Collision Channel Research Articles

Intelligent Dynamic Spectrum Access Using Deep Reinforcement Learning for VANETs

In vehicular ad hoc networks (VANETs), vehicles can communicate with other vehicles or devices through vehicle-to-X communication. However, with the rise of the Internet of Things, it is a real challenge to make the limited spectrum resource meet the increasing communication demands, i.e., the channel contention problem. To solve this problem, this paper proposes a strategy combining multi-hop forwarding through vehicles and dynamic spectrum access. Firstly, a group-based multi-hop broadcast protocol, G-hop, is proposed. G-hop classifies vehicles with similar characteristics such as moving speed and communication distance into same groups using the depth-first-search algorithm. Messages are forwarded within a group in priority and then across groups, which limits both the range and the number of relay vehicles, i.e., channel contenders. Further, we adopt deep reinforcement learning techniques to achieve dynamic spectrum access. We design a Global Optimization algorithm based on Experience Accumulation (GOEA) using deep reinforcement learning. In GOEA, a network structure combined with recurrent neural network and deep Q-network is proposed for learning the time-varying process, and then a reward method is applied to optimize the global utility. Vehicles that need to transmit messages select channels dynamically following the guidance of GOEA. The experimental results demonstrate that the G-hop protocol reduces the packet loss rate from 0.8 to about 0.1. Meanwhile, compared with Slotted Aloha and DQN, our GOEA algorithm reduces the collision probability and channel idle probability by 60%. Moreover, as the number of vehicles increases, the transmission success rate can be improved by 20%.

Measurements of WH and ZH production in the H rightarrow bbar{b} decay channel in pp collisions at 13,text {Te}text {V} with the ATLAS detector

Measurements of the Standard Model Higgs boson decaying into a bbar{b} pair and produced in association with a W or Z boson decaying into leptons, using proton–proton collision data collected between 2015 and 2018 by the ATLAS detector, are presented. The measurements use collisions produced by the Large Hadron Collider at a centre-of-mass energy of sqrt{s} = 13,text {Te}text {V}, corresponding to an integrated luminosity of 139,mathrm {fb}^{-1}. The production of a Higgs boson in association with a W or Z boson is established with observed (expected) significances of 4.0 (4.1) and 5.3 (5.1) standard deviations, respectively. Cross-sections of associated production of a Higgs boson decaying into bottom quark pairs with an electroweak gauge boson, W or Z, decaying into leptons are measured as a function of the gauge boson transverse momentum in kinematic fiducial volumes. The cross-section measurements are all consistent with the Standard Model expectations, and the total uncertainties vary from 30% in the high gauge boson transverse momentum regions to 85% in the low regions. Limits are subsequently set on the parameters of an effective Lagrangian sensitive to modifications of the WH and ZH processes as well as the Higgs boson decay into bbar{b}.

Energy Harvesting Irregular Repetition ALOHA With Replica Concatenation

In this paper, we consider an asynchronous random access scheme called irregular repetition ALOHA (IRA) as a generalization of contention resolution ALOHA (CRA) with varying repetitions. We present an asymptotic performance analysis of CRA and IRA on the collision channel for regular and irregular repetition rates. We also propose an improvement by merging the clean parts of packet replicas in partial collisions, and extend our analysis to this scenario as well. Specific designs of repetition distributions based on the new analysis show that the optimized solutions of irregular repetition slotted ALOHA (IRSA) perform well in both IRA and the enhanced scheme, and they considerably outperform the regular repetition distributions. We also introduce energy harvesting (EH) to both schemes as a practical and sustainable adaptation, where users are able to harvest energy and store it in their finite-capacity batteries. We model the battery state by a discrete-time Markov chain and derive an optimal transmission policy to maximize the asymptotic performance of the system. We provide comprehensive numerical results for both practical and asymptotic scenarios to verify the validity of the proposed analyses, and illustrate the benefits of the proposed systems.

Collision Observation-based Optimization of Low-power and Lossy IoT Network using Reinforcement Learning

The Internet of Things (IoT) has numerous applications in every domain, e.g., smart cities to provide intelligent services to sustainable cities. The next-generation of IoT networks is expected to be densely deployed in a resource-constrained and lossy environment. The densely deployed nodes producing radically heterogeneous traffic pattern causes congestion and collision in the network. At the medium access control (MAC) layer, mitigating channel collision is still one of the main challenges of future IoT networks. Similarly, the standardized network layer uses a ranking mechanism based on hop-counts and expected transmission counts (ETX), which often does not adapt to the dynamic and lossy environment and impact performance. The ranking mechanism also requires large control overheads to update rank information. The resource-constrained IoT devices operating in a low-power and lossy network (LLN) environment need an efficient solution to handle these problems. Reinforcement learning (RL) algorithms like Q-learning are recently utilized to solve learning problems in LLNs devices like sensors. Thus, in this paper, an RL-based optimization of dense LLN IoT devices with heavy heterogeneous traffic is devised. The proposed protocol learns the collision information from the MAC layer and makes an intelligent decision at the network layer. The proposed protocol also enhances the operation of the trickle timer algorithm. A Q-learning model is employed to adaptively learn the channel collision probability and network layer ranking states with accumulated reward function. Based on a simulation using Contiki 3.0 Cooja, the proposed intelligent scheme achieves a lower packet loss ratio, improves throughput, produces lower control overheads, and consumes less energy than other state-of-the-art mechanisms.

Vehicle-Life Interaction in Fog-Enabled Smart Connected and Autonomous Vehicles

Traffic accidents have become a major issue for researchers, academia, government and vehicle manufacturers over the last few years. Many accidents and emergency situations frequently occur on the road. Unfortunately, accidents lead to health injuries, destruction of some infrastructure, bad traffic flow, and more importantly these events cause deaths of hundreds of thousands of people due to not getting treatment in time. Thus, we need to develop an efficient and smart emergency system to ensure the timely arrival of an ambulance service to the place of the accident in order to provide timely medical help to those injured. In addition, we also need to communicate promptly with other entities such as hospitals so that they can make appropriate arrangements and provide timely medical information to emergency personnel on the scene including alerting those related to the injured person(s). In this paper, we have developed an intelligent protocol that uses connected and autonomous vehicles' scenarios in Intelligent Transportation System (ITS) so that prompt emergency services can be provided to reduce the death rate caused. The proposed protocol smartly connects with all the relevant entitles during the emergency while maintaining a smooth traffic flow for the arrival of the ambulance service. Moreover, our protocol also mitigates the broadcasting of messages circulating over the network for delay sensitive tasks. The evaluation results, based on the performance metrics such as channel collision, average packet delay, packet loss, and routing-overhead demonstrate that our proposed protocol outperforms previously proposed protocols such as Emergency Message Dissemination for Vehicular (EMDV), Contention Based Broadcasting (CBB), and Particle Swarm Optimization Contention-based Broadcast (PCBB) protocols. Finally, we discuss several issues and challenges that need to be addressed in the network in order to achieve more a reliable, efficient, connected, and autonomous vehicular network.

Measurements of top-quark pair single- and double-differential cross-sections in the all-hadronic channel in pp collisions at sqrt{mathrm{s}} = 13 TeV using the ATLAS detector

Differential cross-sections are measured for top-quark pair production in the all-hadronic decay mode, using proton-proton collision events collected by the ATLAS experiment in which all six decay jets are separately resolved. Absolute and normalised single- and double-differential cross-sections are measured at particle and parton level as a function of various kinematic variables. Emphasis is placed on well-measured observables in fully reconstructed final states, as well as on the study of correlations between the top-quark pair system and additional jet radiation identified in the event. The study is performed using data from proton-proton collisions at sqrt{s} = 13 TeV collected by the ATLAS detector at CERN’s Large Hadron Collider in 2015 and 2016, corresponding to an integrated luminosity of 36.1 fb−1. The rapidities of the individual top quarks and of the top-quark pair are well modelled by several independent event generators. Significant mismodelling is observed in the transverse momenta of the leading three jet emissions, while the leading top-quark transverse momentum and top-quark pair transverse momentum are both found to be incompatible with several theoretical predictions.

Performance Analysis of Prioritization and Contention Control Algorithm in Wireless Body Area Networks

Abstract A Wireless Body Area Network (WBAN) is the composition of a group of energy-efficient, miniature, invasive/non-invasive, light-weighted sensors that monitor human body health conditions for early detection and treatment for life-threatening diseases. Due to the stringent demands of WBAN, such as energy efficiency, reliability and low delay, the development of an efficient contention control algorithm is exceptionally crucial that aims to maximize throughput by reducing collisions. In this context, this paper proposes an adaptive algorithm, namely, Prioritization and Contention Control (PCC) algorithm, to minimize collisions, latency and energy consumption. The first phase of the proposed algorithm prioritizes sensors using run-time metrics to grant channel access only for the potential nodes to send their data. It leads to a lesser number of collisions among sensors, thereby reducing retransmission attempts. In the second phase, the Contention Window (CW) size is predicted using queue length and collision rate that accurately mimic the current channel status. The dynamic estimation of CW aids in minimizing channel access delay, collisions and energy consumption, thereby enhancing overall network performance. The performance of the proposed PCC algorithm is validated with the 2D Markov model and NS2 simulation in terms of throughput, packet delivery ratio, delay and remaining energy.

Routing into parallel collision channels

We study a Medium Access game modeled as a splittable atomic routing game in a parallel link topology. Each player has to decide how to split her traffic among the links. We take the expected loss probability of a player as her cost and consider various loss scenarios: 1) the M/M/1/1 queue in which an arrival that finds another packet in service is lost, 2) Losses occur as in 1, except that if arrival occurs when another packet is served, then both are lost. We furthermore assume that the packet in service is aborted if there was a collision. 3) Like 2, but the packet in service is not aborted. We study the existence and uniqueness of equilibrium under these three types of losses.

Type Ia supernovae from wide white dwarfs triples

ABSTRACT For ultra-wide systems (with outer orbit >103 au) the galactic field is collisional. Hence, ultra-wide triple white dwarfs (TWDs) can be perturbed, by flyby stars, to sufficiently high outer eccentricity such that the triple becomes dynamically unstable. An unstable triple undergoes multiple binary–single resonant encounters between all three WDs. These encounters might result in a direct collision between any random two WDs and lead to a Type Ia supernova (SN Ia) event. In case where the multiple resonant encounters did not produce a collision a compact binary is formed (while the third WD is ejected), this binary either collides or merges via gravitational wave emission, similar to the classic double-degenerate (DD) channel. In this research study we estimate the galactic rates of SN Ia from the direct collision channel is to be $0.11\!-\!3.76{{\ \rm per\ cent}}$ and primarily $1\!-\!36{{\ \rm per\ cent}}$ from the DD scenario. The spread in the range is due to uncertainty in the underlying population and two types of galaxies, where elliptical galaxies host higher fraction. and primarily $2\!-\!36{{\ \rm per\ cent}}$ from the DD scenario.

An optimal clear channel assessment in IEEE 802.15.4 medium access control protocol for recurrent data transmission and long acknowledgement wait period

AbstractThe IEEE 802.15.4 standard medium access control (MAC) protocol is widely adopted for short‐range communication in current technologies such as Internet of Things, smart grids, and smart cities. The IEEE 802.15.4 standard adopts the carrier‐sense multiple access with collision avoidance channel access mechanism, which performs two consecutive clear channel assessment (CCA). However, performing two consecutive CCA may not be suitable for all types of network scenarios in a star topology‐enabled wireless sensor networks (WSN). In this article, we develop an optimal CCA channel access mechanism to address the recurrent data transmission and the long acknowledgement wait period which is encountered in two network scenarios considered in a star topology‐enabled WSN. To achieve these requirements, the proposed methodology performs five consecutive CCA for successful data transmission without going into the next backoff stage. Initially, an accurate Markov chain model is developed. Later, a set of mathematical expressions are developed for the proposed method. The model is validated using MATLAB 2017a programming tool. Simulation results show that the proposed methodology outperforms the existing CCA approaches in terms of channel collision time, delay, reliability, probability of collision, packet loss rate, and the probability of successful data transmission.

Deterministic Channel Aggregation for LTE in Unlicensed Spectrum

We propose Deterministic Channel Aggregation (DCA) for LTE-Unlicensed, where the LTE eNodeB aggregates a predetermined number of channels in the unlicensed spectrum to achieve high data-rate communications. We introduce the MAC layer design, and analyze the collision probability and channel occupation ratio for DCA. Simulation results validate the effectiveness of DCA and our analytical results, when the eNB coexists with multiple WiFi systems under a range of traffic load conditions. DCA is particularly useful for applications that require high bandwidth and enables efficient access control of mobile broadband applications in the LTE-Unlicensed.

GreenMAC Protocol: A Q-Learning-Based Mechanism to Enhance Channel Reliability for WLAN Energy Savings

We have seen a promising acceptance of wireless local area networks (WLANs) in our day-to-day communication devices, such as handheld smartphones, tablets, and laptops. Energy preservation plays a vital role in WLAN communication networks. The efficient use of energy remains one of the most substantial challenges to WLAN devices. Several approaches have been proposed by the industrial and institutional researchers to save energy and reduce the overall power consumption of WLAN devices focusing on static/adaptive energy saving methods. However, most of the approaches save energy at the cost of throughput degradation due to either increased sleep-time or reduced number of transmissions. In this paper, we recognize the potentials of reinforcement learning (RL) techniques, such as the Q-learning (QL) model, to enhance the WLAN’s channel reliability for energy saving. QL is one of the RL techniques, which utilizes the accumulated reward of the actions performed in the state-action model. We propose a QL-based energy-saving MAC protocol, named greenMAC protocol. The proposed greenMAC protocol reduces the energy consumption by utilizing accumulated reward value to optimize the channel reliability, which results in reduced channel collision probability of the network. We assess the degrees of channel congestion in collision probability as a reward function for our QL-based greenMAC protocol. The comparative results show that greenMAC protocol achieves enhanced system throughput performance with additional energy savings compared to existing energy-saving mechanisms in WLANs.

Multichannel Conflict-Avoiding Codes of Weights Three and Four

Conflict-avoiding codes (CACs) were introduced by Levenshtein as a single-channel transmission scheme for a multiple-access collision channel without feedback. When the number of simultaneously active source nodes is less than or equal to the weight of a CAC, it is able to provide a hard guarantee that each active source node transmits at least one packet successfully within a fixed time duration, no matter what the relative time offsets between the source nodes are. In this article, we extend CACs to multichannel CACs for providing such a hard guarantee over multiple orthogonal channels. Upper bounds on the number of codewords for multichannel CACs of weights three and four are derived, and constructions that are optimal with respect to these bounds are presented.

High-Performance Long Range-Based Medium Access Control Layer Protocol

Long Range (LoRa) has become one of the most promising physical layer technologies for the Internet of Things (IoT) ecosystem. Although it manifests low-power consumption and long-distance communication, LoRa encounters a large number of collisions in the IoT environment, which severely affects the system’s throughput and delay performance. In this paper, a code division carrier sense multiple access (CD/CSMA) protocol that resolves the traditional channel collision problem and implements multi-channel transmission is proposed for the LoRa medium access control (MAC) layer. To reduce data transmission delay and maximize the throughput of the system, the adaptive p-persistent CSMA protocol divides the channel load into four states and dynamically adjusts the data transmission probability. Then, to reduce channel collisions significantly, the code division multiple access (CDMA) protocol is performed on different channel states. Moreover, the combination of the proposed adaptive p-persistent CSMA protocol and the CDMA successfully reduces the number of data retransmissions and makes LoRa more stable. The simulation results demonstrate that the proposed adaptive p-persistent CD/CSMA protocol can achieve near-optimal and occasionally even better performance than some conventional MAC protocols, especially in a heavy load channel.

Reinforcement Learning-Enabled Cross-Layer Optimization for Low-Power and Lossy Networks under Heterogeneous Traffic Patterns.

The next generation of the Internet of Things (IoT) networks is expected to handle a massive scale of sensor deployment with radically heterogeneous traffic applications, which leads to a congested network, calling for new mechanisms to improve network efficiency. Existing protocols are based on simple heuristics mechanisms, whereas the probability of collision is still one of the significant challenges of future IoT networks. The medium access control layer of IEEE 802.15.4 uses a distributed coordination function to determine the efficiency of accessing wireless channels in IoT networks. Similarly, the network layer uses a ranking mechanism to route the packets. The objective of this study was to intelligently utilize the cooperation of multiple communication layers in an IoT network. Recently, Q-learning (QL), a machine learning algorithm, has emerged to solve learning problems in energy and computational-constrained sensor devices. Therefore, we present a QL-based intelligent collision probability inference algorithm to optimize the performance of sensor nodes by utilizing channel collision probability and network layer ranking states with the help of an accumulated reward function. The simulation results showed that the proposed scheme achieved a higher packet reception ratio, produces significantly lower control overheads, and consumed less energy compared to current state-of-the-art mechanisms.

Decision fusion for composite hypothesis testing in wireless sensor networks over a shared and noisy collision channel

We consider the composite hypothesis testing problem of time-bandwidth-constrained distributed detection. In this scenario, the probability distribution of the observed signal when the event of interest is happening is unknown. In addition, local decisions are censored and only those uncensored local decisions will be sent to the fusion center over a shared and noisy collision channel. The fusion center also has a limited time duration to collect transmitted decisions and make a final decision. Two types of medium access control that the sensor nodes apply to send their decisions are investigated: time division multiple access and slotted-Aloha. Unlike using the time division multiple access protocol, the slotted-Aloha-based distributed detection will experience packet collisions. However, in this article, since only uncensored decisions are sent, packet collisions are informative. We derive fusion rules according to generalized likelihood ratio test, Rao test, and Wald test for both the time division multiple access–based distributed detection and the slotted-Aloha-based distributed detection. We see that the fusion rules for the slotted-Aloha-based distributed detection here also exploit packet collisions in the final decision-making. In addition, the asymptotic performances and energy consumption of both schemes are analyzed. Extensive simulation and numerical results are provided to compare the performances of these two schemes. We show that, for a given time delay, the slotted-Aloha-based distributed detection can outperform the time division multiple access–based distributed detection by increasing the number of sensor nodes which results in higher energy consumption.

Time Synchronization Attack and Countermeasure for Multisystem Scheduling in Remote Estimation

We consider time synchronization attack against multisystem scheduling in a remote state estimation scenario where a number of sensors monitor different linear dynamical processes and schedule their transmissions through a shared collision channel. We show that by randomly injecting relative time offsets on the sensors, the malicious attacker is able to make the expected estimation error covariance of the overall system diverge without any system knowledge. For the case that the attacker has full system information, we propose an efficient algorithm to calculate the optimal attack, which spoofs the least number of sensors and leads to unbounded average estimation error covariance. To mitigate the attack consequence, we further propose a countermeasure by constructing shift invariant transmission policies and characterize the lower and upper bounds for system estimation performance. Simulation examples are provided to illustrate the obtained results.

Achieving Zero-Packet-Loss Throughput 1 for a Collision Channel Without Feedback and With Arbitrary Time Offsets

The collision channel without feedback (CCw/oFB) introduced by Massey and Mathys, depicts a scenario where multiple users share a communication channel but have arbitrary time offsets, and can never learn these time offsets due to the lack of feedback. This paper considers an extension of the CCw/oFB, which allows the receiver to use successive interference cancellation (SIC) to cancel the interference caused by those collided packets whose contents have been known by the receiver. We derive the zero-packet-loss throughput regions of this model for both the unsynchronized and slot-synchronized cases. Given an arbitrary number of users and a packet alphabet of arbitrary size, it is shown that these two regions coincide, and the outer boundary of this common region is the set of all points with only nonnegative components that add up to one. It is further shown that all points on this outer boundary with only rational components can be achieved without packet loss in the slot-synchronized case. The constructive proofs are based on a joint design of protocol sequences, identification/location algorithm and erasure correcting codes. These findings indicate that the negative impact of the lack of time synchronization on the throughput performance can be removed by the help of SIC.

Research on multi-address autonomous access technology of uav cluster data link based on priority statistics

As a uav cluster that can change the future combat situation, its data link has the requirements of high speed, low delay, dynamic networking and large network capacity. In this paper, a priority statistics based multi-address autonomous access technology for uav cluster data link is studied. Firstly, the channel load is detected and predicted by time-frequency analysis for different priority services. Then, multi-node information fusion is adopted for multi-address decision-making. Finally, the designed technology is simulated by Qualnet software. The simulation results show that the proposed multi-address autonomous access technology based on priority statistics can effectively reduce in-network channel collisions and realize low-delay transmission of high-priority services.

An Energy-Efficient and Reliable Scheduling Strategy for Dynamic WBANs With Channel Periodicity Exploitation

In dynamic scenarios such as walking, wireless body area networks (WBANs) have unique dynamic features, for instance, the sensor nodes placed on the limbs have approximately periodic link qualities. To realize energy-efficient and reliable communication in dynamic WBANs, this paper focuses on designing a scheduling strategy, that exploits the dynamic channel periodicity. In walking scenarios, we first construct a WBAN testbed, then conduct experiments to show the channel periodicity between the sensor nodes deployed at different positions and quantify the correlation between the received signal strength indication (RSSI) and the acceleration signals. Next, we propose a gait cycle detection algorithm to detect the periodic motion and calculate the acceleration periodicity and phase. Then, we propose a channel periodicity based scheduling (CPBS) strategy that exploits the periodic fluctuations of link qualities, to improve energy efficiency and reliability. With the CPBS strategy, the transmission time is allocated according to the motion state and the rate requirement of each sensor node, which can avoid channel collision. Finally, we evaluate the performance of the CPBS strategy in both periodic and realistic (approximately 23% of the time for aperiodic movements) scenarios. The results show that, compared to stochastic scheduling (SS) strategy, the proposed CPBS strategy can save 17.61% and 13.56% energy consumption in periodic and realistic scenarios, respectively. In addition, the packet loss rate (PLR) can be made as low as zero in both scenarios. Therefore, the proposed CPBS strategy can achieve energy-efficient and reliable communication in dynamic WBANs.