Distributed sensor fusion is a key component of a broad spectrum of applications, such as autonomous systems, where the ability of jointly process multi-sensor data at the edge boosts the range of operating conditions and overall task performance. However, existing distributed sensor fusion approaches encounter limitations in achieving efficient transmission and computation, primarily due to sensor data redundancy, unreliable sensor data transmission, and inflexible sensor fusion methods. In this paper, we propose HERACLES, a distributed sensor fusion framework that connects multi-branched dynamic neural network architectures, which we extend to include branches of different complexity, to (i) a computing methodology that distributes portions of the multi-branched neural network across mobile devices and edge servers, enabling flexible semantic feature extraction and sensor fusion; (ii) a hierarchical modulation-based transmission strategy, where multi-modal semantic features are allocated to different modulation layers to provide varying levels of error protection, and (iii) an infrastructure-level logic that controls the matching between semantic features and modulation layers, and the complexity of the neural model itself to meet an accuracy target while minimizing latency and energy consumption. As a result, HERACLES deeply connects computing, communications and resource allocations in a semantic and context-aware fashion. We evaluate HERACLES using real-world datasets and demonstrate that it can reduce the total delay and energy consumption by 20.39%–89.41% and 4.86%–88.17% (resp.), while maintaining near-optimal inference accuracy. The evaluation code is available at https://github.com/qlt315/HERACLES.

Cell-free (CF) massive multiple-input multiple-output (mMIMO) has been considered as a potential technology for Beyond 5G. However, the performance of CF mMIMO systems has not been thoroughly studied. Most existing analytical studies on CF mMIMO systems rely on deriving average performance metrics. The statistical characteristics of the signal-to-interference-plus-noise ratio (SINR), which capture the tail behavior of SINR, are crucial for metrics such as outage probability and for emerging mission-critical applications that emphasize extreme and rare events, but have not been thoroughly investigated. In this paper, we aim to obtain the distribution of SINR in CF mMIMO systems. Considering a downlink CF mMIMO system with pilot contamination, we first give the closed-form expression of the SINR. Based on our analytical work on the two components of the SINR, i.e., desired signal and interference-plus-noise, we then derive the probability density function and cumulative distribution function of the SINR under maximum ratio transmission (MRT) and full-pilot zero-forcing (FZF) precoding, respectively. Subsequently, the closed-form expressions for two more sophisticated performance metrics, i.e., ergodic rate and outage probability, are obtained. Finally, we perform Monte Carlo simulations to validate our analytical work. Numerous numerical results demonstrate the effectiveness of the derived SINR distribution, ergodic rate, and outage probability.

Millimeter-wave$\!$(mmWave) technology has emerged as a pivotal catalyst for unleashing the full potential of 360° virtual reality (VR). Nonetheless, the explosive growth of VR services, combined with the quality-of-service (QoS) provisioning issues of mmWave, poses formidable challenges in wireless resource allocation for mmWave-enabled 360° VR. In this paper, we propose an innovative 360° VR streaming architecture that addresses three underexplored issues: overlapping fields-of-view (FoVs), statistical QoS provisioning (SQP), and loss-tolerant active data discarding. Specifically, we first design an overlapping FoV-based optimal joint unicast and multicast (JUM) task assignment scheme, which significantly conserves wireless resources by implementing non-redundant task allocation. Leveraging stochastic network calculus (SNC), we develop a comprehensive SNC-based SQP theoretical framework from delay and rate perspectives. Additionally, we propose two corresponding optimal adaptive joint resource allocation and active-discarding (ADAPT-JRAAD) transmission schemes to minimize resource consumption while guaranteeing SQP performance from delay and rate perspectives, respectively. Extensive simulations demonstrate the outstanding performance of the designed optimal JUM task assignment scheme in conserving wireless resources. Moreover, comprehensive comparisons against six benchmarks validate the superiority of the proposed two ADAPT-JRAAD schemes in resource utilization, flexible rate control, and robust queue management.

Industry 5.0 and beyond networks have driven the emergence of numerous mission-critical services exemplified by neXt-generation ultra-reliable low-latency communication (xURLLC). To guarantee low-latency requirements, xURLLC heavily relies on short-blocklength packets with sporadic arrival traffic. As a disruptive multi-access technique, rate-splitting multiple access (RSMA) has emerged as a promising avenue to enhance the quality of service (QoS) and flexibly manage interference for xURLLC. In this paper, we study an innovative RSMA-assisted uplink xURLLC industrial internet-of-things (IIoT) (RSMA-xURLLC-IIoT) network, which takes into account imperfect CSI and finite blocklength (FBL) regimes. We develop a novel theoretical framework leveraging stochastic network calculus (SNC) aimed at revealing insights into statistical QoS provisioning (SQP). Building upon this framework, we formulate the SQP-driven short-packet size maximization and transmit power minimization problems, aiming to guarantee the SQP performance to delay, decoding, and reliability while maximizing the short-packet size and minimizing the transmit power, respectively. By exploiting Monte-Carlo methods, we have thoroughly validated the reliability of the developed theoretical framework. Moreover, through extensive comparison analysis with state-of-the-art multi-access techniques, including non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA), we have demonstrated the superior performance gains achieved by the proposed network.

The cloud radio access network (C-RAN) has become the foundational structure for various emerging communication paradigms, leveraging the flexible deployment of distributed access points (APs) and centralized task processing. In this paper, we propose a cross-layer optimization framework based on a practical finite-length coding communication system in C-RAN, aiming at maximizing bandwidth efficiency while providing statistical quality of service (QoS) for individual services. Based on the theoretical results from effective capacity and finite-length coding, we formulate a joint optimization problem involving modulation and coding schemes (MCS), retransmission count, initial bandwidth allocation and AP selection, which reflects the coordinated decision of parameters across the physical layer, data link layer and transport layer. To tackle such a mixed-integer nonlinear programming (MINLP) problem, we firstly decompose it into a transmission parameter decision (TPD) sub-problem and a user association (UA) sub-problem, which can be solved by a binary search-based algorithm and an auction-based algorithm respectively. Simulation results demonstrate that the proposed model can accurately capture the impact of QoS requirements and channel quality on the optimal transmission parameters. Furthermore, compared with fixed transmission parameter setting, the proposed algorithms achieve the bandwidth efficiency gain up to 27.87% under various traffic and channel scenarios.

In the traditional cellular-based mobile edge computing (MEC), users at the edge of the cell are prone to suffer severe inter-cell interference and signal attenuation, leading to low throughput even transmission interruptions. Such edge effect severely obstructs offloading of tasks to MEC servers. To address this issue, we propose user-centric mobile edge computing (UCMEC), a novel MEC architecture integrating user-centric transmission, which can ensure high throughput and reliable communication for task offloading. Then, we formulate an long-term delay minimization problem by jointly optimizing task offloading, power allocation, and computing resource allocation in UCMEC. To solve the intractable problem, we propose two decentralized joint optimization schemes based on multi-agent deep reinforcement learning (MADRL) and convex optimization, which consider both cooperation and non-cooperation among network nodes. Simulation results demonstrate that the proposed schemes in UCMEC can significantly improve the uplink transmission rate by at least 176.99% and reduce the long-term average total delay by at least 16.36% compared to traditional cellular-based MEC.

Edge service caching can effectively reduce the delay or bandwidth overhead for acquiring and initializing applications. To address single-base station (BS) transmission limitation and serious edge effect in traditional cellular-based edge service caching networks, in this paper, we proposed a novel user-centric edge service caching framework where each user is jointly provided with edge caching and wireless transmission services by a specific BS cluster instead of a single BS. To minimize the long-term average delay under the constraint of the caching cost, a mixed integer non-linear programming (MINLP) problem is formulated by jointly optimizing the BS clustering and service caching decisions. To tackle the problem, we propose JO-CDSD, an efficiently joint optimization algorithm based on Lyapunov optimization and generalized benders decomposition (GBD). In particular, the long-term optimization problem can be transformed into a primal problem and a master problem in each time slot that is much simpler to solve. The near-optimal clustering and caching strategy can be obtained through solving the primal and master problem alternately. Extensive simulations show that the proposed joint optimization algorithm outperforms other algorithms and can effectively reduce the long-term delay and caching cost.

In this paper, fundamentals and performance tradeoffs of next-generation ultra-reliable and low-latency communication (xURLLC) are investigated from the perspective of stochastic network calculus (SNC). An xURLLC-enabled massive MU-MIMO system model has been developed to accommodate xURLLC features. By leveraging and promoting SNC, we provide a quantitative statistical quality of service (QoS) provisioning analysis and derive the closed-form expression of upper-bounded statistical delay violation probability (UB-SDVP). Based on the proposed theoretical framework, we formulate the UB-SDVP minimization problem, which is first degenerated into a one-dimensional integer-search problem by deriving the minimum error probability (EP) detector, and then efficiently solved by the integer-form Golden-Section search algorithm. Moreover, two novel concepts, EP-based effective capacity (EP-EC) and EP-based energy efficiency (EP-EE), have been defined to characterize the tail distributions and performance tradeoffs for xURLLC. Subsequently, we formulate the EP-EC and EP-EE maximization problems, and the EP-EC maximization problem is proven to be equivalent to the UB-SDVP minimization problem, while the EP-EE maximization problem is solved with a low-complexity outer-descent inner-search collaborative algorithm. Extensive simulations demonstrate that the proposed framework can reduce computational complexity compared to reference schemes and provide various tradeoffs and optimization performance of xURLLC concerning UB-SDVP, EP, EP-EC, and EP-EE.

Inter-user interference and pilot contamination are the major obstacles limiting the performance of cell-free massive multiple-input multiple-output (CF mMIMO)-aided ultra-reliable low-latency communication (URLLC) systems. In this paper, user grouping is utilized to address these issues, by allocating users to groups based on frequency band division, preventing interference among users within different groups, and eliminating pilot contamination between different groups. We consider an uplink CF mMIMO-aided URLLC system with user grouping and derive the lower bound for the ergodic rate. Due to the limited blocklength of each group necessitating pilot reuse, a weight sum rate (WSR) maximum problem is formulated by jointly optimizing user grouping, pilot assignment, and power control. We propose a user grouping scheme based on graph theory, where iteratively searching for specific negative loops in the weighted directed graph can approach the optimal user grouping matrix. As the user grouping matrix updates at each iteration, we update the pilot assignment matrix based on graph theory and employ logarithmic function approximation and fractional programming for power control updates. Numerous numerical results demonstrate the effectiveness of user grouping, and the proposed algorithm improves WSR by 25% compared to the non-grouping algorithm, while outperforming other benchmark algorithms.