Frame aggregation (FA) significantly enhances throughput by frame header reduction and payload compression. However, FA affects the performance of latency and deadline adherence due to aggregation delays. In this paper, we investigate time-sensitive FA scheduling to ensure low latency and low delay violation probability while maintaining high throughput. We formulate the complex FA scheduling problem by constraint programming (CP), which considers variable FA sizes and queue availability in FA scheduling and yields near-optimal solutions. To address the NP-hard problem imposed by CP, we propose a composite dispatching cost - genetic algorithm (CDC-GA) for FA scheduling. According to our theoretical analysis, the proposed CDC encourages more aggressive aggregation when frame header overhead is high, thereby optimizing for both throughput and time-centric performance. The GA then refines the initial schedule using a proposed ternary chromosome encoding, which comprehensively captures all necessary scheduling decisions. The proposed CDC-GA approach accommodates various FA structures (e.g., fixed/variable length, with/without data compression), and outperforms the existing approaches, both conventional and learning-based, by adeptly handling variable FA sizes and integrating the impact of FA on time-sensitive performance. Simulation results show that the proposed CDC-GA significantly outperforms the existing approaches, improving throughput by 77% and reducing average latency by 36%.

Massive heterogeneous IoT networks encounter significant random access (RA) challenges due to diverse Quality of Service (QoS) requirements and resource constraints. To address these issues, we first propose a fuzzy logic-assisted multi-criterion access priority ranking (FL-MCAPR) scheme to prioritize RA for IoT devices, integrating delay, channel interference, and energy factors. The resulting suitability values enable adaptive and fine-grained backoff adjustments in large-scale IoT deployments. Next, hybrid RA control schemes with a deployability-descending double-queue (D3Q) structure and access priority-backoff window model optimize preamble and backoff allocation. In addition, preamble parallelization and early-stage collision detection enhance RA throughput by expanding resources and reducing collisions. Using D3Q, the analytical RA throughput is derived, informing an optimization problem to determine Access Class Barring (ACB) factors balancing low delay and energy efficiency, considering all RA resources. Building on these results, the hybrid fuzzy logic-guided RA management (HFL-RAM) scheme is developed for comprehensive RA management, systematically evaluated in terms of delay and throughput. Finally, a lightweight pseudo-Bayesian estimation method is applied, which relies solely on two observable quantities to estimate the contending MTCD traffic. Simulation results demonstrate that the proposed HFL-RAM scheme consistently outperforms conventional approaches, effectively managing traffic heterogeneity across a wide range of traffic loads.

With the rapid development of large multimodal models (LMMs), multimodal understanding applications are emerging. As most LMM inference requests originate from edge devices with limited computational capabilities, the predominant inference pipeline involves directly forwarding the input data to an edge server which handles all computations. However, this approach introduces high transmission latency due to limited uplink bandwidth of edge devices and significant computation latency caused by the prohibitive number of visual tokens, thus hindering delay-sensitive tasks and degrading user experience. To address this challenge, we propose a task-oriented feature compression (TOFC) method for multimodal understanding in a device-edge co-inference framework, where visual features are merged by clustering and encoded by a learnable and selective entropy model before feature projection. Specifically, we employ density peaks clustering based on$K$nearest neighbors to reduce the number of visual features, thereby minimizing both data transmission and computational complexity. Subsequently, a learnable entropy model with hyperprior is utilized to encode and decode merged features, further reducing transmission overhead. To enhance compression efficiency, multiple entropy models are adaptively selected based on the characteristics of the visual features, enabling a more accurate estimation of the probability distribution. Comprehensive experiments on seven visual question answering benchmarks validate the effectiveness of the proposed TOFC method. Results show that TOFC achieves up to 52% reduction in data transmission overhead and 63% reduction in system latency while maintaining identical task performance, compared with neural compression ELIC.

Pilot plays a vital role in acquiring channels, but leading to high pilot overhead in a short packet that includes a pilot part and an effective blocklength part. The removal of pilots for channel estimation is an effective way to reduce age of information (AoI) in short-packet transmissions, by increasing transmission power in effective blocklength and reducing the optimum blocklength, given the total fixed transmission power. We investigate spatial correlation to minimize AoI for unmanned aerial vehicle (UAV)-enabled wireless data collection and wireless power transfer (WPT) in short-packet transmissions, by reducing the number of pilots as much as possible, while guaranteeing block error rate (BLER) performance. A number of sensors in proximity are organized into a cluster. Only the reference sensor needs pilots to estimate channel, while the other sensors utilize spatial correlation to estimate their channels in a cluster requiring no pilots. Channel estimation error is introduced, and considered in BLER and in cluster region determination via elevation angle of UAV. The proposed approach tolerates the variance of channel estimation error up to 0.9, and in such case still provides AoI performance better than the existing work where each sensor requires pilots to estimate channels.

In this paper, we consider a practical integrated sensing and communication (ISAC) scenario in industrial Internet of Things (IIoT). In this scenario, a robot acted as a mobile base station (BS) performing sensing to locate a logistics transport robot (LTR) while also communicating with multiple production lines (PL). We predict the motion parameters of LTR in each time slot and derive the Cramér-Rao bound (CRB) of angle and distance estimation. Afterward, we formulate a joint CRB minimization problem by optimizing the transmit beamforming for communication and sensing. We convert the formulated problem into a two-tier alternating optimization approach by constructing precise surrogates for the non-convex objective functions and constraints. A closed-form expression is derived for solving the outer layer problem. In addition, we employ a convex framework to address the inner layer problem. Numerical results validate the superiority of the proposed algorithm, especially when the BS is far away from the sensing target.

In this paper, we investigate a novel cooperative nonorthogonal multiple access (C-NOMA) strategy for unmanned aerial vehicle (UAV)-enabled wireless communications, where UAV simultaneously works as a user and a time-division-duplex (TDD) relay in two hops. In the first hop, the cell-edge terrestrial users (TUs) transmit signals to UAV. In the second hop, UAV re-transmit TU's signals to base station (BS) together with its own signals based on C-NOMA. We propose a closed-form nearoptimal elevation angle (NOEA) based UAV's height optimization approach, where the elevation angle is utilized to derive a closedform solution to the UAV height optimization, based on the proof that the channel gain in the second hop is concave with respect to the elevation angle between UAV and BS. The proposed NOEA approach achieves near-optimal performance, while requiring no exhaustive search and no iteration. We propose a joint optimization in the TDD relay mode, referred to as JOT, to maximize TU's data rate, while guaranteeing UAV's target data rate. The proposed JOT algorithm allows a low-complexity closed-form optimal power allocation between TU and UAV in two hops, and provide TU's achievable data rate and spectral efficiency higher than the state-of-the-art methods.

Direct current (DC) is added into intensity modulation and direct detection (IM/DD) signals in time domain to make most negative signals be positive in DC biased-optical orthogonal frequency division multiplexing (DCO-OFDM) systems for optical wireless communication (OWC). To the best of the authors' knowledge, this is the first work to utilize DC inherent in DCO-OFDM signals to allow semi-blind joint synchronization and channel estimation for DCO-OFDM OWC systems. We propose a novel semi-blind DC-based joint estimation (SDJE) approach, where sampling time offset (STO) as one of timing synchronizations and channels can be jointly estimated based on DC inherent in DCO-OFDM signals. The proposed SDJE approach is spectral-efficient, requiring no pilot. This is different from state-of-the-art works that employ a number of pilots to perform synchronization and channel estimation. We formulate a cost function by exploring the difference between the received signals and DC signals. The joint STO and channel estimations can be conducted by minimizing the formulated cost function. Furthermore, we propose a dimensionality reduction approach, and design an offline database to reduce complexity. Simulation results show that the proposed SDJE approach provides bit error rate (BER) performance close to the ideal case with no STO and perfect channel state information (CSI).

Blockchain is a decentralized and secure digital ledger system that ensures data integrity through immutable records and cryptographic consensus mechanisms. However, in mobile blockchain networks, the computation-intensive proof-of-work (PoW) mining process often imposes a significant burden on mobile users (MUs) who serve as miners, particularly given their limited computing resources. Mobile edge computing (MEC) offers a promising solution to alleviate the burden on MUs, by enabling them to offload their mining tasks to nearby edge servers. While existing studies have explored MEC-assisted blockchain networks in both single-server and multi-server scenarios, they often overlook crucial aspects of blockchain networks, such as the transmission and computation delays inherent in the mining process. In this work, we investigate a more realistic MEC-assisted mobile blockchain network, where mining tasks are explicitly modeled with delay constraints to better capture real-world performance challenges. To analyze the strategic interactions between MUs and edge computing service providers (ECPs), we formulate a two-stage multi-leader and multi-follower Stackelberg game, which consists of an ECP Resource Pricing (ERP) game at Stage I, and an MU Resource Competition (MRC) game at Stage II. Specifically, in the ERP game at Stage I, ECPs, acting as leaders, set the resource prices for MUs; and in the MRC game at Stage II, MUs, acting as followers, determine their computing resource demands based on the prices of ECPs. We first prove the existence of Nash equilibrium (NE) for both games, and then derive the closed-form conditions for the NE of the MRC game at Stage II. Based on the above, we further propose a sub-gradient-based resource pricing algorithm that can converge to the NE of the ERP game at Stage I. Simulation results show that, when compared to the centralized cooperative solution, our proposed non-cooperative game approach can significantly reduce the computational complexity, while incurring only a modest performance degradation, e.g., the social welfare loss ranges from 6.64% to 9.96%.

This study examines a network-assisted full-duplex (NAFD) cell-free massive MIMO (CF-mMIMO) system, in which half-duplex access points (APs) simultaneously serve multiple uplink and downlink user equipment (UEs) using the same frequency resources. NAFD technology facilitates full-duplex transmission over existing half-duplex hardware through dynamic scheduling of AP operating modes, resulting in significant improvements in system spectral efficiency (SE). To ensure fairness among all UEs, we aim to maximize the minimum SE across UEs by jointly optimizing the AP operation modes and the uplink and downlink power control of the UEs, which helps mitigate severe cross-link interference for UEs with the lowest SE. We propose a novel joint optimization scheme based on integer linearization techniques to address this strongly coupled mixed-integer non-convex problem and achieve a near-optimal solution. Simulation results demonstrate that our proposed scheme outperforms the benchmark approach, providing a more equitable quality of service throughout the coverage area.

In response to the demand for uplink transmission from communication users in near-field (NF) integrated sensing and communication (ISAC) systems, this paper proposes a coupled uplink-downlink (UL-DL) optimization approach. To address the overlapping UL signal and echo, the coupled UL-DL optimization problem requires the simultaneous optimization of both transmit and receive beamforming vectors, thereby substantially increasing the complexity and dimensionality of the problem. Furthermore, in NF systems, the channel incorporates both distance and angle parameters, thereby expanding the parameter space and increasing the spatial degrees of freedom for the signal, thus adding complexity to beamforming optimization. To address these challenges, we introduce a novel framework for coupled UL and DL transmissions in NF ISAC systems. This framework aims to maximize the sum rate of UL and DL users while satisfying the sensing requirements and adhering to the transmit power constraints. Given the highly non-convex nature of the problem, we solve it using an iterative algorithm based on successive convex approximation (SCA). Simulation results demonstrate the superior performance of the proposed algorithm, effectively enhancing both communication and sensing capabilities while addressing the real-time uplink data upload needs in practical NF ISAC applications.

In this study, we consider a more general scenario involving multiple simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) in cell-free massive multiple-input multiple-output (CF-mMIMO) systems. Our objective is to maximize the weighted sum rate for users by decoupling the original problem into two components: the active beamforming design at the access points (APs) and the passive beamforming design at the STAR-RIS. We employ fractional programming to optimize these components alternately. The rank-one constraint in the passive beamforming design, which is proven to be an NP-hard problem, represents the primary challenge. To address this issue, we introduce a novel low-complexity algorithm based on sequential rank-one constraint relaxation (SROCR). Instead of entirely eliminating the rank-one constraint, our SROCR algorithm utilizes a progressive relaxation approach to gradually ease the constraint and identify a generic rank-one suboptimal feasible solution, ultimately converging to a solution that satisfies the rank-one condition. Numerical results demonstrate that our algorithm achieves comparable performance to existing algorithms while significantly reducing complexity, thus outperforming other baseline algorithms.

In this study, we investigate symbiotic radio-assisted cell-free massive multiple-input multiple-output (SCF-mMIMO) systems in which multiple access points serve primary users and backscatter devices. We propose a semi-blind channel estimation scheme based on the expectation maximization (EM) algorithm to reduce pilot overhead and iteratively achieve performance close to that of maximum likelihood (ML) estimation. Unlike the traditional EM algorithm, we derive a modified EM algorithm by incorporating suitable priors for the channel coefficients to estimate the aggregate channel. Simulation results show that the proposed EM algorithm for the SCF-mMIMO system achieves good performance with fewer pilots, approaching that of the ML estimation. In addition, the modified EM algorithm using channel priors outperforms traditional EM algorithms. These results suggest that semi-blind channel estimation holds considerable promise for SCF-mMIMO systems.

In this paper, a 2-layer rate-splitting multiple access (RSMA) framework with a balanced clustering design is proposed for cell-free massive multiple-input multiple-output (CF-mMIMO) systems. Aiming at enhancing spectral efficiency (SE) and meanwhile ensuring low-complexity and scalability in large-scale systems, this work addresses different types of multi-user interferences by utilizing a two-stage optimization scheme with a designed spatial reduction matrix that encompasses the clustering design and joint RSMA. First, a balanced clustering design is developed for 2-layer RSMA to manage intra-and inter-cluster interference more efficiently than conventional user-centric clustering, simultaneously considering AP-user connectivity in CF-mMIMO systems and the impact of cluster similarity on RSMA. By employing the spectral clustering method to solve the bipartite graph partitioning problem with the min-max cut objective, the number of clusters is determined adaptively. Based on the above clustering design, a joint optimization of inner and outer RSMA is proposed to mitigate intra-and inter-cluster interferences simultaneously. Simulation results verify the SE enhancement, low-complexity, and scalability of the proposed 2-layer RSMA framework, compared with benchmark frameworks.

In this article, we focus on the resource allocation and autonomous aerial vehicle (AAV) 3-D trajectory design for the AAV-ground base station (GBS) coordinated multipoint nonorthogonal multiple access (CoMP-NOMA) system to maximize the sum-rate of CoMP users while maintaining users’ high Quality of Service requirements. The main contributions of this article are summarized as follows: 1) with the assistance of closed-form power allocation result, a generalized joint user scheduling and power allocation (G-USPA) algorithm is proposed to derive the optimal user scheduling solution; 2) by revealing the monotone increasing relationship between the sum transmit power and the transmit rates of non-CoMP users, the optimal rate of each non-CoMP users turns out to be its inherent minimum required rate, consequently, the optimal transmit rates and power allocation of all users can also be derived; and 3) moreover, considering the Line of Sight (LoS) and non-LoS factors in the air-ground channel, the 3-D trajectory of AAV is designed based on successive convex approximation to provide a flexible user-centric service. The proposed G-USPA algorithm is compatible with the AAV trajectory design, which is optimized alternatively and can lead to fast convergence. Numerical results verify that the 3-D AAV-GBS CoMP-NOMA model and the G-USPA scheme have a superior performance in terms of total system sum rate and the sum rate of CoMP users over the non-CoMP AAV assisted nonorthogonal multiple access (NOMA) systems.

Carrier frequency offset (CFO) and channel equalization are two critical problems for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) wireless communication systems in ultra-reliable and low latency communication (URLLC). In this paper, we propose a semi-blind precoding aided structure that includes two CFO estimation approaches and an independent component analysis (ICA) based equalization scheme for MIMO OFDM systems in URLLC, requiring no pilots. We design a non-redundant balanced precoding strategy, killing two birds with one stone, where reference signals are superimposed into source signals to simultaneously allow CFO estimation and ambiguity elimination in the ICA-equalized signals. The proposed precoding-aided CFO estimation approach performs by maximizing a cost function formulated via the cross-correlations between the reference signal and the received signal. We further propose a low-complexity closed-form CFO estimation approach, by transforming the formulated cost function into a new expression. To maximize bit error rate (BER) performance, particle swarm optimization (PSO) is employed to perform the joint optimization of precoding constant and the number of OFDM blocks for CFO estimation, while avoiding exhaustive search. The proposed semi-blind precoding-aided structure provides a trade-off between performance, complexity and spectral efficiency for MIMO OFDM systems in URLLC.

We consider a real-time state reconstruction system for industrial metaverse. The time-varying physical process states in real space are captured by multiple sensors via wireless links, and then reconstructed in virtual space. In this paper, we use the spatial-temporal correlation of the sensor data of interest to infer the real-time data of the target sensor to reduce the mean squared error (MSE) of reconstruction for industrial metaverse under short-packet transmission (SPT). Both synchronous and asynchronous transmission modes for multiple sensors are considered. It is proved that the average reconstruction MSE and average block error probability (BLEP) have a positive correlation under inference with synchronous transmission scheme, whereas they have a negative correlation under inference with asynchronous transmission scheme in certain conditions. Additionally, the average reconstruction MSE decreases monotonically with the mean squared spatial correlation (MSSC), which characterizes the global spatial correlation level. With a high BLEP or long transmission period, even under weak MSSC, the inference scheme still significantly reduces the average reconstruction MSE compared to the no inference case. Moreover, closed-form MSSC thresholds are derived for the superiority regions of the inference with synchronous transmission and inference with asynchronous transmission schemes, respectively. Adaptations of blocklength and time shift of asynchronous transmission are conducted to minimize the average reconstruction MSE. Simulation results show that the two inference schemes outperform the no inference case, with an average MSE reduction of more than 50%.

The integration of Internet-of-Things (IoT) and the fifth-generation (5G) networks presents challenges due to low access efficiency caused by massive random access (RA) requests. To this end, both preamble parallelization (PP) and colliding preambles reuse (CPR) modes are proposed as critical RA control methods to enhance access performance. In this paper, we aim to maximize the random access efficiency (RAE) in a smart city scenario to determine the optimal control mode selection between the PP and CPR over the device heterogeneity with limited RA resources. We establish an access order-backoff window (AOBW) mapping model, where RA requirements are mapped onto the backoff time. It offers greater flexibility of backoff window size than previous work to guarantee diverse application and service requirements. Thanks to the derived closed-form expressions of the actual RAE, an RAE maximization algorithm is developed, which optimizes performance across both PP and CPR modes, achieving optimal performance in access delay and access throughput.

In this paper, a novel dual-space (DS) two-stage channel estimation scheme is proposed for massive multiple-input multiple-output hybrid beamforming (m-MIMO-HBF) systems with pilot contamination. This is the first work in m-MIMO which takes into account both HBF and pilot contamination. The proposed channel estimation scheme consists of two stages. In Stage I, a coarse sparse channel estimation is conducted in the beamspace based on the proposed variable-stepsize adaptive matching pursuit (VS-AMP) algorithm, which enhances the channel recovery accuracy by carefully selecting and setting the scaling factor and step size. In Stage II, the impact of pilot contamination from interfering users is initially mitigated through the beamspace separability, while coarse channel estimate is further refined into a quasi-sparse format by subspace projection. Simulation results show that the proposed channel estimation scheme outperforms the state-of-the-art schemes in terms of normalized mean square error (NMSE) of channel estimation and bit error rate. The NMSE of the proposed channel estimation scheme also exhibits higher resilience to interference intensity while maintaining comparable computational complexity.

External passive intermodulation (ePIM) constitutes a pervasive near-field interference within frequency division duplex (FDD) communication systems, originating from passive elements next to antennas, significantly affecting uplink operations. In contrast to existing ePIM suppression methods which spend high complexity for real-time cancellation through modeling, we propose a spatial filtering algorithm employing diagonal loading (DL) based oblique projection techniques to suppress near-field ePIM interference in uplink massive multiple-input multiple-output (MIMO) FDD systems. The angles of arrival (AOA) estimation of far-field users in a multipath model of far-field and near-field mixed signals is facilitated through compressive sensing algorithms, which reduces the interference of near-field ePIM at a low cost. Simulation results demonstrate satisfactory performance of the proposed algorithm, with high robustness against misalignment of steering vectors estimation.

We investigate the relationship between transmission power and clipping distortion for asymmetrically clipped optical orthogonal frequency division multiplexing (ACO-OFDM) visible light communication (VLC) systems with light emitting diode (LED)'s limited bandwidth and upper clipping, which is modeled into the formulation of an achievable data rate problem. We formulate a unimodal function with respect to transmission power, which is proportional to the data rate on a single subcarrier. The local optimal transmission power on a single subcarrier is derived by the unimodal function. We formulate a data rate problem on all subcarriers with respect to transmission power optimization. By analyzing the derivative of data rate, the global optimal transmission power on all subcarriers exists in the range between the maximum and minimum local optimal transmission power over a number of single subcarriers, which is divided into a number of sub-ranges. The local optimal transmission power is used to derive the upper and lower bounds of the derivative of data rate in each sub-range, respectively. To maximize the data rate, the derivative of data rate must be zero, resulting in the target sub-range that the upper bound is higher than zero, and the lower bound is lower than zero, reducing the exhaustive searches into a small number of sub-ranges for the global transmission power optimization. We propose a scaling-based power allocation (SPA) approach, where the global transmission power optimization in the reduced range and the waterfilling algorithm are combined, requiring no exhaustive search, while providing near-optimal performance.

In this paper, we propose a closed form (CF) based channel estimation approach for direct current biased optical-orthogonal frequency division multiplexing (DCO-OFDM) visible light communication (VLC) systems. This is the first work to utilize light emitting diode (LED)'s limited bandwidth to reduce the number of pilots while maintaining good performance. A comb-type pilot pattern is employed, requiring a small number of pilots on few DCO-OFDM subcarriers and blocks. It is high spectral efficiency, as a large number of pilots on all subcarriers and some blocks are not required as in a block-type pilot pattern. The proposed CF approach performs the line-of-sight (LoS) channel estimation and LED's limited bandwidth estimation in a CF via two formulated functions on two subcarriers. Simulation results show that the proposed CF approach provides bit error ratio (BER) and mean square error (MSE) performances better than the block-type pilot based methods in the literature, requiring less pilots.

We study an unmanned aerial vehicle (UAV)-enabled uplink heavy non-orthogonal multiple access (NOMA) system in this paper, where the UL communication becomes extremely important in some hot spot areas such as live concerts or soccer stadiums, and investigate the joint optimization of bandwidth assignment (BA) and power allocation (PA) with UAV three-dimensional (3D) trajectory design to maximize the minimum average rate among all ground users, while meeting their heterogeneous rate requirements. More specifically, we propose a joint BA and PA algorithm by revealing that the inter-user interference in each NOMA group can be eliminated naturally while deriving the sum rate of users. The algorithm is proposed to get the optimal BA and PA solutions with the help of closed-form results and ellipsoid method. After that, in order to solve the UAV 3D trajectory design problem we introduce the elevation angle as a supplementary variable, and the successive convex approximation method is adopted to obtain the UAV 3D trajectory. The joint BA and PA algorithm and the UAV 3D trajectory design can be optimized alternatively, which leads to fast convergence and demonstrates a superior minimum average rate among users and user fairness performance over the previous methods.

In wireless deterministic network (DetNet), it is a great challenge to serve heterogeneous traffic under different delay-bound requirements and wireless channels. This paper investigates the dynamic transmission scheduling policy for wireless DetNet with heterogeneous traffic. To meet the delaybound requirements for diverse traffic types, we propose a queue slicing model, where the queue buffer is divided into multiple slices. In each time slot, the newly arriving packets of different traffic types are allocated to specific queue buffer slices. Based on the queue slicing model, a cross-layer scheduling scheme is proposed, utilizing channel state information (CSI) of the physical layer (PHY) and queue state information of the medium access control (MAC) layer. Our objective is to minimize the delay violation probability under constraints on average transmission power and queue slice length. To solve the problem, we propose a queue slicing based dynamic deterministic scheduling (QS-DDS) algorithm using the Lyapunov drift-plus-penalty optimization method. Numerical results demonstrate that the proposed algorithm provides deterministic transmission for different traffic types. For wireless network with a single traffic type, the proposed algorithm achieves a lower delay violation probability than traditional queuing model based deterministic scheduling policies. Moreover, we validate the effectiveness of Lyapunov optimization method and show a trade-off between the objective function and the average virtual queue backlog.

We investigate straggler-relay association and ener-gy consumption minimization for cooperative relay assisted fed-erated learning (FL) over fading channels to tackle the straggler effect and limited device energy. To the best of our knowledge, this is the first work to explore joint computation-communication optimization for cooperative relay assisted FL over fading chan-nels, where some devices act as relays for stragglers. A closed-form expression for the computation frequency is derived to facilitate low-complexity straggler identification. A bandwidth sharing decode-and-forward relay scheme is proposed, which benefits both straggler and relay. The closed-form expressions for the transmission power, which minimizes the computation and communication energy of devices under global time constraints, are derived. A low-complexity joint straggler-relay association and multi-domain resources optimization (JSAMRO) algorithm is proposed. Simulation results show that the proposed JSAMRO algorithm achieves a significant performance gain in terms of device's energy consumption and availability rate over the comparison schemes.

We design an absorbable intelligent reflecting surfaces (IRS)-based wireless communication system with two modes, where a wave-absorption structure is embedded into a wave-reflection structure. This is the vital work to investigate the dual function of IRS to switch between the wave-absorption mode and the wave-reflection mode, which improves the degree of freedom in terms of optimization. We formulate the spectral efficiency problem with respect to discrete phase shifts and wave-absorption function for the designed absorbable IRS-based wire-less communication system with strong interferences. In order to maximize spectral efficiency, we propose an iterative grouping optimization (IGO) algorithm, to enhance the desired signal power and reduce interference, based on the wave-absorption mode and the wave-reflection mode. The proposed algorithm achieve low complexity, requiring no exhaustive search, while providing spectral efficiency higher than the existing method with no wave-absorption.

Industrial metaverse aims to build an immersive virtual space that can interact with physical space in real-time. Accurate reconstruction of the time-varying physical processes in virtual space is crucial to the realization of industrial metaverse, especially under short-packet transmission (SPT). In this paper, we investigate the suitability of inferring the real-time data of a sensor from the spatially correlated sensor data for SPT in industrial metaverse, in the presence of transmission delay and error as well as imperfect spatial correlation among data. Closed-form expressions for the average mean squared error (MSE) with and without inference are derived. Also, a tight approximation for the average MSE with inference is presented. A closed-form threshold that the inference-aware reconstruction outperforms the case without inference is derived in terms of the average received SNR. Simulation results verify the analytical results and demonstrate that the inference-aware reconstruction enables an average MSE reduction of 32% over the case without inference, and is suitable to the scenarios with low average received SNR, long period, short blocklength and strong mean squared spatial correlation.

We propose a non-redundant pulse amplitude modulation (PAM) mapping scheme for unipolar orthogonal frequency division multiplexing (U-OFDM) optical wireless communication (OWC) systems, robust against light-emitting diode (LED) non-linearity. To the best of our knowledge, this is the first work to utilize the inherent null signals in U-OFDM blocks to reduce signal distortion caused by LED nonlinearity. A large-amplitude signal is mapped into a PAM-dependent signal that consists of a PAM signal, an exceeding margin and a polarity margin. The PAM signal that represents a large part of the original large-amplitude signal replaces the null signal in the adjacent block. At the receiver, the PAM signal and the PAM-dependent signal are combined to recover the amplitude of the original signal, and the polarity margin is used to recognize the polarity of the original signal. The proposed PAM mapping scheme is spectrally efficient, requiring no side information for signal detection. Simulation results verify the proposed scheme.

Age of information (AoI) is an effective performance metric to measure data freshness in short packet communication. In this paper, we investigate AoI for decode-and-forward (DF) relay systems in the time division duplex (TDD) mode in short packet communication with a number of resources, such as blocklength, transmission power and channels. We formulate an average AoI minimization problem for DF relay systems in the TDD mode with multiple resources. We propose a joint multi-resource optimization (JMO) algorithm to minimize average AoI by simultaneously optimizing blocklength, transmission power and channels. Thus, the proposed JMO algorithm can significantly reduce average AoI, as compared to the previous work just considering blocklength optimization (BO). We prove that BO is independent of power allocation (PA) and channel allocation (CA), and can be decoupled to minimize AoI independently. Thus, the complex problem can be decomposed into a BO subproblem and a PA-and-CA subproblem. We propose a BO algorithm to successively optimize blocklengths in two hops using golden section method. We propose a joint power and channel allocation (JPCA) algorithm to further reduce AoI in an iterative manner. We propose a maximum multiplication (MM) based CA criterion, where CA is performed by maximizing the multiplication between two signal-to-noise ratios (SNRs) in two hops. Thus, the proposed MM-based CA criterion provides AoI performance better than the max-min based CA criterion only maximizing the smaller SNR in two hops.

In last decades, joint design of user scheduling and precoding has been investigated for single transmission time interval (TTI). However, this family of single-stage design cannot optimize real-time metrics that are measured in temporal dimension. In this paper, we target on optimizing the experience rate, which is defined as the ratio of a user's data packet size to the total time for completely delivering the user's data. A novel multi-stage dynamic resource programming is formulated as a multi-stage mixed integer nonlinear programming (MINLP) problem. Then, a low-complexity iterative algorithm is proposed for a jointly optimizing user scheduling and precoding. In particular, a second cone programming problem is dedicatedly designed, for providing a high-quality initial point for the iterative algorithm. The simulation results demonstrate that the proposed design endorses enhanced experience rate performance, with fast convergence behavior.

In this paper, symbiotic radio under simultaneous wireless information and power transfer (SWIPT)-enabled cell-free massive multiple-input multiple-output (CF-mMIMO) is investigated, where multiple access points serve all primary users and backscatter devices (BDs). We derive closed-form expressions for the achievable rates of the primary users and BDs, downlink signal-to-interference-plus-noise-ratio (SINR) and the harvested energy of the primary users. We aim to maximize fairness among BDs through joint optimization of the power splitting factor of SWIPT, uplink and downlink power control and backscatter coefficients of BDs. Being different from traditional low-complexity modified bisection search (MBS) schemes applied in other systems where the non-convex issues of the downlink SINR constraints are not considered, a novel MBS-based resource allocation scheme is proposed for symbiotic radio under SWIPT-enabled CF-mMIMO employing successive convex approximation method to address the non-convex issue and thus enhance the applicability of the MBS scheme. Simulation results show that our proposed scheme can obtain a near-optimal solution with low complexity, which could reduce the processing delay of the central processing unit in CF-mMIMO networks.

Enabling Internet-of-Things (IoT) in fifth generation (5G) networks is challenging due to the low access efficiency in the presence of massive random access (RA) requests. To tackle this, we investigate multi-criterion RA ranking and random access efficiency (RAE) maximization for massive IoT networks to deal with devices' heterogeneity and limited RA resources. A fuzzy logic-guided suitability ranking (FL-SR) scheme is proposed, where multiple criteria are considered such as RA delay, movement speed, and battery capacity to ensure that various service and application requirements are met. With normalized suitability and deployability from the FL-SR scheme, the backoff window size gets more flexible than previous work. A fuzzy logic guided random access efficiency optimization (FL-RAEO) algorithm is proposed to maximize the RAE. Thanks to the derived closed-form expressions for the optimal RAE, the FL-RAEO algorithm achieves optimal performance in average access delay, access throughput. and successful access rate.

We propose an adaptive low-complexity orthogonal matching pursuit (ALOMP) channel estimation approach for direct current biased optical-orthogonal frequency division multi-plexing (DCO-OFDM) systems in optical wireless communication (OWC), requiring a single DCO-OFDM block. This is the first investigation to utilize light-emitting diode (LED)'s limited bandwidth to reduce the high complexity of the traditional OMP method in OWC, while maintaining good channel estimation performance. A number of termination factors are designed to reduce the number of iterations, by exploring correlations between channel impulse responses (CIRs) in time domain caused by LED's limited bandwidth. The proposed ALOMP approach provides complexity close to the minimum mean square error (MMSE) method, and requires just two iterations, significantly less than the traditional OMP method with a large number of iterations. We employ OMP to estimate two CIRs in time domain, and formulate two equations. The line-of-sight (LoS) channel and LED's limited bandwidth are estimated separately via the formulated equations rather than jointly in all CIRs in time domain or on all sub carriers in frequency domain in previous works. Also, we derive the lower bound of the proposed ALOMP approach, and the theoretical bit error rate (BER) including channel estimation errors. Simulation results verify the proposed ALOMP approach.

In this paper, we propose an uplink non-orthogonal multiple access (NOMA) based hybrid long-packet and short-packet frame structure for data collection in industrial wireless sensor networks (IWSNs) with heterogeneous quality of service (QoS) requirements of sensors. The proposed NOMA-based hybrid frame structure allows the superimposition between a number of short packets and a long packet during data collection, where the short packets can be firstly decoded to maintain the low-latency requirement, and the long packet is decoded later to achieve a high signal-noise-ratio (SNR). In addition, a joint short packet scheduling and pilot and block length optimization (JSLO) algorithm is proposed to minimize the maximum block error probabilities among short packets while maintaining a high SNR of long packet, with the assistance of optimal closed-form short packet scheduling results and pilot and block length expressions. The JSLO algorithm achieves near-optimal performance and a dramatic complexity reduction compared to exhaustive search and can lead to fast convergence. Numerical results demonstrate the proposed NOMA-based hybrid frame structure and JSLO algorithm can significantly reduce the maximum error probability among short packets and maintain a high level of fairness.

Anomaly detection (AD) in energy consumption for buildings plays a critical role in improving energy efficiency. In distributed systems, federated learning (FL) has gained widespread adoption due to its ability to train robust models while preserving privacy. However, most FL algorithms face challenges in handling heterogeneous data across different buildings, where distribution gaps exist. Moreover, addressing the heterogeneity in data necessitates additional efforts in model personalization, resulting in significant computational overhead, especially in large-scale FL settings. To address these challenges, we propose a sparse attentive aggregation-based federated AD (SAA-FAD) approach. SAA-FAD comprises an extraction network and a lightweight recognition network. The extraction network employs a variational Bayes scheme to extract statistical characteristics from the raw data for computing similarities between clients while preserving privacy. Then, the recognition network is trained with the extracted statistical features. The model is trained in an SAA manner, which leads to superior AD performance on heterogeneous data. Additionally, SAA-FAD achieves a time and memory complexity of$\mathcal {O}(N\cdot {\mathrm {log}}N)$for computing similarity. Simulation results demonstrate that SAA-FAD outperforms other baseline models, particularly in scenarios with heterogeneous data, while offering a significant advantage in terms of computation time.

Industrial Internet of Things (IIoT), which contains different types of devices with heterogeneous Quality-of-Service (QoS) requirements, has encountered significant challenges on guaranteeing the needs of heterogeneous data collection utilizing limited resources. In this article, we investigate the joint frame structure and resource optimization for the hybrid long- and short-packet nonorthogonal multiple access (NOMA)-based data collection with imperfect successive interference cancellation (SIC) in IIoT, where a number of short and long packets can multiplex the same time-frequency resource simultaneously to guarantee their respective heterogeneous QoS requirements. Specifically, the short packet is first decoded to guarantee low latency, afterward the superposed long packet can be decoded to maintain high signal-to-interference-plus-noise-ratio (SINR) performance. A joint short-packet scheduling, pilot length, blocklength, and dynamic power allocation (JSLP) algorithm is proposed to minimize the maximum block error probability among short packets and mitigate the impact of SIC error propagation in NOMA transmission while maintaining a high SINR of long packet, with the assistance of the derived optimal closed-form short-packet scheduling results and pilot and block length expressions. Thanks to the closed-form expressions, the proposed JSLP algorithm demonstrates near-optimal performance and a significant complexity reduction compared to the exhaustive search, leading to fast convergence. Numerical results demonstrate that the designed hybrid NOMA-based frame structure and JSLP algorithm are robust against the SIC error propagation, and can maintain a high level of fairness by significantly mitigating the maximum block error probability gap among short packets.

We propose spatial superimposition (SS) structures to reduce peak-to-average power ratio (PAPR) for unipolar asymmetrically clipped optical-orthogonal frequency division multiplexing (UACO-OFDM) light fidelity (LiFi) systems with multiple light emitting diodes (LEDs) at the transmitter. The traditional μ-law companding method only increases the small amplitudes of signals, while maintaining the maximum value of the signal, which provides the limited PAPR reduction. Hence, we propose an improved nonlinear μ-law companding approach for PAPR reduction by enhancing small-amplitude signals and compressing large-amplitude signals. Linear compression is further used in the transmitted signals to reduce the impact of LED nonlinearity. Multiple LEDs are utilized to compensate for signal distortion caused by joint linear and nonlinear compressions, requiring no decompanding as in the traditional method. However, there are a few negative compensation signals that can be made to be positive by adding a small value of direct current (DC) bias theoretically derived in a closed form. Also, we propose an enhanced SS (eSS) structure, where the turn-on and maximum voltages of LED are jointly considered in the PAPR reduction, requiring no additional DC bias. This is the first work to investigate channel diversity in the proposed structures, while the multiple channels are assumed to be highly correlated with each other due to small LED separation in the previous works. We propose a frequency-domain channel filling (FCF) approach and a time-domain CF (TCF) approach, to mitigate the channel differences, which enables effective equalization of received signals. This is also the first work to investigate the analytical bit error rate (BER) of UACO-OFDM systems with clipping noise. We derive the analytical BERs of the proposed SS and eSS structures, respectively. Simulation results verify the proposed approaches.

Short frame structure and its optimization plays an important role in ultra reliable and low latency communication (URLLC). We investigate and compare the latency and throughput performances of the independent encoding (IE) and joint encoding (JE) frame structures for heterogeneous multi-device URLLC in the finite block length regime. There is a counter-intuitive finding that, despite a longer frame, IE enables a much lower average latency and higher reliability than JE, thanks to lower queuing latency, while JE achieves higher throughput with lower traffic heterogeneity, thanks to less channel dispersion. It is also shown that traffic heterogeneity has less adverse effects on the performance of the IE frame structure, and can even help reduce its average latency with the shortest block length first (SBF) scheduling rule proposed. We also provide an intensive analysis of the trade-off between pilot power and pilot overhead, with near-optimal pilot power and block length derived in closed form. Low-complexity joint pilot power, pilot length and block length optimization algorithms are proposed for IE and JE frame structures. Numerical results verify the effectiveness of the proposed algorithms, and also show that pilot power optimization plays a significant role in enhancing throughput at low to medium SNR.

We propose a non-redundant pulse amplitude modulation (PAM)-coupled unipolar orthogonal frequency division multiplexing (PU-OFDM) optical wireless communication (OWC) system, robust against light-emitting diode (LED) nonlinearity. To the best of our knowledge, this is the first work to utilize the inherent null signals in U-OFDM blocks to reduce signal distortion caused by LED nonlinearity. A large-amplitude signal is mapped into a PAM-dependent signal that consists of a PAM signal, an exceeding margin and a polarity margin. The PAM signal that represents a large part of the original large-amplitude signal replaces the null signal in the adjacent block. At the receiver, the PAM signal and the PAM-dependent signal are combined to recover the amplitude of the original signal, and the polarity margin is used to recognize the polarity of the original signal. The proposed PU-OFDM system is spectrally efficient, requiring no side information for signal detection. In order to map the large-amplitude signals into small-amplitude signals, we design three PU-OFDM signal schemes: i) PAM mapping (PM) for low complexity; ii) enhanced PAM mapping (ePM), enhancing BER performance via time diversity for relative large LED linear range; iii) accumulated PAM mapping (aPM), with strong signal compression ability for severe LED nonlinearity. Simulation results verify the proposed system and schemes.

In this paper, we propose a cooperative time synchronization (CTS) scheme for cell-free massive multiple-input multiple-output (MIMO) systems, where synchronization packets are jointly broadcast by a set of coordinated access points (APs). Thanks to multiple timestamps available at each target node, the proposed CTS scheme enables a much higher accuracy in clock parameters estimation than the conventional non-cooperative synchronization approaches. In addition to the clock parameters between the target nodes and their associated APs, the clock deviations among APs are also jointly estimated, where the clock deviations can be fed back to their associated APs via acknowledgment for further clock adjustment. Furthermore, to mitigate the impact on synchronization performance under the packet loss scenario, a matrix completion-based CTS (MC-CTS) algorithm is proposed that complements the lost timestamp information. Simulation results demonstrate that the proposed CTS scheme presents a robust performance against the clock deviations among coordinated APs with packet overhead of approximately 50% less than that of the non-broadcast synchronization approaches, and the proposed MC-CTS algorithm effectively enhances the estimation performance of clock parameters, compared to just utilizing the CTS algorithm. Hence, the proposed algorithms are particularly suitable for time-sensitive cell-free massive MIMO systems.

In the context of the industrial Internet of Things (IIoT), accommodating massive connectivity presents significant challenges for random access (RA) networks, primarily due to scalability and diverse quality-of-service (QoS) requirements, resulting in severe preamble collisions. We propose a novel Preamble Parallelization Based Random Access with Colliding Preamble Reuse (PP-RACPR) scheme applicable to the RA procedure. Our method enhances the RA procedure by allowing machine-type communication devices (MTCDs) to transmit multiple preambles in parallel during the initial step of the RA procedure, increasing the successful access rate. Additionally, MTCDs are empowered to reuse part of colliding preambles by identifying them earlier in the process, thereby improving the preamble utilization ratio (PAUR) for the RA network. Finally, we conduct a comprehensive mathematical analysis of the proposed scheme, focusing on the system PAUR, and corroborate our analytical framework through extensive simulations, demonstrating its feasibility and efficacy in supporting massive MTCDs and mitigating preamble collisions.

In this paper, we investigate multi-criteria client selection and energy consumption minimization for hierarchical federated learning (HFL) to deal with clients' heterogeneity and limited energy. To the best of our knowledge, this is the first work to investigate multi-criteria client selection for HFL. A fuzzy logic assisted client selection (FLACS) scheme is proposed, where multiple criteria are taken into account, including the distance, clients' battery capacity and computational resource. The FLACS scheme enables a significant performance gain in terms of the clients' average normalized suitability over the previous schemes. A joint communication and learning factors optimization (JCLFO) algorithm is proposed to minimize the system energy consumption. Thanks to the derived closed-form expressions for the optimal aggregation intervals, computation frequency and transmission power, the JCLFO algorithm can achieve the optimal performance in terms of the system energy consumption and converge within only 3 iterations, with a significant complexity reduction over exhaustive search.

Orthogonal time frequency space (OTFS) modulation has become an effective technique for high-mobility scenarios. However, its performance could be severely degraded due to channel aging caused by user mobility and high processing latency. In this paper, an integrated scheme of uplink (UL) channel estimation and downlink (DL) channel prediction is proposed to alleviate channel aging in time division duplex (TDD) multi-input multi-output (MIMO) OTFS systems. Specifically, first, an iterative data-aided channel estimation scheme is proposed to accurately acquire UL channels with the aid of specifically designed frame pattern. Then the discrete prolate spheroidal basis expansion model (DPS-BEM) is used to model the time-varying UL channel estimates, and the dynamic DPS-BEM coefficients are fitted by a set of orthogonal polynomials. A channel predictor is derived to predict DL channels for all antenna pairs and paths by iteratively extrapolating the fitting coefficients. Simulation results verify that the proposed scheme outperforms the existing schemes in terms of normalized mean square error of channel prediction and DL sum-rate.

Supporting wireless transmission of large-scale control systems is a challenging task due to the scarcity of wireless resources in the industrial internet of things (IIOT). To reduce wireless resource consumption while maintaining control stability, this paper investigates the wireless networked predictive control system, where only part of the control devices is permitted to transmit their state information to the centralized controller in each control cycle. For the rest unscheduled control devices, the centralized controller predicts their state information via the Gaussian process regression method. To evaluate the control performance and the wireless resources consumption, we formulate a joint optimization problem of control device scheduling, power allocation, and bandwidth allocation. The joint predictive control and communication optimization (JPCCO) scheduling algorithm is proposed to minimize both the control cost and communication cost. As for control device scheduling, we proposed a fuzzy logic based scheduling ranking (FL-SR) method, where control devices are ranked in descending order according to the fuzzy output. Numerical results show that the proposed JPCCO scheduling method with FL-SR outperforms the previous scheduling methods without predictive control, enabling a more stable wireless networked control system with less wireless resources.

Pilot contamination incurred by strong inter-cell interference seriously degrades the performance of channel estimation in massive multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems. We propose an independent component analysis (ICA) and sparse recovery algorithm based semi-blind channel estimation scheme, referred to as sparse ICA (SICA), for multi-cell massive MIMO-OFDM systems, which does not require any prior information of intercell interference and therefore is more practical. The proposed SICA scheme enables accurate channel estimation by exploiting both the high-order statistics of the received signal and channel sparsity in angle domain. The SICA scheme performs in a semi-blind manner as it is much more robust against pilot overhead than the previous approaches, and requires only one OFDM symbol as pilot to achieve a superior normalized mean square error of channel estimation. Furthermore, the complexity required by SICA is much lower than that required by the previous work, thanks to the negligible complexity of interference sources number estimation based on sparse recovery algorithm.

We consider a three-dimensional (3D) unmanned aerial vehicle (UAV)-terrestrial base station (TBS) coordinated multi-point non-orthogonal multiple access (CoMP-NOMA) scheme where UAV coordinates with TBS to allow joint transmission for the terrestrial users. With the assistance of closed-form power allocation derivations, a joint user scheduling and power allocation (J-USPA) algorithm is proposed to obtain the optimal user scheduling solution, with the consideration of imperfect channel estimation. Moreover, considering the line of sight (LoS) and non-LoS factors in the air-ground channel, the 3D trajectory of UAV is designed to provide a flexible user-centric service. Numerical results verify that the 3D UAV-TBS CoMP-NOMA model and the J-USPA scheme have a superior performance in terms of sum rate of users over the TBS CoMP-NOMA and the UAV assisted NOMA systems without CoMP transmission.

In this paper, we consider a unmanned aerial vehicle (UAV)-enabled uplink (UL) heavy non-orthogonal multiple access (NOMA) system where the UL communication becomes increasingly important in some hot spot regions such as live concerts or football stadiums, and study the maximization of the minimum average rate among all users by jointly optimizing bandwidth assignment (BA) and power allocation (PA) alongside UAV trajectory design, while meeting their specified heterogeneous rate requirements. Specifically, by revealing that the inter-user interference can be naturally eliminated while deriving the sum rate of users in each NOMA group, an one-step BA and PA algorithm is proposed, with the assistance of closed-form results. Afterwards, the elevation angle is introduced as an auxiliary variable to help solve the UAV trajectory design problem. The joint BA and PA algorithm and the UAV trajectory design can be optimized alternatively, which leads to fast convergence and demonstrates a superior minimum average rate among users and user fairness performance over the previous methods.

Multiplexing or transmission diversity via dual links enables the reduction in age of information (AoI). We address the open issue of selection between the two transmission modes for AoI-oriented short-packet system over fading channels, with a comprehensive analysis. Closed-form expressions for the average AoI and peak AoI (PAoI) are derived based on the discrete-time Markov-chain process. Then, to obtain the explicit region of preference (RoP) and quantitative PAoI gains of multiplexing/diversity over the single-queue case, we derive the signal-to-noise ratio (SNR) threshold for transmission mode selection, which is shown to be a decreasing function of the arrival rate and saturates at high arrival rate. Also, the monotonicity of the PAoI gains by multiplexing and diversity, and their achievable gains, are analyzed in a comprehensive manner. It is shown that diversity is able to achieve a PAoI gain of more than 3 dB over the single-queue case at low SNR, while multiplexing has a larger RoP than diversity and is selected at high SNR and high arrival rate. Furthermore, both throughput and PAoI violation probability are considered alongside the average PAoI for a wide range of tradeoff in system design.

We consider a multiple-input single-output (MISO) optical wireless communications (OWC) system with highly correlated channels, causing bit error rate (BER) performance degradation. Because of intensity modulation and direct detection (IM/DD), the transmitted signals are real-valued and non-negative, which limits the utilization of precoding in phase domain. We employ direct current biased optical orthogonal frequency division multiplexing (DCO-OFDM) modulation to design a group of phase rotation (PR) factors in frequency domain for OWC systems, robust against high channel correlations. The proposed PR-based precoding has a number of advantages over the power factor-based design in the literature: i) no change of transmission power on each LED; ii) no signal-to-noise ratio (SNR) and no BER degradation on transmitted signals of all LEDs. We formulate an optimization problem to obtain the optimal PR factors by maximizing the minimum pairwise Euclidean distances between all candidate signals. The optimization problem is non-convex, requiring multi-dimensional exhaustive searches. In order to reduce the complexity, we propose three low-complexity PR-based precoding approaches which provide BER performances better than the power factor-based designs in the literature, and are close to their own analytical results derived, respectively. The proposed approaches are validated via a built testbed, producing comparable performance between measured and simulated BERs.

Age of information (AoI) is an effective performance metric for time-critical industrial Internet of things (IIoT) applications. We investigate status prediction and data aggregation with prediction error awareness, to enhance the AoI performance for short-packet transmission (SPT) in time-critical IIoT. A predict-compare (PredComp) transmission scheme is proposed, where proactive transmission termination is employed in case of prediction error, by comparing the predicted and real updates at source. It is proved to achieve a significant average AoI performance gain over the case without prediction, even under high prediction error probability. In addition, a predict-aggregate-compare (PredAggComp) transmission scheme is proposed, where two status updates are predicted with different prediction horizons and aggregated by utilizing their time correlation. That allows a good tradeoff between the prediction accuracy and the transmission error probability. A closed-form threshold that the PredAggComp scheme outperforms the PredComp scheme is derived. Moreover, prediction horizon adaptation is conducted to minimize the average AoI of the proposed transmission schemes. Simulation results verify the analytical results and show the superiority of the proposed PredComp and PredAggComp schemes, with an average AoI reduction of up to 64% over the case without prediction.

Age of information (AoI) is an effective measure of the information freshness for wireless networked control systems (WNCSs). However, the AoI performance for a closed loop of WNCS with two-way delays has remained unexplored, especially in the finite blocklength (FBL) regime. In this paper, we investigate the peak age of loop (PAoL) performances, including the average, variance and outage probability of PAoL, for WNCSs with FBL over fading channels. Their closed-form expressions are respectively derived regarding the blocklength and the maximum number of allowable transmissions. We prove that the average PAoL is less than the sum of the average peak AoI in uplink (UL) and downlink (DL) due to the coupling between UL and DL. We also show that there is a tradeoff between the average PAoL and the variance/outage probability of PAoL. Based on the comprehensive performance analysis, we study a PAoL-oriented communication and control co-design with an adaptation scheme for transmission power, blocklength and the maximum number of allowable transmissions. Simulation results verify the correctness of the analytical results and show that the proposed PAoL-oriented scheme significantly outperforms the UL only and DL only optimization schemes, with an up to 8-fold reduction in the average control cost.

Multiplexing or transmission diversity via dual queues enables the reduction in age of information (AoI) in industrial Internet-of-Things (IIoT). We address the open issue of how to select between the two transmission modes for AoI-oriented short-packet systems over fading channels. Closed-form expressions for the average peak AoI (PAoI) in the finite block length regime over fading channels are derived based on the average block error probability. The analytical results match the simulation results well. To assist with obtaining the explicit region of preference (RoP) between the multiplexing and diversity modes, the signal-to-noise ratio (SNR) threshold for transmission mode selection is derived, which is shown to be a mono-decreasing function of the arrival rate and to saturate at high arrival rate. The multiplexing mode demonstrates a larger RoP than diversity and is shown to be preferable at high SNR and high arrival rate. While the diversity mode is preferable in the case of low SNR and low arrival rate by presenting a more significant PAoI reduction over the single-queue case.

In this paper, the handover problem is investigated for a cellular vehicle-to-everything (C-V2X) based vehicle platoon. We propose a cooperative multipoint transmission (CoMP)-based seamless handover between roadside units (RSUs) and resource allocation (C-SHRA) algorithm, where a CoMP-based seamless handover is proposed to avoid ping-pong handover for RSU-to-platoon (R2P) link, and a cooperative spectrum-sharing based resource allocation scheme is designed to mitigate both intra-platoon and inter-RSU interferences for intra-platoon vehicle-to-vehicle (V2V) links. A joint block length and transmission power optimization (JBLTPO) algorithm is proposed to maximize the effective throughput of the R2P link in the CoMP mode. Numerical results show that the proposed C-SHRA algorithm is verified for effective communication performance enhancement, and the proposed JBLTPO algorithm achieves the optimal performance in terms of effective throughput with a significant complexity reduction over exhaustive search.

Light emitting diode (LED) is employed to transmit signals for visible light communication (VLC) systems. Asymmetrically clipped optical-orthogonal frequency division multiplexing (ACO-OFDM) is one of multi-carrier modulations to improve data rates. However, ACO-OFDM signals provide high peak-to-average power ratio (PAPR), and are clipped off to work in a nonlinear LED with a limited range of linearity. In this paper, a non-redundant multi-layer superimposed PAPR reduction approach is proposed for ACO-OFDM VLC systems, where two non-redundant signal streams are superimposed with the ACO-OFDM signal stream for PAPR reduction, requiring no pilot and no side information. A number of multi-layer signals are designed to reduce large-amplitude source signals and enhance small-amplitude source signals. The source signals are not interfered by the designed multi-layer signal streams in frequency domain, as odd subcarriers are occupied by source signals, while even subcarriers are occupied by the designed signals. Simulation results show that the proposed approach outperforms a number of existing methods in the literature, and provides performance better than ACO-OFDM with no PAPR reduction, in terms of bit error rate (BER) and complementary cumulative distribution function (CCDF) of PAPR reduction.

Time-dispersive loopback self-interference (SI) is a challenging issue for a full-duplex (FD) amplify-and-forward (AF) relay assisted orthogonal frequency division multiplexing (OFDM) system. In this paper, a utilization scheme is proposed for the uplink. A virtual multiple-input multiple-output (MIMO) system is modeled to enhance the receive diversity, by regarding the residual loopback SI after partial cancellation at relay as additional sources rather than noise. A gain control algorithm is proposed for the FD relay to enable the virtual MIMO modeling, and the optimal target transmission power at relay is derived to maximize the receive signal-to-interference-and-noise ratio (SINR) at base station. The proposed system is more practical as the main computational load is shifted from relay to BS to help the AF relay maintain a low running cost, compared to the previous relay-centric systems with complex SI cancellation at relay. The proposed system significantly outperforms the relay-centric system.

In industrial Internet of Things (IIoT), the existing access class barring (ACB) random access (RA) strategy suffers severe performance degradation with massive contention devices, due to high probability of access collision. In this paper, we propose a collision-aware (CA) ACB RA scheme by reusing the colliding preambles, to enhance the resource utilization. The proposed scheme employs dynamic adjustment of the ACB factor and the preamble resources for delay-sensitive and -non-sensitive devices, respectively. A joint optimization problem is formulated and solved to maximize the preamble utilization ratio (PAUR) subject to the delay constraints and the available preambles. The system performance is evaluated by a Markov Chain based analytical model. Simulation results verify the correctness of our analysis and also show that the proposed CA-ACB RA scheme significantly outperforms the existing ACB RA schemes in terms of PAUR, network throughput, and average access delay.

In this paper, a composite robot aided system is proposed to support the coexistence of enhanced mobile broadband (eMBB), ultra reliable low latency (URLLC) and massive machine type communication (mMTC) traffic in smart factory. The composite robot is deployed to inspect the factory by upstreaming high quality images/videos via eMBB, while collecting information from the mMTC devices and allowing URLLC traffic to overlap upon the scheduled robot transmission. This ensures high energy efficiency of the mMTC traffic and low latency of the URLLC traffic. As the heterogeneous traffic affects each other in a complex manner with shared resources, the objective of this paper is to maximize the minimum average rate of the inspection robot while completing the information collection tasks of all mMTC devices and responding to URLLC requests. In light of dynamic growth of information in mMTC devices and strict delay limits for URLLC traffic, we propose an alternative optimization algorithm of joint optimization of task scheduling, bandwidth allocation and robot trajectory (TSBART). The proposed TSBART algorithm achieves a higher minimum average rate as well as higher quality of service (QoS) than the previous work based on greedy and average resource allocation algorithms, thanks to its higher degree of freedom in optimization.

External passive intermodulation (ePIM), which results from external passive sources around base station (BS), such as rust fence, barn roof and weather may cause different levels of intermodulation power and significant interference in the uplink. In this paper, we propose an adaptive beamforming algorithm based on the general linear combination (GLC) to suppress the ePIM signal, which is the first attempt to design beamforming for ePIM suppression. Rather than the complex and intractable modeling of the ePIM signal, the ePIM and noise correlation (PNC) matrices can be estimated and combined by the GLC to design the beamformer, which is more adaptive and can significantly suppress the ePIM. In addition, The power of the essential factors of the ePIM sources is estimated based on the eigenvalues and eigenvectors of the ePIM signal. Simulation results show that the proposed beamformer provides a near-optimal output signal-to-interference-plus-noise ratio performance and also significantly outperforms the existing beamforming schemes which merely focus on co-channel interference without considering ePIM suppression.

In this paper, by utilizing the temporal correlation of wireless channels, a hierarchical basis expansion model (HBEM) based estimation scheme is proposed for orthogonal frequency division multiplexing systems over doubly selective channel, where the complex exponential basis expansion model (CE-BEM) is used to extract the channel impulse response and the discrete Legendre polynomials BEM is used to refine the CE-BEM coefficients to improve the performance of channel estimation. We design a non-periodic sparse pilot pattern, and hence only scarce subcarriers of a small number of pilots are required for channel estimation, resulting a training overhead reduction of around 50% over the previous CE-BEM based schemes. A block-based signal space matching pursuit algorithm is proposed to enhance the estimation accuracy of CE-BEM coefficients. Furthermore, the proposed HBEM scheme enables a reduction in the number of estimated CE-BEM coefficients by more than 50%, compared to the previous work. A lower bound on the mean square error (MSE) of the proposed HBEM scheme is derived. Simulation results show that the proposed HBEM scheme significantly outperforms the previous CE-BEM based schemes in terms of MSE of channel estimation and bit error rate.

In this paper, we propose a novel cooperative time synchronization (CTS) scheme via broadcasting for time-sensitive cell-free (CF) massive multiple-input multiple-output (MIMO) networks, by allowing distributed access points (APs) to jointly broadcast synchronization messages to the target nodes. With the proposed CTS scheme, more timestamps from distributed APs are available to achieve higher synchronization accuracy over the conventional non-cooperative time synchronization approaches. Parameter estimation is conducted by maximum likelihood estimation of clock offset and clock skew under the Gaussian transmission delay model, and the corresponding Cramer-Rao lower bounds (CRLBs) are derived. In addition, the clock offset and skew deviations among APs are also estimated, which are fed back to APs for further clock adjustment. Thanks to the AP association scheme and broadcast characteristics, the proposed CTS scheme demonstrates robustness against clock parameter deviations, while at a relatively low overhead and computational complexity.

Age of information (AoI), which measures the freshness of information, is a critical performance metric of timesensitive applications of industrial Internet of things (IIoT) with short-packet transmission (SPT). In this paper, we investigate the suitability of predicting the status updates at source and sending them to destination in advance for AoI oriented SPT systems, in the presence of prediction error as well as transmission error. A predictive transmission scheme is proposed, where proactive transmission termination is adopted as soon as a prediction error is detected, and also multiple correlated features of the status is considered. A closed-form expression for the average AoI with respect to prediction horizon (related to prediction error probability) and blocklength (related to transmission error probability) is derived for the multi-feature source scenario. Also, the prediction error probability with respect to prediction horizon is derived in closed form. It is proved that the average AoI performance can benefit from status prediction, even under high prediction error probability. Simulation results demonstrate the correctness of the analytical results, and show that the proposed prediction scheme outperforms the prediction approach with no transmission termination, and there exists an optimal prediction horizon in terms of average AoI. A tight approximation of the optimal prediction horizon is derived for the special case of single-feature status, which achieves a near-optimal performance, with a much lower complexity than exhaustive search.

In this article, we investigate the long-term energy consumption and transmission delay (EC-TD) tradeoff in a wireless-powered body area network that consists of a multiantenna hybrid access point and a number of single-antenna sensor nodes (SNs). The beamforming technique and the simultaneous wireless information and power transfer (SWIPT) technique are adopted. Each SN is equipped with a battery and data buffer for storing harvested energy and sensory data. The long-term energy consumption minimization problem is addressed subject to the constraint of transmission delay. Meanwhile, the residual energy constraints of SNs are considered, which enable the setting up of the available energy of the SNs according to requirements. By employing the Lyapunov optimization theory, the original stochastic optimization problem is transformed into an equivalent instantaneous nonconvex problem in which the long-term EC-TD tradeoff can be adjusted using a system control parameter$V$. A joint power and time allocation scheme is then proposed to solve this instantaneous problem. Moreover, based on the derived upper bounds of the long-term energy consumption and data buffer length, we reveal that the proposed resource allocation scheme achieves an EC-TD tradeoff as$[\mathcal {O}(1/V),\mathcal {O}(V)]$. Since the value of$V$can be adjusted to achieve different energy consumption and transmission delay, the flexibility and applicability of the proposed scheme are enhanced. The simulation results validate the theoretical analysis and verify the effectiveness of the proposed scheme.

In this article, we investigate the grant-free communications with adaptive period for Industrial Internet of Things, where only a fraction of devices is active at a time. To the best of our knowledge, this is the first work to exploit the noncontinuous temporal correlation of the received signal for joint user activity detection (UAD), channel estimation, and signal detection, while all the previous work requires continuous transmission. Two schemes are proposed toward this purpose, namely, periodic block orthogonal matching pursuit (PBOMP) and periodic block sparse Bayesian learning (PBSBL), which outperform the previous schemes in terms of the success rate of UAD, bit error rate, and accuracy in period estimation and channel estimation. The Cramér–Rao lower bounds (CRLBs) of channel estimation by PBOMP and PBSBL are derived. It is shown that the two proposed approaches have close CRLBs and normalized mean-square error at high SNR.

In this paper, we investigate unmanned aerial vehicle (UAV) assisted communication systems that require quasi-balanced data rates in uplink (UL) and downlink (DL), as well as users’ heterogeneous traffic. To the best of our knowledge, this is the first work to explicitly investigate joint UL-DL optimization for UAV assisted systems under heterogeneous requirements. A hybrid-mode multiple access (HMMA) scheme is proposed toward heterogeneous traffic, where non-orthogonal multiple access (NOMA) targets high average data rate, while orthogonal multiple access (OMA) aims to meet users’ instantaneous rate demands by compensating for their rates. HMMA enables a higher degree of freedom in multiple access and achieves a superior minimum average rate among users than the UAV assisted NOMA or OMA schemes. Under HMMA, a joint UL-DL resource allocation algorithm is proposed with a closed-form optimal solution for UL/DL power allocation to achieve quasi-balanced average rates for UL and DL. Furthermore, considering the error propagation in successive interference cancellation (SIC) of NOMA, an enhanced-HMMA scheme is proposed, which demonstrates high robustness against SIC error and a higher minimum average rate than the HMMA scheme.

We investigate a multi-device ultra-reliable low-latency communication system with heterogeneous traffic and finite block length over temporally-correlated fading channels. In light of the challenging demand for accurate channel estimation with limited pilot in a short frame, two frame structures, which respectively adopt independent pilots and shared pilot, are investigated. Block lengths and pilot lengths are jointly optimized for the two frame structures, through instantaneous channel state information (CSI) based dynamic optimization and statistical CSI based static optimization, to strike the tradeoffs among performance, complexity and signaling overhead. The proposed joint optimization algorithms significantly outperform the existing approaches that solely optimize block lengths or pilot lengths. The dynamic optimization algorithms achieve near-optimal performance at dramatic complexity reduction over exhaustive search, and maintain robustness against traffic heterogeneity. Also, the static optimization algorithms are conducted offline, while still outperforming the previous instantaneous CSI based dynamic optimization approaches. It is demonstrated that the independent-pilot frame structure with dynamic optimization is preferable in the scenario with high traffic heterogeneity or high mobility, and that the shared-pilot frame structure with static optimization presents a comparable performance to the former in the case of low mobility, incurring negligible complexity and signaling overhead.

In this paper, we investigate an open topic of a multiuser single-input-multiple-output (SIMO) generalized frequency division multiplexing (GFDM) system in the presence of carrier frequency offsets (CFOs) and in-phase/quadrature-phase (IQ) imbalances. A low-complexity semi-blind joint estimation scheme of multiple channels, CFOs and IQ imbalances is proposed. By utilizing the subspace approach, CFOs and channels corresponding to$U$users are first separated into$U$groups. For each individual user, CFO is extracted by minimizing the smallest eigenvalue whose corresponding eigenvector is utilized to estimate channel blindly. The IQ imbalance parameters are estimated jointly with channel ambiguities by very few subcarriers. The proposed scheme is feasible for a wider range of receive antennas number and has no constraints on the assignment scheme of subsymbols and subcarriers, modulation type, cyclic prefix length and the number of subsymbols per GFDM symbol. Simulation results show that the proposed scheme significantly outperforms the existing methods in terms of bit error rate, outage probability, mean-square-errors of CFO estimation, channel and IQ imbalance estimation, while at much higher spectral efficiency and lower computational complexity. The Cramér-Rao lower bound is derived to verify the effectiveness of the proposed scheme, which is shown to be close to simulation results.

Mobile Edge Caching is a promising technique to enhance the content delivery quality and reduce the backhaul link congestion, by storing popular contents at the network edge or mobile devices (e.g. base stations and smartphones) that are proximate to content requesters. In this work, we study a novel mobile edge caching framework, which enables mobile devices to cache and share popular contents with each other via device-to-device (D2D) links. We are interested in the incentive-related problem of mobile device users: whether and which users are willing to cache and share what contents, taking the user mobility and cost/reward into consideration. The problem is challenging in a large-scale network. We introduce the evolutionary game theory, an effective tool for analyzing large-scale dynamic systems, to analyze the mobile users' content caching and sharing strategies. Specifically, we first derive the users' best caching and sharing strategies, and then analyze how these best strategies change dynamically over time. Based on the above, we further characterize the system equilibrium systematically. Simulation results show that the proposed scheme outperforms the existing schemes in terms of the total transmission cost and the cellular load. In particular, in our simulations, the total transmission cost can be reduced by 42.5%~55.2% and the cellular load can be reduced by 21.5%~56.4%.

We investigate how to strike the balance between pilot overhead and pilot power for short packet ultra reliable and low latency communication (URLLC) systems under continuous fading, as the previous work has not considered the joint impact of pilot overhead and pilot power on the system throughput and has assumed block fading only. It is revealed that, at low to moderate signal to noise ratio (SNR) or in continuous fading, pilot power boosting can reduce pilot overhead and improve throughput significantly, and that at high SNR, pilot overhead plays a dominant role in enhancing the throughput while maintaining a low peak to average power ratio. For throughput formulation, a closed-form expression for the asymptotic block error probability under continuous fading is derived with respect to pilot power, pilot length and block length. And for throughput maximization, the near-optimal pilot power and the near-optimal block length are given in closed form by solving complicated transcendental equations. Based on the analysis, a low-complexity joint pilot power, pilot length and block length optimization (JPLLO) algorithm is proposed, which achieves a near-optimal performance and a dramatic complexity reduction compared to exhaustive search, converging within only 1–3 iterations. The JPLLO algorithm also significantly outperforms the previous joint optimization algorithm under continuous fading.

In this paper, we investigate an unmanned aerial vehicle (UAV) assisted communication system for uplink (UL) heavy scenarios such as a football match, where intensive video uploading from the audience is demanded. Non-orthogonal mul-tiple access (NOMA) is exploited to enhance system throughput, while it suffers a dynamic user rate gap within each NOMA group due to UAV movement. In light of the user fairness issue and the UL heavy demands, we propose joint UL-DL optimization of user scheduling, power allocation (PA) and UAV trajectory (JOSPT). In particular, closed-form optimal solutions are derived for dynamic PA in both UL and DL, which plays a dominant role in system performance. The proposed PA scheme is more effective in maintaining user fairness than the previous work and also computationally efficient and suitable for energy-limited UAV system. The average spectrum efficiency of the proposed NOMA UAV system in UL and DL is also much higher than that of the orthogonal multiple access (OMA) based UAV system, with the heterogeneous rate demands accommodated.

In this paper, we propose a novel dynamic manager selection scheme for vehicles platooning systems and investigate the corresponding resource allocation in the finite blocklength regime, to meet the ultra-reliable and low-latency communication (URLLC) requirements of safety-related data. To the best of our knowledge, this is the first work to investigate the impact of finite blocklength on communications of vehicles platooning systems. By taking into account the factors of communication and the changes in the platoon structure, the proposed dynamic platoon manager selection scheme enables a significant performance enhancement over the conventional fixed manager scheme where the head vehicle in the platoon acts as a manager. Based on the proposed platoon manager selection scheme, a joint resource allocation and coding rate optimization algorithm is proposed to minimize the intra-platoon groupcast latency. Thanks to the closed-form expression of the optimal coding rate and transmission power derived, the proposed optimization algorithm achieves optimal performance in terms of the intra-platoon groupcast latency and converges within only 3 iterations, with a significant complexity reduction over exhaustive search.

Indoor lighting is achieved by multiple light emitting diodes (LEDs) working together, which forms a multi-input single-output (MISO) Light Fidelity (LiFi) system. Due to small separation, the channels between LEDs are highly correlated, causing performance degradation. This is the first work to apply direct-current-biased optical orthogonal frequency division multiplexing (DCO-OFDM) to reduce the adverse effects of high channel correlations in terms of phase for MISO LiFi systems. We propose an optimal phase rotation (PR) based precoding to achieve the reduction of high channel correlation effects for spatial multiplexing (SMP) and spatial modulation (SM) transmissions, respectively. The optimally rotated phase angles are obtained by maximizing minimum Euclidean distances between all candidate signals. The transmission power is not affected by the proposed PR based precoding, while in the previous works, transmitted signals of some LEDs are allocated less power. The proposed PR based precoding is designed offline, applicable to arbitrary multiple transmitted signals with any M-ary quadrature amplitude modulation (M-QAM). Simulation results show that the proposed approach provides bit error rate (BER) performance better than state-of-the-art methods. Analytical results are also derived to provide BER performance close to numerical results.

Timely delivery of information plays an important role in time-sensitive applications like factory automation and monitoring. In this paper, we consider a dual-server short-block wireless communication system to ensure fresh information generated at relatively high update rate to be delivered to destination in real time, where the information is generated at a relatively high update rate, encoded into two short-block queues and delivered in two parallel paths in real time. The age of information (AoI) performance is investigated for the dual-queue system in the presence of block delivery errors. This is the first work to consider both multiple queues and block errors in AoI analysis. An expression for average AoI is derived, based on the Markov-chain process to enable low-complexity system design and optimization, and its correctness is verified by simulations. It is shown that the dual-queue system investigated significantly outperforms the single-queue system in terms of average AoI, peak AoI violation probability and throughput at a relatively high status update rate. The impacts of update rate and blocklength on average AoI are also investigated.

In this article, we focus on a wireless-powered body area network in which the simultaneous wireless information and power transfer (SWIPT) technique is adopted. We consider two scenarios based on whether sensor nodes (SNs) are equipped with battery. For the first time, energy consumption minimization with throughput heterogeneity (ECM-TH) problem is addressed for both scenarios. For the battery-free scenario, a low-complexity time allocation scheme is proposed. This scheme solves the ECM-TH problem based on a hybrid method of gradient descent and bisection search algorithms. Consequently, compared with the interior-point method, our scheme has a lower computational complexity for the same energy consumption performance of the network. For the battery-assisted scenario, the nonconvex ECM-TH problem is first transformed into a convex optimization problem by introducing auxiliary variables. Then, a joint time and power allocation scheme based on the Lagrange dual subgradient method is proposed to solve it. Compared with the battery-free scenario, energy consumption and outage probability are both decreased in the battery-assisted scenario. Moreover, we address a special case wherein the feasible set of the above-mentioned ECM-TH problems may be empty owing to poor channel conditions or high throughput requirements of SNs.

Both information age and delay are critical performance metrics of emerging time-sensitive applications. However, their inherent correlation in the finite block length (FBL) regime has remained uninvestigated as it is affected by block length and update rate in a complex manner. In this paper, closed-form expressions for average age of information (AoI), peak AoI (PAoI) and delay are derived for an FBL Last-Come First-Served system with retransmission and non-preemption policies, based on which a comprehensive analysis of the relationship among the three metrics in the FBL regime is presented. It is proved that there exists a strong tradeoff between delay and AoI/PAoI given a block length, and that AoI, PAoI and delay have positive correlation given an update rate, regardless of the weight. With the goal of minimizing delay and AoI simultaneously, the weighted sum of delay and PAoI (upper bound on AoI) is formulated and proved to be convex with respect to block length and update rate. A low-complexity optimization algorithm is developed with a closed-form expression of the optimal update rate, whose performance approaches the Pareto boundaries of the PAoI-delay and the AoI-delay regions, at much lower complexity than exhaustive search.

In this paper, we investigate the short frame structure optimization in terms of shared-pilot length and finite block length (FBL) for an industrial Internet-of-Things (IIoT) system with heterogeneous traffic requirements on latency, reliability and information size. Both dynamic and static optimization approaches are investigated to allow trade-offs between performance, complexity and signaling overhead. Effective throughput maximization problems are formulated based on statistical and instantaneous channel state information (CSI), respectively, and their monotonicities are proved. A statistical CSI based static joint block length and shared-pilot length (S-JBSPO) algorithm is proposed, which is conducted offline. With no spectral overhead and very low complexity, S-JBSPO outperforms the existing instantaneous CSI based approaches from medium to high SNR. An instantaneous CSI based dynamic JBSPO (D-JBSPO) algorithm is proposed, which maintains a near-optimal and robust throughput performance against a wide range of traffic requirements, and significantly outperforms the previous approaches, thanks to a much higher degree of freedom. D-JBSPO also demonstrates a significant performance gain over S-JBSPO, regardless of the Doppler frequency and the number of devices.

In this paper, we propose a blind direct current bias (DCB) based timing synchronization and a blind null subcarrier (NS) based timing synchronization methods for direct current biased optical-orthogonal frequency division multiplexing (DCO-OFDM) visible light communications (VLC) systems. This is the first work to investigate blind timing synchronization for DCO-OFDM VLC systems, achieving high bandwidth efficiency, unlike the previous works which require a number of pilots. The two blind approaches are robust against the limited bandwidth of light emitting diode (LED), as the timing synchronization is conducted in frequency domain to mitigate the effect of inter-symbol-interference (ISI) caused by LED limited bandwidth, rather than being performed in time domain as the previous works that are vulnerable to the ISI. The DC bias is utilized by the proposed DCB based approach to perform blind timing synchronization, and the null subcarrier is used by the proposed NS based approach. Simulation results show that the proposed blind DCB and NS timing synchronization approaches significantly outperform the state-of-the-art methods in terms of the probability of false detection and bit error rate (BER), and yield BER performance close to ideal case with perfect synchronization, zero forcing (ZF) equalization and perfect channel state information (CSI).

In this paper, we propose an unmanned aerial vehicle (UAV) base station (BS) assisted communication system for a special event (e.g., a football game) with heterogeneous traffic demands by all users and the uplink (UL)-downlink (DL) rate balancing requirement. With respect to UAV's high mobility, we propose a hybrid mode multiple access (HMMA) strategy where both orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) techniques are utilized to meet heterogeneous traffic demands. Specifically, NOMA is utilized to achieve high average data rate, and OMA helps to meet the instantaneous rate demands of users. The proposed HMMA strategy has a high degree of freedom and provides superior minimum average rate across all users and a higher user fairness than the previous work with OMA only or NOMA only, where the instantaneous rate demands of users may not always be guaranteed during UAV's flight time due to dynamic channel changes, the inter-user interference and successive interference cancellation (SIC) error propagation. Furthermore, we investigate joint UL-DL optimization for a UAV assisted wireless system. Based on the channel reciprocity of the air-ground channels, an alternative algorithm is proposed to conduct joint UL-DL optimization of bandwidth assignment and UAV trajectory to accommodate heterogeneous rate demands across users and achieve quasi-balanced average rates in UL and DL.

As mission-critical Internet of Things (MC-IoT) is expected to carry important and private information, its high quality of service (QoS) and high physical layer (PHY) security are indispensable. Nevertheless, most existing PHY security related work is built on the assumption of infinite blocklength, which is not applicable to finite blocklength (FBL) transmission, a typical scenario in MC-IoT such as factory automation. In this paper, we address the PHY security issue of a hybrid duplex relay aided MC-IoT system with FBL. Closed-form expressions for statistical secrecy throughput of full-duplex (FD) and half-duplex (HD) relay systems are derived, respectively, which are verified by numerical results. Based on the closed-form secrecy throughput, joint optimization of blocklength and transmission powers at source and relay is conducted for FD and HD relay systems, respectively. A hybrid duplex relaying scheme is also proposed by selecting the duplex mode with a higher achievable secrecy throughput. Numerical results show that, together with the hybrid relaying scheme, the proposed relay system with joint power allocation and blocklength adaptation, relay mode selection achieves much higher secrecy throughput over the conventional sole FD or HD mode relaying systems. Also, it is revealed that increasing blocklength or transmitting power may not always lead to a higher secrecy throughput and energy efficiency (EE).

We propose an efficient enhanced K-means clustering (E-KMC) algorithm for semi-blind channel estimation of uplink cell-free massive multiple-input multiple-output (MIMO) systems in factory automation, an important application of the internet of things (IoT). The proposed E-KMC algorithm operates with significantly less clusters and complexity than the KMC algorithm while achieving enhanced bit error rate (BER) performance, as the latter converges extremely slowly even with just medium modulation order and a medium number of transmit antennas. A near-optimal short pilot is designed to assist clustering of the E-KMC based channel estimation scheme. The semi-blind receiver structure achieves a BER performance that is very close to the case with perfect channel state information (CSI), as well as a mean square error (MSE) of channel estimation that is very close to the theoretical lower bound derived in the paper. The proposed E-KMC based channel estimation scheme also significantly outperforms other types of semi-blind channel estimation approaches including second-and higher-order statistics based and machine learning based approaches, while at a much lower complexity. In addition, the E-KMC based channel estimation, is conducted at central processing unit (CPU) and avoids excessive fronthaul overhead due to exchange of the estimated CSI between access points (APs) and CPU.

We propose an unmanned aerial vehicle (UAV) ground base station (GBS) coordinated NOMA scheme where UAV and GBS jointly serve the cell-edge users. To the best of our knowledge, this is the first work to investigate air-ground BSs coordination for UAV-assisted NOMA systems, by taking advantage of the interference between UAV and GBS. Therefore, the proposed UAV-GBS coordinated NOMA scheme achieves much higher sum rate of cell-edge users than the non-coordinated UAV-assisted NOMA schemes where interference is suppressed as much as possible. The proposed scheme also outperforms GBSs coordinated NOMA due to more flexible and cost-effective interference management, thanks to the deployment of low-cost UAV BS. We conduct joint optimization of power allocation, user scheduling and UAV trajectory for the UAV-GBS coordinated system. A closed-form optimal solution to power allocation is derived. In addition, a dedicated successive interference cancellation (SIC) ordering approach is proposed. It is proven that the selection of a cell-center user with higher SIC order contributes to a higher rate of cell-edge user, based on which an SIC order based user scheduling algorithm for both cell-center and cell-edge users is presented.

Conventional request-grant based non-orthogonal multiple access (NOMA) incurs tremendous overhead and high latency. To enable grant-free access in NOMA systems, user activity detection (UAD) is essential. In this paper, we investigate compressive sensing (CS) aided UAD, by utilizing the property of quasi-time-invariant channel tap delays as the prior information. This does not require any prior knowledge of the number of active users like the previous approaches, and therefore is more practical. Two UAD algorithms are proposed, which are referred to as gradient based and time-invariant channel tap delays assisted CS (g-TIDCS) and mean value based and TIDCS (m-TIDCS), respectively. They achieve much higher UAD accuracy than the previous work at low signal-to-noise ratio (SNR). Based on the UAD results, we also propose a low-complexity CS based channel estimation scheme, which achieves higher accuracy than the previous channel estimation approaches.

In this article, we propose a semi-blind full-duplex (FD) amplify-and-forward (AF) relay system with adaptive self-interference (SI) processing assisted by independent component analysis (ICA) for low-latency and high-reliability (LLHR) Internet of Things (IoT). The SI at FD relay is not necessarily canceled as much as possible like the conventional approaches, but is canceled or utilized based on a signal-to-residual-SI ratio (SRSIR) threshold at relay. According to the selected SI processing mode at relay, an ICA-based adaptive semi-blind scheme is proposed for signal separation and detection at destination. The proposed FD relay system not only features reduced signal processing cost of SI cancelation but also achieves a much higher degree of freedom in signal detection. The resulting bit error rate (BER) performance is robust against a wide range of SRSIR, much better than that of conventional FD systems, and close to the ideal case with perfect channel state information (CSI) and perfect SI cancelation. The proposed system also requires negligible spectral overhead as only a nonredundant precoding is needed for ambiguity elimination in ICA. In addition, the proposed system enables full resource utilization with consecutive data transmission at all time and same frequency, leading to much higher throughput and energy efficiency than the time-splitting and power-splitting-based self-energy recycling approaches that utilize only partial resources. Furthermore, an intensive analysis is provided, where the SRSIR thresholds for the adaptive SI processing mode selection and the BER expressions with ICA incurred ambiguities are derived.

Crowdsourced mobile edge caching and sharing (Crowd-MECS) is emerging as a promising content delivery paradigm by employing a large crowd of existing edge devices (EDs) to cache and share popular contents. The successful technology adoption of Crowd-MECS relies on a comprehensive understanding of the complicated economic interactions and strategic decision making of different stakeholders in the ecosystem. In this article, we focus on studying the economic and strategic interactions between one content provider (CP) and a large crowd of EDs, where the CP designs the incentive scheme for EDs to cache and share contents, and EDs decide whether to cache and share contents for the CP. We formulate their interactions as a two-stage Stackelberg game. In Stage I, the CP decides the ratio of revenue (as incentives) shared with EDs who choose to cache and share contents, aiming at maximizing its own profit. In Stage II, EDs choose to beagentswho cache and share contents, and meanwhile gain a certain revenue from the CP, orrequesterswho do not cache but request contents in the on-demand fashion. We first analyze the EDs’ best responses and prove the existence and uniqueness of the equilibrium in Stage II by using the nonatomic game theory. Then, we identify the piecewise structure and the unimodal feature of the CP’s profit function, based on which we design a tailored low-complexity 1-D search algorithm to achieve the optimal revenue sharing ratio for the CP in Stage I. The simulation results show that both the CP’s profit and the EDs’ total welfare can be improved significantly (e.g., by 120% and 50%, respectively,) by using the proposed Crowd-MECS system, comparing with the non-MEC system where the CP serves all EDs directly.

An interference alignment (IA) and independent component analysis (ICA)-based semiblind scheme, referred to as IA-ICA, is proposed for downlink dual-user power-domain nonorthogonal multiple access (NOMA) systems in high-reliability low-latency (HRLL) Internet of Things (IoT). At the base station (BS), one user is converted to constructive interference to the other user via phase alignment of each symbol. At both user ends, ICA is used for semiblind signal detection. The phase rotation via nonredundant precoding at the BS does not introduce any spectral overhead, while only 1-2 pilot symbols are required for elimination of ICA incurred ambiguity. Closed-form expressions are derived for the users' symbol error rate (SER) performance in Rayleigh fading with 4-quadrature amplitude modulation (4-QAM), which matches the simulation results very well. Based on the analytical results, we propose an efficient power allocation algorithm that is based on statistical channel state information (CSI) only, and therefore, the signaling overhead involved is negligible. In particular, a near-optimal SER performance can be achieved with equal power allocation between the two users. The proposed IA-ICA-based semiblind NOMA system demonstrates a much better SER performance than the existing approaches even though they are under perfect CSI. Hence, it is a feasible solution for HRLL IoT, with high reliability and very low spectral and signaling overheads.

In this paper, we maximize the system throughput of a point-to-point ultra-reliable low-latency communications (URLLC) system by jointly optimizing its block length and pilot length under the constraints of latency and block error probability. A finite block length (FBL) is adopted to enable low transmission latency. We prove that the throughput is approximately concave with respect to pilot length, given a block length, and that there exists a unique optimal block length in terms of throughput, with a given pilot length. Closed-form expressions are derived for the near-optimal pilot length with a given block length, as well as the asymptotic block error probability with respect to both block length and pilot length. A low-complexity iterative algorithm is proposed for joint optimization of block length and pilot length, which converges within only 1-3 iterations. Simulation results show that the proposed joint optimization scheme achieves a near-optimal throughput performance of an FBL URLLC system, with a much lower complexity than exhaustive search. It also significantly outperforms the previous approaches that considered either block length optimization or pilot length optimization only.

In this paper, we investigate a spectrum- and energy-efficient emergency wireless communication system with an unmanned aerial vehicle (UAV) mounted base station (BS). To the best of our knowledge, this is the first work to investigate joint optimization of three-dimensional (3D) beamforming (BF), power allocation (PA), user scheduling and UAV trajectory, to maximize the average spectrum efficiency (SE) while maintaining high energy efficiency (EE). Furthermore, a closed- form BF design in both angle- and power-domains is proposed for the first time for UAV-BS assisted wireless systems. Thanks to the closed-form solutions, a low-complexity iterative algorithm is proposed for joint optimization, which converges within only one or two iterations. Simulation results show that 3D BF plays a dominant role in the overall performance, and the proposed closed- form 3D BF design achieves near-optimal performance in terms of both EE and SE, while requiring much lower complexity than exhaustive search. The maximum UAV speed with respect to the optimal EE is also obtained.

We propose a low-complexity semi-blind joint multi- carrier frequency offset (CFO) and multi-channel estimation scheme for uplink generalized frequency division multiple access (GFDMA) systems. To the best of our knowledge, this is the first work to investigate the estimation of both CFOs and channels for a wide range of GFDMA systems, allowing arbitrary carrier assignment, modulation type and cyclic prefix length, and a wide range of the number of receive antennas. Thanks to the orthogonality between noise subspace and each signal subspace of U users, a complex U-CFO and U-channel estimation problem is decomposed into 2U one-dimensional problems, and solved in a semi-blind manner. Also, the multi-CFO compensation is performed at receiver rather than transmitter, avoiding spectral overhead due to feedback of multiple CFOs. Simulation results show that the proposed scheme significantly outperforms the existing methods in terms of bit error rate (BER) and root-mean-square-errors (RMSEs) of CFO and channel estimation, at much lower computational complexity than the existing methods.

We propose a robust semi-blind estimation scheme of channel and carrier frequency offset (CFO) for generalized frequency division multiplexing (GFDM) systems. This, to the best of our knowledge, is the first work to propose an integral solution to channel and full-range CFO for a wide range of GFDM systems. Based on the derived equivalent system model with CFO included implicitly, a subspace based method is proposed to perform initial channel estimation blindly, which requires only a small number of received symbols to achieve the second order statistics of the received signal. Then, CFO estimation and channel ambiguity elimination are undertaken in series by utilizing a small number of nulls and pilots in a single sub-symbol. Both channel and CFO estimations are more robust against inter-carrier interference (ICI) and inter-symbol interference (ISI) caused by the nonorthogonal filter of GFDM, compared to the existing methods. The proposed scheme achieves a bit error rate (BER) performance close to the ideal case with perfect CFO and channel estimations especially at medium and high signal-to-noise-ratios (SNRs).

We propose a low-complexity and highperformance joint peak-to-average power ratio (PAPR) reduction and light emitting diode (LED) nonlinearity mitigation approach for unipolar asymmetrically clipped optical-orthogonal frequency division multiplexing (UACO-OFDM) based light fidelity (LiFi) systems. This is the first reported work to apply nonlinear compression based on μ-law companding for UACO-OFDM LiFi systems to reduce PAPR. In order to avoid the effect of the nonlinearity of LED, the transmitted signals are compressed further linearly. The signal distortion caused by nonlinear and linear compression is compensated for using multiple LEDs for simultaneous transmission. The proposed joint PAPR reduction and LED nonlinearity mitigation approach provides low complexity, searching is not required. Also, it is spectrum- and energy-efficient, requiring no pilot and consuming no extra transmission power. Simulation results show that the proposed approach significantly outperforms the state-of-the-art methods in terms of complementary cumulative distribution function (CCDF) and bit error rate (BER), and that it is more robust against the LED nonlinearity than conventional UACO-OFDM LiFi systems and other methods in the literature.

We investigate a downlink multi-cell non-orthogonal multiple access (NOMA) system with coordinated base stations (BSs) and propose a joint resource allocation (RA) scheme alongside adaptive user association to green the system. To the best of our knowledge, this is the first work to investigate joint allocation of subchannels and power for coordinated NOMA systems, while the previous work on RA for coordinated orthogonal multiple access (OMA) systems is not applicable. A serving channel gain based joint RA (SCG-JRA) algorithm is proposed, based on the theoretical proof that the total transmission power is mono-decreasing with respect to the SCGs of non-coordinated users. As for user association, an adaptive fuzzy logic (FL) based multi-criterion approach is proposed to achieve higher robustness against the combined effect of shadowing, fading and inter-cell interference, compared to the previous single-criterion based approaches. Numerical results show that the proposed SCG-JRA with adaptive FL based user association significantly outperforms the previous RA schemes assisted by single-criterion user association, in terms of energy efficiency (EE) and total transmission power, enabling a greener system.

We investigate an amplify-and-forward (AF) full-duplex (FD) relay system, where the FD incurred self-interference (SI), through partial cancellation at relay, is treated as a useful source at destination to enhance degree of freedom in signal detection, while reducing the signal processing cost of SI cancellation. An independent component analysis (ICA) based equalization structure is employed at destination to separate and detect the desired signal from the residual SI in a semi-blind way. The mode of SI cancellation at relay is chosen adaptively based on the threshold of signal-to-interference ratio (SIR) at relay. The proposed FD relay system not only features reduced signal processing cost of SI cancellation, but also achieves much higher energy efficiency (EE) than conventional FD relay systems where SI is canceled as much as possible. Also, the proposed system enables full resource utilization via consecutive data transmission at all time and the same frequency, leading to much higher throughput and EE than the conventional time-splitting and power-splitting based SI recycling approaches that occupy partial resources. Last but not least, the proposed system demonstrates a bit error rate (BER) performance that is robust against a wide range of SI and close to the ideal case with perfect channel state information (CSI) and perfect SI cancellation, while requiring no training sequence for estimation of any channel involved.

Filtered-orthogonal frequency division multiplexing (F-OFDM) is a promising candidate waveform for the fifth generation (5G) wireless communications because of its high flexibility and low out-of-band emission (OOBE). However, it suffers from dramatic peak-to-average-power ratio (PAPR), which is higher than that of OFDM and results in the power amplifier (PA) not working in the high-efficiency region. We propose a hybrid PAPR reduction scheme including precoding, time-domain selected mapping (TSLM) and companding techniques, for F-OFDM systems with independent component analysis (ICA) based blind channel equalization, which can achieve significant PAPR reduction over the previous work. Also, this is the first work to reduce PAPR while enabling the PA to work with the highest possible efficiency. The reciprocal of the hybrid PAPR reduction is embedded in the ambiguity elimination process of ICA, and therefore does not require any dedicated side information from the transmitter or any exclusive signal processing at the receiver, leading to a much higher spectral efficiency (SE) and lower computational complexity than the previous work. The bit error rate (BER) performance of the system with the proposed hybrid PAPR reduction scheme is shown to be close to the ideal case with perfect channel state information (CSI), while no side information and training sequence are required for PAPR reduction and channel estimation, thanks to the effectiveness of the ICA based blind channel equalization.

We propose an iterative semi-blind (ISB) receiver structure to enable ultra-reliable low-latency communications in short-frame full-duplex (FD) systems with carrier frequency offset (CFO). To the best of our knowledge, this is the first paper to propose an integral solution to channel estimation and CFO estimation for short-frame FD systems by utilizing a single pilot. By deriving an equivalent system model with the CFO included implicitly, a subspace-based blind channel estimation is proposed at the initial stage, followed by CFO estimation and channel ambiguities elimination. Then, the refinement of channel and the CFO estimates is conducted iteratively. The integer and fractional parts of CFO in the full range are estimated as a whole and in closed-form at each iteration. The proposed ISB receiver significantly outperforms the previous methods in terms of frame error rate, mean square errors of channel estimation and CFO estimation and output signal-to-interference-and-noise ratio, while at a halved spectral overhead. Cramér-Rao lower bounds are derived to verify the effectiveness of the proposed ISB receiver structure. It also demonstrates high-computational efficiency as well as the fast convergence speed.

We propose an iterative semi-blind carrier frequency offset (CFO) estimation, self-interference (SI) cancelation and signal detection scheme for full-duplex (FD) orthogonal frequency division multiplexing (OFDM) systems. To the best of our knowledge, this is the first work to consider signal detection of FD systems in the presence of both CFO and SI. The CFO estimation, SI cancelation and signal detection are performed initially by a subspace based semi-blind method, which are then enhanced significantly by performing iterations among them. Its CFO compensation is performed on the desired signal estimate, avoiding the introduction of CFO to the SI. The pilots for the desired signal and SI are carefully designed to enable simultaneous transmission of them to achieve FD training mode. Simulation results show that, the proposed iterative scheme, with much lower training overhead, demonstrates a significant performance enhancement over the existing methods. By utilizing the second order statistics of the received signal, a much superior bit error rate (BER) performance can be achieved compared to the case with perfect SI cancelation and CFO compensation. Its output signal-to- interference-and-noise-ratio (SINR) is close to that with perfect SI cancelation, and robust against the input signal-to-interference ratio (SIR).

Combining non-orthogonal multiple access (NOMA) with coordination techniques among base stations (BSs) is a promising solution to enhance spectral efficiency and alleviate inter-cell interference of cell-edge users in 5th generation (5G) networks. In this paper, we consider a multi-cell NOMA network with coordinated BSs in the downlink, and investigate its user coordination mode selection and resource allocation to achieve a green system. A fuzzy logic (FL) based multi-criterion scheme is proposed for user coordination mode selection. It is more robust against shadowing and fading than the previous selection schemes that classify coordinated and non-coordinated users by a single criterion. Also, the FL ranking list is fed into subcarrier allocation, leading to significant reduction in searching complexity of subcarrier allocation. A dramatic performance enhancement is achieved over the previous schemes based on single-criterion, in terms of transmission power and energy efficiency.

We propose a blind selective mapping (BSLM) based peak-to-average power ratio (PAPR) reduction method and an independent component analysis (ICA) based channel equalization structure for millimeter wave (mmWave) filter bank multi-carrier (FBMC) with offset quadrature amplitude modulation (OQAM) systems. On the one hand, a new phase adjustment factor is introduced in the BSLM based PAPR reduction scheme, which is spectrum-efficient as the phase ambiguity incurred can be resolved blindly at the receiver, requiring no side information. On the other hand, we propose an ICA based blind equalization scheme with a new phase shift correction approach, which can resolve the phase ambiguity incurred by ICA equalization. Simulation results show that the proposed structure can provide bit error rate (BER) performance close to zero- forcing (ZF) equalization with perfect channel state information (CSI), and the BSLM based PAPR reduction outperforms the existing methods in the literature, while requiring no side information at the receiver.

A joint multi-carrier frequency offset (CFO) and multi- time of arrival (TOA) estimation algorithm for multiuser single-input multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) systems is proposed. With carefully designed pilots, multiple CFOs and TOAs of K users are separated jointly, dividing a complex 2K-dimensional estimation problem into 2K low-complexity mono-dimensional estimation problems. Two CFO estimation approaches, including a low-complexity closed-form solution and a high-accuracy null-subcarrier assisted accurate estimation approach, are proposed, where the integer and fractional parts of each CFO are estimated as a whole rather separately. Each TOA is estimated regardless of CFO by exploring the features of the inter-carrier interference matrix. The Cramer-Rao lower bounds (CRLBs) of multi-CFO and mutli-TOA estimation are derived for the first time for SIMO OFDM systems. Simulation results show that the proposed CFO and TOA estimators provide higher estimation accuracy than the existing approaches. They also achieve performances close to the CRLBs especially at high signal-to-noise-ratios (SNRs).

Smartphone users are installing more and bigger apps. At the meanwhile, each app carries considerable amount of unused stuff, called software bloat, in its apk file. As a result, the resources of a smartphone, such as hard disk and network bandwidth, has become even more insufficient than ever before. Therefore, it is critical to investigate existing apps on the market and apps in development to identify the sources of software bloat and develop techniques and tools to remove the bloat. In this paper, we present a comprehensive study of software bloat in Android applications, and categorize them into two types, compile-time redundancy and install-time redundancy. In addition, we further propose a static analysis based approach to identifying and removing software bloat from Android applications. We implemented our approach in a prototype called RedDroid, and we evaluated RedDroid on thousands of Android applications collected from Google Play. Our experimental results not only validate the effectiveness of our approach, but also report the bloatware issue in real-world Android applications for the first time.

Light fidelity (LiFi), using light emitting devices such as light emitting diodes (LEDs) which are operating in the visible light spectrum between 400 and 800 THz, provides a new layer of wireless connectivity within existing heterogeneous radio frequency wireless networks. Link data rates of 10 Gbps from a single transmitter have been demonstrated under ideal laboratory conditions. Synchronization is one of these issues usually assumed to be ideal. However, in a practical deployment, this is no longer a valid assumption. Therefore, we propose for the first time a low-complexity maximum likelihood-based timing synchronization process that includes frame detection and sampling clock synchronization for direct current-biased optical orthogonal frequency division multiplexing LiFi systems. The proposed timing synchronization structure can reduce the high-complexity two-dimensional search to two low-complexity one-dimensional searches for frame detection and sampling clock synchronization. By employing a single training block, frame detection can be realized, and then sampling clock offset (SCO) and channels can be estimated jointly. We propose three frame detection approaches, which are robust against the combined effects of both SCO and the low-pass characteristic of LEDs. Furthermore, we derive the Cramér–Rao lower bounds (CRBs) of SCO and channel estimations, respectively. In order to minimize the CRBs and improve synchronization performance, a single training block is designed based on the optimization of training sequences, the selection of training length, and the selection of direct current (DC) bias. Therefore, the designed training block allows us to analyze the trade-offs between estimation accuracy, spectral efficiency, energy efficiency, and complexity. The proposed timing synchronization mechanism demonstrates low complexity and robustness benefits and provides performance significantly better than achieved with existing methods.

Modern software engineering practice increasingly brings redundant code into software products, which has caused a phenomenon called bloatware, leading to software system maintenance, performance and reliability issues as well as security problems. With the rapid advances of smart devices and a more connected world, it is never more important to trim bloatware to improve the leanness, agility, reliability, performance, and security of the interconnected software and network systems. Previous methods have limited scopes and are usually not fully automated. In this paper, we propose a new static-analysis-enabled approach to trimming unused code from both Java applications and Java Runtime Environment (JRE) automatically. We have built a tool called JRed on top of the Soot framework. We have conducted a fairly comprehensive evaluation of JRed based on a set of criteria: code size, code complexity, memory footprint, execution and garbage collection time, and security. Our experimental results show that, Java application size can be reduced by 44.5% on average and the JRE code can be reduced by more than 82.5% on average. The code complexity is significantly reduced according to a set of well-known metrics. Furthermore, we report that by trimming redundant code, 48.6% of the known security vulnerabilities in the Java Runtime Environment JRE 6 update 45 has been removed.

Program obfuscation is an important software protection technique that prevents attackers from revealing the programming logic and design of the software. We introduce translingual obfuscation, a new software obfuscation scheme which makes programs obscure by "misusing" the unique features of certain programming languages. Translingual obfuscation translates part of a program from its original language to another language which has a different programming paradigm and execution model, thus increasing program complexity and impeding reverse engineering. In this paper, we investigate the feasibility and effectiveness of translingual obfuscation with Prolog, a logic programming language. We implement translingual obfuscation in a tool called BABEL, which can selectively translate C functions into Prolog predicates. By leveraging two important features of the Prolog language, i.e., unification and backtracking, BABEL obfuscates both the data layout and control flow of C programs, making them much more difficult to reverse engineer. Our experiments show that BABEL provides effective and stealthy software obfuscation, while the cost is only modest compared to one of the most popular commercial obfuscators on the market. With BABEL, we verified the feasibility of translingual obfuscation, which we consider to be a promising new direction for software obfuscation.

We propose a semi-blind precoding aided maximum likelihood (ML) carrier frequency offset (CFO) estimation method and a precoding aided equalization based on independent component analysis (ICA) receiver structure for multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) wireless communication systems. By carefully designing a constant in the precoding process, the power between reference data and source data can be balanced to enable ML CFO estimation and ambiguity elimination for the ICA output signals at the receiver. This proposed semi-blind non-redundant structure is much more bandwidth-and-energy efficient than the pilot aided ML CFO estimation method, as no real-time training or extra transmission power is required. Simulation results show that the proposed scheme provides a bit error rate (BER) performance the same as the performance of the pilot aided ML CFO estimation method, and close to the ideal case with perfect channel state information (CSI) and no CFO.

We provide a comprehensive energy efficiency (EE) analysis of the full-duplex (FD) and half-duplex (HD) amplify-and-forward (AF) relay-assisted 60-GHz dual-hop indoor wireless systems, aiming to answer the question of which relaying mode is greener (more energy efficient) and to address the issue of EE optimization. We develop an opportunistic relaying mode selection scheme, where FD relaying with one-stage self-interference cancellation (passive suppression) or two-stage self-interference cancellation (passive suppression + analog cancellation) or HD relaying is opportunistically selected, together with transmission power adaptation, to maximize the EE with given channel gains. A low-complexity joint mode selection and EE optimization algorithm are proposed. We show a counter-intuitive finding that with a relatively loose maximum transmission power constraint, FD relaying with two-stage self-interference cancellation is preferable to both FD relaying with one-stage self-interference cancellation and HD relaying, resulting in a higher optimized EE. A full range of power consumption sources is considered to rationalize our analysis. The effects of imperfect self-interference cancellation at relay, drain efficiency, and static circuit power on EE are investigated. Simulation results verify our theoretical analysis.

We propose a joint independent component analysis (ICA) based equalization and carrier frequency offset (CFO) estimation scheme for orthogonal frequency division multiple access (OFDMA) systems, which requires only a single pilot, resulting in a very low spectral overhead. On the one hand, a low-complexity ambiguity elimination method is proposed in the ICA equalized signals. By designing and exploring a fixed order of correlation in the transmitted signals, the permutation ambiguity problem is solved, while the remaining quadrant ambiguity is eliminated by one pilot symbol. One the another hand, linear CFO estimation is performed with a closed-form solution based on the phase difference between adjacent rows in the CFO-corrupted channel structure. Simulation results show that the proposed semi-blind ICA based scheme not only outperforms some existing CFO estimation approaches, but also provides a bit error rate (BER) performance, comparable to the ideal case with perfect channel state information (CSI) and no CFO.

We propose a low-complexity frequency synchronization approach for semi-blind independent component analysis (ICA) based coordinated multi-point (CoMP) orthogonal frequency division multiplexing (OFDM) systems, with multiple carrier frequency offsets (CFOs). The key idea is to introduce a short pilot for both multi-CFO estimation and ambiguity elimination in the ICA equalized signals. First, by minimizing the cross-correlation between original pilots and received pilots with implicit phase rotation correction, the one-dimensional search based CFO estimation can be performed without trial ICI compensation and channel state information (CSI). Second, by maximizing the real part of the cross-correlation between ICA equalized pilots and original pilots, the same pilots can be used again to eliminate the permutation and quadrant ambiguity in the ICA equalized signals. Simulation results show that, with a very low training overhead of 2%, the proposed multi-CFO estimation approach outperforms existing methods. Also, the proposed semi-blind CoMP system can achieve a bit error rate (BER) performance close to the ideal case with perfect CSI and no CFO.

We propose a semi-blind multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM) system, with a precoding aided carrier frequency offset (CFO) estimation approach, and an independent component analysis (ICA) based equalization structure. A number of reference data sequences are carefully designed offline and are superimposed to source data via a non-redundant linear precoding process, which can kill two birds with one stone, without introducing any extra total transmit power and spectral overhead. First, the reference data sequences are selected from a pool of carefully designed orthogonal sequences. The CFO estimation is to minimize the sum cross-correlation between the CFO compensated signals and the rest orthogonal sequences in the pool. Second, the same reference data enable elimination of the permutation and quadrant ambiguity in the ICA equalized signals by maximizing the cross-correlation between the ICA equalized signals and the reference data. Simulation results show that, without extra bandwidth and power needed, the proposed semi-blind system achieves a bit error rate (BER) performance close to the ideal case with perfect channel state information (CSI) and no CFO. Also, the precoding aided CFO estimation outperforms the constant amplitude zero autocorrelation (CAZAC) sequences based CFO estimation approach, with no spectral overhead.

We propose a joint semi-blind equalization and inter-carrier interference (ICI) mitigation scheme for multiple carrier frequency offsets (CFOs) corrupted signals in carrier aggregation (CA) based multiple access coordinated multi-point (CoMP) systems with OFDMA. The CFO-induced ICI is mitigated implicitly via the independent component analysis (ICA) based semi-blind equalization, without requiring an explicit process of estimation of multiple CFOs. Only a small number of pilots are used to resolve the remaining indeterminacies in the ICA equalized signals, introducing a very low training overhead. Simulation results show that the proposed semi-blind ICA based equalization scheme provides a bit error rate (BER) performance closed to the ideal case with perfect CSI and no CFO, and also outperforms the approach with the constant amplitude zero autocorrelation (CAZAC) sequences based explicit CFO estimation.

We propose an orthogonal frequency division multiplexing (OFDM) based semi-blind coordinated multi-point (CoMP) system, with a low complexity estimation approach for multiple carrier frequency offsets (CFOs), and an equalization structure based on the independent component analysis (ICA). Our work is different in that a small number of well-designed pilot symbols are employed to kill two birds with one stone. On the one hand, using the structure of pilots, a complex multi-dimensional search for multiple CFOs is divided into a series of low-complexity mono-dimensional searches. On the other hand, the cross-correlation between the transmitted and the received pilot symbols is explored to allow elimination of the remaining ambiguity in the ICA equalized signals. Simulation results show that with a low training overhead of 3.1%, the proposed semi-blind system can achieve a bit error rate (BER) performance close to the ideal case with perfect channel state information (CSI) and no CFO at the receiver.

In this paper we describe the basic profile of our project Xland: a 3D blog community and the content of our video. Besides, we would also make some annotation in this paper to compensate some information that is not presented by the video.