Multi-user millimeter-wave communication relies on narrow beams and dense cell deployments to ensure reliable connectivity. However, tracking optimal beams for multiple mobile users across multiple base stations (BSs) results in significant signaling overhead. Recent works have explored the capability of out-of-band (OOB) modalities in obtaining spatial characteristics of wireless channels and reducing pilot overhead in single-BS single-user/multi-user systems. However, applying OOB modalities for multi-BS selection towards dense cell deployments leads to high coordination overhead, i.e, excessive computing overhead and high latency in data exchange. How to leverage OOB modalities to eliminate pilot overhead and achieve efficient multi-BS coordination in multi-BS systems remains largely unexplored. In this paper, we propose a novel OOB modality synergy (OMS) based mobility management scheme to realize multi-user beam prediction and proactive BS selection by synergizing two OOB modalities, i.e., vision and location. Specifically, mobile users are initially identified via spatial alignment of visual sensing and location feedback, and then tracked according to the temporal correlation in image sequence. Subsequently, a binary encoding map based gain and beam prediction network (BEM-GBPN) is designed to predict beamforming gains and optimal beams for mobile users at each BS, such that a central unit can control the BSs to perform user handoff and beam switching. Simulation results indicate that the proposed OMS-based mobility management scheme enhances beam prediction and BS selection accuracy and enables users to achieve 91% transmission rates of the optimal with zero pilot overhead and significantly improve multi-BS coordination efficiency compared to existing methods.

Cascaded reconfigurable intelligent surfaces (RISs) greatly improve network coverage and reliability. This paper examines the outage probability (OP) of multi-cascaded RISs (MCRISs)-aided systems over doubly-correlated Rayleigh multiple-input multiple-output (MIMO) channels. The moment-generating function is invoked to convert the OP into a numerical inversion of Laplace integral. To capture profound insights, we conduct an in-depth asymptotic investigation into the outage behavior of MCRIS-aided MIMO communications by leveraging random matrix theory in the high-SNR regime. The asymptotic results reveal that the firsthRISs with the smallest number of reflective elements primarily dictate the bottleneck of reliability performance, wherehis influenced by the variation in the number of reflective elements across the RISs. Specifically, smaller variations result in a largerh, while greater disparities reduce it. Additionally, we identify an “unsaturation effect” that occurs when the performance margin, or residual spatial degree of freedom (DoF), after propagation through the firsthRISs cannot be evenly distributed between the transmitter and the RISs. This effect slows down the decline of the OP with increasing SNR. More cascaded RISs impair the spatial DoF of wireless communications, resulting in the loss of half of the independent fading channels compared to the system without the support of RIS as the cascaded number of RISs increases. Majorization theory is subsequently applied to unveil the negative effect of doubly-spatial correlation on system reliability. Finally, Monte Carlo simulations are carried out for validations.

Many emerging artificial intelligence-driven applications rely on the availability of large-scale, stable, and fresh sensing data, underscoring the Ultra-Reliable Low-Latency Communications (URLLC). This paper proposes to amalgamate Non-Orthogonal Multiple Access (NOMA) and Hybrid Automatic Repeat reQuest with Incremental Redundancy (HARQ-IR) to accommodate reliable and real-time wireless services. The outage probability and the Average Age of Information (AAoI) are used to evaluate the transmission reliability and data freshness for HARQ-IR-NOMA schemes. To reveal physical insights as well as ease system designs, the asymptotic outage probability in the high Signal-to-Noise Ratio (SNR) regime is derived by developing a recursive dominant term approximation method. Moreover, the AAoI is deduced in terms of the outage probability by considering an M/G/1/1 queuing model. The asymptotic AAoI at high SNR is shown to be an increasing function of the diversity order-deficiency, which theoretically justifies the tradeoff between the transmission reliability and the data freshness. Furthermore, the AAoI is minimized by optimizing the transmission powers between users as well as HARQ rounds while maintaining the outage and total power constraints. The minimal AAoI is obtained by developing Geometric Programming (GP)-based and Deep Reinforcement Learning (DRL)-based methods. Finally, the numerical results are presented for verification.

Reconfigurable distributed antenna and reflecting surface (RDARS) has been recently proposed as a promising technology. This architecture enables each element to perform flexibly either in the reflection mode, like the traditional passive reconfigurable intelligent surface (RIS), or in the connection mode, akin to the distributed antenna system (DAS). This dual capability allows RDARS to harness both reflection gain and distribution gain. In this paper, we investigate a dynamic RDARS-aided multiple-input multiple-output communication system, where the optimal configuration of the elements operating in connection mode can provide additional selection gain. Considering the theoretical and practical significances, we address the achievable rate maximization problem by jointly optimizing the mode selection, transmit power allocation and passive beamforming under both perfect and imperfect channel state information (CSI) cases. Due to the involvement of the mode selection design of RDARS, the problem is more challenging than those of the traditional RIS-aided systems with fixed reflection operation. For perfect CSI case, by investigating the inherent properties of the objective function, we propose a greedy-based alternating optimization (AO) algorithm with low-complexity and then extend the proposed algorithm to the general multi-user multi-RDARS scenario. Additionally, we find interesting insights about the mode selection of RDARS in a special scenario with a single-antenna user. The result shows that the RDARS elements leading to the largest distribution gain should be selected to operate in connection mode for the rate maximization. For imperfect CSI case, we develop an efficient alternative direction method of multipliers-based AO algorithm. Numerical results show that RDARS-assisted system outperforms the passive-RIS assisted system and DAS under both perfect and imperfect CSI scenarios with promising reflection, distribution and selection gains.

Reconfigurable distributed antennas and reflecting surface (RDARS) has been recently proposed as a promising technology. This architecture enables each element to operate either in the reflection mode, like the conventional passive reconfigurable intelligent surface (RIS), or in the connection mode, akin to the distributed antenna system (DAS). This dual capability allows RDARS to harness both reflection gain and distribution gain. In this paper, to further unleash the potential of the RDARSaided multiple-input multiple-output (MIMO) communication system in terms of additional selection gain, we formulate an achievable rate maximization problem by jointly designing the mode selection matrix, the power allocation matrix and the reflection coefficient matrix. Due to the mode selection design of RDARS, this problem is more challenging than those of the conventional RIS-aided systems with fixed elements locations. To tackle it, by investigating the beneficial properties of the objective function, we propose a greedy-based alternating optimization (AO) algorithm with low-complexity. Numerical results clearly illustrate the reflection gain, distribution gain and selection gain of RDARS and show that RDARS-assisted system can achieve superior performance than the passive-RIS assisted system and DAS.

We present a framework that enables fast reconstruction and real-time rendering of urban-scale scenes while maintaining robustness against appearance variations across multi-view captures. Our approach begins with scene partitioning for parallel training, employing a visibility-based image selection strategy to optimize training efficiency. A controllable level-of-detail (LOD) strategy explicitly regulates Gaussian density under a user-defined budget, enabling efficient training and rendering while maintaining high visual fidelity. The appearance transformation module mitigates the negative effects of appearance inconsistencies across images while enabling flexible adjustments. Additionally, we utilize enhancement modules, such as depth regularization, scale regularization, and antialiasing, to improve reconstruction fidelity. Experimental results demonstrate that our method effectively reconstructs urban-scale scenes and outperforms previous approaches in both efficiency and quality. The source code is available at: https://yzslab.github.io/REUrbanGS.

Synthesis of talking portraits is a challenging task. Recently, neural radiance fields (NeRF) have been utilized to achieve real-time synthesis and high-fidelity 3D modeling of talking portraits, demonstrating exciting success. However, besides the slow running speed of NeRF, most methods tend to produce artifacts and visual jitter due to the lack of detailed control over NeRF’s implicit expressions. In this work, we propose a novel method for pose-driven talking portrait synthesis based on 3D Gaussian Splatting (3DGS). The pose is composed of three dynamic data types: facial landmark, audio, and camera viewpoint. Our method consists of two modules: the facial landmark generator module and the Gaussian rendering module. The facial landmark generator module is based on a transformer network, which employs a cross-attention mechanism to capture the relationship between audio and facial landmark, outputting the facial landmark corresponding to any given audio. In the 3DGS part, the motion canonical Gaussian of the digital human is first obtained through static reconstruction. The spatial perception module is then used to ascertain the relationship between each type of pose data and the each Gaussian, predicting the offset of the Gaussian. The canonical Gaussian and the offset are combined to obtain the dynamic Gaussian. Extensive experiments show that our method outperforms existing methods in terms of the fidelity and synchronization of speech and video.

With the rapid proliferation of Internet of Things (IoT) devices, overlapping spectrum signals (OSS) have become increasingly prevalent due to concurrent transmissions over the same frequency bands. Accurate recognition of OSS, including their categories, positions, and transmission powers, is crucial for spectrum management and interference mitigation in dense wireless environments. This paper proposes an integrated deep signal recognition (IDSR) architecture based on deep non-negative matrix factorization (Deep-NMF) for multi-parameter OSS estimation. To reduce computational complexity and accelerate iterative convergence, we further develop a collaborative multi-layer signal recognition (CMSR) architecture that decomposes OSS features through a three-layer structure, enabling successive estimation of category, position, and power. Additionally, we incorporate a Dempster-Shafer (D-S) based fusion strategy for signal category recognition and introduce a consensus-constrained NMF algorithm for collaborative localization across monitoring nodes. Particle swarm optimization (PSO) is employed to initialize the power and position estimation to enhance accuracy. Experimental results demonstrate that both IDSR and CMSR outperform traditional blind source separation methods (e.g., PCA, ICA, and standard NMF) and classical deep learning models (CNN and Transformer) in OSS recognition. Specifically, the proposed methods achieve up to 95.6% accuracy in signal classification under SNR = 4 dB, with comprehensive performance gains. Moreover, CMSR attains comparable accuracy to IDSR with lower computational complexity, indicating its potential for real-time edge deployment. The impact of monitoring node placement, signal power ratios, and deployment density is also analyzed, providing practical insights for spectrum monitoring in IoT networks.

The mixed Analog-to-Digital Converter (ADC) architecture is considered a promising solution in balancing the trade-off between high-resolution and low-resolution quantization over the hardware costs, power consumption, and transmission demands in massive multiple-input multiple-output (MIMO) systems. Meanwhile, the Direction of Arrival (DOA) estimation is a prerequisite for accurate beam processing in MIMO systems. Therefore, evaluating the DOA estimation performance in linear array architectures of mixed-ADC based MIMO systems is crucial. However, local bounds, such as the widely used Cramer-Rao Bound (CRB), only offer rigorous performance analysis of the estimator in the high signal-to-noise ratio (SNR) regime. In this paper, we derive a globally effective and closed-form Ziv-Zakai Bound (ZZB) to assess the DOA estimation performance of mixed-resolution quantization structures. We have also provided the CRB for DOA estimation with mixed-resolution quantization and included the classical MUSIC algorithm as a comparison. Additionally, we have analyzed the impact of different prior information, the numbers of snapshots and sensors, quantization bits and parameter settings on the ZZB. Simulation results show that the ZZB provides globally effective bounds under Gaussian and uniform distributions. In particular, in the low SNRs region, the ZZB offers a tighter and more effective bound than CRB and BCRB.

In time division duplexing (TDD) millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) systems, downlink channel state information (CSI) can be obtained from uplink channel estimation thanks to channel reciprocity. However, under high-mobility scenarios, frequent uplink channel estimation is needed due to channel aging. Additionally, large amounts of antennas and subcarriers result in high-dimensional CSI matrices, aggravating pilot training overhead. To address this, we propose a three-domain (3D) channel extrapolation framework across spatial, frequency, and temporal domains. First, considering the effectiveness of traditional knowledge-driven channel estimation methods and the marginal effects of pilots in the spatial and frequency domains, a knowledge-and-data driven spatial-frequency channel extrapolation network (KDD-SFCEN) is proposed for uplink channel estimation via joint spatial-frequency channel extrapolation to reduce spatial-frequency domain pilot overhead. Then, leveraging channel reciprocity and temporal dependencies, we propose a temporal uplink-downlink channel extrapolation network (TUDCEN) powered by generative artificial intelligence for slot-level channel extrapolation, aiming to reduce the tremendous temporal domain pilot overhead caused by high mobility. Numerical results demonstrate the superiority of the proposed framework in significantly reducing the pilot training overhead by 16 times and improving the system’s spectral efficiency under high-mobility scenarios compared with state-of-the-art channel estimation/extrapolation methods.

This paper analyzes the block error rate (BLER) of hybrid automatic repeat request with incremental redundancy (HARQ-IR) assisted short packet communications. The small packet size leads to the occurrence of the time correlation among HARQ rounds. To accurately capture the effect of time correlation, a general correlated Nakagami-m fading channel model is first developed. However, the channel correlation, multiple transmissions, and finite blocklength information theory extremely challenge the analysis of BLER. To confront this, the average BLER is derived in a compact form by capitalizing on linearization approximation and conditional Laplace transform. To reveal more insights, the asymptotic BLER in high signal-to-noise ratio is obtained in a simple form. It is disclosed that full time diversity can be achieved by the proposed HARQ-IR-aided scheme. This justifies the feasibility of using retransmissions to offer reliable short packet communications. The numerical results finally corroborate the validity of our analysis.

This article examines the application of hybrid automatic repeat request with incremental redundancy (HARQ-IR) to reliable mission-critical Internet of Things (IoT) communications, which frequently use short packets to meet low latency of mission. We first analyze the average block error rate (BLER) of HARQ-IR-aided short packet communications. The finite-blocklength information theory and the correlated decoding events preclude the analysis of BLER. To overcome the issue, the recursive formulation of the average BLER motivates us to calculate its value through trapezoidal approximation and Gauss-Laguerre quadrature. Besides, dynamic programming is applied to implement Gauss-Laguerre quadrature to avoid redundant calculations. Moreover, the asymptotic analysis is performed to derive a simple expression for the asymptotic average BLER at high-signal-to-noise ratio (SNR). Then, we study the maximization of long-term average throughput (LTAT) via power allocation meanwhile ensuring power and BLER constraints. To tackle the fractional and nonconvex problem, the asymptotic BLER is employed to convert the original problem into a convex one through geometric programming (GP). Unfortunately, since there is a large approximation error at low SNR, the GP-based solution underestimates the LTAT performance in the circumstance. Alternatively, we develop a deep reinforcement learning (DRL)-based framework to learn the optimal power allocation policy. In particular, the optimization problem is transformed into a constrained Markov decision process problem, which is solved by integrating deep deterministic policy gradient(DDPG) and subgradient method. The numerical results demonstrate that the DRL-based method outperforms the GP-based one at low SNR, albeit at the cost of increasing computational burden.

Distributed Antenna Systems (DASs) employ multiple antenna arrays in remote radio units to achieve highly directional transmission and provide great coverage performance for future-generation networks. However, the utilization of fully digital or hybrid active antenna arrays results in a significant increase in hardware costs and power consumption for DAS. To address these issues, integrating DAS with Reconfigurable Intelligent Surfaces (RIS) offers a viable approach to ensure coverage and transmission performance while maintaining low hardware costs and power consumption. To incorporate the merits of RIS into the DAS from practical consideration, a novel architecture of “Reconfigurable Distributed Antennas and Reflecting Surfaces (RDARS)” is proposed in this paper. Specifically, based on the design of the additional direct-through state together with the existing high-quality fronthaul link, any element of the RDARS can be dynamically programmed to connect with the base station (BS) via fibers and perform theconnected modeas remote distributed antennas of the BS to receive or transmit signals. Additionally, RDARS also inherits the low-cost and low-energy-consumption benefits of fully passive RISs by default configuring the elements as passive to perform thereflection mode. As a result, RDARS encompasses both DAS and RIS as special cases, offering flexible control over the trade-off betweendistribution gainandreflection gainto enhance performance. To unveil the potential of such architecture, the ergodic achievable rate under the RDARS architecture is analyzed and closed-form expression with meaningful insights is derived. The theoretical analysis proves that the RDARS can achieve a higher achievable rate than both DAS and fully passive RIS with the passive beamforming gain provided by elements actingreflection modewhile combating the “multiplicative fading” suffered by RISs through theconnected modeperformed at the RDARS. Simulation results also demonstrate the superiority of the RDARS architecture over DAS and passive RIS-aided systems and its flexible trade-off between performance and cost. To further validate the feasibility and effectiveness, an RDARS prototype with 256 elements is built for real experiments. Experimental results show that the RDARS-aided system with only one element operating inconnected modecan achieve an additional 21% and 170% throughput improvement over DAS and RIS-aided systems, respectively.

A variable-length cross-packet hybrid automatic repeat request (VL-XP-HARQ) is proposed to boost the spectral efficiency (SE) and the energy efficiency (EE) of communications. The SE is firstly derived in terms of the outage probabilities, with which the SE is proved to be upper bounded by the ergodic capacity (EC). Moreover, to facilitate the maximization of the SE, the asymptotic outage probability is obtained at high signal-to-noise ratio (SNR), with which the SE is maximized by properly choosing the number of new information bits while guaranteeing outage requirement. By applying Dinkelbach’s transform, the fractional objective function is transformed into a subtraction form, which can be decomposed into multiple sub-problems through alternating optimization. By noticing that the asymptotic outage probability is a convex function, each sub-problem can be easily relaxed to a convex problem by adopting successive convex approximation (SCA). Besides, the EE of VL-XP-HARQ is also investigated. An upper bound of the EE is found and proved to be attainable. Furthermore, by aiming at maximizing the EE via power allocation while confining outage within a certain constraint, the methods to the maximization of SE are invoked to solve the similar fractional problem. Finally, numerical results are presented for verification.

The emerging immersive and autonomous services have posed stringent requirements on both communications and localization. By considering the great potential of reconfigurable intelligent surface (RIS), this paper focuses on the joint channel estimation and localization for RIS-aided wireless systems. As opposed to existing works that treat channel estimation and localization independently, this paper exploits the intrinsic coupling and nonlinear relationships between the channel parameters and user location for enhancement of both localization and channel reconstruction. By noticing the non-convex, nonlinear objective function and the sparse angle pattern, a variational Bayesian learning-based framework is developed to jointly estimate the channel parameters and user location through leveraging an effective approximation of the posterior distribution. The proposed framework is capable of unifying near-field and far-field scenarios owing to exploitation of sparsity of the angular domain. Since the joint channel and location estimation problem has a closed-form solution in each iteration, our proposed iterative algorithm performs better than the conventional particle swarm optimization (PSO) and maximum likelihood (ML) based ones in terms of computational complexity. Simulations demonstrate that the proposed algorithm almost reaches the Bayesian Cramer-Rao bound (BCRB) and achieves a superior estimation accuracy by comparing to the PSO and the ML algorithms.

In this paper, we investigate a novel reconfigurable distributed antennas and reflecting surface (RDARS) aided multi-user massive multiple-input multiple-output (MIMO) system with imperfect channel state information (CSI) and propose a practical two-timescale (TTS) transceiver design to reduce the communication overhead and computational complexity of the system. In the RDARS-aided system, not only distribution gain but also reflection gain can be obtained by a flexible combination of the distributed antennas and reflecting surface, which differentiates the system from the others and also makes the TTS design challenging. To enable the optimal TTS transceiver design, the achievable rate of the system is first derived in closed-form. The rate expression is general and covers that of the distributed antenna systems (DAS) and reconfigurable intelligent surface (RIS) aided systems as special cases. Then the TTS design aiming at the weighted sum rate maximization is considered. To solve the challenging non-convex optimization problem with high order design variables, i.e., the transmit powers and the phase shifts at the RDARS, a block coordinate descent based method is proposed to find the optimal solutions in semi-closed forms iteratively. Specifically, two efficient algorithms are proposed with provable convergence for the optimal phase shift design, i.e., Riemannian Gradient Ascent based algorithm by exploiting the unit-modulus constraints, and Two-Tier Majorization-Minimization based algorithm with closed-form optimal solutions in each iteration. Simulation results validate the effectiveness of the proposed algorithm and demonstrate the superiority of deploying RDARS in massive MIMO systems to provide substantial rate improvement with a significantly reduced total number of active antennas/RF chains and lower transmit power when compared to the DAS and RIS-aided systems.

To reap the promising benefits of massive multiple-input multiple-output (MIMO) systems, accurate channel state information (CSI) is required through channel estimation. However, due to the complicated wireless propagation environment and large-scale antenna arrays, precise channel estimation for massive MIMO systems is significantly challenging and costs an enormous training overhead. Considerable time-frequency resources are consumed to acquire sufficient accuracy of CSI, which thus severely degrades systems’ spectral and energy efficiencies. In this paper, we propose a dual-attention-based channel estimation network (DACEN) to realize accurate channel estimation via low-density pilots, by jointly learning the spatial-temporal domain features of massive MIMO channels with the temporal attention module and the spatial attention module. To further improve the estimation accuracy, we propose a parameter-instance transfer learning approach to transfer the channel knowledge learned from the high-density pilots pre-acquired during the training dataset collection period. Experimental results reveal that the proposed DACEN-based method achieves better channel estimation performance than the existing methods under various pilot-density settings and signal-to-noise ratios. Additionally, with the proposed parameter-instance transfer learning approach, the DACEN-based method achieves additional performance gain, thereby further demonstrating the effectiveness and superiority of the proposed method.

Accurate channel state information (CSI) is essential for downlink precoding in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems with orthogonal frequency-division multiplexing (OFDM). However, obtaining CSI through feedback from the user equipment (UE) becomes challenging with the increasing scale of antennas and subcarriers and leads to extremely high CSI feedback overhead. Deep learning-based methods have emerged for compressing CSI but these methods generally require substantial collected samples and thus pose practical challenges. Moreover, existing deep learning methods also suffer from dramatically growing feedback overhead owing to their focus on full-dimensional CSI feedback. To address these issues, we propose a low-overhead Incorporation-Extrapolation based Few-Shot CSI feedback Framework (IEFSF) for massive MIMO systems. An incorporation-extrapolation scheme for eigenvector-based CSI feedback is proposed to reduce the feedback overhead. Then, to alleviate the necessity of extensive collected samples and enable few-shot CSI feedback, we further propose a knowledge-driven data augmentation (KDDA) method and an artificial intelligence-generated content (AIGC) -based data augmentation method by exploiting the domain knowledge of wireless channels and by exploiting a novel generative model, respectively. Experimental results based on the DeepMIMO dataset demonstrate that the proposed IEFSF significantly reduces CSI feedback overhead by 64 times compared with existing methods while maintaining higher feedback accuracy using only several hundred collected samples.

Mission critical communications require ultra-high reliability guarantees. It is a natural idea to combine massive multiple-input multiple-output (MIMO) and repetition transmission to further improve the reliability. In this paper, we combine the irregular repetition scheme with the massive MIMO with grant-free access. By introducing random transmission patterns for replicas, the collision problem of grant-free access is mitigated. Considering finite block-length coding, as well as sporadic and periodic arrivals of mission critical traffic, the reliability is thoroughly analyzed and derived in closed form. The theoretical analysis is finally verified through computer simulations. It is observed that the reliability of periodic traffic is superior to the sporadic one in large MIMO systems.

In this paper, reconfigurable intelligent surface (RIS) and hybrid automatic repeat request with incremental redundancy (HARQ-IR) are amalgamated to lower power expenditure, shorten latency and strengthen reliability of the internet of things (IoT) communications. By considering Rician fading channels and multiple RISs, the outage probability of single-input single-output (SISO) HARQ-IR-RIS aided IoT networks is derived in closed-form, with which the asymptotic outage analysis is carried out. The asymptotic results are further extended to single-input multiple-output (SIMO)/multiple-input multiple-output (MIMO) HARQ-IR-RIS aided IoT networks by using random matrix theory. Thereafter, the asymptotic expressions are invoked to reduce the design complexity of the phase shifts, transmit powers, and rate, which aims at minimizing the age of information (AoI) while ensuring the power and outage constraints. Particularly, the optimal phase shifts are firstly determined. The alternating optimization technique is then applied to solve the transmit rate and powers, which are iteratively updated based on majorization-minimization (MM) principle and geometric programming (GP) approximation, respectively. The numerical results consequently corroborate our theoretical analysis. More interestingly, the HARQ-IR-aided scheme with fixed power is found to provide a comparable performance as the proposed scheme with variable power especially for numerous reflecting elements at RIS.

This paper investigates a reconfigurable intelligent surface (RIS)-aided multi-user multiple-input multiple-output (MIMO) system by considering only the statistical channel state information (CSI) at the base station (BS). We aim to maximize its sum-rate via the joint optimization of precoding matrix at the BS and phase shifts vector at the RIS. However, the multi-user MIMO transmissions and the spatial correlations make the optimization cumbersome. For tractability, an asymptotic sum-rate is derived under a large number of the reflecting elements. By adopting the asymptotic sum-rate as the objective function, optimal designs of the transmit precoding matrix and the phase shifts vector can be decoupled and solved individually. More specifically, a high-quality suboptimal solution of the transmit precoding matrix and phase shifts vectors can be obtained by utilizing the water-filling algorithm and the projected gradient ascent (PGA) algorithm, respectively. Comparing to the case of the instantaneous CSI assumed at the BS, the proposed algorithm based on the statistical CSI can achieve comparable performance but with much lower channel estimation overhead, information feedback overhead, and computational complexity, which is more affordable and appealing for practical applications. Moreover, the impact of spatial correlation on the asymptotic sum-rate is examined by using majorization theory.

In this article, we present a hybrid channel estimation algorithm for uniform planar array (UPA)-assisted millimeter-wave (mmWave) massive multiple-input–multiple-output (MIMO) Internet of Things (IoT) systems by exploiting the benefits from both the compressed sensing (CS) and the sparse Bayesian learning (SBL). Compared with existing studies, the distribution characteristics and correlations between propagation paths in the elevation (e)- and azimuth (a)-angle domains are considered to enhance the estimation performance. Specifically, we first redefine the e-angles and the a-angles to simplify the system model. Then, a novel autoregressive (AR)-Gaussian channel prior is proposed to capture both the sparsity and the clustering properties of mmWave massive MIMO IoT channels. After that, we provide a channel approximation method to overcome the channel uncertainty by exploiting the structure of the AR-Gaussian channel prior. The hybrid beamforming (HBF) architecture with limited radio-frequency (RF) chains in mmWave IoT systems is also considered. Finally, we propose a hybrid channel estimation algorithm, which consists of two stages. Based on the different distribution characteristics in different angle domains, the CS-based channel estimation is performed for e-angles on stage one, while the SBL-based channel estimation is applied for a-angles on stage two. Numerical results reveal that compared with the existing CS- and SBL-only methods, the proposed hybrid channel estimation algorithm exhibits better performance in terms of computational complexity, sparsity robustness, and estimation accuracy.

Mobile crowdsensing (MCS) booms the implementation of the Internet of Things (IoT) in different areas due to flexibility and low deployment cost. However, collecting sufficient high quality sensing data is crucial for the success of various applications. Incentive mechanism design plays a critical role in the successful implementation of mobile MCS systems. Most of existing work consider that the platform exactly knows the trajectory of mobile users. However, in most cases, it is difficult to obtain the accurate information of the location of mobile users due to either privacy issue or the lack of information. In this article, we consider nondeterministic mobility of mobile users, where only the probability distribution of users’ mobility is available. We design an effective mechanism to achieve the quality data collection with the objective of maximizing the expected social welfare. Simulation results show that the proposed mechanism achieves her expected social welfare compared with four existing schemes, while satisfying truthfulness, individual rationality, and computational efficiency.

Vehicle-to-vehicle (V2V) communications under dense urban environments usually experience severe keyhole fading effect especially for multi-input multi-output (MIMO) channels, which degrades the capacity and outage performance due to the rank deficiency. To avoid these, the integration of MIMO and hybrid automatic repeat request (HARQ) is proposed to assist V2V communications in this paper. By using the methods of integral transforms, the outage probabilities are derived in closed-form for different HARQ-assisted schemes, including Type I-HARQ, HARQ with chase combining (HARQ-CC), and HARQ with incremental redundancy (HARQ-IR). With the results, meaningful insights are gained by conducting the asymptotic outage analysis. Specifically, it is revealed that full time diversity order can be achieved, while full spatial diversity order is unreachable as compared to MIMO-HARQ systems without keyhole effect. Moreover, we prove that the asymptotic outage probability is a monotonically increasing and convex function of the transmission rate. More importantly, although HARQ-IR performs better than HARQ-CC owing to its higher coding complexity, this advantage becomes negligible in the large-scale array regime. Finally, the numerical results are verified by Monte-Carlo simulations along with some in-depth discussions.

Intelligent reflecting surface (IRS) is an emerging cost-efficient technology to enhance communication performance by implementing a large number of passive reflecting elements with tunable phases in wireless systems. In this paper, we propose a general framework for the IRS-aided MIMO system designs under both single-user and multi-user setups, in which the diverse performance metrics including weighted mutual information and weighted MSE, and the realistic multiple weighted power constraint are taken into consideration. Leveraging the alternating optimization approach, the optimal IRS phase shifts are obtained in semi-closed forms. Specifically, based on the matrix-monotonic optimization theory, it is found that optimizing IRS phase shifts is essentially equivalent to tuning the eigenvalues and the corresponding eigenvectors of the MSE matrix. Then the proposed general framework is extended to a multi-user system by introducing a majorization-minimization (MM)-based method for IRS phase shift optimization. Simulation results show that our proposed optimal design brings significant enhancement on the chosen performance metric compared to the traditional MIMO systems without the IRS, and also significantly outperforms various benchmark designs in both single-user and multi-user systems.

We thoroughly investigate the outage performance of reconfigurable intelligent surface (RIS) aided multi-input multi-output (MIMO) communications by exploiting statistical channel state information (CSI). Kornecker channel model is adopted to characterize the impact of spatial correlations among MIMO antennas and reconfigurable reflectors. Mellin transform and random matrix theory are then utilized to derive the outage probability, with which we further conduct the asymptotic outage analysis to obtain insightful findings. In particular, the asymptotic analysis reveals that the number of reflecting elements at the RIS should not be smaller than the total number of MIMO transmit and receive antennas to get rid of the rank deficiency of the cascaded MIMO channels. Moreover, the asymptotic outage probability is a monotonically increasing and convex function with respect to the transmission rate. The numerical outcomes not only corroborate our analytical results, but also demonstrate the negative impact of the spatial correlation and the benefit of increasing the number of reconfigurable reflectors. Finally, we apply the asymptotic results to optimally devise the phase shifts with a low computational complexity.

In this paper, we investigate an air-ground coordination communication system where ground users (GUs) access suitable UAV base stations (UAV-BSs) to maximize their own throughput and UAV-BSs design their trajectories to maximize the total throughput and keep GU fairness. Note that the action space of GUs is discrete, and UAV-BSs’ action space is continuous. To deal with the hybrid action space, we propose a multi-agent deep reinforcement learning (MADRL) approach, named AG-PMADDPG (air-ground probabilistic multi-agent deep deterministic policy gradient), where GUs transform the discrete actions to continuous action probabilities, and then sample actions according to the probabilities. The proposed method enable the users make decisions based on their local information, which is beneficial for user privacy. Simulation results demonstrate that AG-PMADDPG can outperform the benchmark algorithms in terms of fairness and throughput.

In Mobile Crowd Sensing (MCS), mobile users usually can be both the contributor of sensing data and the customer of the service provided by the data collector or service provider. Most of existing work does not consider the dual-role of mobile users in MCS. In this work, by jointly considering users' dual-role as both the contributor of sensing data and the customer of service, we design a Lyapunov based online algorithm to achieve the stability of dynamic MCS system while maximizing the platform utility. Meanwhile, we demonstrate the impacts of parameter on the balance of these two roles of mobile users. Simulation results show that the proposed method outperforms some existing methods in terms of achieved platform utility and system stability. In addition, it is proved that, the time-averaged platform utility could converge to the maximum benchmark asymptotically.

Reconfigurable intelligent surface (RIS) has recently attracted a spurt of interest due to its innate advantages over massive MIMO on power consumption. In this paper, we study the outage performance of multi-RIS system with the help of hybrid automatic repeat request (HARQ) to improve the RIS system reliability, where the fading channels are modeled by Rician fading and the phase shift setting only depends on the line-of-sight (LoS) component. Both the exact and asymptotic outage probabilities under Type-I HARQ and HARQ with chase combining (HARQ-CC) schemes are derived. Particularly, the tractable asymptotic results empower us to derive meaningful insights for HARQ-aided multi-RIS systems. On the one hand, we find that both the Type-I and the HARQ-CC schemes can achieve full diversity that is equal to the maximal number of HARQ rounds. On the other hand, the closed-form expression of the optimal phase shift setting with respect to outage probability minimization is obtained. The optimal solution indicates that the reflecting link direction should be consistent with the direct link LoS component. Finally, the analytical results are validated by Monte-Carlo simulations.

Secure localization and velocity estimation are of great importance in Internet-of-Things (IoT) applications and are particularly challenging in the presence of malicious attacks. The problem becomes even more challenging in practical scenarios in which attack information is unknown and anchor node location uncertainties occur due to node mobility and falsification of malicious nodes. This challenging problem is investigated in this article. With reasonable assumptions on the attack model and uncertainties, the secure localization and velocity estimation problem is formulated as an intractable maximum a posterior (MAP) problem. A variational-message-passing (VMP)-based algorithm is proposed to approximate the true posterior distribution iteratively and find the closed-form estimates of the location and velocity securely. The identification of malicious nodes is also achieved in the meantime. The convergence of the proposed VMP-based algorithm is also discussed. Numerical simulations are finally conducted and the results show the VMP-based joint localization and velocity estimation algorithm can approach the Bayesian Cramer Rao bound and is superior to other secure algorithms.

In order to combat the challenges of scarce spectrum bandwidth and energy supply in the Internet-of-Things networks, this article investigates a wireless-powered massive multiple-input-multiple-output (MIMO) underlay cognitive radio (CR) system, where the secondary user (SU) harvests the energy from the primary network through a time-switching (TS) strategy. Moreover, the detection technique of maximum-ratio combining (MRC) is applied at base stations (BSs) due to its low complexity. Thereon, the achievable rate of the secondary network is thoroughly analyzed by accounting for spatial correlations at both users and BSs, which significantly differentiates the analysis from the prior literature. With the analytical results, the impacts of the number of antennas and the spatial correlations are quantified. In particular, the negative impact of the spatial correlation on the achievable rate is theoretically proved based on the majorization theory. Furthermore, the TS factor and/or power allocation coefficients are optimally designed based on the statistical channel state information (CSI) only to maximize the achievable rate while ensuring the maximum endurable interference constraint. It is found that the optimal power allocation matrix is a variant of water-filling solution with two water levels associated with sum power constraint and sum weighted power constraint, respectively. The weighting matrix is aligned with the transmit spatial correlation in the SU. The joint design of TS factor and power allocation coefficients is also shown to reach a superior performance over the algorithms-based solely on TS factor or power allocation coefficients optimizations. Finally, the analytical results are validated by conducting simulations.

Reconfigurable intelligent surface (RIS) stands out as a promising technology by enhancing the electromagnetic wave propagation environment with its passive reflecting elements. In this paper, we focus on the ergodic sum-rate analysis and maximization of the RIS-assisted uplink multiuser multiple-input multiple-output (MIMO) multiple-access channel (MAC) under Rician fading by exploiting full statistical channel state information (CSI). The spatial correlations at the base station, the users, and the RIS are also considered. By using the replica method originated in statistical physics, the closed-form asymptotic ergodic sum-rate of the system is first derived in the large-system regime on the account of the unique channel structure of the RIS-assisted MIMO-MAC system. Then, based on the derived asymptotic ergodic sum-rate, we propose an alternating optimization (AO) algorithm to jointly design the transmit covariance matrix of users and the phase-shifting matrix of RIS with full statistical CSI. Simulation results are also demonstrated to verify the accuracy of the derived asymptotic ergodic sum-rate and the superiority of the proposed AO algorithm. The results reveal that the derived closed-form asymptotic ergodic sum-rate matches very well with the Monte Carlo results even for a small number of antennas, and the proposed AO algorithm can achieve up to 10 bps/Hz gain at high signal-to-noise (SNR) regime, which is therefore valuable for future RIS-assisted MIMO-MAC system designs.

An energy-efficient two-way (TW) full-duplex (FD) network with the assistance of an unmanned aerial vehicle (UAV) is proposed, where the UAV acts as a mobile relay to assist the information exchange between two terrestrial transceivers. In particular, the self-interference (SI) channel gains follow complex Gaussian distribution and the perfect channel state information (CSI) of SI channels is unavailable at the receiver. To maximize the energy efficiency (EE), UAV flight speeds are controlled and power adaptation at the UAV relay is performed. The genetic algorithm (GA) is applied to efficiently obtain the optimal solution. Numerical results show that our scheme performs better than the one-way (OW) FDR scheme, fixed power (FP) and fixed flight speed (FS) policy. In addition, the SI cancellation factor on the EE is also demonstrated.

In this paper, we consider the nondeterministic mobility of mobile users, where the platform only has the probability distribution about users' mobility. We design an effective mechanism to achieve high quality data collection with the objective of maximizing the expected social welfare. Simulation results show the better performance of the proposed mechanism compared with four counterparts. In addition, the proposed mechanism also satisfies truthfulness and individual rationality.

Pre-caching popular contents in advance at the network edge such as base stations is a promising method to improve the service quality by reducing the transmission cost and network congestion. In this paper, by jointly considering the mobile users' rating on different contents into the incentive mechanism design, we proposed a rating-aware incentive mechanism for efficiently selecting some BSs to pre-cache the popular contents. The proposed mechanism can achieve higher performance compared with other existing methods in terms of the social welfare. In specific, the proposed mechanism can improve the achieved social welfare by 11.75% and 9.97% compared with the mechanism of random caching and the caching based on bid price, respectively. In addition, the proposed mechanism satisfies the nice properties of individual rationality and truthfulness.

To support massive connectivity and boost spectral efficiency for Internet of Things (IoT), a downlink scheme combining virtual multiple-input-multiple-output (MIMO) and nonorthogonal multiple access (NOMA) is proposed. All the single-antenna IoT devices in each cluster cooperate with each other to establish a virtual MIMO entity, and multiple independent data streams are requested by each cluster. NOMA is employed to superimpose all the requested data streams, and each cluster leverages zero-forcing detection to demultiplex the input data streams. Only statistical channel state information (CSI) is available at the base station to avoid the waste of the energy and bandwidth on frequent CSI estimations. The outage probability and goodput of the virtual MIMO-NOMA system are thoroughly investigated by considering the Kronecker model, which embraces both the transmit and receive correlations. Furthermore, the asymptotic results facilitate not only the exploration of physical insights but also the goodput maximization. In particular, the asymptotic outage expressions provide quantitative impacts of various system parameters and enable the investigation of diversity-multiplexing tradeoff (DMT). Moreover, power allocation coefficients and/or transmission rates can be properly chosen to achieve the maximal goodput. By favor of the Karush-Kuhn-Tucker conditions, the goodput maximization problems can be solved in closed form, with which the joint power and rate selection is realized by using alternately iterating optimization. Besides, the optimization algorithms tend to allocate more power to clusters under unfavorable channel conditions and support clusters with a higher transmission rate under benign channel conditions.

Localization is particularly challenging when the environment has mixed line-of-sight (LOS) and non-LOS paths and even more challenging if the anchors' positions are also uncertain. In the situations in which the parameters of the LOS-NLOS propagation error model and the channel states are unknown and uncertainties for the anchors exist, the likelihood function of a localizing node is computationally intractable. In this paper, assuming the knowledge of the prior distributions of the error model parameters and that of the channel states, we formulate the localization problem as the maximization problem of the posterior distribution of the localizing node. Then we apply variational distributions and importance sampling to approximate the true posterior distributions and estimate the target's location using an asymptotic minimum mean-square-error (MMSE) estimator. Furthermore, we analyze the convergence and complexity of the proposed variational Bayesian localization (VBL) algorithm. Computer simulation results demonstrate that the proposed algorithm can approach the performance of the Bayesian Cramer-Rao bound (BCRB) and outperforms conventional algorithms.

In this paper, a novel 3D cellular model consisting of aerial base stations (aBSs) and aerial user equipments (aUEs) is proposed, by integrating the coordinated multi-point (CoMP) transmission technique with the theory of stochastic geometry. For this new 3D architecture, a tractable model for aBSs' deployment based on the binomial-Delaunay tetrahedralization is developed, which ensures seamless coverage for a given space. In addition, a versatile and practical frequency allocation scheme is designed to eliminate the inter-cell interference effectively. Based on this model, performance metrics including the achievable data rate and coverage probability are derived for two types of aUEs: i) the general aUE (i.e., an aUE having distinct distances from its serving aBSs) and ii) the worst-case aUE (i.e., an aUE having equal distances from its serving aBSs). Simulation and numerical results demonstrate that the proposed approach emphatically outperforms the conventional binomial-Voronoi tessellation without CoMP. Insightfully, it provides a similar performance to the binomial-Voronoi tessellation which utilizes the conventional CoMP scheme; yet, introducing a considerably reduced computational complexity and backhaul/ signaling overhead.

The combination between non-orthogonal multiple access (NOMA) and hybrid automatic repeat request (HARQ) is capable of realizing ultra-reliability, high throughput and massive concurrent connections particularly for emerging communication systems. This paper focuses on characterizing the asymptotic scaling law of the outage probability of the HARQ with chase combining (HARQ-CC)-aided downlink NOMA scheme with respect to the transmit power, i.e., diversity order. The diversity order of the two- user HARQ-CC-aided NOMA system is derived in closed-form, where an integration domain partition trick is developed to obtain the upper and lower bounds of the outage probability. The analytical results show that the diversity order is a decreasing step function of transmission rate given the ratio between the transmit powers allocated to the two users. Moreover, full time diversity can only be achieved under a sufficiently low transmission rate. Additionally, the users' diversity orders follow a descending order according to their respective average channel gains. Monte Carlo simulations finally confirm the analysis.

Considering the widespread use of effective capacity in cross-layer design and the extensive existence of renewal service processes in communication networks, this paper thoroughly investigates the effective capacity for renewal processes. Exact expressions of the effective capacity at a given quality of service (QoS) exponent are derived for the renewal processes with either constant or variable reward. The simple expressions reveal meaningful insights, such as the monotonicity and bounds of the effective capacity. The analytical results are then applied to evaluate the cross-layer throughput for diverse hybrid automatic repeat request (HARQ) systems, including fixed-rate HARQ (FR-HARQ, e.g., Type I HARQ, HARQ with chase combining (HARQ-CC) and HARQ with incremental redundancy (HARQ-IR)), variable-rate HARQ (VR-HARQ), and cross-packet HARQ (XP-HARQ). Furthermore, aiming at maximizing the effective capacity via the optimal rate selection, it is disclosed that the VR-HARQ and XP-HARQ attain almost the same performance, and both of them perform better than FR-HARQ. In contrast, most of the prior outcomes approximate the effective capacity with the lack of insightful discussions that hampers the optimal design of HARQ systems. Finally, the numerical results corroborate the analytical ones.

Non-orthogonal multiple access (NOMA) is considered to be one of the promising multiple access techniques for 5G networks. This paper studies a decode-and-forward cooperative underlay cognitive radio NOMA network. Closed-form expressions for the outage probability of two NOMA secondary destination users (D1and D2) are evaluated. Furthermore, optimal power allocation factors for different distances of D1are found to satisfy the outage probability (OP) fairness for both users. Moreover, the OP for NOMA compared to that of conventional multiple access. Finally, the obtained analytical expressions are corroborated by Monte Carlo simulation.

This paper investigates the performance of Multipleinput multiple-output non-orthogonal multiple access (MIMONOMA) systems with randomly deployed users, where the randomly deployed NOMA users follow Poisson point process (PPP), the spatial correlation between MIMO channels are characterized by using Kronecker model, and the composite channel model is used to capture large-scale fading as well as small-scale fading. The spatial randomness of users' distribution, the spatial correlation among antennas and large-scale fading will severally impact the system performance, but they are seldom considered in prior literature for MIMO-NOMA systems, and the consideration of all these impact factors challenges the analysis. Based on zeroforcing (ZF) detection, the exact expressions for both the average outage probability and the average goodput are derived in closedform. Moreover, the asymptotic analyses are conducted for both high signal-to-noise ratio (SNR) (/small cell radius) and low SNR (/large cell radius) to gain more insightful results. In particular, the diversity order is given by δ = Nr- M + 1, the average outage probability of k-th nearest user to the base station follows a scaling law of O (Dα(Nr-M+1)+2k), the average goodput scales as O(D2) and O(D-2) as D → 0 and D → ∞, respectively, where Nr, M, α and D stand for the number of receive antennas, the total number of data streams, the path loss exponent and the cell radius, respectively. The analytical results are finally validated through the numerical analysis.

This paper develops an interference aware design for cooperative hybrid automatic repeat request (HARQ)-assisted non-orthogonal multiple access (NOMA) scheme for large-scale device-to-device (D2D) networks. Specifically, interference aware rate selection and power allocation are considered to maximize long term average throughput (LTAT) and area spectral efficiency. The design framework is based on stochastic geometry that jointly accounts for the spatial interference correlation at the NOMA receivers as well as the temporal interference correlation across HARQ transmissions. It is found that ignoring the effect of the aggregate interference, or overlooking the spatial and temporal correlation in interference, highly overestimates the NOMA performance and produces misleading design insights. An interference oblivious selection for the power and/or transmission rates leads to violating the network outage constraints. To this end, the results demonstrate the effectiveness of NOMA transmission and manifest the importance of the cooperative HARQ to combat the negative effect of the network aggregate interference. For instance, comparing to the non-cooperative HARQ-assisted NOMA, the proposed scheme can yield an outage probability reduction by 21%. Furthermore, an interference aware optimal design that maximizes the LTAT given outage constraints leads to 17% throughput improvement over HARQ-assisted orthogonal multiple access scheme.

This paper investigates the impact of antenna correlation on full duplex massive multiple-input multiple-output (MIMO) two-way relaying systems. To be general, all nodes in the system are equipped with multiple antennas and arbitrary antenna correlation is considered. Deterministic equivalent of the achievable sum-rate is first given, which enables the derivation of the asymptotic sum-rate when the number of relay antennas is large. The impact of antenna correlation on the asymptotic sum-rate is then thoroughly analyzed. The results reveal that with a large number of relay antennas, the asymptotic sum-rate is independent of the antenna correlation at the relay's transmitter side, however it is Schur-concave with respect to the eigenvalue vectors of the involved antenna correlation matrices at both the users' transceiver sides and the relay's receiver side. In other words, the antenna correlations at both the users' transceiver sides and the relay's receiver side have detrimental effects on the sum-rate. Moreover, the antenna correlation at the users is more significant to the asymptotic sum-rate than that at the relay. The analysis is general and can be easily reduced to consider half-duplex and/or one-way massive MIMO relaying systems. Numerical results are finally provided to validate the analysis. It is also shown that a larger number of user antennas may not always lead to a higher sum-rate, and thus proper selection of the number of user antennas is necessary to maximize the sum-rate.

In this paper, we investigate wireless information and power transfer in full-duplex (FD) two-way massive MIMO amplify-and-forward (AF) relaying network. In the considered network, the relay is an energy demander and applies time switching scheme to harvest energy from the users for amplifying and forwarding the users' signals. Different from prior studies, MIMO users and antenna correlation are considered. Achievable sum-rate is analyzed and the impact of antenna correlations is discovered. It is revealed from the analysis that the transmission power at the MIMO users can be cut down proportionally to 1/Mrand/or the transmission power at the relay can be scaled down proportionally to 1/Mr2to maintain a constant sum-rate, when massive antennas are equipped at the relay. Here Mrdenotes the number of antennas at the relay. Moreover, the optimal time switching ratio for energy harvesting at the relay is found to maximize the sum-rate. Finally, the analytical results are validated by computer simulations.

In this paper, the performance of full-duplex (FD) two-way massive multiple-input multiple-output (MIMO) relay systems is analyzed. One popular linear relaying scheme, i.e., maximum ratio combining/maximum ratio transmission relaying, is particularly investigated. Different from prior analyses, multi-pair of MIMO users and antenna correlation at both the relay and the users are considered. Asymptotic sum-rate under a general case is first derived. Four special power scaling cases are then discussed and the corresponding asymptotic sum-rates are derived in simple forms with clear insights. The analytical results clearly quantify the impacts of self-loop interference, intra-group interference and antenna correlation, and discover the power scaling laws under various cases. It is found that the transmission powers at both the MIMO users and the relay can be scaled down inversely proportional to the number of antennas at the relay while maintaining a desirable sum-rate when the number of antennas at the relay grows large. The FD two-way massive MIMO relay system is finally compared with the half-duplex (HD) counterpart. It is revealed that under two special power scaling cases, the FD system can achieve double sum-rate when compared with the HD system. However, under the other two power scaling cases, self-loop interference and intra-group interference degrade the asymptotic sum-rate and the FD system may perform worse than the HD system. The maximum allowable self-loop interference for the FD system to outperform the HD system is given explicitly. In addition, the impact of antenna correlation at the users and the relay is analyzed. The results show that the antenna correlation at the users has complicate impact on the sum-rate, while the antenna correlation at the relay may not affect the sum-rate under certain power scaling cases.

In this paper, outage performance of hybrid automatic repeat request with incremental redundancy (HARQ-IR) is analyzed. Unlike prior analyses, time-correlated Nakagami-m fading channel is considered. The outage analysis thus involves the probability distribution analysis of a product of multiple correlated shifted Gamma random variables and is more challenging than prior analyses. Based on the findings of the conditional independence of the received signal-to-noise ratios, the outage probability is exactly derived by using conditional Mellin transform. Specifically, the outage probability of HARQ-IR under time-correlated Nakagami-m fading channels can be written as a weighted sum of outage probabilities of HARQ-IR over independent Nakagami fading channels, where the weightings are determined by a negative multinomial distribution. This result enables not only an efficient truncation approximation of the outage probability with uniform convergence but also asymptotic outage analysis to further extract clear insights, which have never been discovered for HARQ-IR even under fast fading channels. The asymptotic outage probability is then derived in a simple form, which clearly quantifies the impacts of transmit powers, channel time correlation, and information transmission rate. It is proved that the asymptotic outage probability is an inverse power function of the product of transmission powers in all HARQ rounds, an increasing function of the channel time correlation coefficients, and a monotonically increasing and convex function of information transmission rate. The simple expression of the asymptotic result enables optimal power allocation and optimal rate selection of HARQ-IR with low complexity. Finally, numerical results are provided to verify our analytical results and justify the application of the asymptotic result for optimal system design.

Virtual Network Mapping (VNM) has been a key issue for network virtualization need to be addressed. A traditional substrate network is usually managed by multicast InPs, and many applications in the substrate network can be characterized by hybrid virtual networks with both unicast and multicast requests. However, to the best of our knowledge, few researches focus on the problem of hybrid virtual network mapping across multiple domains (HVNMMD). In this paper, we investigate the HVNMMD problem and propose two algorithms to solve this problem efficiently: i) the decomposition-based algorithm, HVNMMD_D; and ii) the spectral clustering based algorithm, HVNMMD_SC. We evaluate the performance of our algorithms through conducting simulation experiments. The simulation results show that the proposed algorithms perform better than existing approaches.

Wireless multicast is a spectrum efficient method for group-data transmission. This paper focuses on energy efficient two-stage cooperative multicast transmissions, aiming to minimize the total transmission power while ensuring a practical coverage ratio. To focus on the relationship between the base station (BS) power at the first stage (PBS,C) and the total power consumption, a selective combining based on average received signal strength (SCA) is assumed at the receiver and the user density is supposed to be sufficiently high. Then a mobile relay (MR) arrangement based on sector ring structures is proposed for the second stage transmission, followed by an analytical derivation of the optimal PBS,C conditioned on a desired coverage ratio. In addition, further approximations are exploited to provide a simple theoretical estimation for the optimal PBS,C, whose effectiveness is verified by numerical results. It is shown that compared to the conventional one-stage multicast transmission, the proposed two-stage cooperative scheme can reduce the total power consumption and the BS power consumption by more than 40% and 80%, respectively. Although the proposed scheme is obtained based on SCA, when the user density is higher than 2 × 10-4, a coverage ratio of 95% can be guaranteed by using a practical CP combining with the proposed scheme. Moreover, the effectiveness of the proposed MR arrangement is verified by simulations where it outperforms other three arrangements. It is also shown that targeting at minimizing the total transmission power with guaranteed coverage, the proposed scheme significantly outperforms the existing two-stage scheme in terms of energy consumption and efficiency.

In this paper, the outage performance of an opportunistic amplify-and-forward cooperative downlink cellular system is analyzed. Different from prior works, the randomness of the network topology is taken into account by modeling the user nodes as a homogeneous Poisson point process. Based on this model, outage probability is derived and the impacts of several system parameters are investigated. Under certain conditions, the closed form expression of outage probability is derived. It is found from our results that the diversity order of this opportunistic cooperative system at high signal-to-noise-ratio (SNR) is one. Moreover, optimal power allocation can be found from our results to minimize the outage probability.

In this paper, outage performance of hybrid automatic repeat request (HARQ) technique in interference dominant mobile ad hoc networks (MANETs) is analyzed. Unlike prior analysis, interference and spatial randomness of the nodes (i.e. the randomness in the number of nodes and nodes' locations) are considered. Based on the theory of point processes, the outage probabilities of two popular HARQ techniques, that are type-I HARQ and HARQ with chase combining (HARQ-CC), are derived in closed forms. The outage performance gain of HARQ-CC over type-I HARQ is also discussed and is found to follow the scaling law of Θ (k2(k+1)/α) where α is the path loss exponent and k is the number of retransmissions. It is also demonstrated that both type-I HARQ and HARQ-CC can significantly improve the communication performance even in interference dominant MANETs. Furthermore, it is revealed that in most cases HARQ-CC is superior over type-I HARQ, however, for a dense network type-I HARQ can provide comparable performance with lower complexity than HARQ-CC and is thus more preferable.

In this paper, the uplink coverage probability for heterogeneous cellular systems with range expansion is analyzed and derived in closed-form. Unlike most of the previous analyses of heterogeneous systems, the randomness of not only the number of mobile users but also their locations is taken into account in the analysis based on the theory of stochastic geometry. With the derived analytical results, the impacts of various system parameters on the uplink performance are investigated in detail. The correctness of the analytical results is also verified by simulations. These analytical results can thus serve as a guidance for the design of the heterogeneous systems.

Both Multiple Input and Multiple Output (MIMO) and Smart Antenna (SA) have been widely accepted as promising schemes to improve the spectrum efficiency and coverage of mobile communication systems. This paper addresses the issue of Direction of Arrival (DOA) estimation in Virtual MIMO (VMIMO) systems which adopt SA simultaneously. First of all, we propose a VMIMO scheme for DOA estimation in which a set of User Equipments (UEs) are grouped together to simultaneously communicate with the Base Station (BS) on a given Resource Block (RB). In this scheme, the BS has multiple antennas and can estimate DOA of each UE in the group simultaneously. However, in practical environment because of reflection and refraction of signals, the received signals may be coherent. It is desirable that the DOA estimation in VMIMO could provide high resolution when the number of independent signals is unknown, which can not be achieved by existing algorithms. In order to solve this problem, we propose an Automatic Weighted Subspace Fitting (AWSF) algorithm that can detect the number of independent signals automatically and show accurate DOA estimation. Then with consideration of computational cost and sampling cost while using the AWSF algorithm, we propose a utility factor to determine the optimal number of UEs in VMIMO scheme, i.e., the order of VMIMO. Finally, the performance of the proposed AWSF algorithm and the efficiency of the utility factor criterion are demonstrated by simulations.

Social-based forwarding algorithms provide a new perspective on the study of routing in opportunistic mobile networks, and all of these schemes assume a uniform pattern for message generating rule. However, this is unconvincing due to the heterogeneity of contact rates in human communication patterns. In this paper we propose three social-based communication pattern models and utilize them to evaluate the network performance of different social-based routing protocols based on several human mobility traces. We find that communication patterns could significantly affect the network performance and the influence degree largely depends on the social metrics which these communication patterns are based on. We contend that considering communication pattern is quite important for designing a practical routing algorithm in opportunistic mobile networks.

Online social search (OSS) brings forth a new way to harness the Internet for answers. In this paper, we study the balancing between OSS users' capabilities and responsibilities. Targeting a practical system design, we propose an analytical model that captures the heterogeneity of different referral sessions in OSS, and a distributed socio-aware referral strategy that can achieve the desired balance when the system reaches steady state. We show that configuring the strategy enables the system operator to control the flow of all posed questions in the system. We also discuss the implications of configuring the strategy from a gaming-strategy point of view.

In this paper, we propose a hybrid cargo-level tracking system for logistics. We highlight the special system requirements, discuss the design issues and identify the design principles. Then we propose an innovative hybrid system. As far as we know, this is the first system that exploits both infrastructure-based and infrastructure-less positioning schemes for practical cargo-level tracking. Compared with existing systems, the proposed system provides a ubiquitous cargo-level tracking solution with enhanced availability, reliability, and lower total costs.

Data streaming (DS) over peer-to-peer (P2P) networks has been intensively studied in recent years and there have been various schemes proposed already. To evaluate these schemes, either measurement in experimental implementations, or simulation and theoretical analysis have been used. The former is inadequate as data are collected from different experiments, while the latter lacks a proper theoretical dynamics model. Our research aims at providing a general theoretical model to evaluate DS over P2P systems and analyze their dynamic behaviors. In this paper, with the analysis and abstraction of the characteristics of peers and their organization in DS over P2P, we propose a general population dynamics model for DS over P2P with fixed population. The model depicts the dynamic distribution of peers as a closed Markov queuing network. In particular, the model is scheme-independent and can be used with various schemes. Through theoretical analysis, we prove the model has equilibrium and only one closed-form solution. Besides, we verify the model through simulations, and show that it is a helpful analytical tool with a case study.

Rate adaptation, as a challenging issue for wireless network design, has been an active research topic for years. Existing schemes either assume perfect channel information, or conduct rate adaptation in a black box way, hence can not achieve desirable performance. In this paper, we propose a novel scheme called correlation based rate adaptation (CORA) to address the rate adjustment problem. Unlike previous schemes, CORA splits rate into more atomic components and adjusts them according to the correlation between rate adaptation actions and transmission results. We use IEEE 802.11n as the context for CORA design, where transmission mode has been expanded to spatial dimension in addition to the usual modulation and convolution coding mechanisms. Performance evaluation shows that CORA can conduct rate adaptation in a more logical way and significantly outperform the comparison scheme.

An adaptive framework is proposed for multi-user video transmission over orthogonal frequency division multiple access (OFDMA) systems. Utilizing the channel knowledge, a two-step heuristic sub-carrier assignment algorithm is proposed to achieve unequal error protection for the video data. The approach also solves the fairness issue among different users that may be caused by varying channel quality on different sub-carriers. Meanwhile, multi-user channel gain is also achieved. The proposed framework significantly improves the video transmission quality with little extra computational complexity and system overhead.

Abstract Spectrum sharing in Open Spectrum systems is a challenging problem, particularly since users experience dynamic spectrum availabilities that vary over time and location. In the absence of any centralized infrastructure, users coordinate their spectrum usage to support fair access of spectrum and avoid interference. One critical challenge is to construct an efficient signaling path in the presence of spectrum dynamics, where users exchange control and negotiation information reliably. In this paper, we propose a distributed coordination protocol (HD‐MAC) to construct an in‐band control path, without relying on the existence of a pre‐assigned out‐of‐band control channel. In particular, we propose a distributed group formation to construct coordination path using local common channels, and a heterogeneity‐aware ranking scheme to select channels for data transmissions. Extensive experimental results demonstrate the efficiency and reliability of the proposed approach. Copyright © 2007 John Wiley & Sons, Ltd.

In this paper, we propose an adaptive video transmission scheme to achieve unequal error protection in multichannel ad hoc networks. In our scheme, video data is divided into high priority (HP) and low priority (LP) portions, and mobile nodes have two channels for video transmission. A channel quality metric, busy time ratio (BTR), is employed to characterize the channel quality. The channel with better BTR metric is used for HP data, and the other for LP. Further, adaptive load control and delay-constrained queue management schemes are proposed to improve performance. Simulation results demonstrate the performance of video streaming is greatly enhanced in multichannel multi-hop ad hoc networks.

Proper channel selection is essential to exploit the benefits of multi-channel systems by distributing conflicting transmissions across non-interfering channels. Critical to channel selection is the channel quality metric. We propose a busy time ratio (BTR) metric that captures channel contention and user traffic load under a variety of network dynamics. We also propose a distributed collaborative sensing scheme to reduce sensing overhead and energy consumptions. The proposed algorithms can be implemented using conventional 802.11 hardware with single radio interface. The proposed metric can be integrated with routing and channel selection. Experimental results show that the proposed scheme significantly outperforms the existing channel selection methods.

We propose a hybrid multiple-input-multiple-output (MIMO) architecture to implement unequal error protection (UEP) for video delivery in MIMO systems. On the transmitter side, some of the transmit antennas are used to implement transmit diversity for delivering video data with high priority, while others are employed for spatial multiplexing to transmit video data with low priority. On the receiver side, a hybrid signal detection mechanism is adopted to separate and decode the mixed data. The goal is to exploit the diversity gain to provide better protection to the high priority data, while transmitting the low priority data with spatial multiplexing to achieve high data rate. Analysis and simulation results demonstrate that the proposed UEP mechanism significantly enhances the quality of video reception with high spectrum efficiency.

We provide a sub-channel partitioning based unequal error protection (UEP) scheme for a space-time block coded orthogonal frequency division multiplexing (STBC-OFDM) system. In such a scheme, video data is partitioned into high-priority (HP) and low-priority (LP) layers according to the importance of the data. At the receiver side, OFDM sub-channels are partitioned into high-quality (HQ) and low-quality (LQ) groups according to the estimated channel qualities. Based on the feedback of sub-channel partitioning results, the transmitter assigns HP and LP video data to the corresponding HQ and LQ sub-channels. Through theoretical analysis, we show there is indeed a significant BER difference between the HQ and LQ sub-channels, which can be exploited by UEP. Based on the analysis, we provide a criterion for determining the appropriate transmission power. Through computer simulations, we show that the proposed scheme offers significant performance gain compared to conventional methods. We also demonstrate that the scheme is the least sensitive to channel estimation errors among all compared schemes, and is hardly influenced by the Doppler spread. The feedback overhead can also be reduced with almost no performance penalty by bundling several neighboring sub-channels together and assigning them to the same group.

In this work, we introduce the idea of adaptive sub-channel allocation based unequal error protection (ASCA-UEP) to a space-time block coded orthogonal frequency division multiplexing (STBC-OFDM) system. In such a system, UEP is realized by adaptively allocating and transmitting high-priority and low-priority video data over high-quality and low-quality sub-channels, respectively. Further, we propose two ASCA-UEP schemes in a time division duplex (TDD) system: a receiver-based scheme and a transmitter-based scheme. Analysis and simulation results demonstrate that ASCA-UEP greatly enhances the quality of video reception, and the transmitter-based scheme is more robust to uplink channel noise than the receiver-based scheme, and is thus preferred when the receiver is power-constrained and the transmitter has sufficient power.