Adaptive access-point and channel selection method using Markov approximation

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.

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.

During electroencephalography (EEG)-based motor imagery-brain-computer interfaces (MI-BCIs) task, a large number of electrodes are commonly used, and consume much computational resources. Therefore, channel selection is crucial while ensuring classification accuracy. This paper proposes a channel selection method by integrating the efficient channel attention (ECA) module with a convolutional neural network (CNN). During model training process, the ECA module automatically assigns the channel weights by evaluating the relative importance for BCI classification accuracy of every channel. Then a ranking of EEG channel importance can be established so as to select an appropriate number of channels to form a channel subset from the ranking. In this paper, the ECA module is embedded into a commonly used network for MI, and comparative experiments are conducted on the BCI Competition IV dataset 2a. The proposed method achieved an average accuracy of 75.76% with all 22 channels and 69.52% with eight channels in a four-class classification task, outperforming other state-of-the-art EEG channel selection methods. The result demonstrates that the proposed method provides an effective channel selection approach for EEG-based MI-BCI.

Proper channel selection is essential to exploit the benefits of multi-channel systems by distributing conflicting transmissions across non-interfering channels. Critical to channel selection is the channel quality metric. We propose a busy time ratio (BTR) metric that captures channel contention and user traffic load under a variety of network dynamics. We also propose a distributed collaborative sensing scheme to reduce sensing overhead and energy consumptions. The proposed algorithms can be implemented using conventional 802.11 hardware with single radio interface. The proposed metric can be integrated with routing and channel selection. Experimental results show that the proposed scheme significantly outperforms the existing channel selection methods.

Background Due to the redundant information contained in multichannel electroencephalogram (EEG) signals, the classification accuracy of brain-computer interface (BCI) systems may deteriorate to a large extent. Channel selection methods can help to remove task-independent electroencephalogram (EEG) signals and hence improve the performance of BCI systems. However, in different frequency bands, brain areas associated with motor imagery are not exactly the same, which will result in the inability of traditional channel selection methods to extract effective EEG features. New Method To address the above problem, this paper proposes a novel method based on common spatial pattern- (CSP-) rank channel selection for multifrequency band EEG (CSP-R-MF). It combines the multiband signal decomposition filtering and the CSP-rank channel selection methods to select significant channels, and then linear discriminant analysis (LDA) was used to calculate the classification accuracy. Results The results showed that our proposed CSP-R-MF method could significantly improve the average classification accuracy compared with the CSP-rank channel selection method.

Ubiquitous power Internet of Things (UPIoT) will revolutionize every aspect of the energy sector due to its powerful sensing capability and ubiquitous connectivity. However, the tension between the exponentially increasing number of devices and the limited spectrum resources poses a new challenge on channel selection. In this article, we propose a context-aware learning-based channel selection framework, which can learn the optimal long-term strategy without prior knowledge of global state information. Specifically, we propose a service reliability aware, energy aware, and data backlog aware (SEB) upper confidence bound (UCB)-based channel selection algorithm named SEB-UCB to address the non-adversarial channel selection problem, and propose an SEB exponential weight algorithm for exploration and exploitation (EXP3)-based channel selection algorithm named SEB-EXP3 to address the adversarial channel selection problem. Next, a case study is provided to demonstrate the feasibility of the proposed framework. Finally, we conclude this article and identify several future research issues.

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.

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.

Internet videoconferencing has emerged as a viable medium for communication and entertainment. However, its widespread use is being challenged. This is because videoconference end-users frequently experience perceptual quality impairments such as video frame freezing and voice dropouts due to changes in network conditions on the Internet. These impairments cause extra end-user interaction effortand correspondingly lead to unwanted network bandwidth consumptionthat affects user Quality of Experience (QoE) and Internet congestion. Hence, it is important to measure and subsequently minimize the extra end-user interaction effort in a videoconferencing system. In this paper, we describe a novel active measurement scheme that considers end-user interaction effort and the corresponding network bandwidth consumption to provide videoconferencing interaction QoE measurements. The scheme involves a Multi-Activity Packet-Trains (MAPTs) methodology to dynamically emulate a videoconference session's participant interaction patterns and corresponding video activity levels that are affected by transient changes in network conditions. Also, we describe the implementation and validation of the Vperftool we have developed to measure the videoconferencing interaction QoE on a network path using our proposed scheme.

Time series (TS) analysis is an active application area for Artificial Intelligence (AI) methods, where the objective is to analyze numeric quantities indexed by time for tasks like classification, forecasting, and abnormality detection. In health, TS manifests as biosignals like the electroencephalogram (EEG), where electrical signals from the brain are analyzed. AI and health communities can tremendously benefit each other in TS, with the former offering advanced analytical methods while the latter provides complex data sets and trust-sensitive use cases. But the communities also need to overcome confusing terminologies, hidden assumptions, and a lack of necessary domain contexts for result evaluation and interpretation. In this paper, we attempt to bridge the gap using the problem of channel selection in EEG. We outline challenges in working with EEG data, demonstrate via two experiments how simple explainable AI (XAI) methods can be quite effective for channel selection, irrespective of the EEG tasks/paradigms, and argue that recent TS trends in AI, like LLMs and XAI methods, can benefit health as well. We hope that this work will bring researchers working on TS problems at the intersection of AI and health, closer to work in AI trustworthiness so that they can better leverage results from their respective areas to overcome common challenges. All code and resources are released on GitHub to help others replicate.

This paper presents an automatic selected channel method for improving brain-computer interface (BCI) performance. Finding an effective channel composition in a BCI system is important for parsing the complexity of these systems. In this study, several statistical methods based on an energy extraction method were used to automatically detect the composition of selected channels. We introduce four techniques for automatic channel selection to optimize the energy extraction method, such as mean, high mean, box, and high in box. The performance of all of the proposed techniques was evaluated based on performance accuracy, compression ratio, and channel mapping on a brain scalp. The performance of the proposed BCI framework was compared against a BCI framework that used a manual technique as the energy extraction method. The proposed BCI framework system used a conventional common spatial pattern (CSP) to extract features from two-class motor imagery EEG signals before employing extreme learning machine (ELM) to classify the features of the EEG signal. As a result, the proposed automatic channel selection methods were found effective in finding optimal channels and provided better performance accuracy. In general, the proposed method improved the conventional BCI performance by up to 16% accuracy and 87% channel compression size. Besides that, the automatic technique also yielded better BCI performance accuracy of up to 5% compared to the BCI system that used the manual technique of energy extraction as its EEG channel selection method.

Because of the redundant information contained in the EEG signals, the classification accuracy of motor imagery may be greatly reduced. The channel selection method helps to remove task-independent EEG signals, thereby improving the performance of the BCI system. However, the brain regions associated with motor imagery in different subjects are not exactly same, and the method of channel selection depends on the feedback of classification results. Aiming at the above problems, this paper proposed a new method of channel selection based on backward elimination and threshold. First, we calculated the variance of channels between categories based on the covariance matrix. The backward selection and threshold selection methods were then used to retain the highly differentiated channels. We also used different criterion to evaluate the discriminability between different classes. The selected EEG signals used common spatial pattern (CSP) and support vector machine (SVM) to calculate the classification accuracy. The efficacy of the proposed approach was examined using three data sets. The proposed approach has achieved 77.82% accuracy on the BCI Competition IV data set IIa, 86.02% accuracy on the BCI Competition III data set IIIa, and 86.86% accuracy on the binary class BCI Competition III data set IVa. The performance of the proposed algorithm is compared with other existing algorithms. The results of our experiments demonstrated that the proposed algorithm produces a higher classification accuracy compared to the other algorithms using lesser number of channels.

The electroencephalogram (EEG) signal is the key signal carrier of the brain-computer interface (BCI) system. The EEG data collected by the whole-brain electrode arrangement is conducive to obtaining higher information representation. Personalized electrode layout, while ensuring the accuracy of EEG signal decoding, can also shorten the calibration time of BCI and has become an important research direction. This paper reviews the EEG signal channel selection methods in recent years, conducts a comparative analysis of the combined effects of different channel selection methods and different classification algorithms, obtains the commonly used channel combinations in motor imagery, P300 and other paradigms in BCI, and explains the application scenarios of the channel selection method in different paradigms are discussed, in order to provide stronger support for a more accurate and portable BCI system.

In this paper, we see the objectives of spectrum allocation as evidence information, and propose a novel channel selection method based on Dempster-Shafer (DS) theory. This channel selection method is used in potential game model, and we finally obtain the dynamic spectrum allocation engine based on DS theory and game theory. The simulation results satisfy the requirements of spectrum allocation, which indicate that the method we use is suitable for solving channel selection problems.