Intelligent task-oriented semantic communications (SemComs) have witnessed great progress with the development of deep learning (DL), where multi-task SemComs that perform multiple tasks simultaneously attach great importance due to its high efficiency. However, the study of robust multi-task-oriented semantics transmission is still in early stages. In this paper, we propose a semantic-aware hybrid automatic repeat request (SemHARQ) framework for the robust and efficient transmissions of multi-task semantic features. First, to improve the robustness and effectiveness of semantic coding, a multi-task semantic encoder is proposed. Meanwhile, a feature importance ranking (FIR) method is investigated to ensure the important features delivery under limited channel resources. Then, to accurately detect the possible transmission errors, a novel feature distortion evaluation (FDE) network is designed to identify the distortion level of each feature, based on which an efficient HARQ method is proposed. Specifically, the corrupted features are retransmitted, where the remaining channel resources are used for incremental transmissions. The system performance is evaluated under different channel conditions in multi-task scenarios in the Internet of Vehicles. Extensive experiments show that the proposed framework outperforms state-of-the-art works by more than 20% in rank-1 accuracy for vehicle re-identification, and 10% in vehicle color classification accuracy in the low signal-to-noise ratio regime.

Semantic communications are expected to be an innovative solution to the emerging intelligent applications in the era of connected intelligence. In this paper, a novel scalable multi-task semantic communication system with feature importance ranking (SMSC-FIR) is explored. Firstly, the multi-task correlations are investigated by a joint semantic encoder to extract relevant features. Then, a new scalable coding method is proposed based on feature importance ranking, which dynamically adjusts the coding rate and guarantees that important features for semantic tasks are transmitted with higher priority. Simulation results show that SMSC-FIR achieves performance gain w.r.t. individual intelligent tasks, especially in the low SNR regime.

To meet the extremely low latency requirements of 6G Internet of Things (IoT) services, 6G network should be able to intelligently allocate the network resources. Based on Mobile edge computing (MEC) and network function virtualization (NFV), the 6G NFV/MEC-enabled IoT architecture will be a viable architecture to enable flexible and efficient resource allocation. The architecture will enable the deployment of service function chains (SFCs) in NFV-enabled network edge nodes. However, due to the heterogeneous and dynamic nature of 6G IoT, it is a challenge to deploy SFCs rationally. Therefore, this paper proposes a knowledge-assisted deep reinforcement learning (KADRL) based SFC deployment scheme. The scheme achieves flexible and efficient resource allocation by deploying SFCs at appropriate edge nodes for the requirements of 6G IoT services. Simulation results demonstrate that KADRL can achieve better convergence performance and can meet the requirements of delay-sensitive IoT services.

In order to meet the strict quality of service requirements of delay-sensitive networks, this paper studies resource-autonomous decision-making algorithms for 6G MEC networks to improve key performance indicators (KPIs) such as delay, computing rate, and system stability. This paper considers task offloading and resource allocation decisions in multi-user MEC networks with time-varying channels, where user task data arrive randomly. We designed an autonomous decision-making algorithm for Lyapunov Assisted Deep Reinforcement learning (Ly-DRL) that satisfies the data queue stability and average power constraints and maximizes the network computing rate. We construct a dynamic queue of user task data through queue theory and apply Lyapunov optimization theory to decouple the MINLP problem into subproblems for each time slot. Combining DRL and traditional numerical optimization, the subproblems of each slot are solved with low computational complexity. Simulations show that the algorithm performs best while stabilizing the data Queue.

The rapid development of various autonomous unmanned systems has increased the endogenous intelligence of machines, which empowers functional collaborations among intelligent machines. However, traditional protocols for intelligent machine collaboration have limitations regarding functionality, efficiency, and scalability. Moreover, existing research on intelligent machine collaboration is not yet approaching a unified paradigm that facilitates interactions among machines and between machines and humans. Therefore, aiming to enable a more efficient functional collaboration among intelligent machines, we propose a natural language-based instruction (NLI) protocol, which enjoys the advantages of autonomy, robustness and efficiency. In particular, we specify the NLI protocol architecture by introducing a corpus of NLIs, an NLI generation module, and an NLI parsing module, wherein the corpus contains control instructions, intents and slots, the generation module is used to generate NLIs, and the parsing module is used to parse the intents and slots in the instruction. Furthermore, we case-study the proposed NLI protocol in an electromagnetic interference avoidance scenario based on semi-physical simulation with software-defined radio. Simulation results show that the NLI protocol is more robust and efficient than traditional control protocol in severe wireless channels, which validates the feasibility and effectiveness of implementing intelligent machine collaboration based on NLI.

In response to the explosive growth of data computation in vehicular terminals, computation offloading has emerged as a viable solution to mitigate the limitations of resources. Efficient offloading decisions not only meet the demanding requirements of complex vehicular tasks in terms of time, energy consumption, and computational performance but also minimize competition and resource consumption in the network. However, existing work on task offloading in vehicular networks often exhibits certain limitations, such as incomplete consideration of relevant factors or suboptimal utilization of available resources. This research presents the construction of a three-layer vehicular network environment, which is based on cloud and edge computing paradigms. The design entails the formulation of real-time vehicle location tracking and task priority metrics, while also considering the challenges posed by time-varying channels and signal blockage prevalent in vehicular network environments. In this paper, a novel variant of the Deep Deterministic Policy Gradient (DDPG) algorithm NLDDPG is proposed to iteratively train the model, aiming to optimize a weighted objective function. Simulation results show that this algorithm can improve the efficiency and optimize the task average utility.

Knowing the network states in time has become an indispensable part of network management. However, as users put forward higher requirements for service experience, only focusing on key performance indicators (KPIs) cannot guarantee the quality of user experience. Key quality indicators (KQIs) can reflect the service performance experienced by users and are considered to be an essential factor in optimizing the network. However, it is an urgent problem to represent the network states with different network indicators. In this paper, we propose a novel intelligent representation scheme by exploring the indepth correlation among multi-layer network indicators. Experiments are carried out using the real dataset of the 5G network, and the simulation results demonstrate the feasibility and accuracy of the proposed scheme.

With the popularity and application of the Industrial Internet of Things (1IoT), device anomaly detection is considered as one of the important challenges in IloT implementation. However, the privacy sensitivity of device data and the high heterogeneity of IloT devices make it impossible for traditional schemes to achieve efficient, accurate, and privacy-protected device anomaly detection in IloT networks. In this study, we propose an intelligent anomaly detection architecture for IloT networks based on federated optimization algorithms and deep learning (DL). In particular, an online, adaptive, and semi-supervised device anomaly detection model is designed, and a heterogeneity-aware federated learning algorithm, called Clustered-FedProx, is presented. The Clustered-FedProx algorithm considers the differences in computational power and data statistical distribution among IloT devices, whereby multiple devices can be coordinated to train a global DL model in highly heterogeneous networks. Simulation results show that the proposed scheme can achieve more stable and accurate performance than conventional schemes.

This paper proposes a multi-critic deep Reinforcement learning framework (MCDRL) and a knowledge-embedded multi-critic deep reinforcement learning(KEMCDRL) Decision-making method, the method can ensure users’ real-time QoS delay requirements. Compared with implementing the deep reinforcement learning algorithm directly in the communication system, this method can accelerate the convergence and guarantee the initial QoS performance of the system. Simulation results show that the design method can significantly reduce the convergence time compared with traditional deep reinforcement learning, and has nearly optimal decision delay compared with existing decision-making methods, which can actualize real-time decision-making in a time-varying channel environment.

Person reidentification (re-ID), which is a significant and potential application in the Internet of Things (IoT), aims to retrieve pedestrians of interest given a labeled image in a camera network. Now, it is still existing many challenges that severely influence feature representation in practical scenarios. Many methods adopt the attention mechanism in convolutional neural network (CNN) to improve the ability of feature learning. Although they only apply 1-D attention block in the popular deep learning architecture, the learned features are not discriminative for the feature representation. In this work, we investigate a self-focusing network (SFNet) that considers both the channel-dimensional attention and spatial-dimensional attention to adaptively learn more discriminative features. Namely, we embed the new attention module into the common backbone network, which can focus on the salient region by inhibiting the redundant features. Specifically, we design eight variants of the channel-dimensional attention and spatial-dimensional attention throughout the entire network and explore the most powerful feature representation. The heatmaps of different layers are visualized to intuitively present the performance of SFNet. Furthermore, we compare SFNet with the prior work on three popular person re-ID benchmarks by abundant experiments.

Considering the low-latency and high data rate requirements for automated vehicles (AVs), the millimeter-wave (mmWave) technology can support tens of Gb/s raw sensor information sharing for connected AVs (CAVs). However, the challenging problem is how to achieve fast and robust mmWave beam tracking for CAVs. To solve this problem, we propose a novel vehicle behavior cognition-based particle-filter (VBC-PF)-enabled beam tracking algorithm. The beam-space subset is predicted based on the beam change rate and the position-yaw information from the vehicle behavior cognition in CAVs, which effectively reduces the beam search overhead. In the proposed VBC-PF algorithm, the particle weight updating schemes are designed based on the optimal vehicle behavior cognition to avoid the particle divergence and the error accumulation. Simulation and hardware testbed results verify that the accuracy and efficiency of the proposed VBC-PF algorithm outperform the conventional particle filter (PF) and the extended Kalman filter (EKF) algorithms.

As an enabling technology to address spectrum shortage, spectrum sensing has been investigated a lot. However, the uncertainties in the detection environment, including noise uncertainty and carrier frequency (CF) mismatch, still remain as the main challenges of spectrum sensing, which greatly degrades the sensing performance of typical sensing methods, such as energy detection and cyclostationary detection. To this end, this paper proposes two robust spectrum sensing schemes by leveraging the difference between the phase difference (PD) distribution of noise-perturbed signal and that of Gaussian noise. Specifically, the compact approximation of the PD distribution is first derived to enable the extraction of the features of PD distributions, which are robust to noise uncertainty and CF mismatch. Based on these features, two sensing schemes based on the deep neural network (DNN), referred to as DNN-based PD distribution detection (PDD) and blind PDD (BPDD), are proposed to detect spectrum holes in cases with known CF and unknown CF, respectively. Simulation results show that our proposed schemes are more robust to CF mismatch and noise uncertainty in comparison with the existing sensing schemes. Furthermore, when the CF of the sensed signal is unknown, the proposed BPDD significantly outperforms existing blind sensing schemes.

Millimeter wave (mmWave) communications can potentially meet the high data-rate requirements of unmanned-aerial-vehicle (UAV) networks. However, as the prerequisite of mmWave communications, the narrow directional beam tracking is very challenging because of the 3-D mobility and attitude variation of UAVs. Aiming to address the beam tracking difficulties, we propose to integrate the conformal array (CA) with the surface of each UAV, which enables the full spatial coverage and the agile beam tracking in highly dynamic UAV mmWave networks. More specifically, the key contributions of our work are threefold: 1) a new mmWave beam tracking framework is established for the CA-enabled UAV mmWave network; 2) a specialized hierarchical codebook is constructed to drive the directional radiating element (DRE)-covered cylindrical CA, which contains both the angular beam pattern and the subarray pattern to fully utilize the potential of the CA; and 3) a codebook-based multiuser beam tracking scheme is proposed, where the Gaussian process machine learning-enabled UAV position/attitude prediction is developed to improve the beam tracking efficiency in conjunction with the tracking-error aware adaptive beamwidth control. Simulation results validate the effectiveness of the proposed codebook-based beam tracking scheme in the CA-enabled UAV mmWave network, and demonstrate the advantages of CA over the conventional planner array in terms of spectrum efficiency and outage probability in the highly dynamic scenarios.

The edge computing node plays an important role in the evolution of the artificial intelligence-empowered Internet of things (AIoTs) that converge sensing, communication, and computing to enhance wireless ubiquitous connectivity, data acquisition, and analysis capabilities. With full connectivity, the issue of data security in the new cloud-edge-terminal network hierarchy of AIoTs comes to the fore, for which blockchain technology is considered as a potential solution. Nevertheless, existing schemes cannot be applied to the resource-constrained and heterogeneous IoTs. In this paper, we consider the blockchain design for the AIoTs and propose a novel classified ledger framework based on lightweight blockchain (CLF-LB) that separates and stores data rights at the source and enables a thorough data flow protection in the open and heterogeneous network environment of AIoT. In particular, CLF-LB divides the network into five functional layers for optimal adaptation to AIoTs applications, wherein an intelligent collaboration mechanism is also proposed to enhance the across-layer operation. Unlike traditional full-function blockchain models, our framework includes novel technical modules, such as block regenesis, iterative reinforcement of proof-of-work, and efficient chain uploading via the system-on-chip system, which are carefully designed to fit the cloud-edge-terminal hierarchy in AIoTs networks. Comprehensive experimental results are provided to validate the advantages of the proposed CLF-LB, showing its potentials to address the secrecy issues of data storage and sharing in AIoTs networks.

Concerns have been raised about our recently published article on the fixed region beamforming using frequency diverse subarray for secure mmWave wireless communications. In a comment, the authors thought our precoding vector normalization method of the sidelobe randomization scheme has a flaw and proposed a non-physical-layer-security-oriented (non-PLS-oriented) normalization method by keeping the norm of the steering vector as a unit. However, we believe our PLS-oriented normalization method of the transmit beamforming vector is correct and reasonable from the PLS perspective, i.e., we hope to keep the target use's beampattern gain unit. In this rebuttal, we further clarify and justify our scheme to show its correctness. In addition, we also present a generalized normalization method to compare our proposed PLS-oriented scheme and the non-PLS-oriented scheme in the comment to offer useful insights.

Unmanned aerial vehicles (UAVs) have extensive civilian and military applications, but establishing a UAV network providing high data rate communications with low delay is a challenge. Millimeter wave (mmWave), with its high bandwidth nature, can be adopted in the UAV network to achieve high speed data transfer. However, it is difficult to establish and maintain the mmWave communication links due to the mobility of UAVs. In this paper, a beam management scheme utilizing angular domain information (ADI) is proposed to rapidly establish and reliably maintain the communication links for the mmWave UAV network. Firstly, Gaussian process machine learning (GPML)-enabled position prediction is proposed to facilitate coarse-ADI acquisition through the proposed UAV clustering algorithm. Then, with the proposed confined-ADI acquisition which removes the redundancy in the coarse-ADI acquisition, fast beam tracking with respectively the single-beam pattern and the multi-beam pattern is achieved. Finally, a data-driven beam pattern selection scheme is proposed for improving the spectrum efficiency. Simulation results verify the outstanding performance of the proposed beam management for mmWave UAV networks.

In this paper, we study the high-resolution channel estimation problem for intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) multiple-input-multiple-output (MIMO) communications, which is a prerequisite to guarantee further high-rate data transmission. Considering the typical sparsity of mmWave channels, we formulate the cascaded channel estimation problem from a sparse signal recovery perspective, and then propose a novel two-step cascaded channel estimation protocol to estimate the cascaded user-IRS-base station channel with high-resolution for IRS-assisted mmWave MIMO communications. More specifically, the first step is to estimate the coarse angular domain information (ADI) and further establish the robust uplink by beam training. In the second step, by exploiting the coarse ADI, an adaptive grid matching pursuit (AGMP) algorithm is proposed to estimate the high-resolution cascaded channel state information (CSI) with low complexity. Simulation results verify that the proposed two-step channel estimation protocol significantly outperforms the state-of-the-art scheme, i.e., beam training based channel estimation, and meanwhile can reap near-optimal system performance achieved by perfect CSI.

We study the optimized dynamic antenna parameters configuration for the 3D massive multiple-input multiple- out (MIMO) system in a heterogeneous network (HetNet) with overlaid macrocells and smallcells. In particular, we propose a deep reinforcement learning (DRL) approach to jointly adjust three key antenna parameters, namely, downtilt angle, vertical and horizontal half-power beamwidths of the macro base stations (mBSs) automatically in a dynamic environment with strong user mobility. More specifically, employing the gridded user location information (ULI), we propose a novel mix Q-learning algorithm to efficiently address the challenging joint optimization problem, which integrates a parallel hyper-parameter updating mechanism in dual sub-networks and a technique of prioritized replay buffer. The resultant neural network can efficiently learn the historical experience in an online fashion and achieve excellent sum-rate performance with affordable trials. Moreover, thanks to the proposed gridded ULI, our DRL-empowered antenna configuration framework can easily fit various HetNet deployments with variable user densities. Numerical results show that the average weighted sum-rate is increased by 4.59 bit/s/Hz, and the average performance improvement is up to 24.82% as compared to the reference scheme without gridded ULI.

In recent years, human action recognition has received a considerable amount of research attention because of it's potential in a variety of applications, such as video surveillance, human-computer interaction, and virtual reality (VR). However, many researches on human action recognition performed in single-camera or double-camera system, which achieve reduced performance due to vulnerability to partial occlusion and miss-recognition of back. Some works on human action recognition use multiple cameras but are too complex for practical application. In this paper, we propose a new human action recognition system using triple Kinect sensors for VR application. Particularly, we design a mark detection method to determine the front of user and fusion skeleton data in real time. Features are extracted from three-dimensional (3D) skeleton data sequences, and divided into five parts according to body parts. A classification model based on the part-aware long short-term memory networks is proposed to recognize human motion. Finally, we demonstrate the system with a virtual reality basketball application and the results of experiment validate the feasibility of the proposed system.

In this paper, we consider a user-centric massive multiple-input multiple-output (UC-MMIMO) system, wherein the optimal antenna selection (AS) is very complicated, because of the huge number of deployed antennas. Traditional AS algorithms rely heavily on full and perfect channel state information (CSI). Thus, we propose a novel AS algorithm to achieve low-complexity and less CSI reliance for UC-MMIMO. The proposed AS algorithm consists of the selection stage and the adjustment stage. In the selection stage, antennas are selected by a reinforcement learning (RL) based algorithm in which input data are the locations of users. In the adjustment stage, an adjustment mechanism is designed to further improve the performance. Numerical results show that our algorithm achieves better performance with lower complexity compared with related traditional algorithms.

This paper considers the downlink ultra-dense cloud radio access network (C-RAN), which employs multiple radio remote head (RRH) cooperation to guarantee the minimum achievable transmission rate for each user equipment (UE). However, due to the limited orthogonal frequency resources, it is difficult to achieve this goal. To maximize the coverage probability of the system, we focus on the joint user-centric clustering and frequency allocation problem. To reduce the computational complexity, this problem is split into two sub-problems: user-centric clustering and frequency allocation. Firstly, we propose a novel binary user-centric clustering strategy, which includes serving clusters and silent clusters. This strategy determines the acceptable combination of serving clusters and silent clusters to guarantee the minimum transmission rate for each UE and simplify the complexity of the subsequent frequency allocation. Then based on the generated clusters, a new graph generation method is proposed. The advantage of this graph is that we can allocate frequency resources by simply judging the relationship between the serving clusters in the graph without complicated calculations. Numerical simulation results show that the joint binary user-centric clustering and location-based frequency allocation scheme is superior to the benchmark solutions in terms of the coverage probability.

With the surge of Internet of Things (IoT) applications using unmanned aerial vehicles (UAVs), there is a huge demand for the mobile broadband service with gigabyte per second data rate in the UAV-aided fifth generation (5G) IoT system. However, Doppler frequency shift (DFS) deteriorates the link performance of UAV-aided 5G system in the highly dynamic and mobile scenarios. Therefore, a data-aided DFS estimation and compensation approach is proposed to optimize the DFS estimation process using historical estimation results, aiming to achieve a fast and accurate DFS compensation. The performance of the proposed DFS estimation algorithm is evaluated by both cost function of accuracy based on frame structure and Cramer-Rao lower bound in terms of the mean-squared error and signal-to-noise ratio. Furthermore, an adaptive frequency-domain DFS compensation algorithm is designed by leveraging DFS estimation results to enhance the quality of communication link for UAV-aided 5G system, achieving an optimal tradeoff between accuracy and complexity. Finally, both link-level simulation platform and hardware testbed are designed and developed to evaluate the performance of our proposed data-aided approach over other conventional algorithms.

Millimeter-wave (mmWave) using conventional phased array (CPA) enables highly directional and fixed angular beamforming (FAB), therefore enhancing physical layer security (PLS) in the angular domain. However, as the eavesdropper is located in the direction pointed by the mainlobe of the information-carrying beam, information leakage is inevitable and FAB cannot guarantee PLS performance. To address this threat, we propose a novel fixed region beamforming (FRB) by employing a frequency diverse subarray (FDSA) architecture to enhance the PLS performance for mmWave communications. In particular, we carefully introduce multiple frequency offset increments (FOIs) across subarrays to achieve a sophisticated beampattern synthesis that ensures a confined information transmission only within the desired angle-range region (DARR) in close vicinity of the target user. More specifically, we formulate the secrecy rate maximization problem with FRB over possible subarray FOIs, and consider two cases of interests, i.e., without/with the location information of eavesdropping, both turn out to be NP-hard. For the unknown eavesdropping location case, we propose a seeker optimization algorithm to minimize the maximum sidelobe peak of the beampattern outside the DARR. As for the known eavesdropping location case, a block coordinate descend linear approximation algorithm is proposed to minimize the sidelobe level in the eavesdropping region. Moreover, we propose an inverted subarray subset technique to further randomize the sidelobes against sensitive eavesdropping. By using the proposed FRB, the mainlobes of all subarrays are constructively superimposed in the DARR while the sidelobes are destructively overlayed outside the DARR. Therefore, FRB exhibits prominent effect on confining information transmission within the DARR. Numerical simulations demonstrate that the proposed FDSA-based FRB can provide superior PLS performance over the CPA-based FAB.

We study an energy-efficient unmanned aerial vehicle (UAV) multicast system, in which ground terminals (GTs) requiring a common information (CI) are grouped and a UAV flies to each group to deliver the CI using minimum energy consumption. A machine learning (ML) empowered joint multicast grouping and UAV trajectory optimization framework is proposed to tackle the challenging joint optimization problem. In this framework, we first propose the compressed-feature regression and clustering machine learning (C2ML) for multicast grouping. A support vector regression (SVR) is trained with the silhouette coefficient, a one- dimensional compressed feature regarding the distribution of GTs, to efficiently determine the number of groups that guides the K-means clustering to approach the optimal multicast grouping. With the C2ML- enabled multicast grouping, we solve the UAV trajectory optimization problem by formulating an equivalent centroid-adjustable traveling salesman problem (CA- TSP). An efficient CA-TSP inspired iterative optimization algorithm is proposed for UAV trajectory planning. The proposed ML-empowered joint optimization framework, which integrates the offline C2ML-enabled multicast grouping and the online CA-TSP inspired UAV- trajectory optimization, is shown to achieve excellent energy-saving performance.

Unmanned aerial vehicle (UAV) millimeter-wave (mmWave) communication is emerging as a promising technique for future networks with flexible network topology and ultra-high data transmission rate. Within such full-dimensionally dynamic mmWave network, beam-tracking is challenging and critical, especially when all the UAVs are in motion for some collaborative tasks that require high-quality communications. In this paper, we propose a fast beam tracking scheme, which is built on an efficient position prediction of multiple moving UAVs. In particular, a Gaussian process based machine learning scheme is proposed to achieve fast and accurate UAV position prediction with quantifiable positional uncertainty. Based on the prediction results, the beam-tracking can be confined within some specific spatial regions centered on the predicted UAV positions. In contrast to the full-space searching based scheme, our proposed position prediction based beam tracking requires little system overhead and thus achieves high net spectrum efficiency. Moreover, we also propose a practical communication protocol embedding our beam-tracking scheme, which monitors the channel evolution and triggers the UAV position prediction for beam-tracking, transmit-receive beam pair selection and data transmission. Simulation results validate the advantages of our scheme over the existing works.

Millimeter wave offers large bandwidth for high data-rate unmanned aerial vehicle (UAV)-to-UAV communications. Because of high mobility and attitude variations, it is challenging to maintain the communication link among the navigating UAVs with narrow beam in the mmWave band. To the best of our knowledge, this is the first paper to establish a transmission-oriented UAV attitude prediction model for the UAV-to-UAV mmWave communication link. In particular, a position-attitude prediction based beam tracking algorithm is proposed. First, a Guassian Process (GP) based learning algorithm is presented for the transmitting UAV to predict the position and attitude of the receiving UAV by using the previous position-attitude data and exploiting the relationship between the position and attitude. Then, the analog beamforming vectors are derived by using the predicted spatial angles. Simulation results demonstrate that the proposed learning algorithm can achieve high accurate position-attitude prediction, and the beam tracking algorithm considering UAV attitude variations significantly outperforms the existing algorithms with only position information.

Fine-grained recognition is challenging due to its subtle local inter-class differences versus large intra-class variations. Finding those subtle traits that fully characterize the object is not straightforward. In this paper, we present a novel random part localization model, which first extracts the foreground object using the saliency map, and then localizes the discriminative parts through a set of potential regions in a random way based on their contribution to classification. We train three convolutional neural networks to capture the features that belong to different levels and average their classification results as our final prediction score. Experiments show that our approach achieves competitive performance compared with state-of-the-art methods on three publicly available fine-grained recognition datasets (CUB200-2011, Stanford Cars and FGVC-Aircraft).

In unmanned aerial vehicle (UAV) millimeter wave (mmWave) communications, the inter-UAV wireless channel is fast varying because the high mobility of the UAV transmission platform. In such dynamic scenarios, it is very costly to obtain the inter-UAV channel state information (CSI) with the conventional pilot-aided channel estimation. Aiming to address this critical issue, in this paper, we propose a novel adaptive-structure extreme learning machine (ASELM) enabled fast channel predication to obtain the CSI in a proactive fashion, which can further support agile beam-based inter-UAV mmWave communication. In particular, ASELM copes with the channel variations by adaptively adjusting the number of neurons in the hidden-layer of ELM. Moreover, a sliding window prediction mechanism (SWPM) predicts subsequent-CSI by efficiently reuses the predicted concurrent-CSI to train the ASELM, which is able to save the pilot overhead for channel sampling (estimation) towards longer-range channel prediction and improve prediction accuracy at affordable costs. Simulation results show that the proposed ASELM enabled fast channel predication can achieve lower normalized mean square error than traditional prediction algorithm in the considered inter-UAV mmWave communication scenarios.

We consider an ultra-dense network (UDN), where serious interference may exist due to the densely deployed base stations (BSs) and user equipment (UE). Noting that the conventional interference management (IM) scheme is not readily applicable, in this paper, we propose a novel interference topology management (ITM) scheme to achieve low-complexity IM in UDNs. The proposed ITM scheme consists of two stages, namely, the optimized BS clustering stage and the decentralized UE-cluster association stage. In the first stage, a novel unsupervised learning-based BS clustering algorithm is proposed, which outperforms the conventional clustering method. Then, a decentralized UE-cluster association algorithm is proposed, which doesn't need to exchange the channel information among clusters, and significantly save system overheads as compared to the centralized solutions. The results show that our ITM scheme can improve the system throughput at lower complexity as compared to other related schemes.

In this paper, we propose a high energy efficiency transmission scheme in multi-beam MIMO satellite systems. Satellite is regarded as a two-way decode-and- forward (DF) relay, where multiple pairs of users exchange information within pair. Zero-forcing transceivers are employed at the satellite. The challenge is that of deriving an accurate yet tractable expression of the system-level energy efficiency (EE) to be used as our objective function. To tackle this challenge, firstly, a closed-form expression of the EE is derived under the assumption of perfect satellite channel. Secondly, based on this analytical expression, we formulate a resource allocation optimization problem for the EE maximization by jointly optimizing satellite power and users power, subject to limited transmit power and minimum quality-of-service (QoS) constraints. Finally, the successive convex approximation technique is invoked to transform the original optimization problem into a concave fractional programming problem, which is then efficiently solved by the existed methods. Simulation results demonstrate the effectiveness of the proposed algorithms.

Resource allocation in mobile edge computing (MEC)- based wireless networks with energy harvesting has attracted great attention. However, existing works assume that mobile devices are stationary while harvesting energy. In this paper, an uplink resource allocation strategy is developed in MEC-based heterogeneous networks. A random mobility model is designed to describe the movement of the user equipment (UE). Meanwhile, the UE can harvest energy from six frequency bands while moving along a certain path. The energy harvesting model of the UE is given by an integral expression. The objective of the resource allocation problem is to maximize the energy efficiency (EE) under the constraints of energy consumption, total data rate requirement, sub-carrier allocation, and transmission power. A quantum-behaved particle swarm optimization (QPSO) algorithm is employed to obtain a sub-optimal solution. Numerical results show that the amount of energy harvested by the UE decreases as the moving speed increases. Moreover, the QPSO algorithm has higher EE than an existing particle swarm optimization algorithm.

This paper investigates the artificial noise (AN)-aided robust secure beamforming design for multiple-input multiple-output (MIMO) two-way relaying (TWR) systems based on physical layer network coding (PNC). In terms of signal-to-interference-and-noise ratio (SINR), we propose two robust beamforming designs to optimize worst-case secrecy sum rate in the presence of an eavesdropper, where the eavesdropper's channel state information (ECSI) is imperfect. In low SINR regime, we give a robust joint beamforming design. Since the optimization problem is non-convex, we use a zero-forcing (ZF) constraint on AN beamforming, and after approximating the objective function, the non-convex problem is formulated into a semidefinite programming (SDP). On the other hand, in high SINR regime, we provide a quality-of-service (QoS)-based robust beamforming design, in which the optimal solution can be efficiently obtained by employing an iterative algorithm based on Taylor expansion and semidefinite relaxation (SDR) techniques. Numerical results show the efficiency of the proposed schemes.

Mobile edge computing (MEC) has the potential to enable computation-intensive applications in 5G networks. MEC can extend the computational capacity at the edge of wireless networks by migrating the computation-intensive tasks to the MEC server. In this paper, we consider a multi-user MEC system, where multiple user equipments (UEs) can perform computation offloading via wireless channels to an MEC server. We formulate the sum cost of delay and energy consumptions for all UEs as our optimization objective. In order to minimize the sum cost of the considered MEC system, we jointly optimize the offloading decision and computational resource allocation. However, it is challenging to obtain an optimal policy in such a dynamic system. Besides immediate reward, Reinforcement Learning (RL) also takes a long-term goal into consideration, which is very important to a time-variant dynamic systems, such as our considered multi-user wireless MEC system. To this end, we propose RL-based optimization framework to tackle the resource allocation in wireless MEC. Specifically, the Q-learning based and Deep Reinforcement Learning (DRL) based schemes are proposed, respectively. Simulation results show that the proposed scheme achieves significant reduction on the sum cost compared to other baselines.

Fog computing extends the concept of cloud computing to the edge of network to relieve performance bottleneck and minimize data analytics latency at the central server of a cloud. It uses edge nodes directly to perform data input and data analysis. In public opinion analysis system, edge nodes that collect opinions from users are responsible for some data filtering jobs including sentiment analysis. Therefore, it is crucial to find suitable algorithm that is lightweight in operation and accurate in predictive performance. In this paper, we focus on Chinese sentiment analysis job in fog computing environment and propose a non‐task‐specific method called Channel Transformation Based Convolutional Neural Network (CTBCNN) for Chinese sentiment classification, which uses a new structure called channel transformation based (CTB) convolutional layer to enhance the ability of automatic feature extraction and applies global average pooling layer to prevent overfitting. Through experiments and analysis, we show that our method do achieve competitive accuracy and it is convenient to apply this method to different cases in operation.

Non-orthogonal multiple access (NOMA) has been investigated recently as a candidate radio access technology for the fifth generation (5G) networks due to its high spectrum efficiency (SE). As green radio which focuses on energy efficiency (EE) becomes an inevitable trend, energy efficient design is becoming more and more important. In this paper, we focus on energy efficient resource allocation problem in multi-user downlink NOMA system with the aim to optimize subchannel assignment and power allocation to maximize the system EE. We propose a novel low-complexity suboptimal subchannel assignment algorithm and obtain the optimal power allocation coefficients among subchannel multiplexed users. To further improve the system EE, unequal power allocation across subchannels (UPAAS) scheme including an optimal solution and a suboptimal Dinkelbach-like algorithm is studied. Simulation results show the effectiveness of our proposed resource allocation algorithms.

In this paper, we investigate the cache-enabled small cell networks to provide on-demand video services with differential perceptual qualities, i.e., standard definition video (SDV) and high definition video (HDV). As the extension technology of advanced video coding/H.264, scalable video coding is adopted in the considered networks and videos to be transmitted are divided into a base layer (BL) and N enhancement layers (ELs). In our proposed caching protocol, the n-th small cell base station (SBS) caches BLs and the n-th EL of the most popular videos. Depending on the distances between the typical user and SBSs in the observed cluster, the closest SBS is regarded as the serving node (SN) and the others are cooperative nodes (CNs). When SDV is required, the SN will transmit BL of the required video file to the typical user, while SN and CNs can cooperatively transmit BL and ELs to provide superior video quality if HDV is required. Based on the proposed caching and transmission protocol, we derive the expressions of the key performance indicators, i.e., local serving probability, ergodic service rate and service delay. Numerical results validate the theoretical analysis and show the superiority of our proposed scheme compared to the benchmarks.

In this paper we consider a two-tier heterogeneous network (HetNet) where pico base stations (BSs) can harvest energy from macro BSs. Three cases of special interests are investigated, i.e., pico BSs are deployed 1) without battery and power grid, 2) with power grid, and 3) with battery. In particular, a practical dual-slope path loss model is employed to facilitate the performance analysis. By means of stochastic geometry and Gamma second order moment matching, a compact expression of the distribution of harvested energy is derived. Then the network's energy efficiency (EE) is introduced and optimized with carefully designed system parameters. Finally, an important conclusion is obtained as follows: only when the intensity of pico BSs is high can HetNets with energy harvesting improve the network's EE compared with the conventional HetNets without energy harvesting.

A noncoherent multiuser transmission scheme is proposed for massive multiple-input multiple-output (M-MIMO) systems without explicit channel estimation. In particular, each user uses differential PSK modulation and the receiver employs differential detection. First, we propose a simple user selection scheme to optimize the distributions of power space profile (PSP) of individual users which alleviates the overlap of PSPs. Then, each output stream of the weighing filter is fed into a noncoherent successive interference cancellation (N-SIC) processor, and finally into a soft-input soft-output (SISO) multiple-symbol differential detector(MSDD). Employing the autocorrelation receiver (AcR) and the belief propagation(BP) message passing algorithm, the proposed SISO-MSDD framework can be easily integrated with advanced channel coding. The proposed scheme bears the potential to solve the high channel estimation overhead for conventional coherent M-MIMO systems. Simulation results show that the BER performance can be significantly improved within a few iterations of the proposed scheme.

In this paper, the carrier aggregation (CA) is applied to Heterogeneous Networks (HetNets) consisting of a macro base station (MBS) and low-power pico base stations (PBSs). The PBSs are distributed in the outer space of the disk centered at the MBS and the closed-form solution of the radius of the disk is analyzed. In the CA based HetNet, firstly, a practical user association scheme is proposed to classify the users into macrocell user (MUs) and pico-cell users (PUs). Secondly, a PU-centric cooperative transmission strategy is proposed to increase the data rate of PUs. Then, in order to eliminate the cross-tier interference in HetNet, the MUs and PUs schedule different component carriers (CCs) according to different algorithms. For the MUs, an improved simulated annealing (SA) scheduling algorithm is proposed to maximize the sum rate of the MUs. Compared with the existing SA, the inner loop of the proposed SA is redesigned. Consequently, the best value can be obtained more quickly. For PUs, a location information based low-complexity maximize minimum distance (MMD) algorithm is proposed to reduce the interference from PBSs to PUs as much as possible. Overall, an integrated scheme is proposed to improve the performance. At last, numerical results demonstrate the performance of the proposed schemes.

This paper investigates multicast beamforming for scalable videos in cache-enabled heterogeneous networks, where a macro base station (MBS) and multiple small base stations (SBSs) serve multicast group users on-demand video services. Inspired by the main idea of the scalable video coding, the extension technology of the H.264/advanced video coding, each video is divided into a base layer (BL) and an enhancement layer (EL). The BL can provide the fundamental viewing quality and adding EL to the received BL can guarantee superior perceptual experience. The MBS and multiple SBSs cache the BLs and ELs of the most popular videos, respectively. Employing the transmission schemes in coordinated multi-point, i.e., parallel transmission and joint transmission, the MBS and SBSs parallelly transmit BLs and ELs while the SBSs cooperatively transmit the ELs if higher video qualities are required. To improve the quality of the received videos for users whose requirements can be satisfied locally, our aim is to maximize the weighted sum rate of them and this problem can be converted into an iterative second-order cone programming problem by successive convex optimization with low complexity. Numerical results demonstrate the advantage of our proposed scheme compared to the benchmark scheme, i.e., only MBS serving the end users, even when the transmit power of each SBS is kept low.

This paper proposes a belief propagation (BP) message passing algorithm-based joint multiple symbol differential detection (MSDD) and channel decoding scheme for noncoherent differential ultra-wideband impulse radio (UWB-IR) systems. MSDD is an effective means to improving the performance of noncoherent differential UWB-IR systems. To optimize the overall detection and decoding performance, this paper proposes a novel soft-in soft-out (SISO) MSDD scheme for noncoherent differential UWB-IR. We first propose a new sampling mechanism for the noncoherent auto-correlation receiver to sample the received UWB-IR signal. The proposed sampling mechanism can exploit the dependences (imposed by the differential modulation) among data symbols throughout the whole packet. The signal probabilistic model has a hidden Markov chain structure. We use a factor graph to represent this hidden Markov chain. Then, we apply BP message passing algorithm on the factor graph to develop an SISO MSDD scheme, which is easy to integrate with SISO channel decoding to form a joint MSDD and channel decoding scheme. Performance results of bit error rate simulations and EXIT chart analyses indicate the performance advantages of our scheme over the previous MSDD scheme.

In this paper, a generalized low-complexity beamspace approach is proposed for two-dimensional localization of incoherently distributed sources with a uniform cylindrical array (UCyA) in large scale/massive multiple-input multiple-output (MIMO) systems. The received signal vectors in the antenna-element space are transformed into the beamspace by employing beamforming vectors. As a beneficial result, the total dimensions of the received signal vectors are significantly reduced. In addition, it is shown that the error introduced by the transformation decreases as the number of UCyA antennas increases. The UCyA is composed of multiple uniform circular arrays (UCAs), and the beamspace array response matrices of adjacent UCAs are linearly related. Then, the linear relation is exploited to estimate the nominal elevation direction-of-arrivals (DOAs) directly and the nominal azimuth DOAs based on a low-complexity search algorithm. In contrast, the linear relation in the traditional approach is based on approximations and the associated search algorithm is more complicated. Numerical results demonstrate that the proposed approach outperforms the existing approach in terms of both performance and complexity in the context of massive MIMO systems.

The relay-aided multicast network is considered, where an NT -antenna source multicasts confidential messages to N single-antenna legitimate users via a self-sustainable M-antenna regenerative relay. In particular, the relay is powered by the energy harvested from the radio signal of the source, and there are K unauthorized eavesdroppers wiretapping the channel. Assuming the knowledge of statistical channel state information of eavesdroppers, we aim to minimize the source transmission power via energy-efficient beamforming, subject to the signal-to-noise ratios of legitimate users/relay, the power constraint at the relay, and the outage constraints of the eavesdroppers. An efficient algorithm is developed by using the iterative first-order Taylor expansion and successive convex approximation, where the original nonconvex problem is transformed and solved.

We consider an amplify-and-forward relay network composed of a source, two relays, and a destination. In this network, the two relays are untrusted in the sense that they may perform Byzantine attacks by forwarding altered symbols to the destination. Note that every symbol received by the destination may be altered, and hence, no clean reference observation is available to the destination. For this network, we identify a large family of Byzantine attacks that can be detected in the physical layer. We further investigate how the channel conditions impact the detection against this family of attacks. In particular, we prove that all Byzantine attacks in this family can be detected with asymptotically small miss detection and false alarm probabilities by using a sufficiently large number of channel observations if and only if the network satisfies a non-manipulability condition. No pre-shared secret or secret transmission is needed for the detection of these attacks, demonstrating the value of this physical-layer security technique for counteracting Byzantine attacks.

Soft noncoherent detection, which relies on calculating the a posteriori probabilities (APPs) of the bits transmitted with no channel estimation, is imperative for achieving excellent detection performance in high-dimensional wireless communications. In this paper, a high-performance belief propagation (BP)-based soft multiple-symbol differential detection (MSDD) framework, dubbed BP-MSDD, is proposed with its illustrative application in differential space-time block-code(DSTBC)-aided ultra-wideband impulse radio (UWB-IR) systems. First, we revisit the signal sampling with the aid of a trellis structure and decompose the trellis into multiple subtrellises. Furthermore, we derive an APP calculation algorithm, in which the forward-and-backward message passing mechanism of BP operates on the subtrellises. The proposed BP-MSDD is capable of significantly outperforming the conventional hard-decision MSDDs. However, the computational complexity of the BP-MSDD increases exponentially with the number of MSDD trellis states. To circumvent this excessive complexity for practical implementations, we reformulate the BP-MSDD, and additionally propose a Viterbi algorithm-based hard-decision MSDD (VA-HMSDD) and a VA-based soft-decision MSDD (VA-SMSDD). Moreover, both the proposed BP-MSDD and VA-SMSDD can be exploited in conjunction with soft channel decoding to obtain powerful iterative detection and decoding-based receivers. Simulation results demonstrate the effectiveness of the proposed algorithms in DSTBC-aided UWB-IR systems.

In this paper, we consider low-complexity user scheduling schemes for the multi-pair two-way relay channel, where L pairs of single antenna users are selected from K pairs to perform pair-wise information exchange via an Nr-antenna amplify-and-forward (AF) relay (Nr≥ 2L - 1) with analogue network coding (ANC). We first propose a simple channel norm (CN) based scheme, which enables low-complexity implementation. Then, we propose a near-optimal user scheduling by jointly considering the pair-wise channels norms and orthogonality among all the users (CNO-A), which requires global channel state information (CSI) and centralized computation at the relay. It is noted that CNO-A scheme can achieve comparable performance as the optimal scheduling with reduced computational complexity. Finally, we propose a two-step selective CNO scheme (CNO-S), which strikes a flexible balance between complexity/CSI overhead and performance. CNO-S significantly reduces CSI overhead and computational complexity at the relay, while achieving comparable performance as the CNO-A scheme. Numerical results and complexity analysis not only show that the proposed schemes are feasible and effective, but also demonstrate their advantages over the existing schemes.

This paper proposes a linear precoding that aims at maximizing the sum-rate in a multiuser multiple-input multiple-output (MU-MIMO) simultaneous wireless information and power transfer (SWIPT) system. In this scenario some receivers harvest energy while the others decode information at the same time. The sum-rate for information decoding users is considered as the optimization policy where the transmit power constraint and power harvesting per user constraints are taken into account. Since the objective function of the sum-rate maximization problem is non-convex, it is difficult to find the optimal solution. Thus, we propose an iterative algorithm to find a locally optimal design based on a sequential convex approximation (SCA) method. In this way, the non-convex optimization problem is approximated by a convex program at each iteration. Finally, with the aid of the concept of the Rate-Energy (R-E) region simulation results show that the proposed scheme achieves significant improvement compared to existing works.

This paper considers to mitigate the uplink cross- tier interference (CI) in heterogeneous networks (HetNets) by the idea of opportunistic transmission. Firstly, we introduce the conventional opportunistic interference alignment (OIA) into HetNets and it indeed reduces the CI from the macrocell users (MUs) effectively. Interestingly, based on the unique characteristic in HetNets, the performance of OIA can be further improved. Therefore, the novel opportunistic interference mitigation (OIM) schemes based on adaptive reference signal spaces (A-RSSs) are proposed. In the novel OIM, an A-RSS is defined for each FBS and the A-RSS of a FBS constantly updates as the channel state information (CSI) of its FUs changes. According to the A-RSSs of all FBSs, each MU calculates its scheduling metric and the macrocell base station (MBS) selects MUs by the metrics. However, broadcasting the A-RSS constantly makes the system more complicated, so a complexity- performance tradeoff is considered. To decrease the complexity of the system, the quantization codebook is introduced and the quantized A-RSS based OIM is proposed. At last, extensive simulations are conducted. It is shown that, in terms of femtocells, the performance of novel OIM is better than that of the codebook based OIM (CB-OIM) and the performance of CB-OIM is better than that of conventional OIA.

In this paper, we study the secrecy communication in two-user MISO interference networks where an external eavesdropper is interested in the messages transmitted by both transmitters. We propose a beamforming design to maximize the achievable secrecy sum rate of the transmitters subject to the individual power constraint at each transmitter. To transform this complex non-convex problem into a convex one, we propose an iterative algorithm based on the constrained concave convex procedure (CCCP) and successive convex approximation (SCA). It is observed that the proposed algorithm converges fast to a stationary point within a few iterations. Furthermore, we also propose a low-complexity null-space beamforming design scheme in which the beamforming vectors have closed-form solutions. Simulation results show the effectiveness of the two proposed schemes in improving the secrecy sum rate performance.

Nonparametric belief propagation (NBP) algorithm can result in approximately optimal performance for probabilistic localization in wireless sensor networks without loops theoretically. However, in loopy networks the accuracy of NBP is doubtful and the computational complexity is high. In this paper, a novel approach running NBP on a minimum spanning tree (MST) is proposed, which mitigates the influence of loops and significantly reduces the computational cost as compared with the conventional NBP schemes. In addition, different from other spanning trees, the MST can confine more NBP particles into the bounding circle. Therefore, it shows better resistance to measurement errors. Numerical results show that the proposed method achieves better performance in terms of accuracy in highly connected networks, and the computational cost is much lower than the conventional NBP methods.

In this paper, we pioneer the study of physical-layer security in heterogeneous networks (HetNets). We investigate secure communications in a two-tier downlink HetNet, which comprises one macrocell and several femtocells. Each cell has multiple users and an eavesdropper attempts to wiretap the intended macrocell user. First, we consider an orthogonal spectrum allocation strategy to eliminate co-channel interference, and propose the secrecy transmit beamforming only operating in the macrocell (STB-OM) as a partial solution for secure communication in HetNet. Next, we consider a secrecy-oriented non-orthogonal spectrum allocation strategy and propose two cooperative STBs which rely on the collaboration amongst the macrocell base station (MBS) and the adjacent femtocell base stations (FBSs). Our first cooperative STB is the STB sequentially operating in the macrocell and femtocells (STB-SMF), where the cooperative FBSs individually design their STB matrices and then feed their performance metrics to the MBS for guiding the STB in the macrocell. Aiming to improve the performance of STB-SMF, we further propose the STB jointly designed in the macrocell and femtocells (STB-JMF), where all cooperative FBSs feed channel state information to the MBS for designing the joint STB. Unlike conventional STBs conceived for broadcasting or interference channels, the three proposed STB schemes all entail relatively sophisticated optimizations due to QoS constraints of the legitimate users. To efficiently use these STB schemes, the original optimization problems are reformulated and convex optimization techniques, such as second-order cone programming and semidefinite programming, are invoked to obtain the optimal solutions. Numerical results demonstrate that the proposed STB schemes are highly effective in improving the secrecy rate performance of HetNet.

In this letter, we jointly design relay antenna tilting with user scheduling so as to enhance the sum rate performance of a two-hop relay system, where the relay is equipped with a large-scale antenna array and performs zero-forcing processing. Building the fundamental of the joint design, a tight and tractable sum rate approximation is first derived by employing random matrix theory. Then the relay antenna downtilt and the number of active user pairs are jointly optimized to maximize the approximate sum rate. It is noted that the proposed scheme is independent of instantaneous channel state information. Therefore, it enjoys very low implementation complexity while improving the system performance.

In this paper, distributed user scheduling schemes are proposed for the multi-user MIMO-Y channel, where three NT-antenna users (NT= 2N, 3N) are selected from three clusters to exchange information via an NR-antenna amplify-and-forward (AF) relay (NR= 3N), and N ≥ 1 represents the number of data stream(s) of each unicast transmission within the MIMO-Y channel. The proposed schemes effectively harvest multi-user diversity (MuD) without the need of global channel state information (CSI) or centralized computations. In particular, a novel reference signal space (RSS) is proposed to enable the distributed scheduling for both cluster-wise (CS) and group-wise (GS) patterns. The minimum user-antenna (Min-UA) transmission with NT= 2N is first considered. Next, we consider an equal number of relay and user antenna (ER-UA) transmission with NT= 3N, with the aim of reducing CSI overhead as compared to Min-UA. For ER-UA transmission, the achievable MuD orders of the proposed distributed scheduling schemes are analytically derived, which proves the superiority and optimality of the proposed RSS-based distributed scheduling. These results reveal some fundamental behaviors of MuD and the performance-complexity tradeoff of user scheduling schemes in the MIMO-Y channel.

In this paper, bootstrap percolation is introduced to control the information propagation for efficient cooperative positioning in wireless networks. Particularly, we obtain a novel linear least square (LLS) estimator for the localization of agent nodes. Exploiting the idea of bootstrap percolation, agent nodes sequentially get activated and estimate their positions with an adaptive location updating rule. The rule is designed to first localize the more reliable agent nodes with at least three connections to the active nodes, and then gradually relax such connection constraints in each iteration so as to localize the agent nodes with fewer connections. Due to the activation characteristic, error propogation can be mitigated and energy is well managed. In addition, taking the uncertainty of the positional information into account, positioning errors can be further reduced. Simulations show that the proposed schemes improve the localization accuracy and use fewer links than traditional methods.

We investigate the secrecy beamforming in two-way relay channels (TWRC) with physical layer network coding (PNC). The multi-antenna relay broadcasts the superimposed signal of two user messages with secrecy beamforming after receiving the signals transmitted by the two legitimate users. We first propose a lower bound of the secrecy sum rate to quantify the secrecy performance of the TWRC with PNC. Because the maximization of the lower bound is non-convex under total power constraint, we propose a joint beamforming and power allocation scheme, in which the problem is successively approximated by several convex semidefinite programs. In order to reduce the complexity, we further propose an suboptimal scheme with closed-form solution. Numerical results indicate that the proposed schemes with PNC achieve much better secrecy sum-rate performance than the traditional AF schemes.

A simple distributed user scheduling (DUS) scheme is proposed for the MIMO multi-way relay channel (MWRC), where K (K ≥ 2) M-antenna (M ≥ 2) users are selected from K clusters of users to conduct all-cast information exchange via an M-antenna amplify-and-forward (AF) relay. In particular, the proposed DUS is based on the individual performance metric calculated by each user with its local channel state information (CSI). Therefore, DUS bypasses global CSI and complicated computations at the scheduling center, both are often inevitable with traditional centralized user scheduling (CUS). Furthermore, the outage performance of DUS is analyzed and the achievable multi-user diversity (MuD) order is derived. Numerical results validate the theoretical derivations and show that DUS achieves comparable performances to CUS in the considered scenarios.

In this paper, a novel scheme called blind interference neutralization is provided in the 3- cell interference channel with a shared instantaneous relay. The shared relay not only receives signals from sources, but also sends signals to destinations with a processing matrix. With the proposed scheme, each destination is able to pick up its own desired signal without encountering inter-user interference (IUI). In particular, the sources are blind in the sense that no channel state information (CSI) is required for transmit beamforming. Numerical simulation shows that the proposed scheme, compared with others, can increase the sum rate performance.

Precise ultra-wideband (UWB) ranging requires accurate estimation of time of arrival (TOA). In this paper,a novel TOA estimation approach using checking window is proposed for energy detection (ED) receivers in dense multipath channels. Unlike the traditional methods that treat the multipath components (MPCs) as interference sources, the proposed scheme exploits the MPCs to consolidate the detection of the first path (FP). By detecting dense threshold-crossing (TC) events over successive energy samples with the checking window, the MPCs are collected, and the false TC events that sparsely distributed in the noise region can be recognized and neglected for the FP detection. As a result, the early false detection is reduced and the leading edge detection is improved. Simulations demonstrate the effectiveness and robustness of the proposed scheme in IEEE 802.15.4a channels.

In this paper, a more accurate outage performance analysis is obtained by employing order statistics for multiple-input multiple-out two-way relay system with joint transmit/receive zero-forcing. Furthermore, closed-form upper and lower bounds are first derived for the overall outage probability when there exist spatial correlations at the relay. Analysis and simulation results indicate that the upper bound derived with order statistics is tight under various spatial correlations at the relay, and it is also tighter than that derived by eigenvalues of Wishart matrices over independent identically distributed Rayleigh fading channel. For example, when the relay is equipped with 4, 6 and 8 antennas, the upper bounds derived with order statistics are 2dB, 3dB and 4dB tighter than those with eigenvalues, respectively. In particular, when the numbers of antennas equipped at the users are greater than that equipped at the relay, the derived upper bound is nearly identical to the exact results.

We propose a low-complexity user selection scheme with zero-forcing precoding in multiuser MIMO downlink systems, where the base station (BS) is equipped with large-scale antenna arrays and the number of candidate-users is relatively small. The BS obtains the channel state information (CSI) of the user equipments (UEs) through the pilot-based minimum mean-square error channel estimation. Taking both the channel propagation and the UE location distribution into consideration, we first derive a deterministic approximation of the ergodic sum rate and investigate the optimal number of active UEs, K*, in the sense of sum rate maximization. Then, K* UEs are selected for simultaneous data transmission according to their large-scale channel fading. Small-scale channel fading is not taken into account in the selection procedure, thus reducing the computational complexity dramatically as well as improving the robustness of the proposed scheme in practice. Numerical simulations suggest that whether perfect CSI is available at the BS, our proposed scheme achieves high sum rate performance with very low complexity.

In this paper, two near-optimal detectors based on the convex optimization algorithm are proposed for multiuser detection (MUD) in the ultra-wide bandwidth (UWB) systems. The first detector performs semidefinite relaxation (SDR) to approximate the optimum multiuser detection (OMD) which is a nondeterministic polynomial time hard (NP-hard) problem. When the cutting planes generation algorithm is employed to strengthen the relaxation of the SDR, a tight MUD detector for combinatorial optimization is obtained by adding triangle inequalities to the well-known SDR in strict feasible region. Simulations demonstrate that the semidefinite programming (SDP) approaches can provide bit error rate (BER) performance close to the OMD efficiently using the interior point method, and the tight detector provides a better BER performance than the previous detector with a slightly higher complexity.

This paper reveals symbol extension is capable of improving secrecy performance in the multiple-input single-output (MISO) wiretap channel. We propose a symbol extension scheme jointly with the existing beamforming and artificial noise generation strategy to exploit the time variation of fading channel. After multiplying the data symbol vector by a proper designed square matrix, the data symbol can be extended to multiple time-slots. As a result, without any symbol rate loss, the proposed scheme enhances the secrecy performance in terms of secrecy outage probability. Furthermore, we also analyze the asymptotic secrecy outage probability and derive the achievable diversity order. Both analytical and numerical results show that the proposed scheme can bring more diversity gains into secrecy communication.

The key of time of arrival (TOA) estimation in ultra wideband (UWB) ranging is to detect the first path (FP). In this paper, we propose an optimized FP detector with the adaptive threshold in the absence of prior channel state information (CSI). In particular, the error information (EI) set is introduced to guide the threshold adjustment and determine the TOA estimate with a novel iterative algorithm. The EI set captures the characteristics of major errors regarding the inappropriate threshold. After the iterative process, the proposed scheme is shown to achieve the asymptotic optimal threshold without large number of repeated pulses. Therefore, the proposed scheme efficiently improves the TOA estimation accuracy as compare to the traditional schemes. Simulation results validate the effectiveness and superiority of the proposed scheme.

In this paper, we consider a K-cell multi-user interference network, where S single-antenna users are selected within each cell to carry out the uplink transmission with their M-antenna home base station, where 2 ≤ S ≤ M <; KS. For the considered scenario, a novel intra-cell performance aware opportunistic interference alignment (OIA) scheme is proposed to mitigate the inter-cell interference while reducing the intra-cell power loss caused by zero-forcing receiving. Unlike the traditional OIA schemes, the proposed scheme reuses the reference signal subspace (RSS) to balance not only the inter-cell interference but also the desired signal power and the intra-cell power leakage. It is shown that the refined selection further improves the achievable sum rate as compared to the existing schemes, and this observation is theoretically analyzed. Finally, numerical results validate that our scheme outperforms the existing uplink OIA schemes.

In this paper, semidefinite relaxation (SDR) technology is exploited for the multiple-symbol detection (MSD) over the multiple-input multiple-output (MIMO) ultra-wideband (UWB) systems. The existing scheme generalized likelihood ratio test (GLRT) MSD jointly detect multiple symbols, however, it entails a complexity of O(2M), where M is the observation window size. To this end, SDR is employed to reformulate the GLRT detection into a semidefinite programming (SDP) model, and two detectors, randomization-SDR (RSDR) and eigenvector-SDR (ESDR) are proposed on the order of O(M3.5) and O(M3), respectively. Complexity analysis validates that the SDR-MSD strategy is desirable owing to its reduced complexity, compared with the exponential-complexity sphere decoding (SD) MSD. Furthermore, Monte-Carlo simulations demonstrate that the proposed SDR detectors provide the bit error rate (BER) performance almost the same with that of the SD method, and the RSDR outperforms the ESDR at the price of slightly higher complexity.

In this paper, a MIMO-Y channel based three-group information exchange problem is considered, where users from each group would like to exchange information with the other users in another two groups. In particular, a Max-Min user selection is proposed, which aims to harvest the multiuser diversity gain for reliable transmission in the MIMO-Y channel. The impacts of user configurations on the multiuser diversity gains are investigated by theoretical bounds as well as numerical results. It is shown that, by adding a few users in only one or two groups, the overall system performance can be improved significantly. This observation reveals an interesting behavior of MIMO-Y channel, i.e., the local configuration has a global impact. However, we prove that such asymmetrical multiuser diversity gain does not scale with the number of users. Finally, we show that multiuser diversity gain scales with the number of users if we add an equal number of users in all three groups.

Large-scale multi-cell multiuser multiple-input multiple-output (LS-MIMO) systems can greatly increase the spectral efficiency. But the performance of these systems is deteriorated by pilot contamination. In this paper, first, a pilot design criterion is proposed by exploiting the orthogonality of channel vectors of LS-MIMO systems. Second, following this criterion, Chu sequences based pilots are designed. Because of the proposed pilots, the channel estimate of most terminals of a cell is only interfered by the partial cells rather than all the other cells, where the latter is caused by traditional pilots. As a result, pilot contamination is mitigated. Numerical results verify the effectiveness of the proposed pilots.

In this paper, we study the amplify-and-forward (AF) relaying based signaling scheme for the Y channel consisting of three single-antenna users and a two-antenna relay. In such a particular scenario, traditional signal space alignment for network coding (SSA-NC) scheme is not feasible. Moreover, the time division based multi-user multiple-input multiple-output (MU-MIMO) scheme has to rely on time division mode, i.e., more than two time slots are required to complete the whole communication process, which results in throughput loss. We develop an asymmetric signal space alignment (ASSA) scheme to enable all the users to finish information exchange with each other via the relay within two time slots. Brief degrees of freedom (DOF) analysis and numerical simulations have been provided to demonstrate that the proposed signaling technique significantly outperforms the conventional time division based MU-MIMO scheme.

In this paper, we propose two kinds of interference alignment (IA) schemes with limited feedback for the two-cell interfering multi-user multiple-input multiple-output multiple access channel (MIMO-MAC). Since IA with limited feedback results in residual interference for the quantization error, more effective schemes are introduced to reduce the residual interference in this paper compared with the ever work. The first kind of schemes means that the precoding matrices at the transmitters are the quantization value after obtaining the IA close-form solution of the precoding and decoding matrices at the receivers. This kind of schemes has been generalized to K users in this paper, and decoding matrices design is considered to reduce the quantization error to improve the performance. For the second kind of schemes, the beamforming vectors are chosen in the codebooks directly which guarantee the inter-cell interference (ICI) are most aligned. Monte-Carlo simulations illustrate that the proposed schemes outperform the existing schemes.

In this paper, we propose two new opportunistic interference alignment (IA) schemes to mitigate interference in the K-cell uplink interference channel. Unlike the existing schemes that basically rely on the channel randomness to achieve asymptotical IA for all cells, the proposed schemes employ an active alignment approach to increase the possibility for perfect partial IA for one cell within the network. Specifically, each user adopts the active alignment transmit beamforming such that the user-generated interference is perfectly aligned along the preferred reference interference direction at one of the base-stations (BS) in other cells. With this approach, our schemes increase the chance to obtain degrees-of-freedom gain even with a small number of users. In addition, several new user selection schemes are proposed, and the BS receiver is optimized to enhance performance. Extensive simulations are conducted, and the results show that the proposed schemes outperform the state-of-the-art under the considered scenarios.

This paper considers physical-layer network coding (PNC) with M-ary phase-shift keying (MPSK) modulation in two-way relay channel (TWRC). A low complexity detection technique, termed symbol-based PNC (SPNC), is proposed for the relay. In particular, attributing to the outer product operation imposed on the superposed MPSK signals at the relay, SPNC obtains the network-coded symbol (NCS) straightforwardly without having to detect individual symbols separately. Unlike the optimal multi-user detector (MUD) which searches over the combinations of all users' modulation constellations, SPNC searches over only one modulation constellation, thus simplifies the NCS detection. Despite the reduced complexity, SPNC achieves full diversity in multi-antenna relay as the optimal MUD does. Specifically, antenna selection based SPNC (AS-SPNC) scheme and signal combining based SPNC (SC-SPNC) scheme are proposed. Our analysis of these two schemes not only confirms their full diversity performance, but also implies when SPNC is applied in multi-antenna relay, TWRC can be viewed as an effective single-input multiple-output (SIMO) system, in which AS-PNC and SC-PNC are equivalent to the general AS scheme and the maximal-ratio combining (MRC) scheme. Moreover, an asymptotic analysis of symbol error rate (SER) is provided for SC-PNC considering the case that the number of relay antennas is sufficiently large.

The combination of physical-layer network coding (PNC) and multiple-input multiple-output (MIMO) is expected to improve the throughput of two-way relay network. In this paper, we propose a zero-forcing based MIMO two-way relay scheme in conjunction with a simple Max-Min relay antenna selection. This scheme solves the unpractical constraint encountered by many existing MIMO two-way relay schemes for application, which requires the relay to equip fewer antennas than the end node. Our scheme, on the other hand, benefits from the dedicated relay that has more antennas than the end node. A notable diversity advantage is obtained from judicious relay antenna selection. The reliability of the simple ZF based MIMO two-way relay is therefore improved. Of particular note, this paper extends our previous study to 1) support the more general application with non-binary PNC and 2) give a complete analysis on the attained end-to-end diversity with explicit theoretical result under i.i.d. Rayleigh fading channel.

In this paper, we study the cognitive interference management (CIM) scheme in the coexistence networks of macro-cells and femtocells. A single-channel detection based spectrum allocation algorithm is proposed to enhance the performance of femtocells considering the co-tier interference and co-tier interference. The proposed scheme converts the complex interference environment into several specific channel categories and successfully recognizes these channel patterns, while the group-channel resource allocation algorithm does not distinguish the interference channels in detail and operates on the continuous group of channels. Therefore, femtocell base stations (FBSs) can recognize the spectral environment more completely and allocate more available channels. As a result, our scheme is able to significantly improve the femtocell spectral efficiency and the signal-to-interference-and-noise ratio (SINR) performance of femtocell users (FUEs), meanwhile, it avoids the strong interference on the existing macrocell. Numerical simulations verify the conclusions.

In this paper, we propose a new interference alignment scheme with limited feedback for the two-cell interfering multi-user multiple-input multiple-output multiple access channel (MIMO-MAC), which provides better performance compared with other schemes when the number of feedback bits is same. Then, we analyse the rate loss for the quantization error, and show that the rate loss is only impacted by the residual inter-cell interference. By characterizing the rate loss as a function of the number of feedback bits, a bits allocation algorithm is introduced to further improve the system throughput. Monte-Carlo simulations illustrate that our proposed scheme outperforms the existing schemes.

In this paper, we consider a multi-way relaying channel where 2K users are divided into two groups averagely and each of them exchanges messages with every user of the other group via an intermediate relay. We term it multi-user multi-way relaying X network. We design the beamforming vectors at the users and the relay to achieve an interference alignment (IA) solution. Meanwhile, we investigate the feasibility conditions on the required amount of antennas for each node. Brief theoretical analysis and numerical simulations have been provided to demonstrate that the degrees of freedom (DOF) of 2K2is obtained.

In this paper, we consider the resource allocation problem in the uplink transmission of an orthogonal frequency-division multiple access (OFDMA) based cognitive radio (CR) network. The resource allocation aims to maximize the uplink throughput of secondary users (SUs) in CR network under the constraints of the primary user (PU) interference and the transmit power limits of SUs. In general, the optimal joint power and subchannel allocation is known as NP-hard. To ease the computation complexity while maintain good performance, we propose a novel particle swarm optimization (PSO) based joint uplink power and subchannel allocation algorithm to solve this resource allocation problem. Due to the combinatorial nature of the resource allocation problem, our algorithm in which power continuously changes while the subchannel allocation strategy alters in the iteration process can obtain better performance than the existing decomposition based algorithm that subchannel assignment and power allocation are implemented separately. Simulation results show the effectiveness of the proposed algorithm.

Combining of physical-layer network coding (PNC) and multiple-input multiple-output (MIMO) can significantly improve the performance of the wireless two-way relay network (TWRN). This paper proposes novel Max-Min optimization based relay antenna selection (RAS) schemes for zero-forcing (ZF) based MIMO-PNC transmission. RAS relaxes ZF's constraints on the number of antennas and extends the applications of ZF based MIMO-PNC to more practical scenarios, where the dedicated relay has more antennas than the end node. Moreover, RAS also brings diversity advantages to TWRN and the achievable diversity gains of the proposed schemes are theoretically analyzed. In particular, an equivalence relation is carefully built for the diversity gains obtained by 1) RAS for ZF based MIMO-PNC and 2) transmit antenna selection (TAS) for MIMO broadcasting (BC) with ZF receivers. This equivalence transforms the original problem to a more tractable form which eventually allows explicit analytical results. It is interesting to see that Max-Min RAS keeps the network diversity gain of ZF based MIMO-PNC to be the same as the diversity gain of the point-to-point link within the TWRN. This insight extends the understanding on the behaviors of ZF transceivers with antenna selection (AS) to relatively complicated MIMO-TWRN/BC scenarios.

In this paper, the application of MPSK modulation to physical-layer network coding (PNC) with multiple antennas scheme is investigated. A linear complexity scheme with the capacity of achieving diversity is proposed assuming symbol- level synchronization only. In the proposed scheme, called symbol-based PNC, the constant amplitude of MPSK modulation symbols is exploited and the product of two end transmitted symbols, which is actually related to another modulation symbol, is considered as network code. The autocorrelation of received signal is calculated in the relay node equipped with multiple antennas, which contributes to compact the search space for the intended network code. Two detectors are designed for achieving diversity at the cost of linear complexity. We also investigate the power allocation for symbol-based PNC. The advantages of symbol- based PNC can be summarized as two points. First, it requires symbol-level time synchronization only rather than stringent carrier-phase synchronization. Second, it is linear in complexity with respect to the constellation size. Simulations show that symbol- based PNC can achieve diversity gain.

A physical-layer network coding (PNC) aided two-way relay scheme is proposed for Transmitted-Reference (TR) UWB networks. In particular, a novel noncoherent UWB-PNC detector is investigated for the TR UWB networks. Inheriting the simplicity of the TR-UWB receiver, the proposed PNC detector is based on the autocorrelation receiver (AcR) with simple structure, which effectively suppresses the multi-user interference and harvests the multipath energy. Equipped with the proposed TR UWB-PNC detector, the relay node first detects the bitwise XORed symbol directly from the overlapped information bearing waveforms transmitted from the source nodes, then broadcasts the estimate of the XORed symbol to achieve efficient two-way relay. Simulation results show that, compared with the non-relay, one-way relay and two-way relay with time division multiple access (TDMA) and network coded broadcasting (NCBC), the proposed PNC aided two-way scheme significantly improves both the energy and spectral efficiencies of the TR-UWB networks.

We consider a multiple-input multiple-output (MIMO) two-way relay network with two NT-antenna (NT≥ 2) end nodes and one dedicated NR-antenna (NR>; NT) relay node. A physical-layer network coding (PNC) based joint relay antenna selection and zero-forcing (ZF) spatial multiplexing scheme is proposed to support NTstreams of bidirectional data exchanging with the help of NTout of NRselected antennas at the relay node. The optimum relay antennas are selected by a Max-Min criterion with respect to the post-processing SNR of the whole system and then the linear ZF based MIMO two- way relay transmission is achieved with the help of the selected relay antennas. The optimum relay antenna selection not only fulfills the ZF based scheme's requirement on the number of effective relay antennas but also provides an end-to-end diversity advantage to the NTstreams of bidirectional data exchanging with linear transceiver at each end node. The diversity order of the proposed scheme is analyzed. Explicit diversity order d = NR- 1 is obtained theoretically for NT= 2 streams of bidirectional data exchanging. For NT≥ 2 cases, a conjecture that d = NR- NT+ 1 is obtained based on simulation results.

In this paper, we investigate noncoherent Multiple-Input Multiple-Output (MIMO) ultra-wideband (UWB) systems where the signal is encoded by Differential Space-Time Block Code (DSTBC). Considering the specific signal format of DSTBC-UWB system and employing the property of DSTBC, a noncoherent multiple symbol detection (MSD) scheme is developed by generalized likelihood ratio testing (GLRT) approach. Although the proposed MSD scheme can enhance the performance of DSTBC-UWB system, the complexity of the exhaustive search based MSD exponentially increases with observation window size. To decrease the computational complexity, the original MSD metric is transformed into another equivalent form which can be implemented by sphere decoding (SD) for DSTBC-UWB system. Moreover, a suboptimal Decision-Feedback (DF) based MSD with lower complexity than SD based MSD is proposed to further reduce the computational complexity.

In this paper, a novel dual XOR hybrid automatic retransmission request scheme XOR2-HARQ is proposed for wireless broadcasting. Distinct from the traditional network coding (NC) based HARQ (NC-HARQ), an additional XOR operation is introduced to dynamically combine lost packets from the individual receiver instead of conducting XOR operation only across lost packets from different receivers. Furthermore, based on the linear block code's perspective, we optimize the retransmission strategy to yield optimal diversity gain. Analytical results show that conditioned on the same packet error ratio (PER), the retransmission rounds needed for XOR2-HARQ is strictly less than that of NC-HARQ, and the simulation results consolidate our analysis to show a significant reduction of required retransmissions.

By introducing the leakage concept, the maximum signal-to-leakage-and-noise ratio (SLNR) scheme has been served as a candidate precoding scheme in the advanced long term evolution (LTE-Advanced) communication system. However, the original scheme allocates every user the same transmit power and takes the matched filter to decode the receive signals, which results in the limited bit error rate (BER) performance. With the antenna correlation at BS and the channel estimation error for every user, we design a modified matrix by minimizing the system Mean Square Error (MMSE) after maximizing the SLNR under the total transmit power constraint. At each user, a linear decoder is calculated based on the MMSE criteria in the presence of imperfect channel state information (ICSI). Due to the dynamic power allocation during the parallel data streams and the linear MMSE (LMMSE) receiver, the proposed scheme can mitigate the residual interference induced by ICSI and improve the system BER performance efficiently.

In this paper, we propose a decode-and-forward (DF) two-way relay ultra-wideband (UWB) network with a joint-demodulated non-coherent receiver to boost the system throughput. We consider a three nodes relay network with two terminals exchanging information via a relay node during two time slots. After jointly detecting the simultaneously arrived signals, the relay node broadcasts the XORed signal to each terminal. A novel non-coherent receiver with energy detector (ED) is employed and the power allocation optimization is performed based on the result of the approximate effective signal to noise ratio (SNR). Simulation results show that the proposed technique achieves significant improvement over the existing nonrelay and relay schemes on system throughput.

A multi-layer orthogonal block coded transmission scheme is proposed in this paper to enhance the performance of the noncoherent Ultra-Wideband impulse radio (UWB-IR) system. The design employs a novel Multiple Orthogonal Block Coded Modulation (MOBCM), which transmits multiple information bearing orthogonal codewords with an ingenious layered structure. At the receiver side, a correspondent noncoherent multiple codeword detection technique is developed to jointly detect multiple codewords and exploit the high energy efficiency inherent in the MOBCM. Solid performance gain is achieved and our design reduces the general performance gap between noncoherent and coherent receiver for UWB-IR system.

In this paper, we proposed an effective and unified transmit power allocation (TPA) scheme for the Singular Value Decomposition (SVD)-Assisted multi-user multiple-input multiple-output (MU-MIMO) downlink (DL) and uplink (UL) transmissions in the presence of imperfect channel state information (ICSI). The existing power allocation policies for the SVD-Assisted MU-MIMO system, such as equal power allocation (EPA) in the DL and maximum signal-to-noise ratio (MSNR) policy in UL have been developed under the perfect CSI case. However, the EPA scheme doesn't exploit the CSI and ignores the Bit Error Rate (BER) difference among all singular values meanwhile the MSNR method neglects the noise enhancement caused by the decoding operation at Base Station (BS). Aiming at solving these problems, the proposed TPA scheme takes the smallest singular value and the noise enhancement into account and derives an unified expression for DL and UL. Under the ICSI, the proposed scheme can exploit the power allocation operation to mitigate the residual interference induced by ICSI and improve the system BER performance efficiently.

Hybrid subcarrier exclusivity and sharing (HSEnS) scheme with optimized bit loading in uplink multicell OFDMA system is proposed in this paper. Optimizing bit loading under subcarrier exclusivity (SE) and subcarrier sharing (SS) schemes in two adjacent cells is analyzed. Bit loading criteria and boundary setting procedures for convex optimizing bit loading under SS scheme are proposed. And bit loading boundaries for convex optimizing bit loading under SE and SS scheme can be obtained. The HSEnS scheme is proposed to combine the advantages of SE and SS schemes for making tradeoff between throughput performance and algorithm failure rate when interference is strong, moderate or weak. Simulations support our proposals and HSEnS with optimized bit loading outperforms other schemes.

Existing inter-frame interference (IFI) and intersymbol interference (ISI) mitigation schemes for noncoherent UWB Impulse Radio (UWB-IR) mainly focus on signal processing after nonlinear autocorrelation receiver (AcR) or energy detector (ED). Steering the wheel, a simple but effective IFI and ISI pre-mitigation scheme is proposed in this paper, which realizes IFI and ISI mitigation before ED. Block coded modulation is adopted and the pre-mitigation scheme relies on optimized block code design. The optimization jointly considers the signal interference-patterns before ED and the properties of codewords. Thanks to the matching among codes, interference and detection scheme, leaked signal energy is partially used for detection. IFI and ISI mitigation is thus realized. It is showed in simulations that distinct performance improvement is achieved under moderate IFI and ISI.

Synchronization is a crucial task and big challenge for ultra-wideband (UWB) systems, especially in the presence of inter-symbol interference(ISI) and inter-frame interference (IFI). This paper proposes a blind synchronization and demodulation algorithm that is capable of mitigating ISI and IFI for noncoherent UWB system. Employing a series of codeword matching and averaging operations, the proposal can suppress interference and noise effectively. Moreover, it can acquire synchronization rapidly and improve the bit error rate (BER) performance significantly thanks to efficiently exploiting the observed signals. Simulation results demonstrate the substantial performance improvement compared to existing methods.

In this paper, two novel space-time coded modulation and corresponding noncoherent detection schemes for Ultra-Wideband (UWB) impulse radio system are proposed. An additional orthogonality is introduced to original Orthogonal Space-Time Block Code (OSTBC) for performance enhancement in the proposed two schemes, which is termed as Dual Orthogonal Space-Time Coded Modulation (DOSTCM). The specially designed signal structures from the DOSTCM exploit the advantage of multiantenna system and enable simple but effective noncoherent detection. Simulation results show our schemes achieve outstanding bit error rate (BER) performance.