaining more profits. We conceive this system within the framework of Stackelberg game by treating the SBSs as specific types of resources. We first model the MUs and SBSs as two independent Poisson point processes, and develop, via stochastic geometry theory, the probability of the specific event that an MU obtains the video of its choice directly from the memory of an SBS. Then, based on the probability derived, we formulate a Stackelberg game to jointly maximize the average profit of both the NSP and the VRs. In addition, we investigate the Stackelberg equilibrium by solving a non-convex optimization problem. With the aid of this game theoretic framework, we shed light on the relationship between four important factors: the optimal pricing of leasing an SBS, the SBSs allocation among the VRs, the storage size of the SBSs, and the popularity distribution of the VRs. Monte Carlo simulations show that our stochastic geometry-based analytical results closely match the empirical ones. Numerical results are also provided for quantifying the proposed game-theoretic framework by showing its efficiency on pricing and resource allocation.

We investigate a localization problem using time-difference-of-arrival measurements with unknown and bounded measurement errors. Different from most existing algorithms, we consider the minimization of the worst-case position estimation error to improve the robustness of the algorithm. The localization problem is formulated as a nonconvex optimization problem. We adopt semidefinite relaxation to relax the original problem into a convex optimization problem, which can be solved using existing semidefinite program solvers. Simulation results show that our proposed algorithm has lower worst-case position estimation error than other existing algorithms.

We study the repair problem in distributed storage systems where storage nodes are connected through packet erasure channels and some nodes are dedicated to repair [termed as dedicated-for-repair (DR) storage nodes]. We first investigate the minimum required repair-bandwidth in an asymptotic setup, in which the stored file is assumed to have an infinite size. The result shows that the asymptotic repair-bandwidth over packet erasure channels with a fixed erasure probability has a closed-form relation to the repair-bandwidth in lossless networks. Next, we show the benefits of DR storage nodes in reducing the repair bandwidth, and then we derive the necessary minimal storage space of DR storage nodes. Finally, we study the repair in a nonasymptotic setup, where the stored file size is finite. We study the minimum practical-repair-bandwidth, i.e., the repair-bandwidth for achieving a given probability of successful repair. A combinatorial optimization problem is formulated to provide the optimal practical-repair-bandwidth for a given packet erasure probability. We show the gain of our proposed approaches in reducing the repair-bandwidth.

In this paper, we analyze the maximum likelihood decoding performance of Raptor codes with a systematic low-density generator-matrix code as the pre-code. By investigating the rank of the product of two random coefficient matrices, we derive upper and lower bounds on the decoding failure probability. The accuracy of our analysis is validated through simulations. Results of extensive Monte Carlo simulations demonstrate that for Raptor codes with different degree distributions and pre-codes, the bounds obtained in this paper are of high accuracy. The derived bounds can be used to design near-optimum Raptor codes with short and moderate lengths.

In this paper, we analytically derive an upper bound on the error in approximating the uplink (UL) single-cell interference by a lognormal distribution in frequency division multiple access (FDMA) small cell networks (SCNs). Such an upper bound is measured by the Kolmogorov-Smirnov (KS) distance between the actual cumulative density function (CDF) and the approximate CDF. The lognormal approximation is important because it allows tractable network performance analysis. Our results are more general than the existing works in the sense that we do not pose any requirement on 1) the shape and/or size of cell coverage areas; 2) the uniformity of user equipment (UE) distribution; and 3) the type of multipath fading. Based on our results, we propose a new framework to directly and analytically investigate a complex network with practical deployment of multiple BSs placed at irregular locations, using a power lognormal approximation of the aggregate UL interference. The proposed interference analysis is particularly useful for the 5th generation (5G) systems with more general cell deployment and UE distribution beyond the widely used Poisson distribution.

In this paper, we introduce a sophisticated path loss model incorporating both line-of-sight (LoS) and non-line-of-sight (NLoS) transmissions to study their impact on the performance of dense small cell networks (SCNs). Analytical results are obtained for the coverage probability and the area spectral efficiency (ASE), assuming both a general path loss model and a special case with a linear LoS probability function. The performance impact of LoS and NLoS transmissions in dense SCNs in terms of the coverage probability and the ASE is significant, both quantitatively and qualitatively, compared with the previous work that does not differentiate LoS and NLoS transmissions. Our analysis demonstrates that the network coverage probability first increases with the increase of the base station (BS) density, and then decreases as the SCN becomes denser. This decrease further makes the ASE suffer from a slow growth or even a decrease with network densification. The ASE will grow almost linearly as the BS density goes ultra dense. For practical regime of the BS density, the performance results derived from our analysis are distinctively different from previous results, and thus shed new insights on the design and deployment of future dense SCNs.

In this paper, for the first time, we analytically prove that the uplink (UL) inter-cell interference in frequency division multiple access (FDMA) small cell networks (SCNs) can be well approximated by a lognormal distribution under a certain condition. The lognormal approximation is vital because it allows tractable network performance analysis with closed-form expressions. The derived condition, under which the lognormal approximation applies, does not pose particular requirements on the shapes/sizes of user equipment (UE) distribution areas as in previous works. Instead, our results show that if a path loss related random variable (RV) associated with the UE distribution area, has a low ratio of the 3rd absolute moment to the variance, the lognormal approximation will hold. Analytical and simulation results show that the derived condition can be readily satisfied in future dense/ultra-dense SCNs, indicating that our conclusions are very useful for network performance analysis of the 5th generation (5G) systems with more general cell deployment beyond the widely used Poisson deployment.

In this paper, we introduce a sophisticated path loss model into the stochastic geometry analysis incorporating both line-of-sight (LoS) and non- line-of-sight (NLoS) transmissions to study their performance impact in small cell networks (SCNs). Analytical results are obtained on the coverage probability and the area spectral efficiency (ASE) assuming both a general path loss model and a special case of path loss model recommended by the 3rd Generation Partnership Project (3GPP) standards. The performance impact of LoS and NLoS transmissions in SCNs in terms of the coverage probability and the ASE is shown to be significant both quantitatively and qualitatively, compared with previous work that does not differentiate LoS and NLoS transmissions. From the investigated set of parameters, our analysis demonstrates that when the density of small cells is larger than a threshold, the network coverage probability will decrease as small cells become denser, which in turn makes the ASE suffer from a slow growth or even a notable decrease. For practical regime of small cell density, the performance results derived from our analysis are distinctively different from previous results, and shed new insights on the design and deployment of future dense/ultra-dense SCNs. It is of significant interest to further study the generality of our conclusion in other network models and with other parameter sets.

In this paper, a novel high-resolution through-wall ghost imaging (TWGI) algorithm is proposed for detecting targets hidden behind walls. By using chaotic modulated signals, ghost imaging (GI) derived from optical image reconstruction applications is introduced and applied in through-wall imaging (TWI) scenarios to achieve high-resolution imaging capability. The scattering coefficients of the target imaging plane are treated as levels of brightness in the proposed algorithm, whose spatial distribution can be retrieved by processing the estimated electromagnetic (EM) field at the target imaging plane with the reflected signal captured by a single receiving antenna. Simulation results are presented to show the effectiveness and high quality of reconstruction results by using the proposed TWGI algorithm.

In this paper, we investigate the optimal resource allocation in a power beacon-assisted wireless-powered communication network (PB-WPCN), which consists of a set of hybrid access point (AP)-source pairs and a power beacon (PB). We assume that all sources have no embedded power supply. Thus, each source first harvests energy from the signals broadcast by its associated AP and/or the PB in the downlink (DL) and then uses the harvested energy to transmit its information to the AP in the uplink (UL). The PB is deployed to assist the APs during the DL wireless energy transfer (WET) phase. We formulate an optimization problem for the considered network, in which the DL WET time of each AP-source pair and the energy allocation of the PB are jointly optimized to maximize the weighted sum-throughput of all AP-source pairs in the UL. We also propose a waterfilling-based algorithm to solve the formulated problem in a distributed manner. Numerical results are performed to validate the convergence of the proposed algorithm and demonstrate the impacts of various system parameters.

In this paper, we investigate the performance of a wireless sensor network, in which multiple groups of source nodes communicate with their respective destination nodes with the help of a common relay network. A network code division multiplexing (NCDM) scheme is proposed to remove the inter-session interference among multiple transmission sessions at each destination. We focus on analyzing the soft processing algorithm of the NCDM scheme. Based on the analysis results, a new code design criteria for the construction of the generator matrix is proposed. Simulation results show that by following the proposed code design criteria, the bit error ratio (BER) performance gap between the scheme we studied and the serial session scheme can be managed effectively. In serial session scheme, source nodes in a number of groups communicate with their respective destinations in a time division manner.

We investigate the spectrum sharing problem of a radio frequency (RF)-powered cognitive radio network, where a multi-antenna secondary user (SU) harvests energy from RF signals radiated by a primary user (PU) to boost its available energy before information transmission. In this paper, we consider that both the PU and SU are rational and self-interested. Based on whether the SU helps forward the PU's information, we develop two different operation modes for the considered network, termed as non-cooperative and cooperative modes. In the non-cooperative mode, the SU harvests energy from the PU and then use its available energy to transmit its own information without generating any interference to the primary link. In the cooperative mode, the PU employs the SU to relay its information by providing monetary incentives and the SU splits its energy for forwarding the PU's information as well as transmitting its own information. Optimization problems are respectively formulated for both operation modes, which constitute a Stackelberg game with the PU as a leader and the SU as a follower. We analyze the Stackelberg game by deriving solutions to the optimization problems and the Stackelberg Equilibrium (SE) is subsequently obtained. Simulation results show that the performance of the Stackelberg game can approach that of the centralized optimization scheme when the distance between the SU and its receiver is large enough.

In this paper, we propose a novel network coded non-binary low-density generator matrix (LDGM) code structure for a multi-access relay system, where multiple sources transmit lattice signals to a destination with the help of a relay. Specifically, we first develop a network coded non-binary LDGM code structure by jointly considering lattice-signal transmissions at the sources and the relay. Then we derive the achievable computation rate (ACR) for the proposed system and on that basis optimize the key parameters in the proposed structure to maximize the ACR. Furthermore, we optimize the network coded non-binary LDGM codes based on lattices to approach the ACR. Simulation results show that the optimal setting of the parameters is consistent with that obtained from our analysis and the proposed code structure outperforms the designed reference scheme.

Heterogeneous cellular networks (HCNs) with embedded small cells are considered, where multiple mobile users wish to download network content of different popularity. By caching data into the small-cell base stations, we will design distributed caching optimization algorithms via belief propagation (BP) for minimizing the downloading latency. First, we derive the delay-minimization objective function and formulate an optimization problem. Then, we develop a framework for modeling the underlying HCN topology with the aid of a factor graph. Furthermore, a distributed BP algorithm is proposed based on the network's factor graph. Next, we prove that a fixed point of convergence exists for our distributed BP algorithm. In order to reduce the complexity of the BP, we propose a heuristic BP algorithm. Furthermore, we evaluate the average downloading performance of our HCN for different numbers and locations of the base stations and mobile users, with the aid of stochastic geometry theory. By modeling the nodes distributions using a Poisson point process, we develop the expressions of the average factor graph degree distribution, as well as an upper bound of the outage probability for random caching schemes. We also improve the performance of random caching. Our simulations show that 1) the proposed distributed BP algorithm has a near-optimal delay performance, approaching that of the high-complexity exhaustive search method; 2) the modified BP offers a good delay performance at low communication complexity; 3) both the average degree distribution and the outage upper bound analysis relying on stochastic geometry match well with our Monte-Carlo simulations; and 4) the optimization based on the upper bound provides both a better outage and a better delay performance than the benchmarks.

In this paper, we investigate optimal resource allocation in a power beacon-assisted wireless-powered communication network (PB-WPCN), which consists of a set of hybrid access point (AP)-source pairs and a power beacon (PB). Each source, which has no embedded power supply, first harvests energy from its associated AP and/or the PB in the downlink (DL) and then uses the harvested energy to transmit information to its AP in the uplink (UL). We consider both cooperative and non-cooperative scenarios based on whether the PB is cooperative with the APs or not. For the cooperative scenario, we formulate a social welfare maximization problem to maximize the weighted sum-throughput of all AP-source pairs, which is subsequently solved by a water-filling based distributed algorithm. In the non-cooperative scenario, all the APs and the PB are assumed to be rational and self-interested such that incentives from each AP are needed for the PB to provide wireless charging service. We then formulate an auction game and propose an auction based distributed algorithm by considering the PB as the auctioneer and the APs as the bidders. Finally, numerical results are performed to validate the convergence of both the proposed algorithms and demonstrate the impacts of various system parameters.

Wireless broadcast has been increasingly used to deliver information of common interest to a large number of users. There are two major challenges in wireless broadcast: the unreliable nature of wireless links and the difficulty of acknowledging the correct reception of every broadcast packet by every user when the number of users becomes large. In this paper, by resorting to stochastic geometry analysis, we develop a network coding based broadcast scheme that allows a base station (BS) to broadcast a given number of packets to a large number of users, without user acknowledgment, while being able to provide a performance guarantee on the probability of successful delivery. Further, the BS only has limited statistical information about the environment including the spatial distribution of users (instead of their exact locations and number) and the wireless propagation model. Performance analysis is conducted. On that basis, an upper and a lower bound on the number of packet transmissions required to meet the performance guarantee are obtained. Simulations are conducted to validate the accuracy of the theoretical analysis. The technique and analysis developed in this paper are useful for designing efficient and reliable wireless broadcast strategies.

In this paper, we investigate the performance of a wireless relay network with multiple transmission sessions, in which multiple groups of source nodes communicate with their respective destination nodes via a shared wireless relay network. A multiple transmission session model with network code division multiplexing (NCDM) scheme is proposed to remove the inter-session interference at each destination. The fundamental idea of the NCDM scheme takes advantage of the property of G Θ HT= 0 of the low-density generator matrix (LDGM) codes. Based on the analysis of the NCDM scheme, we investigate the relationship among the equivalent received signal vector, the number of sessions and the column weight of the generator matrix. New code design criteria for the construction of the generator matrix is proposed. We further evaluate the multiple transmission session model with the proposed NCDM scheme in terms of throughput and complexity. Our evaluation demonstrates that the proposed scheme not only has a linear computational complexity, but also shows a similar error performance in the AWGN case and a considerable throughput improvement compared with its counterpart, which is referred to as a serial session scheme, where groups of source nodes communicate with their respective destinations in a time division manner.

In this paper, we propose a novel estimate-and-forward (EF) transmission protocol, and combine it with the essence of compressive sensing (CS) for a network consisting of two correlated sources, one relay and one destination. Compared with the conventional estimate-and-forward (EF) protocol, in our protocol, correlation is exploited in calculating the soft symbols at the relay. Then we transform the network coded soft symbol vector into a sparse vector, which is suitable for compression by using CS. We analyze that the soft symbols in the proposed protocol are more suitable than those in the EF protocol for CS. Simulations show that our protocol can achieve as good bit error rate performance as the uncompressed EF protocol with reduced transmission time at the relay, thus improving the system throughput performance.

In this paper, we propose a threshold-based one-bit soft forwarding (TOB-SF) protocol for a multi-source relaying uplink system with network coding. In the TOB-SF protocol, the relay calculates the log-likelihood ratio (LLR) value of each network coded symbol, compares this LLR value with a pre-optimized threshold, and determines whether to transmit or keep silent. We first derive the bit error rate (BER) expression at the destination, based on which, we optimize the threshold to minimize the BER. Then we theoretically prove that the system can achieve the full diversity gain by using this threshold. Further, we optimize the power allocation at the relay to achieve a higher coding gain. Simulation results show that the proposed TOB-SF protocol outperforms other conventional relaying protocols in terms of error performance.

The non-stationary properties based on Markov chains are proposed to describe the wireless propagation mechanism of High Speed Railway (HSR) under viaduct scenarios. This Markov modeling method reveals the statistical behaviors of the persistence process corresponding to a resolvable multipath component. Based upon the channel measurement on Beijing-Tianjin HSR at 2.35 GHz, the transition probability matrix and the steady-state probability matrix of Markov chains are specified. These proposed model parameters are informative for link-level simulation and prototype verification for HSR communication systems. In addition, the non-stationary properties are first investigated by medium-scale fading entropy and run length to evaluate the degrees of activity and persistence, respectively. Finally, our Markov models are compared with the experimental results by the Kullback-Leibler (KL) distance to obtain the degree of approximation, which show that the second order model provides a good match to the measured data.

There is an increasing demand for broadcasting information of common interest to a large number of users. The unreliable nature of wireless links and the difficulty of acknowledging the correct reception of every broadcast packet by every user when the number of users becomes large are two major challenges for wireless network broadcasting. In this paper we investigate the problem that a base station broadcasts a given number of packets to a given number of users, without user acknowledgment, while being able to provide a guarantee on the probability of successful delivery. Network coding technique is employed to improve both the efficiency and the reliability of the broadcast. Performance analysis is conducted. Based on the analysis, an upper and a lower bound on the number of packet transmissions required to meet the reliability guarantee are obtained. Simulations are conducted to validate the accuracy of the theoretical analysis. The technique and analysis developed in this paper can be useful for designing strategies to deliver information of common interest to a large number of users efficiently and reliably.

Heterogeneous network (HetNet) [1] is considered as an energy efficient system structure to alleviate the problem of rapidly increasing power consumption in the wireless communication system. Significant research on HetNet energy efficiency has been conducted. However, most of them only consider power consumption of Base Stations (BSs) while ignoring influence on energy efficiency of Mobile Devices (MDs) brought by new BSs deployment. In this work, we propose a novel power saving metric for HetNet. Under the coexisting scenario of a single macrocell and a single microcell, we analyze the changes in power consumption at both the BSs side and the MDs side with the deployment of a micro BS. Optimum microcell radii for maximum power saving at the MDs sides and for highest network energy efficiency are obtained through analytical studies. It is found that total power saving for microcell MDs is close to 18% with a proper deployment of a microcell. Finally, extensive simulations have been provided to establish the accuracy of our theoretical analyses.

In this paper, we first analyse bit error rate (BER) bounds of the distributed network coding (DNC) scheme based on the Luby-transform (LT) codes, which is a class of fountain codes, for wireless sensor networks (WSNs). Then we investigate the effect from two parameters of the degree distributions, i.e., the degree value and the proportion of odd degree, to the performance of the LT-based DNC scheme. Based on the analysis and investigation results, a degree distribution design criteria is proposed for the DNC scheme based on fountain codes over Rayleigh fading channels. We compare the performance of the DNC scheme based on fountain codes using degree distributions designed in this paper with other schemes given in the literature. The comparison results show that the degree distributions designed by using the proposed criteria have better performance.

In heterogeneous networks (HetNets), the load between macro-cell base stations (MBSs) and small-cell BSs (SBSs) is imbalanced due to transmit power disparities and ad-hoc deployment of SBSs. This significantly impacts the system performance and user experience. Associating more users to the SBSs is an effective way to solve this problem. In this paper, we formulate the user-BS association problem as a distributed optimization problem with proportional fairness as the objective. Specifically, we propose a novel distribute algorithm based on the belief propagation (BP) method to solve the user-BS association problem via iteratively message passing between the users and BSs. Also, we develop an approximation calculation in the BP method to reduce the computational complexity and transmission overhead of message passing. Simulation results show that the proposed algorithm well approaches the optimal system performance (by exhausting search) with low complexity and fast convergence.

In this paper, we propose a distributed network coding (DNC) scheme based on the Raptor codes for wireless relay networks (WRNs), where a group of source nodes communicate with a single sink through a common relay network in a multi-hop fashion. At the sink, a graph-based Raptor code is formed on the fly. After receiving a sufficient number of encoded packets, the sink begins to decode. The main contributions of this paper are the derivations of upper and lower bit error rate (BER) bounds for the proposed Raptor-based DNC scheme.

A dynamic resource allocation algorithm is investigated for the orthogonal frequency division multiplexing access (OFMDA) system under the network architecture of High-Speed Railway (HSR). The mobile base station (MBS) on the top of the train forwards the signal received from the base station (BS) on the ground to the user equipments (UE) in the train. Due to the high mobility in the BS-MBS link, the inter-carrier interference (ICI) caused by the Doppler shift may degrade the system performance. In this paper, we aim to combat the influence of the ICI to improve the system capacity by means of the resource allocation, which includes subcarrier allocation, subcarrier pairing and power allocation. Simulation results demonstrate that the proposed resource allocation algorithm can improve the system performance dramatically.

Wireless broadcast has been widely utilized to deliver information of common interest to a large number of users. A major challenge for wireless broadcast is that wireless links are often unreliable. Further, it is not feasible for every receiver to acknowledge the correct reception of broadcasted packets. In this paper we investigate the use of wireless broadcast to deliver a given number of packets by a common transmitter to a given number of receivers, without feedback from the receivers, while meeting the reliability constraint, i.e. the probability that all receivers successfully receive all broadcasted packets is above a certain threshold. Rateless codes(RCs) technology is used to assist the broadcast. Performance analysis with the use of RCs is conducted. Simulations are conducted to validate the accuracy of the theoretical analysis. It is shown that the use of RCs can significantly reduce the number of transmissions required to meet the reliability constraint.

In this paper we investigate novel soft mutual information forwarding (MIF) protocols in a two-way relay channel (TWRC), where two sources exchange information with the help of an intermediate relay. Based on the estimated signals from the two sources, the relay calculates the soft mutual information, and then broadcasts it to the two sources. In specific, we propose two MIF protocols, namely, network coded MIF (NC-MIF) and superposition coded MIF (SC-MIF), suitable to different channel conditions. The expressions derived for the received signal-to-noise ratio (SNR) at the sources reveal that if both source-to-relay channels are in good conditions, the NC-MIF outperforms the SC-MIF. Otherwise, the SC-MIF is superior to the NC-MIF. For the TWRC with varying channels, we further develop an adaptive scheme, which enables the dynamic switch between the two protocols, depending on the received SNR at the sources. Furthermore, the threshold that determines the switch of the protocols is developed as a close-form expression. Simulation results show that our adaptive scheme outperforms all the existing relaying protocols in the fading channels.

In many applications of wireless relay networks (WRNs), such as image and video systems, unequal error protection (UEP) among the transmission data from different source nodes is required. All the source nodes in a WRN with different protection requirements form multiple protection groups. In this paper, we focus on estimating the protection parameter, i.e., the protection weight, for each protection group. We first analyze the bit error rate (BER) upper bound of the UEP distributed fountain codes over Rayleigh fading channels for WRNs. Then based on the analysis results, we derive the approximate expression for the BER upper bound with protection weight as the variable. The derived approximate expression can be used to estimate the protection weights for the protection groups according to their various performance requirements. Finally, examples on the application of the approximate expression in estimating protection weights are given.

In this paper, we propose a threshold-based one-bit soft forwarding (TOB-SF) protocol for a multi-source relaying system with network coding, where two sources communicate with the destination with the help of a relay. Specifically in the TOB-SF protocol, the relay calculates the log-likelihood ratio (LLR) value of each network coded symbol, compares this LLR value with a pre-optimized threshold, and determines whether to transmit or keep silent. We are interested in optimizing the TOB-SF protocol in fading channels, and consider both the uncoded and low-density parity check coded systems. In the uncoded system, we first derive the bit error rate (BER) expressions at the destination, based on which, we derive the optimal threshold. Then we theoretically prove that the system can achieve the full diversity gain by using this threshold. Further, we optimize the power allocation at the relay to achieve a higher coding gain. In the coded system, we first optimize the LLR threshold. Then we develop a methodology to track the BER evolution at the destination by using Gaussian approximations. Based on the BER evolution, we further optimize the power allocation at the relay which minimizes the system BER. Simulation results show that the proposed TOB-SF protocol outperforms other conventional relaying protocols in terms of error performance.

In this paper we propose a novel power adaptive network coding (PANC) for a non-orthogonal multiple-access relay channel (MARC), where two sources transmit their information simultaneously to the destination with the help of a relay. In contrast to the conventional XOR-based network coding (CXNC), the relay in PANC generates network coded symbols by considering the coefficients of the source-to-relay channels, and forwards each symbol with a pre-optimized power level. Specifically, by defining a symbol pair as two symbols from the two sources, we first derive the expression of symbol pair error rate (SPER) for the system. Noting that deriving the exact SPER are complex due to the irregularity of the decision regions caused by random channel coefficients, we propose a coordinate transform (CT) method on the received constellation to simplify the derivations of the SPER. Next, we obtain the optimal power level by decomposing it as a multiplication of a power scaling factor and a power adaptation factor. We prove that with the power scaling factor at the relay, our PANC scheme can achieve a full diversity gain, i.e., an order of two diversity gain, while the CXNC can achieve only an order of one diversity gain. In addition, we optimize the power adaptation factor at the relay to minimize the SPER at the destination by considering of the relationship between SPER and minimum Euclidean distance of the received constellation, resulting in an improved coding gain. Simulation results show that (1) the SPER derived based on our CT method can well approximate the exact SPER with a much lower complexity; (2) the PANC scheme with power adaptation optimizations and power scaling factor design can achieve a full diversity, and obtain a much higher coding gain than other network coding schemes.

In wireless relay networks (WRNs), multiple source nodes communicate with a single destination node through a common relay network. Different source nodes may have different error protection or recovery time requirements. In this paper, we focus on unequal error protection (UEP) distributed network coding (DNC) design for WRNs. Specifically, we propose a continuous UEP method based on fountain codes with the objective of satisfying the UEP or unequal recovery time (URT) requirements. Then based upon this UEP method, we develop an adaptive UEP DNC scheme to realize adaptive UEP in WRNs. We analyze the properties of the proposed adaptive UEP DNC scheme and derive the upper and lower bit error rate (BER) bounds for it over Rayleigh fading channels under maximum-likelihood (ML) decoding. Simulation results show that the proposed adaptive UEP DNC scheme has desirable UEP and URT properties. The transmitted data from the source nodes in different protection groups can be recovered successfully under their performance requirements in a shorter time by using the adaptive UEP DNC scheme.

Wireless communication is notoriously lossy due to channel fading, interference and multi-path effects. This work investigates the reliability of all-to-all broadcast in lossy wireless networks where the reliability is measured by the probability that every node in the network receives or decodes the native packet of every other node. To improve the reliability, a novel network coding scheme, namely random neighbour network coding (RNNC) scheme is proposed, which is capable of adaptively generating encoding packets according to the packets received from lossy wireless channels. The network reliability is analysed theoretically and the optimal RNNC scheme that maximises the reliability of a given network is obtained. The theoretical analysis is validated using simulations and it is shown that RNNC can improve the network reliability significantly.

Since the seminal work of Gupta and Kumar, extensive research has been done on studying the capacity of large wireless networks under various scenarios. Most of the existing work focuses on studying the capacity of networks with uniformly or Poissonly distributed nodes. While uniform and Poisson distribution form an important class of spatial distributions, their capability in capturing the spatial distribution of users in various scenarios and application settings is limited. Therefore it is critical to investigate to what extent, the aforementioned results on capacity of networks with uniformly or Poissonly distributed nodes depend on the underlying node distribution being uniform or Poisson. In this paper, we study the capacity of networks under a general node distribution. A capacity upper bound on networks with generally distributed nodes is obtained, which is valid for both finite networks and asymptotically infinite networks. By imposing some mild conditions on the transmission range, we further simplify the result and show that the asymptotic capacity upper bound can be expressed as a product of four factors, which represents respectively the impact of node distribution, link capacity, number of source destination pairs and the transmission range. The upper bound is shown to be tight in the sense that for the special case of networks with uniformly distributed nodes, the bound is in the same order as known results in the literature.

Protocol sequences offer a promising alternative for media access control of mobile ad hoc networks, because they do not require any coordination among the users nor any centralized synchronization. We show that by using suitably designed deterministic scheduling, the delay performance can indeed be much better than using random and pseudo-random sequences. The reported results indicate that protocol sequences can offer practical solutions to complicated multiple-access problems in ad hoc networks, such as vehicular ad hoc networks (VANET). The cumulative distribution function of delay and an upper bound of the individual delay in the cases of protocol sequences are derived.

In this paper, we propose a practical 1-bit soft forwarding protocol for a network-coded two-way relay channel. Different from the conventional estimate-and-forward (EF) protocol, the proposed protocol forwards 1-bit soft information at the relay. We employ the joint trellis coded quantization/modulation (TCQ/M) to implement 1-bit transmission of the soft information. Also, the codebooks in the TCQ are designed to be adaptive to the source-to-relay channel conditions so that the system can achieve the full diversity gain over fading channels. Specifically, in the low source-to-relay channel SNR region, we apply the TCQ/M to the soft information based on the codebook generated by the LloydMax quantizer. In the high source-to-relay channel SNR region, where the soft information is equivalent to its hard decision, we design the codebook by repeating the soft information. It has been shown that the proposed protocol outperforms both the amplify-and-forward (AF) and the decode-and-forward (DF) protocols over fading channels.

Inter-cell interference coordination (ICIC) and resource allocation problems are fundamental challenges for the design of wireless networks. In this paper, we propose a distributed network inter-cell control scheme, and introduce a Belief Propagation (BP) framework to solve the optimization problem. The goal is to maximize the sum rate of those Base Stations (BS). This new approach assumes that the inter-cell interference is a set of stochastic variables. Based on a set of prior distributions, it calculates the posterior distributions of the scheduling variables. The solution allocates PRBs to Mobile Stations (MS) in the cells, including optimization of the transmit powers in each subcarrier. Numerical results demonstrate that this algorithm achieves a good result in typically a couple of iterations.

In this paper we propose a novel network coded LDPC code design for a multiple-access relay channel (MARC). We first investigate the achievable rate region for the MARC. Then we propose a novel physical layer network coded (PNC) LDPC code structure, named PNC-LDPC code. Next, an iterative detection-and-decoding receiver is designed to deal with the multi-user interference at the destination. Based on the code structure and the iterative receiver, we optimize the degree distribution of the PNC-LDPC code to approach the system achievable rate by utilizing the extrinsic mutual information transfer (EXIT) chart. Simulations show that the performance of our PNC-LDPC code, with a code length of 10000, at the destination, is 1:5 dB away from the capacity.

In this work we determine the achievable rate in a multi-source relaying system with Gaussian phase-fading channels. In our system, M sources simultaneously transmit their messages to a common destination in M separate frequency bands with the help of a single relay (an M − 1 − 1 system). The achievable rates of both a separate processing scheme at the relay, and a network coding scheme at the relay, are considered. For the separate processing scheme, we propose an new constrained water-filling algorithm which determines the power allocation at the relay in order to obtain the achievable rate. For the network coding scheme we derive the achievable rate based on the use of a new Galois field rate-splitting theorem, and discuss why power allocation at the relay in this scheme can be set using a traditional water-filling algorithm. We show how our network coding scheme will always obtain higher achievable rates relative to those obtained from a separate processing scheme.

In this paper, we focus on the realization of multiple interpretations (MI) in multi-way relay channels (MWRC) with fading, where multiple sources communicate with each other with the help of a relay. We first propose a novel nested convolutional lattice codes (NCLC) over the finite field, which can achieve the MI for each source in two time slots. Then we derive a theoretical upper bound for the codeword error rate (WER) of the NCLC. We further optimize our NCLC by developing a code design criterion which minimizes the derived WER. In simulations, we construct a specific NCLC based on our code design criterion. Simulation results show that our code can realize MI for each source in two time slots, and validate the derived upper bound in the high normalized signal-to-effective-noise ratio (SENRnorm) region.

In this paper, we focus on network coding design for the wireless sensor networks (WSNs), where multiple source nodes communicate with a common destination node with the help of multiple relay nodes in a two-hop fashion. Specifically, we propose an unequal error protection (UEP) distributed network-channel coding (DNCC) scheme based on Luby-transform (LT) codes. We analyse three properties of the proposed UEP DNCC scheme, i.e. effective weights, turning points, and thresholds of the source nodes' number. Also, we derive the upper and lower bit error rate (BER) bounds for the proposed UEP DNCC scheme over Rayleigh fading channels under maximum-likelihood (ML) decoding. Based on the analysis, it is observed that the proposed UEP DNCC scheme can achieve all protection levels required when the number of source nodes is large enough. Simulation results show that our UEP DNCC scheme can provide desirable UEP to all source nodes.

In this paper, we propose a distributed network coding (DNC) scheme based on the Raptor codes for wireless sensor networks (WSNs), where a group of sensor nodes, acting as source nodes, communicate with a single sink through some other sensor nodes, serving as relay nodes, in a multi-hop fashion. At the sink, a graph-based Raptor code is formed on the fly. After receiving a sufficient number of encoded packets, the sink begins to decode. The main contributions of this paper are the derivation of a bit error rate (BER) lower bound for the LT-based DNC scheme over Rayleigh fading channels under maximum-likelihood (ML) decoding, and the derivations of upper and lower BER bounds for the proposed Raptor-based DNC scheme on the basis of the derived BER bound of LT codes.

Extensive research has been done on studying the capacity of wireless multi-hop networks. These efforts have led to many sophisticated and customized analytical studies on the capacity of particular networks. While most of the analyses are intellectually challenging, they lack universal properties that can be extended to study the capacity of a different network. In this paper, we sift through various capacity-impacting parameters and present a simple relationship that can be used to estimate the capacity of both static and mobile networks. Specifically, we show that the network capacity is determined by the average number of simultaneous transmissions, the link capacity and the average number of transmissions required to deliver a packet to its destination. Our result is valid for both finite networks and asymptotically infinite networks. We then use this result to explain and better understand the insights of some existing results on the capacity of static networks, mobile networks and hybrid networks and the multicast capacity. The capacity analysis using the aforementioned relationship often becomes simpler. The relationship can be used as a powerful tool to estimate the capacity of different networks. Our work makes important contributions towards developing a generic methodology for network capacity analysis that is applicable to a variety of different scenarios.

In this paper, we analyse the performance of distributed network-channel coding (DNCC) with multiple destinations executing a code nulling (MDCN) process. By analysing the formulation deduced from DNCC with the MDCN process, we find that some unstable zero elements and additional noise are generated after right multiplying the parity-check matrix. These unstable zero elements and additional noise are the reasons to degrade BER performance in the two groups of source nodes and two destination nodes (TSTD) network model. Theoretical bit error ratio (BER) curves are drawn according to the calculated equivalent received signal-to-noise ratio (SNR). The analysis results are consistent with the theoretical curves. A design principle for the generator matrix of the DNCC scheme is proposed to solve the BER performance degradation problem. Simulation results show that the problem caused by the MDCN process can be managed effectively and the BER performance can be improved significantly by using the proposed design principle.

In this paper, we introduce the concept of average per-user rate to the multiuser Multiple-Input, Multiple-Output (MIMO) system with the frequency domain packet scheduler (FDPS) at base stations, which provides an estimate of the rate that the system could provide for each admitted user. The proposed admission control is designed by comparing the user's quality of service (QoS) requirements with the transmission rate that the system can offer. The analytical model is based on the generalized 3GPP LTE downlink transmission for which two Spatial Division Multiplexing (SDM) multiuser MIMO schemes are investigated, namely, Single User (SU) and Multi-user (MU) MIMO schemes. The main contribution of this paper is the derivation of the achievable rate for each user in the SDM MIMO systems based on a mathematical model of the Signal to Interference plus Noise Ratio (SINR) distribution with the frequency domain packet scheduler. The achievable rate provides insights into the system's performance from a different perspective.

Multi-source multi-destination wireless relay network coded systems are investigated in this paper. To achieve multiple interpretations at different receivers, we employ nested codes in our proposed system. Besides, an opportunistic scheduling (OS) technique is adopted at the relay to maximize the system capacity. The proposed system model combines the merits of both nested codes and OS. First, we present the detailed coding process of the proposed scheme. Then, we derive the upper bounds on the bit error probability of the schemes with and without OS. Finally, we investigate good codes for our system and carry out simulations to validate the theoretical analysis.

In this paper, we use extrinsic information transfer (EXIT) chart to design irregular low density generator matrix (LDGM) codes to form distributed network-channel codes in a wireless sensor network. We formulate the code design and code search as a linear programming (LP) problem. We consider a real-time wireless network with randomly changeable fading channels, resulting in link failures and time varying network topology. In forming such a dynamic network, the connected number of source nodes at each relay needs to satisfy previously obtained degree distributions. At the same time, the channel quality of the data links connecting the source nodes and relay nodes has to be considered. We propose the optimal relaying selection scheme to present such a solution. Simulation results of the proposed irregular codes show that a considerable performance improvement can be achieved in the waterfall region compared with the existing codes.

A novel iterative detection scheme for MIMO-OFDM systems is proposed in this work. We show that the existing detection schemes are sub-optimum and the iterative process can be optimized by utilizing the non-circular property of the residual interference after interference cancellation. Results show that the proposed iterative scheme outperforms the conventional iterative soft interference cancellation (ISIC) and V-BLAST schemes by about 1.7 and 4.0 dB, respectively, in a 4 × 4 antennas system over exponentially distributed eleven path channels.

This paper presents a novel spatial frequency domain packet scheduling and frequency domain equalization (FDE) algorithm for uplink Single Carrier (SC) Frequency Division Multiple Access (FDMA) multiuser MIMO systems. Our analysis model is confined to 3GPP uplink SC-FDMA transmission with Multi-user (MU) Spatial Division Multiplexing (SDM). The results show that the proposed MU-MIMO scheduler in conjunction with the new FDE singificantly increases the maximum achievable rate and improves the bit error rate (BER) performance for the system under consideration.

In this paper, we consider a multi-access relay interference channel (MARIC), where multiple groups of source nodes communicate with multiple respective destinations, respectively, through a common multi-hop relay network. A joint distributed network-channel coding (DNCC) scheme is proposed to explore both network and channel coding gains. In DNCC, each relay performs a linear network coding and a graph code is formed at each destination. However, multiple groups of source nodes interfere with each other at each destination as each code graph contains bits sent from all other groups of source nodes. To eliminate the inter-group interference, DNCC employs a code-nulling process, so that the graph code at each destination is only the code of its own group of source nodes and does not contain the bits from other group of source nodes. This converts a MARIC into multiple independent multi-access relay channels (MARCs), for each of which a group of source odes communicate with a single destination. Furthermore, for the systematic graph code, such as low density generate matrix (LDGM) code, with respect to each group of source nodes, the LDGM code formed in each decomposed MARC is essentially the same as that formed in the original MARIC. This significantly relax the system design as we can design the distributed LDGM code for each group of source nodes independently as if other group of source nodes does not exist.

Adaptive network coded cooperation (ANCC) has been proposed as an effective scheme to combine network and channel coding for cooperative wireless networks by matching network-on-graph to code-on-graph. Since the real network consists of randomly faded channels, the link failure and topology change are unavoidable. In this paper, we investigate the performance of ANCC in a real wireless sensor network (WSN). Different network codes are constructed to match these instantaneous network topologies. By designing a proper network code through optimizing source node degrees, a significant performance improvement is achieved. In addition, we consider the noisy channel between the source and relay, and propose a soft information relaying based ANCC scheme. Simulation results show that the proposed scheme considerably improves the system performance compared to the hard-decision relaying ANCC scheme.

The average channel capacity for 3GPP LTE downlink multiuser Multiple Input Multiple Output (MIMO) systems is analyzed in this paper. A packet scheduler is used to exploit the available multiuser diversity in all the three physical domains (i.e., space, time and frequency). A mathematical model is established to derive the channel capacity of multiuser MIMO systems with the frequency domain packet scheduler (FDPS). This work provides a theoretical reference for the future version of the LTE standard and a useful source of information for the practical implementation of the LTE systems.

This letter derives mathematical expressions for the received signal-to-interference-plus-noise ratio (SINR) of uplink Single Carrier (SC) Frequency Division Multiple Access (FDMA) multiuser MIMO systems. An improved frequency domain receiver algorithm is derived for the studied systems, and is shown to be significantly superior to the conventional linear MMSE based receiver in terms of SINR and bit error rate (BER) performance.

The LTE downlink multiuser Multiple Input Multiple Output (MIMO) systems are analyzed in this paper. Two Spatial Division Multiplexing (SDM) multiuser MIMO schemes are investigated: Single User (SU) and Multi-user (MU) MIMO schemes. The main contribution of this paper is the establishment of a mathematical model for the Signal to Interference plus Noise Ratio (SINR) distribution for multiuser SDM MIMO systems with frequency domain packet scheduler.

Mathematical expressions are derived for the received signal to interference plus noise ratio of uplink single carrier (SC) frequency division multiple access (FDMA) multi-user multiple-input multiple-output (MIMO) systems with spatial frequency domain packet scheduling. The scheduler is able to exploit the available multi-user diversity in time, frequency and spatial domains. Our analysis model is confined to 3GPP uplink SC-FDMA transmission in which we specifically investigate multi-user spatial divsion multiplexing MIMO schemes.

In this paper, we present upper bounds on the symbol error probability for convolutional encoded continuous phase modulation (CPM) over rings with maximum likelihood sequence detection (MLSD). Both coded CPM schemes with and without feedback from the CPM encoder to the ring convolutional encoder are considered. The bounds are based on the transfer function technique. The paper contribution is in the development of the analytical upper bound on the symbol error probability for the investigated system. The bound can be used as a tool to analyze and design ring convolutional encoded CPM systems.

Joint Source and Channel Coding (JSCC) scheme using ring convolutional coded Continuous Phase Modulation (CPM) is investigated. The channels under this investigation are the Additive White Gaussian Noise (AWGN) channel and the Rayleigh fading Channel. Optimal soft decoding for the proposed JSCC scheme is studied. The soft decoder is based on the A Posteriori Probability (APP) algorithm for trellis coded CPM with ring convolutional codes. The novelties of this work are the development of a JSCC scheme using ring convolutional coded CPM, the use of a soft decoder and the APP algorithm for the combined systems.

This paper presents the analysis of the average channel capacity and the SINR distribution for multiuser Multiple Input Multiple Output (MIMO) systems in combination with the base station based packet scheduler. The packet scheduler is used to exploit the available multiuser diversity in the time, frequency and spatial domains. The analysis model is carried out for 3 GPP LTE downlink transmission. Two Spatial Division Multiplexing (SDM) multiuser MIMO schemes in the context of LTE downlink transmission are investigated. They are Single User (SU) and Multi-user (MU) MIMO schemes. In general, the outage probability for systems using SU-MIMO scheme is larger than the one with MU-MIMO scheme. Compared with the systems without preceding, linear preceding can improve the outage probability. The paper contributions are the derivation of a mathematical expression of the SINR distribution and the average channel capacity for multiuser MIMO systems with a frequency domain packet scheduler.

This paper analyzes the average capacity for a wireless network with joint opportunistic scheduling and wireless network coding. The capacity and the optimal power allocation scheme are derived for a multiuser fading broadcasting channel with perfect channel side information at the transmitter. The packets generated by the source nodes are encoded prior to the transmission to the relay node. The received encoded packets are mixed by XOR operation at the relay node and then broadcasted to the destination nodes. The encoder in the source nodes is designed in such a way that each destination node can give different interpretation of the received packets with their own side information. From the numerical and simulation results, we can see that the proposed simultaneous power and rate adaption for wireless network coding with opportunistic scheduling can significantly improve the average channel capacity.

In this paper, a novel trellis source encoding scheme based on punctured ring convolutional codes is presented. Joint source and channel coding (JSCC) using trellis coded continuous phase modulation (CPM) with punctured convolutional codes over rings is investigated. The channels considered are the additive white gaussian noise (AWGN) channel and the Rayleigh fading channel. Optimal soft decoding for the proposed JSCC scheme is studied. The soft decoder is based on the a posteriori probability (APP) algorithm for trellis coded CPM with punctured ring convolutional codes. It is shown that these systems with soft decoding outperform the same systems with hard decoding especially when the systems operate at low to medium signal-to-noise ratio (SNR). Furthermore, adaptive JSCC approaches based on the proposed source coding scheme are investigated. Compared with JSCC schemes with fixed source coding rates, the proposed adaptive approaches can achieve much better performance in the high SNR region. The novelties of this work are the development of a trellis source encoding method based on punctured ring convolutional codes, the use of a soft decoder, the APP algorithm for the combined systems and the adaptive approaches to the JSCC problem.

Joint source and channel coding (JSCC) using trellis coded quantization (TCQ) in conjunction with trellis coded continuous phase modulation (CPM) is studied. The channel is assumed to be the additive white gaussian noise (AWGN) channel. Analytical bounds on the channel distortion for the investigated systems with maximum-likelihood sequence detection (MLSD) are developed. The bounds are based on the transfer function technique, which was modified and generalized to include continuous-amplitude discrete-time signals. For a memoryless uniform source, the constructed bounds for the investigated systems are shown to be asymptotically tight for increasing channel signal-to-noise ratio (SNR) values. For a memoryless nonuniform source, the constructed bounds are not as tight as the one for the uniform source, however, it still can be used as an indication to how the system performs. It is concluded that the minimum Euclidean distance of the system alone is not enough to evaluate the performance of the considered systems. The number of error events having minimum Euclidean distance and the total distortion caused by those error events also affect the asymptotic performance. This work provides an analysis tool for the investigated systems. The analysis method is very general. It may be applied to any trellis based JSCC schemes.

Joint source and channel (JSC) coding using combined trellis coded quantization (TCQ) and continuous phase modulation (CPM) is studied. The channel is assumed to be the additive white Gaussian noise (AWGN) channel. Optimal soft decoding for JSC coding using jointly designed TCQ/CPM is studied in this paper. The soft decoder is based on the a posteriori probability (APP) algorithm for trellis coded CPM. It is shown that the systems with soft decoding outperform the systems with hard decoding especially when the systems operate at low to medium signal-to-noise ratio (SNR). Furthermore, a TCQ design algorithm for the noisy channel is developed. It has been demonstrated that the combined TCQ/CPM systems are both power and bandwidth efficient compared with the combined TCQ/TCM/8PSK systems. The novelty of this work is the use of a soft decoder and the APP algorithm for combined TCQ/CPM systems.

Joint source and channel (JSC) coding using combined trellis coded quantization (TCQ) and continuous phase modulation (CPM) is studied. The channel is assumed to be the additive white Gaussian noise (AWGN) channel. Analytical bounds on the channel distortion for jointly designed TCQ/CPM systems with maximum likelihood sequence detection (MLSD) are developed. For a memoryless uniform source, our upper bounds for the investigated TCQ/CPM systems are shown to be asymptotically tight for increasing channel signal-to-noise ratio (SNR) values. It is concluded that the minimum Euclidean distance of the CPM system alone is not enough to evaluate the performance of the combined TCQ/CPM systems. The number of the error events having the minimum Euclidean distance and the total distortion caused by those error events also affect the asymptotic performance of the investigated systems. This work provides an analysis tool to estimate the performance for a given combined TCQ/CPM system. The analysis method is very general, it may be applied to any trellis based JSC coding schemes.

Joint source and channel coding (JSCC) using combined trellis coded quantization (TCQ) and trellis coded continuous phase modulation (CPM) is studied. Optimal soft decoding based on the a posteriori probability (APP) algorithm for trellis coded CPM is developed for the considered systems. It has been demonstrated that the combined TCQ/CPM systems are both power and bandwidth efficient compared with the combined TCQ/TCM/8PSK systems. Furthermore, a TCQ design algorithm for the noisy channel is developed. The novelties of this work is the use of a soft decoder and the APP algorithm for combined TCQ/CPM systems.

An iterative soft decoding approach to joint source and channel coding using combined trellis coded quantization (TCQ) and continuous phase modulation (CPM) is proposed. This iterative procedure exploits the structure of the TCQ encoder and the continuous phase modulator. It is observed that the combined TCQ/CPM systems are both power and bandwidth efficient compared with the combined TCQ/TCM system. Based on extrinsic information density evolution, a convergence analysis of combined TCQ/CPM with iterative decoding is presented. The conventional convergence analysis techniques based on the uniform error property of geometrically uniform codes has been modified and generalized to the codes with a non-uniform error property.

In this paper, a novel trellis coded quantization (TCQ) scheme based on ring convolutional codes is presented. Joint source and channel coding (JSCC) using trellis coded continuous phase modulation (CPM) with convolutional codes over rings is investigated. The channel is assumed to be an additive white Gaussian noise (AWGN) channel. Optimal soft decoding for the proposed JSCC scheme is studied. The soft decoder is based on the a posteriori probability (APP) algorithm for trellis coded CPM with ring convolutional codes. It is shown that these systems with soft decoding outperform the same systems with hard decoding especially when the systems operate at low to medium signal-to-noise ratio (SNR). It has been demonstrated that TCQ based on ring convolutional codes is superior to conventional TCQ of the same complexity. The novelties of this work are the development of a TCQ method based on ring convolutional codes, the use of a soft decoder and the APP algorithm for the combined systems

In this paper, a new source encoding scheme is presented. It is a trellis coded quantization scheme which is based on ring convolutional codes. Joint Source and Channel Coding (JSCC) using trellis coded Continuous Phase Modulation (CPM) with convolutional codes over rings is investigated. The channel is assumed to be an Additive White Gaussian Noise (AWGN) channel. Optimal soft decoding for the proposed JSCC scheme is studied. The soft decoder is based on the A Posteriori Probability (APP) algorithm for trellis coded CPM with ring convolutional codes. It is shown that these systems with soft decoding outperform the same systems with hard decoding especially when the systems operate at low to medium Signal-to-Noise Ratio (SNR). It has been demonstrated that TCQ based on ring convolutional codes is superior to conventional TCQ of the same complexity. The novelties of this work are the development of a TCQ method based on ring convolutional codes, the use of a soft decoder and the APP algorithm for the combined systems.

An iterative soft decoding approach to joint source and channel coding using combined trellis coded quantization (TCQ) and continuous phase modulation (CPM) is proposed. The channel is assumed to be AWGN. The performance in terms of the signal-to-distortion ratio (SDR) is compared with that for a combined TCQ with trellis coded modulation (TCM) system. It is observed that the combined TCQ/CPM systems are both power and bandwidth efficient compared with the combined TCQ/TCM system. A convergence analysis, based on the uniform error property of geometrically uniform codes, has been generalized to codes with a nonuniform error property. For source encoding rate R=1 and R=2 bits/sample, the combined systems working at symbol level converge faster than those working at bit level. This can be explained by examining the error events of the combined system. The novelty of this work is the use of a soft decoder and an iterative decoding algorithm for TCQ based joint source channel coding systems. Also, combined TCQ/CPM systems operating at symbol level are considered for the first time.

An iterative decoding approach to joint source and channel coding (JSCC) using combined trellis-coded quantization (TCQ) and continuous phase modulation (CPM) is proposed. The channel is assumed to be the additive white Gaussian noise channel. This iterative procedure exploits the structure of the TCQ encoder and the continuous phase modulator. The performance in terms of the signal-to-distortion ratio (SDR) is compared with that of a combined TCQ/trellis-coded modulation (TCM) system. It is shown that the combined TCQ/CPM systems are both power- and bandwidth-efficient, compared with the combined TCQ/TCM system. For source encoding rate R=2 b/sample, it is observed that the combined TCQ/CPM systems with iterative decoding working at symbol level converge faster than the systems working at bit level. The novelty of this work is the use of a soft decoder and an iterative decoding algorithm for TCQ-based JSCC systems. The combined TCQ/CPM with iterative decoding is considered for the first time.

Symbol error probability bounds for maximum likelihood sequence detection (MLSD) of continuous phase modulation (CPM) signals are studied. The calculation of the upper bound is based on the transfer function technique, which has been generalized. A new method for constructing a lower bound for CPM systems is proposed. From numerical comparisons, it can be seen that the proposed algorithm can substantially improve the lower bound compared to previous approaches by considering the entire set of transmitted data sequences and not only the worst case. This generalized algorithm may be applied to any system which can be described as finite state machine.

The optimum rate-compatible repetition convolutional (RCRC) codes with different parent encoder rates and different constraint lengths are presented. They are constructed according to the optimum distance spectrum (ODS) criterion. The obtained codes provide, e.g., significant throughput gains compared to simple repetition codes, when applied in hybrid type II automatic repeat request (ARQ) schemes.