Resource management is one of the challenges in satellite networks due to their high mobility, wide coverage, long propagation distances, and stringent constraints on energy, communication, and computation resources. Traditional resource allocation approaches rely only on hard and rigid system performance metrics. Meanwhile, incentive mechanisms, which are based on game theory and auction theory, investigate systems from the "economic" perspective in addition to the "system" perspective. Particularly, incentive mechanisms are able to take into account rationality and other behavior of human users into account, which guarantees benefits/utility of all system entities, thereby improving the scalability, adaptability, and fairness in resource allocation. This paper presents a comprehensive survey of incentive mechanism design for resource management in satellite networks. The paper covers key issues in the satellite networks, such as communication resource allocation, computation offloading, privacy and security, and coordination. We conclude with future research directions including learning-based mechanism design for satellite networks.

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.

Federated learning (FL) is a promising distributed machine learning approach with enhanced data privacy protection. However, wireless communication remains a key bottleneck, directly affecting the efficiency and performance of FL. In this paper, we introduce a device-centric cell-free network to mitigate the negative effects of random fading and limited radio resources on FL. The convergence gap, representing the difference between the FL model’s performance and that of the optimal model, is analyzed to evaluate the impact of communication and computation factors, including inter-device interference, on FL performance. Then, access point (AP)-device association, transmission power, and computation frequency are jointly optimized to minimize the convergence gap. Lyapunov techniques are employed to decouple the long-term optimization into a series of online solvable problems. A deep reinforcement learning-based scheme is proposed to optimize the AP association and transmission power for devices, reducing the computational complexity from a prohibitive level to a real-time feasible quadratic level. Additionally, a closed-form solution for the optimal device computation frequency is derived. Simulation results show that the proposed scheme significantly outperforms the traditional cell-free FL and cellular FL schemes in both model training performance and energy efficiency.

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, a comprehensive and detailed uplink performance analysis is provided for cell-free massive multipleinput multiple-output orthogonal frequency division multiplexing (CF m-MIMO OFDM) systems, which consider the impact of multiple user equipment (UE) heterogeneous factors. This is the first performance analysis work on CF m-MIMO OFDM systems that simultaneously accounts for the heterogeneous mobility speed, activation probability and serving priority. Considering that UE's serving priority determines the amount of its allocated time-frequency resources, a novel closed-form expression of uplink spectral efficiency (SE) is derived by weighting each UE's SE based on its allocated time-frequency resources. The derived SE expression can quantify the impact of multiple UE heterogeneous factors on the uplink performance. Additionally, the SE performance analysis of local processing and fully centralized processing is also included for comparison. Simulation results show that CF m-MIMO OFDM systems under multiple UE heterogeneous factors outperform existing CF m-MIMO systems in terms of the 90 %-likely uplink SE, and allow a trade-off among fronthaul overhead, complexity and SE performance.

In this paper, a novel three-stage data-aided dual-space (DADS) (i.e., beamspace and signal subspace) channel estimation scheme is proposed for massive multiple-input multiple-output hybrid beamforming (m-MIMO-HBF) systems subject to pilot contamination. By exploiting the orthogonality of signal subspace, the non-overlapping interference caused by pilot contamination is identified and mitigated in the coarse channel estimate via subspace projection. Thanks to the independence of transmitted data between users, the overlapping interference is suppressed through alternating iterative refinement of the channel estimate and detected data. Additionally, to initially address the under-determined estimation problem arisen from hybrid beamforming (HBF) structures, an improved matching pursuit algorithm is proposed for coarse sparse beamspace channel estimation by appropriately selecting the scaling factor and adjusting the step size in a piecewise manner, followed by enhancement via subspace projection. Furthermore, by accurately detecting the overlap level of interference in the beamspace, the proposed channel estimation scheme selects an appropriate channel enhancement or refinement strategy to address both non-overlapping and overlapping interference subject to several typical channels without significantly increasing computational complexity. Simulation results demonstrate that the proposed channel estimation scheme achieves higher channel estimation accuracy and exhibits stronger resilience to both interference intensity and the number of interference compared to existing channel estimation schemes.

We consider a wireless networked control system (WNCS) with imperfect bidirectional links for real-time applications such as smart grids. To maintain the stability of WNCS, captured by the probability that plant state violates preset values, at minimal cost, heterogeneous physical processes are monitored by multiple sensors. This status information, such as dynamic plant state and Markov Process-based context information, is then received/estimated by the controller for remote control. However, scheduling multiple sensors and designing the controller with limited resources is challenging due to their coupling, delay, and transmission loss. We formulate a Constrained Markov Decision Problem (CMDP) to minimize violation probability with cost constraints. We reveal the relationship between the goal and different updating actions by analyzing the significance of information that incorporates goal-related usefulness and contextual importance. Subsequently, a goal-oriented deterministic scheduling policy is proposed. Two sensing-assisted control strategies and a control-aware estimation policy are proposed to improve the violation probability-cost tradeoff, integrated with the scheduling policy to form a goal-oriented co-design framework. Additionally, we explore retransmission in downlink transmission and qualitatively analyze its preference scenario. Simulation results demonstrate that the proposed goal-oriented co-design policy outperforms previous work in simultaneously reducing violation probability and cost.

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.

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.

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.

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 this paper, we investigate timely and resource-efficient transmission for mixed traffic in industrial Internet-of-Things (IIoT) with short packet communication (SPC). Due to the limited resources in IIoT, fixed resource allocation for multiple users with different arrival rates leads to low resource utilization and high age of information (AoI). We formulate an optimization problem to minimize the required bandwidth for mixed traffic with timely constraints. To avoid resource waste and data collisions, we use traffic prediction to categorize users into high and low traffic states. For users in high traffic state, dedicated bandwidth is reserved for each user while a synchronous multi-channel slotted ALOHA access method is adopted for users in low traffic state. To meet the required timeliness requirements, the closed-form expressions of the peak AoI (PAoI) for users with SPC in different traffic states are derived. Then we explore an Traffic Classification-based Bandwidth Allocation (TCBA) algorithm to minimize the required bandwidth with the timely constraints of mixed traffic. Numerical results are provided to verify our analysis and demonstrate that the proposed TCBA algorithm outperforms the existing methods significantly in terms of bandwidth saving.

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.

Age of Information (AoI) has been proposed to quantify the freshness of information for emerging real-time applications such as remote monitoring and control in wireless networked control systems (WNCSs). Minimization of the average AoI and its outage probability can ensure timely and stable transmission. Energy efficiency (EE) also plays an important role in WNCSs, as many devices are featured by low cost and limited battery. Multi-connectivity over multiple links enables a decrease in AoI, at the cost of energy. We tackle the unresolved problem of selecting the optimal number of connections that is both AoI-optimal and energy-efficient, while avoiding risky states. To address this issue, the average AoI and peak AoI (PAoI), as well as PAoI violation probability are formulated as functions of the number of connections. Then the EE-PAoI ratio is introduced to allow a tradeoff between AoI and energy, which is maximized by the proposed risk-aware, AoI-optimal and energy-efficient connectivity scheme. To obtain this, we analyze the property of the formulated EE-PAoI ratio and prove the monotonicity of PAoI violation probability. Interestingly, we reveal that the multi-connectivity scheme is not always preferable, and the signal-to-noise ratio (SNR) threshold that determines the selection of the multiconnectivity scheme is derived as a function of the coding rate. Also, the optimal number of connections is obtained and shown to be a decreasing function of the transmit power. Simulation results demonstrate that the proposed scheme enables more than 15 folds of EE-PAoI gain at the low SNR than the single-connectivity scheme.

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.

In the realm of industrial Internet of Things (IIoT), the concept of location awareness plays a crucial role in the integrated sensing and communication (ISAC) framework. This paper introduces an innovative methodology for assessing the location awareness of a mobile entity by combining the precision of the positioning algorithm and the timeliness of location estimations based on the age of information (AoI). The assessment employs a novel metric termed as the aging error of localization (AEoL), which encapsulates both the accuracy of localization and its evolution over the data packet lifecycle. This metric bridges a gap in existing research, which predominantly emphasizes geographical precision while neglecting the dynamic spatial attributes of a mobile entity, thereby offering valuable insights into both the precision and temporal aspects of location awareness. The study delves into the evaluation of AEoL under scenarios of perfect and imperfect localization algorithm precision. By considering a scenario where an automated guided vehicle (AGV) adheres to the uniform rectilinear motion (URM) and transmits radio signals via specific queuing models, analytical expressions for the time-average AEoL are derived across varying update rates. These expressions are subsequently validated through numerical simulations. Furthermore, for specific root mean square error (RMSE) scenarios, optimal update rates are recommended, through which the performance of location awareness can be enhanced by reducing the AEoL metric by 10% to 68% compared to the worst-case scenario.

Optimizations premised on open-loop metrics such as Age of Information (AoI) indirectly enhance the system’s decision-makingutility. We therefore propose a novel closed-loop metric named Goal-oriented Tensor (GoT) to directly quantify the impact of semantic mismatches on goal-oriented decision-makingutility. Leveraging the GoT, we consider asampler & decision-makerpair that works collaboratively and distributively to achieve a shared goal of communications. We formulate a two-agent infinite-horizon Decentralized Partially Observable Markov Decision Process (Dec-POMDP) to conjointly deduce the optimal deterministic sampling policy and decision-making policy. To circumvent thecurse of dimensionalityin obtaining an optimal deterministic joint policy through Brute-Force-Search, a sub-optimal yet computationally efficient algorithm is developed. This algorithm is predicated on the search for a Nash Equilibrium between the sampler and the decision-maker. Simulation results reveal that the proposedsampler & decision-makerco-design surpasses the current literature on AoI and its variants in terms of both goal achievementutilityand sparse sampling rate, signifying progress in the semantics-conscious, goal-driven sparse sampling design.

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 consider a wireless networked control system (WNCS) for real-time applications. Different physical processes (e.g., dynamic plant state and Markov Process-based context information) are monitored by multiple sensors via imperfect wireless links and then received/estimated by the controller for remote control. In this paper, we use violation probability as a key performance metric, which captures extreme events that violate the preset threshold of the plant state, to ensure the stability of WNCS. Scheduling multiple sensors with limited resources is challenging, due to the delays and loss in transmission, and their heterogeneous impacts on the WNCS goal. To address this problem, we first show the relationship between the considered goal and the actions of the scheduler and controller. We then present a Markov Decision Problem (MDP) to minimize violation probability with cost constraints. By analyzing the significance of information that incorporates goal-related usefulness and contextual importance, the structural results of the formulated MDP is presented. Then a goal-oriented scheduling and control co-design policy is proposed to improve the violation probability-cost tradeoff. Simulation results show that the proposed policy results in a lower violation probability and a lower cost.

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.

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.

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.

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, 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.

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.

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.

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.

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.

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 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.