Due to the reliance of task processing on specific service models, service caching has emerged as a promising technology for enhancing low-latency performance in vehicular edge computing (VEC) networks. When vehicular long-term service caching mismatches real-time task requirements, the mobility and autonomy of service vehicles enable them to fetch service models from providers actively. However, highly dynamic topology and resource constraints of VEC networks call for a more adaptable approach for jointly designing vehicular service fetching and task offloading. To this end, this paper aims at jointly optimizing active service fetching, task offloading, and communication and computing resource allocation to boost low-latency performance in two-tier caching-assisted VEC networks. In doing so, a total task completion delay minimization problem is formulated, where the social-mobility-aware network topology, vehicle classification, delay tolerance requirements, and resource constraints are taken into account. Since it is a mixed-integer non-linear programming (MINLP) problem, it is typically NP-hard. To solve it effectively, we decompose it into three subproblems, which can be efficiently solved by an iterative algorithm based on block coordinate descent (BCD) with low complexity. Extensive simulation results demonstrate that our proposed algorithm achieves fast convergence and reduces the task completion delay by 9.27%-59.77% compared to five representative baselines.

Collaborative machine learning has been a key technology to enhance the development of intelligent transportation systems with the advantage of privacy protection and communication efficiency. However, model training on vehicles requires a large amount of computation resource especially that the machine learning (ML) models are massive nowadays. In this paper, we shed light on the situation that a part of the vehicles may have low computational capability and not able to participate in the training process. To address this problem, we propose a Vehicular Edge Hybrid Federated Split Learning (VEHFSL) paradigm considering the data privacy protection and resource constraints in Internet of Vehicles (IoV). Firstly, we select the vehicles to participate in the training process by sorting the weighted sum of the expected sojourn time and training data volume of each vehicle. Then, we formulate a joint optimization problem to minimize the training latency of one round by jointly optimizing the training mode selection, computation frequency and transmit power allocation. Given that the formulated optimization problem is mixed-integer nonlinear programming (MINLP), which is typically NP-hard, we design a joint mode selection and resource allocation algorithm (JMSRA) by performing block coordinate descent (BCD) technique to decompose it into three sub-problems and iteratively solving them to achieve a near-optimal solution efficiently with the balance of the computational complexity and optimality of the solution. Finally, extensive simulations are performed to validate the superiority of our proposed VEHFSL paradigm in resource-constrained vehicular networks.

This paper focuses on the energy-efficient unmanned aerial vehicles (UAVs) assisted vehicular networks, where multiple rotary-wing UAVs are deployed to provide uplink service to numerous ground vehicles. The objective is to maximize the long-term system energy efficiency (EE) over the entire service period, through jointly optimizing the vehicle-UAV association, the sub-carrier assignment, the power control of vehicles and the trajectory design of UAVs. The formulated problem involves the limitations on the transmit power of vehicles and the propulsion power of UAVs, the quality of service (QoS) requirements of vehicles, the UAV movement constraints, and co-channel interference among vehicles. This makes the problem a mixed integer non-convex fractional programming problem accompanied by a mass of variables and diverse constraints, which is difficult to be solved within the polynomial time through traditional optimization methods. To cope with the timely decision-making requirement and dynamic moving scenario, we opt to the deep reinforcement learning (DRL) approach. To do so, the formulated problem is first transformed to a Markov decision process (MDP). Then, an improved k-means proximal policy optimization (IKPP) algorithm is proposed to solve the MDP problem. The proposed algorithm involves action reconstruction, the improved k-means algorithm, and proximal policy optimization-clip algorithm, which can help obtain the solutions with low complexity. Simulation results demonstrate the convergence, scalability and real-time of the proposed algorithm, along with its performance advantage over other benchmark algorithms.

Semantic communication emerges as a pivotal technology for realizing the 6G vision. However, existing semantic-aware reconstruction systems rely on syntactic-level loss functions for optimization, failing to focus on the precise recovery of semantic information and resulting in suboptimal semantic fidelity. Meanwhile, the autoregressive decoding architecture adopted in text semantic communication introduces prohibitive high latency. To address these two issues, we propose a robust information bottleneck (RIB) guided non-autoregressive semantic communication (NASC) scheme, named RIB-NASC. First, we pioneer the integration of the RIB criterion into semanticaware reconstruction systems, formulating a direct optimization objective tailored for underlying semantic recovery and establishing an informativeness-robustness trade-off. Second, we derive a tractable variational lower bound for the RIB objective via variational approximation and a novel synonymous mapping-based semantic posterior estimation strategy. Third, we design a lightweight non-autoregressive semantic decoder architecture based on Transformer encoder, enabling high-speed parallel semantic decoding during inference. Extensive simulation results demonstrate that the RIB-NASC scheme significantly outperforms baseline schemes in terms of semantic recovery performance (BLEU score and sentence similarity) and achieves a decoding delay reduction of nearly 96.7% compared to traditional autoregressive decoding.

Asynchronous federated edge learning (Asy-FEEL) has drawn intensive attention due to its ability to effectively address the straggler issue caused by the heterogeneity of the participated mobile devices (MDs). The quality of Asy-FEEL depends highly on the number of participating MDs. Since the local training of federated learning consumes computation resources of MDs, it inevitably reduces the resources that can be devoted to their own tasks (OTs). Therefore, there is always a decreased incentive of MDs to participate in Asy-FEEL, subsequently reducing the amount of Asy-FEEL tasks executed by MDs, thereby failing in achieving satisfied Asy-FEEL performance. How to effectively utilize the limited resources and schedule Asy-FEEL tasks and OTs to satisfy the quality of service requirements on the OTs and at the same time encourage MDs to participate to execute Asy-FEEL tasks is of vital importance. To this end, a joint tasks scheduling and resource allocation problem is formulated and investigated within a dynamic multitasks mobile edge computing (MEC) network, where Asy-FEEL tasks and OTs coexist. Since the problem is a dynamic stochastic optimization problem, a Lyapunov-based dynamic joint tasks scheduling and resources allocation (Lya-DJTR) algorithm is proposed to determine tasks scheduling, computational resource and bandwidth allocation, and transmit power control at MDs simultaneously. Simulation results demonstrate the superiority of the proposed algorithm in improving the efficiency of Asy-FEEL while ensuring the real-time processing of OTs when compared to baseline algorithms.

Vehicle-to-everything (V2X) underlaid cellular networks in underlaid mode suffer catastrophic co-channel interference caused by spectrum sharing, results in a reduced system sum-rate. To cope with this, this work studies a social-mobility-aware nonorthogonal multiple access (NOMA)-enabled V2X underlaid cellular network to mitigate the co-channel interference and improve the sum rate. By jointly optimizing vehicle pairing and resources, a sum-rate maximization problem is formulated under the diverse quality of service requirements of both cellular and vehicular users. The formulated problem is proved to be a nondeterministic polynomial-time (NP)-hard problem and is difficult to solve. As an alternative, we propose a NOMA-based joint vehicle pairing, spectrum assignment, and power control algorithm (NOMA-JVP-SA-PCA), with which the original problem is decomposed into two disjoint subproblems, i.e., 1) joint vehicle pairing and spectrum assignment subproblem and 2) power control subproblem. Dealing the first subproblem, we propose a heuristic social-mobility-aware vehicle pairing algorithm (HSMA-VPA) and a revised Kuhn-Munkres-based spectrum assignment algorithm (KM-SAA) to acquire the vehicle pairing and spectrum assignment solutions. Then, solving the second subproblem, a closed-form power solution is obtained utilizing a 3-D geometric power control approach (3D-PCA). Finally, we solve the original problem through an iterative method. Simulation results show that the proposed NOMA-JVP-SA-PCA effectively enhances the sum rate and outperforms the baseline algorithms around 24%–53% within a specific range.

Caching 360° videos at the network edge can reduce user content request latency and mitigate transmission congestion in backbone networks. Given the fact that user only views a part of content of 360° scope at any time, caching the entire video is resource inefficient. To address this, we focus a multiaccess edge computing (MEC)-based 360° video service system, where the edge server only caches a portion of each video, that is, most likely falling in the Field of View (FoV) of users. To minimize the average video request latency of all users in the system, we formulate a large-scale$\{0-1\}$knapsack problem, which is NP-hard. To tackle it, we proposed a heuristic algorithm where the user viewing patterns extracted from the historical request information are taken into account. Specifically, we first design a cascading cache space allocation method to assign the total cache space of edge server to each segment of videos. After that, the original problem is decomposed into several small-scale yet individual tile caching subproblems with compressed solution space. Then, they are solved by using the dynamic programming algorithm with moderate complexity. To further enhance the caching performance, the PSO-based algorithm is designed to fine tune the parameters involved in the proposed caching algorithm. In addition, we introduce a content-based method to calculate the request probability of the newly generated videos. The effectiveness of the proposed algorithm is evaluated through simulations based on a real world dataset, where the results demonstrate a substantial improvement in both video request latency and cache hit rate compared to the benchmark methods.

The number of categories of instances in the real world is normally huge, and each instance may contain multiple labels. To distinguish these massive labels utilizing machine learning, eXtreme Label Classification (XLC) has been established. However, as the number of categories increases, the number of parameters and nonlinear operations in the classifier also rises. This results in a Classifier Computational Overload Problem (CCOP). To address this, we propose a Multi-Head Encoding (MHE) mechanism, which replaces the vanilla classifier with a multi-head classifier. During the training process, MHE decomposes extreme labels into the product of multiple short local labels, with each head trained on these local labels. During testing, the predicted labels can be directly calculated from the local predictions of each head. This reduces the computational load geometrically. Then, according to the characteristics of different XLC tasks, e.g., single-label, multi-label, and model pretraining tasks, three MHE-based implementations, i.e., Multi-Head Product, Multi-Head Cascade, and Multi-Head Sampling, are proposed to more effectively cope with CCOP. Moreover, we theoretically demonstrate that MHE can achieve performance approximately equivalent to that of the vanilla classifier by generalizing the low-rank approximation problem from Frobenius-norm to Cross-Entropy. Experimental results show that the proposed methods achieve state-of-the-art performance while significantly streamlining the training and inference processes of XLC tasks. The source code has been made public at https://github.com/Anoise/MHE.

In this article, we focus on the problem of product defect detection (PDD) in air conditioner (AC) manufacturing. The challenges are twofold: first, the scale of the objects undergoes significant variations, thereby increasing the difficulty of detection; second, the computing power of terminals is limited, and it may be difficult to meet the quasi-real-time detection requirements. Therefore, to improve detection accuracy and speed, a lightweight object detection model tailored for deployment on the compact wireless Raspberry Pi, is proposed: a deep cascaded U-shape network is presented to effectively capture both global context and local details of objects, which can reduce the feature redundancy and the number of the model parameters. An adaptive multiscale squeeze-and-excitation is designed for feature reuse and fusion, enhancing both detection accuracy and efficiency. Then, to meet the quasi-real-time detection demands and better utilize end-edge-cloud resources in industrial internet of things (IIoTs), an actor–critic-based dynamic offloading (ACDO) algorithm is proposed to minimize the long-term cumulative time of task detection. ACDO utilizes the elapsed time as the reward to directly optimize the mixed variables of the task to achieve efficient offloading. The proposed methods are verified at an AC manufacturing line, which demonstrates accurate and quasi-real-time defect detection, achieving a 64% reduction in runtime and a 1.2% improvement in average mean average precision. In addition, we publish two PDD datasets to accelerate the related research.

This work explores the utilization of rotary-wing unmanned aerial vehicles (UAV) as aerial base station to provide downlink data services to ground users. The objective is to maximize the energy efficiency (EE) of the UAV assisted communication system while guaranteeing fairness among users. In pursuit of this goal, taking the propulsion power consumption of UAV, the flight constraints of UAV, and the limited communication resources into account, we jointly optimize the power allocation, bandwidth allocation, and trajectory design. Since the corresponding formulated problem is non-convex, to solve it efficiently, we first addressed its non-smoothness and then decompose the problem into two sub-problems: the joint power and bandwidth assignment and the trajectory design sub-problems. We prove that the power allocation and bandwidth assignment sub-problem is quasi-convex fractional optimization. A low-complexity iterative algorithm based on the Dinkelbach’s algorithm and the Lagrange duality is proposed to get optimal solution. Successive convex approximation (SCA) together with Dinkelbach’s algorithm are adopted to convert the non-convex trajectory optimization problem into convex to get a suboptimal solution for UAV trajectory. Through solving the two sub-problems iteratively by adopting block coordinate descent (BCD) untill convergence, the original problem is solved. Extensive simulation results demonstrate that the proposed algorithm not only has robust convergence properties but also achieves significant improvements in system EE while ensuring user fairness.

To provide satisfying services for ever-emerging mission-critical applications, the ultra-reliable and low-latency communications (URLLC) need novel design to improve the spectrum efficiency and enhance the robustness. To achieve this, we design a robust rate-splitting and beamforming scheme for the downlink multiuser URLLC system in finite blocklength regime under imperfect channel state information at the transmitter (CSIT) acquisition. Rate-splitting is utilized to deal with the complex inter-user interference and improve the spectrum efficiency. Considering the norm-bounded CSIT error model, we formulate a minimum user rate maximization problem to guarantee the URLLC performance requirements by jointly designing the rate-splitting factors and the common/private beamforming vectors. The corresponding constraints are infinite due to the uncertainty of CSIT and the constraint set is also non-convex. To tackle it, we convert the infinite constraints into finite ones utilizing S-Procedure, and transform the original problem into difference of convex (DC) programming. Efficient approaches based on constrained concave convex procedure and Gaussian randomization are proposed to solve the DC programming and generate initial feasible points. Through extensive simulations, the convergence, robustness and effectiveness proprieties of the design are investigated and confirmed. Compared with the baselines, our design can achieve obvious performance improvement for different blocklength and block error rate requirements.

By encoding the video into different bitrate versions, dynamic adaptive streaming over HTTP (DASH) demonstrates its unique advantages in providing flexible bitrate adaption service in dynamic environments. But, the price is that the amount of video data is dramatically increased. The interaction of massive video data tends to exacerbate the network congestion and degrades the quality of experience (QoE) of users. Edge caching and mobile edge computing (MEC) have been adopted to solve this problem and enhance the QoE. But it is still difficult because of the highly coupled nature of caching and computing, which makes it extremely challenging to coordinate them across multiple edge nodes. To address the problem, this paper devotes itself to investigating collaborative edge caching and computing to maximize QoE for adaptive video streaming. In doing so, an optimization problem is formulated by jointly designing the caching, computing and user bitrate adaption, which turns out to be an integer nonlinear programming (INLP) problem and is NP-hard in strong sense. To solve it, we include caching placement, joint computing and bitrate adaption into a two-stage optimization framework. Specifically, considering the fact that the caching placement is implemented at a relatively long timescale, the caching problem is reformulated based on the statistics of user requests. The reformulated problem is a multiple-choice knapsack problem (MCKP), which is solved by Lagrange dual method after relaxation. The joint computing and bitrate adaption problem is transformed into Markov decision process (MDP) problem, and is solved by deep deterministic policy gradient (DDPG) algorithm. Simulation results validate that the proposed scheme can significantly improve QoE when compared with state-of-the-art baselines.

Meeting the strict Quality of Service (QoS) requirements of terminals has imposed a significant challenge on Multi-access Edge Computing (MEC) systems, due to the limited multi-dimensional resources. To address this challenge, we propose a collaborative MEC framework that facilitates resource sharing between the edge servers, and with the aim to maximize the long-term QoS and reduce the cache switching costs through joint optimization of service caching, collaborative offloading, and computation and communication resource allocation. The dual timescale feature and temporal recurrence relationship between service caching and other resource allocation make solving the problem even more challenging. To solve it, we propose a deep reinforcement learning (DRL)-based dual timescale scheme, called DGL-DDPG, which is composed of a short-term genetic algorithm (GA) and a long short-term memory network-based deep deterministic policy gradient (LSTM-DDPG). In doing so, we reformulate the optimization problem as a Markov decision process (MDP) where the small-timescale resource allocation decisions generated by an improved GA are taken as the states and inputted into a centralized LSTM-DDPG agent to generate the service caching decision for the large-timescale. Simulation results demonstrate that the proposed algorithm outperforms the baseline algorithms in terms of the average QoS and the cache switching costs.

Reconfigurable intelligent surface (RIS) provides a promising way to proactively augment propagation environments for better transmission performance in wireless communications. Existing multi-RIS works mainly focus on link-level optimization with predetermined transmission paths, which cannot be directly extended to system-level management, since they neither consider the interference caused by undesired scattering of RISs, nor the performance balancing between different transmission paths. To address this, we study an innovative multi-hop multi-RIS communication system, where a base station (BS) transmits information to a set of distributed users over multi-RIS configuration space in a multi-hop manner. The signals for each user are subsequently reflected by the selected RISs via multi-reflection line-of-sight (LoS) links. To ensure that all users have fair access to the system to avoid excessive number of RISs serving one user, we aim to find the optimal beam reflecting path for each user, while judiciously determining the path scheduling strategies with the corresponding beamforming design to ensure the fairness. Due to the presence of interference caused by undesired scattering of RISs, it is highly challenging to solve the formulated multi-RIS multi-path beamforming optimization problem. To solve it, we first derive the optimal RISs’ phase shifts and the corresponding reflecting path selection for each user based on its practical deployment location. With the optimized multi-reflection paths, we obtain a feasible user grouping pattern for effective interference mitigation by constructing the maximum independent sets (MISs). Finally, we propose a joint heuristic algorithm to iteratively update the beamforming vectors and the group scheduling policies to maximize the minimum equivalent data rate of all users. Numerical results demonstrate that the proposed transmission framework achieves superior throughput performance than benchmark schemes. Useful insights on how to leverage multi-reflection paths over RISs to boost the throughput performance are also drawn under different settings for the multi-hop multi-RIS communication systems.

This work investigates proactive edge caching for device-to-device (D2D)-assisted wireless networks, where user equipment (UE) can be selected as caching nodes to assist content delivery to reduce the content transmission latency. In doing so, there are two challenges: 1) how to precisely get the user’s preference to cache the proper contents at UEs and 2) how to replace the contents cached at UEs when there are new popular contents emerging. To address these, we develop a user preference learning-based proactive edge caching (UPL-PEC) strategy. In the strategy, we first propose a novel context and social-aware user preference learning method to precisely predict user’s dynamic preferences by jointly exploiting the context correlation among different contents, the influence of social relationships and the time-sequential patterns of user’s content requests. Specifically, the bidirectional long short-term memory networks are adopted to capture the time-sequential patterns of the user’s content requests. And, the graph convolutional networks are developed to capture the high-order similarity representation among different contents from the constructed content graph. To learn the social influence representation, an attention mechanism is designed to generate the social influence weights to users with different social relationship. Based on the learned user preference, a proactive edge caching architecture is proposed to integrate the offline caching content placement and the online caching content replacement policy to continuously cache the popular contents at UEs. Simulation results show that the proposed UPL-PEC strategy outperforms the existing similar caching strategies at about 3.13%–4.62% in terms of the average content transmission latency.

In vehicular ad hoc networks (VANETs), emergency message broadcasting has been considered as one of the important parts for safety-related applications. The efficiency and reliability of the emergency message broadcasting are severely affected by the network structure, message redundancy, channel contention, etc. To address this problem, in this article, we propose roadside unit (RSU)-assisted hybrid emergency message broadcasting (RA-HEMB) protocol for two-way grid roads in urban VANETs. In doing so, the broadcasting target region is first generated based on the instantaneous traffic status and types of the emergency messages. To balance the deliver latency and the reliability, an adaptive forwarding nodes selection scheme based on position is proposed to dynamically determine the forwarding area according to different vehicle densities and vehicle-to-vehicle (V2V) communication ranges. Then, based on the forwarding nodes selection scheme and RSU deployment, an RA-HEMB protocol is established by properly utilizing both the wired links via RSUs and the wireless links to broadcast the emergency message in hybrid vehicular networks. Simulation results show that the proposed RA-HEMB can greatly shorten the broadcasting time needed to notice the nodes within the target region performance compared to the benchmark protocols.

Due to the limited energy, communication bandwidth and computing ability of edge devices in Industrial Internet of Things (IIoT) networks, it is incredibly challenging to compress and transmit those massive manufacturing data collected at the edge, thus greatly degrading the transmission and computing efficiency and finally results in long latency. To address this, we propose a quantized deep compressed sensing network (QDCS-Net) for both linear and nonlinear measurements to help better compress the industrial data to reduce the transmission volume of data and achieve good reconstruction performance. The joint design of customized quantization layers, dual-path structures, and swish activation function in QDCS-Net is adopted to achieve high-precision data reconstruction at high compression ratios. The latency is analyzed for different transmission deployment schemes to get a better edge-cloud collaboration strategy. We evaluate QDCS-Net by using real-world datasets collected from a vibration signal acquisition system. Experimental results demonstrate that the proposed QDCS-Net performs better in recovering industrial signals even at extremely low compression ratios of 1/128, thus can effectively improve data reconstruction accuracy and communication efficiency.

This work investigates proactive edge caching for D2D-assisted wireless networks, where user equipments (UEs) can be selected as caching nodes to assist content delivery. The objective of this work is to achieve a trade-off between the cost for providing caching services and the content transmission latency. Doing so, there are two challenges: 1) Which UEs can be selected as caching nodes; 2) How to place contents on these selected UEs without user’s privacy disclosure. To address these, a novel community detection and attention-weighted federated learning based proactive edge caching (CAFLPC) strategy is proposed. In the strategy, we first group UEs into different communities based on both the mobility and social properties of UEs, and then select important users (IUs) as caching nodes for each community by considering the social importance of UEs. To determine how to place the popular contents in these selected IUs, an attention-weighted federated learning (AWFL) based content popularity prediction framework is proposed. It integrates the attention-weighted federated learning with Bidirectional Long Short Term Memory Network (AWFL_BiLSTM) to achieve a higher content popularity prediction accuracy while protecting user’s privacy. Considering the imbalance of UEs’ active levels and local computing capacities, an attention-weighted aggregation mechanism is proposed to improve the training efficiency and prediction accuracy. Simulations results show that the proposed CAFLPC strategy outperforms the compared existing caching strategies at about 2.2%-35.1% in terms of the transmission latency reduced by per unit cost.

Edge caching has emerged as a promising technique against latency caused by explosive growth of mobile data traffic through caching popular contents at the edge networks. However, the dynamically changing content popularity nature and limited caching capacity make it challenging to design an effective caching scheme to reduce latency. To solve this, a learning-based hierarchical edge caching (LHEC) scheme is proposed in this work. We first propose a novel deep learning architecture, namely Stacked Autoencoder-Long Short Term Memory Network (SAE-LSTMNet) to capture both the correlation of the request patterns among different content and the periodicity in time domain to improve the prediction accuracy of the content popularity. Then, to predict the popularity of these newly-added contents, a dynamic content catalog is introduced and a similarity-based content popularity prediction (SCPP) approach is proposed. Based on the content popularity prediction, a hierarchical edge caching optimization problem is formulated to minimize the average content downloading latency. Since the formulated problem is NP-hard and difficult to be solved, a low-complexity algorithm is proposed to obtain the near-optimal solutions. Simulation results show that the proposed content popularity prediction approach outperforms up to 6.36% in terms of the mean absolute error compared with the state-of-the-art methods and the proposed LHEC scheme reduces the average downloading latency at about 5.3%~7.9% compared with those existing caching schemes.

Abstract In this paper, a novel co‐operative mean field type game based task offloading strategy is proposed for multi‐access edge computing networks. The objective is to optimally determine the tasks offloaded to each edge computing node (ECN) in the network so that to balance the overall system performance and individual revenue of each ECN. To do so, the utility function of each ECN is first formulated, which is the payback of his time and energy cost for processing the tasks, tailed with the penalty from the network for not processing the tasks on time. A mean field type game is then formulated, where each ECN can either cooperate or compete with each other to maximize their own utility. With respect to the proposed game, the direct method is applied to find the final solutions. Simulation results reveal that the proposed strategy can well balance the utility of each ECN and the overall efficiency of the system, leading to a better performance compared with its cooperative‐ and competitive‐counterparts.

Abstract An interference leakage minimisation transceiver design for multiple‐input multiple‐output Interference Channel networks is proposed by making use of the full rank constraint of the desired signal, the low rank constraint and low power constraint of the interference signal. The objective is to suppress interference leakage caused by not only the signal from other users, but also the other streams from the same user. To do so, the transmit precoding matrix and the receive filtering matrix are iteratively optimised through convex optimisation tools at stream level. Furthermore, a Min–Max interference leakage algorithm is also proposed to suppress the maximum interference from user, with the purpose of guaranteeing the fairness among users. Simulation results demonstrate that taking inter‐stream interference into consideration can significantly improve the effectiveness of multiple‐input multiple‐output Interference Channel networks, while the Min–Max method can slightly increase the system capacity under certain conditions. It can be also confirmed that the trade‐off among effectiveness, fairness and robustness exists in the transceiver optimisation of multiple‐input multiple‐output Interference Channel networks.

Unmanned-aerial-vehicles (UAVs)-enabled mobile-edge computing (MEC) networks have shown a huge advantage in providing on-demand communication and computation service for the ground users. To reap the benefit of these integrated networks, reducing their energy consumption becomes a key issue, since both UAVs and ground users are energy-limited devices. To address this problem, this article attempts to provide a novel method that optimizes communication, computation, and control (3C), i.e., user association, computational task offloading, and UAVs flight control, to reduce the communication and computation energy consumption. Specifically, in order to find out the appropriate deployment positions for UAV MEC servers to provide on-demand communication and computation service, we propose the concept of the virtual force field (VFF) based on the user statistical distribution model and then devise a coordinated flight control algorithm for UAV MEC servers. After that, the user association and computational task offloading are optimized alternately. Utilizing the optimal transport theory (OTT), we derive the boundary formulation of the optimal user association and develop an iterative algorithm to approach the optimal association boundary. Then, given the user association, the optimal computational task offloading scheme is investigated. The convergence of the proposed iterative algorithm and the alternating optimization algorithm is proved. The complexity of the 3C optimization method is also analyzed. Simulation results demonstrate that the proposed designs considerably outperform the similar existing algorithm. Comparisons with the benchmark scheme show that the proposed scheme can reduce about 88% energy consumption and also improve energy efficiency performance greatly under the same simulation setups.

With the wide range application of machine learning in healthcare for helping humans drive crucial decisions, data privacy becomes an inevitable concern due to the utilization of sensitive data such as patients records and registers of a company. Thus, constructing a privacy preserving machine learning model while still maintaining high accuracy becomes a challenging problem. In this article, we propose two differentially private algorithms, i.e., Output Perturbation with aGM (OPERA) and Gradient Perturbation with aGM (GRPUA) for empirical risk minimization, a useful method to obtain a globally optimal classifier, by leveraging the analytic Gaussian mechanism (aGM) to achieve privacy preservation of sensitive medical data in a healthcare system. We theoretically analyze and prove utility upper bounds of proposed algorithms and compare them with prior algorithms in the literature. The analyses show that in the high privacy regime, our proposed algorithms can achieve a tighter utility bound for both settings: strongly convex and non-strongly convex loss functions. Besides, we evaluate the proposed private algorithms on five benchmark datasets. The simulation results demonstrate that our approaches can achieve higher accuracy and lower objective values compared with existing ones in all three datasets while providing differential privacy guarantees.

We propose a full-duplex cooperative rate-splitting (FD-CRS) scheme in a downlink two-group multicast system. At the transmitter, two distinct messages requested by the two groups respectively are split and then encoded into one common stream and two private streams, based on the principles of rate splitting multiple access (RSMA). The cell-center-users (CCUs) in one group decode the common stream and their own private stream successively, then cooperatively form a distributed beamformer to assist the cell-edge-users (CEUs) in common stream transmission. To make full utilization of the time resources during cooperation, all the CCUs operate in FD mode to enable information receiving and forwarding simultaneously. Moreover, since it is unfair to sacrifice the cooperator’ energy to forward, each CCU is enabled to harvest energy from the received signal by adopting power-splitting protocol. With the objective of minimizing the system transmission power while guaranteeing all the groups’ target rates, an optimization problem is formulated to jointly design the beamformers, message splitting and power-splitting ratio. We reformulate the non-convex problem by using the difference of convex (DC) programming, and then propose an iterative algorithm based on successive convex approximation to solve it to obtain a local minimum. Further, a robust algorithm combining the semi-positive definite relaxation (SDR) technique and penalty function method is developed for the case with imperfect channel state information. Although our proposed FD-CRS scheme adopts the seemingly energy-wasting wireless power transfer technique, the simulation results still confirm the superiority of the proposed scheme, i.e., it outperforms the other baseline schemes in terms of power consumption under various user deployment, network loads and target rates. That is attributed to the comprehensive utilization of FD cooperation gain, spatial multiplexing gain as well as power multiplexing gain.

Reconfigurable intelligent surface (RIS) provides a promising way to build programmable wireless transmission environments. Owing to the massive number of controllable reflecting elements on the surface, RIS is capable of providing considerable passive beamforming gains. At present, most related works mainly consider the modeling, design, performance analysis and optimization of single-RIS-assisted systems. Although there are a few of works that investigate multiple RISs individually serving their associated users, the cooperation among multiple RISs is not well considered as yet. To fill the gap, this paper studies a cooperative beamforming design for multi-RIS-assisted communication systems, where multiple RISs are deployed to assist the downlink communications from a base station to its users. To do so, we first model the general channel from the base station to the users for arbitrary number of reflection links. Then, we formulate an optimization problem to maximize the sum rate of all users. Analysis shows that the formulated problem is difficult to solve due to its non-convexity and the interactions among the decision variables. To solve it effectively, we first decouple the problem into three disjoint subproblems. Then, by introducing appropriate auxiliary variables, we derive the closed-form expressions for the decision variables and propose a low-complexity cooperative beamforming algorithm. Simulation results have verified the effectiveness of the proposed algorithm through comparison with various baseline methods. Furthermore, these results also unveil that, for the sum rate maximization, distributing the reflecting elements among multiple RISs is superior to deploying them at one single RIS.

Exploiting unmanned aerial vehicles (UAVs) as flying relays is becoming an indispensable strategy to assist terrestrial cellular networks to enhance coverage. One challenging problem for UAV-integrated cellular networks is how to design their deployment and association schemes to provide on-demand coverage with minimum network power consumption. In this paper, the uplink transmission in a UAV-assisted cellular network is studied with the objective of minimizing the transmit power consumption of users and UAVs through designing proper UAV deployment and association schemes. To avoid the computational complexity caused by the estimation of instantaneous position of users, we investigate UAV deployment and association schemes based on the statistical user position. By discretizing the space where UAV can be located, we build a centralized multi-agent$Q$-learning algorithm, with which multiple UAVs update their positions in a joint manner. In the training process of$Q$-learning algorithm, a reward function is built based on the optimal association scheme and its corresponding power consumption. By adopting the optimal transport theory, the existence of the unique optimal association scheme for given statistical user distribution and UAVs’ state is proved. Simulation results demonstrate that the proposed designs considerably outperform the similar existing algorithms. Comparisons with the benchmark scheme show that the proposed scheme can bring about 85% energy efficiency improvement under the same simulation setups.

Wireless traffic prediction has drawn increasing research interests as it can provide guidance to the network optimization. With the predicted information, one can preassign the resources on demand and perform network congestion control adaptively. The network efficiency is therefore enhanced. However, the wireless traffic prediction in the context of mobile scenario, such as Internet of Vehicles (IoVs), is still a challenge issue. The mobile nature of devices, which dynamically changes the topology of network, would brings difficulties to the prediction. This paper focuses on the deep learning based wireless traffic prediction in the IoVs scenario. We first propose a novel method to match up the movement- and communication-behavior of users, by merging two independent datasets on the trajectories of vehicles and communication traffic volumes together. Then a novel STeP-UNet is proposed, in which the SpatioTemporal Partial (STeP) Convolutional Neural Network module is embedded to capture cross-domain features of the wireless traffic pattern, and the UNet structure is utilized to realize the skipping connection from front layer to back layer to fuse different resolutions. Experimental results confirms the promising performance of the proposed model, where 4%~8% performance improvement over other benchmark methods can be achieved.

With the development of Industrial Internet of Things (IIoT), the computation intensive tasks with restrict delay constraints generated at network edge emerge as the main challenge to the terminals with limited power and processing capability. The combination of edge and cloud computing has been demonstrated as one promising solution to such problem. In the edge-cloud collaboration (ECC) framework, the task scheduling among edges and cloud is of great importance on impacting the performance of the system. In this paper, we investigate the task offloading strategy to minimize the energy consumption of the networks. To achieve that, a time delay penalty mechanism, which searches the optimal power for edge to cloud task offloading under given delay constraint, is first proposed. On that basis, a low complexity edge-cloud matching algorithm leveraging the bipartite matching method is developed, to further minimize the execution energy consumption of all devices. Finally, to evaluate its efficiency, the proposed algorithm is deployed and tested on an novel edge-cloud computing collaboration platform. Both simulation and experiment results revel that our proposed scheme can achieve the less energy consumption compared with other alternatives. In addition, it also indicates that our proposed scheme can effectively matching resources from the edge to the cloud, especially for the issues that edge devices fail to meet demands due to limit processing ability.

Edge caching has been explored as an effective technology to alleviate the heavy traffic burden of the backhaul and avoid transmission congestion in vehicular networks. However, high mobility of vehicles could lead to repetitive content caching, resulting in high system cost. Because content popularity changes very frequently in vehicular networks, to provide better service for vehicle users, it is essential to update content frequently. This leads to expensive update cost at the same time. To reduce such cost, we propose a mobility-aware cost effective edge caching strategy, in which vehicle mobility, file encoding technology and dynamic content popularity are jointly taken into consideration. To reduce the complexity of formulated problem, deep reinforcement learning (DRL) approach is adopted. Simulation results show that the proposed mobility-aware coded edge caching strategy can dramatically reduce the system cost (up to 36% compared with classic caching algorithm).

We propose a cooperative nonorthogonal multicast scheme for multiple-input-single-output (MISO) systems, where the transmitter sends the superimposed signal to two multicast groups. After successfully detecting all signals, the cell-center users (CCUs) in one multicast group help relay signal to the cell-edge users (CEUs) in another group to enhance signal reception. Simultaneous wireless information and power transfer is adopted at CCUs to assist information relaying. In such a system, to minimize the transmission power of the system while satisfying the quality of service requirements of all the users, an optimization problem is formulated to jointly design the transmitter beamformers, distributed CCUs beamformer, as well as the power-splitting (PS) ratios. To solve the nonconvex problem, we first equivalently transform it into a difference of convex (DC) programming. Then, a low-complexity iterative algorithm based on the constrained concave convex procedure (CCCP) is proposed to solve the DC programming one. In addition, a robust joint beamforming and PS scheme is proposed by assuming imperfect channel state information (CSI). The infinite constraints caused by CSI uncertainties are converted into finite ones utilizing the S-procedure. To obtain a rank-one locally optimal solution, the penalty function method and CCCP algorithm are adopted. The simulation results reveal that the proposed cooperative scheme can greatly reduce the transmission power and outperform the baselines within a certain range. Moreover, the robustness and effectiveness of the robust design under imperfect CSI case have been validated.

Due to the booming of various devices in Internet-of-Things (IoT) networks, more data should be transmitted over the networks, which will thereby consume more transmission bandwidth and more transmit power. Compressed data aggregation (CDA) has been proposed as an effective way to reduce the amount of the collected data in IoT networks. Although adopting compressed sensing (CS), CDA can sample the source data efficiently, and sparse data reconstruction is still a big challenge. In this work, inspired by this, we propose a learning-based sparse data reconstruction scheme by jointly utilizing CS and deep learning. Our objective is to reduce the volume of data to be transmitted over IoT networks without losing reconstruction accuracy. A deep CS network is designed by adopting an end-to-end learning method to build a measurement matrix and an efficient and high-accuracy reconstruction network. To show the performance of the proposed scheme, six data sets with different structured sparse models and a real sensor data set are utilized in doing experiments. The performance of the proposed scheme in terms of mean-squared error, peak-signal-noise-ratio, and structural similarity is investigated. The results demonstrate the effectiveness of the proposed scheme in reconstruction accuracy for given compression ratio. The results also show that the proposed scheme is suitable for the process of CDA, thus can effectively reduce the amount of data to be transmitted in IoT networks.

When doing image classification, the core task of convolutional neural network (CNN)-based methods is to learn better feature representation. Our analysis has shown that a better feature representation in the layer before softmax operation (BSM-layer) means a better feature embedding location that has a larger distance to the separating hyperplane. By defining this property “Location Property” of CNN, the core task of CNN-based methods can be regarded as to find out the optimal feature embedding location in the BSM-layer. In order to achieve this, in this work, we first propose two feature embedding directions, principal embedding direction (PE-direction) and secondary embedding direction (SE-direction). And then, we further propose a loss-based optimization framework, location property loss (LP-loss), which can make feature representation move in the PE-direction and the SE-direction simultaneously during the training phase. LP-loss consists of two parts, LPPEand LPSE, where LPPEfocuses on PE-direction, and LPSEfocuses on SE-direction. Any loss function focusing on these two embedding directions can be chosen as LPPEand LPSE. Based on the analysis that softmax, L-softmax, and AM softmax can make the feature representation move in PE-direction to a different extent, any of them can be chosen as LPPE. Since there is no existing works can fulfill the purpose of LPSE, a novel loss, secondary optimal feature plane loss (S-OFP loss), is developed. S-OFP loss is designed to make feature representations belonging to the same category embed onto their corresponding S-OFP. It is proved that S-OFP loss is the optimal feature plane in the SE-direction. Experiments are done with shallow, moderate, and deep models on four benchmark data sets, including the MNIST, SVHN, CIFAR-10, and CIFAR-100, and results demonstrate that CNN models can obtain remarkable performance improvements with LPsoftmax, S-OFP and LPAM softmax, S-OFP, which verify the effectiveness of location property.

Reconfigurable intelligent surface (RIS) provides a promising way to build the programmable wireless transmission environments in the future. Owing to the large number of reflecting elements used at the RIS, joint optimization for the active beamforming at the transmitter and the passive reflector at the RIS is usually complicated and time-consuming. To address this problem, this article proposes a low-complexity joint beamforming and reflecting algorithm based on fractional programing (FP). Specifically, we first consider a RIS-aided multi-user communication system with perfect channel state information (CSI) and formulate an optimization problem to maximize the sum rate of all users. Since the problem is nonconvex, we decompose the original problem into three disjoint subproblems. By introducing favorable auxiliary variables, we derive the closed-form expressions of the beamforming vectors and reflecting matrix in each subproblem, leading to a joint beamforming and reflecting algorithm with low complexity. We then extend our approach to handle the case when transmitter-RIS and RIS-receiver channels are not perfect and develop corresponding low-complexity joint beamforming and reflecting algorithm with practical channel estimation. Simulation results have verified the effectiveness of the proposed algorithms as compared to various benchmark schemes.

With the rapid development of the Internet of Things (IoT) technology, how to meet the execution requirements of sensitive services has become a key point to be solved in application scenarios such as smart cities and Internet of Vehicles. Combining with the advantages over edge computing and cloud computing, building the edge-cloud collaboration framework is currently a hot research area. In this paper, a Raspberry Pi edge-cloud collaboration framework (RPECCF) is proposed to effectively reply the complicated application requirements in multi-scenes. Furthermore, to evaluate the performance of the RPECCF, we develop an experiment platform. Experimental results show the RPECCF platform is stable, also allocate the edge and cloud resources properly. The proof-of-concept demonstration of the platform is studied in terms of task latency and framerate of both edge only, edge-cloud collaboration, and cloud only.

This paper deals with the joint time and power allocation problem in the cooperative Non-Orthogonal Division Multiple Access (NOMA) based Mobile Edge Computing (MEC) system. We consider a basic three-node MEC system consisting of a Far User (FU), a Near User (NU), and a Base Station (BS) equipped with the MEC server. In the proposed system, the tasks of the users are divided into two parts which are executed locally and at the edge server, respectively. Moreover, the NU would help offload the FU's task together with his own through NOMA transmission. The optimization problem of joint cooperation time slots assignment and power allocation at both NU and FU is formulated to minimize the total energy consumption of users. To efficiently solve it, an algorithm is proposed through employing Lagrange duality method. Simulation results demonstrate that the proposed algorithm can outperform the existing schemes and improve the energy efficiency of the system remarkably.

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma and accounts for cancer-related deaths. Survival rates are very low when the tumor is discovered in the late-stage. Thus, developing an efficient strategy to stratify patients by the stage of the cancer and inner mechanisms that drive the development and progression of cancers is critical in early prevention and treatment. RESULTS: In this study, we developed new strategies to extract important gene features and trained machine learning-based classifiers to predict stages of ccRCC samples. The novelty of our approach is that (i) We improved the feature preprocessing procedure by binning and coding, and increased the stability of data and robustness of the classification model. (ii) We proposed a joint gene selection algorithm by combining the Fast-Correlation-Based Filter (FCBF) search with the information value, the linear correlation coefficient, and variance inflation factor, and removed irrelevant/redundant features. Then the logistic regression-based feature selection method was used to determine influencing factors. (iii) Classification models were developed using machine learning algorithms. This method is evaluated on RNA expression value of clear cell renal cell carcinoma derived from The Cancer Genome Atlas (TCGA). The results showed that the result on the testing set (accuracy of 81.15% and AUC 0.86) outperformed state-of-the-art models (accuracy of 72.64% and AUC 0.81) and a gene set FJL-set was developed, which contained 23 genes, far less than 64. Furthermore, a gene function analysis was used to explore molecular mechanisms that might affect cancer development. CONCLUSIONS: The results suggested that our model can extract more prognostic information, and is worthy of further investigation and validation in order to understand the progression mechanism.

IEEE 802.11p standard, operating over the 75-MHz spectrum at 5.9-GHz band with one control channel (CCH) and six service channels (SCHs), has been poised to provide V2X services over vehicular ad hoc networks (VANETs). However, due to the absence of central coordinator and the nature of high vehicular mobility, it is difficult to achieve reliable multichannel coordination and adaptive resource reservation to make full use of SCHs, resulting in dramatic throughput degradation. To mitigate this, in this article, we propose an adaptive high-throughput multichannel medium access control (MAC) protocol, namely, AHT-MAC, which can effectively handle the data transmissions over SCHs. With AHT-MAC, the data transmission range (TR) is adjusted according to the beacon TR over the CCH so that a transmitting node can determine proper communication candidates and prepare available resources for both communication nodes before transmissions. Moreover, the communication coordination is done through a two-way handshake. During the handshake, adaptive resource reservation is realized following the proposed resource sharing mechanism, where nodes first utilize as much resource as possible and then share them with others proactively. To increase the success probability of the communication handshake, a request conflict resolution mechanism is also designed to nullify improper handshakes. Therefore, AHT-MAC can reduce the resource wastage due to handshake failures and extra overheads for retransmission requests. Our performance analysis shows that AHT-MAC can significantly improve the system throughput and reduce the channel access period.

As one of the indispensable components in convolutional neural network (CNN), loss function assists in updating parameters of CNN models during the training phase. Generally, different loss functions can assist convolutional neural network (CNN) to learn different feature representations, and different feature representations can be treated as different knowledge learned from objects. In this paper we introduce a novel training framework, namely Loss Transferring (LT), to improve the generalization ability of CNN. LT contains multiple training phases, and each training phase uses a different loss function. Under this framework, CNN models can combine different knowledge of objects by transferring the knowledge learned via one loss function to another. LT contains two components, i.e., loss function set and training strategy. In order to build appropriate loss function set, we establish two basic guides. And according to these basic guides, we design a new loss function in the last layer of CNN models (layer before softmax operation), namely Near Classifier Hyper-Plane (N-CHP) loss, which makes the learned object features belonging to the same category have the minimum intra-class distance and be near the classifier hyper-plane. Based on the two loss function set (MSE, softmaxl and (N-CHP, softmaxl, we setup two specific training methods, LTMSE, softmax and LTN-CHP, softmax, which can be universally applied to different CNN models with low additional computation cost. Meanwhile, two training strategies, multi-phase strategy 1 and multi-phase strategy 2, are further proposed to improve the training efficiency of LT. Extensive experimental results on shallow, moderate and deep models with four benchmark datasets, including MNIST, SVHN, CIFAR-10 and CIFAR-100, demonstrate that CNN models can bring obvious performance improvements when working with LTMSE, softmax and LTN-CHP, softmax, which verifies the effectiveness of LT and the proposed two basic guides.

In cellular Internet of Things networks, massive access and highly dynamic traffic of machine type communication devices may cause network congestion, heavy energy consumption or even service unavailability. To reduce the amount of data transmission and improve the efficiency of data aggregation in sporadic machine type communications, we develops a two-stage compressed data aggregation scheme by jointly utilizing compressive sensing and sparse autoencoder. A deep compressive sensing network (DCSNet) is designed by using deep learning method to reconstruct signals from compressive measurements. Experimental results demonstrate that, our scheme can effectively reduce the data traffic, achieve higher reconstruction accuracy in different sparse conditions of signal and brake the obstacles that compressive sensing method has a large reconstruction error when the measured data is small.

This paper investigates an energy efficiency (EE) and delay aware user association problem in millimeter wave (mmWave) downlink networks, in which the wire backhaul links are replaced by the wireless backhaul links to transmit traffic data. To characterize the mmWave network delay performance, the queue theory is adopted and an M/M/1 tandem queuing model is introduced to capture the data transmission latency over mmWave backhaul links and access links. Besides, in order to measure the network EE and delay performance simultaneously, a novel EE-delay aware utility function is constructed and the user association problem is formulated. It is proved that the formulated problem is NP-hard. To solve it, one many-to-one matching game is reformulated and a two-steps user association algorithm is proposed. Simulation results show that the proposed association scheme can obtain significant utility improvement, achieving up to 21% gains compared with the classic association schemes. Moreover, the proposed scheme also demonstrates its superiority on load balancing management in mmWave backhaul networks.

Diabetic retinopathy severity assessment is an important domain in which deep learning has benefited medical imaging analysis. In this regard, CNNs which perform well in ImageNet are incapable of extracting subtle lesion features from high-resolution retinal fundus images. So novel convolutional networks with higher input size were developed. But no prior work give deep investigation on the impact of image resolution in the context of DR severity assessment. In this paper, we first explore how the performance of diabetic retinopathy severity assessment task would change if higher-resolution input images were used. Next, we adopt the stepwise strategy of training convolutional networks with high input scales to avoid overfitting. Finally, rigorous analyses on the impact of image resolution are given, showing that as model expands with higher input image resolutions, the performance grows logarithmically while both time and space complexity increase exponentially. Our model obtains new state-of-the-art kappa score in the task of diabetic retinopathy severity assessment task on EyePACS dataset with convolutional networks whose input size is 896 × 896, and great progress in classification of mild diabetic retinopathy. There is great potential for generalizing this solution to other medical image analysis problems.

A multi-relay cooperative quadrature spatial modulation (QSM) system is proposed in this paper. The distinguishable feature of the multi-relay cooperative QSM lies in the enlarged aggregate throughput of the system since extra information bits at the source can be transmitted through the selection of the relays. During the communication process, only one relay is activated which adopts the amplify-and-forward (AF) relaying protocol. Furthermore, a closed-form expression of the average pairwise error probability (APEP) of the proposed multi-relay cooperative QSM system is given and employed to derive the tight upper bound of the average bit error probability (ABEP) over Rayleigh fading channels. Monte-Carlo simulation shows that the proposed multi-relay cooperative QSM system achieves better ABEP performance compared with the traditional multi-relay cooperative spatial modulation (SM) system.

Machine (deep) learning-enabled accurate traffic modeling and prediction is an indispensable part for future big data-driven intelligent cellular networks, since it can help autonomic network control and management as well as service provisioning. Along this line, this paper proposes a novel deep learning architecture, namely Spatial–Temporal Cross-domain neural Network (STCNet), to effectively capture the complex patterns hidden in cellular data. By adopting a convolutional long short-term memory network as its subcomponent, STCNet has a strong ability in modeling spatial–temporal dependencies. Besides, three kinds of cross-domain datasets are actively collected and modeled by STCNet to capture the external factors that affect traffic generation. As diversity and similarity coexist among cellular traffic from different city functional zones, a clustering algorithm is put forward to segment city areas into different groups, and consequently, a successive inter-cluster transfer learning strategy is designed to enhance knowledge reuse. In addition, the knowledge transferring among different kinds of cellular traffic is also explored with the proposed STCNet model. The effectiveness of STCNet is validated through real-world cellular traffic datasets using three kinds of evaluation metrics. The experimental results demonstrate that STCNet outperforms the state-of-the-art algorithms. In particular, the transfer learning based on STCNet brings about 4%~13% extra performance improvements.

In this paper, a novel 3-dimensional (3-D) constellation design is proposed for spatial modulation (SM) multiple-antenna system (MIMO) employing maximum ratio combing (MRC) detector. The 3-D constellation is established for spatial modulated systems to minimize the system symbol error rate (SER) by jointly exploiting the potential of both antenna and signal domains. A low-complexity and efficient design algorithm is proposed to find out the 3-D constellation sub-optimally. The performance of the jointly mapped SM (JM-SM) with the designed 3-D constellation is investigated. Simulation results show that the proposed JM-SM schemes can bring obvious performance gain compared with the existing adaptive SM systems under the same spectral efficiency.

This paper investigates the downlink transmission of massive multiple-input-multiple-output (MIMO) simultaneous wireless information and power transfer (SWIPT) systems. The base station (BS) is equipped with large scale antenna array to provide users with concurrent information and energy supplies. Considering the short communication range between users and the BS in SWIPT systems, the transmission channels are modeled as Rician fading channels to capture both the line-of-sight (LOS) and non-LOS propagations. The approximate and asymptotic expressions of the achievable rate are first derived, and a sum achievable rate optimization problem is formulated based on the asymptotic expression subject to the quality-of-service (QoS) and transmit power constraints. An iterative optimization framework is proposed to solve the original non-linear non-convex problem, and iterative successive convex approximation (SCA) method is introduced in the framework to transmit the non-convex subproblem into convex form. The convergence and effectiveness of the proposed framework are analyzed and proved through analysis and intensive simulations. Results show that the proposed framework can achieve the optimal system performance as the exhaustive search method does.

This paper considers wireless information surveillance systems, in which one suspicious communication link is intercepted by a legitimate monitor to prevent possible criminal or terrorist attacks from the link. To enhance the information surveillance performance, a novel proactive eavesdropping mechanism is proposed with the help of a cooperative relay. It is assumed that the relay operates in full-duplex (FD) mode to assist monitor for information relaying and jamming transmission simultaneously. Based on the proposed mechanism, the joint relay beamforming and power allocation scheme is further proposed and the closed form optimal solutions are derived. Simulation results show that the proposed proactive eavesdropping mechanism can achieve better surveillance performance than both proactive eavesdropping schemes without the help of cooperative relay and cooperative surveillance with pure information forwarding. Simulation results also reveal that the proposed power allocation scheme boosts surveillance performance significantly compared with those equal power allocation scheme.

This paper focuses on the secure transmission of wireless-powered relay systems with the imperfect eavesdropper channel state information. For efficient energy transfer and information relaying, a novel two-phase protocol is proposed, in which the relay operates in a full-duplex (FD) mode to achieve the simultaneous wireless power and information transmission. Compared with those existing protocols, the proposed design possesses two main advantages: 1) it fully exploits the available hardware resource (antenna element) of relay and can offer a higher secrecy rate and 2) it enables the self-energy recycling (S-ER) at relay, in which the loopback interference generated by the FD operation is harvested and reused for information relaying. To maximize the worst-case secrecy rate (WCSR) through jointly designing the source and relay beamformers coupled with the power allocation ratio, an optimization problem is formulated. This formulated problem is proved to be non-convex and the challenge to solve it is how to concurrently solve out the beamformers and the power allocation ratio. To cope with this difficulty, an alternative approach is proposed by converting the original problem into three subproblems. By solving these subproblems iteratively, the closed-form solutions of robust beamformers and power allocation ratio for the original problem are achieved. Simulations are done and results reveal that the proposed S-ER -based secure transmission scheme outperforms the traditional time-switching based relaying scheme at a maximum WCSR gain of 80%. Results also demonstrate that the WCSR performance of the scheme reusing all antennas for information reception is much better than that of schemes exploiting only one antenna.

The medium access probability (MAP) of a random access protocol can severely impact network throughput especially for delay-sensitive applications, since it determines whether a node should transmit packets in a given slot or not. This paper focuses on network throughput maximization through optimizing the MAPs of all users under delay quality-of-service constraints in wireless interference-limited networks. Specifically, first, the total delay for transmitting one packet for a user is analyzed and derived based on an M/G/1 model. Then, the stochastic property of the aggregated interference is analyzed and its distribution is modeled as a log-normal distribution. Based on the delay and interference models, an optimization problem is formulated to derive the optimal MAPs so that the network throughput is maximized under the delay constraints. Two network traffic scenarios, homogeneous and heterogeneous user traffic, are discussed, respectively. For the case of homogeneous traffic, a closed-form expression of the optimal MAP is derived; for the case of heterogeneous traffic, a global E-optimal algorithm based on the branch-and-bound framework and convex relaxation technology is proposed with relatively low complexity. Simulations results show that the proposed algorithms can achieve superior network throughput performance over existing schemes.

This paper gives models and analytic techniques for studying shaping and policing data traffic in fog networks. The traffic in these networks is expected to be highly diverse and bursty, and regulation will be required as an integral part of congestion control. We generalize the Leaky Bucket model to shape and police traffic source for rate-based congestion control in high-speed fog networks. In particular, the Markov modulated fluid sources reflect the bursty characteristics of data traffic. To measure the performance of the model in shaping and policing traffic, we derive four performance metrics. The experimental results show that with proper design the Leaky Bucket model effectively controls a 4-way trade-off between throughput, loss probability, delay and burstiness of data traffic. Numerical results also reveal that the model performance is sensitive to certain traffic source characteristics.

Spatial modulation (SM) conveys information bits by utilizing both the antenna index and complex symbols to form a 3-D constellation. Similar to 2-D modulation, the structure of 3-D constellation could greatly affect the transmission reliability. In this paper, a generalized 3-D constellation design is proposed to optimize the constellation diagram used for each antenna, i.e., to optimize the complex symbols and their total number for each antenna and finally to enhance the transmission reliability. The optimal design method with exhaustive search algorithm may cause prohibitive computational complexity, especially when the cardinality of 3-D constellation is large. To overcome this issue, a recursive design algorithm is proposed with a computational complexity increasing polynomially with the cardinality of 3-D constellation. Extensions of the proposed methods to the SM constellation design for massive multiple-input multiple-output transmission, generalized spatial modulation (GSM) constellation design, and the SM constellation design with transmit antenna correlation are also discussed. Simulations are done to validate those theoretical analysis, and results show that the proposed 3-D constellation design is a generalized design scheme and can be adopted in any SM/GSM systems without constraints on the number of transceiver antennas. It is also shown that the proposed approach offers better symbol-error-rate performance than other solutions.

The IEEE 1609.4 standard suggests that the control channel (CCH) should be devoted to the beacon delivery, where the multiple access control (MAC) plays an important role in scheduling the network resources to multiple nodes. This paper proposes a novel OFDM-based group contention MAC (OGCMAC) for vehicular ad hoc networks (VANETs), in which a new CCH architecture, a modified multi-carrier burst contention (MCBC) and a group contention strategy are established. The CCH architecture is proposed to reduce the resource consumption of contention windows (CWs). Based on the proposed architecture, MCBC is modified to adapt to VANETs. The proposed CCH architecture supports parallel contention mechanisms and allows losers to change their target resource blocks (RBs) during the modified MCBC. Furthermore, to accelerate the RB assignment, a group contention strategy is developed by dividing the entire frame into several groups with different sizes. With the objective to maximize the ratio of the RBs allocated free of collisions within a single frame, a greedy group partition algorithm is proposed to determine the appropriate group sizes according to the MCBC capability. Evaluations are done to validate the proposed mechanism. Results show that OGCMAC achieves the highest throughput among all the evaluated MACs, because of its high channel usage rate. RBs are allocated faster by employing the proposed optimal group contention policy. It is also shown that the proposed greedy group partition algorithm can reduce the resource waste by properly adopting the existing CWs without extra resource consumption.

This paper proposes a spatial modulated simultaneous wireless information and power transfer (SM SWIPT) scheme to save multiple radio frequency (RF) chains at the transmitter. As SM is adopted, only one RF chain is equipped at the transmit side. To harvest energy and in the same time to transfer information, the received signal is split into two parts according to the defined power splitting factors. The power splitting factors are determined by maximizing the throughput of the information decoding (ID) receiver under the given energy harvesting (EH) constraint. An iterative power splitting algorithm (ISPA) is developed to solve the maximization problem. Its performance is investigated through simulations. In addition, the computational complexity of the proposed algorithm is analyzed. Results show the superiority of the proposed SM SWIPT in throughput through comparison with single-input multiple-output (SIMO) SWIPT and beamformed MIMO SWIPT. What is more, unlike the conventional beamformed MIMO SWIPT that requires perfect channel state information at the transmitter (CSIT), the proposed scheme is open loop and requires no any CSIT, thus can further enhance the superiority in energy efficiency.

This paper studies the joint transceiver design and power splitting (PS) for a downlink multiuser multiple-input multiple-output (MU-MIMO) simultaneous wireless information and power transfer (SWIPT) system. The objective of this work is to minimize transmit power by jointly optimizing the transmitter at a base station (BS), the PS factors and information decoding (ID) receivers at mobile stations (MSs) subject to both the mean-square-error (MSE) and energy harvesting (EH) constraints. To solve the formulated nonconvex optimization problem, a framework is proposed to iteratively solve a joint transmitter and PS factors optimization (JTxPS) sub-problem and a receiver side minimum mean-square error (MMSE) minimization subproblem. The nonconvex JTxPS sub-problem is reformulated as a convex semidefinite programming (SDP) and thus solved. Simulation results show the effectiveness of the proposed scheme.

By providing substantial gains in terms of both spectral and energy-efficiency, Massive MIMO is expected to be the promising enabler for the fifth generation (5G) communications. However the performance of massive MIMO is greatly affected by pilot contamination due to the insufficiency of pilot sequences. To overcome this, we propose a linear programming based pilot allocation with the purpose of alleviating the effect of pilot contamination and maximizing the system throughput. We first formulate the pilot allocation as a user clustering problem, which can be converted to a linear programming one by introducing the integer factor and constraint relaxation. An efficient linear programming algorithm is proposed to solve the problem. Simulation results demonstrate that the proposed scheme outperforms the other candidates in the presence of pilot contamination.

In this paper, a promising wireless communication concept which is termed as Spatial Modulation (SM) is considered. In particular the focus is on an interference scenario between a SM multiple-input- multiple-output (MIMO) system and a conventional spatial multiplexing (SMX) MIMO system. It has been shown that the interference caused by SM has a peculiar behavior. Since SM exploits the spatial position of the active antenna as an additional dimension for data transmission, the interference caused by SM system has no fixed interference subspace which can not be estimated accurately. Hence, conventional interference suppression techniques can not be applied accordingly. In this paper, we discuss this unique problem generated by SM systems, and propose three methods to overcome the adverse effects caused by such interference.

Cross-layer communication could bring a significant performance improvement for wireless communication systems through allowing information exchanges among multiple layers. For delay-sensitive video application, cross-layer optimization could be more critical by jointly considering the network resource allocation and application performance requirement, especially in an interference-limited distributed network. To improve the quality of video transmission over an ad hoc network, this paper proposes a cross-layer scheme to maximize the average received video quality by jointly considering the application performance and network transmission strategy. In this scheme, important system parameters including the distribution of the node and the resulting interference, link transmission policy, and queueing delay, as well as the video encoding rate are jointly considered to achieve the best received video quality. To this end, a threshold-based transmission strategy and an interference approximation model are developed first. Then, the queueing delay is analyzed based on the M/M/1 queue model. Finally, the problem of maximizing video transmission quality is formulated as a cross-layer optimization problem. To solve this optimization problem, a distributed algorithm is proposed based on the optimization and game theories. The convergence performance of this distributed algorithm is analyzed and shown with relatively lower complexity through simulation. Furthermore, both numerical and simulation results show that the proposed scheme improves the system performance considerably based on the comparison with other two existing schemes.

It is well known that the data transport capacity of a wireless network can be increased by leveraging the spatial and frequency diversity of the wireless transmission medium. This has motivated the recent surge of research in cooperative and dynamic-spectrum-access (which we also refer to as cognitive spectrum access) networks. Still, as of today, a key open research challenge is to design distributed control strategies to dynamically jointly assign: 1) portions of the spectrum and 2) cooperative relays to different traffic sessions to maximize the resulting network-wide data rate. In this paper, we make a significant contribution in this direction. First, we mathematically formulate the problem of joint spectrum management and relay selection for a set of sessions concurrently utilizing an interference-limited infrastructure-less wireless network. We then study distributed solutions to this (nonlinear and nonconvex) problem. The overall problem is separated into two subproblems: 1) spectrum management through power allocation with given relay selection strategy; and 2) relay selection for a given spectral profile. Distributed solutions for each of the two subproblems are proposed, which are then analyzed based on notions from variational inequality (VI) theory. The distributed algorithms can be proven to converge, under certain conditions, to VI solutions, which are also Nash equilibrium (NE) solutions of the equivalent NE problems. A distributed algorithm based on iterative solution of the two subproblems is then designed. Performance and price of anarchy of the distributed algorithm are then studied by comparing it to the globally optimal solution obtained with a newly designed centralized algorithm. Simulation results show that the proposed distributed algorithm achieves performance that is within a few percentage points of the optimal solution.

Physical layer security approaches enable secure message transmission without upper layer data encryption and thus draw intensive attention recently years. Following this topic, this paper proposes a novel approach to improve the security of multiple-input single-output (MISO) communications links in the presence of non-colluding passive Poisson distributed eavesdroppers. In the proposed approach, it is assumed that the channel state information (CSI) of the main channel is known and that of the eavesdropper channel is unknown. Through beamforming vectors, the transmitter transmits information signal to the legitimate receiver along with artificial noise (AN) to confuse the eavesdroppers. Secrecy outage probability (SOP) is adopted to describe the secrecy performance, and based on it, security region (SR) is used from the perspective of space to illustrate the security. In obtaining the SOP of the described transmission link, stochastic geometry theory is adopted. It is shown that the stochastic geometry theory provides a powerful tool in obtaining a solution of SOP. Furthermore, the secrecy performance between transmitting approaches with AN and without AN is compared. The SR is plotted and the factors impacting security are analyzed accordingly.

The secrecy rate optimization of wireless communication systems with full-duplex (FD) relays and jamming signals is investigated in this work. Cooperated with FD relays, a novel secrecy transmission mechanism is proposed targeting at creating interference at eavesdroppers by adopting jamming signals. In the proposed mechanism, relays work in FD mode to receive information signals and forward them together with extra jamming signals. The global channel state information (CSI) is assumed available at all transmit nodes. Based on the proposed scheme, the secrecy rate of relay communication system is analyzed. Simulation results are also included to support the theoretical analysis. Results show that the proposed scheme can obviously enhance the secrecy rate of relay communication systems.

Sums of exponentially distributed random variables (RVs) play important roles in performance analysis of various communication systems. Their logarithmic expectations can not only facilitate capacity analysis but also provide efficient analytical expressions of the system capacity. However, the analytical expressions for the logarithmic expectations have been rarely systematically provided in literature, resulting in inconvenience for related performance analysis. To overcome this issue, in this work, the analytical expressions for the logarithmic expectations of the sums of independent exponential RVs are summarized. Especially, for the case where the sum is composed of both independent non-identically distributed (i.n.i.d.) exponential RVs and independent identically distributed (i.i.d.) exponential RVs, a new closed-form probability density function (PDF) is derived. Compared to the previous PDF expressions, the derived PDF expression is much concise and easy to be determined. To demonstrate the effectiveness of the derived logarithmic expectations, case studies are performed by applying the derived logarithmic expectations to the analysis and derivations of the ergodic capacity of several multiple antenna systems. It is shown that the proposed approach can significantly facilitate the performance evaluation of multiple antenna communication systems.

In this paper, we propose and analyze a new relaying scheme for the three-node cooperative relaying system, named as incremental hybrid decode-amplify-forward relaying (IHDAF), where the relay may choose to keep silent or transmit message in decode-and-forward (DF) or amplify-and-forward (AF) mode based on the qualities of the channels among the source, relay, and destination. Closed-form expressions of the outage probability and bit error rate (BER) of the proposed IHDAF protocol are derived. The optimal relationship of the two important signal-to-noise ratio (SNR) thresholds at the relay and destination to decide whether cooperation is necessary and the cooperative mode has been achieved. Moreover, the effects of the power allocation schemes, SNR thresholds, and relay locations on the outage probability and BER of the IHDAF protocol are studied. Theoretical analysis and simulation results show that the IHDAF relaying scheme outperforms the incremental-selective DF (ISDF), incremental DF (IDF), and cooperative STBC schemes, especially when the relay is close to the destination.

The recent development of high-speed trains (HSTs), as a high-mobility intelligent transportation system, and the growing demands of broad-band services for HST users, introduce new challenges to wireless communication systems for HSTs. The deployment of mobile relay stations on top of the train carriages is one of the promising solutions for HST wireless systems. For a proper design and evaluation of HST wireless communication systems, we need accurate channel models that can mimic the underlying channel characteristics for different HST scenarios. In this paper, a novel nonstationary geometry-based stochastic model (GBSM) is proposed for wideband multiple-input multiple-output HST channels in rural macrocell scenarios. The corresponding simulation model is then developed with angle parameters calculated by the modified method of equal areas. Both channel models can also be used to model nonstationary vehicle-to-infrastructure channels in vehicular communication networks. The system functions and statistical properties of the proposed channel models are investigated based on a theoretical framework that describes nonstationary channels. Numerical and simulation results demonstrate that the proposed channel models have the capability to characterize the nonstationarity of HST channels. The statistical properties of the simulation model, verified by the simulation results, can match those of the proposed theoretical GBSM. An excellent agreement is achieved between the stationary intervals of the proposed simulation model and those of relevant measurement data, demonstrating the utility of the proposed channel models.

Cognitive radio (CR) is considered one of the prominent techniques for improving the utilization of the radio spectrum. A CR network (i.e., secondary network) opportunistically shares the radio resources with a licensed network (i.e., primary network). In this work, the spectral-energy efficiency trade-off for CR networks is analyzed at both link and system levels against varying signal-to-noise ratio (SNR) values. At the link level, we analyze the required energy to achieve a specific spectral efficiency for a CR channel under two different types of power constraint in different fading environments. In this aspect, besides the transmit power constraint, interference constraint at the primary receiver (PR) is also considered to protect the PR from a harmful interference. Whereas at the system level, we study the spectral and energy efficiency for a CR network that shares the spectrum with an indoor network. Adopting the extreme-value theory, we are able to derive the average spectral and energy efficiency of the CR network. It is shown that the spectral efficiency depends upon the number of the PRs, the interference threshold, and how far the secondary receivers (SRs) are located. We characterize the impact of the multi-user diversity gain of both kinds of users on the spectral and energy efficiency of the CR network. Our analysis also proves that the interference channels (i.e., channels between the secondary transmitter and PRs) have no impact on the minimum energy efficiency.

Errors encountered in digital wireless channels are not independent but rather form bursts or clusters. Error models aim to investigate the statistical properties of bursty error sequences at either packet level or bit level. Packet-level error models are crucial to the design and performance evaluation of high-layer wireless communication protocols. This paper proposes a general design procedure for a packet-level generative model based on a sampled deterministic process with a threshold detector and two parallel mappers. In order to assess the proposed method, target packet error sequences are derived by computer simulations of a coded enhanced general packet radio service system. The target error sequences are compared with the generated error sequences from the deterministic process-based generative model using some widely used burst error statistics, such as error-free run distribution, error-free burst distribution, error burst distribution, error cluster distribution, gap distribution, block error probability distribution, block burst probability distribution, packet error correlation function, normalized covariance function, gap correlation function, and multigap distribution. The deterministic process-based generative model is observed to outperform the widely used Markov models. Copyright © 2013 John Wiley & Sons, Ltd.

This paper proposes a power allocation scheme to maximize the sum capacity of all users for signal-to-leakage-and-noise ratio SLNR precoded multiuser multiple-input single-output downlink. The designed scheme tries to explore the effect of the power allocation for the SLNR precoded multiuser multiple-input single-output system on sum capacity performance. This power allocation problem can be formulated as an optimization problem. With high signal-to-interference-plus-noise ratio assumption, it can be converted into a convex optimization problem through the geometric programming and hence can be solved efficiently. Because the assumption of high signal-to-interference-plus-noise ratio cannot be always satisfied in practice, we design a globally optimal solution algorithm based on a combination of branch and bound framework and convex relaxation techniques. Theoretically, the proposed scheme can provide optimal power allocation in sum capacity maximization. Then, we further propose a judgement-decision algorithm to achieve a trade-off between the optimality and computational complexity. The simulation results also show that, with the proposed scheme, the sum capacity of all the users can be improved compared with three existing power allocation schemes. Meanwhile, some meaningful conclusions about the effect of the further power allocation based on the SLNR precoding have been also acquired. The performance improvement of the maximum sum capacity power allocation scheme relates to the transmit antenna number and embodies different variation trends in allusion to the different equipped transmit antenna number as the signal-to-noise ratio SNR changes.Copyright © 2013 John Wiley & Sons, Ltd.

In this paper, a novel trellis coded generalized spatial modulation (TCGSM) scheme is presented and analyzed. Similar to that of the traditional generalized spatial modulation (GSM), a subset of the entire transmit antennas is selected for transmission at each time slot. Nevertheless, in the proposed TCGSM scheme, the bits in the spatial domain are first coded by the trellis encoder before antenna selection. The purpose is to combat the correlation of the MIMO channel and hence improve the system performance. We give the detailed system model as well as the trellis encoding/decoding algorithm for the proposed scheme. The performance of the scheme is evaluated through both theoretical analysis and simulations. The results indicate that the proposed scheme is spectral efficient and robust against the channel correlation.

Interference alignment (IA) is a promising interference management technique to efficiently eliminate multiuser interference in a K-user interference network. However, channel state information (CSI) at the transmitter is indispensable for the interference alignment precoding matrices design. In spite of the fact that the CSI can be obtained through feedback from receivers to transmitters in a frequency-division duplex (FDD) system or through the reciprocity in a time-division duplex (TDD) system, it may be imperfect for the reason such as estimation inaccuracy, feedback delay and time-varying of channel. In this paper, the impact of imperfect CSI on the achievable sum rate of interference alignment network is considered and a channel prediction technique based on Kalman filtering is proposed to verify the theoretical analysis. Through analysing, we find that the sum rate performance of an interference aligned network is affected by an integrated parameter, i.e., the product of user number, transmit power and channel error variance. Simulation results reveal that the performance of interference alignment is more sensitive to the uncertainty of CSI at high signal-to-noise ratio (SNR) regime than that at low SNR regime. It is also verified that the performance can be improved through channel prediction, comparing with interference alignment based on the delayed feedback CSI.

In this paper, we proposed a scheme, with which the transmitted signals at the eavesdropper node can be nulled out to avoid intercepting in the case of one eavesdropper. The amplify-and-forward (AF) based cooperative protocol is considered to form the transmission link. Assuming the globe channel state information is available at all the transmission nodes, we simulate the transmission system and show the secrecy rate of the proposed scheme. The simulation results show that the performance of the secrecy capacity of the proposed scheme is better than the bound method and the direct transmission.

Hybrid Automatic Retransmission reQuest (HARQ) is an effective technique to improve the reliability of wireless communication systems by detecting, correcting, and retransmitting the erroneous packets. Packet-level error sequences obtained from physical layer wireless communication systems are important for the design and performance evaluation of high layer protocols, e.g., HARQ. In this paper, we utilize the open source Vienna long-term evolution (LTE) simulator to study the impact of HARQ on the burst error statistics of packet-level error sequences. Moreover, we propose a generative model that can generate packet-level error sequences with predicted burst error statistics similar to those of error sequences obtained from wireless systems with HARQ. Simulation results demonstrate that the proposed generative model is accurate and efficient in predicting the behavior of HARQ in terms of a set of burst error statistics rather than predicting the packet error rate (PER) only.

This study addresses the issue of intercell interference coordination in a multicell orthogonal frequency division multiple access network with universal frequency reuse, which aims at improving the network spectral efficiency, especially for the cell edge areas. According to different degrees of information sharing within the engaged network, both a joint and a distributed solution are proposed respectively trying to determine the most appropriate power control strategy along with the user scheduling policy. The joint power control and user scheduling scheme with full access to the network information is capable of gaining considerable enhancement on the network capacity and also a performance improvement for users suffering from strong intercell interferences. The distributed scheme with the same purpose manages to adapt the power control and user scheduling strategy on the basis of dynamic programming with respect to both current and future utility of the network with only local information available. Numerical results have shown the advantages of the proposed schemes in achieving better spectral efficiency under both joint and distributed circumstances.

Physical-layer cooperation allows leveraging the spatial diversity of wireless channels without requiring multiple antennas on a single device. However, most research in this field focuses on optimizing physical-layer metrics, with little consideration for network-wide and application-specific performance measures. This paper studies cross-layer design techniques for video streaming over cooperative networks. The problem of joint rate control, relay selection, and power allocation is formulated as a mixed-integer nonlinear problem, with the objective of maximizing the sum peak signal-to-noise ratio (PSNR) of a set of concurrent video sessions. A global optimization algorithm based on the branch and bound framework and on convex relaxation of nonconvex constraints is then proposed to solve the problem. The proposed algorithm can provide a theoretical upper bound on the achievable video quality and is shown to provably converge to the optimal solution. In addition, it is shown that cooperative relaying allows nodes to save energy without leading to a perceivable decrease in video quality. Based on this observation, an uncoordinated, distributed, and localized low-complexity algorithm is designed, for which we derive conditions for convergence to a Nash equlibrium (NE) of relay selection. The distributed algorithm is also shown to achieve performance comparable in practice to the optimal solution.

Narrow band interference (NBI) suppression is one of major issues for ultra wideband (UWB) wireless communication system operating over huge spectrum occupied by narrow band wireless systems. In this paper, we propose an interference mitigation scheme based on complex-valued adaptive notch filter, which smartly exploits the substantial correlation difference of signals and removes the NBIs in UWB signal with noise by estimating the corresponding central frequencies. To obtain high speed convergence and maintain small signal distortion, a low complexity gradient algorithm for one-order basic adaptive notch filter cell is deduced. Considering that the data rate prior to despreading is extremely high, a novel time-division multiplexing (TDM) parallel approach for eliminating a single NBI is presented, which effectively simplify the hardware design especially in high speed digital signal processing. Based on the one-order basic adaptive notch filter cell, three different implementations including direct forms, linear cascade forms and TDM parallel cascade forms to eliminate multiple NBIs are developed and discussed. Theoretical analysis and simulation results indicate that the proposed scheme possesses the advantages of high convergence speed, small distortion and high stability. Furthermore, the propose scheme utilized as a preprocessing unit prior to despreading can considerably improve the interference tolerance margin of UWB systems, leading to it's suitable for the low complexity direct sequence (DS)-UWB receiver.

In this paper, we present a novel user grouping method for grouped OFDM-IDMA systems. Aiming at maximizing the system capacity, we adaptively distribute users among the pre-allocated subcarrier groups according to their respective channel conditions. Using the notion of SNR evolution function, we first analyze the achievable capacity of the system and formulate the optimization problem as a weighted bipartite matching problem. Due to the analytically intractability of the SNR evolution function, we opt to use either the worst case or best case of the achievable capacity to approximate the original problem. Then Kuhn-Munkres method is employed to solve the approximated problems. The performance of the proposed scheme is evaluated by both theoretical analyses and simulations. Results show that with our proposed algorithm, the system capacity is markedly improved and is very close to the theoretical capacity upper bound.

ABSTRACT We consider a cognitive radio system where a secondary network shares the spectrum band with a primary network. Aiming at improving the frequency efficiency of the secondary network, we set a multiantenna relay station in the secondary network to perform two‐way relaying. Three linear processing schemes at the relay station based on zero forcing, zero forcing‐maximum ratio transmission, and minimum mean square error criteria are derived to guarantee the quality of service of primary users and to suppress the intrapair and interpair interference among secondary users (SUs). In addition, the transmit power of SUs is optimized to maximize the sum rate of SUs and to limit the interference brought to PUs. Numerical results show that the proposed multiuser two‐way relay processing schemes and the optimal power control policies can efficiently limit the interference caused by the secondary network to primary users, and the sum rate of SUs can also be greatly improved. Copyright © 2011 John Wiley & Sons, Ltd.

Cognitive radio (CR) has great potential to improve the spectral efficiency of future wireless networks. This paper focuses on maximizing the sum-capacity of cognitive wireless networks based on signal to leakage noise ratio (SLNR) precoding and hybrid opportunistic spectrum access scheme. We propose a globally optimal power allocation scheme based on a combination of the Branch and Bound framework (B&B) and convex relaxation technique to maximize the sum capacity of all secondary users (SUs). Simulation results indicate that, with the proposed power allocation scheme, the sum capacity of the secondary network can be improved compared to conventional SLNR-precoding-based power allocation schemes.

In this paper, we investigate the performance of the orthogonal complementary code (OCC) based direct sequence ultra wideband (DS-UWB) system. OCC has perfect partial autocorrelation and cross-correlation characteristics. With the application of OCC in DS-UWB system, we can offer multiple access interference (MAI) free operation in both synchronous and asynchronous transmissions over MAI-AWGN channel. Theoretical analysis illustrates that multipath interference (MI) as well as MAI can be also mitigated over lognormal multipath fading channels in OCC based DS-UWB system. Our simulation also shows the superiority of the OCC based DS-UWB to unitary code based system.

This paper proposes a non-stationary wideband geometry-based stochastic model (GBSM) for multiple-input multiple-output (MIMO) high-speed train (HST) channels. The proposed model has the ability to investigate the non-stationarity of HST environment caused by the high speed movement of the receiver. Based on the proposed model, the space-time-frequency (STF) correlation function (CF) and STF local scattering function (LSF) are derived for different taps. Numerical results show the non-stationarity of the proposed channel model.

In this paper, a novel unified channel model framework is proposed for cooperative multiple-input multiple-output (MIMO) wireless channels. The proposed model framework is generic and adaptable to multiple cooperative MIMO scenarios by simply adjusting key model parameters. Based on the proposed model framework and using a typical cooperative MIMO communication environment as an example, we derive a novel geometry-based stochastic model (GBSM) applicable to multiple wireless propagation scenarios. The proposed GBSM is the first cooperative MIMO channel model that has the ability to investigate the impact of the local scattering density (LSD) on channel characteristics. From the derived GBSM, the corresponding multi-link spatial correlation functions are derived and numerically analyzed in detail.

The knowledge of channel statistics can be very helpful in making sound opportunistic spectrum access decisions. It is therefore desirable to be able to efficiently and accurately estimate channel statistics. In this paper, we study the problem of optimally placing sensing/sampling times over a time window so as to get the best estimate of the parameters of an on-off renewal channel. We are particularly interested in a sparse sensing regime with a small number of samples relative to the time window size. Using Fisher information as a measure, we analytically derive the best and worst sensing sequences under a sparsity condition. We also present a way to derive the best/worst sequences without this condition using a dynamic programming approach. In both cases the worst turns out to be the uniform sensing sequence, where sensing times are evenly spaced within the window. Interestingly the best sequence is also uniform but with a much smaller sensing interval that requires a priori knowledge of the channel parameters. With these results we argue that without a priori knowledge, a robust sensing strategy should be a randomized strategy. We then compare different random schemes using a family of distributions generated by the circular \beta ensemble, and propose an adaptive sensing scheme to effectively track time-varying channel parameters. We further discuss the applicability of compressive sensing in the context of this problem.

Abstract In order to alleviate the narrowband interference (NBI) to ultra wideband (UWB) systems, we propose two non‐linear UWB chirp waveforms based on the arctrigonometric and archyperbolic function in this paper. The proposed UWB pulses can obtain good performance in NBI suppression. Both of the two chirp pulses require only the time domain processing because of the inherent relationship between the frequency domain and the time domain. Theoretical analysis and simulation results show that the direct sequence pulse binary amplitude modulation (DS‐BPAM) UWB systems with the proposed chirp waveforms can achieve excellent NBI suppression performance and outperform the linear chirp waveform based UWB system significantly. Copyright © 2010 John Wiley & Sons, Ltd.

ABSTRACT This paper proposes two power‐efficient resource allocation policies with statistical delay Quality of Service (QoS) guarantees for uplink time‐division multiple access (TDMA) communication links. Specifically, the first policy aims at maximizing the system throughput while fulfilling the delay QoS and average power constraints, and the second policy is devised as an effort to minimize the total average power subject to individual delay QoS constraints. Convex optimization problems associated with the resource allocation policies are formulated based on a cross‐layer framework, where the queue at the data link layer is served by the resource allocation policy. By employing the Lagrangian duality theory and the dual decomposition theory, two subgradient iteration algorithms are developed to obtain the globally optimal solutions. The aforementioned resource allocation policies have been shown to be deterministic functions of delay QoS requirements and channel fading states. Moreover, numerical results are provided to demonstrate the performance of the proposed resource allocation policies. Copyright © 2010 John Wiley & Sons, Ltd.

In this paper, we investigate the interference mitigation from a cross-layer perspective for a cognitive radio (CR) multiple-input multiple-output (MIMO) network coexisting with a primary time-division-duplexing (TDD) system. The channel allocation in the media access control (MAC) layer and a subspace-based precoding scheme in the physical layer of the CR network are jointly considered to minimise the interference to the primary user and maximise the CR throughput. Two distributed cross-layer algorithms, namely, joint iterative channel allocation and precoding (JICAP) and non-iterative channel allocation and precoding (NICAP), are proposed for the cases with and without channel information among CR nodes, respectively. Moreover, a channel estimation scheme is also proposed to enable the NICAP. The effectiveness of the proposed algorithms over non-cross-layer counterpart is demonstrated via simulations.

The problem of optimal resource allocation to share high-quality multimedia content in cognitive ad hoc networks with cooperative relays is addressed in this paper. Cooperative transmission is a promising technique to increase the capacity of wireless links by exploiting spatial diversity without multiple antennas at each node. However, mainstream research in this field focuses on optimizing physical layer performance measures, with little consideration for application-specific and network-wide performance measures. In this paper, the problem of joint video encoding rate control, power control, relay selection and channel assignment is formulated as a mixed-integer nonlinear problem(MINLP), and a solution algorithm based on a combination of the branch and bound framework and convex relaxation techniques is then proposed. The proposed solution jointly allocates channel, power, video encoding rate, and relay nodes for secondary users to maximize the video quality under the constraints posed by delay-sensitive video applications. Performance evaluation results show that cognitive networks with cooperative relaying can provide considerably higher video quality (in terms of the average peak signal-to-noise ratio (PSNR)) than solutions that do not rely on cooperation or without dynamic spectrum allocation.

In this paper, we investigate the ARQ technology based wireless cooperative network, which can improve the spectral efficiency and decrease the collision probability. With the application of channel estimation, the source and the relays in our scheme can decide the best terminal and the optimal cooperation protocol by their timers. Comparing to opportunistic relay selection (ORS), the spectral efficiency and collision probability could be improved. Meanwhile, comparing to Incremental Transmit Relay Selection (ITRS), the new scheme benefits from AF cooperative protocol and obtains more power gains, it is also more robust to the networks topology. Additionally, the complexity in the destination becomes lower.

It is well known that the data transport capacity of a wireless network can be increased by leveraging the spatial and frequency diversity of the wireless transmission medium. This has motivated the recent surge of research in cooperative and dynamic-spectrum-access networks. Still, as of today, a key open research challenge is to design distributed control strategies to dynamically jointly assign (i) portions of the spectrum and (ii) cooperative relays to different traffic sessions to maximize the resulting network-wide data rate.

Physical-layer cooperation allows leveraging the spatial diversity of the wireless channel without requiring multiple antennas on a single device. However, most research in this field focuses on optimizing physical layer metrics, with little consideration for network-wide and application-specific performance measures. This paper studies cross-layer design techniques for video streaming over cooperative networks. The problem of joint video rate control, relay selection, and power allocation is formulated as a mixed-integer nonlinear problem, with the objective of maximizing the sum peak signal-to-noise ratio (PSNR) of a set of concurrent video sessions. An asynchronous, distributed and localized low-complexity algorithm is designed, based on the iterative solution of convex optimization problems at each individual node. In addition, a global-optimization centralized algorithm based on convex relaxations of non-convex constraints is also proposed as performance benchmark. The distributed algorithm is shown to achieve performance within a few percentage points of the optimal solution. It is also shown that cooperative relaying allows nodes to reduce the overall power consumption without leading to a perceivable decrease in video quality.

To improve power efficiency in vehicle-to-roadside infrastructure (V2I) communication networks, this paper proposes a joint power and sub-carrier assignment policy under delay aware quality of service (QoS) requirements. Due to the real-time nature of the V2I transmissions, the proposed policy should satisfy delay aware QoS requirements with a minimized power consumption. In particular, we develop a cross-layer framework in which orthogonal frequency division multiplexing (OFDM) is employed at the physical layer and the proposed power and sub-carrier assignment policy works at the data link layer. Under the assumption that the instantaneous channel state information (CSI) of all users is known, the optimization problem can be formulated and solved with the help of a time-sharing factor. The obtained results show that both the optimal power allocation and the optimal sub-carrier assignment depend on the delay aware QoS requirements of each user. We also theoretically prove that the proposed power allocation policy converges to the classical water-filling policy if we do not consider the QoS requirements. Experimental results reveal that the proposed policy offers a superior performance over the existing resource allocation policies.

In this paper, we investigate a cooperative multiple input multiple output (MIMO) system with multi-antenna relays. In order to minimize the system outage probability, we propose a new relay selection policy for this system based on the opportunistic relaying scheme. The relays are endowed with the ability of channel estimation and the maximal ratio receiver combining (MRRC) technology and beamforming are employed in the first and the second cooperative phase, respectively. Statistical optimal power allocation algorithm, close-form expression of the system outage probability and also the diversity order are presented in detail. Finally, simulation results prove the accuracy of the the theoretical analysis and also the efficiency of the new scheme which can obtain more power gains or wider coverage compared with the opportunistic relaying scheme.

Abstract In this paper, we extend the geometrical one‐ring multiple‐input multiple‐output (MIMO) channel model with respect to frequency selectivity. Our approach enables the design of efficient and accurate simulation models for wideband space‐time MIMO channels under isotropic scattering conditions. Two methods will be provided to compute the parameters of the simulation model. Especially, the temporal, frequency and spatial correlation properties of the proposed wideband space‐time MIMO channel simulator are studied analytically. It is shown that any given specified or measured discrete power delay profile (PDP) can be incorporated into the simulation model. The high accuracy of the simulation model is demonstrated by comparing its statistical properties with those of the underlying reference model with specified correlation properties in the time, frequency and spatial domain. As an application example of the new MIMO frequency‐selective fading channel model, we study the influence of various channel model parameters on the system performance of a space‐time coded orthogonal frequency division multiplexing (OFDM) system. For example, we investigate the influence of the antenna element spacings of the base station (BS) antenna as well as the mobile station (MS) antenna. It turns out that an increasing of the antenna element spacing at the BS side results in a higher diversity gain than an increasing of the antenna element spacing at the MS side. Furthermore, the diversity gain brought in by space‐time block coding schemes is investigated by simulation. Our results show that transmitter diversity can significantly reduce the symbol error rate (SER) of multiple antenna systems. Finally, the influence of the Doppler effect and the impact of imperfect channel state information (CSI) on the system performance is also investigated. Copyright © 2009 John Wiley & Sons, Ltd.

This paper focuses on using the mindset of co-opetition for collaborative multimedia resource allocation. The co-opetition suggests a judicious mixture of competition and cooperation. We present a novel co-opetition strategy based on the Kalai-Smorodinsky bargaining solution (KSBS), and apply it to video rate allocation. The proposed strategy makes satisfied users stop competing for resources such that QoS of unsatisfied users can be improved. Our strategy is evaluated through comparing to existing competition-based strategies. Numerical results indicate that, the co-opetition strategy can result in an improved number of satisfied users. Algorithm with low complexity is also presented.

Ultra wideband (UWB) is a well known technology in wireless personal area networks (WPANs). Our work analyzes a multiple access code-selected M-ary direct sequence (DS) UWB communication system. The objective is to get a high data rate multiple access M-ary modulation scheme of UWB system. M-ary modulation is obtained by assigning a DS code set to each user, which is different with the traditional M-ary modulation schemes, such as pulse position modulation (PPM) and pulse amplitude modulation (PAM) etc. For this multiple access code-selected M-ary modulation UWB system, with the increase of the modulation level M, we can keep the same number of users, the data transmission rate and the multiple access performance while reduce the required transmitter power. In this paper, the signal processing algorithm is provided and the simulation results prove the efficiency of this multiple access M-ary UWB system.

Power control is critical for wireless communications that allow spectrum sharing among secondary users and primary users. In this paper, we derive an optimal distributed power control strategy aiming at the total capacity maximization of secondary network with interference constraints to primary users. Due to the nonconvexity of system utility, geometric programming is introduced to transform nonconvex optimization problems into convex optimization problems. Furthermore, system utility is usually coupled which means each utility depends not only on its local variables but also on the variables of other utilities. We introduce auxiliary variables and extra equality constraints to transfer the coupling in utility to coupling in constraints. The solution of the proposed power control strategy is shown to be globally optimal and leads to excellent performance.

We focus on the problems of physical network coding based on DNF, in the scenario of two-way wireless relaying systems, in frequency-flat Nakagami-Rice fading channels. We propose a novel joint design scheme which considers physical network coding and source coding together. The sequence of symbols with hybrid cardinalities is converted into a sequence of symbols with unifying 4-ary cardinality by using an appropriately designed source coding. The a priori probabilities used as the input of source coding to achieve the optimal lossless source coding can be obtained in advance, according to the statistics of channel conditions. The complexity of the transceiver systems can be reduced significantly, and the reliability of the transmission can be improved, although the price of our scheme is that it has to transmit a little bit more extra symbols. Simulation results demonstrate that the proposed joint design scheme is advantageous because we only use a very simple method to unify the cardinality of hybrid symbols, while the cost of our scheme is acceptable.

We investigate the power control policy which minimizes the average transmit power subject to delay quality of service (QoS) constraints for continuous and discrete constellation multi-level quadrature amplitude modulation (MQAM) schemes. The problem is formulated based on a cross-layer framework. In the framework, the queue at the data link layer is serviced by the power control and modulation schemes at the physical layer, using the theory of effective capacity. The solution for the continuous constellation MQAM case is derived by solving the Karush-Kuhn-Tucker (KKT) optimality conditions. For the discrete constellation MQAM case, we propose the region boundaries related to the required delay QoS. Our analysis provides the fundamental power limits for the fading channels while fulfilling the delay QoS requirement. Furthermore, the numerical results are presented to show the power saving performance of the proposed power control schemes.

This paper presents a novel coopetition paradigm based on bargaining theory for collaborative multimedia resource management. The paradigm consists of a judicious mixture of competition and cooperation. For competition, the well-known Kalai-Smorodinsky Bargaining Solution (KSBS) is adopted as the fairness criteria, and for cooperation each user stops competing for resources as long as it achieves a predefined threshold of Quality o Service (QoS). We apply the propose paradigm to rate allocation amongst multiple video users and compare its performance to other two schemes, traditional KSBS, and generalized KSBS for similar video quality. Results indicate that our paradigm adapts the best to the variation of resources as well as the user number. Also, importantly, our paradigm can result in an improved number of satisfied users while simultaneously avoid penalizing same users in the case of scarce resources. Complexity of the proposed paradigm is also analyzed.

We present a model for multiuser multi-input multi-output (MIMO) high speed downlink packet access (HSDPA) with covariance based linear precoders. The linear procoders, which are designed for single stream detection with rake receivers, are independent of the equalizers employed in multiple antenna receivers to support multistream detection. These symbol based equalizers are designed to minimize the mean square error (MSE) of the signal estimates. Both theoretical analysis and numerical results show the applicability of the proposed approach in HSDPA. There is no limitation on the number of users as long as enough code channels are available. Also, all the users have free choice on how many antennas they can deploy. This approach extends the covariance based precoding concept from MISO to MIMO systems.

In this paper, an M-ary code selected direct sequence bipolar pulse amplitude modulation (MCSDS-BPAM) ultra wideband system is introduced and the code selection criteria of this system is investigated. The particular DS code sequence used in MCSDS-MPAM scheme is selected by the log2M/2 bits from the DS code set, which offers the system higher data rate without increasing the system bandwidth or changing the pulse shape compared to other M-ary modulation schemes. The system performance can be varied by the selection of DS code set from the common DS code set without any extra expense. The criteria based on four parameters used to evaluate the system performance under different code sets are given over an ideal AWGN channel and correlation receivers.

The generation of multiple uncorrelated Rayleigh fading waveforms is often demanded for simulating wideband fading channels, multiple-input multiple-output (MIMO) channels, and diversity-combined fading channels. In this letter, an improved deterministic sum-of-sinusoids (SoS) channel simulator with a new parameter computation method is proposed to simulate a large number of uncorrelated Rayleigh fading processes. Compared with the existing SoS channel simulators, the proposed deterministic SoS model yields a much better simulation efficiency while still preserving satisfactory approximations to the desired statistical properties of the reference model.

A number of turbo equalization (TE) methods with reduced complexity have recently been introduced, in which the maximum a posteriori probability (MAP) equalizer is replaced by suboptimal and low complexity ones. These methods can save the computational complexity significantly, but with sacrificed system performance. In this paper, the authors first use the extrinsic information transfer (EXIT) chart tool to visually explain why these suboptimal methods have the worse performance. Then, the authors propose a novel iterative method for TE which takes advantage of both parallel and serial concatenation turbo-like schemes. In the novel iterative method, the EXIT chart switches between the two schemes as a balance. The transmitter side is also changed correspondingly by utilizing the concept of repetition code to acquire a structure similar to turbo codes. It is shown from both analytical and simulation results that the proposed iterative method for TE results in the excellent system performance while its realization complexity is kept relatively low.

This paper gives a new way to employ the second order characteristics of fading channels, i.e., the level crossing rate and average fading duration. The purpose of the paper is to define a new up bound of the spacing between pilot symbols by using level crossing rate and average fading duration to improve the spectral efficiency in pilot symbol assisted modulation systems. And find an up bound to make the system has both efficiency and reliability. It has also been proved that the new definition can be used as an adaptive adjustment of pilot symbols when the transmitter knows the channel state information in an adaptive system.

Simulating wideband fading channels, multiple-input multiple-output (MIMO) channels, and diversity-combined fading channels often demands the generation of multiple uncorrelated Rayleigh fading waveforms. In this letter, two appropriate parameter computation methods, namely the method of exact Doppler spread (MEDS) and Lp-norm method (LPNM), for deterministic sum-of-sinusoids (SoS) channel simulators are investigated to guarantee the uncorrelatedness between different simulated Rayleigh fading processes. Numerical and simulation results show that the resulting deterministic SoS channel simulator can accurately and efficiently reproduce all the desired statistical properties of the reference model.

Abstract This paper studies the peak‐to‐average power ratios (PAPRs) in multicarrier modulation (MCM) systems with seven different orthogonal bases, one Fourier base and six wavelet bases. It is shown by simulation results that the PAPRs of the Fourier‐based MCM system are lower than those of all wavelet‐based MCM (WMCM) systems. A novel threshold‐based PAPR reduction method is then proposed to reduce the PAPRs in WMCM systems. Both numerical and simulation results indicate that the proposed PAPR reduction method works very effectively in WMCM systems. Copyright © 2006 John Wiley & Sons, Ltd.

If the degree distribution is chosen carefully, the irregular LDPC codes can outperform the regular ones. In this paper, we proposed an LDPC coded BICM scheme in image transmission system to improve both efficiency and reliability. Simulation results show that LDPC codes are good coding schemes over fading channel in image communication. Simultaneously, irregular codes can obtain a code gain of about 0.7 dB than regular ones when BER is 10/sup -4/. So the irregular LDPC codes are more suitable for image transmission than the regular codes.

This paper investigates a low-density parity-check (LDPC) coded orthogonal frequency-division multiplexing (OFDM) wireless communication system based on IEEE 802.11a standard. And an initialization algorithm is proposed for the decoding of LDPC coded modulation in OFDM system. The LDPC decoding procedure is simplified without the estimation of channel noise power. Simulation results show that this algorithm is effective and the decoding performance is satisfied when maximum iteration number is 10.

A novel peak to average power ratio (PAPR) reduction method, threshold method, for wavelet based multicarrier modulation (WMCM) systems is proposed and presented in this paper. Theory analysis and simulation results both prove that our proposed PAPR reduction method to be a feasible and efficient one for multicarrier modulation system (MCM)

In this paper performance of different basis in multicarrier modulation (MCM) system over time-variant channels are analyzed and filter bank generated by different orthogonal Daubechies wavelets and bi-orthogonal wavelets are tested and compared. Due to the high spectral containment of wavelet filters, the wavelet packet MCM (WP-MCM) system performs better than Fourier based MCM system over time-variant channels. With turbo code applied in WP-MCM system, the BER performance improves greatly over time-variant channels with few iterations in the regions of low signal to noise ratio.

As a class of block codes, LDPC codes with any desired code rate and code length are easily constructed. We examine the performance of three high-rate (0.769, 0.889, 0.935) LDPC codes in image transmission. Simulation results show that LDPC codes are good coding schemes over fading channels in image communication with lower system complexity. The distorted images can be recovered by using the three high-rate LDPC codes at SNR of 10 dB, 11.5 dB and 14 dB respectively. These high-rate codes can achieve relatively higher bandwidth efficiency than the low-rate codes can. Moreover, we discovered that the code rate has more influence on the performance than the code length does.

A new scheme is proposed about how to make use of the unequal error protection (UEP) property of irregular low-density parity-check (LDPC) codes. By means of the construction of a weight-increasing parity-check matrix and systematic encoding, the mapping between the important information data and the elite bits of an irregular LDPC code becomes traceable and tractable. Thus, the inherent UEP property of irregular LDPC codes is not the theoretical interest any more. The proposed scheme makes it practical to implement UEP in system applications. Taking an irregular LDPC code with code length 10000 and code rate one half as an example, simulation results are presented over AWGN channels. The validity of the proposed scheme is verified by simulation.

Two adaptive coded modulation schemes employing LDPC (low-density parity-check) code are proposed for Rayleigh fading channels. Scheme 1: we fix the code rate (rate=1/2), and only change the modulation methods according to the channel state, that is, during poor channel conditions, QPSK modulation is employed, as channel conditions improve, more efficient modulation scheme such as 8PSK is used. Scheme 2: the modulation scheme is fixed (BPSK modulation), and the code rate is changed according to the channel state. Through simulation, we get the performance of these two adaptive LDPC schemes under different demands of BER, and the comparison between adaptive LDPC and nonadaptive LDPC is also given.

Unequal error protection (UEP) is applied in image transmission with the nonuniform signal constellation design over multilevel coded wavelet packet multicarrier modulation (MLC-WP-MCM) system. Different data division schemes are analyzed and compared. Simulation results show that image transmission by UEP with bit-based data division scheme presents much higher PSNR values and surprisingly better image quality in our proposed system

In this paper, based on the characteristics of Turbo Codes and Trellis Coded Modulation (TCM), an improved method Turbo Trellis Coded Modulation (Turbo TCM) with two different mapping strategies is presented over AWGN (add white gauss noise) channels. The performance of different mapping strategies of Turbo TCM in AWGN channels for image transmission is analyzed and investigated. Furthermore, taking 16QAM modulation of Turbo TCM as the example, two different signal constellations are compared and computer simulation results are showed.

In this paper, we research the code-matched interleaver's application condition and design method for turbo codes. The innovation is that firstly we give the parameters according to which we devise the code-matched interleavers combined with different generator matrix. Secondly, a modified code-matched interleaver design method is proposed in this paper. Compared with the code-matched interleaver design method given in (W. Feng et al., 2002), this modified code-matched interleaver's complexity is decreased when the interleaver size is longer while the effect that improve the code error performance at moderate to high signal-to-noise ratio is not decreased. The results are given when the input data is random sequence generated by the computer.

Multilevel coded I-Q modulation (I-Q MCM) is proposed in this paper. By utilizing the orthogonal character of QAM modulation, the in-phase and quadrature components of the signal can be encoded and decoded independently in I-Q MCM, which can greatly decrease the decoding complexity and time delay. Through simulation, we can also see this I-Q MCM doesn't give rise to any performance loss compared with traditional MCM scheme.

Based on different partitioning rules for MQAM constellations. Nonstandard MLC/MQAM schemes had been proposed to reduce the calculation of the individual capacity. In this paper, performance of the proposed MLC/MQAM schemes in well-defined OFDM systems are discussed with punctured convolutional codes as component codes. Compared with traditional MLC/MQAM and MQAM without MLC in OFDM systems, simulation results show that the nonstandard MLC/MQAM schemes can get better performance and lower complexity.

Performance of a new nonalgebraic maximum-likelihood decoding method of block codes based on generalized array code (GAC) structure is studied. Conventionally, algebraic method is widely used in decoding of block codes, which emits soft-decision information in demodulation, thus degrading the system performance greatly. If soft-decision decoding is adapted, the complexity is intractable, especially for long block codes. We propose to use GAC decomposition to decrease the complexity of block codes. Because GAC has a regular structure, the soft-decision trellis decoding becomes feasible and easy. Simulation results show that by using decomposition method, code (7, 4, 3) and code (15, 5, 7) can obtain a coding gain of up to 24 dB and 34 dB, respectively in comparison with uncoded BPSK at BER around 10/sup -5/. Furthermore, the method is proven to be effective in compressed image transmission system under fading environment, where it makes image transmission quality significantly improved.

Low-density parity check coded bit-interleaved coded modulation (BICM) schemes are analyzed in this paper. A simplified decoding method without iteration between demodulator and decoder is provided. And the performance over additive white Gaussian noise (AWGN) and Rayleigh fading channels are analyzed. Through the simulation results we can conclude that the schemes with low complexity decoding method have good performance both over AWGN and Rayleigh fading channels.

In this paper, the influences of iterations and the length of the interleaver on the performance of turbo codes in regions of low signal to noise ratio over Rayleigh fading channels are studied on the wavelet based orthogonal frequency division multiplexing (WOFDM). The results show that to improve the performance of turbo codes in WOFDM systems you have two ways to choose: increase the iteration number or enlarge the length of your interleaver. But, both the ways can bring side effects to your systems, it is also the case on coded OFDM systems. According to the demand of concrete system there must be a trade-off.

Abstract For the narrow band Wireless Code Division Multiple Access (WCDMA) system, there are some channel coding schemes proposed and applied like Turbo code and convolutional codes. But for the 4G Code Division Multiple Access (CDMA) wideband systems, we have to use new channel coding schemes with high bandwidth efficiency. In this case, multilevel coding (MLC) scheme is easy to map to Multiple Quardrature Amplitude Modulation (MQAM) modulation strategy to be used for 4G, and MLC+MQAM will be a potential channel coding scheme for the error correcting of next generation of mobile systems. A novel criterion, that is ‘capacity rule’ plus ‘mapping rule’, for the design of the optimum MLC scheme for radio systems over Rayleigh fading channels is proposed in this paper. Based on this theory, a few of key issues related to design an optimum MLC system are investigated. These include a novel optimum design criterion proposed, different mapping strategies, different decoding methods of MLC/MSD and MLC/Parallel Decoding on Levels (PDL) and their performance comparison over Additive White Gaussian Noise (AWGN) and Rayleigh fading channels respectively. Copyright © 2003 John Wiley & Sons, Ltd.

A new distance metric is proposed to design optimal multilevel codes/parallel decoding of levels (MLC/PDL) system. Combining the constellation's intra-subset and inter-subset distances jointly, the metric describes not only the difference between the points in the same subset, but also the difference between the subsets that include the corresponding points. The metric reflects the real distinction in the points. The validity of the metric is proven through a series of simulations including 8PSK with Ungerboeck (1982) partitioning (UP) and block partitioning (BP), respectively. On the basis of the metric, we design an MLC/PDL system with surprising reliability, short time delay and decoding complexity.

Optimal multilevel codes (MLC) with QAM constellations are considered with focus on both AWGN and Rayleigh fading channels. With the proposition of three novel set partitioning rules for QAM constellations, a new MLC scheme with a very simple MSD (multistage decoding) structure comes into being, which is operated on the capacity rule. Compared with traditional MLC scheme the new MLC structure greatly reduces the calculation of the individual capacities in the MLC system because the new set partitioning rules ensure the independency of two dimension symbols. Furthermore the new system cannot cause any performance loss and the time delay is just 1/2 of that with traditional partitioning rules. As an example 64QAM constellation with three new set partitioning rules are provided. Their performance is simulated over AWGN and Rayleigh fading channels by choosing BCH codes as the component codes.

In this paper some nonstandard partitioning rules for 16QAM constellation are proposed. With these nonstandard partitioning rules a new MSD structure is generated. For the fixed component codes length the new structure can greatly reduce the complexity of MLC(Multilevel codes) system. The time delay is just 1/2 of that with traditional partitioning rules. Simulation results show that the new partitioning rules and the new MSD structure do not create any performance loss. Furthermore for the fixed time delay the new MLC system is better than the traditional MLC system and the performance improvement is about 0.25 dB with BER equal to 10/sup -5/.

A new trellis decoding method of block codes based on the generalized array codes (GACs) structure over a Rayleigh fading channel is presented. Further researches are also carried out to introduce this GAC-based decoding method to an image communication system. Several GACs, code (8, 4, 4) and its shortened code (7, 4, 3), code (16, 5, 8) and its shortened code (15, 5, 7), code (15, 9, 4) and code (30, 19, 4) with the same minimum Hamming distance, are considered in the system. The results show that the minimum Hamming distance is the key factor of the error-correction capability of GAC rather than the coding length in a Rayleigh fading channel. Furthermore, under the soft decision trellis decoding strategy, the quality of the image, whether uncompressed or DCT-compressed, is greatly improved with the help of the GAC.

An efficient multilevel coding system is constructed in this paper. Parallel decoding of levels (PDL) is adopted to guarantee short time delay. System performance with Ungerboeck partitioning (UP) and block partitioning (BP) is evaluated respectively and results show that with very short block component codes, such a MLC/PDL system can bring very good performance. For example, multilevel code (15,9,4)(15,11,3)(15,11,3) can obtain a coding gain up to 27dB compared with uncoded QPSK when BER is around 10/sup -5/.

In this paper, based on the characteristics of multilevel codes (MLC) and space-time block codes (STBC), the concatenation scheme combining MLC and STBC is proposed over Rayleigh fading channels. According to the fact that MLC system can achieve optimal performance according to the "capacity rule and BP (block partitioning) rule", the same topics, i.e. the optimal scheme involving in the optimal code rate design and the optimal set partitioning of MLC-STBC system are investigated in this paper. After analysis and computer simulations, a novel criterion for optimum MLC-STBC is proposed.

In this paper, based on the characteristics of multilevel codes (MLC) and space-time block codes (STBC), a concatenation scheme (MLC-STBC) combining MLC and STBC is presented over Rayleigh fading channels. The unequal error protection (UEP) of MLC-STBC in Rayleigh fading channels for image transmission is investigated. Furthermore, taking 8ASK modulation as the example, a non-uniform signal constellation for realizing UEP of MLC-STBC is designed. Both theoretical analysis and computer simulation show that MLC-STBC scheme is a high rate and high power efficiency scheme, UEP is its inherent character and non-uniform signal constellation can improve its UEP ability. Its UEP character can enhance the transmission quality in the same bandwidth over a Rayleigh fading channel. MLC-STBC is an excellent scheme for image transmission.

We present multilevel hierarchical coding with diagonal and block interleaving scheme applied in a Rayleigh fading channel in which both the capacity rule and block partitioning have been taken into accord. Simulated by computer, the performance of level 3 is more than 1 dB better than no interleaving at two levels at error probability of 10/sup -5/. This scheme can use in the diagonally layered space-time architecture.

Space-time block codes (STBC) are designed to achieve the maximum possible diversity benefit. However, STBC needs to be concatenated with an outer code which provides a significant coding gain. Based on the capacity rule and a multilevel codes/multi-stage decoding (MLC/MSD) system, this paper presents three concatenation schemes of STBC and MLC/MSD. Punctured convolutional codes are selected as component codes according to the capacity rule. The performances of different hard and soft concatenation schemes are analyzed and performance comparisons are made for three mapping strategies, named Ungerboeck partitioning (UP), block partitioning (BP) and mixed partitioning (MP). Then, we present the best concatenation scheme for a mobile environment.

A new multilevel code (MLC) structure with multiple antennas is presented, in which space diversity is achieved by using space-time block codes. Through performance analysis and computer simulation the asymptotic behavior of the new MLC scheme is provided, which is proportional to the product of the space diversity S and the minimum Hamming distance L' of component codes. When BCH codes are chosen as component codes and block partitioning (BP) with 8ASK modulation is adopted, the performances of the new MLC approach and the original MLC scheme are compared. The simulation results show that with the same bandwidth efficiency the new MLC scheme has a better power efficiency than the original MLC system without space diversity and the new system can achieve better trade-off between performance and complexity than the original MLC system without space diversity.

Multilevel coding (MLC) schemes based on channel capacity with multistage decoding (MSD) and parallel decoding on levels (PDL) are considered and compared. The channel models AWGN and Rayleigh fading are used in order to study the performance of MLC systems under different conditions. The investigation is done for 8ASK modulation and three set partitioning strategies. In each scheme BCH codes with different code lengths are used as component codes. Numerical results indicate that MSD is a sub-optimal decoding method of MLC for both channels, while PDL is most robust to varying channels if block partitioning (BP) is used. For Ungerboeck partitioning (UP) and mixed partitioning (MP) strategy, the MSD method is strongly recommended to use for the MLC system, while for the BP strategy, PDL is suggested to use as a simple decoding method compared with MSD.

This paper employs the four-state Markov models as the long-bursting error probability models over mobile fast-fading channels. The (2,1,7) convolutional codes with Viterbi (1971) decoding and the wavelet noise cancellation are combined as a new error-correcting scheme, whose performance in image communication system over mobile fast-fading channels is researched.

A new trellis decoding method of block codes based on generalized array codes (GAC) structure is adopted for error control in a Rayleigh fading channel. The trellis decoding method has lower complexity than the Viterbi decoding method does, and soft-decision can be easily added. We also make researches on the application of this decoding strategy to compressed image transmission system in a Rayleigh fading channel. Simulation results show that the soft-decision trellis decoding of block codes obtains significant coding gain in a Rayleigh fading channel. Furthermore, the decoding technique can make the image transmission quality greatly improved with a rather low decoding complexity.

According to the "capacity rule", the performance of multilevel coding (MLC) schemes with 8ASK modulation and three set partitioning strategies over AWGN channels is investigated. Two different decoding methods, which are multistage decoding (MSD) and parallel decoding on level (PDL), are used. In each scheme BCH codes with code lengths of 127 are used as component codes. Numerical results indicate that MSD is a sub-optimal decoding method of MLC for AWGN channels. For Ungerboeck partitioning (UP) and mixed partitioning (MP), the MSD method is strongly recommended to use for the MLC system, while for block partitioning (BP), PDL is suggested to use as a simple decoding method compared with MSD.

This paper employs the four-state Markov models as the long-bursting error probability models over mobile fast-fading channels. The (2, 1, 7) convolutional codes with Viterbi (1971) decoding and the wavelet noise cancellation are combined as a new error-correcting scheme, whose performance in an image communication system over mobile fast-fading channels is researched.

A new nonalgebraic decoding method based on generalized array codes (GAC) structure for a Rayleigh fading channel is studied. The trellis structure of the decoding method makes it easy to add soft decision. 2-level and 8-level quantification are considered respectively in decoding a (7, 4, 3) code, a (15, 5, 7) code, a (15, 9, 4) code and a (30, 19, 4) code. Simulation results show that the decoding strategy adapts to the Rayleigh fading channel successfully, especially for soft-decision decoding. Soft-decision decoding can achieve up to 6 dB more coding gain than hard-decision decoding when the BER is around 10/sup -5/.

The interleaved (2,1,7) convolutional codes in the same city in eight different channels with different modulation styles, different vehicle speeds and different rates of information throughput are simulated, and their performance is researched. The interleaved (2,1,7) convolutional codes with Viterbi decoding are adopted as the anti-interference scheme in mobile image communication system, and their performance is studied and compared with the (2,1,3) convolutional codes.

In this paper, the 4-state Markov model is presented as a long-burst error probability model of a mobile channel, and punctured convolutional codes (PCC) are applied to an image transmission system over mobile fast-fading channels. We propose a new scheme of unequal error protection (UEP) in image transmission by means of adjusting the different code rates, the constraint length of the mother code and interleaving degree. Simulation results show the superiority of UEP directly.

This paper focuses on the influence of soft derision decoding on multilevel coding (MLC) schemes with different mapping strategies, named UP (Ungerboeck's (1982) partitioning), BP (block partitioning) and MP (mixed partitioning), in Rayleigh fading channels. Based on the capacity rule, 8ASK MLC systems are constructed using punctured convolution codes. The performance comparisons are made for soft and hard decision decoding by using soft decision decoding for selected coding levels. The error propagation resulted from the lower level is also discussed. The results can present some references for the optimal criterion for MLC design in fading channels.

The performance of an improved multilevel coding (MLC) system with intralevel interleaving and iterative multilevel decoding in Rayleigh fading channels is studied. BCH codes are selected as component codes and code rates are distributed according to "capacity rule". The Ungerboeck partitioning scheme and 8ASK modulation are used. The simulation results indicate that interleaving technique and iterative decoding can improve the performance of MLC system greatly.

Two novel pseudonym random interleaving schemes, whose interleaving degrees obey a uniform distribution and a Rayleigh distribution respectively, are proposed in this paper. The performance of BCH (63,39,4) and BCH (127,78,7) codes using these two schemes as well as a periodic interleaving scheme in Rayleigh fading channels are obtained by simulation. The results show that pseudonym random interleaving can decrease the average time delay on some occasions and it is also a good cipher scheme.

Propagation characteristics of radio signal in UHF band places fundamental limitations on actual cellular and microcellular system design. The downlink and uplink radio signals are very important for service quality, so accurate prediction of signal characteristics for high grade of service (GOS) in urban areas where subscribers' density is high is required for better performance of cellular mobile systems. In this paper, we present some competing models in cellular/microcellular system design. The effects of terrain on predicted 900 MHz signals are analyzed. Results of radio signal propagation measurements for Jinan city, China, are compared to those predicted based on Hata-urban (1980), Bertoni-Walfish (1988), Egli, plane earth models.

We adopt the (2,1,3) convolutional code combined with an interleaving technique as the anti-interference measure. We simulate and discuss the performance of error-correction of a standard image transmitted through eight mobile channels which have four different modulation styles and two different vehicle speeds; we also compare the performance with that in an AWGN channel. We study the influence of the fast fading characteristics on the reliability of image transmission, the error-correcting performance of the convolutional codes in the mobile channel and the significance of the interleaving technique. At the same time we prove the results obtained are similar to previous ones. We also obtain some significant conclusions and propose some ideas for the mobile image communication system.