Adaptive Modulation for Open All-Photonics Networks

Orthogonal Frequency Division Multiplexing (OFDM) is a multicarrier transmission scheme. OFDM transmits data by using a large number of narrow bandwidth carriers. In an OFDM transmission system, each subcarrier is attenuated individually under the frequency-selective and fast fading channel if the same fixed transmission scheme is used for all OFDM subcarriers thus it results in highest attenuation and hence poor performance. The purpose of this paper is to introduce the adaptive modulation to get an understanding of the differences between fixed and adaptive modulations schemes. In this work adaptive modulation is implemented by dividing whole subcarriers into blocks of adjacent subcarriers. Based on calculated average instantaneous signal to noise (SNR) same modulation scheme is applied to all subcarriers of same block. Here average bit error rate (BER) performance of OFDM system under fixed modulation and adaptive modulation is observed. Average BER performance of these modulation techniques is observed with various inverse fast Fourier transform (IFFT) size and using simpler adaptive quadrature amplitude modulation (QAM) schemes. The simulation results show that BER performance of OFDM system using adaptive modulation is better than fixed modulation.

We propose an adaptive multicarrier modulation technique, where the transmit rate and power are adapted in both time and frequency. We derive the optimal adaptation strategy and determine its performance. We also compare this performance with that of multicarrier systems that adapt only in the temporal or spectral domains. Our results indicate that as long as these domains have enough degrees of freedom, adaptive modulation across one domain performs almost the same as the more complex two dimensional adaptive policy. If however rate or power is restricted to be constant over the time and/or frequency domains, two dimensional adaption policy shows significantly better performances than adaptive modulation across one domain.

Digital Mammography: Wavelet Transform and Kalman-Filtering Neural Network in Mass Segmentation and Detection

In multi-access wireless communication systems, power control and adaptive modulation are two important means to increase spectral efficiency, combat the time varying fading environment, and reduce cochannel interference. The overall uplink transmitting power is optimized while the overall network throughput is fixed. Each link can select a range of targeted signal-to-interference-plus-noise ratio (SINR) according to its current channel condition and each link's time average SINR is maintained as a constant to ensure fairness. Adaptive M-QAM or M-PSK modulation with antenna diversity is applied to increase spectral efficiency. The scheme can be interpreted as "water filling" each link's SINR in the time domain and allocating network throughput to different links at each time. From simulation results, our scheme reduces about 40% of overall transmitting power and increases average spectral efficiency by about 0.9 bit/s/Hz.

Correlation between the channels is the primary factor that restricts data rates in multiple-input multiple-output visible light communications (MIMO-VLC). Different receiver designs have been proposed to reduce this correlation value, while adaptive modulations that perform better under the correlated channel effect have been studied on the transmitter side. In this study, we conducted a performance analysis of MIMO-VLC systems by utilizing adaptive MIMO modulations at the transmitter side and angular diversity receiver (ADR) structures at the receiver side. As adaptive modulations, created using spatial diversity (SD) and spatial multiplexing (SMP) modulations, SD/SMP switching, adaptive SMP (aSMP), and SD-assisted adaptive SMP (SD-aSMP) are considered. Analytical and simulation results were obtained for achievable data rates in an indoor environment. Cumulative distribution function (CDF) graphs were used to assess the overall performance based on these data rates. GADR has an advantage over ADR in low SNR regions but performs similarly in high SNR regions. The SD-aSMP scheme demonstrated the best performance across all SNR regions as a hybrid modulation, taking advantage of diversity gain in low SNR and multiplexing gain in high SNR regions. The adaptive SMP schemes benefit from increasing the number of LEDs and increasing the elevation angle up to 60 degrees.

The Independent Curriculum provides thinking in learning, allowing teachers to adjust the material to the needs of students. However, challenges such as limited resources, teacher readiness, and variations in student learning styles are still challenges in its implementation (Kemendikbud, 2022). This study aims to examine the implementation of adaptive learning modules as a solution to improve learning effectiveness. Adaptive modules that integrate technology and personalization of teaching materials are expected to improve students' understanding in a way that is more appropriate to their individual needs (Mayer, 2009). The approach used in this study includes literature studies and field observations, which show that adaptive modules can significantly improve student learning outcomes. However, the implementation of this adaptive learning module still requires support in the form of adequate teacher training and supporting technological infrastructure so that learning can run effectively and efficiently (Batubara, 2019). This study provides recommendations to strengthen teacher readiness and provide the resources needed in the implementation of adaptive modules.

An adaptive sub-carrier modulation in OFDM transmission is an effective scheme in order to increase transmission rate because it is possible for OFDM to determine a modulation scheme of each sub-carrier independently according to its channel state information. However, in this case, it is necessary to transmit allocation information of modulation from the transmitter to the receiver and this leads to degradation of performance. In this paper, an adaptive modulation scheme for OFDM that makes possible less information transmission from the transmitter to the receiver for allocation information is proposed. Moreover, sub-carrier power control is combined in order to improve performance further. As the result of theoretical analysis and simulation, better performance of the proposed scheme was confirmed.

Multimodal sentiment analysis aims at extracting effective information from different modalities such as text, audio, and visual to infer the speaker’s sentiment state. Due to the high heterogeneity across modalities, most existing approaches decouple modalities into specific and invariant features, which can capture effective cross-modal representations to some extent. However, in multimodal tasks, different modalities exhibit varying strengths and weaknesses, with strong modalities dominating the overall optimization direction of the network, leading to under-optimization of weak modalities. To address the modality imbalance issue, we propose a Modality Modulation Adaptive Fusion Network (MMAFNet) to optimize the learning of valuable information from each modality. Specifically, for modality-specific features, we design a specific feature gradient modulation strategy to stimulate the weak modalities learning and adaptively modulate the corresponding gradients by measuring different importance to better optimize each modality. For modality-invariant features, in terms of the distance between different modalities, we propose an invariant feature parameter reset strategy that prevents overfitting irrelevant information while enhancing the feature extraction capability of weaker modalities. Finally, we incorporate an adaptive fusion module to combine modality-specific and invariant features based on their respective weights. Overall, we analyze the characteristics of various features and propose modality modulation strategies that mitigate modality imbalance. Extensive experiments on two multimodal sentiment analysis datasets, demonstrate the superior performance of our method.

This study aims to identify student learner profiles based on a combination of prior knowledge and Self-Regulated Learning (SRL) levels and to analyze the differential impact of an adaptive learning module on knowledge and SRL improvement in each profile. Using a mixed-methods explanatory sequential design, 92 undergraduate physics education students were selected through purposive sampling. K-Means cluster analysis was applied to form learner profiles, followed by a six-week pre–post intervention and qualitative interviews. The results identified three learner profiles (Proficient-Autonomous Learner, Resilient-Developing Learner, and Proficient-Fragile Achiever). The result showed that the adaptive module significantly improved Results showed significant knowledge gains across profiles, while SRL improvements differed significantly. The Proficient–Fragile Achiever group demonstrated the largest SRL gain (p < .001; large effect size, d > 0.80), associated with more frequent scaffolding support. In conclusion, the effectiveness of adaptive modules is highly dependent on learner profiles, with the most significant benefits in their ability to provide external support for building self-regulation skills. These findings imply that learning technology design should incorporate SRL as a key variable for personalization, and institutions can utilize these platforms as intervention tools for students with weak learning independence.

A novel hybrid control strategy for reducing torque ripple in permanent magnet brushless direct current motors has been developed by combining the deterministic nature of direct torque control and the adaptive nature of fuzzy logic control. Direct torque control using space vector pulse width modulation has been developed. This technique ensures the near-sinusoidal nature of the stator and rotor current. The impact of commutation ripple corresponding to the sinusoidal current is reduced compared to square wave current in conventional direct torque control. Further, the fuzzy logic controller provides adaptive modulation in space vector to mitigate the torque ripple. The response of the proposed method has been analyzed in the time domain as well as in the frequency domain. Experimental results establish a significant reduction in torque ripple and minimization of high-frequency components. A comparative study with conventional direct torque control methods has also been presented.

In this paper, link adaptation (LA) schemes based on adaptive modulation (AM), where the transmit parameters can be adapted to the varying channel conditions, are investigated for spatial modulation (SM). We propose machine learning (ML) based adaptive spatial modulation (ASM) as the optimal technique for modulation selection (MS) compared with the exhaustive search based ASM scheme. Specifically, two supervised machine learning classifiers (MLCs), K-nearest neighbors (KNN) and support vector machine (SVM), are proposed to design the MS in ASM and make the tradeoff between the system complexity and the bit error rate (BER) performance. The proposed classifiers are employed to solve the MS problem in ASM as a multi-class classification problem and obtain the best modulation selection candidate (MSC) that achieves the maximum minimum Euclidean distance. Simulation results show that, under the same spectral efficiency (SE), the proposed schemes provide considerable system complexity reduction and obtain a sub-optimal BER performance compared with the typical ASM scheme.

This paper proposes a multipath interference canceller (MPIC) associated with orthogonal code-multiplexing that achieves much higher peak throughput than 2 Mbit/s with adaptive data modulation for high-speed packet transmission in the wideband direct sequence-code division multiple access (W-CDMA) forward link, and evaluates its throughput performance by computer simulation. The simulation results elucidate that sufficient multipath interference (MPI) suppression is achieved by a four-stage MPIC with 6-12 orthogonal code-multiplexing using one iterative channel estimation with pilot and decision feedback data symbols and further that the interference rejection weight control according to the number of observed multipaths is effective in improving the throughput. It is also demonstrated that MPIC exhibits a superior MPI suppression effect to a chip equalizer in the lower received signal energy per bit-to-background noise spectrum density (E/sub b//N/sub 0/) channel around 0-3 dB owing to the successive channel estimation at each stage. We show that the maximum peak throughput using MPIC is approximately 2.1 fold that without MPIC in a two-path and three-path Rayleigh fading channel and that the peak throughput of 8.0 Mbit/s is achieved using 64 QAM data modulation in a two-path fading channel within a 5 MHz bandwidth. Furthermore, the required average E/sub b//N/sub 0/ for satisfying the same throughput with MPIC is decreased by more than 2.0 dB.

While adapting pretrained vision models to downstream dense prediction tasks is widely used, current methods often overlook adaptation efficiency, especially in the context of multi-task learning (MTL). Although parameter-efficient fine-tuning (PEFT) methods can enhance parameter efficiency, broader aspects such as GPU memory and training time efficiency remain underexplored. In this paper, we propose a new paradigm that simultaneously achieves efficiency in Parameters, GPU Memory, and Training Time for Multi-Task Dense Vision Adaptation. Specifically, we propose a dual-branch framework, in which a frozen pretrained backbone serves as the generic main branch, and the proposed Bi-Directional Task Adaptation (BDTA) modules are integrated in parallel to form a task bypass branch that extracts adaptation features required by multiple specific tasks. This adaptation module is lightweight, efficient, and does not require backpropagation through the large pre-trained backbone, thus avoiding resource-intensive gradient computations. Moreover, a Mixture of Task Experts mechanism (MoTE) is further proposed to integrate adaptation features across tasks and scales, thereby obtaining more robust representations tailored for dense prediction tasks. On the PASCAL-Context benchmark, our method achieves over 2× relative performance improvement compared to the best prior multi-task PEFT method, while using only ~30% of the parameters, ~50% of the memory, and ~60% of the training time, demonstrating superior overall adaptation efficiency.

This paper discusses system design strategies and performance evaluation for adaptive modulation techniques used in new and up-and-coming wireless access systems. After a brief discussion on basic modulation parameter (MP) settings as well as functionalities specific for adaptive modulation systems, this paper discusses how to design a narrowband time-division multiple-access/time-division duplex (TDMA/TDD)-based adaptive modulation system and confirms through laboratory experiments that the narrowband TDMA/TDD-based adaptive modulation system dramatically enhances system robustness to multipath fading and flexibility in throughput and transmission quality control. Next, as an extension to many fields of application for adaptive modulation, this paper discusses subcarrier-level adaptive modulation for orthogonal frequency-division multiplexing (OFDM)-based one-cell reuse broadband cellular systems. One key result is that nonsubcarrier transmit power control (TPC) applied adaptive modulation systems can achieve almost the same performance as subcarrier TPC applied adaptive modulation systems provided that the required signal-to-noise-plus-interference power ratio increment in the MP sets is designed to be sufficiently small. Finally, this paper explains the dynamic parameter controlled OFDM/TDMA system as an example of a practical scheme for one-cell reuse broadband wireless access systems. The analysis confirms that this adaptive system can achieve a spatial reuse efficiency defined by (average throughput in multicell conditions)/(average throughput in single cell conditions) of 0.8 as well as an average media access control payload throughput of about 150 Mb/s using about 100 MHz of bandwidth.

The effect of DC offset on multi-input multi-output (MIMO) direct transceivers with adaptive modulation (AM) is discussed in this paper. A variable-rate variable-power (VRVP) AM system with perfect channel state information (P-CSI) at both the transmitter and receiver in a MIMO scenario is considered. The DC offset is modeled as a zero mean complex Gaussian distributed random variable. By this modeling of the DC offset, the analytical expression for degraded bit error rate (BER) is derived. To derive this analytical expression, we establish a reasonable approximation. The good agreement between the analytical and simulation results shows that the approximation is valid and confirms the accuracy of the analytical expressions. Moreover, an approach to improve the degraded BER in these systems is introduced. For this purpose, we introduce a design for AM MIMO systems that takes account of DC offset and its effectiveness is confirmed. Throughput analysis for the AM MIMO system in the presence of DC offset is presented in this paper too. An analytical expression for throughput is derived and approximated to a simpler equation. At last, throughput results are compared to the simulation outcomes.