Channel Resources Research Articles

An Adaptive Dynamic Channel Allocation Algorithm Based on a Temporal–Spatial Correlation Analysis for LEO Satellite Networks

Low Earth orbit (LEO) satellites that can be used as computing nodes are an important part of future communication networks. However, growing user demands, scarce channel resources and unstable satellite–ground links result in the challenge to design an efficient channel allocation algorithm for the LEO satellite network. Edge computing (EC) provides sufficient computing power for LEO satellite networks and makes the application of reinforcement learning possible. In this paper, an adaptive dynamic channel allocation algorithm based on a temporal–spatial correlation analysis for LEO satellite networks is proposed. First, according to the user mobility model, the temporal–spatial correlation of handoff calls is analyzed. Second, the dynamic channel allocation process in the LEO satellite network is formally described as a Markov decision process. Third, according to the temporal–spatial correlation, a policy for different call events is designed and online reinforcement learning is used to solve the channel allocation problem. Finally, the simulation results under different traffic distributions and different traffic intensities show that the proposed algorithm can greatly reduce the rejection probability of the handoff call and then improve the total performance of the LEO satellite network.

A Collision Reduction Adaptive Data Rate Algorithm Based on the FSVM for a Low-Cost LoRa Gateway

LoRa (Long Range), a wireless communication technology for low power wide area networks (LPWANs), enables a wide range of IoT applications and inter-device communication, due to its openness and flexible network deployment. In the actual deployment and operation of LoRa networks, the static link transmission scheme does not make full use of the channel resources in the time-varying channel environment, resulting in a poor network performance. In this paper, we propose a more effective adaptive data rate (ADR) algorithm for low-cost gateways, we firstly analyze the impact of the different hardware parameters (RSSI, SNR) on the link quality and classify the link quality using the fuzzy support vector machine (FSVM). Secondly, we establish an end device (ED) throughput model and energy consumption model and design different adaptive rate algorithms, according to the different link quality considering both the link-level performance and the MAC layer performance. The proposed algorithm uses machine learning to classify the link quality, which can accurately classify the link quality using a small amount of data, compared to other adaptive rate algorithms, and the link parameter adaptation algorithm can maximize the throughput while ensuring the link stability, by considering the link-level performance and the MAC layer performance, compared to other algorithms. The results show that it outperforms the standard LoRaWAN ADR algorithm in both the single ED and the multi ED scenarios, in terms of the packets reception rate (PRR) and the network throughput. Compared to the LoRaWAN ADR in 32 multi-ED scenarios, the proposed algorithm improves the throughput by 34.12% and packets the reception rate by 26%, significantly improving the network throughput and the packets reception rate.

Optimal sensor scheduling for state estimation under limited channel resources

In this paper, we investigate the estimation-oriented sensor scheduling problem over industrial cyber-physical systems. Different from the existing works, we consider a more innovative and practical scenario where channel resources are limited. A novel transmission scheduling framework for sensors monitoring multiple sub-processes is proposed, where the most important sensory data will transmitted with higher priority and reliability. For two-system case, we first present an explicit optimal scheduling strategy under the off-line scenario. In order to further improve the performance, we propose an on-line scheduling strategy based on the arrival information feedback. Moreover, we have theoretically proved the feasibility and superiority of our proposed on-line schedule to the optimal off-line one. Besides, in order to reduce the computation complexity, we present sub-optimal algorithms for more complicated multi-system scenario, and derive out the conditions under which the optimal or sub-optimal schedules can be designed explicitly and separately, which conspicuously reduces the computation complexity. Moreover, the theoretical gap between our proposed sub-optimal schedules and optimal schedule can be determined by solving two relaxed problems. Simulations are conducted to demonstrate and verify the correctness and advantages of proposed schedules.

CeRA-eSP: Code-Expanded Random Access to Enhance Success Probability of Massive MTC.

With the growing interest in the Internet of Things (IoT), research on massive machine-type communication (mMTC) services is being actively promoted. Because mMTC services are required to serve a large number of devices simultaneously, a lack of resources during initial access can be a significant problem when providing mMTC services in cellular networks. Various studies on efficient preamble transmission have been conducted to solve the random access problem of mMTC services. However, supporting a large number of devices simultaneously with limited resources is a challenging problem. In this study, we investigate code-expanded random access (CeRA), which extends the limited preamble resources to the code domain to decrease the high collision rate. To solve the existing CeRA phantom codeword and physical uplink shared channel (PUSCH) resource shortage problems, we propose an optimal preamble codeword set selection algorithm based on mathematical analysis. The simulation results indicate that the proposed code-expanded random access scheme to enhance success probability (CeRA-eSP) achieves a higher random access success rate with a lower access delay compared to the existing random access schemes.

Reliability Optimization for Channel Resource Allocation in Multihop Wireless Network: A Multigranularity Deep Reinforcement Learning Approach

This article investigates the high-reliable data transmission for multihop and multichannel wireless sensor networks (WSNs), which jointly optimizes the channel allocation and channel access mechanisms. We propose a novel wireless paradigm empowered by mobile-edge computing (MEC) and deep reinforcement learning (DRL) to improve the data process ability of WSNs and formulate the joint resource allocation problem for reliability maximization as a partially observable Markov decision process (POMDP). Meanwhile, we introduce the distributed decision-making (DDM) framework to decouple channel optimization into two subproblems: 1) channel allocation and 2) channel access. Correspondingly, we present an asynchronous channel allocation algorithm for multiagent scenario and enable the neighbor cooperation to tackle the nonstationary problem, which can significantly improve the network convergence speed. Besides, we present a collision-free channel access algorithm including three submodules that can simultaneously eliminate vanishing nodes, hidden terminal, and exposed terminal problems in large-scale WSNs. Simulation results demonstrate that the proposed algorithm significantly improves network performance in terms of convergence, throughput, collision, and packet delivery ratio (PDR).

Smartphone-Based Social Distance Detection Technology with Near-Ultrasonic Signal.

With the emergence of COVID-19, social distancing detection is a crucial technique for epidemic prevention and control. However, the current mainstream detection technology cannot obtain accurate social distance in real-time. To address this problem, this paper presents a first study on smartphone-based social distance detection technology based on near-ultrasonic signals. Firstly, according to auditory characteristics of the human ear and smartphone frequency response characteristics, a group of 18 kHz–23 kHz inaudible Chirp signals accompanied with single frequency signals are designed to complete ranging and ID identification in a short time. Secondly, an improved mutual ranging algorithm is proposed by combining the cubic spline interpolation and a two-stage search to obtain robust mutual ranging performance against multipath and NLoS affect. Thirdly, a hybrid channel access protocol is proposed consisting of Chirp BOK, FDMA, and CSMA/CA to increase the number of concurrencies and reduce the probability of collision. The results show that in our ranging algorithm, 95% of the mutual ranging error within 5 m is less than 10 cm and gets the best performance compared to the other traditional methods in both LoS and NLoS. The protocol can efficiently utilize the limited near-ultrasonic channel resources and achieve a high refresh rate ranging under the premise of reducing the collision probability. Our study can realize high-precision, high-refresh-rate social distance detection on smartphones and has significant application value during an epidemic.

Investigation of the competitive nature of eMBB and mMTC 5G services in conditions of limited communication resource.

The main contribution of the investigation is the Markov model of the process of resource allocation management between subscribers of eMBB and mMTC services within the 5G cluster. The proposed model, considers the organization of the channel resource in the format of resource blocks. The presented model allows to estimate the average duration of IoT sessions, the average number of active multimedia/IoT sessions, the average number of channel resource units occupied by multimedia/IoT traffic, the average number of resource blocks occupied by multimedia/IoT traffic. The metrics are generalized by three management schemes of the investigated process: balanced, competitive and perspective. The first and third schemes enable static/dynamic distribution of channel resources into reserved and common segments for subscribers of eMBB and mMTC services. The proposed model is illustrated with an example showing how to assess the availability and efficiency of channel resource use of the 5G cluster of the cyber-physical system of the Situation Center of the Department of Information Technology of Vinnytsia City Council (Vinnytsia, Ukraine). The article also shows how to use the proposed model to select the 5G network parameters to keep the probabilities of rejection of multimedia and IoT requests below a set threshold.

A Robust Power Allocation Algorithm for Cognitive Radio Networks Based on Hybrid PSO.

The use of a cognitive radio power allocation algorithm is an effective method to improve spectral utilization. However, there are three problems with traditional cognitive radio power allocation algorithms: (1) based on the ideal channel model analysis, channel fluctuation is not considered; (2) they do not consider fairness among cognitive users; and (3) some algorithms are complex and locating the optimal power allocation scheme is not an easy task. For the above problems, this study establishes a robust model which adds the cognitive user transmission rate variance constraint to solve the maximum channel capacity time power allocation scheme by considering the worst-case channel transmission model, and finally solves this complex non-convex optimization problem by using the hybrid particle swarm algorithm. Simulation results show that the algorithm has good robustness, improves the fairness among the cognitive users, makes full use of the channel resources under the constraints, and has a simple algorithm, fast convergence, and good optimization results.

Practical multiport event-triggered control for ASV via the intervened LVS guidance

A crucial and challenging issue in path following control of the autonomous surface vehicle (ASV) is input constraint and communication burden. In this paper, an intervened logic virtual ship (ILVS) guidance is developed to generate the reference signal, and further to tackle the input saturation constraint by employing the dynamic feedback evaluation rule. The main superiority of this design is to intervene the logic virtual ship (LVS) guidance principle to avoid the persistent large stabilization error. Associated with ILVS guidance framework, a robust adaptive neural event-triggered algorithm is proposed for the ASV by fusing the adaptive technique and the multiport event-triggered approach. The constrains of the model nonlinear term and the uncertain actuators gain are released by constructing online learning parameters. Furthermore, the channel resource from the sensor to the controller (SC) as well as the controller to the actuator (CA) is reduced for merits of the multiport event-triggered approach. The feedback error signals and the control inputs will be updated while the triggered condition is occurred. That also can eliminate the low frequency disturbance, caused by marine environment, to improve the robustness of the closed-loop system. Through the Lyapunov stability criterion, all state variables in the triggered point and triggered interval are achieved semi-global uniform ultimate bounded (SGUUB) stability. Finally, the control performance of the proposed method is evaluated on simulation platform, showing the satisfied tracking accuracy with the low transmission load.

Resource Allocation for Underlay Interfering D2D Networks With Multiantenna and Imperfect CSI

Underlay device-to-device (D2D) communications improve the spectral efficiency by simultaneously allowing direct communication between D2D-users on the same channels as cellular-users (CUs). However, most related works consider perfect channel-state-information (CSI) with single-antenna transmissions and usually assign each channel to one D2D pair. In this work, we formulate an optimization problem for maximizing the aggregate rate of all D2D pairs and CUs in single and multiple antenna configurations under imperfect CSI, by optimizing channel and power resources. Our formulation guarantees probability of outage below a specified threshold and fairness in channel allocation across D2D pairs. The resulting problem is a stochastic-mixed-integer-non-convex problem, we solve it approximately by alternating between power-allocation and channel-assignment sub-problems. The stochastic objective and outage constraints are addressed by the concept of order-of-statistics in the single-antenna case and the Bernstein-type inequality in the multiple-antenna configuration. The power-allocation sub-problem is solved by exploiting a quadratic-transformation, while the channel-assignment sub-problem is solved by integer relaxation. Furthermore, two computationally efficient algorithms are proposed to approximately solve the problem in a partially decentralized manner. We also establish convergence guarantees for the different algorithms proposed in this work. Simulation results show that the proposed approach achieves higher throughput compared to the state-of-the-art alternatives.

User Selection for Federated Learning in a Wireless Environment: A Process to Minimize the Negative Effect of Training Data Correlation and Improve Performance

This article describes our study about federated learning by using the front view-based training process in autonomous driving as an example. The overlap of camera views among users creates training data correlation. It is shown that this correlation undermines the optimization performance of the federated learning process. To reduce the negative effect of correlation within training data while limiting wireless channel resource utilization, a federated learning protocol with user selection is proposed. The protocol selects a limited number of users with sufficient spatial separation in each round of federated learning. An exclusion zone is applied to maintain separation during user selection. Experimental results show that in an example deployment scenario with a user density of 0.05, applying a discrete exclusion zone (DEZ) to prevent selecting the first three nearest users and applying a geographical exclusion zone (DEZ) to avoid selecting users within 70 m have equivalent effects on reducing training data. Furthermore, both methods can provide the performance of five-user federated learning approaches, an ideal case without correlation in the training data.

Hybrid NOMA-FDMA Assisted Dual Computation Offloading: A Latency Minimization Approach

Edge computing has been considered as a promising solution for enabling computation-intensive yet latency-sensitive applications at resource-constrained wireless devices (WDs). In this paper, exploiting the advanced small-cell dual connectivity (DC), we investigate a paradigm of dual computation offloading in which a WD can simultaneously offload partial workloads to a cloudlet-server co-located at the macro base station (MBS) and an edge-server (ES) co-located at a small-cell based station (SBS). To facilitate the multi-user dual computation offloading, we exploit a hybrid model of non-orthogonal multiple access (NOMA) and frequency division multiple access (FDMA). Specifically, due to the SBSs’ limited channel resources, we consider that the WDs form different NOMA-groups for offloading their respective workloads to different SBSs, which improves the spectrum efficiency. Meanwhile, all WDs use FDMA for offloading their workloads to the MBS, which avoids the WDs’ co-channel interference. We formulate a joint optimization of the WDs’ partial offloading decisions, their FDMA transmission to the MBS, different NOMA-groups’ transmission to the SBSs, as well as the computing-rate allocation of the ESs and the cloudlet-server, with the objective of minimizing the overall latency for completing all WDs’ workloads. Despite the strict non-convexity of the joint optimization problem, we propose a layered yet cell-based distributed algorithm for obtaining the optimal dual offloading solution. Based on the optimal dual offloading solution, we further investigate how to properly group WDs into different NOMA-groups for offloading workloads to the corresponding SBSs, and propose a cross-entropy based learning algorithm for finding the optimal NOMA grouping scheme. Numerical results are finally provided to validate the effectiveness and efficiency of our proposed algorithms.

Hypergraph-Based Resource Allocation for Ultra-Dense Wireless Network in Industrial IoT

As next generation wireless communication and smart manufacturing evolve, Industrial Internet of Things (IIoT) has emerged. Due to the advantage in coverage and improved capacity of cellular networks, femtocells are densely deployed in macrocell of cellular networks contributing to Ultra-dense Wireless Network (UDWN). Nevertheless, it is faced with the challenge of how to reduce interference to improve spectrum efficiency and system performance for Non-orthogonal Multiple Access (NOMA) in IIoT. To this end, we identify and formulate the channel resource allocation issue as a sum-rate maximization problem under the guarantee of Quality of Service (QoS) and fairness. To solve the problem, we first construct a hypergraph by considering interference relationships. Thereafter, we convert the original problem into a maximum weighted clique set problem in hypergraph theory, based on which a heuristic resource allocation algorithm is proposed. Moreover, we analyse the property of the proposed algorithm and its complexity. Finally, simulation results show that the proposed method outperforms graph-based algorithm and other hypergraph-based algorithm in improving the system throughput.

Preparing Wi-Fi 7 for Healthcare Internet-of-Things.

The healthcare Internet of Things (H-IoT) is an interconnection of devices capable of sensing and transmitting information that conveys the status of an individual’s health. The continuous monitoring of an individual’s health for disease diagnosis and early detection is an important application of H-IoT. Ambient assisted living (AAL) entails monitoring a patient’s health to ensure their well-being. However, ensuring a limit on transmission delays is an essential requirement of such monitoring systems. The uplink (UL) transmission during the orthogonal frequency division multiple access (OFDMA) in the wireless local area networks (WLANs) can incur a delay which may not be acceptable for delay-sensitive applications such as H-IoT due to their random nature. Therefore, we propose a UL OFDMA scheduler for the next Wireless Fidelity (Wi-Fi) standard, the IEEE 802.11be, that is compliant with the latency requirements for healthcare applications. The scheduler allocates the channel resources for UL transmission taking into consideration the traffic class or access category. The results demonstrate that the proposed scheduler can achieve the required latency for H-IoT applications. Additionally, the performance in terms of fairness and throughput is also superior to state-of-the-art schedulers.

Strategy and Business Model Reformulation for Development of Zakat, Infaq, Alms, and Waqf (ZISWAF) (Case Study Bank XYZ, Pekanbaru)

Indonesia has enormous potential for zakat and waqf, but Bank XYZ has not maximized the collection through the sharia business unit (UUS). Therefore, it is necessary to reformulate the strategy and business model of developing ZISWAF Bank XYZ in order to obtain optimal results. This study used a qualitative descriptive method with a case study at Bank XYZ. The research was conducted from February 2021 to October 2021. The response survey was determined based on purposive sampling consisting of internal practitioners, academics, BWI, BAZNAS, and ziswaf institutions affiliated with Islamic banks. The data collection technique was based on observation, in-depth interviews, and questionnaires to respondents. Data analysis used IPA, BMC, SWOT, and QSPM approaches. Based on IPA analysis, this study recommends that three selected elements be developed: value proposition, channel, and critical resource. Technology adjustment and strengthening of bank image as ZISWAF recipients, improving bank channels through optimizing office networks and utilizing social media marketing and customer databases, as well as improving SOPs to improve the business model of Bank XYZ in the future.

Research on the Sustainability of Channel Strategy Selection on the Overall Efficiency of Listed Retail Enterprises: Evidence from China

Appropriate channel strategies can help to improve the operational efficiency of retail firms and stimulate the domestic demand market. To study the sustainability of retail enterprises in the COVID-19 era, this paper uses the data of 29 Chinese listed retail enterprises from 2012 to 2021 as a sample, and uses data envelopment analysis (DEA) to evaluate and analyze the efficiency of the listed retail enterprises that implement different channel strategies. The results of the study show that: (1) The implementation of channel strategy will re-integrate the information flow, capital flow, commercial flow and logistics of enterprises, and affect the efficiency of enterprises through four paths: market demand, channel resources, channel technology and channel system. (2) The implementation of dual-channel strategy enterprises, in the short term, has greater technical efficiency than single-channel strategy enterprises. (3) The channel integration strategy of online ordering and store delivery has a significant positive impact on the overall efficiency of listed retail enterprises. (4) The inventory turnover rate has a significant positive impact on the overall efficiency of the listed retail enterprises. The level of accounts receivable, the level of retail technology, the level of investment in fixed assets and the size of enterprises have a negative impact on the overall efficiency of the listed retail enterprises.

MuSher: An Agile Multipath-TCP Scheduler for Dual-Band 802.11ad/ac Wireless LANs

Future WLAN devices will combine both IEEE 802.11ad and 802.11ac interfaces. The former provides multi-Gbps rates but is susceptible to blockage, whereas the latter is slower but offers reliable connectivity. A fundamental challenge is thus how to combine those complementary technologies, to make the most of the advantages they offer. In this work, we leverage Multipath TCP (MPTCP) to use both interfaces simultaneously in order to achieve a higher overall throughput as well as seamlessly switch to a single interface when the other one fails. We find that standard MPTCP often performs sub-optimally and can yield a throughput much lower than that of single path TCP over the faster of the two interfaces. We analyze the cause of these performance issues in detail and then design <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MuSher</i> , an agile MPTCP scheduler that allows MPTCP to fully utilize the channel resources available to both interfaces. Our evaluation in realistic scenarios shows that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">MuSher</i> can provide a throughput improvement of 50%/130% under WLAN/Internet settings respectively, compared to the default MPTCP scheduler. It further speeds up the recovery of a traffic stream after disruption by a factor of 8x/75x.

Deep reinforcement learning based flexible preamble allocation for RAN slicing in 5G networks

One of the most difficult challenges in radio access network slicing occurs in the connection establishment phase where multiple devices use a common random access channel in order to gain access to the network. It is now very well known that random access channel congestion is a serious issue in case of sporadic arrival of machine-to-machine nodes and may result in a significant delay for all nodes. Hence, random access channel resources are also needed to be allocated to different services to enable random access network slicing. In the random access channel procedure, the nodes transmit a selected preamble from a predefined set of preambles. If multiple nodes transmit the same preamble at the same random access channel opportunity, a collision occurs at the eNodeB. To isolate the one service class from others during this phase, one approach is to allocate different preamble subsets to different service classes. This research proposes an adaptive preamble subset allocation method using deep reinforcement learning. The proposed method can distribute preambles to different service classes according to their priority providing virtual isolation for service classes. The results indicate that the proposed mechanism can quickly adapt the preamble allocation according to the changing traffic demands of service classes.

Racial, Ethnic, and Socioeconomic Disparities Drive Appointment No-Show in Patients with Chronic Eye Disease.

Visit no-shows (NS) reduce clinic efficiency and effective resource allocation. Inadequate follow-up among patients with chronic eye disease increases risk of disease progression. Our study identifies demographic, medical, and socioeconomic characteristics that increase odds of NS among patients with chronic eye conditions at high risk of vision-threatening complications. This is a retrospective case-control study of data abstracted over a 5-year period (January 2013-December 2018) in an urban academic ophthalmology practice. Follow-up appointments of patients ≥ 18years of age with a diagnosis of glaucoma, diabetic retinopathy, or age-related macular degeneration were included. Age, sex, race, ethnicity, language preference, zip code, and relevant medical history were recorded. A multivariate mixed logistic regression model was utilized to determine any association between demographic factors and visit NS. A total of 106,652 visits for 4,598 unique patients were included in this study. Of these, 13,240 (12.4%) visits were NS. Patient characteristics that increased the odds of NS included Hispanic ethnicity (p < 0.0001), Black race (p < 0.0001), and a history of mental illness (p < 0.0001). Socioeconomic factors that increased the odds of NS included median household income < $40,000 (p = 0.002), Medicare insurance (p < 0.0001), and Medicaid insurance (p < 0.0001). Our results highlight the influence of ethnic, racial, medical, and socioeconomic characteristics on appointment NS among patients with chronic eye disease. Future interventions aimed at reducing appointment NS could channel resources to the at-risk populations identified in this analysis to improve access to care for those who need it most.

An On-Demand Channel Bonding Algorithm Based on Outage Probability for Large-Scale Industrial Internet of Things

In Industrial Internet of Things (IIoT), a large number of wireless nodes communicate through limited channel resources. Using IEEE802.11ac/ah with multiple users multiple-input–multiple-output (MU-MIMO) and channel bonding technology in IIoT, bonding multiple channels for transmission links can improve data transmission quality. How to reasonably bond limited channel resources on demand for data transmissions in IIoT has become a key issue to improve network performance and ensure communication quality of the nodes. In this article, the definition of outage probability is extended from the amount of information to the signal noise ratio (SNR), which changes the outage probability from a statistical quantity to a quantity that can be directly calculated. This article further analyzes various factors affecting the outage probability, and derives the direct calculation formula of the outage probability in single-hop and multihop data transmission, which allows the outage probability to be directly calculated by some simple parameters. Based the outage probability, this article proposes a dynamic channel bonding algorithm based on outage probability (DCB-OP), which can bond multiple channels for the data transmissions with a high outage probability to improve the success rate of data transmissions. The experimental results show that in IEEE 802.11ac/ah networks, the DCB-OP algorithm can improve the utilization of channel resources and increase the throughput by about 40% compared with no channel bonding. Compared with the general channel bonding algorithm, DCB-OP can make the network throughput higher.