Channel Selection Problem Research Articles

Two-stage sparse multi-objective evolutionary algorithm for channel selection optimization in BCIs.

Channel selection has become the pivotal issue affecting the widespread application of non-invasive brain-computer interface systems in the real world. However, constructing suitable multi-objective problem models alongside effective search strategies stands out as a critical factor that impacts the performance of multi-objective channel selection algorithms. This paper presents a two-stage sparse multi-objective evolutionary algorithm (TS-MOEA) to address channel selection problems in brain-computer interface systems. In TS-MOEA, a two-stage framework, which consists of the early and late stages, is adopted to prevent the algorithm from stagnating. Furthermore, The two stages concentrate on different multi-objective problem models, thereby balancing convergence and population diversity in TS-MOEA. Inspired by the sparsity of the correlation matrix of channels, a sparse initialization operator, which uses a domain-knowledge-based score assignment strategy for decision variables, is introduced to generate the initial population. Moreover, a Score-based mutation operator is utilized to enhance the search efficiency of TS-MOEA. The performance of TS-MOEA and five other state-of-the-art multi-objective algorithms has been evaluated using a 62-channel EEG-based brain-computer interface system for fatigue detection tasks, and the results demonstrated the effectiveness of TS-MOEA. The proposed two-stage framework can help TS-MOEA escape stagnation and facilitate a balance between diversity and convergence. Integrating the sparsity of the correlation matrix of channels and the problem-domain knowledge can effectively reduce the computational complexity of TS-MOEA while enhancing its optimization efficiency.

Identifying a selection mechanism of distribution channel for the supply chain: The barriers to the application of web 3.0

The application of Web 3.0 will bring unprecedented changes to the supply chain, where many industries are seeing significant cost reductions. However, due to the requirement of stability for supply chain, sometimes the total cost of supply chain cannot decrease effectively under Web 2.0, which may hinder the application of Web 3.0. Following the application from Web 2.0 to Web 3.0, this paper discusses the selection of distribution channel whose cost is under user stickiness and the degree of digitalization. The system optimum model, which is commonly used in dynamic traffic allocation, is applied to solve the distribution channel selection problem. The results implies that when the degree of digitalization is low, the distribution channel cost first decreases and then increases as user stickiness increases. This trend of channel cost illustrates the price induction on user stickiness initially, and gradually adjust the price to be higher when user stickiness makes channel transformation costly. Customers with unclear user stickiness perceptions and weak channel control can easily be trapped in the transition from Web 2.0 to Web 3.0. Distributors will also be forced to accept high product costs due to the price induction from product distribution channels.

Cross-Subject EEG Channel Selection Method for Lower Limb Brain-Computer Interface

Article Cross-Subject EEG Channel Selection Method for Lower Limb Brain-Computer Interface Mingnan Wei 1,2, Mengjie Huang 3,*, and Jiaying Ni 3 1 School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China 2 Department of Computer Science, University of Liverpool, Liverpool, L69 3BX, United Kingdom 3 Design School, Xi'an Jiaotong-Liverpool University, Suzhou, 215123, China * Correspondence: Mengjie.Huang@xjtlu.edu.cn Received: 27 April 2023 Accepted: 30 June 2023 Published: 26 September 2023 Abstract: Lower limb motor imagery (MI) classification is a challenging research topic in the area of brain-computer interfaces (BCIs), and entails numerous signal channels to provide sufficient information about the background neural activity. However, practical applications often lack the environment to accommodate excessive channels due to the time-consuming setup process, inconvenient movement, and restricted application scenarios. The existing channel selection algorithms (designed for the individual subject) place a great deal of focus on the classified performance comparisons, whereas the significance of actual locations and neural functions of brain regions is disregarded. Although these algorithms require significant computation resources, their selected solutions cannot be re-used for other subjects to realize the cross-subject channel selection and improve the reusability of model due to poor interpretability and inapplicability. To date, there have been no investigations about the cross-subject channel selection problem for the lower limb MI stepping tasks. This study proposes an optimal cross-subject lower limb channel selection that selectively retains significant channels, narrows the computation scope of the selection, and obtains the optimal selection solutions. Through stepping-based MI experiments, the proposed optimal channel selection enables effective recognition in low-channel settings, thereby contributing a lot to the development of generic and convenient lower limb BCI systems. Additionally, statistical analysis reveals a significant difference in energy spectrum between left and right stepping-based MI tasks in the and bands of the frontal lobe channels, providing new evidence that the frontal lobe dramatically affects lower limb MI tasks.

Domain knowledge-assisted multi-objective evolutionary algorithm for channel selection in brain-computer interface systems.

For non-invasive brain-computer interface systems (BCIs) with multiple electroencephalogram (EEG) channels, the key factor limiting their convenient application in the real world is how to perform reasonable channel selection while ensuring task accuracy, which can be modeled as a multi-objective optimization problem. Therefore, this paper proposed a two-objective problem model for the channel selection problem and introduced a domain knowledge-assisted multi-objective optimization algorithm (DK-MOEA) to solve the aforementioned problem. The multi-objective optimization problem model was designed based on the channel connectivity matrix and comprises two objectives: one is the task accuracy and the other one can sensitively indicate the removal status of channels in BCIs. The proposed DK-MOEA adopted a two-space framework, consisting of the population space and the knowledge space. Furthermore, a knowledge-assisted update operator was introduced to enhance the search efficiency of the population space by leveraging the domain knowledge stored in the knowledge space. The proposed two-objective problem model and DK-MOEA were tested on a fatigue detection task and four state-of-the-art multi-objective evolutionary algorithms were used for comparison. The experimental results indicated that the proposed algorithm achieved the best results among all the comparative algorithms for most cases by the Wilcoxon rank sum test at a significance level of 0.05. DK-MOEA was also compared with a version without the utilization of domain knowledge and the experimental results validated the effectiveness of the knowledge-assisted mutation operator. Moreover, the comparison between DK-MOEA and a traditional classification algorithm using all channels demonstrated that DK-MOEA can strike the balance between task accuracy and the number of selected channels. The formulated two-objective optimization model enabled the selection of a minimal number of channels without compromising classification accuracy. The utilization of domain knowledge improved the performance of DK-MOEA. By adopting the proposed two-objective problem model and DK-MOEA, a balance can be achieved between the number of the selected channels and the accuracy of the fatigue detection task. The methods proposed in this paper can reduce the complexity of subsequent data processing and enhance the convenience of practical applications.

The channel selection and coordination considering platform private‐label products

AbstractThis paper studies online channel selection problems related to introducing a platform's private‐label products to the manufacturer's own product. We examine the decision between two prevalent online sales modes: reselling mode and marketplace mode. Our results demonstrate that when the own‐label products are sold in the marketplace mode, both the manufacturer and platform prefer to sell platform's private‐label products in the marketplace mode, but the former prefers a low commission rate and the latter a high rate. However, a “win‐win” zone exists in the marketplace mode for the manufacturer and platform under a moderate commission rate. When the own‐label products are sold in the reselling mode, with the increase in the commission rate, the platform first prefers the reselling mode, then the marketplace mode, and finally the reselling mode. When the own‐label products are sold in the reselling mode, the environmental impact depends on the commission rate and the environmental advantages of the private‐label products. For quantity coordination, when the two products are sold in different modes, the manufacturer and platform can be coordinated under appropriate conditions. For social welfare coordination, even if both products are sold in the reselling mode, the two firms are coordinated under a low carbon emission.

Supplier or co-optor: Optimal channel and logistics selection problems on retail platforms

In the platform economy, many brands have already supplied products to platforms (e.g., Amazon.com and JD.com) who operate “self-run (SR)” stores. They face the challenges of whether or not operating flagship stores on the platforms and how to determine the optimal logistics choice (provided by the platforms or 3PL providers). To address these challenges, we explore the optimal channel and logistics selection strategies for brands operating on platforms through building theoretical models. This paper considers three levels of the logistics service difference between platform logistics and 3PL. It guides brands to make operational strategies of being suppliers or co-optors and choosing different logistics services. Our results imply that the brand's optimal choice of operational strategy between being “a pure supplier” or “a co-optor with the optimal logistics service selection” is affected by the logistics service level difference between platform logistics and 3PL, platform's annual service fee, and logistics fees charged by the platform and 3PL provider. Besides, we have identified the condition which could achieve a triple-win situation for the brand, the platform and consumers when the brand determines the optimal channel strategy or logistics mode strategy, respectively. We extend the models to consider consumer return behavior, spillover effect and endogenous platform logistics fee, and find that when considering consumer return behavior and spillover effect, the main results are robust. However, when considering the endogenous platform logistics fee, the brand is extremely reluctant to become a co-optor to adopt platform logistics for its flagship store.

Hybrid Radio Resource Management for 6G Subnetwork Crowds

In-X subnetworks are expected to be located at the very edge of the 6G 'network of networks' and provide localized wireless connectivity for in-vehicle, in-robot, in-body communication. By nature in-X subnetworks can lead to dense crowds, calling for efficient radio resource management techniques. In this article, we introduce a hybrid radio resource management framework where the decision capabilities can either happen at a global agent operating on an umbrella 6G network, or locally at each subnetwork, depending on the quality of the backhaul link, the crowd density and the local spectrum sensing capabilities. We present a possible application of the proposed framework for the problem of radio channel selection in a dense crowd. Possible research directions leveraging the proposed framework are also described.

Reinforcement Learning for Link Adaptation and Channel Selection in LEO Satellite Cognitive Communications

In this letter, we solve the link adaptation and channel selection problem in next generation satellite cognitive networks under dynamically varying channel availability and time-varying channel statistics. Primary user (PU) activity in Low Earth Orbit (LEO) satellite cognitive communications forces the set of available transmission channels for a secondary user (SU) to vary dynamically over time. We consider the scenario where the channel state varies in a piecewise-stationary mode, referred to as quasi-static (block-fading) channels. We formalize the problem as a reinforcement learning problem, and propose Discounted Structured and Sleeping Thompson Sampling (dSTS), which maximizes the SU’s throughput by selecting the optimum modulation and coding scheme (MCS) and the transmission channel under volatile and piecewise-stationary settings. When channel characteristics are unknown as well as piecewise-stationary, the proposed algorithm adapts the SU’s link-rate by exploiting the structure of the transmission success probability in transmission rates over the selected available channel. Furthermore, channel state information (CSI) is absent and feedback is limited to 1-bit (success/failure).

A DRL-Based Intelligent Jamming Approach for Joint Channel and Power Optimization

The traditional jamming methods mainly focus on the optimization of a single domain, such as frequency domain. However, in the practical wireless communication, the jamming methods in single dimension domain are limited and difficult to cope with multidomain scenarios. To overcome these issues, this paper investigates the problem of joint decision-making for jamming channel and power in a dynamic spectrum environment. Firstly, the Markov decision process (MDP) is used to formulate the jamming channel and power joint selection problem. Then, a deep reinforcement learning- (DRL-) based jamming algorithm is proposed with the function of parallel learning and joint decision-making. Specially, to accelerate the learning speed of the algorithm, the prioritized experience reply (PER) technology is introduced. Finally, a practical jamming testbed is built to evaluate the proposed algorithm. The simulation results and the testbed results demonstrate the effectiveness of the proposed algorithm, which can guarantee the jamming effect and maximize the utilization of jamming resources.

Dynamic selection of clarification channels in rumor propagation containment.

Rumors refer to spontaneously formed false stories. As rumors have shown severe threats to human society, it is significant to curb rumor propagation. Rumor clarification is an effective countermeasure on controlling rumor propagation. In this process, anti-rumor messages can be published through multiple media channels, including but not limited to online social platforms, TV programs and offline face-to-face campaigns. As the efficiency and cost of releasing anti-rumor information can vary from media channel to media channel, provided that the total budget is limited and fixed, it is valuable to investigate how to periodically select a combination of media channels to publish anti-rumor information so as to maximize the efficiency (i.e., make as many individuals as possible know the anti-rumor information) with the lowest cost. We refer to this issue as the dynamic channel selection (DCS) problem and any solution as a DCS strategy. To address the DCS problem, our contributions are as follows. First, we propose a rumor propagation model to characterize the influences of DCS strategies on curbing rumors. On this basis, we establish a trade-off model to evaluate DCS strategies and reduce the DCS problem to a mathematical optimization model called the DCS model. Second, based on the genetic algorithm framework, we develop a numerical method called the DCS algorithm to solve the DCS model. Third, we perform a series of numerical experiments to verify the performance of the DCS algorithm. Results show that the DCS algorithm can efficiently yield a satisfactory DCS strategy.

Machine Learning-Based Two-Stage Task Offloading Optimization for Power Distribution Internet of Things

The increase in the number of services in the power distribution grid leads to a massive increase in task data. Power distribution internet of things (PDIoT) is the specific application of internet of things (IoT) in the power distribution grid. By deploying a large number of PDIoT devices, the voltage, active power, reactive power, and harmonic parameters are collected to support distribution grid services such as fault identification and status detection. Therefore, PDIoT utilizes massive devices to collect and offload tasks to the edge server through 5G network for real-time data processing. However, how to dynamically select edge servers and channels to meet the energy-efficient and low-latency task offloading requirements of PDIoT devices still faces several technical challenges such as task offloading decisions coupling among devices, unobtainable global state information, as well as interrelation of various quality of service (QoS) metrics such as energy efficiency and delay. To this end, we firstly construct a joint optimization problem to maximize the weighted difference between energy efficiency and delay of devices in PDIoT. Then, the joint optimization problem is decomposed into a large-timescale server selection problem and a small-timescale channel selection problem. Next, we propose an ML-based two-stage task offloading algorithm, where the large-timescale problem is solved by two-side matching in the first stage, and the small-timescale problem is solved by adaptive ε -greedy learning in the second stage. Finally, simulation results show that compared with the task offloading delay-first matching algorithm and the matching theory-based task offloading strategy, the proposed algorithm performs superior in terms of energy efficiency and delay.

Two-Timescale Resource Allocation for Automated Networks in IIoT

The rapid technological advances of cellular technologies will revolutionize network automation in industrial internet of things (IIoT). In this paper, we investigate the two-timescale resource allocation problem in IIoT networks with hybrid energy supply, where temporal variations of energy harvesting (EH), electricity price, channel state, and data arrival exhibit different granularity. The formulated problem consists of energy management at a large timescale, as well as rate control, channel selection, and power allocation at a small timescale. To address this challenge, we develop an online solution to guarantee bounded performance deviation with only causal information. Specifically, Lyapunov optimization is leveraged to transform the long-term stochastic optimization problem into a series of short-term deterministic optimization problems. Then, a low-complexity rate control algorithm is developed based on alternating direction method of multipliers (ADMM), which accelerates the convergence speed via the decomposition-coordination approach. Next, the joint channel selection and power allocation problem is transformed into a one-to-many matching problem, and solved by the proposed price-based matching with quota restriction. Finally, the proposed algorithm is verified through simulations under various system configurations.

Interference-Aware Spectrum Resource Management in Dynamic Environment: Strategic Learning With Higher-Order Statistic Optimization

Wireless spectrum environment is complex and uncertain in practical scenarios. For spectrum resource management, there are two crucial challenges to be resolved. First, channel states are always time-varying, thus violent fluctuations of interference links bring difficulties to interference mitigation. Second, the optimization efficiency degrades when jointly considering frequency coordination and power adaption. In this paper, we focus on the interference-aware resource management in a dynamic radio environment. First, we propose a new metric named effective weighted aggregate interference to capture the fluctuations of time-varying interference links. Then, we investigate the joint channel selection and power assignment problem from the perspective of game theoretic, and propose a novel mechanism called action freezing to significantly improve the optimization efficiency. Furthermore, we develop a higher-order statistic optimization based multi-agent strategic learning (HSOMSL) algorithm to alleviate the effect of fluctuating payoffs and obtain stable solutions. Simulation results illustrate that the proposed algorithm has lower experienced interference, higher achievable throughput, and better energy efficiency compared to existing state-of-the-art algorithms. In addition, it is verified to be effective in mobile scenarios.

Internet Traffic based Channel Selection in Multi-Radio Multi-Channel Wireless Mesh Networks

Wireless Mesh Networks(WMNs) are the outstanding technology to facilitate wireless broadband Internet access to users. Routers in WMN have multiple radio interfaces to which multiple orthogonal/partially overlapping channels are assigned to improve the capacity of WMN. This paper is focused on channel selection problem in WMN since proper channel selection to radio interfaces of mesh router increases the performance of WMN. To access the Internet through WMN, the users have to associate with one of the mesh routers. Since most of the Internet Servers are still in wired networks, the major dominant traffic of Internet users is in downlink direction i.e. from the gateway of WMN to user. This paper proposes a new method of channel selection to improve the user performance in downlink direction of Internet traffic. The method is scalable and completely distributed solution to the problem of channel selection in WMN. The simulation results indicate the significant improvement in user performance.

Multi-agent few-shot meta reinforcement learning for trajectory design and channel selection in UAV-assisted networks

Unmanned aerial vehicle (UAV)-assisted communications have been considered as a solution of aerial networking in future wireless networks due to its low-cost, high-mobility, and swift features. This paper considers a UAV-assisted downlink transmission, where UAVs are deployed as aerial base stations to serve ground users. To maximize the average transmission rate among the ground users, this paper formulates a joint optimization problem of UAV trajectory design and channel selection, which is NP-hard and non-convex. To solve the problem, we propose a multi-agent deep Q-network (MADQN) scheme. Specifically, the agents that the UAVs act as perform actions from their observations distributively and share the same reward. To tackle the tasks where the experience is insufficient, we propose a multi-agent meta reinforcement learning algorithm to fast adapt to the new tasks. By pretraining the tasks with similar distribution, the learning model can acquire general knowledge. Simulation results have indicated the MADQN scheme can achieve higher throughput than fixed allocation. Furthermore, our proposed multiagent meta reinforcement learning algorithm learns the new tasks much faster compared with the MADQN scheme.

Game-Based Channel Selection for UAV Services in Mobile Edge Computing

Computation offloading is a hot research topic in mobile edge computing (MEC). Computation offloading among multiedge nodes in heterogeneous networks can help reduce offloading cost. In addition, the unmanned aerial vehicles (UAVs) play a key role in MEC, where UAVs in the air communicate with ground base stations to improve the network performance. However, limited channel resources can lead to the increase of transmission delay and the decline of communication quality. Effective channel selection mechanisms can help address those issues by improving transmission rate and ensuring communication quality. In this paper, we study channel selection during communication between multiple UAVs and base stations in an MEC system with heterogeneous networks. To maximize the transmission rate of each UAV user, we formulate a channel selection problem and model it as a noncooperative game. Then, we prove the existence of Nash equilibrium (NE). In addition, we design a multiple UAV-enabled transmission channel selection (UTCS) algorithm to obtain the equilibrium strategy profile of all the UAV users. Experimental results validate that UTCS algorithm can converge after a finite number of iterations and it outperforms random transmission algorithm (RTA) and sequential transmission algorithm (STA).

EEG Channel Selection Using Multiobjective Cuckoo Search for Person Identification as Protection System in Healthcare Applications.

Recently, the electroencephalogram (EEG) signal presents an excellent potential for a new person identification technique. Several studies defined the EEG with unique features, universality, and natural robustness to be used as a new track to prevent spoofing attacks. The EEG signals are a visual recording of the brain's electrical activities, measured by placing electrodes (channels) in various scalp positions. However, traditional EEG-based systems lead to high complexity with many channels, and some channels have critical information for the identification system while others do not. Several studies have proposed a single objective to address the EEG channel for person identification. Unfortunately, these studies only focused on increasing the accuracy rate without balancing the accuracy and the total number of selected EEG channels. The novelty of this paper is to propose a multiobjective binary version of the cuckoo search algorithm (MOBCS-KNN) to find optimal EEG channel selections for person identification. The proposed method (MOBCS-KNN) used a weighted sum technique to implement a multiobjective approach. In addition, a KNN classifier for EEG-based biometric person identification is used. It is worth mentioning that this is the initial investigation of using a multiobjective technique with EEG channel selection problem. A standard EEG motor imagery dataset is used to evaluate the performance of the MOBCS-KNN. The experiments show that the MOBCS-KNN obtained accuracy of 93.86% using only 24 sensors with AR20 autoregressive coefficients. Another critical point is that the MOBCS-KNN finds channels not too close to each other to capture relevant information from all over the head. In conclusion, the MOBCS-KNN algorithm achieves the best results compared with metaheuristic algorithms. Finally, the recommended approach can draw future directions to be applied to different research areas.

EEG Channel Selection for Person Identification Using Binary Grey Wolf Optimizer

Electroencephalogram signals (EEG) have provided biometric identification systems with great capabilities. Several studies have shown that EEG introduces unique and universal features besides specific strength against spoofing attacks. Essentially, EEG is a graphic recording of the brain’s electrical activity calculated by sensors (electrodes) on the scalp at different spots, but their best locations are uncertain. In this paper, the EEG channel selection problem is formulated as a binary optimization problem, where a binary version of the Grey Wolf Optimizer (BGWO) is used to find an optimal solution for such an NP-hard optimization problem. Further, a Support Vector Machine classifier with a Radial Basis Function kernel (SVM-RBF) is then considered for EEG-based biometric person identification. For feature extraction purposes, we examine three different auto-regressive coefficients. A standard EEG motor imagery dataset is employed to evaluate the proposed method, including four criteria: (i) Accuracy, (ii) F-Score, (iii) Recall, and (v) Specificity. In the experimental results, the proposed method (named BGWO-SVM) obtained 94.13% accuracy using only 23 sensors with 5 auto-regressive coefficients. Besides, BGWO-SVM finds electrodes not too close to each other to capture relevant information all over the head. As concluding remarks, BGWO-SVM achieved the best results concerning the number of selected channels and competitive classification accuracies against other meta-heuristics algorithms.

EEG CHANNEL SELECTION USING DIFFERANTIAL EVOLUTION ALGORITHM AND PARTICLE SWARM OPTIMIZATION FOR CLASSIFICATION OF ODORANT-STIMULATED RECORDS

A significant advancement has been made in the evolutionary computing and swarm intelligence methods in past decades. These methods have been commonly used to calculate well optimized solutions. Methods select the best elements or cases among set of alternatives. In EEG signal processing applications, efficient channel selection algorithms are required to reduce high dimensionality and remove redundant features. To do this, we examined optimal 5 electrodes out of 14 using Particle Swarm Optimization (PSO) and Differential Evolution Algorithm (DEA). The proposed work is related with pleasant-unpleasant EEG odors classification problem. Classification error rates were calculated by Linear Discriminant Analysis (LDA), k-NN (k Nearest Neighbor), Naive Bayes (NB), Regression Tree (RegTree) classifiers and used as fitness function for optimization algorithms. The results showed that PSO with selected 5 channels gave lowest error rates compared with DEA for all runs. RegTree classifier generated optimal fitness function value among other classifiers. PSO algorithm can effectively support channel selection problem to identify the best channels to maximize classification performance.

Joint relay and channel selection against mobile and smart jammer: A deep reinforcement learning approach

This paper investigates the joint relay and channel selection problem using a deep reinforcement learning (DRL) algorithm for cooperative communications in a dynamic jamming environment. The latest types of jammers include the mobile and smart jammer that contains multiple jamming patterns. This new type of jammer poses serious challenges to reliable communications such as huge environment states, tightly coupled joint action selections and real-time decision requirements. To cope with these challenges, a DRL-based relay-assisted cooperative communication scheme is proposed. In this scheme, the joint selection problem is constructed as a Markov decision process (MDP) and a double deep Q network (DDQN) based anti-jamming scheme is proposed to address the unknown and dynamic jamming behaviors. Concretely, a joint decision-making network composed of three sub-networks is designed and the independent learning method of each sub-network is proposed. The simulation results show that the user agent is able to anticipate the jammer behaviors and elude the jamming in advance. Furthermore, compared with the sensing-based algorithm, the Q learning-based algorithm and the existing DRL-based anti-jamming approaches, the proposed algorithm maintains a higher average normalized throughput.