DRL-Assisted Resource Allocation for NOMA-MEC Offloading with Hybrid SIC

From MDPI via Jisc Publications RouterHistory: accepted 2021-05-11, pub-electronic 2021-05-14Publication status: PublishedMulti-access edge computing (MEC) and non-orthogonal multiple access (NOMA) are regarded as promising technologies to improve the computation capability and offloading efficiency of mobile devices in the sixth-generation (6G) mobile system. This paper mainly focused on the hybrid NOMA-MEC system, where multiple users were first grouped into pairs, and users in each pair offloaded their tasks simultaneously by NOMA, then a dedicated time duration was scheduled to the more delay-tolerant user for uploading the remaining data by orthogonal multiple access (OMA). For the conventional NOMA uplink transmission, successive interference cancellation (SIC) was applied to decode the superposed signals successively according to the channel state information (CSI) or the quality of service (QoS) requirement. In this work, we integrated the hybrid SIC scheme, which dynamically adapts the SIC decoding order among all NOMA groups. To solve the user grouping problem, a deep reinforcement learning (DRL)-based algorithm was proposed to obtain a close-to-optimal user grouping policy. Moreover, we optimally minimized the offloading energy consumption by obtaining the closed-form solution to the resource allocation problem. Simulation results showed that the proposed algorithm converged fast, and the NOMA-MEC scheme outperformed the existing orthogonal multiple access (OMA) scheme

Unraveling plant adaptation to nitrogen limitation from enzyme stoichiometry aspect in Karst soils: a case study of Rhododendron Pudingense

Enzyme stoichiometry can reflect the resource limitation of soil microbial metabolism, and research on the relationships between plants and resource limitation in Karst Microhabitats is scarcely investigated. To clarify the extracellular enzyme stoichiometry characteristics in soil across different karst microhabitats and how the Rhododendron pudingense adapts to nutrient restrictions, plot investigation experiments were set up in Zhenning County, Qinglong County, and Wangmo County of Guizhou Province which included total three karst microhabitats, i.e., soil surface (SS), rock gully (RG), and rock surface (RS), by analyzing he rhizosphere soil nutrient, extracellular enzyme activity, and nutrient content of R. pudingense. The findings indicated that all karst microenvironments experienced varying levels of nitrogen (N) limitation, with the order of N limitation being as follows: SS > RG > RS. Notably, there were significant discrepancies in N content among different plant organs (p< 0.05), with the sequence of N content as follows: leaf > stem > root. However, no significant differences were observed in nutrient content within the same organ across different microenvironments (p > 0.05). A noteworthy discovery was the significant allometric growth relationship between C-P in various organs (p< 0.05), while roots and stems exhibited a significant allometric growth relationship between N-P (p< 0.05). The study highlighted the substantial impact of Total Nitrogen (TN) and N-acquiring enzymes (NAE) on nutrient allocation within the components of R. pudingense. Overall, the research demonstrated that N was the primary limiting factor in the study area’s soil, and R. pudingense’s nutrient allocation strategy was closely associated with N limitations in the karst microenvironment. Specifically, the plant prioritized allocating its limited N resources to its leaves, ensuring its survival. This investigation provided valuable insights into how plants adapt to nutrient restrictions and offered a deeper understanding of soil-plant interactions in karst ecosystems

DRL-Assisted Resource Allocation for NOMA-MEC Offloading with Hybrid SIC

Multi-access edge computing (MEC) and non-orthogonal multiple access (NOMA) are regarded as promising technologies to improve the computation capability and offloading efficiency of mobile devices in the sixth-generation (6G) mobile system. This paper mainly focused on the hybrid NOMA-MEC system, where multiple users were first grouped into pairs, and users in each pair offloaded their tasks simultaneously by NOMA, then a dedicated time duration was scheduled to the more delay-tolerant user for uploading the remaining data by orthogonal multiple access (OMA). For the conventional NOMA uplink transmission, successive interference cancellation (SIC) was applied to decode the superposed signals successively according to the channel state information (CSI) or the quality of service (QoS) requirement. In this work, we integrated the hybrid SIC scheme, which dynamically adapts the SIC decoding order among all NOMA groups. To solve the user grouping problem, a deep reinforcement learning (DRL)-based algorithm was proposed to obtain a close-to-optimal user grouping policy. Moreover, we optimally minimized the offloading energy consumption by obtaining the closed-form solution to the resource allocation problem. Simulation results showed that the proposed algorithm converged fast, and the NOMA-MEC scheme outperformed the existing orthogonal multiple access (OMA) schem

Joint Resource Allocation for Hybrid NOMA Assisted MEC in 6G Networks

Multi-access Edge Computing (MEC) is an essential technology for expanding computing power of mobile devices, which can combine the Non-Orthogonal Multiple Access (NOMA) in the power domain to multiplex signals to improve spectral efficiency. We study the integration of the MEC with the NOMA to improve the computation service for the Beyond Fifth-Generation (B5G) and the Sixth-Generation (6G) wireless networks. This paper aims to minimize the energy consumption of a hybrid NOMA-assisted MEC system. In a hybrid NOMA system, a user can offload its task during a time slot shared with another user by the NOMA, and then upload the remaining data during an exclusive time duration served by Orthogonal Multiple Access (OMA). The original energy minimization problem is non-convex. To efficiently solve it, we first assume that the user grouping is given, and focuses on the one group case. Then, a multilevel programming method is proposed to solve the non-convex problem by decomposing it into three subproblems, i.e., power allocation, time slot scheduling, and offloading task assignment, which are solved optimally by carefully studying their convexity and monotonicity. The derived solution is optimal to the original problem by substituting the closed expressions obtained from those decomposed subproblems. Furthermore, we investigate the multi-user case, in which a close-to-optimal algorithm with low-complexity is proposed to form users into different groups with unique time slots. The simulation results verify the superior performance of the proposed scheme compared with some benchmarks, such as OMA and pure NOMA

Reinforcement Learning based Latency Minimization in Secure NOMA-MEC Systems with Hybrid SIC

—In this paper, physical layer security (PLS) in a non-orthogonal multiple access (NOMA)-based mobile edge computing (MEC) system is investigated, where hybrid successive interference cancellation (SIC) decoding is considered. Specifically , users intend to complete confidential tasks with the help of the MEC server, while an eavesdropper attempts to intercept the offloaded tasks. By jointly designing computational resource allocation, task assignment, and power allocation, a latency minimization problem is formulated. Based on the interactions between local computing time and MEC processing time, the closed-from solutions of computational resource allocation and task assignment are derived. After that, a strategy selection mechanism is established to select offloading strategies based on the corresponding conditions. Moreover, according to the analysis of hybrid SIC decoding, the conditions of different decoding orders in secure NOMA networks are derived. Furthermore, a reinforcement learning based algorithm is proposed to solve the power allocation problems for NOMA and OMA offloading strategies. This work is extended to a multiuser scenario, in which a matching-based algorithm is proposed to solve the formulated sub-channel assignment problem. Simulation results indicate that: i) the proposed solution can significantly reduce the latency and provide dynamic strategy selection for various scenarios; ii) the NOMA offloading strategy with hybrid SIC decoding can outperform other strategies in the considered system. Index Terms—Mobile edge computing (MEC), Non-orthogonal multiple access (NOMA), physical layer security (PLS), reinforcement learning, sub-channel assignment

Strength and Environmental Behaviours of Municipal Solid Waste Incineration Fly Ash for Cement-Stabilised Soil

Many sandy soil foundations need to be solidified during traffic construction in Guangxi, China. Because it has a similar chemical composition as cement, municipal solid waste incineration fly ash (MSWIFA) can strengthen sandy soil. However, the chloride ions and heavy metals in MSWIFA may have a negative influence on the solidification of sandy soil. Thus, FA resource use faces great challenges. This study evaluates the feasibility of using MSWIFA to solidify sandy soil. The acetic acid buffer solution method was used in the leaching test to simulate the weak acid groundwater environment in the Guangxi karst landform. The effects of the treatment methods (washing with ferrous sulphate solution, pre-treatment of organics via chelation, and adding sugarcane ash) on the strength and environmental characteristics of fly ash cement-stabilised soil (FACS) are discussed in detail. The results indicate that the FACS unconfined compressive strength (UCS) decreased by 24.82–46.64% when 5% cement was replaced with FA. Sugarcane ash effectively improved the strength of FACS by more than 10%. The leaching concentrations of Zn and Cu in the FACS meet the concentration limit set by GB 16889-2008. The leaching concentrations of Cr and Pb after washing with 6% ferrous sulphate solution were reduced by more than 30%. Meanwhile, the FACS strength developed faster. Organic chelating agents solidified most heavy metals

Study of Transformer Harmonic Loss Characteristic in Distribution Network Based on Field-Circuit Coupling Method

One of the primary causes of additional losses in dry-type distribution transformers is harmonic disturbances in the distribution network. It is critical to investigate the change law of trans-former losses under harmonic conditions. The effect of harmonics on transformer core losses and winding losses is first investigated in this paper. The field-circuit coupling method is then used to create a finite element model of a three-phase phase dry type distribution transformer. Finally, the relationship between core loss and harmonic voltage, winding loss and harmonic current is calculated and analyzed for each harmonic frequency. The AC resistance factor model is found to be more accurate than the conventional model in calculating transformer harmonic winding losses. This paper’s findings have significant theoretical implications for the analysis of harmonic losses and loss reduction in distribution networks

Star Identification Algorithm Based on Multi-Dimensional Features and Multi-Layered Joint Screening for Star Sensors

The algorithm for star identification is a crucial technology for determining the orientation of spacecraft using star sensors. Traditional star identification algorithms achieve matching by seeking a unique or a few optimal solutions. However, in high-noise environments, some solutions may be lost, which could result in matching failure. A new lost-in-space architecture algorithm aimed at rapid identification under high star position noise conditions by directly using star positions for final matching is proposed in this paper. The main idea of this algorithm is to construct sufficiently redundant navigation triangles, fully utilizing the physical relationships of the features and forming a screening method from high to low dimensions and from loose to strict. During identification, a multi-layer joint screening matching method is adopted to screen triangles as a whole, narrowing the range of matches quickly while retaining error tolerance. In a series of simulation experiments, this algorithm achieved identification rates of 99.51%, 99.06%, and 98.42% for 2.0 pixel star position noise, 1.0 Mv star magnitude noise, and 5 false stars, respectively. In terms of practical application, all 1000 star images taken by the star sensor in orbit have been successfully identified, and it only takes 28ms to identify each image. In addition, star images taken by consumer-grade cameras from the ground also show that the algorithm has strong robustness to star position noise, magnitude error and false star interference in more severe environments. This method provides partial algorithmic reference for non-specialized design of star sensors for low-cost, large-scale satellites in the future