Considering the edge-cloud environment and uploading large amounts of data from IoT devices through the edge node towards the cloud, this paper investigates an aggregation method of data streams compressed by Generalized Deduplication (GD) for the edge node. The simplest way is to decode GD-compressed data streams, aggregate raw streams and re-encode it into a single GD-compressed data stream. However, this involves a large computational complexity for aggregation due to the decoding and re-encoding of linear codes underlain the GD. From the above observation, this paper presents a novel aggregation method, called Aggregable GD (AGD) to aggregate multiple GD streams into a single compressed stream. AGD consists of two main key ideas: The first one is to reduce the computational complexity of elimination of the redundancy among multiple GD streams by partly extracting data from the compressed stream. The second one is to create an additional dictionary corresponding to each leaf node to eliminate redundancy within a single stream from a single leaf node. By applying these, in addition to the reduction of computational complexity, the compression performance is also expected to be improved compared to the ordinary decoding-aggregating-re-encoding method. For this expectation, this paper clarifies that AGD dramatically improves the computational complexity of aggregation by mathematical estimation. Furthermore, by the computer simulation using the Reed-Solomon code as the underlying linear code of GD, we demonstrate that AGD performs better than the ordinary method from the viewpoint of the compression rate.

This paper investigates the problem of distributed resource management in underwater acoustic communication networks (UACNs) involving multiple transmitters and receivers. In this setting, each transmitter autonomously selects a power allocation strategy based solely on local observations, without reliance on a central controller. Given that the optimization problem incorporating fairness and quality of service (QoS) constraints is non-convex and NP-hard, it is reformulated as a Markov Decision Process (MDP). To address the high complexity of underwater networks and the large state and action spaces, we propose a distributed learning framework based on a multi-agent dueling deep Q-network (MAD3QN). The proposed scheme enables each transmitter to dynamically adjust its transmission power based on local observations by integrating the Jain fairness index, QoS interruption penalty, and energy consumption constraints. Furthermore, by incorporating a dueling network architecture and a neighborhood cooperation mechanism, the learning efficiency is significantly enhanced, leading to a stable and effective resource optimization policy. Simulation results demonstrate that the proposed distributed learning algorithm outperforms existing approaches in terms of convergence speed, network fairness, and communication rate.

Emerging applications, such as AI, AR/VR, and large-scale data processing, have significantly increased the demand for distributed data center resources. Consequently, the inter-datacenter wide area networks (WANs) face a surge in large-scale, long-distance network traffic. As the number of data centers increases, network congestion exhibits localized characteristics. Centralized link-grained schedules lack scalability, fairness and prolong the control cycle. To address this issue, we propose a Hop-by-hop Multi-Topology Traffic Engineering method (MTTE) on overlay network to provide on-time transmission services for Inter-DC WANs. The method divides the next hop set of hop-by-hop multipath routing into multiple topologies, which supports cost-based dynamic path switching at intermediate nodes. When traffic volume exceeds the adjustment of topologies, feedback is sent hop-by-hop to the upstream nodes, ultimately triggering rate control at the edge. Then, local bottlenecks are modeled as the Local Bandwidth Maximization (LBM) problem, enabling the adjustment of topology ratios, feedback, and transmission rates. Simulations show that MTTE can effectively optimize the link resources by adaptively adjusting the topology ratio. Compared to state-of-the-art heuristic-based centralized methods, MTTE improves the on-time ratio and fairness by increasing link utilization by approximately 30%.

Long-term rain attenuation statistics of the Ka and Ku band satellite signals obtained from 1986 to 2006 at Osaka Electro-Communication University in Neyagawa, Osaka, are investigated for the characteristics of their worst month cumulative time percentages. The long-term worst month statistics over more than 20 years are, on average, in good agreement with the ITU-R predictions, while their yearly values show fairly large year-to-year variations. The yearly variations are found to be caused by two different factors, attributed to the coefficients a and b of an exponential function, respectively, which connects the worst month statistics of each year with the long-term average cumulative time percentages. Specifically, the coefficient a means an overall increase of the yearly worst month statistics from the long-term average statistics. This coefficient is shown to be roughly determined by total rainfall in each year, and it can be more precisely estimated by the maximum monthly rainfall during each year. On the other hand, the coefficient b means the gradient of the worst month statistics against the long-term average statistics in logarithmic scale. This coefficient is found to be closely related to the occurrence of heavy rainfall due to convective clouds such as shower in summer, rather than the amount of rainfall during a year.

The evaluation of automotive wire harnesses requires both interpretability and diversity in judgment criteria to enhance the practicality and reliability of diagnostic models. This study primarily focuses on optimizing heatmap generation for model interpretability by introducing score class activation mapping (Score-CAM) as a replacement for the previously used gradient-weighted CAM (Grad-CAM) in wire harness diagnosis. Score-CAM provides a more robust and accurate representation of feature importance as interpreted by a convolutional neural network (CNN), addressing limitations in stability and reliability inherent to Grad-CAM. Additionally, crosstalk voltage (XTV) is proposed as a supplementary diagnostic criterion alongside common mode current (CMC), enriching the evaluation framework by providing an additional perspective for comprehensive diagnosis. Experimental results demonstrate that the integration of Score-CAM significantly enhances heatmap quality and model interpretability, while the inclusion of XTV offers valuable complementary insights. These advancements collectively contribute to the development of more effective and interpretable diagnostic systems for automotive wire harnesses.

In product electromagnetic compatibility (EMC) design, accurately obtaining the S-parameter characteristics of electrical and electronic devices is a fundamental and crucial requirement. S-parameters are measured after mounting the device on a test board; however, these characteristics typically include parasitic impedances caused by factors such as the board structure, device mounting pads, and soldering. This can lead to the device exhibiting different characteristics when mounted on different boards. In this study, we investigated a method to extract parasitic impedances that contribute to characteristic differences in devices mounted on different boards. This method simultaneously estimates which device the parasitic impedance is associated with and its value based on analysis using simulated annealing, a parameter optimization technique, and the properties of cascaded ABCD parameters. We verified the method’s validity using a common mode choke as an example, a representative EMC design component, through both circuit simulations and experiments. Both results demonstrate that the method can identify the value of the parasitic impedance and distinguish which device it is part of.

This paper proposes complexity reduced overloaded MIMO spatial multiplexing with colored noise cancellation, which can be applied for uplinks in wireless systems with massive MIMO where the number of the transmit antennas is less than that of receive antennas. This paper analyzes the characteristics of the colored noise cancellation. The analysis proves that the proposed colored noise cancellation can shrink the search space for the brute force search needed in the log-likelihood ratio (LLR) estimation, which reduces the complexity of the colored noise cancellation. Because the complexity of the colored noise cancellation dominates that of the proposed spatial multiplexing, the proposed spatial multiplexing is made less complex than the conventional spatial multiplexing by reducing the complexity of the colored noise cancellation. Computer simulation reveals that the receiver with the colored noise cancellation can be implemented with about 80% less computational complexity than that with full search space, even though the transmission performance is less than 1 dB degraded at the BER of 10^-4 in a 2 × 6 MIMO channels, where 6 streams are spatially multiplexed.

To address the sum-rate degradation caused by channel aging, accurate prediction of future channels based on pilot signals is critical. Existing model-based or neural network-based approaches relying on sequential prediction suffer from error propagation and significant sum-rate loss in multi-frame channel prediction. While Transformer models have recently enabled parallel processing through encoder self-attention mechanisms, their application in massive MIMO systems remains underexplored. Besides, considering the limitations of conventional self-attention and positional encoding strategies, We propose a Parallel Synchronous Transformer with Hybrid Attention Mechanism incorporating hybrid positional encoding. Firstly, the Hybrid Attention Mechanism with hybrid position encoding ensures high adaptability and efficient attention calculation. Moreover, this method with Hybrid Attention Mechanism synchronously predicts several frames in future channels by historical channel states information (CSI), effectively eliminating error propagation and achieving near-optimal sum-rate performance. The simulation results demonstrate that the proposed model achieves a prediction normalized mean square error (NMSE) below -15 dB in two distinct user mobility scenarios. Notably, the system’s achievable sum-rate closely approaches that of the ideal CSI case. These findings highlight the model’s exceptional accuracy and its ability to maintain a high sum-rate, even under high-mobility conditions.

In this paper, a new signal detection scheme for overloaded multiple-input multiple output (MIMO) systems is proposed. In MIMO signal detection with belief propagation (BP), a factor graph consists of many loops that arise high complexity and cause performance degradation due to outliers. Various schemes have been proposed to overcome these issues. In BP with maximal ratio combining (MRC) and minimum mean square error (MMSE) pre-cancellation, a conventional scheme applies MRC on received signals in advance to obtain diversity gain to improve bit error rate (BER) performance as compared with BP using only MMSE pre-cancellation. To achieve further performance improvement, BP using a combined channel matrix is proposed in this paper. In the proposed scheme, the number of observation nodes is increased by combining a part of an original channel matrix to the channel matrix with MRC and MMSE pre-cancellation to enhance diversity gain. Numerical results obtained through computer simulation show that the BER performance of the proposed scheme is superior to that of BP or BP with MRC and MMSE pre-cancellation. The proposed scheme improves BER performance by about 3.1 dB at a BER of 10^-3 as compared with the BP with MRC and MMSE pre-cancellation. The computational complexity of the proposed scheme increases as the number of rows of the additional channel submatrix. It is in the same order of magnitude as that of the original BP based MIMO detection scheme.