This paper investigates two distinct types of block errors - undetected errors (confusions) and erasures - in additive white Gaussian noise (AWGN) channels with error-bounded block decoders operating in the finite blocklength (FBL) regime. While block error rate (BLER) is a common metric, it does not distinguish between confusions and erasures, which can have significantly different impacts in cross-layer protocol design, despite upper-layer protocols universally assuming physical (PHY) errors manifest as packet erasures rather than undetected corruptions - an assumption lacking rigorous PHY-layer validation. We present a systematic analysis of confusions and erasures under BLER-constrained maximum likelihood (ML) decoding. Through sphere-packing analysis, we provide analytical bounds for both block confusion and erasure probabilities, and derive the sensitivities of these bounds to blocklength and signal-to-noise ratio (SNR). To the best of our knowledge, this is the first study on this topic in the FBL regime. Our findings provide theoretical validation for the block erasure channel abstraction commonly assumed in medium access control (MAC) and network layer protocols, confirming that, for practical FBL codes, block confusions are negligible compared to block erasures, especially at large blocklengths and high SNR.

The technological landscape is rapidly evolving toward large-scale systems. Networks supporting massive connectivity through numerous Internet of Things (IoT) devices are at the forefront of this advancement. In this paper, we examine Wireless Power Transfer (WPT)-enabled networks, where a server requires to collect data from these IoT devices to compute a task with massive Ultra-Reliable and Low-Latency Communication (mURLLC) services. We focus on information freshness, using Age-of-Information (AoI) as the key performance metric. Specifically, we aim to minimize the maximum AoI among IoT devices by optimizing the scheduling policy. Our analytical findings demonstrate the convexity of the problem, enabling efficient solutions. We introduce the concept of AoI-oriented cluster capacity and analyze the relationship between the number of supported devices and network AoI performance. Numerical simulations validate our proposed approach's effectiveness in enhancing AoI performance, highlighting its potential for guiding the design of future IoT systems requiring mURLLC services.

The extreme requirements for high reliability and low latency in the upcoming Sixth Generation (6G) wireless networks are challenging the design of multi-hop wireless transport networks. Inspired by the advent of the virtualization concept in the wireless networks design andopennessparadigm as fostered by the Open-Radio Access Network (O-RAN) Alliance, we target a revolutionary resource allocation scheme to improve the overall transmission efficiency. In this paper, we investigate the problem of automatic repeat request (ARQ) in multi-hop decode-and-forward (DF) relaying in the finite blocklength (FBL) regime, and propose a dynamic scheme of multi-hop hybrid ARQ (HARQ), which maximizes the end-to-end (E2E) communication reliability in the wireless transport network.We also propose an integer dynamic programming (DP) algorithm to efficiently solve the optimal Dynamic Multi-Hop HARQ (DMH-HARQ) strategy. Constrained within a certain time frame to accomplish E2E transmission, our proposed approach is proven to outperform the conventional listening-based cooperative ARQ, as well as any static HARQ strategy, regarding the E2E reliability. It is applicable without dependence on special delay constraint, and is particularly competitive for long-distance transport network with many hops.

Distributed edge learning (DL) is considered a cornerstone of intelligence enablers, since it allows for collaborative training without the necessity for local clients to share raw data with other parties, thereby preserving privacy and security. Integrating DL into the 6G networks requires a coexistence design with existing services such as high-bandwidth (HB) traffic like eMBB. Current designs in the literature mainly focus on communication round-wise designs that assume a rigid resource allocation throughout each communication round (CR). However, rigid resource allocation within a CR is a highly inefficient and inaccurate representation of the system’s realistic behavior, especially when CR duration far exceeds the channel coherence time due to large model size or limited resources. This is due to the heterogeneous nature of the system, as clients inherently may need to access the network at different time instants. This work zooms into one arbitrary CR, and demonstrates the importance of considering a time-dependent design for sharing the resource pool with HB traffic. We first formulate a time-slot-wise optimization problem to minimize the consumed time by DL within the CR while constrained by a DL energy budget. Due to its intractability, a session-based optimization problem is formulated assuming a CR lasts less than a large-scale coherence time. Some scheduling properties of such multi-server joint communication scheduling and resource allocation framework have been established. An iterative algorithm has been designed to solve such non-convex and non-block-separable-constrained problems. Simulation results confirm the importance of the efficient and accurate integration design proposed in this work.

This paper investigates a multi-user MIMO down-link network operating in the finite blocklength (FBL) regime. We propose an efficient power allocation scheme that balances overall system performance and user fairness. Specifically, we design a power allocation scheme to maximize the overall FBL throughput of the system. To address the non-convexity of the formulated problem, we employ a successive convex approximation (SCA) -based approach, transforming the non-convex problem into a series of convex subproblems to obtain the optimal power allocation. Subsequently, we introduce a fairness-oriented power allocation scheme that maximizes the minimum user FBL throughput. By combining these two approaches, we develop a unified power allocation scheme that effectively balances overall system performance and user fairness. Simulation results demonstrate that the proposed scheme efficiently addresses the trade-off between system-wide FBL performance and user fairness, providing a flexible solution for diverse application scenarios.

Physical layer deception (PLD) is a novel security mechanism that combines physical layer security (PLS) with deception technologies to actively defend against eavesdroppers. In this paper, we establish a novel semantic model for PLD that evaluates its performance in terms of semantic distortion. By analyzing semantic distortion at varying levels of knowledge on the receiver's part regarding the key, we derive the receiver's optimal decryption strategy, and consequently, the transmitter's optimal deception strategy. The proposed semantic model provides a more generic understanding of the PLD approach independent from coding or multiplexing schemes, and allows for efficient real-time adaptation to fading channels.

Distributed learning (DL) is considered a cornerstone of intelligence enabler, since it allows for collaborative training without the necessity for local clients to share raw data with other parties, thereby preserving privacy and security. Integrating DL into the 6G networks requires coexistence design with existing services such as high-bandwidth (HB) traffic like eMBB. Current designs in the literature mainly focus on communication round (CR)-wise designs that assume a fixed resource allocation during each CR. However, fixed resource allocation within a CR is a highly inefficient and inaccurate representation of the system's realistic behavior. This is due to the heterogeneous nature of the system, where clients inherently need to access the network at different times. This work zooms into one arbitrary communication round and demonstrates the importance of considering a time-dependent resource-sharing design with HB traffic. We propose a time-dependent optimization problem for minimizing the consumed time and energy by DL within the CR. Due to its intractability, a session-based optimization problem has been proposed assuming a large-scale coherence time. An iterative algorithm has been designed to solve such problems and simulation results confirm the importance of such efficient and accurate integration design.

In this paper, we study an industrial Internet of Thing (IIoT) network supporting massive ultra-reliable and low-latency communications, where each user has strict timeliness requirements. We propose an optimal framework to maximize the effective throughput via jointly choosing the uplink transmission blocklength for multiple users. To address the formulated non-convex problem, we first characterize the quasi-concavity of the effective throughput to users’ blocklength. Then, following the characterization, the problem is reformulated to a quasi-convex one. Utilizing Dinkelbach’s transformation, an efficient algorithm is developed to obtain the optimal solution. Finally, through simulations, we confirm our analytical model and the superiority of the proposed design in comparison to benchmarks.

The evolution towards 6G communication technology heightens the demand for data freshness. Consequently, Age of Information (AoI), a key metric quantifying data freshness, has raised significant attention from academia and industry. This paper investigates a Multi-access Edge Computing (MEC) network with multiple servers designed to support mission-critical, low-latency computational services. We characterize the transmission reliability with FBL codes in the communication phase. Using extreme value theory, we analyze the occurrence of extreme queue length violations during the computation time phase. Based on the characterizations, we develop an optimal framework incorporating server selection and scheduling strategies for minimizing the average AoI. Via numerical simulations, we validate our algorithm’s effectiveness in enhancing AoI performance, demonstrate how varying parameters affect system performance, and illustrate the potential of our method for guiding future MEC system designs.

Enabling Mobile edge computing (MEC) services with massive connectivity and low energy consumption is crucial for future Internet of Things (IoT) infrastructures. In this article, we investigate an IoT network, where MEC is adopted as the computing framework for complicated IoT services while nonorthogonal multiple access (NOMA) is introduced to enable the interdependent data input offloading from multiple IoT devices to an edge server. The MEC service frame consists of a communication phase and a computation phase, where the latter phase requires the complete data offloaded in the former one to complete a specific task. To minimize the weighted sum energy consumption of both users and the edge server, a joint resource allocation original problem is formulated, which is unfortunately nonconvex. To tackle the difficulty, we first decompose the problem into subproblems, and characterize the structure of optimal solution to the subproblems. Following the characterization, the original problem is reformulated into a tractable one. We then develop a Branch-Reduce-and-Bound (BRB) based algorithm to obtain the optimal solution. Additionally, to further investigate the MEC scenario with the offloading of multiple users, we apply the state-of-the-art hybrid NOMA (H-NOMA) scheme to evaluate its benefits to multiuser MEC. We rigorously prove that, with interdependent user data inputs, pure NOMA (P-NOMA) is not only a special case but also an optimal case of H-NOMA. Via simulation, the analytical findings and the proposed algorithm are validated and evaluated.

In this paper, we consider a multi-access mobile edge computing (MEC) network with multiple sensors and one MEC server in industrial Internet of Things networks, where the MEC server provides a joint computation service (in the computation phase) for a set of sub-tasks offloaded by different sensors (in the communication phase). Due to the requirements of low latency and ultra reliability, we utilize finite blocklength information theory to characterize the reliability of the communication phase and exploit extreme value theory to investigate the delay violation probability in the computation phase. Following these characterizations, we derive the average end-to-end error probability of the entire service and provide two average end-to-end reliability-optimal design frameworks considering fixed frames structure and dynamic frames structure, in both of which the goal is to minimize the average end-to-end error probability by optimally allocating the total time length to each frame, as well as allocating each frame length to the communication phase and the computation phase. For the fixed frames structure, the original problem is decomposed, and the joint convexity of the decomposed sub-problems is rigorously proved, and the optimal solutions are obtained by the proposed optimal time allocation algorithm. Moreover, for the dynamic frames structure, we reformulate the optimization problem by introducing an average time constraint. By exploiting Lagrange multipliers, we transform the reformulated optimization problem into a dual problem with strong duality, the solutions of which can be obtained by the proposed time allocation algorithm. Via simulations, we validate the proven convexity and the approximation in our analytical model and evaluate the performance for both fixed frames length structure and dynamic frames length structure.

Physical layer security (PLS) is a promising technology to secure wireless communications by exploiting the physical properties of the wireless channel. However, the passive nature of PLS creates a significant imbalance between the effort required by eavesdroppers and legitimate users to secure data. To address this imbalance, in this article, we propose a novel framework of physical layer deception (PLD), which combines PLS with deception technologies to actively counteract wiretapping attempts. Combining a two-stage encoder with randomized ciphering and non-orthogonal multiplexing, the PLD approach enables the wireless communication system to proactively counter eavesdroppers with deceptive messages. Relying solely on the superiority of the legitimate channel over the eavesdropping channel, the PLD framework can effectively protect the confidentiality of the transmitted messages, even against eavesdroppers who possess knowledge equivalent to that of the legitimate receiver. We prove the validity of the PLD framework with in-depth analyses and demonstrate its superiority over conventional PLS approaches with comprehensive numerical benchmarks.

In this paper, we focus on an ultra-reliable low latency communication (URLLC) scenario, where the access point (AP) is supposed to support latency-critical communication via a non-orthogonal multiple access (NOMA) scheme. Moreover, we allow the device with the stronger channel acting as a relay for cooperatively enhancing the transmission reliability for the other device. Based on the considered NOMA-assisted cooperative scheme, we characterize out the maximum error probability between two devices as the objective to be minimized. Together with an energy constraint for the whole transmission period, we formulate a problem jointly optimizing the blocklength assigned to two phases, i.e., the NOMA phase and the cooperative phase, and power resources allocated in each transmission attempt. To address this non-convex problem, we reformulate the problem by introducing auxiliary variables and construct a tight convex approximation at any feasible local point, based on which we further propose an efficient algorithm for iteratively improving the local point until a convergence to a sub-optimum. Via numerical results, we validate the convergence of the proposed iterative algorithm and confirm the reliability advantages of NOMA-assisted cooperative scheme, compared with multiple benchmarks.

In this paper, towards enabling massive connectivity in the next generation ultra-reliable low latency communication (URLLC) Internet-of-Things (IoT) network, we investigate a scalable clustered network, where the user scheduling at the access point (AP) is completely replaced by the cooperation scheduling among clustered IoT users, in order to alleviate the overload at AP. In particular, while serving the clustered network, the AP simply broadcasts out all data for the whole network. Each clustered user attempts to decode the broadcast signal. Afterwards, cooperation retransmissions will be scheduled among users for compensating the overall transmission reliability. Considering limited energy and blocklength resources, we start with the cooperation case based on a cluster head and aim at fairly minimizing the maximum error probability among all users, while the resource allocation and cooperation scheduling are jointly designed. To deal with the inherent nonconvexity, we construct a tight convex approximation for the problem based on an arbitrary feasible point, which enables an iterative algorithm for constantly improving the solution until a convergence to a suboptimal. Next, to further exploit the high cooperation flexibility in clustered URLLC network, we extend the whole design to the case allowing arbitrary cooperation among users, i.e., the case without cluster head. Finally, simulation results validate the convergence of our proposed algorithms and highlight the reliability benefits over benchmarks. The impact of cluster head selection and the high cooperation flexibility of the case without cluster head are also illustrated.

In this paper, we study a multi-access mobile edge computing (MEC) network in the industrial Internet-of-Things (IoT) scenario, which aims at providing a joint computation service for a group of sub-tasks offloaded from multiple user equipments (UEs). The whole MEC service, including a communication phase and a computation phase, is required to satisfy both a low latency and a high reliability requirement. We derive the end-to-end reliability (error probability) of the whole MEC service and provide corresponding reliability-optimal design frameworks, where both the perfect channel state information (CSI) and outdated CSI scenarios are considered. In particular, we characterize the low-latency communication behavior with the consideration of the finite blocklength (FBL) impact, and exploit the extreme value theory to study the delay violation probability in the computation phase. Following the characterizations, in the perfect CSI scenario, a design framework minimizing the instantaneous end-to-end error probability is provided, i.e., via optimally choosing the time length for each user’s offloading and the time length for the computation phase. We rigorously prove the convexity of the problem, investigate the relationships among the variables in the optimal solution, based on which a low-complexity method is proposed achieving the optimal solution. In addition, for the scenario with only the outdated CSI, after deriving the expected end-to-end error probability conditioned on the outdated CSI value, a corresponding optimal time allocation design is provided as well, where the convexity of the formulated problem is characterized and the optimal solution is obtained. Via simulations, we validate our analytical model and evaluate the network performance under the design.

To support abundant mission-critical applications, the next-generation ultra-reliable low latency communication (URLLC) is expected to meet more stringent requirements. In this work, to promote the advancement of URLLC, we target at exploring the fundamental trade-offs in finite blocklength (FBL) regime. Taking the short blocklength impacts into account, we integrate broadcasting into time-division multiple access (TDMA) strategy and adopt a hybrid broadcasting-TDMA (BC-TDMA) strategy for the multiple access URLLC services. Within hybrid BC-TDMA, user grouping has been implemented, such that grouped users can be served over the shared large blocklength and thus get rid of the performance hindrance from short blocklengths. We formulate a problem for fairly minimizing the error probability for all users via optimizing the user grouping decision together with the joint power and blocklength allocation. For given grouping, we characterize four necessary optimality conditions for the joint resource allocation and accordingly construct the optimal closed-form resource allocation solution. The analytical characterizations have also enabled two criteria for efficiently filtering out the optimal grouping in an iterative manner. Finally, via simulations, we examine our proposed algorithms for both obtaining optimal resource allocation and filtering the optimal grouping. The extremely low complexity and significant reliability advantages of our proposed hybrid BC-TDMA solution are also highlighted in comparison to benchmarks.

In federated learning (FL), local workers learn a global model collaboratively using their local data by communicating trained models to a central server for privacy concerns. Due to its local nature, FL is typically subject to various heterogeneities, including system and statistical heterogeneity. To address these concerns, Federated Proximal (FedProx) has been considered a promising FL paradigm to provide more stable learning convergence in the presence of computation stragglers and statistical heterogeneity. However, in wireless networks with unreliable communication channels, the errors of packet transmissions should be considered, introducing additional heterogeneity. For the first time, we rigorously prove the convergence of FedProx in the presence of transmission packet errors in heterogeneous networks. In addition, we propose a joint client selection and resource allocation strategy that maximizes the number of effective participating users for convergence acceleration. The method is combined with a random weight mechanism to reduce the statistical bias caused by the client selection strategy. An efficient low-complexity algorithm for solving the optimization problem is developed. The proposed method achieves faster convergence and requires fewer communication rounds to attain accuracy than existing state-of-the-art client selection methods.

Integrated Sensing, Communication, and Computation (ISCC) multi-functional networks represent a new paradigm in wireless communications, enabling comprehensive environmental perception, data processing, and communication. However, realizing the full potential of these networks requires addressing cooperative gain-a challenge given the competitive nature of the tasks associated with the various functionalities. This paper investigates the concept of semantic communication as a potential pathway towards achieving this cooperative gain. Despite the considerable body of research in semantic communications, the area of semantic reliability remains relatively unexplored, and characterization of semantic reliability within ISCC networks is particularly limited. In this study, we focus on the performance of semantic reliability within the ISCC framework. We formulate a joint resource allocation problem aimed at maximizing semantic reliability, thereby addressing the trade-off between different functionalities with limited resources. This approach transforms the traditionally competitive objectives into a cooperative framework from the perspective of semantic communications. Our analytical findings validate the efficacy of this approach, highlighting the benefits of focusing on semantic communication over traditional data communication in ISCC networks.

Data security is one of the key concerns in the next generation of ultra-reliable and low-latency networks, especially with machine-type communications. In this work, we propose a novel metric, leakage-failure probability, to represent the reliable-secure performance of the considered system. We discover that the system performance can be enhanced by counter-intuitively trading the reliability for security, i.e., allocating less blocklength in the short-packet transmission. In order to solve the corresponding blocklength allocation problem, we propose a novel optimization framework, for which a lower-bounded approximation of the decoding error probability in the finite blocklength regime is provided. Based on that, we reformulate the optimization problem into a convex one and propose an iterative searching method. We show the efficiency and the convergence of such a method analytically. Furthermore, we discuss the extendability of the proposed framework with an example of the effective secure throughput as the metric. Via numerical results, we verify the performance of the optimization problem and demonstrate the reliability-security tradeoff under various setups.

How to provide information security while fulfilling ultra reliability and low-latency requirements is one of the major concerns for enabling the next generation of ultra-reliable and low-latency communications service (xURLLC), specially in machine-type communications. In this work, we investigate the reliability-security tradeoff by defining the leakage-failure probability, a metric that jointly characterizes both reliability and security performances for short-packet transmissions. We discover that the system performance can be enhanced, counter-intuitively, by allocating fewer resources for the transmission with finite blocklength (FBL) codes. In order to solve the corresponding optimization problem for the joint resource allocation, we propose an optimization framework, that leverages lower-bounded approximations for the decoding error probability in the FBL regime. We characterize the convexity of the reformulated problem and establish an efficient iterative searching method, the convergence of which is guaranteed. To show the extendability of the framework, we further discuss the blocklength allocation schemes with practical requirements of reliable-secure performance, as well as the transmissions with the statistical channel state information (CSI). Numerical results verify the accuracy of the proposed approach and demonstrate the reliability-security tradeoff under various setups.

To support ultra-reliable and low-latency services for mission-critical applications, transmissions are usually carried via short blocklength codes, i.e., in the so-called finite blocklength (FBL) regime. Different from the infinite blocklength regime where transmissions are assumed to be arbitrarily reliable at the Shannon’s capacity, the reliability and capacity performances of an FBL transmission are impacted by the coding blocklength. The relationship among reliability, coding rate, blocklength and channel quality has recently been characterized in the literature, considering the FBL performance model. In this paper, we follow this model, and prove the joint convexity of the FBL error probability with respect to blocklength and transmit power within a region of interest, as a key enabler for designing systems to achieve globally optimal performance levels. Moreover, we apply the joint convexity to general use cases and efficiently solve the joint optimization problem in the setting with multiple users. We also extend the applicability of the proposed approach by proving that the joint convexity still holds in fading channels, as well as in relaying networks. Via simulations, we validate our analytical results and demonstrate the advantage of leveraging the joint convexity compared to other commonly-applied approaches.

Enabling ultra-reliable and low-latency communication services while providing massive connectivity is one of the major goals to be accomplished in future wireless communication networks. In this paper, we investigate the performance of a hybrid multi-access scheme in the finite blocklength (FBL) regime that combines the advantages of both non-orthogonal multiple access (NOMA) and time-division multiple access (TDMA) schemes. Two latency-sensitive application scenarios are studied, distinguished by whether the queuing behaviour has an influence on the transmission performance or not. In particular, for the latency-critical case with one-shot transmission, we aim at a certain physical-layer quality-of-service (QoS) performance, namely the optimization of the reliability. And for the case in which queuing behaviour plays a role, we focus on the link-layer QoS performance and provide a design that maximizes the effective capacity. For both designs, we leverage the characterizations in the FBL regime to provide the optimal framework by jointly allocating the blocklength and transmit power of each user. In particular, for the reliability-oriented design, the original problem is decomposed and the joint convexity of sub-problems is shown via a variable substitution method. For the effective-capacity-oriented design, we exploit the method of Lagrange multipliers to formulate a solvable dual problem with strong duality to the original problem. Via simulations, we validate our analytical results of convexity/concavity and show the advantage of our proposed approaches compared to other existing schemes.

In this work, we study a downlink multi-user network, where a single access point (AP) is supposed to accomplish data transmissions to all users under low latency constraints. To more effectively cope with the multiple access demand, we consider a hybrid strategy for the multi-user downlink service in finite blocklength (FBL) regime, which combines broadcasting with time-division multiple access (TDMA). In the hybrid strategy, the users are first clustered into different groups. Different groups are served in a TDMA manner with dedicated time slots, while users within each group are served together via a broadcasting signal from the AP. By taking into account the fairness of transmission reliability among all users, we formulate a problem minimizing the maximum error probability among users via jointly determining the user grouping and allocating blocklength among all groups. To address the complicated non-convex problem, we first characterize the optimal blocklength allocation under each given grouping decision, which leads to an optimal closed-form allocation solution via solving an equation system. Based on the characterized features, we are enabled to efficiently distill out the optimal grouping from all possible groupings, which forms the efficient optimal solution for the optimal joint design. Afterwards, aiming at a complexity reduction, we further propose a low-complexity iterative solution, in which the grouping is iteratively improved via the introduced operations until a convergence to a suboptimum. Finally, via simulations, we validate the proposed solutions and reveal the close optimality of the iterative solution. In addition, the hybrid strategy has shown a significant reliability advantage in comparison to pure broadcasting or TDMA, and this performance advantage becomes further enlarged in case of more users.

We consider a mobile edge computing (MEC) network supporting low-latency, critical offloading workloads. The task offloading from the user to the server is operated under a truncated Hybrid Automatic Repeat reQuest (HARQ) process, i.e., we consider finite retransmission attempts. Both the HARQ type-I and type-II schemes are studied. For each scheme, we first characterize the total error probability and the total energy cost, while the impact of finite blocklength (FBL) on the stochastic retransmission behavior is considered. Following the characterizations, we are interested in optimal frameworks for each considered HARQ type, where the number of potential retransmission attempts is optimized together with the duration of each transmission, while the CPU frequency at the edge node is adjusted via voltage scaling. The objective is to minimize the total energy cost with error probability threshold. We show that the resulting stochastic optimization problems can be solved by means of convex optimization. We furthermore demonstrate that sharp minima exist among the energy consumption, underlying the importance of near-optimal parameter choice in the studied scenarios. Our results underline the importance of trading off communication and computational characteristics in delay-critical MEC setups with FBL codes.

In this paper, we focus on a relaying network working with a decode-and-forward (DF) principle. A source reports latency-critical information updates to the destination with the help of the relay under periodic request, while this two-hop transmission is operating with finite blocklength (FBL) codes. To evaluate the data freshness at destination, we characterize the average age-of-information (AoI) of the two-hop relaying. Based on the characterization, we consider a problem minimizing the average AoI by jointly optimizing the blocklengths allocated to both hops. To address this non-convex problem, we construct a tight convex approximation for the average AoI at a feasible local point (values of the two blocklengths). Then, we propose an efficient algorithm which iteratively applies the convex approximation, solves the approximated convex problem and updates the local point until a convergence to a suboptimum. Via numerical results, we validate the convergence of the proposed iterative algorithm and confirm the high performance and high efficiency of the proposed solution. The performance advantage of relaying in improving the data freshness is also shown in comparison to direct transmission.

In this work, we study an energy harvesting (EH)-enabled low-latency communication network where a full-duplex server continuously performs wireless power transfer (WPT) to a half-duplex sensor. The sensor is designed to operate periodically in each updating round, during which the sensor firstly harvests energy via the WPT process, then collects measurement data and wirelessly transmits an update to the server based on the harvested energy. We assume that no energy can be reserved at the end of each round, due to the deployed capacitor-structured energy container. Leveraging the recent characterization on the error probability in the finite blocklength (FBL) regime, we derive the average Age-of-Information (AoI) in the considered network and construct a problem minimizing the average AoI via optimizing the duration of the updating round. The convexity of the optimization problem is shown, following which an efficient optimal solution is provided. At last, via Monte Carlo simulations, the convexity of the problem can also be visualised, and the average AoI performance of the network is evaluated.

It is becoming increasingly clear that an important task for wireless networks is to minimize the age of information (AoI), i.e., the timeliness of information delivery. While mainstream approaches generally rely on the real-time observation of user AoI and channel state, there has been little attention to solve the problem in a complete (or partial) absence of such knowledge. In this article, we present a novel study to address the optimal blind radio resource scheduling problem in orthogonal frequency division multiplexing access (OFDMA) systems towards minimizing long-term average AoI, which is proven to be the composition of time-domain-fair clustered round-robin and frequency-domain-fair intra-cluster sub-carrier assignment. Heuristic solutions that are near-optimal as shown by simulation results are also proposed to effectively improve the performance upon presence of various degrees of extra knowledge, e.g., channel state and AoI.

Taking into consideration of vehicle mobility and fairness, in this paper we provide a cooperative offloading algorithm maximizing the energy efficiency for a vehicular edge computing network, while guaranteeing the shortest delay of the worst-case vehicle. In particular, through exploiting the geometrical feature of the unidirectional road, we formulate a mixed integer convex problem by jointly designing the offloading selection and allocating the computation resource simultaneously. The optimization is carried out by a proposed two-step optimization algorithm: We first optimize the server selection to obtain the minimized achievable delay, and subsequently optimize jointly the selection and resource allocation to maximize the energy efficiency while maintaining the optimal achievable delay. Via simulations, we show the advantage of proposed algorithms and evaluate the system performance.

This work is related to ultra-reliable and low latency communication (URLLC) in Internet-of-Thing (IoT) networks. In particular, we consider a multi-device IoT network performing blind retransmissions on shared radio resources. We characterize the reliability and goodput performances of such network in the finite blocklength regime. In addition, following the characterization we provide two designs minimizing the error probability and maximizing the network goodput (under reliability constraints), respectively. In particular, the optimal solution is obtained for the reliability-oriented design. In addition, an efficient solution is proposed for the second design maximizing the goodput, which provides a performance tightly close to the one obtained via exhaustive search. Through simulation, we validate our analytical model and evaluate the system performance.

We consider a low-latency communication network operating with finite blocklength (FBL) codes. During the transmission, the minimum mean squared error (MMSE) channel estimation is assumed to be applied to obtain the instantaneous but imperfect Channel State Information (CSI) for the rate selection. We aim at optimizing the FBL throughput of the system under given reliability constraints. First, we provide an optimal frame structure design by optimally allocating the total frame length for MMSE training of channel estimation and data transmission. In addition, we further improve the FBL throughput considering channel dynamics which optimally selects the coding rate per frame. Combining the frame structure and the coding rate selection, a joint optimization problem is studied and solved by a sub-optimal algorithm. In the simulation study, we validate the proposed analytical model and evaluate the FBL throughput of the proposed solution in comparison to benchmark schemes.

The partial offloading technologies in the cooperative mobile edge computing (MEC) networks are considered as promising solutions to enable the emerging latency-sensitive and compute-intensive applications. In this paper, we characterize the performance model of an energy-aware MEC networks with multiple servers cooperatively computing a set of interdependent tasks. To minimize the total delay of the whole process of the set of tasks, an optimal offloading design is provided under given energy constraints. In particular, we provide an optimal solution to the offloading problem, which makes a 3-dimensional decision (matrix) representing at which time instant to offload which task to which server. Via simulation, we investigate the performance of the proposed design for the tasks with different interdependency structures. In particular, the impacts of the number of MEC servers, CPU frequencies and in the proposed algorithm on the system performance are studied. In addition, the tradeoff between the delay performance and computation complexity is addressed.

This paper considers simultaneous wireless information and power transfer (SWIPT) mechanisms in a relaying-assisted ultra-reliable low latency communication network operating with finite blocklength codes. The reliability of the network is maximized by the optimal selection of SWIPT parameters under both a power splitting (PS) protocol and a time switching (TS) protocol. In addition, we propose a protocol to improve the reliability performance by introducing a tradeoff between the PS and TS protocols. To further improve the reliability, a joint design is provided, which aligns the optimal selection of SWIPT parameters together with a blocklength allocation between two relaying hops. Via simulations, we validate our analytical model and show that the proposed algorithm achieves the same performance as that obtained with exhaustive search. In addition, we evaluate the considered network, and characterize the impact of blocklength, transmit power, and packet size on the reliability of the considered SWIPT-enabled relaying network. Finally, the performance advantages of the proposed protocol (in comparison with the PS and TS protocols) and the proposed joint designs are investigated.

This paper considers simultaneous wireless information and power transfer (SWIPT) mechanisms in a relaying network with finite blocklength (FBL) codes. The reliability of the network is optimized under both a power splitting (PS) protocol and a proposed joint PS and time switching (TS) protocol. Under both protocols, we first determine the overall error probability formulation of the SWIPT-enabled two-hop transmission in the FBL regime. Subsequently, we state and solve optimization problems minimizing the overall error probability. Via numerical analysis, we show the appropriateness of our analytical model and demonstrate the performance advantage of the proposed protocol in comparison to TS and PS protocols. In addition, we provide interesting insights on the system behavior by characterizing the impact of the blocklength, transmit power and packet size on the reliability performance.