Adaptive Channel Selection Through Collaborative Sensing

In the field of brain-computer interface (BCI) based on motor imagery (MI), multi-channel electroencephalography (EEG) data is commonly utilized for MI task recognition to achieve sensory compensation or precise human-computer interaction. However, individual physiological differences, environmental variations, or redundant information and noise in certain channels can pose challenges and impact the performance of BCI systems. In this study, we introduce a channel selection method utilizing Hybrid-Recursive Feature Elimination (H-RFE) combined with residual graph neural networks for MI recognition. This channel selection method employs a recursive feature elimination strategy and integrates three classification methods, namely random forest, gradient boosting, and logistic regression, as evaluators for adaptive channel selection tailored to specific subjects. To fully exploit the spatiotemporal information of multi-channel EEG, this study employed a graph neural network embedded with residual blocks to achieve precise recognition of motor imagery. We conducted algorithm testing using the SHU dataset and the PhysioNet dataset. Experimental results show that on the SHU dataset, utilizing 73.44% of the total channels, the cross-session MI recognition accuracy is 90.03%. Similarly, in the PhysioNet dataset, using 72.5% of the channel data, the classification result also reaches 93.99%. Compared to traditional strategies such as selecting three specific channels, correlation-based channel selection, mutual information-based channel selection, and adaptive channel selection based on Pearson coefficients and spatial positions, the proposed method improved classification accuracy by 34.64%, 10.8%, 3.25% and 2.88% on the SHU dataset, and by 46.96%, 5.04%, 5.81% and 2.32% on the PhysioNet dataset, respectively.

Time-Slotted Channel Hopping (TSCH) from the IEEE 802.15.4-2015 standard uses channel hopping to combat interference and frequency-selective fading. Due to the pseudorandom nature of TSCH standard channel hopping process, the energy usage and end-to-end delay achieved in statically configured TSCH networks are suboptimal when compared to using a dynamically updated set of channels. We investigate and compare the advantages of several different adaptive channel selection metrics and methods under the presence of external, frequency-specific interference. In our experiments, PRR-based channel quality assessment with downstream-driven channel selection shows the best results. It is able to reliably distinguish between heavily-interfered and lightly-interfered channels, reduces the number of packet retransmissions up to 2.7 times, achieves up to 22 % lower average radio-on time, and shows close-to 100 % PDR even under heavy interference.

This paper investigates the channel selection and slot time configuration in a cognitive radio network with a number of potential channels. Each channel alternates between ON state (i.e., the primary user is using the channel) and OFF state (i.e., the primary user does not use the channel), and the state evolution process is modeled as a continuous‐time Markov process. The traffic parameters (the transition rates) of the Markov process also evolve with time, modeled as a discrete‐time Markov process. A secondary user adopts a slotted structure with dynamic slot length. At each slot, the secondary user needs to determine which channel to sense and, if the channel is sensed idle, how long the slot length should be. Considering both the amount of data that the secondary user can transmit and the duration when the secondary user interferes with primary activities, a reward definition is given. Based on the reward definition, an adaptive channel selection and slot length configuration method is proposed, which includes a reward maximization procedure to maximize the achieved reward and an update procedure for the channel state belief vector and traffic parameter state belief vector. Numerical results are given to demonstrate the effectiveness and features of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.

We analyze the problem of autonomous cognitive radios (CRs) competing for multiple potentially available channels that may offer different rewards due to their non-homogeneity. The non-homogeneity in channels may lead to payoff distribution conflict among CRs, as each CR would prefer to select the more desirable channels. In our model, CRs are not able to observe the channel selections of other competing CRs. Rather, they get an imperfect signal from which the channel selections must be inferred. We study an adaptive win-shift lose-randomize (WSLR) strategy that (without centralized coordination) enables the CRs to autonomously reach an efficient and fair payoff distribution outcome. We study the autonomous channel selection problem under different primary user (PU) occupancy models; analyze the proposed strategy under imperfect signals. We also investigate the impact of deviations by a selfish CR on the performance of the proposed strategy.

This article proposes an access-point and channel selection method for Internet of Things environments. Recently, the number of wireless nodes has increased with the growth of Internet of Things technologies. In order to accommodate traffic generated by the wireless nodes, we need to utilize densely placed wireless access-points. This article introduces a joint optimization problem of access-point and channel selection for such an environment. The proposed method deals with the optimization problem, using Markov approximation which adapts to dynamic changes in network conditions. Markov approximation is a distributed optimization framework, where a network is optimized by individual behavior of users forming a time-reversible continuous-time Markov chain. The proposed method searches optimal solution for the access-point and channel selection problem on the time-reversible continuous-time Markov chain. Simulation experiments demonstrate the effectiveness of the proposed method.

In this paper, we propose an adaptive channel quality metric that is suitable for uplink channel estimation in ultra-narrowband networks. The proposed metric relies on channel measurements rather than on packet information, therefore it doesn't need a large number of packets to initialize, and is simple enough that can be computed per channel for a large number of channels. Our results show that the proposed metric should be able to identify channels with the least amount of interference for a wide range of expected received packet powers.

Channel quality and utilization metric for interference estimation in Wireless Mesh Networks

The performance of MIMO systems can be improved or optimized if channel state information (CSI) is known at the transmitter. In such systems, the transmitter adapts to the channel based on some channel metric(s). The design and performance of the adaptive scheme depends on the time-varying nature of the metric(s). Some previous work in this area [P.J. Smith et al., May 2005] includes a derivation of level crossing rates (LCR) and average fade durations (AFD) for channel eigenvalues. In this paper, we extend this work to study the temporal behavior of three channel metrics. In particular, we derive the approximate LCR and AFD for the eigenvalue sum, eigenvalue range and simultaneous eigen mode outages. The analysis has been verified through simulations.

In this paper, we propose a reinforcement learning-based approach for enabling vehicles to make intelligent channel selection choices across TV whitespace spectrum. In order for vehicle communication networks to dynamically access TV whitespace in a secondary manner, it is imperative that these communication systems be capable of coexisting with other types of secondary wireless networks operating within the same frequency range. Consequently, we first propose a TV whitespace channel sharing scheme that would facilitate the coexistence between WLAN, WRAN, and vehicular communication networks. Using the channel utilization variations observed by a collection of mobile vehicular communication systems, we then devised a reinforcement learning-based adaptive channel selection algorithm that employs channel utilization sensing in order to reinforce the decisions made by the vehicular communication system. Moreover, the parameters of the proposed learning approach are adaptively tuned in order to achieve better adaptation to a particular environment. A computer emulation environment composed of actual real-world sensing measurement data and a simulated TV whitespace network is created in order to accurately model the characteristics of future wireless environment, as well as to test the proposed learning-based channel access approach. Experimental results show a significant performance improvement with respect to vehicle communication.

The individual-specific steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) is characterized by individual calibration data, resulting in satisfactory performance. However, existing individual-specific SSVEP-BCIs employ generalized channels and task-related subspaces, which seriously limit their potential advantages and lead to suboptimal solutions. In this study, AS-TRCA was proposed to develop a purely individual-specific SSVEP-BCI by fully exploiting individual-specific knowledge. AS-TRCA involves optimal channel learning and selection (OCLS) as well as optimal subspace selection (OSS). OCLS aims to pick the optimal subject-specific channels by employing sparse learning with spatial distance constraints. Meanwhile, OSS adaptively determines the appropriate number of optimal subject-specific task-related subspaces by maximizing profile likelihood. The extensive experimental results demonstrate that AS-TRCA can acquire meaningful channels and determine the proper number of task-related subspaces for each subject compared to traditional methods. Furthermore, combining AS-TRCA with existing advanced calibration-based SSVEP decoding methods, including deep learning methods, to establish a purely individual-specific SSVEP-BCI can further enhance the decoding performance of these methods. Specifically, AS-TRCA improved the average accuracy as follows: TRCA 7.21%, SSCOR 7.61%, TRCA-R 6.58%, msTRCA 7.70%, scTRCA 4.47%, TDCA 2.91%, and bi-SiamCA 3.23%. AS-TRCA is promising for further advancing the performance of SSVEP-BCI and promoting its practical applications.

Underwater object detection is difficult for existing models to balance accuracy and efficiency due to problems such as complex optical interference, blurred features of small targets, and the limited computing power of edge devices. To address the limitations of YOLOv12 in underwater scenarios, including insufficient multi-scale feature modeling (mAP50: 0.652) and high computational complexity (21.2 GFLOPs), we propose YOLOv12-LSE, a lightweight framework that achieves the collaborative optimization of underwater detection performance and efficiency through three innovations: First, the C2PSA_LSKA module is designed, integrating channel-space joint attention and lightweight large-core decomposition strategies to enhance the multi-scale object detection capability and suppress edge blur interference. Second, the Slim Neck architecture is constructed based on gradient sparse convolution and cross-level feature aggregation to reduce computational redundancy and retain shallow detail features. Finally, efficient channel attention is introduced into the detection head to improve the feature discrimination of low-contrast targets through adaptive channel selection. Experiments on the DUO dataset show that the mAP50 of YOLOv12-LSE reaches 0.681, significantly improving by 4.45% compared to the baseline model YOLOv12. The precision increases by 6.3%, while the GFLOPs decrease by 7.08% to 19.7, the lowest in the same series. The ablation experiment verified the collaborative optimization effect of each module, and the visualization results further proved its robustness in low-illumination, dense targets, and edge-blurred scenes. This study breaks through the bottleneck of the "precision-efficiency" trade-off with a lightweight design, providing a high-precision real-time detection solution for underwater mobile devices (such as AUVs) and promoting the deep integration of the attention mechanism and edge computing.

This paper presents the design and implementation of PUMA, a declarative constraint-solving platform for policy-based routing and channel selection in multi-radio wireless mesh networks. In PUMA, users formulate channel selection policies as optimization goals and constraints that are concisely declared using the PawLog declarative language. To efficiently execute PawLog programs in a distributed setting, PUMA integrates a high performance constraint solver with a declarative networking engine. We demonstrate the capabilities of PUMA in defining distributed protocols that cross-optimize across channel selection and routing. We have developed a prototype of the PUMA system that we extensively evaluated in simulations and on the ORBIT testbed. Our experimental results demonstrate that PUMA can flexibly and efficiently implement a variety of centralized and distributed channel selection protocols that result in significantly higher throughput compared to single channel and identical channel assignment solutions.

This paper presents PUMA, a novel declarative constraint-solving architecture that achieves efficient policy-based adaptive routing and channel selection for wireless networks. PUMA is based on declarative networking, a database-inspired extensible infrastructure using query languages to specify behavior. In PUMA, users specify high-level declarative policies that dictate their channel selection constraints and routing protocol behavior. The runtime system generates the actual implementation that meets these specifications. Moreover, PUMA provides avenues to optimize across routing and channel selection policies. We have developed a prototype of PUMA that is extensively evaluated in simulations and on the ORBIT testbed.

Dynamic spectrum Access (DSA) is used to make use of the spectrum in wireless network. In most situations, control channel is used as a common approach to negotiate between secondary users. However, based on this feature, the whole network can be destroyed by influencing the control channel only. This paper analyzed and simulated two channel selection methods (Random channel selection and Receiver-directed channel selection) without control channel. And we design a dynamic spectrum access environment and use different topologies to simulate and analysis the algorithms. The results prove that those methods are available. The Receiver-directed channel selection is more match to the regular control channel standards in throughput by comparisons, and the attack probability of network is decreased by the successful spectrum channel handoff.

Channel selection is important for automatic speech recognition as the signal quality of one channel might be significantly better than those of the other channels and therefore, microphone array or blind source separation techniques might not lead to improvements over the best single microphone. The mayor challenge, however, is to find this particular channel who is leading to the most accurate classification. In this paper we present a novel channel selection method, based on class separability, to improve multi-source far distance speech-totext transcriptions. Class separability measures have the advantage, compared to other methods such as the signal to noise ratio (SNR), that they are able to evaluate the channel quality on the actual features of the recognition system. We have evaluated on NISTs RT-07 development set and observe significant improvements in word accuracy over SNR based channel selection methods. We have also used this technique in NISTs RT-07 evaluation.