Channel Resources Research Articles

Enabling WLAN and WPAN Coexistence via Cross-Technology Communication.

In the limited frequency spectrum shared by various wireless communication technologies, cross-technology interference is an important factor which determines communication performance. A variety of coexistence methods to reduce the impact of this interference have been studied, but most of them cannot explicitly coordinate the shared spectrum and are not practical. This paper presents an explicit coexistence mechanism using cross-technology communication among heterogeneous wireless technologies. This mimics “carrier sense multiple access with collision avoidance” (CSMA/CA) via bidirectional cross-technology communication, which is called CTC-CSMA/CA. It allows communication between heterogeneous wireless technologies in order to achieve CSMA/CA. This accurately assigns required channel resources by directly sending and receiving feedback. CTC-CSMA/CA is a highly compatible technology because it does not require any modification to the IEEE 802.11 standard or any extra hardware. In addition, Zigbee can operate with a low duty cycle by synchronizing it to a periodic Wi-Fi beacon. We implemented CTC-CSMA/CA using a commodity Wi-Fi access point and a commercial Zigbee platform. Our experiments showed that the channels are coordinated more accurately by our method, which significantly improves Zigbee throughput, than by conventional schemes. We expect the proposed scheme to be an important application case in designing future cross-technology communication.

The Application of Reprogramming Technology in a Wireless Sensor Network

Wireless sensor networks can collect physical world information in a large, long-term and real-time manner in a complex environment. Wireless sensor networks have been successfully applied in many fields. With the development of wireless sensor network, wireless sensor network applications need to update the executable code on the sensor due to the change of application requirements. The researchers call this a wireless sensor network application reprogramming, or simply reprogramming. The efficiency of reprogramming is closely related to the size of the updated code that needs to be distributed and the amount of traffic to be distributed. This paper firstly expands the classical logic language based on the characteristics of wireless sensor network, and implements the language by compiling/interpretation, which can generate the smaller update code. Secondly, the paper also makes full use of the location information of the sensor and the data distribution protocol of low message complexity of telecommunication and channel resources. The research results of this paper can effectively reduce the communication volume of the distribution process.

Algorithm for dynamic channel reservation of aggregated data flow

In modern telecommunications systems, dynamic reservation of channel resources for an aggregated data stream is one of the most important tasks, as it allows for efficient use of available resources and provides high-quality service to subscribers. Developing an algorithm for dynamic reservation is a challenging task, as it requires ensuring optimal resource allocation among subscribers during variable network load. This article discusses an approach to developing an algorithm for dynamic reservation of channel resources for an aggregated data stream that provides efficient resource allocation and ensures high-quality service to subscribers. Specifically, approaches to dynamic resource allocation based on network load and approximation and optimization methods that reduce algorithm complexity and ensure its effective operation will be considered. The results of this work can be useful for telecommunications system developers and for research in the field of telecommunications. The article proposes an algorithm for dynamic reservation of channel resources for an aggregated data stream. The implementation of this algorithm is performed on a VPN gateway designed to manage data streams, filter them, and organize priority access to data streams in the VPN tunnel. In the event of a network load, the developed algorithm should first evaluate the network status and, based on the results obtained, perform data stream access control functions, taking into account their priorities. Based on the developed algorithm for dynamic reservation of channel resources for an aggregated real-time data stream, this section establishes a system of criteria for efficient utilization of channel resources for protected corporate multiservice communication networks under conditions of network resource availability for servicing the proposed workload from the access network and network congestion. The approach to prioritizing data streams takes into account user categories and service classes, which, unlike existing tools, allows for the task of prioritized servicing under load conditions and determines the best use of the available channel resources at the transport layer of protected corporate multiservice communication networks.

Massively Parallel, Nonlinear Processing for 6G: Potential Gains and Further Research Challenges

In order to support the plethora of the new, pending, transformative use-cases, 6G systems will need to be capable of providing not only high-throughput but also ultra-massive device connectivity. In this direction, 6G systems will need to fully exploit the available spatial, frequency and time resources of the wireless channel. Moving from linear to nonlinear processing has the potential to substantially improve the capabilities of such 6G systems. However, the processing complexity and latency requirements of traditional nonlinear processing become impractical when a large number of concurrently transmitted information streams is targeted. This work discusses how by efficiently massively parallelizing nonlinear processing, we can enable practical 6G systems with substantially improved capabilities compared to current systems. Examples of such gains are demonstrated and further research challenges are discussed towards unlocking the full potential of future massively parallel, nonlinear processing. In addition, how massively parallel, nonlinear processing can redefine the way we design future physical layer architectures is also examined.

Efficient Reinforcement Learning-Based Transmission Control for Mitigating Channel Congestion in 5G V2X Sidelink

Channel congestion has been an open challenge for vehicular networks due to the limited resource of communication channels. Explosion of channel access requests from a massive number of transmitter vehicles can exhaust bandwidth and then degrade transmission quality. The rapid drop of messages (because of the high bit error rate in the transmission congestion condition) can threaten the safety of connected vehicles. Maintaining congestion-free communications is then essential to improve the reliability for vehicular networks, including Cellular-V2X (C-V2X)-based cooperative intelligent transport systems and road-safety applications. In this work, we present a novel intelligent transmission control model, namely DEEPCUT, to automatically adjust the message broadcasting rate of a transmitter vehicle. DEEPCUT works based on a Double Deep Q-learning Networks with Prioritized Experience Relay framework. DEEPCUT encourages the transmitter vehicle to (1) reduce its broadcasting rate if the vehicle is maintaining a safe distance from its neighbors and (2) increase the rate if the vehicle is approaching the others at a high-risk distance, all done by using reward/punish strategies. The evaluation results show that DEEPCUT can cut up 16% redundant data while increasing 22% packet reception rate compared with baseline models, particularly in crowd vehicular communications. Our risk-based transmission control can be an excellent complement to address the congestion when the channel cannot satisfy every vehicle’s resource requests. At best, the risk assessment-based approach in our congestion control method can provide a novel material to enhance Decentralized Congestion Control (DCC) for 5G V2X sidelink in the coming specifications.

On the Cost of Achieving Downlink Ultra-Reliable Low-Latency Communications in 5G Networks

We determine the cost of serving Ultra-Reliable Low-Latency Communications (URLLC) traffic (1 ms one-way latency, 99.999% reliability) in 5G New Radio (NR) Macro cellular networks. The cost is measured as the degradation of the enhanced Mobile Broadband (eMBB) downlink system capacity when serving a certain offered load of URLLC traffic on the same radio carrier. A methodology for assessing the cost of URLLC is presented, which takes into account all the aspects related to configuring a suitable resource allocation and link adaptation strategy, as well as the associated control channel overhead due to the use of mini-slots with very frequent control channel resources for monitoring downlink data assignments. When taking a holistic view on the performance, advanced system-level simulation results show that 1 Mbps of URLLC traffic results in an eMBB throughput reduction of up to 60 Mbps, i.e. URLLC traffic can be up to 60 times more costly than traditional best-effort. The presented results can be used by cellular service providers, such as operators, for understanding and dimensioning the tradeoffs and impact towards traditional network services when adding URLLC services to their portfolio.

Time-Slot Reservation and Channel Switching Using Markovian Model for Multichannel TDMA MAC in VANETs

Efficient communication in the intelligent transport system requires the vehicles to transmit safety and non-safety messages timely without any loss. In multi-channel MAC, SCH channels have different properties in terms of the frequency range, frequency limit, power limit, distance covered, and channel parameters. Based on these channel properties, messages must be transmitted using the most appropriate channel. Existing multi-channel MAC approaches randomly choose the available SCH channel to transmit the messages. Random selection of the channel leads to overuse of the channel resources or lack of the resources due to inappropriate channel selection. The proposed approach provides a way to reserve the most appropriate SCH leading to optimized resource utilization. For collision-free allocation of resources to a large number of contending vehicles, we have proposed a variable size TDMA approach for SCH channel allocation. The paper also focuses on reducing the transmission collision and re-usability of the time slot for timely message transmission. Markovian modeling determines the probability of successfully reserving the time slot and switching to the required SCH. Simulation results prove that the probability of time slot reservation in the required SCH is approximately 0.6 for the proposed approach and approximately 0.2 for other approaches.

A Proportional Scheduling Protocol for the OFDMA-Based Future Wi-Fi Network

The IEEE 802.11ax (Wi-Fi 6) and IEEE 802.11be (Wi-Fi 7) adopt the OFDMA technology to provide high-speed and uninterrupted communications in the dense network. Until the advent of IEEE 802.11ax standard, Wireless LAN (WLAN) predominantly uses the Random Access (RA) mechanism to access the network. IEEE 802.11ax and IEEE 802.11be (proposed) provide another access mechanism for WLAN (i.e., Wi-Fi), which is known as Scheduled Access (SA). This mechanism utilizes the OFDMA technology to provide highspeed and smooth communications in congested areas. By the way, the performance of the OFDMA based wireless LAN largely depends on the scheduling protocol. Many researchers propose RA and SA protocols independently, which do not consider the simultaneous implementation of both mechanisms. This paper proposes a Proportional Resource Scheduling (PRS) scheme for the OFDMA-based wireless LAN that simultaneously implements RA and SA mechanisms for data transmission. We design two algorithms for the resource scheduling for the PRS protocol. Algorithm 1 provides the initial scheduling information, which is received by Algorithm 2 as the input. After performing revision, Algorithm 2 provides the final scheduling information to the access point. The PRS distributes the channel resources proportionally to the stations according to their available loads. Thus, it utilizes the resources efficiently and increases the throughput and fairness in accessing the channel. We construct analytical models both for the SA and RA mechanisms and conduct rigorous simulations to measure the efficiency of the PRS protocol. The analyses validate the robustness of the proposed protocol in throughput, goodput, fairness, and retransmissions. The main contribution of the proposed protocol is that it provides a framework for simultaneous implementation of RA and SA mechanisms for the future wireless LAN.

A Unified Approach to Semantic Information and Communication Based on Probabilistic Logic

Traditionally, studies on technical communication (TC) are based on stochastic modeling and manipulation. This is not sufficient for semantic communication (SC) where semantic elements are logically connected, rather than stochastically correlated. To fill this void, by leveraging a logical programming language called probabilistic logic (ProbLog), we propose a unified approach to semantic information and communication through the interplay between TC and SC. Building on the well-established existing TC layer, we introduce, in this paper, a SC layer that utilizes knowledge bases of communicating parties for the exchange of semantic information. These knowledge bases are logically described, manipulated, and exploited using ProbLog. To allow efficient interactions between SC and TC layers, various measures are proposed in this paper using the entropy of a clause in a knowledge base. These measurements can account for various technical problems in SC, such as message selection to improve the receiver’s knowledge base. Extending this, we present few selected examples of how the SC and TC layers interact with each other, s while taking into account constraints of physical channels and efficiently utilizing channel resources.

Multi-Agent Deep Reinforcement Learning for Channel Resource Allocation in Vehicular Networks

Multi-Agent Deep Reinforcement Learning for Channel Resource Allocation in Vehicular Networks

ACE: A Routing Algorithm Based on Autonomous Channel Scheduling for Bluetooth Mesh Network

The Internet of Things (IoT) interconnects massive cyber-physical devices (CPD) to provide various applications, such as smart home and smart building. Bluetooth Mesh is an emerging networking technology, which can be used to organize a massive network with Bluetooth Low Energy (BLE) devices. Managed-flooding protocol is used in Bluetooth Mesh to route the data packets. Although it is a highly desirable option when data transmission is urgent, it is inefficient in a larger and denser mesh network due to the collisions of broadcast data packets. In this paper, we introduce ACE: a Routing Algorithm based on Autonomous Channel Scheduling for Bluetooth Mesh Network. ACE relies on the existing Bluetooth Mesh messages to distribute routes without additional traffic overhead and conducts a beacon-aware routing update adaptively as the topology evolves. In ACE, BLE channel resources can be efficiently utilized by a channel scheduling scheme for each node locally and autonomously without any neighborly negotiation. We implement ACE on the nRF52840 from Nordic Semiconductor and evaluate its effectiveness on our testbed. Compared to the Bluetooth Mesh, our experiments proved that ACE could reduce the end-to-end latency by 16%, alleviate packets collisions issues, and increase the packet delivery ratio (PDR) by 30% under heavy traffic. Moreover, simulation results verified that ACE has better scalability when the size and density of networks become larger and denser.

The Orbital Channel Resource Control on Non-Markov Priority Systems of Mass Service Basis. Part 2. Dispatch Method

The article develops a methodology for scheduling an orbital channel resource with operational priority assignment based on the calculation of the parameters of a non-Markov service model with a limited residence time of a request in the system, which differs from known methods in that it takes into account the priority of various network subscribers, taking into account the high dynamics of their movement and the average waiting time in the service system with priority in the allocation of the orbital channel resource of the satellite command-relay system. The analysis of the influence of service quality indicators of information data flows is carried out.It is shown that the developed method of priority scheduling makes it possible to meet the requirements for the quality of service. A complex of experimental studies was carried out, which showed the possibility of ensuring the required level of stability with the deterioration in difficult conditions of radio electronic equipment, which made it possible to formulate practical proposals for improving the complex of coordination and distribution of resources of in the satellite command relay system.

Introducing The Lancet Global Health Commission on financing primary health care: putting people at the centre

Introducing The Lancet Global Health Commission on financing primary health care: putting people at the centre

5G Ultra-Reliable and Low Latency Communication Resource Scheduling for Power Business Quality Assurance

The resource scheduling method of 5G URLLC (Ultra-Reliable and Low Latency Communication) is studied in this paper to assure the Quality of Service (QoS) of various power business, which utilizes the limited spectrum and power in low-band cellular communication system to meet the requirement of power terminal about transmission rate, scheduling delay and fairness in an efficient manner. Firstly, based on the high reliability and low latency characteristics of URLLC, a multi-cell downlink system model is built. Then, a resource allocation problem model for maximizing downlink throughput is proposed and solved step by step. Power allocation algorithm bases on pricing mechanism and non-cooperative game and an Delay-based Proportional Fair algorithm (DPF）is designed to schedule channel resource dynamicly..Simulation results show that the proposed resource scheduling method can reduce the scheduling delay of power terminals under the constraints of transmission reliability and fairness while meeting the different QoS requirements. The proposed method outperforms some known algorithms.

ACCsiNet: Asymmetric Convolution-Based Autoencoder Framework for Massive MIMO CSI Feedback

Channel state information (CSI) is a critical part for massive multiple-input multiple-output (MIMO) system. However, it is a big challenge to send a large amount of CSI from the receiver to the transmitter with limited channel resources. In this letter, we propose asymmetric convolution-based autoencoder framework (ACCsiNet) to handle the CSI compression and decompression problem. Specifically, asymmetric convolution block (AC-Block) is used to enhance the feature extraction ability of convolution. Further, a lightweight method is applied, which can greatly reduce the storage space at the receiver. Considering the practical deployment, multi-model fusion schemes including multi-rate and multi-scenario fusion are also discussed to strengthen the generalization ability of the network. Experimental results show that the proposed ACCsiNet can improve the NMSE and cosine similarity <inline-formula> <tex-math notation="LaTeX">$\rho $ </tex-math></inline-formula> performance, especially for outdoor scenario. The results also verify that both the lightweight and multi-model fusion schemes can reach a near-optimal performance of the proposed ACCsiNet, but further significantly reduce the parameter amount by more than 83&#x0025; and 90&#x0025;, respectively.

An Analysis on Contemporary MAC Layer Protocols in Vehicular Networks: State-of-the-Art and Future Directions

Traffic density around the globe is increasing on a day-to-day basis, resulting in more accidents, congestion, and pollution. The dynamic vehicular environment induces challenges in designing an efficient and reliable protocol for communication. Timely delivery of safety and non-safety messages is necessary for traffic congestion control and for avoiding road mishaps. For efficient resource sharing and optimized channel utilization, the media access control (MAC) protocol plays a vital role. An efficient MAC protocol design can provide fair channel access and can delay constraint safety message dissemination, improving road safety. This paper reviews the applications, characteristics, and challenges faced in the design of MAC protocols. A classification of the MAC protocol is presented based on contention mechanisms and channel access. The classification based on contention is oriented as contention-based, contention-free, and hybrid, whereas the classification based on channel access is categorized as distributed, centralized, cluster-based, cooperative, token-based, and random access. These are further sub-classified as single-channel and multi-channel, based on the type of channel resources they utilize. This paper gives an analysis of the objectives, mechanisms, advantages/disadvantages, and simulators used in specified protocols. Finally, the paper concludes with a discussion on the future scope and open challenges for improving the MAC protocol design.

TARS: A Novel Mechanism for Truly Autonomous Resource Selection in LTE-V2V Mode 4.

Effective communication in vehicular networks depends on the scheduling of wireless channel resources. There are two types of channel resource scheduling in Release 14 of the 3GPP, i.e., (1) controlled by eNodeB and (2) a distributed scheduling carried out by every vehicle, known as Autonomous Resource Selection (ARS). The most suitable resource scheduling for vehicle safety applications is the ARS mechanism. ARS includes (a) counter selection (i.e., specifying the number of subsequent transmissions) and (b) resource reselection (specifying the reuse of the same resource after counter expiry). ARS is a decentralized approach for resource selection. Therefore, resource collisions can occur during the initial selection, where multiple vehicles might select the same resource, hence resulting in packet loss. ARS is not adaptive towards vehicle density and employs a uniform random selection probability approach for counter selection and reselection. As a result, it can prevent some vehicles from transmitting in a congested vehicular network. To this end, the paper presents Truly Autonomous Resource Selection (TARS) for vehicular networks. TARS considers resource allocation as a problem of locally detecting the selected resources at neighbor vehicles to avoid resource collisions. The paper also models the behavior of counter selection and resource block reselection on resource collisions using the Discrete Time Markov Chain (DTMC). Observation of the model is used to propose a fair policy of counter selection and resource reselection in ARS. The simulation of the proposed TARS mechanism showed better performance in terms of resource collision probability and the packet delivery ratio when compared with the LTE Mode 4 standard and with a competing approach proposed by Jianhua He et al.

The FinTech Dividend: How Much Money Is FinTech Likely to Mobilize for Sustainable Development?

AbstractFinTech offers a new way to mobilize resources for all kinds of uses – including for funding sustainable development. Roughly 3%–13% of funding required for the UN’s Sustainable Development Goals (SDGs)– or around $50 billion to $125 billion ‐‐ could come from a ‘FinTech Dividend.’ Such a dividend derives from the use of FinTech platforms to increase savings and investment (overall), channel resources into publicly‐funded as well as privately‐funded SDG‐related activities and policies, and encourage the use of internet platforms, which deliver novel goods and services that relate to the seventeen SDGs. Less than half of UN members have FinTech laws and policies – making FinTech a ripe area for right‐regulating. Unfortunately, in areas like institutional reform – no amount of money can guarantee achieving the SDGs, without wider legal and administrative reforms. And no clear data about the exact policies needed to help grow an economy (or pay for SDG spending) serve as any guide. With total investment in FinTech stuck at around $150 billion to $200 billion – the hoped for deluge of FinTech dollars on SDG activities may remain a trickle for years to come.

Computation Offloading in MIMO Based Mobile Edge Computing Systems Under Perfect and Imperfect CSI Estimation

Intelligent offloading of computation-intensive tasks to a mobile cloud server provides an effective mean to expand the usability of wireless devices and prolong their battery life, especially for low-cost internet-of-things (IoT) devices. However, realization of this technology in multiple-input multiple-output (MIMO) systems requires sophisticated design of joint computation offloading and other network functions such as channel state information (CSI) estimation, beamforming, and resource allocation. In this paper, we study the computation task offloading and resource allocation optimization in MIMO based mobile edge computing systems considering perfect/imperfect-CSI estimation. Our design aims to minimize the maximum weighted energy consumption subject to practical constraints on available computing and radio resources and allowable latency. The optimal and low-complexity algorithms are proposed to solve the underlying mixed integer non-linear problems (MINLP). For the perfect-CSI, we employ bisection search to find the optimal solution. The low-complexity algorithms are developed by decomposing the original optimization problem into the offloading optimization (OP) and power allocation (PA) subproblems and solve them iteratively. Moreover, the difference of convex functions (DC) method is employed to deal with non-convex structure of (PA) subproblems in the imperfect-CSI scenario. Numerical results confirm the advantages of proposed designs over conventional local computation strategies in energy saving and fairness.

Research and Design of Adaptive Networking Method Based on LoRa

An adaptive networking method based on LoRa(Long Range) technology is proposed. The data transmission of wireless sensor networks widely used in the Internet of things is studied. A star network composed of Lora wireless transmission technology is designed to build an adaptive data transmission network. The design process of network topology, hardware and adaptive networking method of adaptive networking are introduced. Aiming at solving the problems of limited node capacity, adjacent frequency interference, and unreasonable channel resource allocation in the LoRa network, an adaptive frequency hopping mechanism is used for networking. The system uses I. MX6ULL as the main control and integrates 8 LoRa modules at the same time, which greatly improves the node capacity of the gateway. The server can realize real-time viewing and monitoring of node equipment, and real-time evaluation of channel communication quality.