Sparse Message Passing Based Preamble Estimation for Crowded M2M Communications

Due to the massive number of devices in the M2M communication era, new challenges have been brought to the existing random-access (RA) mechanism, such as severe preamble collisions and resource block (RB) wastes. To address these problems, a novel sparse message passing (SMP) algorithm is proposed, based on a factor graph on which Bernoulli messages are updated. The SMP enables an accurate estimation on the activity of the devices and the identity of the preamble chosen by each active device. Aided by the estimation, the RB efficiency for the uplink data transmission can be improved, especially among the collided devices. In addition, an analytical tool is derived to analyze the iterative evolution and convergence of the SMP algorithm. Finally, numerical simulations are provided to verify the validity of our analytical results and the significant improvement of the proposed SMP on estimation error rate even when preamble collision occurs.Comment: submitted to ICC 2018 with 6 pages and 4 figure

Joint Optimization of Energy Consumption and Completion Time in Federated Learning

Federated Learning (FL) is an intriguing distributed machine learning approach due to its privacy-preserving characteristics. To balance the trade-off between energy and execution latency, and thus accommodate different demands and application scenarios, we formulate an optimization problem to minimize a weighted sum of total energy consumption and completion time through two weight parameters. The optimization variables include bandwidth, transmission power and CPU frequency of each device in the FL system, where all devices are linked to a base station and train a global model collaboratively. Through decomposing the non-convex optimization problem into two subproblems, we devise a resource allocation algorithm to determine the bandwidth allocation, transmission power, and CPU frequency for each participating device. We further present the convergence analysis and computational complexity of the proposed algorithm. Numerical results show that our proposed algorithm not only has better performance at different weight parameters (i.e., different demands) but also outperforms the state of the art.Comment: This paper appears in the Proceedings of IEEE International Conference on Distributed Computing Systems (ICDCS) 2022. Please feel free to contact us for questions or remark

Enhancing Federated Learning with spectrum allocation optimization and device selection

Machine learning (ML) is a widely accepted means for supporting customized services for mobile devices and applications. Federated Learning (FL), which is a promising approach to implement machine learning while addressing data privacy concerns, typically involves a large number of wireless mobile devices to collect model training data. Under such circumstances, FL is expected to meet stringent training latency requirements in the face of limited resources such as demand for wireless bandwidth, power consumption, and computation constraints of participating devices. Due to practical considerations, FL selects a portion of devices to participate in the model training process at each iteration. Therefore, the tasks of efficient resource management and device selection will have a significant impact on the practical uses of FL. In this paper, we propose a spectrum allocation optimization mechanism for enhancing FL over a wireless mobile network. Specifically, the proposed spectrum allocation optimization mechanism minimizes the time delay of FL while considering the energy consumption of individual participating devices; thus ensuring that all the participating devices have sufficient resources to train their local models. In this connection, to ensure fast convergence of FL, a robust device selection is also proposed to help FL reach convergence swiftly, especially when the local datasets of the devices are not independent and identically distributed (non-iid). Experimental results show that (1) the proposed spectrum allocation optimization method optimizes time delay while satisfying the individual energy constraints; (2) the proposed device selection method enables FL to achieve the fastest convergence on non-iid datasets.Comment: This paper is accepted by IEEE/ACM Transactions on Networkin

LSTM-Aided Hybrid Random Access Scheme for 6G Machine Type Communication Networks

In this paper, an LSTM-aided hybrid random access scheme (LSTMH-RA) is proposed to support diverse quality of service (QoS) requirements in 6G machine-type communication (MTC) networks, where massive MTC (mMTC) devices and ultra-reliable low latency communications (URLLC) devices coexist. In the proposed LSTMH-RA scheme, mMTC devices access the network via a timing advance (TA)-aided four-step procedure to meet massive access requirement, while the access procedure of the URLLC devices is completed in two steps coupled with the mMTC devices' access procedure to reduce latency. Furthermore, we propose an attention-based LSTM prediction model to predict the number of active URLLC devices, thereby determining the parameters of the multi-user detection algorithm to guarantee the latency and reliability access requirements of URLLC devices.We analyze the successful access probability of the LSTMH-RA scheme. Numerical results show that, compared with the benchmark schemes, the proposed LSTMH-RA scheme can significantly improve the successful access probability, and thus satisfy the diverse QoS requirements of URLLC and mMTC devices

Shelterbelt Poplar Forests Induced Soil Changes in Deep Soil Profiles and Climates Contributed Their Inter-site Variations in Dryland Regions, Northeastern China

The influence of shelterbelt afforestation on soils in different-depth profiles and possible interaction with climatic conditions is important for evaluating ecological effects of large-scale afforestation programs. In the Songnen Plain, northeastern China, 720 soil samples were collected from five different soil layers (0–20, 20–40, 40–60, 60–80, and 80–100 cm) in shelterbelt poplar forests and neighboring farmlands. Soil physiochemical properties [pH, electrical conductivity (EC), soil porosity, soil moisture and bulk density], soil carbon and nutrients [soil organic carbon (SOC), N, alkaline-hydrolyzed N, P, available P, K and available K], forest characteristics [tree height, diameter at breast height (DBH), and density], climatic conditions [mean annual temperature (MAT), mean annual precipitation (MAP), and aridity index (ARID)], and soil texture (percentage of silt, clay, and sand) were measured. We found that the effects of shelterbelt afforestation on bulk density, porosity, available K, and total P were observed up to 100 cm deep; while the changes in available K and P were several-fold higher in the 0–20 cm soil layer than that in deeper layers (p < 0.05). For other parameters (soil pH and EC), shelterbelt-influences were mainly observed in surface soils, e.g., EC was 14.7% lower in shelterbelt plantations than that in farmlands in the 0–20 cm layer, about 2.5–3.5-fold higher than 60–100 cm soil inclusion. For soil moisture, shelterbelt afforestation decreased soil water by 7.3–8.7% in deep soils (p < 0.05), while no significant change was in 0–20 cm soil. For SOC and N, no significant differences between shelterbelt and farmlands were found in all five-depth soil profiles. Large inter-site variations were found for all shelterbelt-induced soil changes (p < 0.05) except for total K in the 0–20 cm layer. MAT and silt content provided the greatest explanation powers for inter-site variations in shelterbelt-induced soil properties changes. However, in deeper soils, water (ARID and MAP) explained more of the variation than that in surface soils. Therefore, shelterbelt afforestation in northeastern China could affect aspects of soil properties down to 100 cm deep, with inter-site variations mainly controlled by climate and soil texture, and greater contribution from water characteristics in deeper soils

Intelligent Reflecting Surface Aided Power Control for Physical-Layer Broadcasting

Reconfigurable intelligent surface (RIS), a recently introduced technology for future wireless com-munication systems, enhances the spectral and energy efficiency by intelligently adjusting the propaga-tion conditions between a base station (BS) and mobile equipments (MEs). An RIS consists of manylow-cost passive reflecting elements to improve the quality of the received signal. In this paper, westudy the problem of power control at the BS for the RIS aided physical-layer broadcasting. Our goalis to minimize the transmit power at the BS by jointly designing the transmit beamforming at the BSand the phase shifts of the passive elements at the RIS. Furthermore, to help validate the proposedoptimization methods, we derive lower bounds to quantify the average transmit power at the BS as afunction of the number of MEs, the number of RIS elements, and the number of antennas at the BS.The simulation results demonstrated that the average transmit power at the BS is close to the lowerbound in an RIS aided system, and is significantly lower than the average transmit power in conventionalschemes without the RIS

CRISPR-cas technology: A key approach for SARS-CoV-2 detection

The CRISPR (Clustered Regularly Spaced Short Palindromic Repeats) system was first discovered in prokaryotes as a unique immune mechanism to clear foreign nucleic acids. It has been rapidly and extensively used in basic and applied research owing to its strong ability of gene editing, regulation and detection in eukaryotes. Hererin in this article, we reviewed the biology, mechanisms and relevance of CRISPR-Cas technology and its applications in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) diagnosis. CRISPR-Cas nucleic acid detection tools include CRISPR-Cas9, CRISPR-Cas12, CRISPR-Cas13, CRISPR-Cas14, CRISPR nucleic acid amplification detection technology, and CRISPR colorimetric readout detection system. The above CRISPR technologies have been applied to the nucleic acid detection, including SARS-CoV-2 detection. Common nucleic acid detection based on CRISPR derivation technology include SHERLOCK, DETECTR, and STOPCovid. CRISPR-Cas biosensing technology has been widely applied to point-of-care testing (POCT) by targeting recognition of both DNA molecules and RNA Molecules

Homocysteine levels in first-episode patients with psychiatric disorders

A high homocysteine (Hcy) level is a risk factor for schizophrenia, depression, and bipolar disorder. However, the role of hyperhomocysteinemia as either an independent factor or an auxiliary contributor to specific psychiatric symptoms or disorders remains unclear. This study aimed to examine Hcy levels in first-episode inpatients with psychotic symptoms and various psychiatric diseases to elucidate the association between Hcy levels and psychiatric disorders. This study enrolled 191 patients (aged 18–40 years) with psychiatric disorders. Seventy-five patients were diagnosed with schizophrenia, 48 with acute and transient psychotic disorders, 36 with manic episodes with psychosis, 32 with major depressive episodes with psychosis, and 56 healthy controls. Serum Hcy levels were measured using the enzyme cycle method. A Hcy concentration level of > 15 μmol/L was defined as hyperhomocysteinemia. Hcy levels were significantly higher in first-episode patients with psychiatric disorders compared to healthy controls (5.99 ± 3.60 vs. 19.78 ± 16.61 vs. 15.50 ± 9.08 vs. 20.00 ± 11.33 vs. 16.22 ± 12.06, F = 12.778, P < 0.001). Hcy levels were significantly higher in males with schizophrenia, acute and transient psychotic disorder, and major depressive disorder but not in mania [schizophrenia, (t = -4.727, P < 0.001); acute and transient psychotic disorders, (t = -3.389, P = 0.001); major depressive episode with psychosis, (t = -3.796, P < 0.001); manic episodes with psychosis, (t = -1.684, P = 0.101)]. However, serum Hcy levels were not significantly different among the psychiatric disorder groups (F = 0.139, P = 0.968). Multivariate linear regression showed that males had an increased risk for homocysteinemia. (95% CI = 8.192–15.370, P < 0.001). These results suggest that first-episode patients with psychiatric disorders have higher Hcy levels than in the general population, and men are at greater risk for psychiatric disorders. In conclusion, elevated Hcy levels may contribute to the pathogenesis of first-episode patients with psychotic symptoms