Channel Access Research Articles

Veto powers and access capabilities in the design of preferential trade agreements

Studies of domestic political actors’ influence on the design of preferential trade agreements (PTAs) have focused on veto players as obstacles to policy change, assuming their roles as access points or channels for economic interests. Access point and veto player theories predict that multiple access points lead to a protectionist bias and that numerous veto players have a constraining influence over PTAs. This study argues that after the veto player and access point theories are combined, the effect of variation in the number of veto players and access points on the institutional features of PTAs depends on the types of exporters (endowment-based or intra-industry exporters) with whom said political actors interact. Quantitative tests show that as the value of intra-industry exports rises, veto players and access points have a less constraining effect on the scope and autonomy of PTAs. This study employed a novel dataset to approximate the values of intra-industry and endowment-based exports of 256 Standard International Trade Classification (SITC) products manufactured by 500 trading partners. The findings highlight the importance of considering the broader influence of political actors and economic interests on PTA design.

Health Information Transfer in Rural India: A Study from Kangra Valley of Western Himalayas

ABSTRACT A comprehensive and informed approach to managing personal health and making sound medical decisions relies heavily on accurate, current health information. This vital knowledge is attainable through a diverse array of sources. The provision and accessibility of health information assume paramount importance in enhancing healthcare quality and outcomes. Within the Indian context, where 70% of the population resides in rural areas, a pressing demand for health information persists, compounded by formidable obstacles to accessing healthcare services. This study seeks to illuminate the impediments and sources obstructing the flow and retrieval of health information within rural domains. Emphasis is placed on investigating the information-seeking behaviors, health-related needs, challenges, and comprehension of both rural inhabitants and healthcare institutions. Furthermore, the research delves into the interplay between traditional practices and conventional medical approaches, scrutinizing the role of folk medicine alongside established medical modalities. The study specifically focuses on the Kangra Valley in Himachal Pradesh, chosen as a representative locale to decipher the structure, dynamics, and hurdles intrinsic to these systems. To assess the channels of information dissemination and access, a dual-pronged survey strategy was employed. The initial survey targeted health information providers and traditional healers, while the subsequent survey encompassed a randomized sampling of rural communities. Findings underscore the profound significance of both traditional healers and modern healthcare establishments in facilitating the distribution of high-caliber medical insights. Revelations highlight a substantial knowledge gap among rural residents concerning health matters, exacerbated by a pronounced disparity between population coverage and actual visits to healthcare centers. Moreover, the study underscores the potential of libraries to function as information hubs, mirroring health organizations in enhancing the diffusion of health information. By aligning with healthcare entities, libraries emerge as potent agents of bolstered public awareness, informed decision-making, and the empowerment of individuals in assuming control over their health and well-being.

False Protection of Real-Time Traffic with Quieting in Heterogeneous Wi-Fi 7 Networks: An Experimental Study

To provide limited delays for remote sensing and control, gaming, and virtual reality applications, the Wi-Fi 7 standard introduces the Restricted Target Wake Time (R-TWT) mechanism, which reserves time intervals for particular stations with such real-time traffic. As legacy stations do not support R-TWT, the access point forbids channel access during these intervals for legacy stations. Quiet Intervals have been announced for this purpose. Since the support for the Quieting Framework can be configured as mandatory in some networks, Quiet Intervals are assumed to be valid protection for R-TWT. The paper describes experimental results with mass-market devices that disprove this assumption. The paper reveals significant inconsistencies between the standard and widely used devices, e.g., the inability to schedule multiple Quiet Intervals. It will be a significant problem for Wi-Fi 7 devices using R-TWT in heterogeneous networks with legacy devices and will require much effort from academia and industry to solve.

Data Freshness Optimization Under CAA in the UAV-Aided MECN: A Potential Game Perspective

As a promising enabler for edge intelligence, Unmanned Aerial Vehicles (UAV) have become more and more important in Mobile Edge Computing Networks (MECN), such as communication, computation, collection and control service supply. Although Age of Information (AoI) minimization is indispensable for fresh information collection and computation in the UAV-aided MECN, some attackers can launch attacks to deteriorate the availability of precious channel resources, such as revealed channel access attacks (CAAs). Moreover, recent research has not considered the system's active probability and security issues concurrently, e.g., CAA, in the average AoI minimization process. In this paper, to deal with this problem, we consider an AoI-oriented channel access problem under CAA with a game theory viewpoint. Firstly, to obtain a MECN-based AoI indicator under CAA, the system model with active probability consideration is established. Next, the channel access-based AoI minimization problem is formulated from the viewpoint of the Ordinary Potential Game (OPG). Furthermore, two algorithms called AACSD and DCASD are proposed to determine channel access strategies, by which the Nash Equilibrium (NE) solution of the OPG could be reached. Finally, experiments are conducted under homogeneous and heterogeneous parameter settings, and the simulation results evaluate the correctness and effectiveness of our proposals.

Smart Optimization Solution for Channel Access Attack Defense Under UAV-Aided Heterogeneous Network

6G-based wireless communication system is poised to redefine the next-generation network landscape by enabling novel services and applications such as intelligent link establishment, power control, data collection, transmission, and distribution. However, security issues, particularly recently revealed Channel Access Attack (CAA), present significant challenges to performance optimization tasks in the heterogeneous wireless networks of 6G, namely, Age of Information (AoI) Oriented Network (AoN), Throughput Oriented Network (ToN), and Latency Oriented Network (LoN). To address these challenges, this paper presents a game theory-based smart optimization solution to enable Unmanned Aerial Vehicles (UAV) to resist CAA within a 6G-based heterogeneous network. Our methodology begins by outlining the advantages and challenges associated with UAV usage, followed by the design of performance indicators and intelligent resource allocation schemes under the influence of CAA. Subsequently, we introduce definitions and categories within game theory, encompassing the concept and equilibrium of three typical game models. The efficacy of our proposed framework is validated through simulation results, which demonstrate the achievement of optimal AoI, enhanced throughput, and reduced latency compared with baseline methodologies when countering CAA in a UAV-assisted heterogeneous network.

Towards V2I Age-Aware Fairness Access: A DQN Based Intelligent Vehicular Node Training and Test Method

Vehicles on the road exchange data with base station (BS) frequently through vehicle to infrastructure (V2I) communications to ensure the normal use of vehicular applications, where the IEEE 802.11 distributed coordination function (DCF) is employed to allocate a minimum contention window (MCW) for channel access. Each vehicle may change its MCW to achieve more access opportunities at the expense of others, which results in unfair communication performance. Moreover, the key access parameters MCW is the privacy information and each vehicle are not willing to share it with other vehicles. In this uncertain setting, age of information (AoI) is an important communication metric to measure the freshness of data, we design an intelligent vehicular node to learn the dynamic environment and predict the optimal MCW which can make it achieve age fairness. In order to allocate the optimal MCW for the vehicular node, we employ a learning algorithm to make a desirable decision by learning from replay history data. In particular, the algorithm is proposed by extending the traditional DQN training and testing method. Finally, by comparing with other methods, it is proved that the proposed DQN method can significantly improve the age fairness of the intelligent node.

FASURA: A Scheme for Quasi-Static Fading Unsourced Random Access Channels

FASURA: A Scheme for Quasi-Static Fading Unsourced Random Access Channels

Do the Channels of Access to Finance Differ with Regard to the Income Level of Countries?

Bu araştırma, ülkelerin gelişmişlik durumu ile finansmana erişim kanalları arasındaki ilişkiye odaklanmaktadır. Bu kapsamda, bireylerin finansmana erişim aracı olarak kullandıkları kanalları gösteren ülke ölçeğindeki Dünya Bankası (DB) verisi kullanılmıştır. Bu kanallar, banka gibi finansal kurumlar ile aile, akraba, arkadaş ve tasarruf kulübü şeklindedir. Çalışmada, bankalar formel; aile, akraba, arkadaş ve tasarruf kulüpleri ise enformel kurum olarak tanımlanmıştır. Söz konusu veriler 2011, 2014 ve 2017 yıllarını kapsamaktadır. Her bir yıla ilişkin ülke verisi zaman serisi şeklinde düzenlenmiştir. Ülkelerin gelişmişlik düzeyi ise Dünya Bankası’nın verilerine göre dört kategori şeklinde sınıflandırılmıştır. Ülkelerin gelir sınıflaması ile söz konusu mekanizmaları kullanma oranları arasındaki ilişkiyi ve farklılaşmayı anlamak için sırasıyla korelasyon ve ANOVA testi yapılmıştır. Bulgularımız, ülkelerin gelir düzeyi arttıkça formel kurumları, gelir düzeyi azaldıkça ise enformel kurumları kullanma oranının arttığını ortaya koymakta ve gelir düzeyine göre bunun istatistiksel olarak anlamlı bir biçimde farklılaştığını göstermektedir.

Correction for Pardhe et al., "H2O2-Driven Hydroxylation of Steroids Catalyzed by Cytochrome P450 CYP105D18: Exploration of the Substrate Access Channel".

Correction for Pardhe et al., "H2O2-Driven Hydroxylation of Steroids Catalyzed by Cytochrome P450 CYP105D18: Exploration of the Substrate Access Channel".

Understanding Cannabis-Related Information Needs: An Analysis of Inquiries to the National Cancer Institute's Cancer Information Service.

Purpose: Given increased cannabis use for medical and nonmedical purposes alike, there is growing public interest related to the potential risks and benefits of cannabis use, particularly related to cancer. The purpose of this descriptive study was to analyze cannabis inquiries to the National Cancer Institute's Cancer Information Service (CIS). Materials and Methods: From September 2018 to June 2023, 190,070 noncannabis and 425 cannabis inquiries were documented by the CIS. Cannabis inquiries were delineated into two categories: nonmedical cannabis (NMC, n=240) or medical cannabis (MC, n=185). Chi-square tests were performed to determine differences between noncannabis and cannabis inquiries and descriptive analyses were used to identify patterns within cannabis-specific inquiries. Results: Statistically significant differences between noncannabis and cannabis inquiries were observed. In addition, there were variations in MC and NMC inquiries. For example, 73% of MC inquiries originated from cancer survivors and caregivers, whereas almost half of NMC inquiries (48%) were from individuals identifying as tobacco users. MC and NMC inquiries also differed by CIS access channel (e.g., instant chat, telephone), language used (English vs. Spanish), discussions of cancer continuum phases and cancer sites, and referrals provided to individuals for additional information and resources. Conclusion: Cannabis-related information needs of the public-as documented by the CIS-varied by several factors. Health information sources such as the CIS can help address cannabis-related questions and concerns, while noting differences in who is inquiring, how, and why.

Accuracy–Security Tradeoff With Balanced Aggregation and Artificial Noise for Wireless Federated Learning

In federated learning (FL), a number of devices train their local models and upload the corresponding parameters or gradients to the base station (BS) for global model updates. However, the eavesdropper can recover data from parameters or gradients, resulting in data leakage. To defend against eavesdropping attacks, in this paper, we propose an algorithm that divides the transmit power proportionally between the transmitted signal and artificial noise (AN) to counteract the eavesdropper for wireless FL. In this algorithm, due to the limited communication resources, the ratio of signal power to total power and the aggregation frequency need to be carefully chosen, to guarantee the model accuracy and security at the same time. In order to achieve this goal, we maximize the secrecy rate with the system/user power and model performance constraints. To make this problem tractable, we derive two bounds of the secrecy rate and loss function, which allows us to obtain closed-form expressions for the power of AN and the aggregation frequency. Furthermore, in order to make our analysis more realistic, we consider the FL model with channel fading and additive white Gaussian noise (AWGN) over uplink and downlink respectively. Specifically, we discuss the convergence of FL over noisy multiple access channels (MACs). Simulation results confirm the convergence and the effectiveness of the proposed algorithm.

The role of SIC on the design of next generation multiple access

The interplay of physical layer enhancements and classic random access protocols is the objective of this paper. Successive interference cancellation (SIC) is among the major enhancements of the physical layer. Considering the classic representatives of random access protocols, Slotted ALOHA and Channel Sensing Multiple Access (CSMA), we show that two regimes can be identified as a function of the communication link spectral efficiency. In case of high levels of spectral efficiency, multi-packet reception enabled by SIC is of limited benefit. Sum-rate performance is dominated by the effectiveness of the Medium Access Control (MAC) protocol. On the contrary, for low spectral efficiency levels, sum-rate performance is essentially dependent on physical layer SIC capability, while the MAC protocol has a marginal impact. Limitations due to transmission power dynamic range are shown to induce unfairness among nodes. However, the unfairness issue fades away when the system is driven to work around the sum-rate peak achieved for low spectral efficiency. This can also be confirmed by looking at Age of Information (AoI) metric. The major finding of this work is that SIC can boost performance, while still maintaining a fair sharing of the communication channel among nodes. In this regime, the MAC protocol appears to play a marginal role, while multi-packet reception endowed by SIC is prominent to provide high sum-rate, low energy consumption, and low AoI.

RETRACTED ARTICLE: Enhancing throughput using channel access priorities in frequency hopping network using federated learning

The data are sent by the nodes taking part in frequency hopping communications (FHC) utilising carrier frequencies and time slots that are pseudo-randomly assigned. Because of this, a high degree of protection against eavesdropping and anti-interference capabilities is provided. When using FHC in an environment, sharing time and frequency resources, avoiding collisions, and differentiating services are all made more complex as a result of this. A protocol for FHC that is based on dispersed wireless networks is presented by the authors of this research. It is a mechanism for multiple access control, which is prioritised and distributed. The ratio of empty channels metric can be found in the previous sentence. It is possible to provide priority in channel access by assigning different preset ratios of empty channel thresholds to the various traffic classes. Frames from frequency spread segments that have a partial collision are included as well. An analytical model is simulated for the analysis in terms of collision probability, transmission probability, and frame service time in order to carry out a theoretical examination of the performance of FHC. The objective of this inquiry is to determine how well FHC works. The analytical model has been proven correct by the exhaustive simulations as well as the theoretical findings. Cloud platforms are often used in the instruction of the most cutting-edge machine learning techniques of today, such as deep neural networks. This is done in order to take advantage of the cloud's capacity to scale elastically. In order to satisfy the criteria of these sorts of applications, federated learning, has been proposed as a distributed machine learning solution. This is done in order to fulfil the requirements of these kinds of applications. In federated learning (FL), even though everyone who uses the system works together to train a model, nobody ever shares their data with anybody else. Each user trains a local model with their own data, and then communicates the updated models with a FL server so that the data can be aggregated and a global model can be constructed. This process ensures that each user's model is unique. This process is repeated until a global model has been developed. This kind of training not only reduces the amount of network overhead that is necessary to transfer data to a centralised server, but it also safeguards the personal information of the users. Within the framework of this work, we looked at the feasibility of using the FL technique of learning on the many devices that are part of the dispersed network. On a centralised server, we conduct an analysis of the performance of the FL model by comparing its accuracy and the amount of time it takes to train using a range of various parameter value combinations. Additionally, the accuracy of these federated models may be made to reach a level that is comparable to that of the accuracy of central models.

Learning-Based Autoencoder for Multiple Access and Interference Channels in Space Optical Communications

Learning-Based Autoencoder for Multiple Access and Interference Channels in Space Optical Communications

A Hierarchical Deep Learning Approach for Optimizing CCA Threshold and Transmit Power in Wi-Fi Networks

The nodes, e.g., access points and clients, in current WiFi networks rely on carrier sense multiple access (CSMA) for channel access. This means they rely on a clear channel assessment (CCA) threshold to determine when it is safe to transmit a packet. In practice, this threshold is fixed, and nodes do not adapt their CCA threshold given varying channel conditions. To this end, this paper proposes and studies the use of deep reinforcement learning (DRL) to optimize the CCA threshold and transmit power of an AP. It allows an AP to learn the optimal policy for setting its CCA threshold and transmit power given its queue length and interference level. We outline a semi-Markov decision process (Semi-MDP) for the problem at hand and solve it using a decentralized deep hierarchical reinforcement learning (HRL) approach. We then evaluate its performance using traffic generated via a Poisson distribution and also from a trace file. The results show that when compared to two competing solutions, an AP with HRL reduces its average queue length by 24.4% and 52.4%, respectively.

Age of Information Optimization in Multi-Channel Based Multi-Hop Wireless Networks

The proliferation of IoT devices, with various capabilities in sensing, monitoring, and controlling, has prompted diverse emerging applications, highly relying on effective delivery of sensitive information gathered at edge devices to remote controllers for timely responses. To effectively deliver such information/status updates, this paper undertakes a holistic study of AoI in multi-hop networks by considering the relevant and realistic factors, aiming for optimizing information freshness by rapidly shipping sensitive updates captured at a source to its destination. In particular, we consider the multi-channel with OFDM (orthogonal frequency-division multiplexing) spectrum access in multi-hop networks and develop a rigorous mathematical model to optimize AoI at destination nodes. Real-world factors, including orthogonal channel access, wireless interference, and queuing model, are taken into account for the very first time to explore their impacts on the AoI. To this end, we propose two effective algorithms where the first one approximates the optimal solution as closely as we desire while the second one has polynomial time complexity, with a guaranteed performance gap to the optimal solution. The developed model and algorithms enable in-depth studies on AoI optimization problems in OFDM-based multi-hop wireless networks. Numerical results demonstrate that our solutions enjoy better AoI performance and that AoI is affected markedly by those realistic factors taken into our consideration.

Analysis of IEEE 802.11p-Based Intra-Platoon Message Broadcast Delay in a Platoon of Vehicles

For platooning applications, vehicles in a platoon require timely and reliable delivery of road-safety information in order to maintain the stable operation of the platoon. Intra-platoon message broadcast delay (InPB-delay) is a critical metric that is used to measure the message delivery performance in a platoon of vehicles, where a V2V multi-hop broadcast mode is used for message delivery. In this paper, we study the modeling and analyses of the InPB-delay for a platoon of connected and automated vehicles (CAVs), which employs an IEEE 802.11p EDCA mechanism for channel access. A 2-D Markov chain model is first built to describe a backoff procedure of a platoon broadcasting node, which takes into account the four access categories of different priorities in the EDCA mechanism. Based on the Markov model, a theoretical performance model is further derived to investigate the impacts of major system parameters on the InPB-delay, taking into account the effects of both platoon vehicles and non-platoon vehicles. The derived performance model is validated through simulation results and the impacts of major system parameters are investigated through numerical results. The InPB-delay model can be used to provide reference values for system parameter settings in order to ensure the stable operation of a platoon.

OTOP: Optimized Transmission Power Controlled OBSS PD-Based Spatial Reuse for High Throughput in IEEE 802.11be WLANs

As wireless local area network (WLAN) devices become more prevalent in various environments, the IEEE 802.11ax standard (Wi-Fi 6), released in 2021, aims to improve the spectral efficiency. The overlapping basic service set (OBSS) packet detection (PD) based spatial reuse (SR) scheme is a main technology for this purpose which can increase channel access opportunities by adjusting parameters. However, the standard only provides boundary values for parameter adjustment and does not provide details on how to optimize the performance, which can lead to a performance degradation. The IEEE 802.11be standard (Wi-Fi 7), established to satisfy extremely high throughput (EHT) demands of next-generation applications, includes the OBSS PD based SR scheme and the coordinated spatial reuse (CSR) scheme to maximize area throughput in dense networks. The CSR scheme optimizes the transmission performance through coordination between access points (APs), which results in additional signaling overhead and protocol complexity. In this paper, an enhanced SR scheme named optimized transmission power based OBSS PD (OTOP) SR is proposed to achieve high throughput in dense networks. The OTOP scheme enhances the parameter control operation of OBSS PD based SR to take advantage of its distributed nature which keeps the signaling overhead and protocol complexity low. The OTOP scheme derives the optimal transmission power that can maximize the transmission success probability of WLAN devices based on stochastic geometry analysis. The simulation results show that the proposed OTOP scheme performs better than the existing SRGM and OBSS PD based SR schemes in terms of throughput, frame error rate, and fairness.

On Flow Control and Optimized Back-Off in Non-Saturated CSMA

Medium Access Control (MAC) main functions encompass contention for channel access, packet scheduling, error control, and data integrity. Channel contention is a collective function involving all stations in the network, while data integrity pertains to data flows of each individual station. We propose a design where contention related functions are separated from other data management functions. The hinge connecting contention and other data management functions is a flow control algorithm, aiming at guaranteeing stability of contention queues and load on the MAC channel. With reference to Carrier-Sense Multiple Access (CSMA), we define an analytical model of contention queues under non saturated traffic. An asymptotic analysis of the model for large number of stations yields a closed form of the optimal flow control rate. The insight gained from the model is used to design an adaptive flow control algorithm that guarantees throughput optimality for all values of the number of stations.

LTE Device Radio Frequency Fingerprints Blind Extraction Based on Temporal-Frequency Domain PRACH Signals

A frequency-domain cepstrum correlation and temporal transient filtered radio frequency fingerprints (RFF) blind extraction method is proposed for long term evolution uplink device authentication in this work. With the merits of the constant-modulus nature of the Zadoff-Chu (ZC) sequence and the cyclic prefix (CP) structure of the physical random access channel (PRACH) frame, the device RFF integrated in both the frequency-domain ZC sequence and the transient ramp-on/ramp-off sequences are extracted. For the frequency-domain RFF extraction, the cepstrum of the ZC sequence is calculated to enlarge the gap between the constant RFF and the random fading effects, and the correlation between two cepstrum fragments is then calculated to mitigate the random fading components. For the temporal RFF extraction, the ramp-on and ramp-off features induced by the device-specific power amplifier are extracted by exploiting the CP structure of the PRACH frame, where the CP part and the duplicate part are adaptively filtered and the final adapted weight of the least mean square filter is used as RFF. No prior PRACH parameter knowledge is required in RFF extraction and both the frequency-domain and the temporal RFF are combined as the input of the convolutional neural network to enhance the classification accuracy. With four-months sampling of PRACH signals emitted from universal software radio peripheral devices, the experimental results show that the authentication accuracy of the proposed combined RFF can reach as high as 99.9% in the line-of-sight (LOS) scenario and 99.6% in the non-LOS scenario.