Probability Of Successful Packet Transmission Research Articles

Green grant-free power allocation for ultra-dense Internet of Things: A mean-field perspective

Grant-free access, in which each Internet-of-Things (IoT) device delivers its packets through a randomly selected resource without spending time on handshaking procedures, is a promising solution for supporting the massive connectivity required for IoT systems. In this paper, we explore grant-free access with multi-packet reception capabilities, with an emphasis on ultra-low-end IoT applications with small data sizes, sporadic activity, and energy usage constraints. We propose a power allocation scheme aimed at maximizing throughput while minimizing power consumption by considering the traffic and energy constraints of IoT devices. Our approach employs a stochastic geometry framework and mean-field game theory to model and analyze the mutual interference among active IoT devices. Additionally, we utilize a Markov chain model to capture and track the queue length of IoT devices, enabling the derivation of the transmission success probability at steady-state. The simulation results illustrate the optimal power allocation strategy and evaluate the proposed approach’s performance in terms of packet transmission success probability and average delay.

A Formula for the Probability of Successful Packet Transmission in Cognitive Radio Networks

In this article, we derive a closed-form expression for the probability of successful packet transmission (PSPT) over Rayleigh fading channels in cognitive radio networks (CRNs). The derived expression is novel, as it does not require prior channel state information. This is achieved by employing a probabilistic model for the packet transmission time, avoiding the less realistic deterministic model that is widely used in the literature. Furthermore, the derived expression allows trading accuracy with computational complexity. Our expression is computationally efficient with linear computational complexity. Simulation results demonstrate high accuracy in evaluating the PSPT, with controllable computational complexity.

Analyzing Novel Grant-Based and Grant-Free Access Schemes for Small Data Transmission

Fifth Generation (5G) New Radio (NR) does not support data transmission during random access (RA) procedures, which results in unnecessary control signalling overhead and power consumption, especially for small data transmission (SDT). Motivated by this, 3GPP has proposed 4/2-step SDT RA schemes based on the existing grant-based (4-step) and grant-free (2-step) RA schemes, with the aim to enable data transmission during RA procedures in Radio Resource Control (RRC) Inactive state. To compare the 4/2-step SDT RA schemes with the benchmark 4/2-step RA schemes, we provide a spatio-temporal analytical framework to evaluate the RA schemes, which jointly models the preamble detection, Physical Uplink Shared Channel (PUSCH) decoding, and data transmission procedures. Based on this analytical model, we derive the analytical expressions for the overall packet transmission success probability and average throughput in each RACH attempt. We also derive the average energy consumption in each RACH attempt. Our results show that 2-step SDT RA scheme provides the highest overall packet transmission success probability, and the lowest average energy consumption, but the performance gain decreases with the increase of device intensity.

Highly Reliable Transmission and Channel Assignment for CR-IoT Networks

In this article, we propose a highly reliable transmission strategy based on exploiting channel diversity for Internet of Things (IoT) networks equipped with cognitive radio (CR) capabilities (referred to as CR-IoT networks). Specifically, we investigate the channel-assignment problem with the objective of minimizing the number of assigned channels for each CR transmission subject to link reliability, data rate, and success probability constraints. We use the theory of continuous-time Markov chains to derive mathematical expressions for channel availability and reliability. These expressions are employed in a binary linear program model to formulate the channel assignment optimization problem, which is suboptimally solved using a polynomial-time sequential fixing procedure. Theoretical performance analysis of the proposed channel assignment scheme is derived in terms of a lower bound on the achievable probability of successful packet transmission. Through simulation experiments, we demonstrate significant performance enhancement compared to existing reliability-unaware CR channel assignment algorithms.

Energy-Efficient Routing in Wireless Sensor Networks

Efficient data collection is the core concept of implementing Industry4.0 on IoT platforms. This requires energy aware communication protocols for Wireless Sensor Networks (WSNs) where different functions, like sensing and processing on the IoT nodes must be supported by local battery power. Thus, energy aware network protocols, such as routing, became one of fundamental challenges in IoT data collection schemes.In our research, we have developed novel routing algorithms which guarantee minimum energy consumption data transfer which is achieved subject to pre-defined reliability constraints. We assume that data is transmitted in the form of packets and the routing algorithm identifies the paths over which the packets can reach the Base Station (BS) with minimum transmission energy, while the probability of successful packet transmission still exceeds a pre-defined reliability parameter. In this way, the longevity and the information throughput of the network is maximized and the low energy transmissions will considerably extend the lifetime of the IoT nodes. In this paper we propose a solution that maximizes the lifetime of the nodes.

Resource Allocation for High-Speed Train Communication Based on Deep Reinforcement Learning

With the advent of high-speed railway era, users’ demands for high link capacity and high reliability are increasing. In this paper, a resource allocation algorithm based on deep reinforcement learning (DRL) is proposed in the high-speed railway (HSR) scenario. The communication link between high-speed train and high-speed train (T2T) is taken as an agent, and the link capacity and packet transmission success probability (PTSP) are taken as optimization objectives, which are mapped into the reward function. The agents gain experience by constantly interacting with the HSR communication environment to update the neural networks in DRL and eventually learned the optimization policy. The simulation shows that when the trains’ speed is 350km/h and the transmission packet size is 2385Bits, the high reliability of T2T link can still be maintained, and the capacity of high-speed train to Infrastructure (T2I) link is improved by 1Mbps compared with the baseline, which proves that the proposed algorithm can mitigate the interference introduced by spectrum reuse and doppler shift.

Grant-Free Opportunistic Uplink Transmission in Wireless-Powered IoT: A Spatio-Temporal Model

Ambient radio frequency (RF) energy harvesting is widely promoted as an enabler for wireless-power Internet of Things (IoT) networks. This article jointly characterizes energy harvesting and packet transmissions in grant-free opportunistic uplink IoT networks energized via harvesting downlink energy. To do that, a joint queuing theory and stochastic geometry model is utilized to develop a spatio-temporal analytical model. Particularly, the harvested energy and packet transmission success probability are characterized using tools from stochastic geometry. Moreover, each device is modeled using a two-dimensional discrete-time Markov chain (DTMC). Such two dimensions are utilized to jointly track the scavenged/depleted energy to/from the batteries along with the arrival/departure of packets to/from devices buffers over time. Consequently, the adopted queuing model represents the devices as spatially interacting queues. To that end, the network performance is assessed in light of the packet throughput, the average delay, and the average buffer size. The effect of base stations (BSs) densification is discussed and several design insights are provided. The results show that the parameters for uplink power control and opportunistic channel access should be jointly optimized to maximize average network packet throughput, and hence, minimize delay.

Recycling Cellular Energy for Self-Sustainable IoT Networks: A Spatiotemporal Study

This paper investigates the self-sustainability of an overlay Internet of Things (IoT) network that relies on harvesting energy from a downlink cellular network. Using stochastic geometry and queueing theory, we develop a spatiotemporal model to derive the steady state distribution of the number of packets in the buffers and energy levels in the batteries of IoT devices given that the IoT and cellular communications are allocated disjoint spectrum. Particularly, each IoT device is modelled via a two-dimensional discrete-time Markov Chain (DTMC) that jointly tracks the evolution of the data buffer and energy battery. In this context, stochastic geometry is used to derive the energy generation at the batteries and the packet transmission success probability from buffers taking into account the mutual interference from other active IoT devices. To this end, we show the Pareto-Frontiers of the sustainability region, which define the network parameters that ensure stable network operation and finite packet delay. Furthermore, the spatially averaged network performance, in terms of transmission success probability, average queueing delay, and average queue size are investigated. For self-sustainable networks, the results quantify the required buffer size and packet delay, which are crucial for the design of IoT devices and time critical IoT applications.

Robust H∞ Fault Detection for Networked Control Systems with Markov Time-Delays and Data Packet Loss in Both S/C and C/A Channels

This paper investigates the robust H∞ fault detection problem for networked control systems with Markov time-delays and data packet loss in both S/C and C/A channels. First, the time-delay from sensor to controller (S/C) and the time-delay from sensor to actuator (C/A) are described by two different Markov chains. Two random variables obeying the Bernoulli distribution are used to describe the packet loss between the sensor and the controller together between the controller and the actuator. Based on this, a fault detection filter is constructed and the closed-loop system mathematical model is established. Then, the solution method of the fault detection filter and controller gain matrix is given. The relationship between the probability of successful packet transmission and the ability to suppress external disturbance is obtained. Finally, simulation verifies the effectiveness of the proposed method.

Performance Analysis of Grant-Free Multiple Access for Supporting Sporadic Traffic in Massive IoT Networks

Grant-free multiple access (GFMA) protocol has been regarded as a key element to support sporadic traffic generated from massive internet-of-things (IoT) networks. In GFMA protocol, each IoT device transmits data packets without grant from a base station (BS) via pre-reserved uplink resources. Packet collisions inherently occur when multiple IoT devices transmit packets by using the same radio resource, but the collision effect can be alleviated with multi-packet reception (MPR) capability of the BS. Since a number of studies have focused on improving the physical layer performance such as bit error rate, they may be hard to provide intuitions from the MAC layer perspective when a number of IoT devices sporadically generate uplink packets and attempt the GFMA. In this paper, we thoroughly investigate the GFMA from the MAC layer perspective. We provide an analytical framework based on a Markov chain to capture the performance of the GFMA in terms of packet transmission success probability, ergodic throughput, and access delay. Through simulations, we validate our analytical framework and verify the necessity of adopting MPR technique for supporting a massive number of IoT devices generating sporadic traffic.

Fragment-based transmission using minimum power consumed routing (FBTMPCR) algorithm of a MIMO integrated MANET

The establishment of an efficient broadcast routing technique in a multiple input multiple output (MIMO)- based mobile ad hoc network is a challenge in wireless communication. MIMO configuration provides enhancement of data throughput even under conditions of interference and multipath fading. Improvement of transmission efficiency by increasing saturation throughput, reducing transmission delay, and minimizing bit error rate is the main motivation of the present work. In this paper a hybrid routing algorithm, fragment-based transmission using minimum power consumed routing, has been developed. It is an integration of the cooperative transmission process and fragmentation of packet payload. The routing algorithm is developed on selecting the best suitable relay node considering effective received signal strength and probability of successful packet transmission. The broadcasting mechanism is done by using multiple fragment of packet transmission through MIMO channel by using a suitable relay node. The performance analysis of this proposed routing algorithm increases the signal to noise interference ratio, resulting in minimization of BER, improvement of saturation throughput, and reduced transmission delay.

Study of Adjustment Delay Scheme on IEEE 802.15.4 Networks at Beacon Enabled Mode

In the contention mechanism used to slotted carrier sense multiple access with collision avoidance (CSMA/CA) on IEEE 802.15.4 standard. Device nodes will perform a backoff process as soon as the clear channel assessment (CCA) detects as busy condition. The challenge of CSMA/CA as following: first, when the device nodes detect the channel in busy condition, the device nodes have to increase the value of backoff exponent (BE) which cause range of blind backoff process also increase. Second, when the device nodes detect the channel in busy condition, the device nodes have to increase number of backoff stage which cause more energy consumptions for entering next backoff stage. This article proposes a scheme to improve IEEE 802.15.4 medium access control, called adjustment delay scheme (ADES). ADES not only reduce probability of collision but also reduce probability of going to next backoff stage. The validity of study is proven by simulation experiments. ADES performs better than IEEE 802.15.4 standard in term of the probability of successful packet transmission, network goodput, bandwidth utilization as well as energy consumption in the networks.

Adjustment Delay Scheme to Improve Performance IEEE 802.15.4 Networks

The challenges of CSMA/CA as following: first, when the device nodes detect the channel in busy condition, the device nodes have to increase the value of backoff exponent which cause range of blind backoff process also increase. Second, the blind backoff process will cause lower channel utilization and more energy consumptions. This article proposes a scheme to improve IEEE 802.15.4 medium access control, called adjustment delay scheme (ADES). This article also presents a comprehensive Markov chain analysis to predict the probability of successful transmission, network goodput, bandwidth utilization and total network energy consumption. The validity of the model analysis is proven by precisely matching the simulation experiments. ADES performs better than those of other algorithms in term of the probability of successful packet transmission, network goodput, bandwidth utilization as well as energy consumption in the networks.

Neighbors-Aware Proportional Fair scheduling for future wireless networks with mixed MAC protocols

In this paper, we consider a beyond-5G scenario, where two types of users, denoted as scheduled and uncoordinated nodes, coexist on the same set of radio resources for sending data to a base station. Scheduled nodes rely solely on a centralized scheduler within the base station for the assignment of resources, while uncoordinated nodes use an unslotted Carrier Sense Multiple Access (CSMA) protocol for channel access. We propose and evaluate through simulations: (a) a novel centralized resource scheduling algorithm, called Neighbors-Aware Proportional Fair (N-PF) and (b) a novel packet length adaptation algorithm, called Channel-Aware (CA) Packet Length Adaptation algorithm for the scheduled nodes. The N-PF algorithm considers the uplink channel state conditions and the number of uncoordinated nodes neighboring each scheduled node in the aggregate scheduling metric, in order to maximize packet transmission success probability. The CA algorithm provides an additional degree of freedom for improving the performance, thanks to the fact that scheduled nodes with lower number of hidden terminals, i.e., having higher packet capture probability, are assigned longer packet transmission opportunities. We consider two benchmark schemes: Proportional Fair (PF) algorithm, as a resource scheduling algorithm, and a discrete uniform distribution (DUD) scheme for packet lengths distribution. Simulation results show that the proposed schemes can result in significant gain in terms of network goodput, without compromising fairness, with respect to two benchmark solutions taken from the literature.

Co-DDTMA: Cooperative Distributed TDMA for Vehicular Networks

In recent years, Vehicular Ad Hoc Networks (VANET) has experienced a rapid development due to the advancement of wireless communication technologies, and now emerges as a promising way to provide road safety, traffic efficiency and infotainment applications. However, it is a challenge to design a reliable and efficient Medium Access Control (MAC) protocol for VANET due to its frequent topology changes and unreliable wireless links. Cooperative communication, on the other hand, can enhance the reliability of wireless links by exploiting the broadcast nature of the wireless communication. A cooperative scheme for MAC is proposed for VANET in this paper, referred to as Cooperative Distributed TDMA (Co-DTDMA). In the Co-DTDMA, neighboring nodes utilize its idle slots for cooperatively retransmitting a packet which has failed to reach the destination. Since the cooperative retransmission is conducted in node's own idle slot, the proposed scheme does not interrupt the normal transmission. Both theoretical analysis and simulation results demonstrate that the proposed scheme greatly increases the probability of successful packet transmission and decreases the packet transmission delay.

Performance analysis and optimisations of code resources in wireless dual-channel ad hoc network

To improve code resource utilisation efficiency, we propose a novel ad hoc network called dual-channel network DCN consisting of one control channel CC and multiple data channels DCs. By utilising the original frequency hopping FH sequence, all nodes in the DCN networks exchange control information through CC. However, when some nodes pairs have the requests for data transmission, multiple DC data channels would be established by using different FH sequences to deliver data packets. These different FH sequences are obtained from the original FH sequence used for CC by selecting different shifted phases randomly. We analyse networking capacity and derive the expression of packet transmission success probability. This paper also presents a frequency point hitting optimisation method called sliding correlation SC to reduce frequency point hitting probability. Analytical and simulation results demonstrate that DCN networking capacity can satisfy the common tactical requirement, and SC can reduce the hitting number effectively.

Performance analysis and optimisations of code resources in wireless dual-channel ad hoc network

To improve code resource utilisation efficiency, we propose a novel ad hoc network called dual-channel network (DCN) consisting of one control channel (CC) and multiple data channels (DCs). By utilising the original frequency hopping (FH) sequence, all nodes in the DCN networks exchange control information through CC. However, when some nodes pairs have the requests for data transmission, multiple DC data channels would be established by using different FH sequences to deliver data packets. These different FH sequences are obtained from the original FH sequence used for CC by selecting different shifted phases randomly. We analyse networking capacity and derive the expression of packet transmission success probability. This paper also presents a frequency point hitting optimisation method called sliding correlation (SC) to reduce frequency point hitting probability. Analytical and simulation results demonstrate that DCN networking capacity can satisfy the common tactical requirement, and SC can reduce the hitting number effectively.

Carrier Sense Random Packet CDMA Protocol in Dual-Channel Networks

Code resource wastage is caused by the reason that many hopping frequency (FH) sequences are unused, which occurs under the condition that the number of the actual subnets needed for the tactical network is far smaller than the networking capacity of code division net- working. Dual-channel network (DCN), consisting of one single control channel and multiple data channels, can solve the code resource wastage effectively. To improve the anti-jamming capability of the control channel of DCN, code division multiple access (CDMA) technology was introduced, and a carrier sense random packet (CSRP) CDMA protocol based on random packet CDMA (RP-CDMA) was proposed. In CSRP-CDMA, we provide a carrier sensing random packet mechanism and a packet-segment acknowledgement policy. Furthermore, an analytical model was developed to evaluate the per- formance of CSRP-CDMA networks. In this model, the im- pacts of multi-access interference from both inter-clusters and intra-clusters were analyzed, and the mathematical expressions of packet transmission success probability, normalized network throughput and signal interference to noise ratio, were also derived. Analytical and simulation results demonstrate that the normalized network through- put of CSRP-CDMA outperforms traditional RP-CDMA by 10%, which can guarantee the resource utilization efficiency of the control channel in DCNs.

Analysis of superframe duration adjustment scheme for IEEE 802.15.4 networks

The challenge of the IEEE 802.15.4 beacon-enabled mode is how to improve throughput and bandwidth utilization in contention access period (CAP) and contention free period (CFP), respectively. This article proposes a scheme to improve IEEE 802.15.4 medium access control, called superframe duration adjustment scheme (SUDAS), which analyzes the overall of the IEEE 802.15.4 not only CAP but also CFP. SUDAS is expected to effectively allocate guaranteed time slot to the requested devices, it adjusts the length of the slot in superframe duration based on the length of the packet data. This article also presents a comprehensive Markov chain analysis for SUDAS, especially for star topology, to predict the probability of successful transmission, network goodput, average bandwidth utilization, as well as total network energy consumption. The validity of the analytical model is proven by closely matching the simulation experiments. SUDAS performs better than other algorithms in terms of the probability of successful packet transmission, network goodput, average bandwidth utilization, and total energy consumption.

Opportunistic Channel Access and RF Energy Harvesting in Cognitive Radio Networks

Radio frequency (RF) energy harvesting is a promising technique to sustain operations of wireless networks. In a cognitive radio network, a secondary user can be equipped with RF energy harvesting capability. In this paper, we consider such a network where the secondary user can perform channel access to transmit a packet or to harvest RF energy when the selected channel is idle or occupied by the primary user, respectively. We present an optimization formulation to obtain the channel access policy for the secondary user to maximize its throughput. Both the case that the secondary user knows the current state of the channels and the case that the secondary knows the idle channel probabilities of channels in advance are considered. However, the optimization requires model parameters (e.g., the probability of successful packet transmission, the probability of successful RF energy harvesting, and the probability of channel to be idle) to obtain the policy. To obviate such a requirement, we apply an online learning algorithm that can observe the environment and adapt the channel access action accordingly without any a prior knowledge about the model parameters. We evaluate both the efficiency and convergence of the learning algorithm. The numerical results show that the policy obtained from the learning algorithm can achieve the performance in terms of throughput close to that obtained from the optimization.