In scenarios with extremely harsh channel conditions and severely limited communication resources, the reliability and effectiveness of semantic communication require urgent enhancement to satisfy the increasing demands of 6G technology. To address this issue, we propose an importance prioritized framework that integrates both message importance and feature importance to identify critical semantics for reliable and efficient semantic transmission. Considering service personalization and task intelligence, we analyze the message importance by factoring the receiver’s preferences and the communication tasks requirements. Specifically, a large AI model is introduced to quantify message importance, while an importance-based metric for semantic accuracy is established to evaluate the overall reliability of semantic communication. To safeguard significant messages in harsh channel conditions, an unequal error protection strategy based on message importance is employed. Furthermore, we propose a novel approach for feature importance analysis based on loss variation to accurately identify critical features. A feature importance prediction network is designed for algorithm deployment. Additionally, a semantic compression strategy based on feature importance is utilized to prioritize the transmission of essential features in limited communication resources scenarios. Extensive experimental results demonstrate substantial performance advantages of our framework and methods, especially in low signal-to-noise ratio and communication resource shortages, providing a reliable and efficient solution for semantic communication in adverse communication environments.

Robust speaker verification under noisy conditions remains an open challenge. Conventional deep learning methods learn a robust unified speaker representation space against diverse background noise and achieve significant improvement. In contrast, this paper presents a noise-conditioned mixture-of-experts framework that decomposes the feature space into specialized noise-aware subspaces for speaker verification. Specifically, we propose a noise-conditioned expert routing mechanism, a universal model based expert specialization strategy, and an SNR-decaying curriculum learning protocol, collectively improving model robustness and generalization under diverse noise conditions. The proposed method can automatically route inputs to expert networks based on noise information derived from the inputs, where each expert targets distinct noise characteristics while preserving speaker identity information. Comprehensive experiments demonstrate consistent superiority over baselines.

The channel nonlinearity and high-speed mobility in low-earth orbit (LEO) constellation communication are key bottlenecks that constrain the performance of waveform transmission. To address these challenges, we propose a universal continuous phase modulation (CPM) spread-spectrum communication method with high spectral efficiency, and a Doppler-insensitive signal detection mechanism. First, an enhanced dual-phase CPM spread-spectrum (DP-CPM-SS) waveform is investigated. By analyzing the principles and power spectrum characteristics of DP-CPM-SS, we correct the modulation index and design the optimalWiener filtering reception for CPM, improving the spectral efficiency and noise resilience. Subsequently, a CPM noncoherent detection integrating frequency estimation and phase pre-compensation is developed. The performance lower bound of the partial matched filter-fast fourier transform (PMF-FFT) frequency estimation algorithm is derived, revealing the relationship among the length and number of partial matched filters, the normalized frequency offset and the mean square error (MSE) of frequency estimation. Additionally, error probability performance and frequency offset adaptation range are analyzed. Numerical results show that the proposed method exhibits superior and stable performance under severe Doppler effects, which is a promising scheme for LEO constellation communication.

By focusing on the intrinsic meaning of information, semantic communication (SemCom) marks a fundamental paradigm shift from physical bit transmission to personalized semantic service. Considering the importance of personalized features related to the speaker in speech for source recovery and understanding, we propose a semantic-driven framework for personalized speech transmission, named PerSemCom, which combines speaker acoustic features with semantic information. Specifically, we first introduce an efficient semantic extraction mechanism to achieve the conversion from speech to text transcriptions, and design a semantic corrector coupled with multi-domain knowledge to mitigate the effects of wireless channel distortion. Building upon the reliable transcriptions at receiver, we further establish a speaker embedding vector knowledge base and achieve high-fidelity speech reconstruction through quantitative modeling of speaker-specific acoustic features. Extensive experimental results demonstrate that our proposed framework outperforms existing schemes in terms of subjective perception at harsh channel conditions. Complexity analysis and latency measurements also show competitive advantages in computational efficiency and real-time capabilities. Reconstructed personalized speech samples have been publicly available at https://kwtankw.github.io/PerSemCom/.

Unmanned aerial vehicles (UAVs) acting as aerial base stations are regarded as an effective solution for emergency communication, due to their high mobility and low cost. In this paper, we consider the differentiated communication requirements in disaster relief scenarios, where the uplink demands high throughput and the downlink requires low latency and high reliability. To simultaneously capture the timeliness and reliability requirements of downlink transmissions, we propose a novel QoS evaluation metric based on finite blocklength theory. Building on this, we formulate a system utility maximization problem and solve it by jointly optimizing UAV trajectory and ground node access control. To address this problem, we propose a novel algorithm named PW-QMIX, which integrates a priority-based heuristic access control policy with a QMIX-based trajectory optimization scheme, effectively tackling the challenges posed by the high-dimensional joint action space. Furthermore, to tackle the challenge of dynamic observation caused by UAV movement and sensing limitations, we design a weight generation network (WGN) and incorporate it into the input layer of QMIX. The WGN dynamically generates weight matrices based on observed nodes, enhancing UAV’s adaptability to local observation variations. Simulation results validate the significance of jointly optimizing uplink and downlink communications. Moreover, compared with state-of-the-art algorithms, the proposed PW-QMIX demonstrates significant advantages in convergence and scalability.

Semantic communication (SemCom) is emerging as a key technology for efficient and robust data transmission in future networks. To improve its reliability, the integration of hybrid automatic repeat request (HARQ) has garnered increasing attention. However, current semantic HARQ methods often rely on redundant retransmissions and do not effectively utilize past semantic and channel information, leading to inefficient resource usage. In this paper, we propose a semantic communication framework that incorporates cross-training feedback integration. This system dynamically fuses semantic features and channel states from previous transmissions to optimize the current encoding process. At the transmitter, a fusion module utilizes the semantic representation from the previous decoding to refine the current transmission. At the receiver, the channel buffer and the semantic combiner work together to progressively integrate features, thereby improving the reliability of semantic decoding. Extensive experiments on image transmission under various channel conditions and bandwidth settings demonstrate that the proposed method achieves significant performance gains.

Vehicle-to-ship (V2S) communication plays a crucial role in maritime emergency rescue operations. This paper investigates the V2S large-scale channel model in offshore environments. We conducted V2S channel measurements and proposed machine learning-based models to predict path loss. Along the propagation path, obstacles enter the first Fresnel zone and cause energy loss. To characterize the extent of obstruction in the Fresnel zone during radio propagation, we innovatively introduced the relative propagation gap as an input parameter. The proposed model, utilizing the Random Forest algorithm, achieves a root mean square error (RMSE) of 1.51 dB, making a reduction of 6.03 dB compared to least squares model and 7.67 dB compared to ITU model.

Adopting federated learning (FL) in unmanned aerial vehicle (UAV) networks is a promising paradigm, which can empower UAV networks with enhanced intelligence to support complex applications. Considering the imbalanced data properties, limited energy and unstable wireless connection of UAVs, an effective client scheduling scheme is critical for the design of efficient FL. In this paper, in order to properly consider the priority criteria, we first propose two importance metrics from the perspectives of data attributes and local updates, namely data importance measurement (DIM) and gradient importance measurement (GIM). Then, take into account DIM and GIM, an optimization problem is formulated to jointly optimize the client scheduling, computation and communication of UAVs. Due to the non-convex nature of this problem, we decompose it into two sub-problems and derive their optimal closed-form solutions. Simulations demonstrate that, compared to benchmark schemes, our proposal ensures better performance on test accuracy, convergence and energy saving.

Unmanned aerial vehicles (UAVs) significantly augment mobile edge computing (MEC) networks with their flexible deployment. In this paper, we investigate a priority-driven multi-UAV cooperative MEC system, in which the task priority are jointly determined by the task queue and task type. The system aims to maximize the task priority gain, subject to the constraints on offloading decision, UAV trajectory design and task scheduling. To solve this problem, we develop a priority scheduling insert based heterogeneous Q-mixing networks (PSI-HQMIX) framework, where the PSI scheme dynamically updates the position of tasks within the queues and the HQMIX algorithm is used to obtain the optimal offloading decisions and trajectories. Simulation results demonstrate that the proposed algorithm outperforms benchmark algorithms in terms of the achieved average priority gains and convergence.

Semantic and task-oriented communications have emerged as significant paradigm shifts for next-generation communication networks, which extracts and transmits task-relevant information rather than raw data for downstream tasks. However, most existing work focused on bit-level loss functions, such as mean square error (MSE) and cross-entropy (CE), rather than directly optimizing at the semantic level. These approaches often lack interpretability of semantic representation and result in higher system complexity and less efficient transmission for various tasks. To this end, we develop a novel task-oriented semantic communication system for multitask scenarios and further develop a semantic-level framework. This framework can extract structured semantic representation by compressing raw data with different labels into mutually orthogonal subspaces. Simulation results demonstrate that the proposed framework not only extracts structured semantic representation, but also outperforms existing benchmarks in terms of data recovery and AI inference performance.

Unmanned aerial vehicles (UAVs) are recognized as effective data collectors for wireless sensor networks. The Age of Information (AoI), a metric indicating data freshness, is crucial for decision making in time-sensitive applications. It can be significantly reduced by jointly optimizing UAV trajectories and communication scheduling of sensor nodes (SNs). However, rapid changes in the environment make it challenging to predesign UAV trajectories and communication scheduling decisions using traditional methods, especially when central controllers are absent and the numbers of UAVs and SNs vary. In this article, we propose hypernetwork-based QMIX (HyperQMIX), a population-invariant multiagent deep reinforcement learning (MADRL) algorithm capable of transferring policies across tasks with varying population sizes. First, we design neural network modules adaptable to varying input and output dimensions, facilitated by parameter generation through a hypernetwork. Then, HyperQMIX leverages these modules to process fluctuations in state and action dimensions. This approach ensures that the network structure remains consistent regardless of population sizes, thereby enhancing the algorithm’s scalability. Extensive simulations demonstrate that HyperQMIX significantly outperforms state-of-the-art algorithms in terms of learning efficiency and converged performance. Moreover, agents pretrained with HyperQMIX perform well in tasks of different population sizes without additional training. Fine-tuning these models achieves performance comparable to training from scratch.

The reciprocity of wireless channels is a prerequisite for physical layer secret key generation (SKG). However, inherent factors, such as noise, asynchronous observations, and hardware impairments, disrupt the ideal reciprocity in the channel state information (CSI) observed by the two legitimate parties. To address this issue, we propose a mutual information-driven autoencoder (MIAE) architecture to extract reciprocal channel features from the non-ideal channel observations of legitimate parties. MIAE is constructed with an AutoEncoder Network (AENet) and a mutual information neural estimator (MINE). Specifically, AENet employs a structure with dual encoders and a shared decoder. The two encoders, integrated with convolutional block attention modules (CBAMs), are designed to focus on reciprocal features within CSI observations and to compensate for temporal variations caused by asynchronous observations. The shared encoder and the correspondingly designed loss function further concentrate the autoencoder’s reciprocity enhancement capability into the encoders. MINE is integrated into the proposed MIAE to estimate the mutual information between the channel features of the two parties. This estimation is then used to formulate a mutual information loss, which guides the encoders to learn channel features that closely match the optimal distribution, thereby boosting the key generation rate. Furthermore, a complete SKG scheme is designed based on the proposed channel feature extractor, MIAE. Simulation results show that our proposed MIAE can extract channel features with strong reciprocity and thus achieve excellent SKG performance based on the extracted features. The generalization performance of the proposed MIAE architecture for communication scenarios of different scales has also been examined.

Most AI-based channel estimation methods with static environment assumptions suffer from performance degradation due to the distribution shift caused by the varying channel environment. As one of the solutions, transfer learning also faces the problem of catastrophic forgetting, where the model tents to fail in previous estimation tasks after learning from new ones. In this paper, we propose a continuous learning-based channel estimation (CLCE) scheme that integrates a series of subnetworks to preserve historical knowledge and achieve an ongoing process of self-improvement. To determine whether the wireless environment is previously unobserved, we first propose a distance-based unsupervised out-of-distribution (OOD) detection algorithm to perceive the distribution shift of the channel environment. The OOD detection algorithm is developed based on the representation of channel data in the latent space of a variational autoencoder (VAE), which is designed to infer the latent variable that implies the characteristics of the wireless environment. Then, a new channel estimation subnetwork is initiated with meta-learning to adapt to the dynamic channel environments with a small set of OOD channel data. Simulation results reveal that our proposed scheme can accurately detect unobserved channel environments without introducing additional detection network, and efficiently adapt to them with few online samples. Furthermore, the mean square error (MSE) result of the channel estimations across multiple environments demonstrates that CLCE effectively mitigates catastrophic forgetting and outperforms the competitors.

As a potential technique, unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) can provide flexible coverage and computing services for real-time applications such as emergency search, traffic control and disaster rescue. In this paper, we investigate a freshness sensitive multi-UAV assisted MEC system where tasks arrive stochastically. The system aims to minimize the age of information (AoI), subject to the constraints on computation offloading, trajectory control and communication resource allocation. Due to the dynamic environment and the coupling of variables, we develop a multi-agent reinforcement learning (MARL) scheme, in which a federated updating method is introduced. Through our scheme, smart mobile devices, UAVs and cloud center can collaborate to learn interactive policies. Simulation results validate that our scheme outperforms local computing, remote computing, and centralized solutions in terms of both the average AoI and convergence.

The radio map has emerged as a promising tool for optimizing spectrum resource utilization and shaping the future landscape of intelligent wireless networks. However, the deployment of radio maps across the network introduces computational and latency challenges, restricting their real-time applications from the user’s perspective. In this paper, we introduce an innovative scheme for constructing and distributing radio maps in unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks. Initially, we transform the distribution of radio maps into a collaborative process between UAV server and smart mobile devices (SMD), proposing four distribution modes tailored to different network conditions. This ensures that each SMD can access radio maps with the lowest cost. Additionally, our scheme integrates a deep reinforcement learning (DRL) framework, fostering seamless coordination between UAV server and SMD to enhance overall system performance and operational efficiency. Simulation results validate the efficiency and efficacy of our proposed scheme in optimizing radio map distribution strategies and resource allocation, further confirming the potential real-time applications of radio maps in future wireless networks.

The 6G wireless communication demands intelligent and versatile interaction between humans and machines that can deal with various intelligent tasks. Semantic communication that focuses on transmitting the meanings rather than the data is expected to be one of the promising technologies to achieve this goal. However, most existing semantic communication systems optimize the whole system under a single objective, lacking the scalabi-lity to dynamic scenarios. For a dynamic scenario with changing channel conditions and background knowledge, we propose a layered semantic communication system (LSCS), which takes advantage of layered coding architec-ture at the semantic and syntactic levels. In addition, a symbolic attention-based denoising network is designed at the receiver to recover transmitted meanings. Simulation results demonstrate that the proposed LSCS can adapt to dynamic scenarios and achieve superior performance over benchmarks under different channel conditions, especially in the low signal-to-noise ratio (SNR) region.

Secret-key generation (SKG) at physical layer is considered as a promising solution for lightweight key distribution. On the analysis of the secret-key capacity over multipath fading channels, existing studies generally neglect the channel observation method, which has a direct impact on the secret-key capacity. Moreover, there is a lack of concise and interpretable expressions for the secret-key capacity based on the channel state information. Motivated by the above, we analyze the performance of SKG based on MMSE estimation of channel impulse response over multipath fading channels. The general expression of secret-key capacity is derived. We find that the secret-key capacity depends on the estimation signal-to-noise ratios (SNRs) of channel-tap gains. Then, the condensed-parameter expressions of exact secret-key capacity and the expressions of asymptotic secret-key capacity at high SNR are derived in two specific PDP cases. We show that the bandwidth and the transmission SNR determine the upper bound of secret-key capacity over channels with different degrees of freedom for the flat PDP case. In addition, we find that the asymptotic secret-key capacities under these two PDPs follow a uniform form, which is a linear function of the channel estimation SNR (in dB). Finally, we simulate the channel probing process under different transmission and channel parameters and exploit the simulated channel estimates to estimate the secret-key capacity through numerical methods. The simulation results demonstrate the theoretical analysis and conclusions.

Unmanned aerial vehicles (UAVs) as mobile base stations are recognized as effective means for emergency communications. The performance of such systems depends on the movement of UAVs and scheduling of ground users (GUs). However, devising an efficient algorithm to jointly optimize UAV trajectories and user scheduling is still challenging, especially in real-time scenarios lacking central controllers. Multi-agent deep reinforcement learning (MADRL) provides a promising solution to this problem. Nevertheless, as the numbers of UAVs and GUs increase, existing MADRL algorithms encounter scalability and sample efficiency issues. In this paper, we develop a novel symmetry-augmented MADRL approach for learning scalable UAV trajectory design and user scheduling policies. The core idea is to utilize symmetries to reduce the multi-agent state-action space and enhance sample efficiency. Specifically, we design a family of neural networks to learn individual policies, namely entity permutation equivariant policy networks (EP2Nets). EP2Nets effectively leverage the permutation symmetry to reduce redundancy in the state-action space. Additionally, we achieve data augmentation by exploiting rotational and reflection symmetries, further boosting sample efficiency. Finally, a Symmetric QMIX (SymmQMIX) algorithm is proposed by integrating the EP2Net and data augmentation method into the QMIX algorithm. Simulation results indicate that SymmQMIX significantly outperforms QMIX and other symmetry-enhanced algorithms, achieving a 4.5-fold increase in converged performance and a 100-fold improvement in sample efficiency.

Non-orthogonal multiple access (NOMA) with mixed numerology is a promising technology that blends flexibility and high spectral efficiency. However, since NOMA enables multiplexing of the same time-frequency resources for different users and mixed-numerology allows superimposing sub-signals with different numerologies, high peak-to-average power ratio (PAPR) as well as power fluctuation problem in NOMA detection becomes cumbersome especially when applying PAPR reduction techniques. This study considers minimizing PAPR in mixed numerology NOMA systems using selected-mapping (SLM) method. The Riemann sequence is one of the simplest phase sequence that can be used to generate a set of signal copies for PAPR reduction. Our analysis reveals that as the amplitude variation caused by the Riemann sequence grows, the upper bound of PAPR for signal copies decreases correspondingly. Leveraging this insight, we introduce a new maximum-range Riemann (MRR)-based phase sequences, in which the amplitude factor can be adjusted to control the power fluctuations. Compared to previous works, our study delves deeper into the influence of phase sequence design of SLM on PAPR reduction performance. Simulations show that the proposed method offers significant performance advantages of PAPR reduction and bit error rate (BER) improvement even with consideration of power-amplifier.

Unmanned aerial vehicles as base stations (UAV-BSs) are recognized as effective means for tackling eruptive communication service requirements especially when terrestrial infrastructures are unavailable. Quality of service (QoS) received by ground terminals (GTs) highly depends on the spatial movement of UAV-BSs. In this paper, we investigate the cooperative trajectory design problem of multiple UAV-BSs towards fair throughput maximization of GTs. Considering the restriction of coverage and sensing, we first propose a heterogeneous-graph-based formulation of relations between GTs and UAV-BSs. Subsequently, we design a framework named graph vision and communication (GVis&Comm) to 1) let each UAV-BS efficiently manage time-varying local observations; 2) facilitate cooperation between UAV-BSs through explicit information exchange. To further reduce the overhead of over-the-air cooperation, we realize discretization of the message passing process among UAV-BSs while still enabling end-to-end training. By leveraging multi-agent reinforcement learning (MARL), UAV-BSs as agents learn a distributed trajectory design policy. Extensive numerical simulation shows that our framework on the one hand achieves remarkable efficiency in processing local observations of each UAV-BS, and on the other improves the overall network performance via close cooperation among UAV-BSs.

Some Internet of Things (IoT) applications represented by vehicular networks, Internet of Medical Things (IoMT), and fire alarm systems have high requirements on the freshness of receiving information. Due to limited computing capability of IoT devices, mobile edge computing (MEC) is applied to reduce packet calculation time and improve packet freshness. In this paper, we investigate a MEC system for sharing vehicle status information and use the age-of-information (AoI) to define the freshness of information in the MEC system. The whole system is modeled as a two-stage tandem queue model with multi source-destination pairs. We derive the closed-form expression for the average AoI of partial computing and analyze the impact of system parameters on the average AoI, which provides guidance on how to set parameters to maximize the information freshness of the MEC system. As a more flexible scheme, partial computing we used reduces the AoI of the MEC system compared to remote computing. Numerical analysis validates our theory.

Deep Neural Network (DNN)-based physical layer techniques are attracting considerable interest due to their potential to enhance communication systems. However, most studies in the physical layer have tended to focus on the application of DNN models to wireless communication problems but not to theoretically understand how does a DNN work in a communication system. In this paper, we aim to quantitatively analyze why DNNs can achieve comparable performance in the physical layer comparing with traditional techniques and their cost in terms of computational complexity. We further investigate and also experimentally validate how information is flown in a DNN-based communication system under the information theoretic concepts.

With the development of wireless communication and Internet of Things (IoT), there are massive wireless devices that need to share the limited spectrum resources. Dynamic spectrum access (DSA) is a promising paradigm to remedy the problem of inefficient spectrum utilization brought upon by the historical command-and-control approach to spectrum allocation. In this article, we investigate the distributed DSA problem for multiusers in a typical multichannel cognitive radio network. The problem is formulated as a decentralized partially observable Markov decision process (Dec-POMDP), and we propose a centralized off-line training and distributed online execution framework based on cooperative multi-agent reinforcement learning (MARL). We employ the deep recurrent$Q$-network (DRQN) to address the partial observability of the state for each cognitive user. The ultimate goal is to learn a cooperative strategy which maximizes the sum throughput of a cognitive radio network in a distributed fashion without information exchange between cognitive users. Finally, we validate the proposed algorithm in various settings through extensive experiments. The experimental results show that the proposed CoMARL-DSA algorithm outperforms the state-of-the-art deep$Q$-learning for spectrum access (DQSA) in terms of successful access rate and collision rate by at least 14% and 12%, respectively.

Recently, deep neural network (DNN)-based physical layer communication techniques have attracted considerable interest. Although their potential to enhance communication systems and superb performance have been validated by simulation experiments, little attention has been paid to the theoretical analysis. Specifically, most studies in the physical layer have tended to focus on the application of DNN models to wireless communication problems but not to theoretically understand how does a DNN work in a communication system. In this paper, we aim to quantitatively analyze why DNNs can achieve comparable performance in the physical layer comparing with traditional techniques, and also drive their cost in terms of computational complexity. To achieve this goal, we first analyze the encoding performance of a DNN-based transmitter and compare it to a traditional one. And then, we theoretically analyze the performance of DNN-based estimator and compare it with traditional estimators. Third, we investigate and validate how information is flown in a DNN-based communication system under the information theoretic concepts. Our analysis develops a concise way to open the “black box” of DNNs in physical layer communication, which can be applied to support the design of DNN-based intelligent communication techniques and help to provide explainable performance assessment.

With innovative applications of unmanned aerial vehicle (UAV) ad hoc networks in various areas, their demands on broad bandwidth, large capacity and low latency become prominent. The combination of millimeter wave, directional antenna and time division multiple access techniques, which enables concurrent transmissions, is promising to deal with it. In this paper, we study the resource allocation problem in UAV ad hoc networks. Specifically, the slot assignment, antenna boresight and transmit power are jointly optimized to promote the network capacity. First, we formulate the optimization problem as the maximization of the fairness-weighted network capacity, subject to the constraint on priority guarantee. Then, because the formulated problem is a mixed integer non-linear programming problem (MINLP), which is NP-hard, two algorithms called dual-based iterative search algorithm (DISA) and sequential exhausted allocation algorithm (SEAA) are respectively proposed to efficiently solve it with acceptable complexity. DISA slacks the MINLP into a continuous-variable optimization problem and solves it with the Lagrangian dual method in an iterative manner. As a heuristic method, SEAA schedules links sequentially, i.e., from high-priority to low-priority ones. Numerical results demonstrate that both DISA and SEAA can efficiently allocate resources for UAVs, while guaranteeing the fairness and priority of links.

Machine learning (ML) has been widely used for efficient resource allocation (RA) in wireless networks. Although superb performance is achieved on small and simple networks, most existing ML-based approaches are confronted with difficulties when heterogeneity occurs and network size expands. In this paper, specifically focusing on power control/beamforming (PC/BF) in heterogeneous device-to-device (D2D) networks, we propose a novel unsupervised learning-based framework named heterogeneous interference graph neural network (HIGNN) to handle these challenges. First, we characterize diversified link features and interference relations with heterogeneous graphs. Then, HIGNN is proposed to empower each link to obtain its individual transmission scheme after limited information exchange with neighboring links. It is noteworthy that HIGNN is scalable to wireless networks of growing sizes with robust performance after trained on small-sized networks. Numerical results show that compared with state-of-the-art benchmarks, HIGNN achieves much higher execution efficiency while providing strong performance.

A joint channel estimation and message passing (MP) data detection receive scheme is proposed for orthogonal time frequency space (OTFS) system in this paper. First, in order to mitigate inter-Doppler interference, we treat the interference from data as Gaussian variables, whose means and variances are fed back from MP detector. Secondly, based on the priori information on the channel, we derive a maximum a posteriori (MAP) delay-Doppler bins detection method to extract the proper position of the channel taps. Thirdly, in order to circumvent the performance loss incurred by the conventional least squares channel estimation, a novel minimum mean square error (MMSE) delay-Doppler domain channel estimator is constructed, which utilized the information both from the channel power delay profile and the Doppler spectrum. Simulation results show that the proposed algorithm performs better than the conventional algorithm in literature both in terms of the channel estimation accuracy and the bit error rate performance at the cost of less guard symbols.

Abstract In the linear minimum mean square error (LMMSE) estimation for orthogonal frequency division multiplexing (OFDM) systems, the channel correlation function (CCF) is required. Some methods have been proposed to calculate the CCF. Instead of providing a novel method to obtain the CCF, a scheme is developed for the estimator to select among different CCFs. In this paper, an enhanced LMMSE estimation is proposed that is able to select the best‐matched CCF within a candidate set. To this end, a parameter comparison scheme is proposed, in which the possible channel statistics for the LMMSE estimation can be evaluated using the sampled noise MSE. Analytical expressions are thus derived to indicate the accuracy of the proposed scheme. Furthermore, fuzzy bound is provided as the performance metric, which reflects the resolution of the parameter comparison scheme. As an example of application, the enhanced LMMSE method is used with the block pilot pattern in the OFDM systems, and the possible CCF candidates for typical scenarios are presented. The complexity of the estimator is also analyzed and a simplified parameter comparison algorithm is proposed to reduce the complexity. Finally, the theoretical analysis and performance comparison are demonstrated by simulation experiments.

In this paper, we devise a highly efficient machine learning-based channel estimation for orthogonal frequency division multiplexing (OFDM) systems, in which the training of the estimator is performed online. A simple learning module is employed for the proposed learning-based estimator. The training process is thus much faster and the required training data is reduced significantly. Besides, a training data construction approach utilizing least square (LS) estimation results is proposed so that the training data can be collected during the data transmission. The feasibility of this novel construction approach is verified by theoretical analysis and simulations. Based on this construction approach, two alternative training data generation schemes are proposed. One scheme transmits additional block pilot symbols to create training data, while the other scheme adopts a decision-directed method and does not require extra pilot overhead. Simulation results show the robustness of the proposed channel estimation method. Furthermore, the proposed method shows better adaptation to practical imperfections compared with the conventional minimum mean-square error (MMSE) channel estimation. It outperforms the existing machine learning-based channel estimation techniques under varying channel conditions.

Recently, machine learning-based channel estimation has attracted much attention. The performance of machine learning-based estimation has been validated by simulation experiments. However, little attention has been paid to the theoretical performance analysis. In this paper, we investigate the mean square error (MSE) performance of machine learning-based estimation. Hypothesis testing is employed to analyze its MSE upper bound. Furthermore, we build a statistical model for hypothesis testing, which holds when the linear learning module with a low input dimension is used in machine learning-based channel estimation, and derive a clear analytical relation between the size of the training data and performance. Then, we simulate the machine learning-based channel estimation in orthogonal frequency division multiplexing (OFDM) systems to verify our analysis results. Finally, the design considerations for the situation where only limited training data is available are discussed. In this situation, our analysis results can be applied to assess the performance and support the design of machine learning-based channel estimation.

Due to the flexibility in 3-D space and high probability of line-of-sight (LoS) in air-to-ground communications, unmanned aerial vehicles (UAVs) have been considered as means to support energy-efficient data collection. However, in emergency applications, the mission completion time should be main concerns. In this article, we propose a UAV-aided data collection design to gather data from a number of ground users (GUs). The objective is to optimize the UAV’s trajectory, altitude, velocity, and data links with GUs to minimize the total mission time. However, the difficulty lies in that the formulated time minimization problem has mutual effect with trajectory variables. To tackle this issue, we first transform the original problem equivalently to the trajectory length problem and then decompose the problem into three subproblems: 1) altitude optimization; 2) trajectory optimization; and 3) velocity and link scheduling optimization. In the altitude optimization, the aim is to maximize the transmission region of GUs which can benefit trajectory designing; then, in the trajectory optimization, we propose a segment-based trajectory optimization algorithm (STOA) to avoid repeat travel; besides, we also propose a group-based trajectory optimization algorithm (GTOA) in large-scale high-density GU deployment to relieve massive computation introduced by STOA. Then, the velocity and link scheduling optimization is modeled as a mixed-integer nonlinear programming (MINLP) and block coordinate descent (BCD) is employed to solve it. Simulations show that both STOA and GTOA achieve shorter trajectory compared with the existing algorithm and GTOA has less computational complexity; besides, the proposed time minimization design is valid by comparing to the benchmark scheme.

Multiaccess edge computing (MEC) is regarded as a promising solution to overcome the limit on the computation capacity of mobile devices. This article investigates an energy-efficient unmanned aerial vehicle (UAV)-enabled MEC framework incorporating nonorthogonal multiple access (NOMA), where multiple UAVs are deployed as edge servers to provide computation assistance to terrestrial users and NOMA is adopted to reduce the energy consumption of task offloading. A utility is formed to mathematically evaluate the weighted energy cost of the system. Due to the coupling of parameters, the minimization of utility is a highly nonconvex problem and therefore, the problem is decomposed into two more tractable subproblems, i.e., the optimal allocation of radio and computation resources given UAV trajectories, and the trajectory planning based on given resource allocation schemes. These two problems are converted to convex ones via successive convex approximation (SCA) and quadratic approximation, respectively. Then, an efficient iterative algorithm is proposed where these two subproblems are alternately solved to gradually approach the optimal resource management of the proposed system. Sufficient numerical results show that our proposed strategy has a remarkable advantage over existing systems in terms of energy efficiency.

Field programmable gate array (FPGA) is ubiquitous nowadays and is applied to many areas. Dynamic partial reconfiguration (DPR) is introduced to most modern FPGAs, enabling changing the function of a part of the FPGA by dynamically loading new bitstreams to the logic regions without affecting the function of other parts of the FPGA. However, delivering the powerful capacity of the DPR FPGA to the user depends on the efficient partitioning and scheduling technology. This article proposes the module merging technique for the partitioning and scheduling problem to reduce the reconfiguration overhead and improve the schedule performance. An exact approach based on the integer linear programming (ILP) for the partitioning and scheduling problem with module merging is proposed. The ILP-based approach is capable of solving the problem optimally, and can be used to further improve the performance of schedules produced by other non-optimal algorithms; however, it is time-consuming to solve large-scale problems. Therefore, a K-sliced-ILP algorithm based on the methodology of divide-and-conquer is proposed, which is able to reduce the time complexity significantly with the solution quality being degraded marginally. Experiments are carried out with a set of real-life applications, and the result demonstrates the effectiveness of the proposed methods.

Mixed-numerology transmission is proposed to support a variety of communication scenarios with diverse requirements. However, as the orthogonal frequency division multiplexing (OFDM) remains as the basic waveform, the peak-to average power ratio (PAPR) problem is still cumbersome. In this paper, based on the iterative clipping and filtering (ICF) and optimization methods, we investigate the PAPR reduction in the mixed-numerology systems. We first illustrate that the direct extension of classical ICF brings about the accumulation of inter-numerology interference (INI) due to the repeated execution. By exploiting the clipping noise rather than the clipped signal, the noise-shaped ICF (NS-ICF) method is then proposed without increasing the INI. Next, we address the in-band distortion minimization problem subject to the PAPR constraint. By reformulation, the resulting model is separable in both the objective function and the constraints, and well suited for the alternating direction method of multipliers (ADMM) approach. The ADMM-based algorithms are then developed to split the original problem into several subproblems which can be easily solved with closed-form solutions. Furthermore, the applications of the proposed PAPR reduction methods combined with filtering and windowing techniques are also shown to be effective.

This paper investigates the air-to-air channel model for Unmanned Aerial Vehicle (UAV) communication links. Although the 3-D ellipsoid model is widely used for characterization of wireless channels, present studies only include the angular distribution of scatterers while the power distribution is absent. Besides, all scatterers are assumed homogeneous in existing work, which is inaccurate for low-altitude UAVs. For above-mentioned problems, our work has two main contributions. First, a precise description of statistical characteristics of receiving power in both delay and direction of arrival (DoA) is provided based on the current 3-D ellipsoid model. Second, we extend the original model to a composite model including two independent ellipsoid models. Apart from surrounding scatterers, obtrusive objects like skyscrapers are specially considered in our model as far clusters. These far clusters could cause distinctive rays with excessive delay even from a distance and influence the statistical characteristics of the channel evidently. Numerical results show that the occurrence of far clusters increases the spreads of delay and DoA.

With the emergence of Internet of Things (IoT), where any device is able to connect to the Internet and monitor/control physical elements, several applications were made possible, such as smart cities, smart health care, and smart transportation. The wide range of the requirements of these applications drives traditional IoT to cognitive IoT (CIoT) that supports smart resource allocation, automatic network operation and intelligent service provisioning. To enable CIoT, there is a need for flexible and reliable wireless communication. In this paper, we propose to combine cognitive radio (CR) with a biological mechanism called reaction–diffusion to provide efficient spectrum allocation for CIoT. We first formulate the quantization of qualitative connectivity-flexibility tradeoff problem to determine the optimal cluster size (i.e., number of cluster members) that maximizes clustered throughput but minimizes communication delay. Then, we propose a bio-inspired algorithm which is used by CIoT devices to form cluster distributedly. We compute the optimal values of the algorithm’s parameters (e.g., contention window) of the proposed algorithm to increase the network’s adaption to different scenarios (e.g., spectrum homogeneity and heterogeneity) and to decrease convergence time, communication overhead, and computation complexity. We conduct a theoretical analysis to validate the correctness and effectiveness of proposed bio-inspired algorithm. Simulation results show that the proposed algorithm can achieve excellent clustering performance in different scenarios.

Exploiting unmanned aerial vehicles (UAVs) as flying base stations (BSs) to assist the terrestrial cellular networks is promising in 5G and beyond. Despite the inherent potentials, one challenging problem is how to optimally deploy multiple UAVs to achieve on-demand coverage for ground user equipment (UE). In this article, we model the deployment problem as minimizing the number of UAVs and maximizing the load balance among them, which is subject to two main constraints, i.e., UAVs should form a robust backbone network and they should keep connected with the fixed BSs. To solve this optimization problem with low complexity, we decompose the problem into two subproblems and propose a hybrid algorithm to solve them stepwise. First, a centralized greedy search algorithm is used to heuristically obtain the minimum number of UAVs and their suboptimal positions in a discontinuous space. Then, a distributed motion algorithm is adopted which enables each UAV to autonomously control its motion toward the optimal position in a continuous space. The proposed algorithm is applicable to various scenarios where UAVs are deployed alone or with fixed BSs regardless of the UE distribution. Extensive simulations validate the proposed algorithm.

Mobile edge computing (MEC) has risen as a promising paradigm to provide high quality of experience via relocating the cloud server in close proximity to smart mobile devices (SMDs). In MEC networks, the MEC server with computation capability and storage resource can jointly execute the latency-sensitive offloading tasks and cache the contents requested by SMDs. In order to minimize the total latency consumption of the computation tasks, we jointly consider computation offloading, content caching, and resource allocation as an integrated model, which is formulated as a mixed integer nonlinear programming (MINLP) problem. We design an asymmetric search tree and improve the branch and bound method to obtain a set of accurate decisions and resource allocation strategies. Furthermore, we introduce the auxiliary variables to reformulate the proposed model and apply the modified generalized benders decomposition method to solve the MINLP problem in polynomial computation complexity time. Simulation results demonstrate the superiority of the proposed schemes.

Unmanned aerial vehicle (UAV) has been witnessed as a promising approach for offering extensive coverage and additional computation capability to smart mobile devices (SMDs), especially in the scenario without available infrastructures. In this paper, a UAV-assisted mobile edge computing system with stochastic computation tasks is investigated. The system aims to minimize the average weighted energy consumption of SMDs and the UAV, subject to the constraints on computation offloading, resource allocation, and flying trajectory scheduling of the UAV. Due to nonconvexity of the problem and the time coupling of variables, a Lyapunov-based approach is applied to analyze the task queue, and the energy consumption minimization problem is decomposed into three manageable subproblems. Furthermore, a joint optimization algorithm is proposed to iteratively solve the problem. Simulation results demonstrate that the system performance obtained by the proposed scheme can outperform the benchmark schemes, and the optimal parameter selections are concluded in the experimental discussion.

In channel-hopping (CH)-based distributed cognitive radio networks (CRNs), the time duration that secondary users (SUs) spend for establishing communication links is called access delay. To evaluate access delay, we propose an access delay model by jointly considering imperfect spectrum sensing and multi-channel multi-SU transmission, from the cross-layer perspective. The model considers two typical scenarios. The first scenario assumes that the SUs do not use contention scheme (CS) which indicates that the time slot is relatively shorter to just allow a transmission. The second scenario assumes that the SUs employ CS [i.e., modified Distributed Coordination Function (DCF)-based Carrier Sense Multiple Access/Collision Avoidance (CSMA/CA) in this paper], which indicates that the time slot is long enough to regulate multiple transmissions. We then propose a bio-inspired algorithm for the first scenario and a self-adaptive step-length algorithm for the second scenario to search for the optimal values of spectrum sensing parameters. The theoretical analysis and simulation results validate the proposed access delay model and show that the proposed algorithms can reduce the most redundant computation. They also show that the optimization of cross-layer parameters can significantly decrease SUs' access delay. Moreover, we conduct a cost-benefit analysis to evaluate the performance of the two scenarios.

The distributed mobile robotic network consists of a group of mobile nodes, such as mobile sensors, unmanned vehicles, unmanned submarines, unmanned air vehicles, or mobile robots. The mobile robotic network keeping a regular topology can utilize efficient network protocols and is also promising in many application scenarios. We propose a distributed algorithm that controls multiple distributed robotic nodes to form regular topology, including straight line, ring, triangular lattice, and square lattice. Our algorithm generates artificial forces, including the attractive force towards a reference point to gather the distributed nodes, the repulsive force from neighboring nodes to keep the desirable distance among them, the formation force to form a specific shape, and the obstacle avoidance force to avoid possible obstacles, such that each node simply follows the resultant force to move. The algorithm works in a fully distributed manner, converges fast, and is easy to deploy, requiring only one-hop local network geometry information. And, it is effective under both 2D and 3D scenarios. A computer demo is developed to demonstrate the effectiveness of the algorithm for large numbers of robotic nodes.

The problem of quantization for Log-likelihood ratios in the presence of a practical automatic-gain-control (AGC) is elaborated in this paper. A median based quantization approach is proposed, in which the median of the absolute value of loglikelihood ratios (LLRs) is set as the quantized midpoint. This approach can diminish the mutual information loss caused by quantization. When applied to bit-interleaved coded modulation scheme, the proposed quantizer proves to be robust to different transmission schemes in both AWGN and rayleigh channels. The implementation issue of median estimate using subset averaged median estimator is also covered in details.

Mobile edge computing (MEC) brings computation capacity to the edge of mobile networks in close proximity to smart mobile devices (SMDs) and contributes to energy saving compared with local computing, but resulting in increased network load and transmission latency. To investigate the tradeoff between energy consumption and latency, we present an energy-aware offloading scheme, which jointly optimizes communication and computation resource allocation under the limited energy and sensitive latency. In this paper, single and multicell MEC network scenarios are considered at the same time. The residual energy of smart devices' battery is introduced into the definition of the weighting factor of energy consumption and latency. In terms of the mixed integer nonlinear problem for computation offloading and resource allocation, we propose an iterative search algorithm combining interior penalty function with D.C. (the difference of two convex functions/sets) programming to find the optimal solution. Numerical results show that the proposed algorithm can obtain lower total cost (i.e., the weighted sum of energy consumption and execution latency) comparing with the baseline algorithms, and the energy-aware weighting factor is of great significance to maintain the lifetime of SMDs.

Due to the flying nature of unmanned aerial vehicles (UAVs), it is very attractive to deploy UAVs as aerial base stations and construct airborne networks to provide service for on-ground users at temporary events (such as disaster relief, military operation, and so on). In the constructing of UAV airborne networks, a challenging problem is how to deploy multiple UAVs for on-demand coverage while at the same time maintaining the connectivity among UAVs. To solve this problem, we propose two algorithms: a centralized deployment algorithm and a distributed motion control algorithm. The first algorithm requires the positions of user equipments (UEs) on the ground and provides the optimal deployment result (i.e., the minimal number of UAVs and their respective positions) after a global computation. This algorithm is applicable to the scenario that requires a minimum number of UAVs to provide desirable service for already known on-ground UEs. Differently, the second algorithm requires no global information or computation, instead, it enables each UAV to autonomously control its motion, find the UEs and converge to on-demand coverage. This distributed algorithm is applicable to the scenario where using a given number of UAVs to cover UEs without UEs' specific position information. In both algorithms, the connectivity of the UAV network is maintained. Extensive simulations validate our proposed algorithms.

Cognitive radio (CR) has emerged as an advanced and promising technology to exploit the wireless spectrum opportunistically. In cognitive radio networks (CRNs), any pairwise communicating nodes are required to rendezvous on a commonly available channel prior to exchange information. In the earlier research, the most popular method is selecting a Common Control Channel (CCC) in CRNs to establish the rendezvous. However, employing a CCC has many problems such as the control channel saturation, vulnerability to jamming attacks, and inapplicability to dynamic network scenarios. Therefore, the blind rendezvous, which requires neither CCC nor the information of the target user's available channels, has recently attracted a lot of research interests. As a contribution to this research area, in this paper we propose a Sender-Jump Receiver-Wait (SJ-RW) blind rendezvous algorithm, which has fully satisfied the following requirements: 1) guaranteeing rendezvous; 2) realizing full rendezvous diversity, i.e., any pair of users can rendezvous on all commonly available channels; 3) requiring no time-synchronization; 4) supporting both symmetric and asymmetric models; 5) supporting multi-user/multi-hop scenarios and 6) consuming short Time-to-Rendezvous (TTR). Theoretical analysis, computer simulations and experiment with testbed have validated the proposed SJ-RW algorithm.

Summary Extracting secret keys from the common randomness of wireless channels has attracted prominent attention recently. Orthogonal frequency‐division multiplexing (OFDM) systems can provide extra randomness in view of the use of multiple subchannels. So far, the secret key capacity of OFDM systems is still an open issue. In this paper, the secret key capacity of OFDM systems based on the subchannel state information is analyzed, and an expression of the secret key capacity is derived under the assumption that the subchannels are independent. To increase the secret key capacity, a power allocation scheme based on geometric program is proposed. Furthermore, an underlying propagation protocol is designed to realize the power allocation scheme. Performance simulations show that the proposed scheme achieves greater secret key capacity in comparison with equal power allocation scheme, especially at low signal‐to‐noise ratio region. Besides, the secret key bits mismatch rate during the secret key generation based on the power allocated subchannels is decreased.

This study addresses the problem of power allocation and mode selection in a hybrid coordinated multi‐cell transmission system with limited backhaul capacity. The power consumption is considered as the extra expense due to data exchange via backhaul links. A novel hybrid multi‐cell scheme is presented, in which each mobile station (MS) can dynamically select either interference channel or multi‐cell multi‐input and multi‐output cooperation mode. To maximise energy efficiency (EE), the optimal power allocation scheme with low complexity is proposed. Moreover, the authors propose an energy‐efficient mode selection metric for each MS, which reflects both benefits and extra expense brought by the selected cooperation mode. Based on the proposed metric, the optimal mode selection strategy maximising EE is obtained. Numerical results show that the hybrid multi‐cell scheme outperforms the traditional single cooperation mode scheme in terms of EE.

Jointly detecting the signals from the source and relay in a spatial multiplexing (SM) multiple-input multiple-output (MIMO) relaying system improves the transmit reliability significantly. However, the existing joint detection schemes for SM MIMO relaying systems, which achieve full diversity, such as the near maximum likelihood (ML) decoder, suffer from high complexity. In this paper, we propose a weighted combining (WC) algorithm, which is applied before the detector in the SM MIMO decode-and-forward relaying system. The proposed algorithm merges the received signal vectors from the source and relay into a combined signal without expanding their dimension, and formulates an equivalent MIMO channel matrix for the combined signal, resulting in a lower complexity for the subsequent detection. We analyze the performance of the proposed WC algorithm with ML detection in terms of the diversity order and computational complexity. An approximate upper bound on the symbol error probability (SEP) for the proposed algorithm is also derived. Simulation results show that in symmetric networks, the proposed WC algorithm achieves substantially lower complexity, while maintaining an SEP performance similar to that of the benchmark NML decoder. The consistency of the derived upper bound on the SEP is also verified by simulations.

This article studies the scheduling of real-time streaming applications on multiprocessor systems-on-chips with predictable memory hierarchy. An iteration-based task-FIFO co-scheduling framework is proposed for this problem. We obtain FIFO size distributions using Pareto space searching, based on which the task-to-processor mapping is obtained with the potential FIFO allocation being taken into account; then, the FIFO-to-memory allocation is optimized to minimize the total memory access cost; finally, a self-timed throughput analysis method that considers memory and direct memory access controller contention is utilized to analyze the throughput. Our methods are validated by a set of synthesized and practical applications on different platforms.

Many applications such as streaming applications are both computation and communication intensive. The Multi-Processor System-on-Chip (MPSoC) based on Network-on-Chip (NoC) outperforms the multiprocessors with bus-based networking architecture in communication bandwidth and scalability, making it a better choice for implementing systems running these applications. It's important to schedule both the computation and communication onto processors and the networking architecture so as to satisfy the stringent timing requirements. To reduce or avoid inter-processor communication, task duplication has been employed in scheduling. Most of the available techniques for the duplication-based scheduling problem use heuristics to solve the problem, and seldom has any work studied further improving the schedule performance, despite the fact that the heuristic cannot provide quality guarantee. To fill in this gap, this paper introduces a duplication and mapping constrained task-communication co-scheduling problem that assumes the duplication strategy and task-to-processor mapping are known a priory, and proposes two Integer Linear Programming (ILP) formulations, i.e., CF-ILP and CA-ILP, to solve two editions of this problem, i.e., the contention-free problem and the contention-aware problem. The proposed ILP formulations optimize the ordering and timing of the communication and computation, thus improving the performance. Both synthesized and real applications are tested on a set of platforms to evaluate the performance of the proposed methods. The experimental results demonstrate the effectiveness of the proposed methods.

Cognitive radio (CR) has emerged as a promising technology to increase the utilization of spectrum resource. A pivotal challenge in CR lies on secondary users' (SU) finding each other on the frequency band, i.e., the spectrum locating. In this demo, we implement two kinds of multi-channel rendezvous technology to solve the problem of spectrum locating: (i) the common control channel (CCC) based rendezvous scheme, which is simple and effective when a control channel is always available; and (ii) the channel-hopping (CH) based blind rendezvous, which could also obtain guaranteed rendezvous on all commonly available channels of pairwise SUs in a short time without a CCC. Furthermore, the cognitive nodes in the demonstration could adjust their communication channels autonomously according to the dynamic spectrum environment for continuous data transmission.

This paper describes our demonstration of a self-adaptive network architecture reconfiguration technology in Cognitive Radio Networks (CRNs) under complex and vicious environment. The technology enables CRN to switch between three kinds of typical network architectures, i.e., centralized, ad hoc and cooperative relay in an autonomous and flexible way. This self-adaptive switch can solve the problem of physical breakdown and invalid communication link in vicious network environment, and hence enhance the network robustness. We implement the technology on a CRN testbed consisting of GNU Radio and USRPs and verify its performance including switching time and throughput.

The blind rendezvous, which requires neither Common Control Channel (CCC) nor the information of the target user's available channels, has recently attracted a lot of research interests. As a contribution to this research area, in this paper we propose a Sender-Jump Receiver-Wait blind rendezvous algorithm, which has fully satisfied the following requirements: 1) guaranteeing rendezvous; 2) realizing full rendezvous diversity, i.e., any pair of users can rendezvous on all commonly available channels; 3) requiring no time-synchronization; 4) supporting both symmetric and asymmetric models; 5) supporting multi-user/multi-hop scenarios and 6) consuming short Time-to-Rendezvous (TTR). Theoretical analysis and computer simulations have validated our algorithm.

In order to improve the connectivity and survivability of Cognitive Radio Networks (CRNs) under complex and vicious communication environment, we propose a network architecture self-adaption technology. The technology enables CRN to switch between three kinds of architectures, i.e., centralized, single-hop ad hoc and cooperative relay in an autonomous and flexible way. This self-adaptive switching can deal with physical breakdown and invalid communication link due to great distance or spectrum heterogeneity, and enhance the network robustness thereby. The work patterns of each architecture and the switching scheme between them were demonstrated, and theoretical switching time was also calculated. Moreover, a testbed based on GNU Radio and USRPs was set up to test its performance including switching time and throughput. Testing results prove the effectiveness of the technology.

In-phase and quadrature-phase (I/Q) imbalance is a critical issue limit the achievable operating signal-to-noise ratio (SNR) at the receiver in direct conversion architecture. In recent literatures, the second-and fourth-order circularity property of communication signals have been used for designing compensator to eliminate the I/Q imbalance. In this paper, we investigate whether moment circularity of an order higher than four can be used in receiver I/Q imbalance compensation. It is shown that the sixth-order moment E[z4z*2] is a suitable statistic for measuring the sixth-order circularity of representative communication signals such as M-QAM and M-PSK with M > 2. Two blind algorithms are then proposed to update the coefficients of I/Q imbalance compensator by restoring the sixth-order circularity of the compensator output signal. Simulation results show that the new proposed methods based on sixth-order statistic converges faster or gives lower steady-state variance than the reference methods that are based on second-and fourth-order statistics.

Lattice reduction (LR) aided multiple-input multiple-output (MIMO) detectors have been considered as an option to obtain near-maximum likelihood (ML) performance. We first give the analysis to show that large signal-to-noise ratio (SNR) corresponds to the short length of the dual basis vectors. Then, in order to further alleviate the complexity of LR aided MIMO detectors while maintaining acceptable performance, we study the dual-lattice reduction methods and propose a fast dual-lattice reduction (FDLR) algorithm which minimizes the orthogonality deficiency of dual-basis. And a tree search method is presented to implement the FDLR algorithm, which enables a flexible trade-off between performance and complexity. Compared to the existing dual Lenstra-Lenstra-Lovasz (DLLL) algorithm, out proposed FDLR algorithm requires less iteration time and yields more orthogonal basis vectors. Simulation results show that FDLR aided detectors achieve better performance and lower complexity than DLLL aided detectors, especially for large MIMO system.

Jointly detecting signals from the source and relay in a multi-input multi-output (MIMO) relaying system can achieve lower symbol error probability (SEP) and higher diversity order. In the literature, the best detector for spatial multiplexing decode-and-forward (DF) MIMO relaying systems is the near maximum likelihood (NML) decoder. However, both NML decoder and its variation are computationally intensive, especially when high-order modulations and multiple data streams are adopted. In order to develop a more efficient detection scheme, we propose a weighted combining (WC) algorithm which is applied before the final detector. The proposed algorithm merges the signal vectors from the source and relay without expanding their dimension and formulates an equivalent MIMO channel matrix for the combined signal, resulting in a much lower complexity for the subsequent detection. Simulation results show that by using the proposed WC algorithm with the ML detector, the same diversity gain as that of the more complex NML detection scheme can be achieved. In particular, in a symmetric network topology, the performance of the proposed WC algorithm is comparable to that of NML.

In this study, the authors propose a novel secret key generation scheme to address the consecutively identical secret key bits problem in stationary environments. First, by randomising the phase of probe signals with stochastic coefficients, they sum the channel estimates in a moving window to get new records with remarkable fluctuations. Then, they propose an adaptive equal probability quantisation approach to ensure the randomness of the secret key. Furthermore, considering the worst scenario, the security of the scheme is evaluated in terms of the adversary's mean square error (MSE) for the single‐antenna system. While for the multi‐antenna system, they propose an artificial noise‐aided strategy to compromise the adversary's MSE performance. The simulation results reveal that the security of the scheme is guaranteed and a better security performance is achieved compared with the prior works. Finally, they validate the feasibility of the proposed scheme in real stationary environments. The testing results show that their scheme achieves remarkable performance in bit mismatch rate and key generation rate, and the generated keys pass the National Institute of Standards and Technology test.

This paper presents a mixed-decimation multipath delay feedback (M2 DF) approach for the radix-2kfast Fourier transform. We employ the principle of folding transformation to derive the proposed architecture, which activates the idle period of arithmetic modules in multipath delay feedback (MDF) architectures by integrating the decimation-in-time operations into the decimation-in-frequency-operated computing units. Furthermore, we compare the proposed design with other efficient schemes, namely, the MDF and the multipath delay commutator (MDC) scheme theoretically and experimentally. Relying on the obtained expressions and statistics, it can be concluded that the M2DF design serves as an efficient alternative to the MDF scheme, since it achieves improved efficiency in the utilization of arithmetic resources without deteriorating the superiorities of feedback structures. In addition, the recommended design performs better in memory requirement and computing delay compared with the MDC approach.

In this study, the outage‐limited optimal power allocation problem for hybrid automatic repeat request (HARQ) protocol over multiple‐input–multiple‐output (MIMO) block Rayleigh fading channels is addressed. Considering three typical HARQ protocols, the authors first derive the approximate outage probabilities of the MIMO HARQ protocols at the high signal‐to‐noise ratio region. On the basis of the approximations, they then formulate an optimisation problem of minimising the average total power usage with the constraints of the targeted outage probability and the maximum number of transmission rounds. A closed‐form optimal power sequence is obtained by solving the outage‐limited optimisation problem. Applying the solutions, the monotonicity of the optimal power sequence is further investigated. It is found that the optimal power sequence is monotonically increasing when the targeted outage probability is smaller than a threshold. They also study the impacts of the system parameters on the optimal power sequence and the power allocation gain under various conditions. Finally, the numerical and simulation results are presented to verify the theoretical derivation.

A novel approach for ensuring confidential wireless communication is proposed and analysed from a geometrical perspective. In this method, both the legitimate receiver and transmitter generate artificial noise (AN) to impair the eavesdropper's channel. The authors use the concept of insecure region to characterise the security performance when the eavesdropper's channel is unknown. The insecure region is defined as the region where the eavesdropper may decode the secret message. With the aim of minimising the size of the insecure region, an optimum power allocation strategy between the information bearing signal and the AN is proposed. Simulation results show that the proposed method achieves a good performance.

This paper considers a multi-cell OFDMA downlink system with several decode-and-forward (DF) relay stations (RSs) aiding the base station (BS) transmissions. The opportunistic DF protocol proposed in [1] is applied. The problem considered is the maximization of the system sum rate with a total power constraint in each cell. An iterative low-complexity resource allocation (RA) algorithm is proposed to optimize mode selection (decision whether relaying should be used or not and which relay), subcarrier assignment (MSSA) and power allocation (PA) alternatively. During the MSSA stage, instead of the original objective function, a lower bound is maximized so that the problem is decoupled into subproblems which can be solved in linear time. During the PA stage, an algorithm based on single condensation and Lagrange duality PA (SC-LDPA) is designed to optimize PA with the tentative MSSA results. Through numerical experiments, the convergence of the low-complexity algorithm (LCA) as well as its benefit compared with a centralized algorithm (CA) are illustrated.

This paper studies masked beamforming schemes for secure communication in broadcast multiple-input multiple-output (MIMO) systems with a passive multiple-antenna eavesdropper. Assuming no information about the eavesdropper is available at the transmitter, we aim to maximize the transmit power of the artificial noise while meeting mean square error (MSE) constraints at the legitimate receivers and the total power constraint at the transmitter. Based on imperfect channel state information (CSI) of the legitimate receivers at the transmitter, we present a worst-case robust masked beamforming algorithm. By exploiting alternating iterative optimization, the proposed algorithm recasts the non-convex optimization problem as two semidefinite program (SDP) based subproblems, which are solvable with interior-point methods. Simulation results are provided to illustrate the secrecy performance of the proposed algorithm.

This study addresses the problem of carrier frequency offset (CFO) tracking in co‐operative space‐frequency block‐coded orthogonal frequency division multiplexing (OFDM) systems with multiple CFOs. Considering that the inserted pilot tones are decayed by data subcarriers in the presence of multiple CFOs, a novel recursive residual CFO tracking (R‐RCFOTr) algorithm is proposed. This method first removes CFO‐induced inter‐carrier interference from data subcarriers, and then updates the residual CFO (RCFO) estimation of each OFDM block recursively. When used in conjunction with a multiple CFOs estimator, the proposed R‐RCFOTr can effectively mitigate the impacts from the multiple RCFOs with affordable complexity. Finally, simulation results are provided to validate the effectiveness of our proposed R‐RCFOTr algorithm, which has performance close to that of perfect CFO estimation at moderate and high signal‐to‐noise ratio, and significantly outperforms conventional CFO tracking algorithm for large CFOs.

This paper studies a novel underlay MIMO cognitive radio (CR) network where the instantaneous or statistical channel state information (CSI) of the interfering channels to the primary receivers (PRs) is completely unknown to the CR. We first show that low-rank CR interference is preferable for improving the throughput of the PRs compared with spreading less power over more transmit dimensions. Based on this observation, we then propose a rank minimization CR transmission strategy assuming a minimum information rate must be guaranteed on the CR main channel. We propose a simple solution referred to as frugal waterfilling (FWF) that uses the least amount of power required to achieve the rate constraint with a minimum-rank transmit covariance matrix. We also present two heuristic approaches that have been used in prior work to transform rank minimization problems into convex optimization problems. We demonstrate that the direct FWF solution leads to higher PR throughput even though it has higher interference “temperature” (IT) compared with the heuristic methods. This calls into question the use of IT as a metric for CR interference.

In this paper, we propose a class of full diversity rate one space-time block codes (STBC) satisfying the generalized orthogonal constraint (GOC). First an explicit construction of completely decoupled STBC is proposed to obtain a rate one STBC with linear decoding complexity for any number of transmit antennas. Then we propose an adaptation strategy for the codes to achieve full diversity by utilizing partial phase information of the channel obtained via a feedback link. With a few feedback bits, the proposed rate one code has full diversity while reserving the same decoding complexity as Orthogonal STBCs. Moreover, the full diversity can be still achieved even if the simple zero-forced decoding is used at the receiver.

This paper considers a multi-cell relay aided orthogonal frequency division multiple access (OFDMA) downlink system, in which all stations are coordinated by a central controller for resource allocation (RA). The decode-and-forward (DF) protocol with selection relaying (SR) is applied. The problem considered is the maximization of the weighted sum of per cell min-rate (WSMR) with a total power constraint in each cell. An iterative RA algorithm is proposed to optimize mode selection (decision whether the relay should help or not), subcarrier assignment (MSSA) and power allocation (PA) alternatively. Each iteration is composed of the MSSA stage and the PA stage. During the MSSA stage, instead of the original objective function, a lower bound is maximized leading to lower complexity. The lower complexity problem is decoupled into mixed integer linear programs (MILP) that can easily be solved by typical MILP solvers. During the PA stage, an algorithm based on single condensation and geometric programming PA (SC-GPPA) is designed to optimize PA with the tentative MSSA results. The convergence of the proposed RA algorithm is proven. Finally, the performance of the RA algorithm and the benefit of using SR are illustrated through numerical experiments.

The authors propose a novel and practical approach to estimate resources and perform a distributed admission control in multi-hop ad hoc networks based on multi-rate enabled IEEE 802.11 technology. The main challenge is to determine if there exist sufficient resources [i.e. the available bandwidth (AB)] for a new incoming flow to be admitted rather than quantifying the exact amount of existing resources. In order to determine the AB along a multi-hop path, the authors take into consideration the channel rate at each hop as well as the channel idle ratio of relevant neighbouring nodes. Furthermore, the admission control is performed at the same time as the AB is determined which minimises overhead. The proposed approach can be applied hop-by-hop in a distributed manner by the end-user, thus being suitable for wireless ad hoc networks. Analysis and simulation based on the Network Simulator version 2 (NS2) platform verify the proposed approach.

This letter investigates masked beamforming schemes for multiuser multiple-input multiple-output (MIMO) downlink systems in the presence of an eavesdropper. With noisy and outdated channel state information (CSI) at the base station (BS), we aim to maximize the transmit power of an artificial noise, which is broadcast to jam any potential eavesdropper, while meeting individual minimum mean square error (MMSE) constraints of the desired user links. To this end, we adopt a Bayesian approach and derive an average MSE uplink-downlink duality with imperfect CSI. Using the duality, a robust beamforming algorithm is proposed. Simulation results show the effectiveness of the proposed scheme.

This paper addresses the problem of beamforming (BF) design for orthogonal frequency division multiplexing (OFDM) based relay networks over frequency-selective channels. Both frequency-domain (FD) BF and time-domain (TD) BF are investigated. The later requires less feedback from the destination to perform BF. The BF vectors are designed by maximizing the minimum signal-to-noise-ratio (SNR) over all subcarriers at the destination, first under the total power constraint (TPC) and then under the per-relay power constraint (PPC). We show that both TPC and PPC BF designs lead to a quasi-convex optimization problem, which can be solved by bisection search method efficiently. Simulation results demonstrate that based on max-min SNR criterion, the performance of TD-BF rapidly approaches that of FD-BF when increasing the filter length. Moreover, it is found that for TD-BF, the minimum filter length required to achieve optimum performance under PPC is longer than that under TPC.

Described in the specifications of WAVE (Wireless Access in Vehicular Environments) standards, broadcast is the main traffic in vehicular networks when all vehicles monitor the control channel. In this paper, we show a simple Markov model to analyze the saturation throughput of control channel broadcast, our analysis reveals that existing IEEE 802.11p parameter settings can result in degraded network performance. In particular, the argument that the contention window size determines the performance of broadcast is concluded. Moreover, we propose a novel scheme which can achieve an optimal throughput by adapting the contention window size to the networks size. Both theoretical analyses and simulation results show the effectiveness of our proposal.

To determine the end-to-end throughput capacity of a multi-hop route in wireless networks, existing work either use a simplistic approach to divide the 1-hop throughput capacity by the number of contending links in the bottleneck region, which has limitations in terms of accuracy, or rely on complicated non-linear equations, which is impractical to solve for a large number of hops. In this paper, we present an optimization methodology to analytically calculate the end-to-end throughput capacity of IEEE 802.11-based multi-hop wireless networks. The calculation considers the interference due to neighboring nodes and assess the impact of hidden node collision as well as multi-rate terminals (i.e., nodes can transmit at different rates) on throughput capacity. The proposed methodology provides a very accurate calculation of the end-to-end throughput capacity when compared to existing work, and yet it is more practical to implement.

A distributed approach to perform admission control in multi-hop ad hoc networks is proposed. Existing algorithms normally compute the total amount of available resources in the network, however our method evaluates if there are sufficient resources to satisfy the bandwidth demand of a new incoming flow rather than quantifying the total amount, which is impractical when considering implementation. Our methodology also considers multi-rate scenarios and can be implemented hop-by-hop in a distributed manner, which makes the approach scalable and suitable for wireless ad hoc networks. The distributed admission control algorithm introduced in this paper is assessed via analysis and simulation.

This letter addresses the problem of beamforming design for an amplify-and-forward (AF) based two-way relaying network (TWRN) which consists of two terminal nodes and several relay nodes. Considering a two-time-slot relaying scheme, we design the optimal beamforming coefficients to maximize the sum-rate of AF-based TWRN under total relay power constraint (TRPC). Although the optimization problem is neither convex nor concave, we show that the global optimal solution can be obtained by the branch-and-bound algorithm. To address the computational complexity concern, we also propose a low-complexity suboptimal solution which is obtained by optimizing a cost function over one real variable only. Simulation results show that the proposed optimal solution outperforms existing schemes significantly. Moreover, we show that the suboptimal solution only suffers small sum-rate losses compared to the optimal solution.

We propose a new analytical approach to evaluate the average packet error rate (PER) of a conventional packet transmission system over a quasi static fading channel, by presenting an integral inequality lemma. The basic idea of the approach is that, given the PER for the AWGN channel as a function of signal-to-noise ratio (SNR), the average PER over Rayleigh fading channel can be generally upper bounded by a quite simple inequality, i.e.,1 - exp(-wo/γ̅), for both coded and uncoded schemes, where wo, defined by an integral expression, corresponds exactly to the inversion of coding gain; and this bound is tight in the high SNR region or for long packet systems. We further apply the integral inequality to extend our research to more general Nakagami-m fading channel.

We consider the problem of transmit beamforming (BF) design for cyclic prefixed (CP) transmissions over MISO frequency selective channels. Both CP single carriers (SC) and orthogonal frequency-division multiplexing (OFDM) systems are investigated. To reduce receiver complexity, frequency domain BF is adopted. The BF is designed by minimizing the arithmetic mean of the error probabilities at the receiver, first under the total power constraint (TPC) and then under the per-antenna power constraint (PPC). The solutions under the PPC are obtained using convex optimization tools. The simulation results show that although BF for SC only slightly outperforms BF for OFDM under TPC, the gap in performance becomes large under PPC. It is also shown that for large number of transmit antennas, the phase-rotation BF (PRB) is nearly optimal under PPC.

Despite the very substantial body of research on the performance analysis of cooperative relay systems, most studies focus on either the symbol error ratio (SER) or outage behavior. This paper analyzes the asymptotic average packet error rate (PER) of the packet cooperative relay system for both AF (Amplify-and-Forward) and DF (Decode-and-Forward) schemes in the high signal-to-noise ratio (SNR) region, and studies the effect of packet length on average PER performance. It is shown that the system achieves the same diversity gain in terms of PER as in terms of SER, but with different coding gain depending on packet length. If we consider practical packet lengths, the DF scheme always achieves better performance than the AF scheme; with shorter packet length, the advantage of DF over AF is more significant. For large enough packet length, AF performance approaches that of DF.

In this paper, a new joint symbol detection and channel tracking algorithm is proposed for the coded MIMO-OFDM systems over time-varying frequency-selective fading channels. The iterative detection/decoding and channel estimation are iteratively employed based on the Variational Bayes expectation-maximization (VBEM) algorithm to improve the system performance. A modified list sphere decoder (LSD) is derived to make the data detection feasible for large systems, which takes into account the statistical information about the channel uncertainty and provides soft symbols for channel estimation. With the autoregressive process channel model and the soft symbols calculated from detector, the time-varying channel impulse responses are tracked by the Kalman smoother. The VBEM iterations are embedded in the turbo-processing of the receiver for incorporation of the coding constraints. Simulation results demonstrate that the proposed algorithm has robust performance over time-varying channels.

In this paper we propose a novel connection admission control scheme named Rate Transition Area assisted Guaranteed Handover Scheme (GHS-RTA), which utilizes the geographical information, rate transition areas and overlap areas to intelligently decide when to block a new call. This scheme helps avoid possible inter-cell and intra-cell handover failures for HAP communications systems with adaptive modulation and coding in the physical layer. Simulation results show that the GHS-RTA can improve the average new call blocking probability greatly (by a minimum of 21.5% for the system model with the parameter values chosen) while maintaining zero inter-cell and intra-cell handover call dropping probabilities compared with Extended Time-based Channel Reservation Algorithm, and that the larger the rate transition area and the overlap area, the better the average new call blocking performance.

In this letter, we propose a lattice-based full diversity design for rate-one quasi-orthogonal space time block codes (QSTBC) to obtain an improved diversity product for eight transmit antennas where the information bits are mapped into 4-D lattice points instead of the common modulation constellations. Particularly, the diversity product of the proposed code is directly determined by the minimum Euclidean distance of the used lattice and can be improved by using the lattice packing. We show analytically and by using simulation results that the proposed code achieves a larger diversity product than the rate-one QSTBCs reported previously.

Based on the variational Bayes expectation-maximization (VBEM) algorithm, a low complexity semi-blind Bayesian iterative receiver with joint signal detection and channel tracking is proposed in this paper for MIMO-OFDM systems over time-varying multi-path channels. Since the VBEM algorithm provides distribution estimation of all parameters, the detection performance can be improved by taking the channel estimation error into account. In addition, with the aid of the soft information provided by the signal detector, the recursive VBEM (RVBEM) algorithm is introduced to track the time-varying channels. Due to the high complexity of the RVBEM algorithm, a novel time-frequency domain recursive VBEM (TF-LCRVBEM) algorithm with low complexity is further proposed. The TFLCRVBEM algorithm simply predicts the channel impulse responses (CIRs) on time domain and recursively refines them on all subcarriers. The complexity analysis results demonstrate that the TF-LCRVBEM algorithm totally avoids computation of matrix inversion and obtains linear complexity. Moreover, the simulation results show that the proposed receiver not only dramatically outperforms the conventional receiver, but also provides performance close to the optimal receiver with perfect channel state information (PCSI).

In this paper, we investigate the timing and carrier frequency offset (CFO) synchronization problem in decode and forward cooperative systems operating over frequency selective channels. A training sequence which consists of one OFDM block having a tile structure in the frequency domain is proposed to perform synchronization. Timing offsets are estimated using correlation-type algorithms. And since some subcarriers are nulled in the proposed tile structure, CFOs are readily estimated using subspace-based methods. By judiciously designing the size of the tile, these algorithms are shown to have better performance, in terms of synchronization errors and bit error rate, than the computationally demanding SAGE algorithm.

Recently, full rate and full diversity two-group (2Gp) and four-group (4Gp) decodable space-time block codes (STBC) derived from quasi-orthogonal STBC (QSTBC) and designed under diversity product maximization criterion have been proposed. In this paper, we derive an upper bound of diversity product for those STBCs and discover that the diversity product of the current 2Gp-QSTBC and 4Gp-QSTBC has the potential to approach the upper bound for 8 transmit antennas. To this end, we propose an improved design of 2Gp and 4Gp STBC with increased diversity product for 8 transmit antennas by allowing sufficient number of dimensions for constellation rotation. The diversity product of the proposed two-group decodable STBC achieves the derived upper bound.

A low complexity iterative receiver is proposed in this paper for MIMO-OFDM systems in time-varying multi-path channel based on the variational Bayes (VB) method. According to the VB method, the estimation algorithms of the signal distribution and the channel distribution are derived for the receiver. With the aid of the soft-output QRD-M algorithm, whose complexity is fixed and relatively low, the signal distribution can be obtained conveniently. In particular, a sequential channel estimation algorithm, which completely avoids the computation of matrix inversion and multiplication, is introduced for the channel distribution estimation. Moreover, the distribution estimations of the signals and the channels are performed in a cyclical iteration way. The simulation results show that the performance loss of the proposed receiver is only ldB for fast varying channels and less than 0.5 dB for slow varying channels at the bit error rate of 10-4after 3 iterations, compared with the optimum receiver with perfect channel state information.

The sphere decoding (SD) algorithm is widely considered to be an efficient approach to obtain maximum likelihood (ML) performance in MIMO detection. At present, almost all of the research about the SD algorithm is based on the assumption of independent and identically distributed channel coefficients. However, the channel coefficients are often correlated in practice, which cause the complexity of the SD algorithm to vary. In this paper, we give a theoretical analysis of the complexity of Fincke and Pohst's(FP) SD algorithm in spatial correlated MIMO channels; the exact expression of the expected complexity is derived. We present simulation results obtained from this expression to show the effect of spatial correlation on the complexity of the algorithm, for different Signal-to-Noise Ratios (SNR) and level of spatial correlations.

A practical variational Bayes (VB) iterative receiver with joint signal detection and channel estimation is proposed in this paper for MIMO-OFDM systems in time-varying multipath channel. Since the VB method provides distribution-estimates of the parameters, the soft-input soft-output (SISO) QRD-M algorithm is exploited to estimate the signal distribution, and several channel estimation algorithms including the low complexity recursive channel estimation (LCRCE) algorithm are derived for the channel distribution estimation. It is noted that the LCRCE algorithm not only completely avoids computation of matrix inversion and matrix multiplication, but also greatly reduces the recursion numbers. The simulation results show that the performance loss of the proposed receiver is only ldB for fast varying channels and less than 0.5 dB for slow varying channels at the bit error rate of 10 4 after 3 iterations, compared with the optimal receiver with perfect channel state information.

This paper addresses the problem of data detection in cooperative space-time block coded (STBC) orthogonal frequency division multiplexing (OFDM) systems in the presence of multiple carrier frequency offsets (CFO). An enhanced iterative maximum-likelihood detector (EIMLD) is proposed. This method consists of first removing inter-carrier interference (ICI), and then performing iterative symbol detection and inter-symbol interference reduction. Simulation results show that EIMLD significantly outperforms existing iterative methods. Comparisons with the zero-forcing and minimum-mean square error detectors, which require complex matrix inversion, are also carried out.

In this paper, we propose an evolutionary topology unware TDMA MAC protocol (E-TUTM) which improves upon the existing threaded time spread multiple access (T-TSMA) protocol while preserving the advantage of the T-TSMA protocol. According to the current network topology and traffic load, our proposed E-TUTM protocol can control each node to utilize the shared wireless channel effectively by the protocol threading technique and the hybrid channel access strategy. In this paper, we present the E-TUTM protocol and analyze the performance of it. Simulation results show that our proposed E-TUTM protocol improves the performance dramatically as compared with the T-TSMA protocol.

In this paper we proposed a typical cooperative MIMO system grounded on wireless mobile ad hoc networks and brought forward the problem of time efficiency in cooperative MIMO system. And then adopting time efficiency, we analyzed the Shannon capacity limit of the cooperative MIMO system. The analysis shows two implications. First, only when intra-cluster channel is better enough than inter-cluster channel, cooperative MIMO can bring increment of channel capacity; second, there should be an optimal number of cooperative partners in a cooperative MIMO system. For instance it's optimal to use 3 cooperative partners in the proposed typical cooperative MIMO system, when it can achieve a channel capacity increment of about 2 bps/hz compared with direct transmission.

The stack algorithm is a promising tree-search algorithm with relatively low computation complexity for multi-input multi-output (MIMO) systems. Recent researches show that it obtains low detection complexity at the price of performance degradation. To achieve a better compromise between computational complexity and detection performance, a sphere constraint stack detection algorithm (SC-Stack) is proposed in this paper. With the aid of sorted QR decomposition based on the MMSE criterion (MMSE-SQRD), the proposed algorithm constrains conventional stack algorithm by a sphere radius obtained from partial serial interference cancellation (PSIC) algorithm. The SC-Stack algorithm avoids abundant metric computation by excluding a large number of nodes from the stack according to the sphere radius. The simulation results of computational complexity and detection performance presented in this paper show that the SC-Stack algorithm improves detection performance with lower complexity than the conventional stack algorithm. Moreover, the proposed algorithm achieves almost the same performance as sphere decoding algorithm while expanding far fewer nodes. So it is more feasible in practical systems.

In this paper, we propose an evolutionary time spread multiple access (E-TSMA) protocol which is independent of topology changes for ad hoc networks. Our proposed protocol is based on protocol threading technique and a novel contention schedule with reservation and carrier sense. According to the topology density of the network and the transmission requirement, our proposed protocol can control each node to utilize its assigned slots and its non-assigned slots effectively. The protocol improves upon the existing threaded time spread multiple access (T-TSMA) protocol while preserving the advantage of the topology transparency and eliminating the maximum nodal degree constraint. In this paper we present the protocol and analyze the performance of it. Simulation results show that our proposed E-TSMA protocol is better than the T-TSMA protocol.

In this paper, a new joint symbol detection and channel estimation algorithm is proposed for MIMO-OFDM systems over frequency-selective fading channels using the variational Bayesian expectation-maximization (VBEM) algorithm. Since the VBEM algorithm can provide distribution-estimates of the parameters, the statistical information about the channel uncertainty is exploited to improve the evaluation of the extrinsic information in the soft-input soft-output detector which identifies the significant symbol combinations via list sphere decoder. In addition, two channel estimators are derived based on the posterior distributions of the transmitted symbols which are obtained from the space-time detection. The VBEM iterations are embedded in the turbo-processing of the receiver. Simulation results demonstrate that the proposed VBEM algorithm has more robust performance over the conventional EM techniques.

Recently, a single-symbol decodable transmit strategy based on preprocessing at the transmitter has been introduced to decouple the quasi-orthogonal space-time block codes (QOSTBC) with reduced complexity at the receiver . Unfortunately, it does not achieve full diversity, thus suffering from significant performance loss. To tackle this problem, we propose a full diversity scheme with four transmit antennas in this letter. The proposed code is based on a class of restricted full-rank single-symbol decodable design (RFSDD) and has many similar characteristics as the coordinate interleaved orthogonal designs (CIODs), but with a lower peak-to-average ratio (PAR).

Space-frequency coding is an attractive approach to exploit the space and frequency diversity provided by an orthogonal frequency-division multiplexing (OFDM)-based frequency-selective multiple-input multiple-output (MIMO) fading channels. Due to the prohibitive complexity of acquiring knowledge of the fading coefficients, noncoherent space- frequency coding (NSFC) which dose not need the knowledge of channel is proposed and the design criteria is presented. However, the existing NSFCs adopt the scheme that one block occupies all the subcarriers of the OFDM system, and this large code size increases the coding and decoding complexity exponentially. In this paper, we address a new view of the transmission of space-frequency codes which transforms the transmission in frequency selective channels into flat fading channels. Then, a sub-block noncoherent space-frequency coding is proposed which divides the space-frequency plane into small sub-blocks and each one constitutes a space-frequency codeword. This reduces the code size and the coding-decoding complexity. By a modified signal model, an asymptotic analysis of the pairwise error probability (PEP) is derived and thus the design criteria for full diversity-achieving code is explicitly defined. We also propose a code construct that achieves the promised order of diversity and demonstrate our conclusion by computer simulation.

We present an "opportunistic" scheduling policy with the objective of improving delay performance for time-sensitive users in wireless networks. Since packet delay depends on both resource allocation and time-varying capacity of wireless channels, we introduce a search radius (SR) into the framework of packet fair queueing (PFQ) and employ maximum relative SNR (Max-rSNR) as scheduling rule with the purpose of providing short-term temporal fairness guarantee and improving user throughput. We make theoretical analysis of the delay performance of each user and find that each user's delay is directly related to SR, moreover the value of SR should be restricted within a limited range in order to provide better delay performance to each user. Based on this, we propose a feasible iterative algorithm to achieve an appropriate SR. We conduct an extensive set of simulations, which characterizes the performance of our scheduling scheme.

Differential unitary space-frequency modulation reduces the complexity of multiple-input multiple-output-orthogonal frequency division multiplexing (MIMO-OFDM) systems significantly. But the conventional single symbol differential detection (SSDD) results in a high error floor over a severe multipath spreading channel. To overcome this limitation, a multiple symbol differential stack algorithm is proposed by embedding a recursion of maximum-likelihood metric in the stack algorithm. The proposed algorithm is suitable for arbitrary nondiagonal unitary space-frequency constellations and enhances the flexibility to multipath spread compared with SSDD.

In this paper, we present statistical fairness opportunistic scheduling (SFOS), a wireless scheduling algorithm with the objective of improving system throughput while providing statistical fairness guarantee. In particular, SFOS provides statistical fairness by using a virtual time variation, while improving system throughput by using our designed utility function which relates to the transmission rate. We develop a general analytical framework for SFOS, which shows that the fairness index is bounded by the utility. Further, we investigate the design rule of the utility function over Rayleigh fading channels and discrete transmission rates and present a reference design. Simulation results evaluate SFOS can significantly improve system throughput while providing statistical fairness guarantee.

In this paper, a new joint channel tracking and symbol detection algorithm is proposed for MIMO-OFDM systems over the time-varying frequency-selective fading channel. The iterative detection/decoding and channel estimation are combined to improve system performance. With the aid of the candidate list from list sphere decoder (LSD), the channel frequency response of each subcarrier is estimated by the recursive expectation-maximization (EM) algorithm which uses sequential processing over all the significant symbol combinations in the limited candidate list. Moreover, with the prior knowledge of the second-order statistics of the fading channel in time domain, the time-varying channel impulse responses are tracked by the Kalman predictor based on the estimated channel frequency response. Simulation results demonstrate that the proposed algorithm can track the time- varying channel effectively and offer substantial performance gains over the conventional soft decision-directed Kalman filtering techniques.

This paper proposes an evolutionary-dynamic TDMA slot assignment protocol (E-DTSAP) for ad hoc networks. According to the topology density of the network and the bandwidth requirement, the proposed protocol changes the frame length and the transmission schedule dynamically. Moreover, it allows the transmitter to reserve one or more unscheduled slots from the set of unassigned slots in its neighborhood by coordinating the announcement and confirmation with the neighboring nodes up to two hops away. The important sections of the proposed protocol including frame format, packet format and slot assignment are presented and analyzed. The simulation results show that our proposed protocol is better than the conventional second category MAC protocols and 802.11.

This paper investigates iterative channel estimation for multiple-input multiple-output (MIMO) systems with orthogonal frequency division multiplexing (OFDM) transmission technique. Using the candidate list obtained by the list sphere decoder (LSD), the soft information is calculated and fed back to the channel estimator which is based on the maximum a posteriori (MAP) version of the expectation-maximization (EM) algorithm. A soft symbol threshold was set to avoid the performance deterioration due to the unreliable detected symbols. With the aid of the decomposed component signals obtained at the E step, the soft symbols with low reliability are recalculated and the more accurate soft information is used in the channel estimation. Simulation results show that the newly proposed enhanced EM-MAP algorithm outperforms the conventional EM-ML and EM-MAP algorithms.

We first examine the impact of channel errors and collisions on the performance of legacy IEEE 802.11 fragmentation, and find the optimal fragment size depends on both channel condition and network size. Then, we present an improved contention-free fragmentation burst (CFF) scheme, by which the source immediately retransmits the failed fragment without backoff procedure except for the first one. Finally, a channel adaptive fragmentation (ADF) scheme based on CFF is proposed to combat both channel errors and collisions. ADF can distinguish frame transmission failure caused by collision from that by channel error in moderate noisy channel. Simulations show our CFF and ADF have distinct performance improvements in noisy channel and large size networks.