Arctic regions, such as northern Canada, face significant challenges in achieving consistent connectivity and low-latency computing services due to the sparse coverage of Low Earth Orbit (LEO) satellites. To enhance service reliability in remote areas, this paper proposes a hybrid satellite architecture for task offloading that combines Medium Earth Orbit (MEO) and LEO satellites. We develop an optimization framework to maximize task offloading admission rate while balancing the energy consumption and delay requirements. Accounting for satellite visibility and limited computing resources, our approach integrates dynamic path selection with frequency and computational resource allocation. Because the formulated problem is NP-hard, we reformulate it into a mixed-integer convex form using disjunctive constraints and convex relaxation techniques, enabling efficient use of off-the-shelf optimization solvers. Simulation results show that, compared to a standalone LEO network, the proposed hybrid LEO-MEO architecture improves the task admission rate by 15% and reduces the average delay by 12%. These findings highlight the architecture’s potential to enhance connectivity and user experience in remote Arctic areas.

Data center networks have been gaining a lot of attention in recent years. These networks are scaling up quickly with the explosive nature of current applications. Lately, a lot of efforts have been exerted to improve the performance of these networks compared to the often performance-lagging standard Clos-based topologies. One of the approaches for performance improvement is to use alternative data center network topologies. Consequently, researchers explored topologies based on Expander Graphs (EGs), such as Jellyfish, Xpander, and STRAT, where they exploited the sparse and incremental nature of these new topologies. This paper investigates the STructured Re-Arranged Topology (STRAT) as a potentially robust and efficient design for next-generation data centers. Robustness and throughput metrics are adopted to benchmark the performance of STRAT against the well-known Expander architectures, which show better performance than that of present topologies, (e.g., Fat-Tree, BCube). This paper shows that STRAT has better structural properties than Jellyfish and Xpander, making it more robust to switch and link failures. Specifically, STRAT possesses lower average shortest path length and diameter, higher spectral gap, and higher algebraic connectivity. These exceptional properties allow STRAT to achieve better throughput. Such observations are validated through extensive flow and packet level simulations, demonstrating STRAT’s superior performance in terms of the flow completion time as compared to other Expanders.

This study applies and validates causal graphical models for the task of assessing the risk of a stroke given the stroke patient data. A probabilistic causal (Bayesian) network is designed to evaluate the risk factors identified by a stroke expert. Structural Equation Modeling is applied on empirical data to provide a quantitative assessment of causal relationships among the variables in the Bayesian Network, thus statistically validating the expert's knowledge. Several scenarios of risk assessment using the inference mechanism on the Bayesian network are demonstrated.

Ultra-Reliable and Low Latency Communications (URLLC) is one of the essential services in 5G networks and beyond. The coexistence of URLLC alongside other services, namely, enhanced Mobile BroadBand (eMBB) and massive Machine-Type Communications (mMTC), calls for developing spectrally efficient multiplexing techniques. In this work, we study the problem of scheduling URLLC traffic in a downlink system in the presence of eMBB traffic. Based on the proposed superposition/puncturing scheme, a resource allocation problem is formulated with the objective to minimize the rate loss of the eMBB service and URLLC packet segmentation loss while satisfying the eMBB and URLLC quality of service (QoS) constraints. The resulting problem is formulated as a mixed-integer non-linear program (MINLP) which is generally very hard to solve in polynomial time. Hence, we reformulate the problem as a one-to-one pairing problem and we derive its feasibility region as well as the optimal solutions for the power and spectral resource allocation. Subsequently, we propose a low complexity algorithm to support the many-to-many pairing. Simulation results show that the proposed algorithm achieves higher URLLC packet admission rate and lower rate loss for eMBB. For instance, the URLLC packet admission rate, unlike baseline methods, is shown to be preserved under the proposed method even at higher URLLC load. It is shown that at least 30% more URLLC users can be served without degrading their QoS, while keeping the impact on eMBB rate minimal. Detailed numerical evaluation is presented to quantify the benefits of the proposed method.

This paper studies the coexistence of enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communication (URLLC) services in a cellular network that is assisted by a reconfigurable intelligent surface (RIS). The system model consists of one base station (BS) and one RIS that is deployed to enhance the performance of both eMBB and URLLC in terms of the achievable data rate and reliability, respectively. We formulate two optimization problems, a time slot basis eMBB allocation problem and a mini-time slot basis URLLC allocation problem. The eMBB allocation problem aims at maximizing the eMBB sum rate by jointly optimizing the power allocation at the BS and the RIS phase-shift matrix while satisfying the eMBB rate constraint. On the other hand, the URLLC allocation problem is formulated as a multi-objective problem with the goal of maximizing the URLLC admitted packets and minimizing the eMBB rate loss. This is achieved by jointly optimizing the power and frequency allocations along with the RIS phase-shift matrix. In order to avoid the violation in the URLLC latency requirements, we propose a novel framework in which the RIS phase-shift matrix that enhances the URLLC reliability is proactively designed at the beginning of the time slot. For the sake of solving the URLLC allocation problem, two algorithms are proposed, namely, an optimization-based URLLC allocation algorithm and a heuristic algorithm. The simulation results show that the heuristic algorithm has a low time complexity, which makes it practical for real-time and efficient multiplexing between eMBB and URLLC traffic. In addition, using only 60 RIS elements, we observe that the proposed scheme achieves around 99.99% URLLC packets admission rate compared to 95.6% when there is no RIS, while also achieving up to 70% enhancement on the eMBB sum rate.

Ultra-Reliable and Low Latency Communications (URLLC) is one of the essential services in 5G networks and beyond. The coexistence of URLLC alongside other service classes, namely, enhanced Mobile BroadBand (eMBB) and massive Machine-Type Communications (mMTC), calls for developing spectrally efficient multiplexing techniques. In this work, we study the problem of scheduling URLLC traffic in a downlink system with the presence of eMBB traffic class. Based on the superposition/puncturing scheme, a resource allocation problem is formulated with the objective to minimize the eMBB data rate loss while satisfying eMBB and URLLC quality of service (QoS) constraints. The resulting problem is formulated as a mixed integer non-linear programming (MINLP) which is generally NP hard and hence complex to solve. Hence, we derive its feasibility region as well as the optimal solutions for the power and spectral resource allocation. Subsequently, we propose a low complexity algorithm to serve URLLC traffic. Simulation results show that the proposed algorithm achieves higher reliability for URLLC and higher eMBB data rate compared to the puncturing schemes. The results also show that the eMBB QoS requirements, which are represented by the eMBB rate loss threshold, has a negative effect on the URLLC reliability for high URLLC load. Therefore, the eMBB rate and the eMBB loss threshold should be jointly optimized considering QoS of both eMBB and URLLC. Index Terms—eMBB, multiplexing, puncturing, superposition, URLLC, 6G.

This paper proposes a novel framework to emerge the reconfigurable intelligent surface (RIS) in cellular networks wherein enhanced mobile broadband (eMBB) and ultra-reliable and low-latency communication (URLLC) services coexist. In order to avoid the violation in the URLLC latency requirements, the framework proposes RIS phase shift matrix that enhances the URLLC reliability is proactively designed at the beginning of the time slot. The system model consists of a single base station (BS) and a single RIS which deployed to enhance the channel environments of the eMBB and the URLLC users. To allocates the eMBB users, we formulate a time-slot basis eMBB allocation problem which has the goal of maximizing the eMBB sum-rate by jointly optimizing the power allocation at the BS and the RIS phase shift matrix while satisfying the eMBB rate constraint. Since the formulated problem is a non-convex problem which hard to be solved directly, we adopt the alternating optimization approach to decompose the eMBB allocation problem optimization problem into a power allocation and a RIS phase shift matrix sub-problems. Then, the URLLC allocation problem is formulated as a multi-objective problem with the goal of maximizing the URLLC admitted packets and minimizing the eMBB rate loss by jointly optimizing the power and frequency allocation. Then, we proposed a heuristic algorithm to allocate the URLLC load. The proposed algorithm has a low time complexity which makes it a efficient method for multiplexing URLLC and eMBB traffics. Finally, simulation results show that using only 60 RIS elements, we observe that the proposed scheme achieves around 99.99% URLLC packets admission rate compared to 95.6% when there is no RIS, while also achieving up to 70% enhancement on the eMBB rates.